attention.py 327 KB
Newer Older
1
# Copyright (c) 2022-2025, 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

cyanguwa's avatar
cyanguwa committed
16
import numpy as np
17
from packaging.version import Version as PkgVersion
18
19

import torch
yuguo's avatar
yuguo committed
20
from torch.utils.cpp_extension import IS_HIP_EXTENSION
21

22
import transformer_engine_torch as tex
23
from transformer_engine.debug.pytorch.debug_state import TEDebugState
24
25
26
27
28
from transformer_engine.pytorch.utils import (
    get_cudnn_version,
    nvtx_range_pop,
    nvtx_range_push,
)
29
from transformer_engine.pytorch.cpp_extensions.fused_attn import (
30
31
    fused_attn_fwd,
    fused_attn_bwd,
32
    FusedAttnBackend,
33
34
35
36
37
38
39
    META_QKV,
    META_O,
)
from transformer_engine.pytorch.fp8 import (
    FP8GlobalStateManager,
    get_fp8_te_dtype,
    get_fp8_torch_dtype,
40
)
41
from transformer_engine.pytorch.float8_tensor import Float8Tensor
42
from transformer_engine.pytorch.tensor._internal.float8_tensor_base import Float8TensorBase
43
from transformer_engine.pytorch.module import LayerNormLinear, Linear
44
from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
45
46
47
48
49
from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
50
    get_default_init_method,
51
52
53
54
)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
55
    AttnBiasTypes,
56
    QKVLayouts,
57
    dist_group_type,
58
    TE_DType,
59
60
61
62
)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
63
    get_distributed_rank,
64
    checkpoint,
65
66
67
    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
68
69
    gather_along_first_dim,
    reduce_scatter_along_first_dim,
70
)
71
from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
72
from transformer_engine.pytorch.graph import is_graph_capturing
73
from transformer_engine.pytorch.dot_product_attention.inference import InferenceParams
74
75
76
77
78
from transformer_engine.pytorch.tensor.quantized_tensor import (
    QuantizedTensor,
    prepare_for_saving,
    restore_from_saved,
)
79

80
81
82
83
84
# Import attention utils
import transformer_engine.pytorch.dot_product_attention.utils as dpa_utils
from transformer_engine.pytorch.dot_product_attention.utils import FlashAttentionUtils as fa_utils
from transformer_engine.pytorch.dot_product_attention.utils import AttentionLogging as attn_log
from transformer_engine.pytorch.dot_product_attention.rope import apply_rotary_pos_emb
85
from .cpu_offload import mark_activation_offload
86
87


88
89
90
# Setup Attention Logging
attn_log.setup_logging()

91
# Global vars for flash attn v2 and v3 imports
92
flash_attn_cuda_bwd = None
93
94
flash_attn_func = None
flash_attn_varlen_func = None
95
96
97
98
_flash_attn_fwd = None
_flash_attn_bwd = None
_flash_attn_varlen_fwd = None
_flash_attn_varlen_bwd = None
99
try:
100
    fa_utils.version = PkgVersion(get_pkg_version("flash-attn"))
101
except PackageNotFoundError:
102
    pass  # only print warning if use_flash_attention_2 = True in get_attention_backend
103
else:
104
    if torch.cuda.is_available() and get_device_compute_capability() >= (10, 0):
105
106
107
108
        if fa_utils.version_required_blackwell <= fa_utils.version <= fa_utils.max_version:
            fa_utils.is_installed = True
    elif fa_utils.version_required <= fa_utils.version <= fa_utils.max_version:
        fa_utils.is_installed = True
109

110
    if fa_utils.is_installed:
111
        from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd
112
        from flash_attn.flash_attn_interface import flash_attn_func, flash_attn_varlen_func
113
114
        from flash_attn.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd
        from flash_attn.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd
115
        from flash_attn.flash_attn_interface import (
116
            _flash_attn_varlen_forward as _flash_attn_varlen_fwd,
117
118
        )
        from flash_attn.flash_attn_interface import (
119
            _flash_attn_varlen_backward as _flash_attn_varlen_bwd,
120
121
        )

122
123
        # Setup Flash attention utils
        fa_utils.set_flash_attention_version()
124
    elif (
125
        torch.cuda.is_available()
yuguo's avatar
yuguo committed
126
        and (IS_HIP_EXTENSION or get_device_compute_capability() >= (8, 0))
127
        and dpa_utils._NVTE_FLASH_ATTN
128
    ):
129
        attn_log.fa_logger.warning(
130
            "Supported flash-attn versions are %s. Found flash-attn %s.",
131
            dpa_utils._get_supported_versions(
132
                (
133
                    fa_utils.version_required
134
                    if get_device_compute_capability() < (10, 0)
135
                    else fa_utils.version_required_blackwell
136
                ),
137
                fa_utils.max_version,
138
            ),
139
            fa_utils.version,
140
        )
yuguo's avatar
yuguo committed
141
142
if not IS_HIP_EXTENSION:
    try:
143
        fa_utils.fa3_version = PkgVersion(get_pkg_version("flash-attn-3"))
yuguo's avatar
yuguo committed
144
    except PackageNotFoundError:
145
        pass  # only print warning if use_flash_attention_3 = True in get_attention_backend
yuguo's avatar
yuguo committed
146
    else:
147
148
        from flash_attn_3.flash_attn_interface import flash_attn_func as flash_attn_func_v3
        from flash_attn_3.flash_attn_interface import (
yuguo's avatar
yuguo committed
149
            flash_attn_varlen_func as flash_attn_varlen_func_v3,
150
        )
151
152
        from flash_attn_3.flash_attn_interface import (
            flash_attn_with_kvcache as flash_attn_with_kvcache_v3,
yuguo's avatar
yuguo committed
153
        )
154
155
        from flash_attn_3.flash_attn_interface import _flash_attn_forward as _flash_attn_fwd_v3
        from flash_attn_3.flash_attn_interface import _flash_attn_backward as _flash_attn_bwd_v3
yuguo's avatar
yuguo committed
156
157
    
        fa_utils.set_flash_attention_3_params()
158

159
# Global vars for available attention backends and ALiBi cache
160
161
162
_attention_backends = {
    "attention_params": None,
    "use_flash_attention": None,
163
    "flash_attention_backend": None,
164
165
166
167
    "use_fused_attention": None,
    "fused_attention_backend": None,
    "use_unfused_attention": None,
    "backend_selection_requires_update": False,
168
}
169

170
171
172
173
174
175
176
177
178
179
180
_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,
}

181
__all__ = ["DotProductAttention", "MultiheadAttention"]
182
183


184
185
186
187
188
def maybe_contiguous(tensor: torch.Tensor) -> torch.Tensor:
    """Make tensor contiguous if final stride is not 1."""
    return tensor.contiguous() if tensor.stride(-1) != 1 else tensor


189
190
191
def flash_attn_p2p_communicate(
    rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm
):
192
    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
193
194
195
196
    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
197
198
199
200
201
202
            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
            )
203
204
205
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
206
207
208
209
210
211
            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
            )
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
            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


231
232
233
234
235
236
237
238
239
240
241
242
243
244
@jit_fuser
def flash_attn_fwd_out_correction_init(
    out_init_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_init_step: torch.Tensor,
    seq_dim: int,
):
    """Merge partial outputs of the first step in Attention with context parallelism"""
    softmax_lse_corrected_exp = torch.exp(softmax_lse_init_step - softmax_lse).movedim(2, seq_dim)
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_init_step * softmax_lse_corrected_exp
    return out_corrected.to(out_init_step.dtype)


245
@jit_fuser
246
247
248
249
250
def flash_attn_fwd_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
251
    seq_dim: int,
252
):
253
    """Merge partial outputs of each step in Attention with context parallelism"""
254
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(2, seq_dim)
255
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
256
    out_corrected = out_per_step * softmax_lse_corrected_exp
257
258
259
    out.add_(out_corrected)


260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
@jit_fuser
def flash_attn_fwd_second_half_out_correction(
    out: torch.Tensor,
    out_per_step: torch.Tensor,
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
    seq_dim: int,
):
    """Merge second half of partial outputs of each step in Attention with context parallelism"""
    out_ = out.select(seq_dim, 1)
    softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)[..., 1, :]
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse_).movedim(2, seq_dim)
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_per_step * softmax_lse_corrected_exp
    out_.add_(out_corrected)


277
@jit_fuser
278
279
280
281
def flash_attn_fwd_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
282
    """Merge softmax stats of each step in Attention with context parallelism"""
283
284
    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
285
    new_scale = max_scale + torch.log1p(torch.exp(min_scale - max_scale))
286
    softmax_lse.copy_(new_scale)
287
288


289
290
291
292
293
294
295
296
297
298
299
300
301
@jit_fuser
def flash_attn_fwd_second_half_softmax_lse_correction(
    softmax_lse: torch.Tensor,
    softmax_lse_per_step: torch.Tensor,
):
    """Merge second half of softmax stats of each step in Attention with context parallelism"""
    softmax_lse_ = softmax_lse[..., 1, :]
    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.log1p(torch.exp(min_scale - max_scale))
    softmax_lse_.copy_(new_scale)


302
303
@jit_fuser
def get_cu_seqlens_on_cp_rank(
304
305
306
307
308
309
    cu_seqlens: torch.Tensor,
    cu_seqlens_padded_on_cp_rank: torch.Tensor,
    cp_size: int,
    cp_rank: int,
    first_half: bool,
    second_half: bool,
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
):
    """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


328
@jit_fuser
329
def get_seq_chunk_ids_for_reordering_before_attn(cp_size, device):
330
331
    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
332
333
334
    To make sure tokens are ordered correctly for compute, we need to reorder sequence chunks to
    be contigupus before attention compute. This function is to compute sequence chunk ids for
    reordering.
335
336
    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
337
338
339
    for rank in range(cp_size):
        chunk_ids[rank] = 2 * rank
        chunk_ids[rank + cp_size] = 2 * cp_size - 2 * rank - 1
340
341
342
    return chunk_ids


343
@jit_fuser
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
def get_seq_chunk_ids_for_reordering_after_attn(cp_size, device):
    """
    Context parallelism assigns two discontiguous sequence chunks to each GPU for load balancing.
    We need to reorder sequence chunks back to discontiguous after attention compute. This function
    is to compute sequence chunk ids for reordering.
    """
    chunk_ids = torch.empty(2 * cp_size, dtype=torch.int32, device=device)
    for rank in range(cp_size):
        chunk_ids[2 * rank] = rank
        chunk_ids[2 * rank + 1] = 2 * cp_size - rank - 1
    return chunk_ids


@jit_fuser
def reorder_seq_chunks_for_a2a_before_attn(x, chunk_ids_for_a2a, seq_dim, cp_size):
    """Reorder sequence chunk for A2A communication before attention compute."""
    # [cp, b, s, np//cp, hn] -> [b, cp, s, np//cp, hn]
    # or [cp, s, b, np//cp, hn] -> [cp, s, b, np//cp, hn]
    x = x.movedim(0, seq_dim).contiguous()
    # [b, cp, s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
    # or [cp, s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
    x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 2) :])
    # reorder the sequence chunks
    x = torch.index_select(x, dim=seq_dim, index=chunk_ids_for_a2a)
    return x


@jit_fuser
def reorder_seq_chunks_for_a2a_after_attn(x, chunk_ids_for_a2a, seq_dim, cp_size):
    """Reorder sequence chunk for A2A communication after attention compute."""
    # [b, cp*2, s//2, np//cp, hn] -> [cp*2, b, s//2, np//cp, hn]
    # or [cp*2, s//2, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
    x = x.movedim(seq_dim, 0).contiguous()
    # reorder the sequence chunks
    x = torch.index_select(x, dim=0, index=chunk_ids_for_a2a)
    # [cp*2, b, s//2, np//cp, hn] -> [cp, 2, b, s//2, np//cp, hn]
    # or [cp*2, s//2, b, np//cp, hn] -> [cp, 2, s//2, b, np//cp, hn]
    x = x.view(cp_size, 2, *x.shape[1:])
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
    return x


def flash_attn_a2a_communicate(
    a2a_inputs: Union[torch.Tensor, List[torch.Tensor]],
    chunk_ids_for_a2a: torch.Tensor,
    seq_dim: int,
    cp_size: int,
    cp_group: dist_group_type,
    cp_stream: torch.cuda.Stream,
    before_attn: bool,
) -> Union[torch.Tensor, List[torch.Tensor]]:
    """A2A communication for context parallelism."""
    a2a_inputs = [a2a_inputs] if not isinstance(a2a_inputs, list) else a2a_inputs
    a2a_outputs, a2a_reqs = [None] * len(a2a_inputs), [None] * len(a2a_inputs)
    if before_attn:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # reorder the sequence chunks
409
410
                    x = reorder_seq_chunks_for_a2a_before_attn(
                        x, chunk_ids_for_a2a, seq_dim, cp_size
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
                    )
                    # [b, cp*2, s//2, np//cp, hn] -> [b, cp*s, np//cp, hn]
                    # or [cp*2, s//2, b, np//cp, hn] -> [cp*s, b, np//cp, hn]
                    a2a_outputs[i - 2] = x.view(*x.shape[:seq_dim], -1, *x.shape[(seq_dim + 2) :])
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, s, np, hn] -> [b, s, cp, np//cp, hn]
                # or [s, b, np, hn] -> [s, b, cp, np//cp, hn]
                x = x.view(*x.shape[:-2], cp_size, x.shape[-2] // cp_size, x.shape[-1])
                # [b, s, cp, np//cp, hn] -> [cp, b, s, np//cp, hn]
                # or [s, b, cp, np//cp, hn] -> [cp, s, b, np//cp, hn]
                a2a_inputs[i] = x.movedim(-3, 0).contiguous()
    else:
        for i in range(len(a2a_inputs) + 2):
            if 0 < i < len(a2a_inputs) + 1:
                a2a_outputs[i - 1] = torch.empty_like(a2a_inputs[i - 1])
                a2a_reqs[i - 1] = torch.distributed.all_to_all_single(
                    a2a_outputs[i - 1], a2a_inputs[i - 1], group=cp_group, async_op=True
                )
            if i < len(a2a_inputs):
                x = a2a_inputs[i]
                # [b, cp*s, np//cp, hn] -> [b, cp*2, s//2, np//cp, hn]
                # or [cp*s, b, np//cp, hn] -> [cp*2, s//2, b, np//cp, hn]
                x = x.view(*x.shape[:seq_dim], cp_size * 2, -1, *x.shape[(seq_dim + 1) :])
                # reorder the sequence chunks
436
437
                a2a_inputs[i] = reorder_seq_chunks_for_a2a_after_attn(
                    x, chunk_ids_for_a2a, seq_dim, cp_size
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
                )
            if i > 1:
                with torch.cuda.stream(cp_stream):
                    a2a_reqs[i - 2].wait()
                    x = a2a_outputs[i - 2]
                    # [cp, 2, b, s//2, np//cp, hn] -> [b, 2, s//2, cp, np//cp, hn]
                    # or [cp, 2, s//2, b, np//cp, hn] -> [2, s//2, b, cp, np//cp, hn]
                    x = x.movedim(0, -3).movedim(0, seq_dim).contiguous()
                    # [b, 2, s//2, cp, np//cp, hn] -> [b*s, np, hn]
                    # or [2, s//2, b, cp, np//cp, hn] -> [s*b, np, hn]
                    a2a_outputs[i - 2] = x.view(-1, x.shape[-3] * x.shape[-2], x.shape[-1])
    torch.cuda.current_stream().wait_stream(cp_stream)
    return a2a_outputs[0] if len(a2a_inputs) == 1 else a2a_outputs


453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
_cu_seqlens_info_with_cp_cache = {}


def _get_cu_seqlens_info_with_cp(
    batch_size: int,
    max_seqlen: int,
    cp_size: int,
    cu_seqlens: torch.Tensor,
):
    """Cumulative sequence lengths with CP being considered."""
    global _cu_seqlens_info_with_cp_cache
    if (batch_size, max_seqlen, cp_size) not in _cu_seqlens_info_with_cp_cache:
        _cu_seqlens_info_with_cp_cache[(batch_size, max_seqlen, cp_size)] = (
            cu_seqlens // cp_size,
            cu_seqlens // (cp_size * 2),
        )
    return _cu_seqlens_info_with_cp_cache[(batch_size, max_seqlen, cp_size)]


472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
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
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
def get_fa_args(
    forward: bool,
    use_flash_attn_3: bool,
    qkv_format: str,
    cu_seqlens_q=None,
    cu_seqlens_kv=None,
    max_seqlen_q=None,
    max_seqlen_kv=None,
    dq=None,
    dk=None,
    dv=None,
):
    """Get forward/backward arguments for flash-attn v2 and v3."""
    if use_flash_attn_3:
        if forward:
            if qkv_format == "thd":
                return [
                    *[None] * 4,  # k_new, v_new, qv, out
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    *[None] * 3,  # cu_seqlens_k_new, seqused_q, seqused_k
                    max_seqlen_q,
                    max_seqlen_kv,
                    *[None]
                    * 8,  # page_table, kv_batch_idx, leftpad_k, rotary_cos, rotary_sin, q_descale, k_descale, v_descale
                ]
            return [
                *[None]
                * 9,  # k_new, v_new, qv, out, cu_seqlens_q, cu_seqlens_kv, cu_seqlens_k_new, seqused_q, seqused_k
                max_seqlen_q,
                max_seqlen_kv,
                *[None]
                * 8,  # page_table, kv_batch_idx, leftpad_k, rotary_cos, rotary_sin, q_descale, k_descale, v_descale
            ]
        if qkv_format == "thd":
            return [
                cu_seqlens_q,
                cu_seqlens_kv,
                None,  # sequed_q
                None,  # sequed_k
                max_seqlen_q,
                max_seqlen_kv,
                dq,
                dk,
                dv,
            ]
        return [
            None,  # cu_seqlens_q
            None,  # cu_seqlens_kv
            None,  # sequed_q
            None,  # sequed_k
            max_seqlen_q,
            max_seqlen_kv,
            dq,
            dk,
            dv,
        ]
    if forward:
        if qkv_format == "thd":
            return [
                cu_seqlens_q,
                cu_seqlens_kv,
                max_seqlen_q,
                max_seqlen_kv,
            ]
        return []
    if qkv_format == "thd":
        return [
            dq,
            dk,
            dv,
            cu_seqlens_q,
            cu_seqlens_kv,
            max_seqlen_q,
            max_seqlen_kv,
        ]
    return [
        dq,
        dk,
        dv,
    ]


555
class AttnFuncWithCPAndKVP2P(torch.autograd.Function):
556
    """
557
558
559
    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.
560
561
562
563
564

    This implementation also supports hierarchical CP, which parallelizes attention
    heads in low-level CP groups and parallelizes sequence dimension in high-level CP
    groups. For more details, please refer to `LongVILA <https://arxiv.org/abs/2408.10188>`_
    and `USP <https://arxiv.org/abs/2405.07719>`_.
565
566
567
    """

    @staticmethod
568
569
570
571
572
573
574
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
575
        cu_seqlens_kv,
576
        max_seqlen_q,
577
        max_seqlen_kv,
578
579
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
580
581
582
583
584
585
586
587
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
588
589
        fp8,
        fp8_meta,
590
591
592
        cp_group,
        cp_global_ranks,
        cp_stream,
593
        quantizers,
594
        pad_between_seqs,
595
        use_flash_attn_3,
596
    ):
597
        # pylint: disable=missing-function-docstring
598
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
599
600
601
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
        if isinstance(cp_group, list):
            assert (
                qkv_format != "thd"
            ), f"{qkv_format} format is not supported with hierarchical CP implementation yet!"
            assert attn_bias_type == "no_bias", (
                f"{attn_bias_type} bias type is not supported with hierarchical CP implementation"
                " yet!"
            )
            cp_group_a2a = cp_group[0]
            cp_size_a2a = get_distributed_world_size(cp_group_a2a)
            rank_a2a = get_distributed_rank(cp_group_a2a)
            cp_group = cp_group[1]
        else:
            cp_group_a2a = None
            cp_size_a2a = 1
            rank_a2a = 0

619
620
        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
621
622
        send_dst = cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
623
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0"))
624

625
626
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
627

628
        batch_dim = None
629
        seq_dim = None
630
        cu_seqlens_q_half, cu_seqlens_kv_half = None, None
631
        if qkv_format in ["bshd", "sbhd"]:
632
            seq_dim = qkv_format.index("s")
633
            qkv_layout = qkv_format + "_" + qkv_format[:-2] + "2" + qkv_format[-2:]
634
635
636
637
638
639
640
641
642
            cu_seqlens_q_padded, cu_seqlens_kv_padded = None, None
            if use_fused_attention:
                batch_dim = qkv_format.index("b")
                cu_seqlens_q, cu_seqlens_q_half = _get_cu_seqlens_info_with_cp(
                    q.shape[batch_dim], max_seqlen_q, cp_size, cu_seqlens_q
                )
                cu_seqlens_kv, cu_seqlens_kv_half = _get_cu_seqlens_info_with_cp(
                    q.shape[batch_dim], max_seqlen_kv, cp_size, cu_seqlens_kv
                )
643
644
        else:
            qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format
645
646
            cu_seqlens_q_padded = cu_seqlens_q_padded // cp_size
            cu_seqlens_kv_padded = cu_seqlens_kv_padded // cp_size
647
648
649
650
651

        max_seqlen_q = max_seqlen_q // cp_size
        max_seqlen_kv = max_seqlen_kv // cp_size
        cu_seqlens_q_per_step = [None for _ in range(cp_size)]
        cu_seqlens_kv_per_step = [None for _ in range(cp_size)]
652

653
        fused_attn_backend = None
654
        qkv_dtype = q.dtype
655
656
657
        amax_per_step = None
        S_quantizer_per_step = [None for _ in range(cp_size)]
        O_CP_quantizer_per_step = [None for _ in range(cp_size)]
658
659
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
660
661
662
663
664
665
666
667
668
669
670
        is_output_fp8 = False

        (
            QKV_quantizer,
            O_quantizer,
            O_CP_quantizer,
            S_quantizer,
            dQKV_quantizer,
            dQKV_CP_quantizer,
            dO_quantizer,
            dP_quantizer,
671
        ) = dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=True)
672

673
674
675
        if fp8:
            if use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
676

677
678
679
680
                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
681
682
683
684
685
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
                if is_input_fp8:
                    QKV_quantizer = q._quantizer
                    q, k, v = q._data, k._data, v._data
                else:
686
687
                    q_f16, k_f16, v_f16 = q, k, v
                    if cp_size_a2a == 1 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
688
                        q = QKV_quantizer(q_f16)._data
689
                    if int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
690
691
692
693
694
                        k, v = [QKV_quantizer(x)._data for x in [k_f16, v_f16]]
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                # partial result quantizer
                for i in range(cp_size):
                    S_quantizer_per_step[i] = S_quantizer.copy()
695
                    S_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
696
                    O_CP_quantizer_per_step[i] = O_CP_quantizer.copy()
697
                    O_CP_quantizer_per_step[i].amax = amax_per_step[1][i].reshape((1,))
698
699
700
701
702
703
704
705
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            q_f16 = q
            if use_fused_attention:
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

        if cp_size_a2a > 1:
706
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size_a2a, q.device)
707

708
709
710
711
712
            q, k, v = flash_attn_a2a_communicate(
                [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, True
            )
            if not fp8:
                q_f16 = q
713
            elif not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
714
                q_f16 = q
715
                q = QKV_quantizer(q_f16)._data
716

717
718
719
        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!"
720
        if causal:
721
722
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
723
                q, k, v = [x.view(x.shape[0], 2, x.shape[1] // 2, *x.shape[2:]) for x in [q, k, v]]
724
725
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
726
                q, k, v = [x.view(2, x.shape[0] // 2, *x.shape[1:]) for x in [q, k, v]]
727
        if attn_bias is not None:
728
            assert len(attn_bias.shape) == 4, (
729
730
731
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
732
733
734
            assert (
                attn_bias.shape[-2] % 2 == 0 and attn_bias.shape[-1] % (2 * cp_size) == 0
            ), "Sequence length does not meet divisible requirements!"
735
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
736
737
738
739
740
741
            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),
742
743
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
744
745
            attn_bias = attn_bias.view(
                *attn_bias.shape[:-1], 2 * cp_size, attn_bias.shape[-1] // (2 * cp_size)
746
            )
747
        assert q.shape[-1] % 8 == 0, "hidden size per attention head should be multiple of 8"
748

749
750
751
752
753
        softmax_lse_in_packed_format = False
        if qkv_format == "thd":
            if use_fused_attention:
                softmax_lse_in_packed_format = get_cudnn_version() >= (9, 6, 0)
            else:
754
                softmax_lse_in_packed_format = fa_utils.v2_6_0_plus or use_flash_attn_3
755

756
        flash_attn_fwd = None
757
758
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
759
760
761
762
            if use_flash_attn_3:
                flash_attn_fwd = (
                    _flash_attn_fwd_v3  # pylint: disable=possibly-used-before-assignment
                )
763
764
                fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
            else:
765
766
767
768
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
769
770
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
771
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
772
                    fa_forward_kwargs["window_size"] = (-1, 0) if causal else (-1, -1)
773
                elif fa_utils.v2_7_0_plus:
774
775
                    fa_forward_kwargs["window_size_left"] = -1
                    fa_forward_kwargs["window_size_right"] = 0 if causal else -1
776
                if fa_utils.v2_4_plus:
777
                    fa_forward_kwargs["alibi_slopes"] = None
778
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
779
                    fa_forward_kwargs["block_table"] = None
780
                if fa_utils.v2_6_0_plus:
781
                    fa_forward_kwargs["softcap"] = 0.0
782

783
784
785
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
786
        attn_bias_inputs = [None, None]
787
788
789
790
        # 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)]
791
        attn_biases = [None for _ in range(cp_size)]
792
793
794
795
796
797
798

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

        p2p_comm_buffers = [None for _ in range(cp_size)]
799
        if qkv_format in ["bshd", "sbhd"]:
800
801
802
            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)
803
804
        send_recv_reqs = [[], []]

805
        out = None
806
        for i in range(cp_size + 1):
807
            if i < cp_size:
808
                with torch.cuda.stream(flash_attn_streams[i % 2]):
809
                    # wait until KV is received
810
                    for req in send_recv_reqs[(i + 1) % 2]:
811
812
                        req.wait()

813
814
815
816
817
818
819
820
821
822
823
824
                    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,
                        )

825
                    if not fp8 or is_input_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
826
827
828
                        kv_inputs[i % 2] = p2p_comm_buffers[i]
                    else:
                        # KV exchange is in BF16/FP16, cast received KV in each step
829
                        kv_inputs[i % 2] = QKV_quantizer(p2p_comm_buffers[i])._data
830
831
                    if causal:
                        if i == 0:
832
                            if pad_between_seqs:
833
834
835
836
837
838
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                                cu_seqlens_kv_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_kv, cu_seqlens_kv_padded, cp_size, rank, True, True
                                )
839
840
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
841
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
842
843
844
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [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:]
                                )
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
861
                            if use_fused_attention:
862
863
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
864
865
866
867
868
869
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias[..., idx, :],
                                            attn_bias[..., (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
870
                                    ).contiguous()
871
872
873
874
875
876
877
878
879
880
881
882

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
883
                                fp8_meta_kwargs = {}
884
885
886
887
888
889
890
891
892
893
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
894
895
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
896

897
898
899
900
901
902
                                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],
903
904
905
906
907
                                    q_part,
                                    k_part,
                                    v_part,
                                    fake_dtype=qkv_dtype,
                                    fused_attention_backend=fused_attn_backend,
908
909
910
911
912
913
914
915
916
                                    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,
917
                                )
918
919
920
921
922
                                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
923
                            else:
924
925
926
927
928
929
930
931
932
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q,
                                    max_seqlen_kv=max_seqlen_kv,
                                )
933
                                fa_outputs = flash_attn_fwd(
934
                                    q_inputs[i % 2],
935
936
937
938
939
940
941
942
943
944
945
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
946
                                    causal=True,
947
                                    **fa_forward_kwargs,
948
                                )
949
                                if not fa_utils.v2_7_0_plus:
950
951
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
952
                                    if not use_flash_attn_3:
953
954
955
956
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
957
                                    if not use_flash_attn_3:
958
                                        rng_states[i] = fa_outputs[3]
959
                        elif i <= rank:
960
                            if pad_between_seqs:
961
962
963
964
965
966
967
968
969
970
971
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                                )
                                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,
                                )
972
973
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
974
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // (cp_size * 2)
975
976
977
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv_half
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                                q_inputs[i % 2] = q.view(q.shape[0], -1, *q.shape[-2:])
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][:, 0, ...]
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                                q_inputs[i % 2] = q.view(-1, *q.shape[-3:])
                                # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2][0]
                            elif qkv_format == "thd":
                                q_inputs[i % 2] = q
                                # [2, t, np, hn] -> [2, t/2, np, hn]
                                kv_inputs[i % 2] = tex.thd_read_half_tensor(
                                    kv_inputs[i % 2], cu_seqlens_kv_padded, 0
                                )
994
                            if use_fused_attention:
995
                                kv_inputs[i % 2] = kv_inputs[i % 2].contiguous()
996
997
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
998
                                    attn_bias_inputs[i % 2] = attn_bias[..., idx, :].contiguous()
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
1011
                                fp8_meta_kwargs = {}
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
1022
1023
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1024
1025
1026
1027
1028
1029
                                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],
1030
1031
1032
1033
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
                                    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,
1048
                                )
1049
1050
1051
1052
1053
                                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
1054
                            else:
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q,
                                    max_seqlen_kv=max_seqlen_kv // 2,
                                )
                                if use_flash_attn_3 or (
1065
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1066
                                ):
1067
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1068
                                elif fa_utils.v2_7_0_plus:
1069
1070
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1071
                                fa_outputs = flash_attn_fwd(
1072
                                    q_inputs[i % 2],
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
1084
                                    causal=False,
1085
                                    **fa_forward_kwargs,
1086
                                )
1087
                                if not fa_utils.v2_7_0_plus:
1088
1089
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1090
                                    if not use_flash_attn_3:
1091
1092
1093
1094
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1095
                                    if not use_flash_attn_3:
1096
                                        rng_states[i] = fa_outputs[3]
1097
                        else:
1098
                            if pad_between_seqs:
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
                                cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                    cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, False, True
                                )
                                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,
                                )
1110
1111
                            elif qkv_format == "thd":
                                cu_seqlens_q_per_step[i] = cu_seqlens_q // (cp_size * 2)
1112
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1113
1114
1115
                            else:
                                cu_seqlens_q_per_step[i] = cu_seqlens_q_half
                                cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
                            if qkv_format == "bshd":
                                # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                                q_inputs[i % 2] = q[:, 1, ...]
                                # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                                kv_inputs[i % 2] = kv_inputs[i % 2].view(
                                    k.shape[0], -1, 2, *k.shape[-2:]
                                )
                            elif qkv_format == "sbhd":
                                # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                                q_inputs[i % 2] = q[1]
                                # [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:]
                                )
                            elif qkv_format == "thd":
                                # [t, np, hn] -> [t/2, np, hn]
                                q_inputs[i % 2] = tex.thd_read_half_tensor(
                                    q, cu_seqlens_q_padded, 1
                                )
1135
                            if use_fused_attention:
1136
                                q_inputs[i % 2] = q_inputs[i % 2].contiguous()
1137
1138
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
1139
1140
1141
1142
1143
1144
                                    attn_bias_inputs[i % 2] = torch.cat(
                                        (
                                            attn_bias_[..., 1, :, idx, :],
                                            attn_bias_[..., 1, :, (2 * cp_size - idx - 1), :],
                                        ),
                                        dim=-1,
1145
                                    ).contiguous()
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157

                                q_part = q_inputs[i % 2]
                                k_part = (
                                    kv_inputs[i % 2][..., 0, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][0]
                                )
                                v_part = (
                                    kv_inputs[i % 2][..., 1, :, :]
                                    if qkv_format in ["bshd", "sbhd"]
                                    else kv_inputs[i % 2][1]
                                )
1158
                                fp8_meta_kwargs = {}
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
                                if fp8:
                                    q_part = QKV_quantizer.create_tensor_from_data(
                                        q_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    k_part = QKV_quantizer.create_tensor_from_data(
                                        k_part, fake_dtype=qkv_dtype, internal=True
                                    )
                                    v_part = QKV_quantizer.create_tensor_from_data(
                                        v_part, fake_dtype=qkv_dtype, internal=True
                                    )
1169
1170
                                    fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                    fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1171
1172
1173
1174
1175
1176
                                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],
1177
1178
1179
1180
                                    q_part,
                                    k_part,
                                    v_part,
                                    qkv_dtype,
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
                                    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,
1195
                                )
1196
1197
1198
1199
1200
                                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
1201
                            else:
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
                                fa_forward_args_thd = get_fa_args(
                                    True,
                                    use_flash_attn_3,
                                    qkv_format,
                                    cu_seqlens_q=cu_seqlens_q_per_step[i],
                                    cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                    max_seqlen_q=max_seqlen_q // 2,
                                    max_seqlen_kv=max_seqlen_kv,
                                )
                                if use_flash_attn_3 or (
1212
                                    fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
1213
                                ):
1214
                                    fa_forward_kwargs["window_size"] = (-1, -1)
1215
                                elif fa_utils.v2_7_0_plus:
1216
1217
                                    fa_forward_kwargs["window_size_left"] = -1
                                    fa_forward_kwargs["window_size_right"] = -1
1218
                                fa_outputs = flash_attn_fwd(
1219
                                    q_inputs[i % 2],
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
                                    (
                                        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]
                                    ),
                                    *fa_forward_args_thd,
1231
                                    causal=False,
1232
                                    **fa_forward_kwargs,
1233
                                )
1234
                                if not fa_utils.v2_7_0_plus:
1235
1236
                                    out_per_step[i] = fa_outputs[4]
                                    softmax_lse_per_step[i] = fa_outputs[5]
1237
                                    if not use_flash_attn_3:
1238
1239
1240
1241
                                        rng_states[i] = fa_outputs[7]
                                else:
                                    out_per_step[i] = fa_outputs[0]
                                    softmax_lse_per_step[i] = fa_outputs[1]
1242
                                    if not use_flash_attn_3:
1243
                                        rng_states[i] = fa_outputs[3]
1244
                    else:
1245
                        if pad_between_seqs:
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
                            cu_seqlens_q_per_step[i] = get_cu_seqlens_on_cp_rank(
                                cu_seqlens_q, cu_seqlens_q_padded, cp_size, rank, True, True
                            )
                            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,
                            )
1257
1258
                        elif qkv_format == "thd":
                            cu_seqlens_q_per_step[i] = cu_seqlens_q // cp_size
1259
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv // cp_size
1260
1261
1262
                        else:
                            cu_seqlens_q_per_step[i] = cu_seqlens_q
                            cu_seqlens_kv_per_step[i] = cu_seqlens_kv
1263
                        if use_fused_attention:
1264
1265
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
1266
1267
1268
1269
1270
1271
                                attn_bias_inputs[i % 2] = torch.cat(
                                    (
                                        attn_bias[..., idx, :],
                                        attn_bias[..., (2 * cp_size - idx - 1), :],
                                    ),
                                    dim=-1,
1272
                                ).contiguous()
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284

                            q_part = q
                            k_part = (
                                kv_inputs[i % 2][..., 0, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][0]
                            )
                            v_part = (
                                kv_inputs[i % 2][..., 1, :, :]
                                if qkv_format in ["bshd", "sbhd"]
                                else kv_inputs[i % 2][1]
                            )
1285
                            fp8_meta_kwargs = {}
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
                            if fp8:
                                q_part = QKV_quantizer.create_tensor_from_data(
                                    q_part, fake_dtype=qkv_dtype, internal=True
                                )
                                k_part = QKV_quantizer.create_tensor_from_data(
                                    k_part, fake_dtype=qkv_dtype, internal=True
                                )
                                v_part = QKV_quantizer.create_tensor_from_data(
                                    v_part, fake_dtype=qkv_dtype, internal=True
                                )
1296
1297
                                fp8_meta_kwargs["s_quantizer"] = S_quantizer_per_step[i]
                                fp8_meta_kwargs["o_quantizer"] = O_CP_quantizer_per_step[i]
1298
1299
1300
1301
1302
1303
                            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],
1304
1305
1306
1307
                                q_part,
                                k_part,
                                v_part,
                                qkv_dtype,
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
                                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,
1318
                            )
1319
1320
1321
1322
1323
                            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
1324
                        else:
1325
1326
1327
1328
1329
1330
1331
1332
1333
                            fa_forward_args_thd = get_fa_args(
                                True,
                                use_flash_attn_3,
                                qkv_format,
                                cu_seqlens_q=cu_seqlens_q_per_step[i],
                                cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                                max_seqlen_q=max_seqlen_q,
                                max_seqlen_kv=max_seqlen_kv,
                            )
1334
                            fa_outputs = flash_attn_fwd(
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
                                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]
                                ),
                                *fa_forward_args_thd,
1347
                                causal=False,
1348
                                **fa_forward_kwargs,
1349
                            )
1350
                            if not fa_utils.v2_7_0_plus:
1351
1352
                                out_per_step[i] = fa_outputs[4]
                                softmax_lse_per_step[i] = fa_outputs[5]
1353
                                if not use_flash_attn_3:
1354
1355
1356
1357
                                    rng_states[i] = fa_outputs[7]
                            else:
                                out_per_step[i] = fa_outputs[0]
                                softmax_lse_per_step[i] = fa_outputs[1]
1358
                                if not use_flash_attn_3:
1359
                                    rng_states[i] = fa_outputs[3]
1360
1361
1362
1363

            if i > 0:
                # wait until fwd restuls correction of last step is done
                if i > 1:
1364
                    flash_attn_streams[(i - 1) % 2].wait_event(fwd_results_correction_done)
1365

1366
                with torch.cuda.stream(flash_attn_streams[(i - 1) % 2]):
1367
1368
1369
1370
1371
1372
1373
1374
                    if use_fused_attention:
                        # [b, np, sq, 1] -> [b, np, sq] or
                        # [t, np, 1] -> [t, np]
                        softmax_lse_per_step[i - 1].squeeze_(-1)
                        if softmax_lse_in_packed_format:
                            softmax_lse_per_step[i - 1] = (
                                softmax_lse_per_step[i - 1].transpose(0, 1).contiguous()
                            )
1375
                    if fp8:
1376
                        out_per_step[i - 1] = out_per_step[i - 1].dequantize(dtype=torch.float32)
1377
1378
                    if i == 1:
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
1379
1380
                        if qkv_format == "thd":
                            out = torch.zeros_like(q if not fp8 else out_per_step[0]).view(q.shape)
1381
1382
1383
1384
                    elif (i - 1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(
                            softmax_lse, softmax_lse_per_step[i - 1]
                        )
1385
                    else:
1386
                        if qkv_format == "thd":
1387
                            tex.thd_second_half_lse_correction(
1388
1389
1390
                                softmax_lse,
                                softmax_lse_per_step[i - 1],
                                cu_seqlens_q_padded,
1391
                                softmax_lse_in_packed_format,
1392
                            )
1393
                        else:
1394
1395
1396
                            flash_attn_fwd_second_half_softmax_lse_correction(
                                softmax_lse.view(*softmax_lse.shape[:-1], 2, -1),
                                softmax_lse_per_step[i - 1],
1397
                            )
1398
1399

                if i < cp_size:
1400
                    flash_attn_streams[(i - 1) % 2].record_event(fwd_results_correction_done)
1401
1402
1403

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

1404
1405
1406
1407
        second_half_lse_seqlen = None
        if causal and rank < (cp_size - 1):
            second_half_lse_seqlen = softmax_lse_per_step[-1].shape[-1]

1408
1409
        softmax_lse = softmax_lse.to(torch.float)
        for i in range(cp_size):
1410
            if i <= rank or not causal:
1411
                if qkv_format in ["bshd", "sbhd"]:
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
                    if i == 0:
                        out = flash_attn_fwd_out_correction_init(
                            out_per_step[0],
                            softmax_lse,
                            softmax_lse_per_step[0],
                            seq_dim,
                        )
                        out = out.view(q.shape)
                    else:
                        flash_attn_fwd_out_correction(
                            out.view(*out_per_step[i].shape),
                            out_per_step[i],
                            softmax_lse,
                            softmax_lse_per_step[i],
                            seq_dim,
                        )
1428
                elif qkv_format == "thd":
1429
1430
1431
1432
1433
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1434
                        cu_seqlens_q_padded,
1435
                        False,
1436
                        softmax_lse_in_packed_format,
1437
                    )
1438
            else:
1439
                if qkv_format in ["bshd", "sbhd"]:
1440
1441
                    flash_attn_fwd_second_half_out_correction(
                        out,
1442
                        out_per_step[i],
1443
                        softmax_lse,
1444
                        softmax_lse_per_step[i],
1445
                        seq_dim,
1446
                    )
1447
                elif qkv_format == "thd":
1448
1449
1450
1451
1452
                    tex.thd_out_correction(
                        out,
                        out_per_step[i],
                        softmax_lse,
                        softmax_lse_per_step[i],
1453
                        cu_seqlens_q_padded,
1454
                        True,
1455
                        softmax_lse_in_packed_format,
1456
                    )
1457
1458

        kv = p2p_comm_buffers[-1]
1459
1460
1461
1462
1463
1464
1465
1466
        if qkv_format == "bshd":
            out = out.view(out.shape[0], -1, *out.shape[-2:])
            ctx.batch_size = out.shape[0]
        elif qkv_format == "sbhd":
            out = out.view(-1, *out.shape[-3:])
            ctx.batch_size = out.shape[1]

        if cp_size_a2a > 1:
1467
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, out.device)
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
            out = flash_attn_a2a_communicate(
                out, chunk_ids_for_a2a, seq_dim, cp_size_a2a, cp_group_a2a, cp_stream, False
            )
            if use_fused_attention:
                if qkv_format == "bshd":
                    # [b*s, np, hn] -> [b, s, np, hn]
                    out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                elif qkv_format == "sbhd":
                    # [s*b, np, hn] -> [s, b, np, hn]
                    out = out.view(-1, ctx.batch_size, *out.shape[-2:])
        elif not use_fused_attention:
1479
            out = out.view(-1, *out.shape[-2:])
1480

1481
1482
        if fp8 and use_fused_attention:
            amax_cp_fwd = amax_per_step.amax(dim=1)
1483
1484
            S_quantizer.amax.copy_(amax_cp_fwd[0])
            O_CP_quantizer.amax.copy_(amax_cp_fwd[1])
1485

1486
        out_fp8 = None
1487
        out_f16 = out.to(qkv_dtype)
1488

1489
        if fp8 and (is_output_fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1"))):
1490
1491
1492
            out_fp8 = O_quantizer(out_f16)  # final result

        out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16
1493
1494

        if fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
1495
            q_save, kv_save, out_save = q, kv, out_fp8._data
1496
        elif fp8 and is_input_fp8:
1497
            q_save, kv_save, out_save = q, kv, out_f16
1498
        else:
1499
            q_f16 = q_f16.view(q.shape)
1500
1501
            q_save, kv_save, out_save = q_f16, kv, out_f16

1502
        tensors_to_save, tensor_objects = prepare_for_saving(
1503
1504
1505
            q_save,
            kv_save,
            out_save,
1506
            softmax_lse,
1507
1508
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
1509
1510
            *cu_seqlens_q_per_step,
            *cu_seqlens_kv_per_step,
1511
1512
            *rng_states,
            *attn_biases,
1513
        )
1514
1515
1516
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

1517
1518
1519
        ctx.cp_group_a2a = cp_group_a2a
        ctx.cp_size_a2a = cp_size_a2a
        ctx.rank_a2a = rank_a2a
1520
1521
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
1522
        ctx.cp_stream = cp_stream
1523
1524
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
1525
        ctx.max_seqlen_kv = max_seqlen_kv
1526
        ctx.softmax_scale = softmax_scale
1527
        ctx.qkv_format = qkv_format
1528
        ctx.attn_mask_type = attn_mask_type
1529
1530
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
1531
        ctx.deterministic = deterministic
1532
        ctx.use_fused_attention = use_fused_attention
1533
        ctx.softmax_lse_in_packed_format = softmax_lse_in_packed_format
1534
        ctx.second_half_lse_seqlen = second_half_lse_seqlen
1535
1536
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
1537
1538
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
1539
        ctx.use_flash_attn_3 = use_flash_attn_3
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555

        ctx.qkv_dtype = qkv_dtype
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dQKV_CP_quantizer = dQKV_CP_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.S_quantizer = S_quantizer
        if ctx.fp8:
            ctx.QKV_quantizer = QKV_quantizer.copy()
            ctx.QKV_quantizer.scale = QKV_quantizer.scale.clone()
            ctx.O_quantizer = O_quantizer.copy()
            ctx.O_quantizer.scale = O_quantizer.scale.clone()
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
1556
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.forward")
1557

1558
        return out_ret
1559
1560
1561

    @staticmethod
    def backward(ctx, dout):
1562
        # pylint: disable=missing-function-docstring
1563
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
1564
1565
1566
        cp_size_a2a = ctx.cp_size_a2a
        rank_a2a = ctx.rank_a2a

1567
1568
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)
1569
1570
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size * cp_size_a2a + rank_a2a]
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size * cp_size_a2a + rank_a2a]
1571
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0"))
1572

1573
        q, kv, out, softmax_lse, cu_seqlens_q_padded, cu_seqlens_kv_padded, *other_tensors = (
1574
            restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
1575
1576
1577
1578
1579
        )
        cu_seqlens_q_per_step = other_tensors[:cp_size]
        cu_seqlens_kv_per_step = other_tensors[cp_size : cp_size * 2]
        rng_states = other_tensors[cp_size * 2 : cp_size * 3]
        attn_biases = other_tensors[cp_size * 3 : cp_size * 4]
1580

1581
1582
        causal = "causal" in ctx.attn_mask_type
        padding = "padding" in ctx.attn_mask_type
1583
1584

        seq_dim = None
1585
        if ctx.qkv_format in ["bshd", "sbhd"]:
1586
            seq_dim = ctx.qkv_format.index("s")
1587
1588
1589
            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
1590

1591
        if attn_biases[0] is not None:
1592
1593
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
1594
                *ctx.attn_bias_shape, dtype=attn_biases[0].dtype, device=attn_biases[0].device
1595
1596
1597
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
1598
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3] // 2, *attn_dbias.shape[-2:]
1599
1600
1601
            )
        else:
            attn_dbias = None
1602
            attn_dbias_ = None
1603

1604
1605
        softmax_lse_ = None
        if causal and ctx.second_half_lse_seqlen is not None:
1606
            if ctx.qkv_format == "thd":
1607
                softmax_lse_ = tex.thd_read_second_half_lse(
1608
1609
1610
1611
                    softmax_lse,
                    cu_seqlens_q_padded,
                    ctx.softmax_lse_in_packed_format,
                    ctx.second_half_lse_seqlen,
1612
                )
1613
1614
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
1615
                softmax_lse_ = softmax_lse.view(*softmax_lse.shape[:-1], 2, -1)
1616
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
1617
1618
1619
1620
1621
1622
            if ctx.use_fused_attention:
                if ctx.softmax_lse_in_packed_format:
                    softmax_lse_ = softmax_lse_.transpose(0, 1).contiguous()
                # [b, np, sq//2] -> [b, np, sq//2, 1] or
                # [t//2, np] -> [t//2, np, 1]
                softmax_lse_.unsqueeze_(-1)
1623
        if ctx.use_fused_attention:
1624
1625
1626
1627
            if ctx.softmax_lse_in_packed_format:
                softmax_lse = softmax_lse.transpose(0, 1).contiguous()
            # [b, np, sq] -> [b, np, sq, 1] or
            # [t, np] -> [t, np, 1]
1628
            softmax_lse.unsqueeze_(-1)
1629
            dout = dout.contiguous()
1630

1631
        dq = None
1632
        dout_dtype = dout.dtype
1633
1634
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
1635
1636
1637
        amax_per_step = None
        dP_quantizer_per_step = [None for _ in range(cp_size)]
        dQKV_CP_quantizer_per_step = [None for _ in range(cp_size)]
1638
1639
1640
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
1641

1642
                if ctx.is_output_fp8:
1643
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
1644
                    ctx.dO_quantizer = dout._quantizer
1645
                else:
1646
                    dout = ctx.dO_quantizer(dout)
1647
1648
1649
1650
1651
1652
                fused_attn_dqkv_dtype = TE_DType[dout._data.dtype]
                dq_fp8 = torch.empty((cp_size, *q.shape), dtype=dout._data.dtype, device=q.device)
                dkv_fp8 = torch.empty(
                    (cp_size, *kv.shape), dtype=dout._data.dtype, device=kv.device
                )
                dkv_fp8_ = torch.empty_like(dkv_fp8)
1653
                p2p_comm_buffers = [[kv, dkv_fp8], [torch.empty_like(kv), dkv_fp8_]]
1654
                dout = dout._data
1655
                fp8_meta_kwargs = {}
1656
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
1657
1658
1659
                amax_per_step = torch.zeros((2, cp_size), dtype=torch.float32, device=q.device)
                for i in range(cp_size):
                    dP_quantizer_per_step[i] = ctx.dP_quantizer.copy()
1660
                    dP_quantizer_per_step[i].amax = amax_per_step[0][i].reshape((1,))
1661
                    dQKV_CP_quantizer_per_step[i] = ctx.dQKV_CP_quantizer.copy()
1662
                    dQKV_CP_quantizer_per_step[i].amax = amax_per_step[1][i].reshape((1,))
1663
1664
1665
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
            if ctx.fp8_meta is not None:
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    kv = ctx.QKV_quantizer.create_tensor_from_data(
                        kv, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q = q.dequantize(dtype=ctx.qkv_dtype)
                    kv = kv.dequantize(dtype=ctx.qkv_dtype)
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    if cp_size_a2a == 1:
                        dout = dout.dequantize(dtype=dout_dtype)
                    else:
                        ctx.dO_quantizer = dout._quantizer
                        dout = dout._data
1683
1684
1685
1686
1687
1688
1689
1690
            dq = torch.empty_like(q)
            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 = {}
1691
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
1692
1693
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

1694
1695
1696
1697
        if cp_size_a2a > 1:
            if not ctx.use_fused_attention:
                out = out.view(ctx.batch_size, -1, *out.shape[-2:])
                dout = dout.view(*out.shape)
1698
1699
1700
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(
                cp_size_a2a, out.device
            )
1701
1702
1703
1704
1705
1706
1707
1708
1709
            out, dout = flash_attn_a2a_communicate(
                [out, dout],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                True,
            )
1710
            if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
1711
1712
1713
1714
                dout = ctx.dO_quantizer.create_tensor_from_data(
                    dout, fake_dtype=dout_dtype, internal=True
                )
                dout = dout.dequantize(dtype=dout_dtype)
1715

1716
1717
1718
1719
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        send_recv_reqs = []

1720
        flash_attn_bwd = None
1721
1722
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
1723
1724
1725
1726
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
1727
1728
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
1729
1730
1731
1732
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
1733
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
1734
                if fa_utils.v2_4_plus:
1735
                    fa_backward_kwargs["alibi_slopes"] = None
1736
                if fa_utils.v2_4_1_plus:
1737
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
1738
                if fa_utils.v2_6_0_plus:
1739
                    fa_backward_kwargs["softcap"] = 0.0
1740

1741
1742
1743
1744
1745
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

1746
1747
            send_tensor = p2p_comm_buffers[i % 2]
            recv_tensor = p2p_comm_buffers[(i + 1) % 2]
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
            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
                )
1777

1778
            kv = p2p_comm_buffers[i % 2][0]
1779
1780
            q_, kv_, out_, dout_ = None, None, None, None
            dq_, dk_, dv_ = None, None, None
1781
            # In reversed order of fwd
1782
            if causal:
1783
                if i == (cp_size - 1):
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        q_, kv_, out_, dout_ = q, kv, out, dout
1798
                    if ctx.use_fused_attention:
1799
1800
1801
1802
1803
1804
1805
1806
                        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]]
1807
                        if attn_dbias is not None:
1808
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
1829
                                dout_part, fake_dtype=dout_dtype, internal=True
1830
                            )
1831
1832
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1833
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1834
                            ctx.max_seqlen_q,
1835
1836
1837
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1838
1839
1840
1841
1842
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1843
                            dout_dtype,
1844
                            fused_attn_dqkv_dtype,
1845
                            aux_ctx_tensors,
1846
                            fused_attn_backend,
1847
1848
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
                            cu_seqlens_kv_padded=cu_seqlens_kv_padded,
1849
1850
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1851
                            qkv_layout=qkv_layout,
1852
                            attn_mask_type=ctx.attn_mask_type,
1853
                            attn_bias_type=ctx.attn_bias_type,
1854
1855
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1856
                        )
1857
1858
1859
1860
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1861
                    else:
1862
                        dq_ = torch.empty_like(q_)
1863
                        dkv_ = torch.empty_like(kv_)
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=ctx.max_seqlen_kv,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
1887
                            fa_backward_kwargs["window_size"] = (-1, 0)
1888
                        elif fa_utils.v2_7_0_plus:
1889
1890
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = 0
1891
                        if not ctx.use_flash_attn_3:
1892
1893
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
1894
1895
                            dout_,
                            q_,
1896
1897
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
1898
1899
                            out_,
                            softmax_lse,
1900
                            *fa_backward_args_thd,
1901
1902
                            causal=True,
                            **fa_backward_kwargs,
1903
                        )
1904
                elif i >= (cp_size - rank - 1):
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                        q_, out_, dout_ = [
                            x.view(x.shape[0], -1, *x.shape[-2:]) for x in [q, out, dout]
                        ]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk//2, 2, np, hn]
                        kv_ = kv[:, 0]
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                        q_, out_, dout_ = [x.view(-1, *x.shape[-3:]) for x in [q, out, dout]]
                        # [2, sk//2, b, 2, np, hn] -> [sk//2, b, 2, np, hn]
                        kv_ = kv[0]
                    elif ctx.qkv_format == "thd":
                        q_, out_, dout_ = q, out, dout
                        # [2, t, np, hn] -> [2, t/2, np, hn]
                        kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_kv_padded, 0)
1921
                    if ctx.use_fused_attention:
1922
                        kv_ = kv_.contiguous()
1923
1924
1925
1926
1927
1928
1929
1930
                        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]]
1931
                        if attn_dbias is not None:
1932
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
1953
                                dout_part, fake_dtype=dout_dtype, internal=True
1954
                            )
1955
1956
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
1957
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1958
                            ctx.max_seqlen_q,
1959
1960
1961
                            ctx.max_seqlen_kv // 2,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
1962
1963
1964
1965
1966
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
1967
                            dout_dtype,
1968
                            fused_attn_dqkv_dtype,
1969
                            aux_ctx_tensors,
1970
                            fused_attn_backend,
1971
1972
1973
1974
                            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
                            ),
1975
1976
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1977
                            qkv_layout=qkv_layout,
1978
                            attn_mask_type="padding" if padding else "no_mask",
1979
                            attn_bias_type=ctx.attn_bias_type,
1980
1981
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
1982
                        )
1983
1984
1985
1986
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
1987
                    else:
1988
                        dq_ = torch.empty_like(q_)
1989
                        dkv_ = torch.empty_like(kv_)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=ctx.max_seqlen_kv // 2,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2013
                            fa_backward_kwargs["window_size"] = (-1, -1)
2014
                        elif fa_utils.v2_7_0_plus:
2015
2016
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2017
                        if not ctx.use_flash_attn_3:
2018
2019
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2020
2021
                            dout_,
                            q_,
2022
2023
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2024
2025
                            out_,
                            softmax_lse,
2026
                            *fa_backward_args_thd,
2027
2028
                            causal=False,
                            **fa_backward_kwargs,
2029
2030
                        )
                else:
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
                    if ctx.qkv_format == "bshd":
                        # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                        q_, out_, dout_ = q[:, 1], out[:, 1], dout[:, 1]
                        # [b, 2, sk//2, 2, np, hn] -> [b, sk, 2, np, hn]
                        kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                    elif ctx.qkv_format == "sbhd":
                        # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                        q_, out_, dout_ = q[1], out[1], dout[1]
                        # [2, sk//2, b, 2, np, hn] -> [sk, b, 2, np, hn]
                        kv_ = kv.view(-1, *kv.shape[-4:])
                    elif ctx.qkv_format == "thd":
                        # [t, np, hn] -> [t/2, np, hn]
                        q_, out_, dout_ = [
                            tex.thd_read_half_tensor(x, cu_seqlens_q_padded, 1)
                            for x in [q, out, dout]
                        ]
                        kv_ = kv
2048
                    if ctx.use_fused_attention:
2049
                        q_, out_, dout_ = [x.contiguous() for x in [q_, out_, dout_]]
2050
2051
2052
2053
2054
2055
2056
2057
                        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]]
2058
                        if attn_dbias is not None:
2059
                            aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080

                        q_part = q_
                        k_part = kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0]
                        v_part = kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1]
                        out_part = out_
                        dout_part = dout_

                        if ctx.fp8:
                            q_part = ctx.QKV_quantizer.create_tensor_from_data(
                                q_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            k_part = ctx.QKV_quantizer.create_tensor_from_data(
                                k_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            v_part = ctx.QKV_quantizer.create_tensor_from_data(
                                v_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            out_part = ctx.O_quantizer.create_tensor_from_data(
                                out_part, fake_dtype=ctx.qkv_dtype, internal=True
                            )
                            dout_part = ctx.dO_quantizer.create_tensor_from_data(
2081
                                dout_part, fake_dtype=dout_dtype, internal=True
2082
                            )
2083
2084
                            fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                            fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2085
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2086
                            ctx.max_seqlen_q // 2,
2087
2088
2089
                            ctx.max_seqlen_kv,
                            cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv_per_step[cp_size - i - 1],
2090
2091
2092
2093
2094
                            q_part,
                            k_part,
                            v_part,
                            out_part,
                            dout_part,
2095
                            dout_dtype,
2096
                            fused_attn_dqkv_dtype,
2097
                            aux_ctx_tensors,
2098
                            fused_attn_backend,
2099
2100
2101
2102
                            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,
2103
2104
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
2105
                            qkv_layout=qkv_layout,
2106
                            attn_mask_type="padding" if padding else "no_mask",
2107
                            attn_bias_type=ctx.attn_bias_type,
2108
2109
                            deterministic=ctx.deterministic,
                            **fp8_meta_kwargs,
2110
                        )
2111
2112
2113
2114
                        if ctx.fp8:
                            dq_ = dq_._data
                            dk_ = dk_._data
                            dv_ = dv_._data
2115
                    else:
2116
                        dq_ = torch.empty_like(q_)
2117
                        dkv_ = torch.empty_like(kv_)
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                            cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                            max_seqlen_q=ctx.max_seqlen_q // 2,
                            max_seqlen_kv=ctx.max_seqlen_kv,
                            dq=dq_,
                            dk=(
                                dkv_[..., 0, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[0]
                            ),
                            dv=(
                                dkv_[..., 1, :, :]
                                if ctx.qkv_format in ["bshd", "sbhd"]
                                else dkv_[1]
                            ),
                        )
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2141
                            fa_backward_kwargs["window_size"] = (-1, -1)
2142
                        elif fa_utils.v2_7_0_plus:
2143
2144
                            fa_backward_kwargs["window_size_left"] = -1
                            fa_backward_kwargs["window_size_right"] = -1
2145
                        if not ctx.use_flash_attn_3:
2146
2147
                            fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                        flash_attn_bwd(
2148
2149
                            dout_,
                            q_,
2150
2151
                            kv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[0],
                            kv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv_[1],
2152
2153
                            out_,
                            softmax_lse_,
2154
                            *fa_backward_args_thd,
2155
2156
                            causal=False,
                            **fa_backward_kwargs,
2157
2158
2159
                        )
            else:
                if ctx.use_fused_attention:
2160
2161
2162
2163
                    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]]
2164
                    if attn_dbias is not None:
2165
                        aux_ctx_tensors += [attn_biases[cp_size - i - 1]]
2166
2167
2168
2169
2170
2171
2172
2173
                    q_part = q
                    k_part = kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0]
                    v_part = kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1]
                    out_part = out
                    dout_part = dout

                    if ctx.fp8:
                        q_part = ctx.QKV_quantizer.create_tensor_from_data(
2174
                            q_part, fake_dtype=ctx.qkv_dtype, internal=True
2175
2176
                        )
                        k_part = ctx.QKV_quantizer.create_tensor_from_data(
2177
                            k_part, fake_dtype=ctx.qkv_dtype, internal=True
2178
2179
                        )
                        v_part = ctx.QKV_quantizer.create_tensor_from_data(
2180
                            v_part, fake_dtype=ctx.qkv_dtype, internal=True
2181
2182
                        )
                        out_part = ctx.O_quantizer.create_tensor_from_data(
2183
                            out_part, fake_dtype=ctx.qkv_dtype, internal=True
2184
2185
                        )
                        dout_part = ctx.dO_quantizer.create_tensor_from_data(
2186
                            dout_part, fake_dtype=dout_dtype, internal=True
2187
                        )
2188
2189
                        fp8_meta_kwargs["dp_quantizer"] = dP_quantizer_per_step[i]
                        fp8_meta_kwargs["dqkv_quantizer"] = dQKV_CP_quantizer_per_step[i]
2190
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
2191
                        ctx.max_seqlen_q,
2192
2193
2194
                        ctx.max_seqlen_kv,
                        cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv_per_step[cp_size - i - 1],
2195
2196
2197
2198
2199
                        q_part,
                        k_part,
                        v_part,
                        out_part,
                        dout_part,
2200
                        dout_dtype,
2201
                        fused_attn_dqkv_dtype,
2202
                        aux_ctx_tensors,
2203
                        fused_attn_backend,
2204
2205
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
2206
2207
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
2208
                        qkv_layout=qkv_layout,
2209
                        attn_mask_type=ctx.attn_mask_type,
2210
                        attn_bias_type=ctx.attn_bias_type,
2211
2212
                        deterministic=ctx.deterministic,
                        **fp8_meta_kwargs,
2213
                    )
2214
2215
2216
2217
2218
2219

                    if ctx.fp8:
                        dq_ = dq_._data
                        dk_ = dk_._data
                        dv_ = dv_._data

2220
                else:
2221
2222
                    dq_ = torch.empty_like(q)
                    dkv_ = torch.empty_like(kv)
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
                    fa_backward_args_thd = get_fa_args(
                        False,
                        ctx.use_flash_attn_3,
                        ctx.qkv_format,
                        cu_seqlens_q=cu_seqlens_q_per_step[cp_size - i - 1],
                        cu_seqlens_kv=cu_seqlens_kv_per_step[cp_size - i - 1],
                        max_seqlen_q=ctx.max_seqlen_q,
                        max_seqlen_kv=ctx.max_seqlen_kv,
                        dq=dq_,
                        dk=dkv_[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[0],
                        dv=dkv_[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else dkv_[1],
                    )
                    if ctx.use_flash_attn_3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2236
                        fa_backward_kwargs["window_size"] = (-1, -1)
2237
                    elif fa_utils.v2_7_0_plus:
2238
2239
                        fa_backward_kwargs["window_size_left"] = -1
                        fa_backward_kwargs["window_size_right"] = -1
2240
                    if not ctx.use_flash_attn_3:
2241
2242
                        fa_backward_kwargs["rng_state"] = rng_states[cp_size - i - 1]
                    flash_attn_bwd(
2243
2244
2245
2246
2247
                        dout,
                        q,
                        kv[..., 0, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[0],
                        kv[..., 1, :, :] if ctx.qkv_format in ["bshd", "sbhd"] else kv[1],
                        out,
2248
                        softmax_lse,
2249
                        *fa_backward_args_thd,
2250
2251
                        causal=False,
                        **fa_backward_kwargs,
2252
2253
                    )

2254
2255
            if ctx.fp8:
                dq = dq_fp8[(rank + i + 1) % cp_size]
2256
2257
2258
            if causal and ctx.qkv_format in ["bshd", "sbhd"] and i >= (cp_size - rank - 1):
                # [b, sq, np, hn] -> [b, 2, sq//2, np, hn] or
                # [sq, b, np, hn] -> [2, sq//2, b, np, hn]
2259
                dq_ = dq_.view(*dq.shape)
2260

2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
            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:
2272
                if i > (cp_size - rank - 1):
2273
                    dq.add_(dq_)
2274
2275
                elif i == (cp_size - rank - 1):
                    if rank == (cp_size - 1):
2276
2277
                        dq.copy_(dq_)
                    else:
2278
2279
2280
2281
2282
2283
                        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])
2284
                        elif ctx.qkv_format == "thd":
2285
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "copy", "add")
2286
                elif i > 0:
2287
2288
2289
2290
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
2291
                    elif ctx.qkv_format == "thd":
2292
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "add")
2293
                else:
2294
2295
2296
2297
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
2298
                    elif ctx.qkv_format == "thd":
2299
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q_padded, "none", "copy")
2300
2301
2302
2303
2304
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
2305

2306
            if attn_dbias is not None:
2307
                idx = (rank + i + 1) % cp_size
2308
                if i == (cp_size - 1) or not causal:
2309
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
2310
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2311
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
2312
2313
                    attn_dbias[..., (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size - rank - 1):
2314
2315
2316
2317
                    # [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)]
2318
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1] // 2)
2319
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
2320
                    attn_dbias_[..., 1, :, (2 * cp_size - idx - 1), :].copy_(dbias_[..., 1, :])
2321

2322
2323
2324
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
2325

2326
2327
2328
2329
2330
2331
2332
            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]
2333
            if ctx.use_fused_attention:
2334
                if ctx.qkv_format in ["bshd", "sbhd"]:
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
                    dkv_ = _combine_tensors([dk_, dv_], -2)
                elif ctx.qkv_format == "thd":
                    dkv_ = torch.cat(
                        (dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0
                    )  # pylint: disable=used-before-assignment
            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:])
                dkv_ = dkv_.movedim(-3, 0)
                if causal and (i < (cp_size - rank - 1) or i == (cp_size - 1)):
                    # [2, b, sk, np, hn] -> [2, b, 2, sk//2, np, hn] or
                    # [2, sk, b, np, hn] -> [2, 2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(*dkv.shape)
2349

2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
            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:
2361
                if i == (cp_size - 1):
2362
                    if rank == 0:
2363
2364
2365
2366
2367
2368
                        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, ...])
2369
                        elif ctx.qkv_format == "thd":
2370
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "copy")
2371
2372
                    else:
                        dkv.add_(dkv_)
2373
2374
                elif i >= (cp_size - rank - 1):
                    if i == 0 and rank == (cp_size - 1):
2375
2376
2377
2378
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
2379
                        elif ctx.qkv_format == "thd":
2380
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "copy", "none")
2381
                    else:
2382
2383
2384
2385
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
2386
                        elif ctx.qkv_format == "thd":
2387
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_kv_padded, "add", "none")
2388
2389
2390
2391
2392
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
2393
2394
2395
2396
2397
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

2398
        if ctx.fp8 and ctx.use_fused_attention:
2399
            amax_cp_bwd = amax_per_step.amax(dim=1)
2400
2401
            ctx.dP_quantizer.amax.copy_(amax_cp_bwd[0])
            ctx.dQKV_CP_quantizer.amax.copy_(amax_cp_bwd[1])
2402
2403
2404
2405
            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:])
2406
2407
2408
2409
2410
2411
2412
            dq = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dq_fp8, fake_dtype=torch.float32, internal=True
            )
            dkv = ctx.dQKV_CP_quantizer.create_tensor_from_data(
                dkv_fp8, fake_dtype=torch.float32, internal=True
            )
            dq, dkv = [x.dequantize(dtype=torch.float32) for x in [dq, dkv]]
2413
2414
            dq, dkv = [x.sum(dim=0).to(dout_dtype) for x in [dq, dkv]]

2415
        if causal:
2416
2417
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
2418
                dq = dq.view(dq.shape[0], -1, *dq.shape[-2:])
2419
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
2420
                dkv = dkv.view(*dkv.shape[0:2], -1, *dkv.shape[-2:])
2421
2422
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
2423
                dq = dq.view(-1, *dq.shape[-3:])
2424
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
2425
2426
                dkv = dkv.view(dkv.shape[0], -1, *dkv.shape[-3:])

2427
2428
2429
        if ctx.qkv_format == "thd" and not ctx.use_fused_attention:
            dq[cu_seqlens_q_padded[-1] :].fill_(0)
            dkv[:, cu_seqlens_kv_padded[-1] :].fill_(0)
2430

2431
        if ctx.fp8 and ctx.is_input_fp8:
2432
2433
            assert torch.uint8 not in [dq.dtype, dkv.dtype]
            dq, dkv = [ctx.dQKV_quantizer(x)._data for x in [dq, dkv]]
2434
2435
2436
        dk, dv = dkv[0], dkv[1]

        if cp_size_a2a > 1:
2437
            chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size_a2a, q.device)
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
            dq, dk, dv = flash_attn_a2a_communicate(
                [dq, dk, dv],
                chunk_ids_for_a2a,
                seq_dim,
                cp_size_a2a,
                ctx.cp_group_a2a,
                ctx.cp_stream,
                False,
            )
            if ctx.qkv_format == "bshd":
                dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
            elif ctx.qkv_format == "sbhd":
                dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

2452
2453
2454
        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)
2455
2456
        # converting torch.uint8 to float8tensor
        if ctx.fp8 and ctx.is_input_fp8:
2457
2458
2459
            dq = ctx.dQKV_quantizer.create_tensor_from_data(dq, fake_dtype=dout_dtype)
            dk = ctx.dQKV_quantizer.create_tensor_from_data(dk, fake_dtype=dout_dtype)
            dv = ctx.dQKV_quantizer.create_tensor_from_data(dv, fake_dtype=dout_dtype)
2460
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVP2P.backward")
2461

2462
2463
2464
        return (
            None,
            dq,
2465
2466
            dk,
            dv,
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2478
            attn_dbias,
2479
2480
2481
2482
2483
            None,
            None,
            None,
            None,
            None,
2484
2485
            None,
            None,
2486
            None,
2487
            None,
2488
            None,
2489
        )
2490
2491


2492
2493
def get_kv_seq_info_after_all_gather(
    local_chunk_id, cp_size, max_seqlen_q, max_seqlen_kv, window_size, causal
2494
):
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
    """Compute KV sequence index range and update window size after all-gather."""
    local_chunk_end_idx = (local_chunk_id + 1) * max_seqlen_kv
    full_seq_end_idx = max_seqlen_kv * cp_size * 2

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

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

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

    return (seq_start_idx, seq_end_idx), (window_size_left, window_size_right)
2517
2518
2519
2520


class AttnFuncWithCPAndKVAllGather(torch.autograd.Function):
    """
2521
2522
    Attention implementation with context parallelism. KV all-gather between CP ranks is exposed.
    Refer section 3.3.2 of `The Llama 3 Herd of Models <https://arxiv.org/abs/2407.21783>`_.
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
    """

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
2545
2546
        cp_group,
        cp_stream,
2547
        use_flash_attn_3,
2548
    ):
2549
        # pylint: disable=missing-function-docstring
2550
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2551
2552
2553
2554
2555
2556
        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)

2557
2558
        qkv_dtype = q.dtype

2559
2560
        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
2561
        assert not padding, f"{attn_mask_type} mask type is not supported!"
2562
2563
2564
2565
2566
        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 (
2567
            use_fused_attention or fa_utils.v2_3_plus
2568
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
2569

2570
        flash_attn_fwd = None
2571
2572
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
2573
2574
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
2575
            else:
2576
2577
2578
2579
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
2580
2581
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
2582
                if fa_utils.v2_4_plus:
2583
                    fa_forward_kwargs["alibi_slopes"] = None
2584
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
2585
                    fa_forward_kwargs["block_table"] = None
2586
                if fa_utils.v2_6_0_plus:
2587
                    fa_forward_kwargs["softcap"] = 0.0
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598

        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)
2599
2600
        if use_fused_attention or qkv_format == "thd":
            cu_seqlens_q = cu_seqlens_q // (2 * cp_size)
2601
2602
2603
2604
        if cu_seqlens_q_padded is not None and qkv_format == "thd":
            cu_seqlens_q_padded = cu_seqlens_q_padded // (2 * cp_size)
        else:
            cu_seqlens_q_padded = None
2605

2606
2607
2608
2609
        # [b, s, np, hn] -> [b, 2, s//2, np, hn] or [s, b, np, hn] -> [2, s//2, b, np, hn]
        q = q.view(*q.shape[:seq_dim], 2, q.shape[seq_dim] // 2, *q.shape[(seq_dim + 1) :])
        # [b, s, np, hn] or [s, b, np, hn] -> [s, b, np, hn]
        k, v = [x.movedim(seq_dim, 0).contiguous() for x in [k, v]]
2610

2611
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2612
2613
        k_ag, _ = gather_along_first_dim(k, cp_group)
        v_ag, _ = gather_along_first_dim(v, cp_group)
2614
2615

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2616
2617
        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:])
2618
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2619
2620
2621
2622
2623
2624
2625
2626
2627
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        cp_stream.wait_stream(torch.cuda.current_stream())

        # create two streams to resolve wave quantization issue of Flash Attn in each step
        flash_attn_streams = [torch.cuda.current_stream(), cp_stream]
2628
2629

        local_seq_chunk_ids = [rank, 2 * cp_size - rank - 1]
2630
2631
2632
        kv_seq_range_per_step = [None, None]
        window_size_per_step = [None, None]
        cu_seqlens_kv_per_step = [None, None]
2633
2634
2635
2636
2637
2638
2639
2640
        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]):
2641
2642
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2643
2644
2645
2646
2647
2648
2649
2650
2651
                    q_ = q.select(seq_dim, i).contiguous()
                    kv_seq_range_per_step[i], window_size_per_step[i] = (
                        get_kv_seq_info_after_all_gather(
                            local_seq_chunk_ids[i],
                            cp_size,
                            max_seqlen_q,
                            max_seqlen_kv,
                            window_size,
                            causal,
2652
                        )
2653
2654
2655
2656
2657
2658
                    )
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv_ = seq_end_idx - seq_start_idx
2659
                    if use_fused_attention or qkv_format == "thd":
2660
                        cu_seqlens_kv_per_step[i] = dpa_utils.get_full_cu_seqlens(
2661
2662
                            k.shape[1], max_seqlen_kv_, k.device
                        )
2663
2664
2665
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [s_range, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
2666
2667
2668
2669
                    if use_fused_attention:
                        out_per_step[i], [softmax_lse_per_step[i], rng_states[i]] = fused_attn_fwd(
                            is_training,
                            max_seqlen_q,
2670
                            max_seqlen_kv_,
2671
                            cu_seqlens_q,
2672
                            cu_seqlens_kv_per_step[i],
2673
2674
2675
                            q_,
                            k_,
                            v_,
2676
                            qkv_dtype,
2677
2678
2679
2680
2681
2682
2683
2684
                            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,
2685
2686
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
                            window_size=window_size_per_step[i],
2687
2688
                        )
                    else:
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
                        fa_forward_args_thd = get_fa_args(
                            True,
                            use_flash_attn_3,
                            qkv_format,
                            cu_seqlens_q=cu_seqlens_q,
                            cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                            max_seqlen_q=max_seqlen_q,
                            max_seqlen_kv=max_seqlen_kv_,
                        )
                        if use_flash_attn_3 or (fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus):
2699
                            fa_forward_kwargs["window_size"] = window_size_per_step[i]
2700
                        elif fa_utils.v2_7_0_plus:
2701
2702
                            fa_forward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_forward_kwargs["window_size_right"] = window_size_per_step[i][1]
2703
2704
2705
2706
                        fa_outputs = flash_attn_fwd(
                            q_,
                            k_,
                            v_,
2707
                            *fa_forward_args_thd,
2708
2709
                            causal=causal,
                            **fa_forward_kwargs,
2710
                        )
2711
                        if not fa_utils.v2_7_0_plus:
2712
2713
                            out_per_step[i] = fa_outputs[4]
                            softmax_lse_per_step[i] = fa_outputs[5]
2714
                            if not use_flash_attn_3:
2715
2716
2717
2718
                                rng_states[i] = fa_outputs[7]
                        else:
                            out_per_step[i] = fa_outputs[0]
                            softmax_lse_per_step[i] = fa_outputs[1]
2719
                            if not use_flash_attn_3:
2720
                                rng_states[i] = fa_outputs[3]
2721
2722
2723
2724

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if qkv_format == "bshd":
2725
                        out[:, i - 1].copy_(out_per_step[i - 1])
2726
                    elif qkv_format == "sbhd":
2727
                        out[i - 1].copy_(out_per_step[i - 1])
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744

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

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

        ctx.save_for_backward(
            q,
            k,
            v,
            cu_seqlens_q,
            cu_seqlens_q_padded,
2745
            *cu_seqlens_kv_per_step,
2746
2747
2748
2749
            *out_per_step,
            *softmax_lse_per_step,
            *rng_states,
        )
2750
2751

        ctx.qkv_dtype = qkv_dtype
2752
2753
        ctx.kv_seq_range_per_step = kv_seq_range_per_step
        ctx.window_size_per_step = window_size_per_step
2754
2755
2756
2757
2758
2759
2760
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_bias_type = attn_bias_type
2761
        ctx.attn_mask_type = attn_mask_type
2762
2763
        ctx.deterministic = deterministic
        ctx.use_fused_attention = use_fused_attention
2764
        ctx.use_flash_attn_3 = use_flash_attn_3
2765
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.forward")
2766
2767
2768
2769
        return out

    @staticmethod
    def backward(ctx, dout):
2770
        # pylint: disable=missing-function-docstring
2771
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2772
2773
2774
        cp_size = get_distributed_world_size(ctx.cp_group)
        rank = get_distributed_rank(ctx.cp_group)

2775
2776
2777
2778
2779
2780
        (*saved_tensors,) = ctx.saved_tensors
        (q, k, v, cu_seqlens_q, cu_seqlens_q_padded) = saved_tensors[:5]
        cu_seqlens_kv_per_step = saved_tensors[5:7]
        out_per_step = saved_tensors[7:9]
        softmax_lse_per_step = saved_tensors[9:11]
        rng_states = saved_tensors[11:13]
2781
2782
        kv_seq_range_per_step = ctx.kv_seq_range_per_step
        window_size_per_step = ctx.window_size_per_step
2783

2784
        seq_dim = ctx.qkv_format.index("s")
2785
2786
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

2787
        dout = dout.view(q.shape)
2788
        dq = torch.empty_like(q)
2789
        dk = torch.zeros((k.shape[0] * cp_size, *k.shape[1:]), dtype=k.dtype, device=k.device)
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
        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()

2800
        # [s, b, np, hn] -> [cp, s, b, np, hn]
2801
2802
        k_ag, _ = gather_along_first_dim(k, ctx.cp_group)
        v_ag, _ = gather_along_first_dim(v, ctx.cp_group)
2803
2804

        # [cp, s, b, np, hn] -> [cp*2, s//2, b, np, hn]
2805
2806
        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:])
2807
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_before_attn(cp_size, k.device)
2808
2809
2810
2811
2812
2813
        k_ag = torch.index_select(k_ag, dim=0, index=chunk_ids_for_kv_ag)
        v_ag = torch.index_select(v_ag, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
        k_ag = k_ag.view(-1, *k.shape[1:])
        v_ag = v_ag.view(-1, *v.shape[1:])
        ctx.cp_stream.wait_stream(torch.cuda.current_stream())
2814
2815
2816

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

2817
        flash_attn_bwd = None
2818
2819
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
2820
2821
            if ctx.use_flash_attn_3:
                flash_attn_bwd = _flash_attn_bwd_v3
2822
2823
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
2824
2825
2826
2827
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
2828
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
2829
                if fa_utils.v2_4_plus:
2830
                    fa_backward_kwargs["alibi_slopes"] = None
2831
                if fa_utils.v2_4_1_plus:
2832
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
2833
                if fa_utils.v2_6_0_plus:
2834
                    fa_backward_kwargs["softcap"] = 0.0
2835
2836
2837
2838

        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]):
2839
2840
                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                    # or [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
2841
2842
2843
2844
2845
2846
2847
2848
2849
                    q_ = q.select(seq_dim, i).contiguous()
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i][0],
                        kv_seq_range_per_step[i][1],
                    )
                    max_seqlen_kv = seq_end_idx - seq_start_idx
                    k_, v_ = [x[seq_start_idx:seq_end_idx] for x in [k_ag, v_ag]]
                    # [cp*s, b, np, hn] -> [b, s_range, np, hn] or [s_range, b, np, hn]
                    k_, v_ = [x.movedim(0, seq_dim).contiguous() for x in [k_, v_]]
2850
                    out_ = out_per_step[i]
2851
                    dout_ = dout.select(seq_dim, i).contiguous().view(out_.shape)
2852
2853
2854
2855
                    if ctx.use_fused_attention:
                        aux_ctx_tensors = [softmax_lse_per_step[i], rng_states[i]]
                        dq_per_step[i], dk_per_step[i], dv_per_step[i], _ = fused_attn_bwd(
                            ctx.max_seqlen_q,
2856
                            max_seqlen_kv,
2857
                            cu_seqlens_q,
2858
                            cu_seqlens_kv_per_step[i],
2859
2860
2861
2862
2863
                            q_,
                            k_,
                            v_,
                            out_,
                            dout_,
2864
                            ctx.qkv_dtype,
2865
                            TE_DType[dout.dtype],
2866
2867
2868
                            aux_ctx_tensors,
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            cu_seqlens_q_padded=cu_seqlens_q_padded,
2869
                            cu_seqlens_kv_padded=cu_seqlens_kv_per_step[i],
2870
2871
2872
2873
2874
                            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,
2875
2876
                            window_size=window_size_per_step[i],
                            deterministic=ctx.deterministic,
2877
2878
2879
2880
2881
                        )
                    else:
                        dq_per_step[i], dk_per_step[i], dv_per_step[i] = [
                            torch.empty_like(x) for x in [q_, k_, v_]
                        ]
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
                        fa_backward_args_thd = get_fa_args(
                            False,
                            ctx.use_flash_attn_3,
                            ctx.qkv_format,
                            cu_seqlens_q=cu_seqlens_q,
                            cu_seqlens_kv=cu_seqlens_kv_per_step[i],
                            max_seqlen_q=ctx.max_seqlen_q,
                            max_seqlen_kv=max_seqlen_kv,
                            dq=dq_per_step[i],
                            dk=dk_per_step[i],
                            dv=dv_per_step[i],
                        )
                        if not ctx.use_flash_attn_3:
2895
                            fa_backward_kwargs["rng_state"] = rng_states[i]
2896
2897
2898
                        if ctx.use_flash_attn_3 or (
                            fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus
                        ):
2899
                            fa_backward_kwargs["window_size"] = window_size_per_step[i]
2900
                        elif fa_utils.v2_7_0_plus:
2901
2902
                            fa_backward_kwargs["window_size_left"] = window_size_per_step[i][0]
                            fa_backward_kwargs["window_size_right"] = window_size_per_step[i][1]
2903
                        flash_attn_bwd(
2904
2905
2906
2907
2908
2909
                            dout_,
                            q_,
                            k_,
                            v_,
                            out_,
                            softmax_lse_per_step[i],
2910
                            *fa_backward_args_thd,
2911
2912
                            causal="causal" in ctx.attn_mask_type,
                            **fa_backward_kwargs,
2913
2914
2915
2916
2917
                        )

            if i > 0:
                with torch.cuda.stream(flash_attn_streams[i - 1]):
                    if ctx.qkv_format == "bshd":
2918
                        dq[:, i - 1].copy_(dq_per_step[i - 1])
2919
                    elif ctx.qkv_format == "sbhd":
2920
2921
2922
2923
2924
2925
                        dq[i - 1].copy_(dq_per_step[i - 1])
                    # [b, s_range, np, hn] or [s_range, b, np, hn] -> [s_range, b, np, hn]
                    dk_per_step[i - 1], dv_per_step[i - 1] = [
                        x.movedim(seq_dim, 0).contiguous()
                        for x in [dk_per_step[i - 1], dv_per_step[i - 1]]
                    ]
2926
2927
2928
                    # wait until dkv update of last step is done
                    if i > 1:
                        flash_attn_streams[i - 1].wait_event(dkv_update_done)
2929
2930
2931
2932
2933
2934
                    seq_start_idx, seq_end_idx = (
                        kv_seq_range_per_step[i - 1][0],
                        kv_seq_range_per_step[i - 1][1],
                    )
                    dk[seq_start_idx:seq_end_idx].add_(dk_per_step[i - 1])
                    dv[seq_start_idx:seq_end_idx].add_(dv_per_step[i - 1])
2935
2936
2937
2938
2939
                    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)

2940
2941
2942
        # [cp*s, b, np, hn] -> [cp*2, s//2, b, np, hn]
        dk = dk.view(2 * cp_size, -1, *dk.shape[-3:])
        dv = dv.view(2 * cp_size, -1, *dv.shape[-3:])
2943
        chunk_ids_for_kv_ag = get_seq_chunk_ids_for_reordering_after_attn(cp_size, dk.device)
2944
2945
2946
        dk = torch.index_select(dk, dim=0, index=chunk_ids_for_kv_ag)
        dv = torch.index_select(dv, dim=0, index=chunk_ids_for_kv_ag)
        # [cp*2, s//2, b, np, hn] -> [cp*s, b, np, hn]
2947
2948
2949
2950
2951
        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)

2952
2953
2954
        dq = dq.view(*dq.shape[:seq_dim], -1, *dq.shape[(seq_dim + 2) :])
        dk = dk.movedim(0, seq_dim).contiguous()
        dv = dv.movedim(0, seq_dim).contiguous()
2955
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndKVAllGather.backward")
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
2977
            None,
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
        )


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

    @staticmethod
    def forward(
        ctx,
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
        window_size,
        fp8,
        fp8_meta,
        cp_group,
        cp_stream,
3013
        quantizers,
3014
        use_flash_attn_3,
3015
    ):
3016
        # pylint: disable=missing-function-docstring
3017
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3018
3019
3020
3021
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
3022
        qkv_dtype = q.dtype
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032

        causal = "causal" in attn_mask_type
        padding = "padding" in attn_mask_type
        assert not padding, f"{attn_mask_type} mask type is not supported!"
        assert attn_bias_type == "no_bias", f"{attn_bias_type} bias type is not supported!"
        assert q.shape[-1] % 8 == 0, "Hidden size per attention head should be multiple of 8!"
        assert (
            window_size == (-1, 0)
            or window_size == (-1, -1)
            or use_fused_attention
3033
            or fa_utils.v2_3_plus
3034
        ), "Sliding window attention only can work with FusedAttention or FlashAttention >= 2.3!"
3035

3036
        flash_attn_fwd = None
3037
3038
        if not use_fused_attention:
            fa_forward_kwargs = {"softmax_scale": softmax_scale}
3039
3040
            if use_flash_attn_3:
                flash_attn_fwd = _flash_attn_fwd_v3
3041
3042
                fa_forward_kwargs["window_size"] = window_size
            else:
3043
3044
3045
3046
                if qkv_format == "thd":
                    flash_attn_fwd = _flash_attn_varlen_fwd
                else:
                    flash_attn_fwd = _flash_attn_fwd
3047
3048
                fa_forward_kwargs["dropout_p"] = dropout_p
                fa_forward_kwargs["return_softmax"] = False
3049
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3050
                    fa_forward_kwargs["window_size"] = window_size
3051
                elif fa_utils.v2_7_0_plus:
3052
3053
                    fa_forward_kwargs["window_size_left"] = window_size[0]
                    fa_forward_kwargs["window_size_right"] = window_size[1]
3054
                if fa_utils.v2_4_plus:
3055
                    fa_forward_kwargs["alibi_slopes"] = None
3056
                if fa_utils.v2_5_7_plus and qkv_format == "thd":
3057
                    fa_forward_kwargs["block_table"] = None
3058
                if fa_utils.v2_6_0_plus:
3059
                    fa_forward_kwargs["softcap"] = 0.0
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073

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

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

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

3074
        fused_attn_backend = None
3075
3076
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
        is_input_fp8 = False
3077
3078
3079
        is_output_fp8 = False

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
3080
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
3081
3082
3083
        )
        if fp8:
            if use_fused_attention:
3084
                fused_attn_backend = FusedAttnBackend["FP8"]
3085
3086
3087
3088
                assert isinstance(k, q.__class__) and isinstance(
                    v, q.__class__
                ), "q, k, and v must have the same type."
                is_input_fp8 = isinstance(q, Float8Tensor)
3089
                is_output_fp8 = fp8_meta is not None and fp8_meta["recipe"].fp8_mha
3090
                if is_input_fp8:
3091
                    QKV_quantizer = q._quantizer
3092
3093
3094
3095
                    q_fp8, k_fp8, v_fp8 = q, k, v
                    q, k, v = q_fp8._data, k_fp8._data, v_fp8._data
                elif int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                    q_f16, k_f16, v_f16 = q, k, v
3096
                    q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3097
                fp8_meta_kwargs = {}
3098
3099
                fp8_meta_kwargs["s_quantizer"] = S_quantizer
                fp8_meta_kwargs["o_quantizer"] = O_quantizer  # partial result quantizer
3100
3101
3102
3103
3104
3105
3106
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
            if use_fused_attention:
                fp8_meta_kwargs = {}
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

3107
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, q.device)
3108
3109
3110
3111
        q, k, v = flash_attn_a2a_communicate(
            [q, k, v], chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, True
        )

3112
        if fp8 and not is_input_fp8 and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3113
            q_f16, k_f16, v_f16 = q, k, v
3114
            q, k, v = [QKV_quantizer(x)._data for x in [q_f16, k_f16, v_f16]]
3115
3116
3117

        batch_size = q.shape[batch_dim]
        if use_fused_attention:
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
            q_part, k_part, v_part = q, k, v
            if fp8:
                q_part = QKV_quantizer.create_tensor_from_data(
                    q, fake_dtype=qkv_dtype, internal=True
                )
                k_part = QKV_quantizer.create_tensor_from_data(
                    k, fake_dtype=qkv_dtype, internal=True
                )
                v_part = QKV_quantizer.create_tensor_from_data(
                    v, fake_dtype=qkv_dtype, internal=True
                )
3129
3130
3131
3132
3133
3134
            out, aux_ctx_tensors = fused_attn_fwd(
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3135
3136
3137
3138
                q_part,
                k_part,
                v_part,
                qkv_dtype,
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
                fused_attn_backend,
                attn_scale=softmax_scale,
                dropout=dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=attn_mask_type,
                attn_bias_type=attn_bias_type,
                attn_bias=attn_bias,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                window_size=window_size,
                **fp8_meta_kwargs,
            )
3151
3152
            if fp8:
                out = out._data
3153
        else:
3154
3155
3156
3157
3158
3159
3160
3161
3162
            fa_forward_args_thd = get_fa_args(
                True,
                use_flash_attn_3,
                qkv_format,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_kv,
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
            )
3163
            fa_outputs = flash_attn_fwd(
3164
3165
3166
                q,
                k,
                v,
3167
                *fa_forward_args_thd,
3168
                causal=causal,
3169
                **fa_forward_kwargs,
3170
            )
3171
            if not fa_utils.v2_7_0_plus:
3172
                out, softmax_lse = fa_outputs[4], fa_outputs[5]
3173
                rng_state = fa_outputs[7] if not use_flash_attn_3 else None
3174
3175
            else:
                out, softmax_lse = fa_outputs[0], fa_outputs[1]
3176
                rng_state = fa_outputs[3] if not use_flash_attn_3 else None
3177
3178
            aux_ctx_tensors = [softmax_lse, rng_state]

3179
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, out.device)
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
        out = flash_attn_a2a_communicate(
            out, chunk_ids_for_a2a, seq_dim, cp_size, cp_group, cp_stream, False
        )

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

        if fp8:
3193
            if is_output_fp8:
3194
3195
                out_fp8 = O_quantizer.create_tensor_from_data(
                    out, fake_dtype=qkv_dtype, internal=False
3196
3197
                )
                out_ret = out_fp8
3198
                out = out_fp8._data
3199
            else:
3200
                out_fp8 = O_quantizer.create_tensor_from_data(
3201
                    out, fake_dtype=qkv_dtype, internal=True
3202
                )
3203
                out_f16 = out_fp8.dequantize(dtype=qkv_dtype)
3204
3205
3206
3207
                out_ret = out_f16
        else:
            out_ret = out

3208
        if not fp8 or int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
3209
            q_save, k_save, v_save, out_save = q, k, v, out
3210
3211
3212
3213
3214
3215
3216
3217
3218
        else:
            if is_input_fp8:
                q_save, k_save, v_save = q, k, v
            else:
                q_save, k_save, v_save = q_f16, k_f16, v_f16
            if is_output_fp8:
                out_save = out
            else:
                out_save = out_f16
3219

3220
        tensors_to_save, tensor_objects = prepare_for_saving(
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
            q_save,
            k_save,
            v_save,
            out_save,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        )
3231
3232
3233
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects

3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
        ctx.batch_size = batch_size
        ctx.cp_group = cp_group
        ctx.cp_stream = cp_stream
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.softmax_scale = softmax_scale
        ctx.qkv_format = qkv_format
        ctx.attn_mask_type = attn_mask_type
        ctx.attn_bias_type = attn_bias_type
        ctx.deterministic = deterministic
        ctx.window_size = window_size
        ctx.use_fused_attention = use_fused_attention
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        ctx.fp8_meta = fp8_meta
3249
3250
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
3251
        ctx.use_flash_attn_3 = use_flash_attn_3
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266

        ctx.qkv_dtype = qkv_dtype
        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.QKV_quantizer = QKV_quantizer
        ctx.O_quantizer = O_quantizer
        ctx.S_quantizer = S_quantizer
        if ctx.fp8:
            ctx.QKV_quantizer = QKV_quantizer.copy()
            ctx.QKV_quantizer.scale = QKV_quantizer.scale.clone()
            ctx.O_quantizer = O_quantizer.copy()
            ctx.O_quantizer.scale = O_quantizer.scale.clone()
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
3267
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.forward")
3268
3269
3270
3271
        return out_ret

    @staticmethod
    def backward(ctx, dout):
3272
        # pylint: disable=missing-function-docstring
3273
        nvtx_range_push("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3274
3275
        cp_size = get_distributed_world_size(ctx.cp_group)

3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
        (
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
            *aux_ctx_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)
3287
3288
3289
3290
3291

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

3292
        dout_dtype = dout.dtype
3293
3294
        fused_attn_backend = None
        fused_attn_dqkv_dtype = None
3295
3296
3297
        if ctx.fp8:
            if ctx.use_fused_attention:
                fused_attn_backend = FusedAttnBackend["FP8"]
3298
                if ctx.is_output_fp8:
3299
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
3300
                    ctx.dO_quantizer = dout._quantizer
3301
                else:
3302
                    dout = ctx.dO_quantizer(dout)
3303
                fused_attn_dqkv_dtype = TE_DType[dout._data.dtype]
3304
                dout = dout._data
3305
                fp8_meta_kwargs = {}
3306
3307
3308
3309
                fp8_meta_kwargs["s_quantizer"] = ctx.S_quantizer
                fp8_meta_kwargs["dp_quantizer"] = ctx.dP_quantizer
                fp8_meta_kwargs["dqkv_quantizer"] = ctx.dQKV_quantizer

3310
3311
3312
            else:
                assert False, "FP8 is only supported with Fused Attention!"
        else:
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
            if ctx.fp8_meta is not None:
                if ctx.is_output_fp8:
                    assert isinstance(dout, Float8Tensor), "dout must be Float8Tensors for FP8 MHA!"
                    ctx.dO_quantizer = dout._quantizer
                    dout = dout._data
                if ctx.is_input_fp8:
                    q = ctx.QKV_quantizer.create_tensor_from_data(
                        q, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    k = ctx.QKV_quantizer.create_tensor_from_data(
                        k, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    v = ctx.QKV_quantizer.create_tensor_from_data(
                        v, fake_dtype=ctx.qkv_dtype, internal=True
                    )
                    q, k, v = [x.dequantize(dtype=ctx.qkv_dtype) for x in [q, k, v]]
3329
3330
            if ctx.use_fused_attention:
                fp8_meta_kwargs = {}
3331
                fused_attn_dqkv_dtype = TE_DType[dout_dtype]
3332
3333
3334
3335
3336
3337
                fused_attn_backend = FusedAttnBackend["F16_arbitrary_seqlen"]

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

3338
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_before_attn(cp_size, out.device)
3339
3340
3341
        out, dout = flash_attn_a2a_communicate(
            [out, dout], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, True
        )
3342
3343
3344
3345
3346
3347
3348
3349
3350
        if not ctx.fp8 and ctx.fp8_meta is not None and ctx.is_output_fp8:
            out = ctx.O_quantizer.create_tensor_from_data(
                out, fake_dtype=ctx.qkv_dtype, internal=True
            )
            dout = ctx.dO_quantizer.create_tensor_from_data(
                dout, fake_dtype=dout_dtype, internal=True
            )
            out = out.dequantize(dtype=ctx.qkv_dtype)
            dout = dout.dequantize(dtype=dout_dtype)
3351

3352
        flash_attn_bwd = None
3353
3354
        if not ctx.use_fused_attention:
            fa_backward_kwargs = {"softmax_scale": ctx.softmax_scale}
3355
3356
3357
3358
            if ctx.use_flash_attn_3:
                flash_attn_bwd = (
                    _flash_attn_bwd_v3  # pylint: disable=possibly-used-before-assignment
                )
3359
3360
3361
                fa_backward_kwargs["window_size"] = ctx.window_size
                fa_backward_kwargs["deterministic"] = ctx.deterministic
            else:
3362
3363
3364
3365
                if ctx.qkv_format == "thd":
                    flash_attn_bwd = _flash_attn_varlen_bwd
                else:
                    flash_attn_bwd = _flash_attn_bwd
3366
                fa_backward_kwargs["dropout_p"] = ctx.dropout_p
3367
                if fa_utils.v2_3_plus and not fa_utils.v2_7_0_plus:
3368
                    fa_backward_kwargs["window_size"] = ctx.window_size
3369
                elif fa_utils.v2_7_0_plus:
3370
3371
                    fa_backward_kwargs["window_size_left"] = ctx.window_size[0]
                    fa_backward_kwargs["window_size_right"] = ctx.window_size[1]
3372
                if fa_utils.v2_4_plus:
3373
                    fa_backward_kwargs["alibi_slopes"] = None
3374
                if fa_utils.v2_4_1_plus:
3375
                    fa_backward_kwargs["deterministic"] = ctx.deterministic
3376
                if fa_utils.v2_6_0_plus:
3377
                    fa_backward_kwargs["softcap"] = 0.0
3378
3379

        if ctx.use_fused_attention:
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
            q_part = q
            k_part = k
            v_part = v
            out_part = out
            dout_part = dout

            if ctx.fp8:
                q_part = ctx.QKV_quantizer.create_tensor_from_data(
                    q_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                k_part = ctx.QKV_quantizer.create_tensor_from_data(
                    k_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                v_part = ctx.QKV_quantizer.create_tensor_from_data(
                    v_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                out_part = ctx.O_quantizer.create_tensor_from_data(
                    out_part, fake_dtype=ctx.qkv_dtype, internal=True
                )
                dout_part = ctx.dO_quantizer.create_tensor_from_data(
3400
                    dout_part, fake_dtype=dout_dtype, internal=True
3401
3402
                )

3403
3404
3405
3406
3407
            dq, dk, dv, _ = fused_attn_bwd(
                ctx.max_seqlen_q,
                ctx.max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
3408
3409
3410
3411
3412
                q_part,
                k_part,
                v_part,
                out_part,
                dout_part,
3413
                dout_dtype,
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
                fused_attn_dqkv_dtype,
                aux_ctx_tensors,
                fused_attn_backend,
                cu_seqlens_q_padded=cu_seqlens_q_padded,
                cu_seqlens_kv_padded=cu_seqlens_kv_padded,
                attn_scale=ctx.softmax_scale,
                dropout=ctx.dropout_p,
                qkv_layout=qkv_layout,
                attn_mask_type=ctx.attn_mask_type,
                attn_bias_type=ctx.attn_bias_type,
                window_size=ctx.window_size,
                deterministic=ctx.deterministic,
                **fp8_meta_kwargs,
            )
3428
3429
3430
3431
            if ctx.fp8:
                dq = dq._data
                dk = dk._data
                dv = dv._data
3432
3433
3434
        else:
            softmax_lse, rng_state = aux_ctx_tensors
            dq, dk, dv = [torch.empty_like(x) for x in [q, k, v]]
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
            fa_backward_args_thd = get_fa_args(
                False,
                ctx.use_flash_attn_3,
                ctx.qkv_format,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_kv=cu_seqlens_kv,
                max_seqlen_q=ctx.max_seqlen_q,
                max_seqlen_kv=ctx.max_seqlen_kv,
                dq=dq,
                dk=dk,
                dv=dv,
            )
            if not ctx.use_flash_attn_3:
3448
3449
                fa_backward_kwargs["rng_state"] = rng_state
            flash_attn_bwd(
3450
3451
3452
3453
3454
3455
                dout,
                q,
                k,
                v,
                out,
                softmax_lse,
3456
                *fa_backward_args_thd,
3457
3458
                causal=causal,
                **fa_backward_kwargs,
3459
3460
            )

3461
        chunk_ids_for_a2a = get_seq_chunk_ids_for_reordering_after_attn(cp_size, q.device)
3462
3463
3464
3465
        dq, dk, dv = flash_attn_a2a_communicate(
            [dq, dk, dv], chunk_ids_for_a2a, seq_dim, cp_size, ctx.cp_group, ctx.cp_stream, False
        )

3466
        if ctx.qkv_format == "bshd":
3467
            dq, dk, dv = [x.view(ctx.batch_size, -1, *x.shape[-2:]) for x in [dq, dk, dv]]
3468
        elif ctx.qkv_format == "sbhd":
3469
3470
3471
            dq, dk, dv = [x.view(-1, ctx.batch_size, *x.shape[-2:]) for x in [dq, dk, dv]]

        if ctx.fp8:
3472
3473
3474
3475
3476
3477
3478
3479
3480
            dq = ctx.dQKV_quantizer.create_tensor_from_data(
                dq, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dk = ctx.dQKV_quantizer.create_tensor_from_data(
                dk, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
            dv = ctx.dQKV_quantizer.create_tensor_from_data(
                dv, fake_dtype=dout_dtype, internal=not ctx.is_input_fp8
            )
3481
            if not ctx.is_input_fp8:
3482
                dq, dk, dv = [x.dequantize(dtype=dout_dtype) for x in [dq, dk, dv]]
3483
        nvtx_range_pop("transformer_engine.AttnFuncWithCPAndQKVOA2A.backward")
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506

        return (
            None,
            dq,
            dk,
            dv,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
3507
3508
3509
            None,
            None,
            None,
3510
            None,
3511
3512
3513
        )


3514
def attn_forward_func_with_cp(
3515
3516
3517
3518
3519
    is_training,
    q,
    k,
    v,
    cu_seqlens_q,
3520
    cu_seqlens_kv,
3521
    max_seqlen_q,
3522
    max_seqlen_kv,
3523
3524
    cu_seqlens_q_padded,
    cu_seqlens_kv_padded,
3525
3526
3527
3528
    dropout_p,
    cp_group,
    cp_global_ranks,
    cp_stream,
3529
    cp_comm_type,
3530
3531
3532
3533
3534
3535
3536
    softmax_scale=None,
    qkv_format="bshd",
    attn_mask_type="causal",
    attn_bias_type="no_bias",
    attn_bias=None,
    deterministic=False,
    use_fused_attention=False,
3537
    window_size=None,
3538
3539
    fp8=False,
    fp8_meta=None,
3540
    quantizers=None,
3541
    pad_between_seqs=False,
3542
    use_flash_attn_3=False,
3543
) -> torch.Tensor:
3544
3545
3546
3547
    """
    Attention implementation with context parallelism.
    """

3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
    if cp_comm_type == "a2a+p2p":
        assert isinstance(
            cp_group, list
        ), "Hierarchical CP implementation needs multi-level CP groups!"
        assert len(cp_group) == 2, "Current implementation only supports two-level CP groups!"
        if get_distributed_world_size(cp_group[0]) == 1:
            cp_group = cp_group[1]
            cp_comm_type = "p2p"
        elif get_distributed_world_size(cp_group[1]) == 1:
            cp_group = cp_group[0]
            cp_comm_type = "a2a"
    else:
        assert isinstance(
            cp_group, dist_group_type
        ), f"Unsupported process group for CP communication type {cp_comm_type}!"

3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
    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 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!"""
    )
3576
    assert qkv_format != "thd" or (
3577
        cu_seqlens_q_padded is not None and cu_seqlens_kv_padded is not None
3578
    ), "cu_seqlens_padded cannot be None with context parallelism + THD format!"
3579
3580
3581

    sliding_window_attn = (
        window_size is not None and window_size != (-1, 0) and window_size != (-1, -1)
3582
    )
3583
3584
3585
3586
    assert not sliding_window_attn or cp_comm_type in [
        "a2a",
        "all_gather",
    ], "The context parallel running configs cannot support sliding window attetnion!"
3587

3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
    args = [
        is_training,
        q,
        k,
        v,
        cu_seqlens_q,
        cu_seqlens_kv,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
        dropout_p,
        softmax_scale,
        qkv_format,
        attn_mask_type,
        attn_bias_type,
        attn_bias,
        deterministic,
        use_fused_attention,
    ]

3609
    if cp_comm_type in ["p2p", "a2a+p2p"]:
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
        args += [
            fp8,
            fp8_meta,
            cp_group,
            cp_global_ranks,
            cp_stream,
            quantizers,
            pad_between_seqs,
            use_flash_attn_3,
        ]
3620
3621
3622
3623
        out = AttnFuncWithCPAndKVP2P.apply(*args)
    elif cp_comm_type == "all_gather":
        args.pop(5)
        args.pop(8)
3624
        args += [window_size, cp_group, cp_stream, use_flash_attn_3]
3625
3626
        out = AttnFuncWithCPAndKVAllGather.apply(*args)
    elif cp_comm_type == "a2a":
3627
        args += [window_size, fp8, fp8_meta, cp_group, cp_stream, quantizers, use_flash_attn_3]
3628
        out = AttnFuncWithCPAndQKVOA2A.apply(*args)
3629
3630
3631
    else:
        raise ValueError(f"Unsupported communication type: {cp_comm_type}!")

3632
3633
3634
    return out


cyanguwa's avatar
cyanguwa committed
3635
class _SplitAlongDim(torch.autograd.Function):
3636
3637
3638
    """"""

    @staticmethod
3639
3640
3641
3642
3643
    def forward(
        ctx,
        mixed_x_layer: torch.Tensor,
        split_dim: int,
        split_size_or_sections: Union[int, List[int], Tuple[int]],
3644
        squeeze=False,
3645
    ) -> Tuple[torch.Tensor, ...]:
3646
        # pylint: disable=missing-function-docstring
cyanguwa's avatar
cyanguwa committed
3647
3648
        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
        if isinstance(mixed_x_layer, Float8TensorBase) and not isinstance(
            mixed_x_layer, Float8Tensor
        ):
            return tuple(
                Float8TensorBase(
                    fp8_scale_inv=mixed_x_layer._scale_inv,
                    fp8_dtype=mixed_x_layer._fp8_dtype,
                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
                    quantizer=mixed_x_layer._quantizer,
                )
                for x in torch.split(
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
                    dim=split_dim,
                )
            )
3666
        if isinstance(mixed_x_layer, Float8Tensor):
3667
3668
3669
            return tuple(
                Float8Tensor.make_like(
                    mixed_x_layer,
3670
3671
                    data=x.squeeze(split_dim) if squeeze else x,
                    shape=x.squeeze(split_dim).shape if squeeze else x.shape,
3672
3673
                )
                for x in torch.split(
3674
3675
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
3676
3677
3678
                    dim=split_dim,
                )
            )
3679
3680
3681
3682
        out_list = torch.split(mixed_x_layer, split_size_or_sections, dim=split_dim)
        if squeeze:
            out_list = [x.squeeze(split_dim) for x in out_list]
        return out_list
3683
3684

    @staticmethod
3685
    def backward(ctx, *grad_outputs):
3686
        # pylint: disable=missing-function-docstring
3687
3688
        assert len(grad_outputs) > 0, "No gradients received for backprop!"

cyanguwa's avatar
cyanguwa committed
3689
3690
        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
3691
3692
3693
            assert len(grad_outputs) == len(
                split_sizes
            ), "Unequal number of gradients vs split sections for backprop!"
cyanguwa's avatar
cyanguwa committed
3694
3695
3696
3697
3698
        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

3699
3700
3701
3702
3703
3704
3705
3706
        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]
3707
3708
3709
3710
3711
3712
3713
                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
                ):
3714
3715
3716
                    noop_ok = False
                    break
            if noop_ok:
3717
3718
3719
                ret = torch.Tensor().to(
                    device=grad_outputs[0].device, dtype=grad_outputs[0]._data.dtype
                )
3720
3721
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
3722
3723
3724
3725
3726
                ret.set_(
                    grad_outputs[0]._data.untyped_storage(),
                    grad_outputs[0]._data.storage_offset(),
                    new_shape,
                    strides,
3727
                )
3728
3729
3730
3731
3732
                return (
                    Float8Tensor.make_like(grad_outputs[0], data=ret, shape=ret.shape),
                    None,
                    None,
                )
3733
3734

            grad_outputs_data = [x._data for x in grad_outputs]
3735
            data = torch.cat(grad_outputs_data, dim=split_dim)
3736
            return (
3737
3738
                Float8Tensor.make_like(grad_outputs[0], data=data, shape=data.shape),
                None,
3739
3740
3741
                None,
                None,
            )
3742
3743
        noop_ok = True
        strides = grad_outputs[0].stride()
3744
        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
cyanguwa's avatar
cyanguwa committed
3745
        shape = list(grad_outputs[0].shape)
3746
        for i, tensor in enumerate(grad_outputs):
cyanguwa's avatar
cyanguwa committed
3747
3748
            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
3749
3750
3751
3752
3753
3754
3755
            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
            ):
3756
3757
3758
                noop_ok = False
                break
        if noop_ok:
3759
            ret = torch.Tensor().to(device=grad_outputs[0].device, dtype=grad_outputs[0].dtype)
3760
            new_shape = list(shape)
cyanguwa's avatar
cyanguwa committed
3761
            new_shape[split_dim] = sum(split_sizes)
3762
3763
3764
3765
3766
            ret.set_(
                grad_outputs[0].untyped_storage(),
                grad_outputs[0].storage_offset(),
                new_shape,
                strides,
3767
            )
cyanguwa's avatar
cyanguwa committed
3768
            return ret, None, None
3769

3770
        return torch.cat(grad_outputs, dim=split_dim), None, None
3771
3772
3773
3774
3775
3776
3777
3778
3779


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

    def __init__(
        self,
3780
        softmax_scale: float,
3781
        attention_type: str = "self",
3782
3783
3784
3785
3786
3787
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

3788
        self.softmax_scale = softmax_scale
3789
        self.attention_type = attention_type
3790
3791
3792
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

3793
        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
3794
3795
3796
3797
3798
3799

        # 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)

3800
3801
        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
3802
3803
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None
        )
3804

3805
3806
3807
3808
3809
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3810
        qkv_layout: str = "sbh3d",
3811
3812
        cu_seqlens_q: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None,  # pylint: disable=unused-argument
3813
        attn_mask_type: str = "causal",
3814
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3815
        window_size: Optional[Tuple[int, int]] = None,
3816
3817
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
3818
        alibi_slopes: Optional[torch.Tensor] = None,
3819
        inference_params: Optional[InferenceParams] = None,
3820
    ) -> torch.Tensor:
3821
        """Unfused attention fprop"""
3822
3823
3824
        assert (
            qkv_layout in QKVLayouts
        ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
3825
3826
3827
3828
3829
3830

        # get q_format and kv_format for training and inference
        qkv_format, q_format, _ = dpa_utils.get_qkv_format(qkv_layout, inference_params)
        if inference_params is not None and inference_params.is_paged:
            key_layer, value_layer = inference_params.convert_paged_to_nonpaged(self.layer_number)

3831
        if qkv_format == "bshd":
3832
            # convert to sbhd and use sbhd implementation for now
3833
3834
3835
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
        if qkv_format == "sbhd_2bshd":
            key_layer, value_layer = [x.transpose(0, 1) for x in [key_layer, value_layer]]

        total_tokens, batch_size = None, None
        if qkv_format == "thd_2bshd":
            total_tokens, batch_size = query_layer.shape[0], key_layer.shape[0]
            query_layer = tex.convert_thd_to_bshd(
                query_layer,
                cu_seqlens_q,
                batch_size,
                inference_params.max_ctx_len,
            )
            query_layer, key_layer, value_layer = [
                x.transpose(0, 1) for x in [query_layer, key_layer, value_layer]
            ]
3851
3852
3853
3854
3855
        batch_size, max_seqlen_q, max_seqlen_kv = (
            query_layer.shape[1],
            query_layer.shape[0],
            key_layer.shape[0],
        )
3856

3857
3858
3859
3860
        if "padding" in attn_mask_type and attention_mask is None:
            attention_mask = dpa_utils.get_padding_mask(
                batch_size, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv
            )
3861
3862
3863
3864
3865
3866
3867
3868
3869
        attn_mask_type, attention_mask, actual_seqlens_q, actual_seqlens_kv = (
            dpa_utils.get_full_mask(
                max_seqlen_q,
                max_seqlen_kv,
                attn_mask_type=attn_mask_type,
                attention_mask=attention_mask,
                window_size=window_size,
                attention_type=self.attention_type,
            )
3870
        )
3871

3872
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
3873
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
3874
3875
3876
3877
3878
3879
3880
3881
3882

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

3883
        if key_layer.shape[2] != query_layer.shape[2]:
3884
3885
3886
            assert (
                query_layer.shape[2] % key_layer.shape[2] == 0
            ), "The number of attention heads must be divisible by the number of GQA groups!"
3887
            key_layer = key_layer.repeat_interleave(
3888
3889
                int(query_layer.shape[2] / key_layer.shape[2]), dim=2
            )
3890
            value_layer = value_layer.repeat_interleave(
3891
3892
                int(query_layer.shape[2] / value_layer.shape[2]), dim=2
            )
3893

3894
        # [sq, b, np, hn] -> [sq, b * np, hn]
3895
        query_layer = query_layer.reshape(output_size[2], output_size[0] * output_size[1], -1)
3896
3897
3898
3899
3900
3901
3902
3903
        # [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]
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
3904
            dtype=query_layer.dtype,
3905
3906
3907
            device=torch.cuda.current_device(),
        )

3908
        scale = self.softmax_scale
3909
        if apply_qk_layer_scaling:
3910
            scale /= self.layer_number
3911
3912

        # Raw attention scores. [b * np, sq, sk]
3913
3914
3915
3916
3917
3918
        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,
3919
                alpha=scale,
3920
            ).view(*output_size)
3921
3922
3923
3924
3925
3926
3927

        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]
            )
3928
            matmul_result = matmul_result.view(*output_size) + core_attention_bias
3929
            matmul_result *= scale
3930

3931
3932
3933
3934
        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":
3935
3936
                _, core_attention_bias = dpa_utils.get_alibi(
                    _alibi_cache,
3937
3938
3939
                    output_size[1],
                    output_size[2],
                    output_size[3],
3940
3941
                    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,
3942
3943
                    alibi_slopes=alibi_slopes,
                    bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
3944
                )
3945
3946
3947
3948
3949
            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,
3950
                alpha=scale,
3951
            )
3952
3953
            matmul_result = (matmul_result.view(*output_size) + core_attention_bias).to(
                dtype=query_layer.dtype
3954
            )
3955
3956
3957

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
3958
        attention_probs = self.scale_mask_softmax(
3959
            matmul_result, attention_mask, attn_mask_type, softmax_scale
3960
        )
3961

3962
3963
3964
3965
3966
        # 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)

3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
        # 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]
3982
        value_layer = value_layer.reshape(value_layer.size(0), output_size[0] * output_size[1], -1)
3983
3984

        # change view [b * np, sq, sk]
3985
        attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
3986
3987
3988
3989
3990
3991
3992

        # 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)

3993
        if q_format == "sbhd":
3994
3995
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
3996

3997
3998
3999
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

4000
        if q_format == "bshd":
4001
4002
4003
4004
4005
            # [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)
4006

4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
        if q_format == "thd":
            # [b, np, sq, hn] --> [b, sq, np, hn]
            context_layer = context_layer.permute(0, 2, 1, 3).contiguous()

            # [b, sq, np, hn] --> [tq, np, hn]
            context_layer = tex.convert_bshd_to_thd(
                context_layer,
                cu_seqlens_q,
                total_tokens,
            )

            # [tq, np, hn] --> [tq, hp]
            context_layer = context_layer.view(total_tokens, -1)

4021
4022
4023
4024
4025
        return context_layer


class _PrepareQKVForFA(torch.autograd.Function):
    """This class converts QKV from interleaved (s, b, ...) layout
4026
    to separate contiguous q, k, v tensors in (b, s, ...) layout."""
4027
4028

    @staticmethod
4029
4030
4031
4032
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
4033
        value_layer: torch.Tensor,
4034
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
4035
        # pylint: disable=missing-function-docstring
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
        # 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
4047
4048
4049
4050
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
4051
        dv: torch.Tensor,
4052
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4053
        # pylint: disable=missing-function-docstring
4054
4055
4056
4057
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

4058

4059
class FlashAttention(torch.nn.Module):
4060
    """Dot product attention, using HazyResearch flash-attn package:
4061
    https://github.com/Dao-AILab/flash-attention
4062
4063
4064
4065
    """

    def __init__(
        self,
4066
        softmax_scale: float,
4067
4068
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
4069
4070
        attention_type: str = "self",
        layer_number: Optional[int] = None,
4071
        deterministic: bool = False,
4072
4073
4074
    ) -> None:
        super().__init__()

4075
        if fa_utils.is_installed:
4076
            assert (
4077
4078
                fa_utils.version >= fa_utils.version_required
            ), f"FlashAttention minimum version {fa_utils.version_required} is required."
4079
            assert (
4080
4081
                fa_utils.version <= fa_utils.max_version
            ), f"FlashAttention maximum version {fa_utils.max_version} is supported."
4082

4083
        self.softmax_scale = softmax_scale
4084
4085
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
4086
4087
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
4088
        self.deterministic = deterministic
4089
        self.logger = logging.getLogger("FlashAttention")
4090
        self.logger.setLevel(attn_log._log_level)
4091
        if not self.logger.hasHandlers():
4092
            self.logger.addHandler(attn_log._stream_handler)
4093
4094
4095
4096
4097
4098

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
4099
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4100
4101
4102
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
4103
4104
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
4105
        attn_mask_type: str = "causal",
4106
        window_size: Optional[Tuple[int, int]] = None,
4107
        alibi_slopes: Optional[torch.Tensor] = None,
4108
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
4109
        cp_global_ranks: List[int] = None,
4110
        cp_stream: torch.cuda.Stream = None,
4111
        cp_comm_type: str = "p2p",
4112
4113
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
4114
        quantizers=None,
4115
4116
        inference_params: Optional[InferenceParams] = None,
        flash_attention_backend: Optional[PkgVersion] = PkgVersion("0"),
4117
4118
4119
    ) -> torch.Tensor:
        """flash-attn fprop"""

4120
4121
4122
4123
        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."
4124
4125
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
4126
        ), "FlashAttention currently only supports CUDA tensors."
4127
4128
        assert (
            qkv_layout in QKVLayouts
4129
        ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"
4130

4131
4132
4133
4134
4135
4136
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
4137
        context_parallel = cp_size > 1
4138

4139
4140
        # get q_format and kv_format for training and inference
        qkv_format, q_format, kv_format = dpa_utils.get_qkv_format(qkv_layout, inference_params)
4141

4142
        # convert q, k, v to bshd if they are in sbhd; qkv_format doesn't change
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
        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 = [
4156
4157
                        x.transpose(0, 1).contiguous()
                        for x in (query_layer, key_layer, value_layer)
4158
                    ]
4159
4160
            elif q_format == "sbhd" and kv_format == "bshd":
                query_layer = query_layer.transpose(0, 1).contiguous()
4161
            if context_parallel:
4162
                query_layer, key_layer, value_layer = [
4163
4164
4165
4166
4167
                    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 = [
4168
                    x.transpose(0, 1).contiguous()
4169
4170
                    for x in (query_layer._data, key_layer._data, value_layer._data)
                ]
4171
                query_layer, key_layer, value_layer = [
4172
                    Float8Tensor.make_like(x, data=x._data, shape=x._data.shape)
4173
4174
                    for x in (query_layer, key_layer, value_layer)
                ]
4175
4176
4177
4178
4179
            elif q_format == "sbhd" and kv_format == "bshd":
                query_layer._data = query_layer._data.transpose(0, 1).contiguous()
                query_layer = Float8Tensor.make_like(
                    query_layer, data=query_layer._data, shape=query_layer._data.shape
                )
4180
            if context_parallel:
4181
4182
                query_layer._data, key_layer._data, value_layer._data = [
                    x.contiguous() for x in (query_layer._data, key_layer._data, value_layer._data)
4183
                ]
4184

4185
4186
4187
4188
4189
4190
4191
4192
        # get batch_size, max_seqlen and cu_seqlens
        batch_size, context_len = None, None
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"]:
                batch_size = query_layer.shape[0]
                max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
4193

4194
4195
4196
4197
                if "padding" in attn_mask_type:
                    assert (
                        not context_parallel
                    ), "Padding mask not supported with context parallelism!"
4198

4199
4200
4201
4202
4203
                    # [b * s, h, d]
                    query_layer, key_layer, value_layer = [
                        x.reshape(x.shape[0] * x.shape[1], *x.shape[2:])
                        for x in [query_layer, key_layer, value_layer]
                    ]
4204

4205
                    if self.attention_type == "self":
4206
                        assert (
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
4219
4220
                            max_seqlen_q == max_seqlen_kv
                        ), "Maximum sequence length for Q and KV should be the same."
                        if cu_seqlens_q is None:
                            assert (
                                attention_mask is not None
                            ), "Please provide attention_mask for padding!"
                            cu_seqlens_q, indices_q = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask
                            )
                        else:
                            indices_q = dpa_utils.get_indices(max_seqlen_q, cu_seqlens_q)
                        cu_seqlens_kv = cu_seqlens_q
                        query_layer, key_layer, value_layer = dpa_utils.PackTensors.apply(
                            indices_q, query_layer, key_layer, value_layer
4221
                        )
4222
                    else:
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
                        if cu_seqlens_q is None or cu_seqlens_kv is None:
                            assert (
                                attention_mask is not None
                            ), "Please provide attention_mask for padding!"
                            cu_seqlens_q, indices_q = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask[0]
                            )
                            cu_seqlens_kv, indices_kv = dpa_utils.get_cu_seqlens_and_indices(
                                attention_mask[1]
                            )
                        else:
                            indices_q = dpa_utils.get_indices(max_seqlen_q, cu_seqlens_q)
                            indices_kv = dpa_utils.get_indices(max_seqlen_kv, cu_seqlens_kv)
                        query_layer = dpa_utils.PackTensors.apply(indices_q, query_layer)
                        key_layer, value_layer = dpa_utils.PackTensors.apply(
                            indices_kv, key_layer, value_layer
                        )
4240
                else:
4241
4242
4243
4244
4245
4246
                    # Cumulative sequence lengths for unpadded data
                    if cu_seqlens_q is None:
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
4247
                        )
4248
4249
4250
4251
4252
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
4253
                        )
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
            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!"
                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()
        else:
            if qkv_format in ["sbhd_2bshd", "bshd"]:
                # q is in bshd in both cases from conversion above or the original input
                batch_size, context_len = query_layer.shape[:2]
                cu_seqlens_q = cu_seqlens_q[: batch_size + 1]
                cu_seqlens_kv = cu_seqlens_kv[: batch_size + 1]
                # convert from bshd to thd_2bshd for flash_attn_varlen_func/_with_kvcache;
                # kernel assumes tensor is contiguous
                if isinstance(query_layer, Float8Tensor):
                    query_layer._data = tex.convert_bshd_to_thd(
                        query_layer._data,
                        cu_seqlens_q,
                        batch_size * context_len,
4277
                    )
4278
4279
                    query_layer = Float8Tensor.make_like(
                        query_layer, data=query_layer._data, shape=query_layer._data.shape
4280
                    )
4281
4282
4283
4284
4285
                else:
                    query_layer = tex.convert_bshd_to_thd(
                        query_layer,
                        cu_seqlens_q,
                        batch_size * context_len,
4286
                    )
4287

4288
4289
4290
        use_flash_attn_3 = False
        if flash_attention_backend is not None and flash_attention_backend > PkgVersion("3.0.0b"):
            use_flash_attn_3 = True
4291
4292
4293
        if context_parallel and all(
            not isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]
        ):
4294
4295
4296
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
4297
            with self.attention_dropout_ctx():
4298
                output = attn_forward_func_with_cp(
4299
4300
4301
4302
4303
4304
4305
4306
                    self.training,
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
4307
4308
                    cu_seqlens_q if qkv_format == "thd" else None,
                    cu_seqlens_kv if qkv_format == "thd" else None,
4309
                    self.attention_dropout if self.training else 0.0,
4310
4311
4312
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
4313
                    cp_comm_type,
4314
                    softmax_scale=self.softmax_scale,
4315
                    qkv_format="bshd" if qkv_format == "sbhd" else qkv_format,
4316
                    attn_mask_type=attn_mask_type,
4317
                    deterministic=self.deterministic,
4318
                    window_size=window_size,
4319
                    quantizers=quantizers,
4320
                    pad_between_seqs=False,
4321
                    use_flash_attn_3=use_flash_attn_3,
4322
4323
                )
        else:
4324
4325

            from .cpu_offload import CPUOffloadEnabled
4326

4327
            if CPUOffloadEnabled:
4328
4329
4330
                mark_activation_offload(
                    query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv
                )
4331

4332
            with self.attention_dropout_ctx():
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
                #       | API                     | use cases
                # ----------------------------------------------------------------------
                # FA v2 | flash_attn_func         | bshd/sbhd + not padding
                #       | flash_attn_varlen_func  | bshd/sbhd + padding
                #       |                         | thd + padding
                #       |                         | KV cache (not-paged/paged), i.e.
                #       |                         |     bshd/sbhd/thd + padding
                # FA v3 | flash_attn_func         | bshd/sbhd + not padding
                #       | flash_attn_varlen_func  | bshd/sbhd + padding
                #       |                         | thd + padding
                #       | flash_attn_with_kvcache | KV cache (not-paged/paged), i.e.
                #       |                         |     bshd/sbhd/thd + padding
4345
4346
4347
                fa_optional_forward_args_thd = []
                if qkv_format in ["bshd", "sbhd"] and "padding" not in attn_mask_type:
                    func = (
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
                        flash_attn_func if not use_flash_attn_3 else flash_attn_func_v3
                    )  # pylint: disable=possibly-used-before-assignment
                else:
                    if not use_flash_attn_3:
                        func = flash_attn_varlen_func
                    elif inference_params is None:
                        func = flash_attn_varlen_func_v3  # pylint: disable=possibly-used-before-assignment
                    else:
                        func = flash_attn_with_kvcache_v3  # pylint: disable=possibly-used-before-assignment
                    if not use_flash_attn_3 or inference_params is None:
                        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 not use_flash_attn_3:
                    fa_optional_forward_kwargs = {}
                    if fa_utils.v2_3_plus:
                        fa_optional_forward_kwargs["window_size"] = window_size
                    if fa_utils.v2_4_plus:
                        fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                    if fa_utils.v2_4_1_plus:
                        fa_optional_forward_kwargs["deterministic"] = self.deterministic
                    if inference_params is not None:
                        # use block_table kwarg to support thd_2bshd for non-paged
                        fa_optional_forward_kwargs["block_table"] = (
                            inference_params.cache_manager.page_table[:batch_size]
                            if inference_params.is_paged
                            else inference_params.cache_manager.batch_indices_post_step.unsqueeze(
                                1
                            )[:batch_size]
                        )
                    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,
4388
                    )
4389
                else:
4390
4391
                    fa_3_optional_forward_kwargs = {}
                    fa_3_optional_forward_kwargs["window_size"] = window_size
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
                    if inference_params is None:
                        fa_3_optional_forward_kwargs["deterministic"] = self.deterministic
                    else:
                        fa_3_optional_forward_kwargs["cu_seqlens_q"] = cu_seqlens_q
                        fa_3_optional_forward_kwargs["max_seqlen_q"] = max_seqlen_q
                        cache_seqlens = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                        fa_3_optional_forward_kwargs["cache_seqlens"] = cache_seqlens
                        # flash_attn_with_kvcache accepts thd_2bshd for non-paged
                        if inference_params.is_paged:
                            fa_3_optional_forward_kwargs["page_table"] = (
                                inference_params.cache_manager.page_table[:batch_size]
                            )
4404
                    if fp8:
4405
                        QKV_quantizer = quantizers["scaling_fwd"][META_QKV]
4406
                        torch_dtype = get_fp8_torch_dtype(fp8_meta["recipe"], fprop_tensor=True)
4407
                        torch_orig_dtype = query_layer.dtype
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418

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

4419
4420
4421
4422
4423
                        # "fp8_mha" decides outputs in fp8, while inputs are inferred from
                        # the real dtype
                        assert isinstance(key_layer, query_layer.__class__) and isinstance(
                            value_layer, query_layer.__class__
                        ), "q, k, and v must have the same type."
4424
                        if not isinstance(query_layer, Float8Tensor):
4425
                            query_layer, key_layer, value_layer = (
4426
                                QKV_quantizer(x) for x in [query_layer, key_layer, value_layer]
4427
                            )
4428
4429
4430
4431
                        batch_size = cu_seqlens_q.shape[0] - 1
                        num_heads_k = key_layer.shape[-2]
                        fa_3_optional_forward_kwargs["q_descale"] = (
                            query_layer._scale_inv.unsqueeze(0).repeat(batch_size, num_heads_k)
4432
                        )
4433
                        fa_3_optional_forward_kwargs["k_descale"] = key_layer._scale_inv.unsqueeze(
4434
                            0
4435
4436
4437
                        ).repeat(batch_size, num_heads_k)
                        fa_3_optional_forward_kwargs["v_descale"] = (
                            value_layer._scale_inv.unsqueeze(0).repeat(batch_size, num_heads_k)
4438
                        )
4439
4440
4441
                        query_layer, key_layer, value_layer = (
                            convert_to_torch_float8(x, torch_dtype)
                            for x in [query_layer, key_layer, value_layer]
4442
                        )
4443
                    try:
4444
                        output = func(
4445
4446
4447
4448
4449
4450
4451
4452
                            query_layer,
                            key_layer,
                            value_layer,
                            *fa_optional_forward_args_thd,
                            softmax_scale=self.softmax_scale,
                            causal="causal" in attn_mask_type,
                            **fa_3_optional_forward_kwargs,
                        )
4453
4454
                        if isinstance(output, (List, Tuple)):
                            output = output[0]
4455
                    except TypeError as e:
4456
                        if fa_utils.v3_0_0_beta:
4457
4458
4459
4460
                            e.args = (
                                e.args[0]
                                + ". Please update your flash-attn v3 (beta) installation as it "
                                + "may have added more supported arguments to its API. \n"
4461
                                + fa_utils.v3_installation_steps,
4462
4463
4464
4465
4466
4467
4468
4469
                            ) + e.args[1:]
                        raise

                    if fp8:
                        output = output.to(dtype=torch_orig_dtype)
                    if fp8 and fp8_meta["recipe"].fp8_mha:
                        O_quantizer = quantizers["scaling_fwd"][META_O]
                        output = O_quantizer(output)
4470

4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
4491
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"] and "padding" in attn_mask_type:
                output = dpa_utils.UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
        elif qkv_format in ["bshd", "sbhd_2bshd"]:
            # all KV caching cases use thd_2bshd for calculation
            # convert results back to bshd from thd_2bshd
            if isinstance(query_layer, Float8Tensor):
                output._data = tex.convert_thd_to_bshd(
                    output._data,
                    cu_seqlens_q,
                    batch_size,
                    context_len,
                )
                output = Float8Tensor.make_like(output, data=output._data, shape=output._data.shape)
            else:
                output = tex.convert_thd_to_bshd(
                    output,
                    cu_seqlens_q,
                    batch_size,
                    context_len,
                )
4492

4493
        if q_format == "sbhd":
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
            # (bs)hd -> bs(hd) -> sb(hd)
            if fp8 and fp8_meta["recipe"].fp8_mha:
                output_data = (
                    output._data.reshape(batch_size, max_seqlen_q // cp_size, -1)
                    .transpose(0, 1)
                    .contiguous()
                )
                output = Float8Tensor.make_like(
                    output,
                    data=output_data,
                    shape=output_data.shape,
                )
            else:
                output = output.view(batch_size, max_seqlen_q // cp_size, -1).transpose(0, 1)
4508
        elif q_format == "bshd":
4509
4510
            # (bs)hd -> bs(hd)
            output = output.reshape(batch_size, max_seqlen_q // cp_size, -1)
4511
        elif q_format == "thd":
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
            # thd -> t(hd)
            output = output.reshape(output.shape[0], -1)

        return output.contiguous()


def _combine_tensors(
    tensors: List[torch.Tensor],
    dim: int,
) -> torch.Tensor:
    """Combine tensors along a particular dimension"""

    num_tensors = len(tensors)
    new_shape = list(tensors[0].shape)
    new_shape.insert(dim, num_tensors)
    if isinstance(tensors[0], Float8Tensor):
        new_stride = list(tensors[0]._data.stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0]._data.dtype)
        combined_tensor.set_(
            tensors[0]._data.untyped_storage(),
            tensors[0]._data.storage_offset(),
            new_shape,
            new_stride,
        )
        combined_tensor = Float8Tensor.make_like(tensors[0], data=combined_tensor, shape=new_shape)
    else:
        new_stride = list(tensors[0].stride())
        new_stride.insert(dim, int(new_stride[dim - 1] / num_tensors))
        combined_tensor = torch.Tensor().to(device=tensors[0].device, dtype=tensors[0].dtype)
        combined_tensor.set_(
            tensors[0].untyped_storage(), tensors[0].storage_offset(), new_shape, new_stride
4544
4545
        )

4546
4547
    return combined_tensor

4548

4549
4550
4551
4552
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
4553
4554
4555
4556
4557
4558
4559
    def forward(
        ctx,
        is_training,
        max_seqlen_q,
        max_seqlen_kv,
        cu_seqlens_q,
        cu_seqlens_kv,
4560
4561
        cu_seqlens_q_padded,
        cu_seqlens_kv_padded,
4562
4563
        page_table_k,
        page_table_v,
4564
4565
4566
4567
4568
4569
4570
4571
4572
4573
        q,
        k,
        v,
        attn_bias,
        attn_scale,
        dropout_p,
        fast_zero_fill,
        qkv_layout,
        attn_bias_type,
        attn_mask_type,
4574
        window_size,
4575
4576
4577
4578
4579
        rng_gen,
        fused_attention_backend,
        use_FAv2_bwd,
        fp8,
        fp8_meta,
4580
        quantizers,
4581
        deterministic,
4582
    ):
4583
        # pylint: disable=missing-function-docstring
4584
        # "fp8_mha" decides outputs in fp8, while inputs are inferred from the real dtype
4585
        is_input_fp8 = False
4586
        is_output_fp8 = fp8_meta["recipe"].fp8_mha if "recipe" in fp8_meta else False
4587
4588
4589
4590

        # FP16/BF16 attn:                  fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = False: fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = True:  fake_dtype = torch.float8_e4m3fn
4591
4592
4593
        fake_dtype = q.dtype

        QKV_quantizer, O_quantizer, S_quantizer, dQKV_quantizer, dO_quantizer, dP_quantizer = (
4594
            dpa_utils.get_attention_quantizers(fp8, quantizers, cp_specific_quantizers=False)
4595
        )
4596
4597
        if fp8:
            fused_attention_backend = FusedAttnBackend["FP8"]
4598
4599
4600
            assert isinstance(k, q.__class__) and isinstance(
                v, q.__class__
            ), "q, k, and v must have the same type."
4601

4602
            is_input_fp8 = isinstance(q, Float8Tensor)
4603
            q_fp8, k_fp8, v_fp8 = None, None, None
4604
            if is_input_fp8:
4605
                q_fp8, k_fp8, v_fp8 = q, k, v
4606
4607
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4608
                qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
4627
4628
                match qkv_group:
                    case 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                        qkv_fp8 = QKV_quantizer(qkv)
                        q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1, 1, 1], True)
                    case 2:
                        q_fp8 = QKV_quantizer(q)
                        dim = qkv_layout.split("_")[1].find("2")
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                        kv_fp8 = QKV_quantizer(kv_c)
                        k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1, 1], True)
                    case 3:
                        q_fp8 = QKV_quantizer(q)
                        k_fp8 = QKV_quantizer(k)
                        v_fp8 = QKV_quantizer(v)
                    case _:
                        raise "Invalid qkv_layout " + qkv_layout
4629
            # q_fp8, k_fp8, v_fp8, out_fp8: torch.float8_e4m3fn
4630
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
4631
4632
4633
4634
4635
4636
4637
4638
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q_fp8,
                k_fp8,
                v_fp8,
4639
                fake_dtype,
4640
4641
                fused_attention_backend,
                attn_bias,
4642
4643
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4644
4645
                None,
                None,
4646
4647
                S_quantizer,
                O_quantizer,
4648
4649
4650
4651
4652
4653
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4654
                window_size,
4655
4656
                rng_gen,
            )
4657
            if is_output_fp8:
4658
                out_ret = out_fp8
4659
            else:
4660
                out_ret = out_fp8.dequantize().view(out_fp8.shape)
4661
4662
            # is_output_fp8 = False: out_save.dtype = torch.float16 or torch.bfloat16
            # is_output_fp8 = True:  out_save.dtype = torch.float8_e4m3fn
4663
4664
            out_save = out_ret

4665
            if not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
4666
                # 1: qkv packed, 2: kv packed, 3: qkv separate
4667
                if is_input_fp8:
4668
                    qkv_group = len(qkv_layout.replace("paged_kv_", "").split("_"))
4669
4670
4671
4672
                    if qkv_group == 1:
                        dim = qkv_layout.find("3")
                        qkv = _combine_tensors([q, k, v], dim)
                        qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
4673
4674
                        qkv_no_fp8 = qkv_c.dequantize().view(qkv.shape)
                        q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1, 1, 1], True)
4675
                    if qkv_group == 2:
4676
                        q = q.dequantize()
4677
                        dim = qkv_layout.replace("paged_kv_", "").split("_")[1].find("2")
4678
4679
                        kv = _combine_tensors([k, v], dim)
                        kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
4680
4681
                        kv_no_fp8 = kv.dequantize()
                        k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1, 1], True)
4682
                    if qkv_group == 3:
4683
4684
4685
                        q = q.dequantize()
                        k = k.dequantize()
                        v = v.dequantize()
4686
                if is_output_fp8:
4687
4688
4689
                    out_save = out_fp8.dequantize()

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8)
4690
        else:
4691
            # q, k, v, out_ret: torch.float16 or torch.bfloat16
4692
            out_ret, aux_ctx_tensors = fused_attn_fwd(
4693
4694
4695
4696
4697
4698
4699
4700
                is_training,
                max_seqlen_q,
                max_seqlen_kv,
                cu_seqlens_q,
                cu_seqlens_kv,
                q,
                k,
                v,
4701
                fake_dtype,
4702
4703
                fused_attention_backend,
                attn_bias,
4704
4705
                cu_seqlens_q_padded,
                cu_seqlens_kv_padded,
4706
4707
                page_table_k,
                page_table_v,
4708
4709
                None,  # s_quantizer
                None,  # o_quantizer
4710
4711
4712
4713
4714
4715
                attn_scale,
                dropout_p,
                fast_zero_fill,
                qkv_layout,
                attn_bias_type,
                attn_mask_type,
4716
                window_size,
4717
4718
                rng_gen,
            )
4719
            out_save = out_ret
4720
            fp8_tensors = (None, None, None, None)
4721

4722
4723
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))

4724
        from .cpu_offload import CPUOffloadEnabled
4725

4726
        if CPUOffloadEnabled:
4727
4728
4729
4730
4731
            if ctx.fp8:
                tensor_list = fp8_tensors
            else:
                tensor_list = [q, k, v, out_save]

4732
            qkv_layout = "sbhd_sbhd_sbhd"
4733
4734
            mark_activation_offload(*tensor_list)
            mark_activation_offload(*aux_ctx_tensors)
4735

4736
4737
        ctx.is_input_fp8 = is_input_fp8
        ctx.is_output_fp8 = is_output_fp8
4738
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
4739
4740
        tensors_to_save, tensor_objects = prepare_for_saving(
            *fp8_tensors,
4741
4742
4743
            *qkvo_tensors,
            cu_seqlens_q,
            cu_seqlens_kv,
4744
4745
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4746
4747
            *aux_ctx_tensors,
        )
4748
4749
        ctx.save_for_backward(*tensors_to_save)
        ctx.tensor_objects = tensor_objects
4750
        ctx.fp8_meta = fp8_meta
4751
4752
4753
4754
4755

        ctx.dQKV_quantizer = dQKV_quantizer
        ctx.dO_quantizer = dO_quantizer
        ctx.dP_quantizer = dP_quantizer
        ctx.S_quantizer = S_quantizer
4756
4757
4758
        if ctx.fp8:
            ctx.S_quantizer = S_quantizer.copy()
            ctx.S_quantizer.scale = S_quantizer.scale.clone()
4759

4760
4761
4762
4763
4764
4765
4766
4767
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        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
4768
        ctx.window_size = window_size
4769
        ctx.fused_attention_backend = (
4770
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
4771
        )
4772
        ctx.use_FAv2_bwd = use_FAv2_bwd
4773
        ctx.deterministic = deterministic
4774

4775
        return out_ret
4776
4777
4778

    @staticmethod
    def backward(ctx, d_out):
4779
        # pylint: disable=missing-function-docstring
4780
        if ctx.is_output_fp8:
4781
4782
4783
            assert isinstance(
                d_out, Float8Tensor
            ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
4784

4785
4786
4787
4788
4789
        # FP16/BF16 attn:                  fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = False: fake_dtype = torch.float16 or torch.bfloat16
        # FP8 attn, is_output_fp8 = True:  fake_dtype = torch.float8_e5m2
        fake_dtype = d_out.dtype

4790
        d_out = d_out.contiguous()
4791
        (
4792
4793
4794
4795
            q_fp8,
            k_fp8,
            v_fp8,
            out_fp8,
4796
4797
4798
4799
4800
4801
            q,
            k,
            v,
            out,
            cu_seqlens_q,
            cu_seqlens_kv,
4802
4803
            cu_seqlens_q_padded,
            cu_seqlens_kv_padded,
4804
4805
4806
4807
4808
            *other_tensors,
        ) = restore_from_saved(ctx.tensor_objects, ctx.saved_tensors)

        aux_ctx_tensors = other_tensors

4809
4810
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
4811
        rest = [None]
4812
        if ctx.use_FAv2_bwd:
4813
            softmax_lse, rng_state = aux_ctx_tensors
4814
4815
4816
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
4817
            d_out, q, k, v, out = [maybe_contiguous(x) for x in (d_out, q, k, v, out)]
4818
            flash_attn_cuda_bwd(
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834
4835
4836
4837
                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,
4838
            )
4839
4840
4841
            dq = dq[..., : d_out.shape[-1]]
            dk = dk[..., : d_out.shape[-1]]
            dv = dv[..., : d_out.shape[-1]]
4842
        else:
4843
4844
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
4845
                    if ctx.is_output_fp8:
4846
4847
                        d_out_fp8 = d_out
                    else:
4848
                        d_out_fp8 = ctx.dO_quantizer(d_out)
4849
4850
4851
                    dqkv_dtype = TE_DType[d_out_fp8._data.dtype]
                    # q_fp8, k_fp8, v_fp8, out_fp8:      torch.float8_e4m3fn
                    # d_out_fp8, dq_fp8, dk_fp8, dv_fp8: torch.float8_e5m2
4852
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
4853
4854
4855
4856
4857
4858
4859
4860
4861
                        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,
4862
4863
                        fake_dtype,
                        dqkv_dtype,
4864
                        aux_ctx_tensors,
4865
                        ctx.fused_attention_backend,
4866
4867
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4868
4869
4870
                        ctx.S_quantizer,
                        ctx.dP_quantizer,
                        ctx.dQKV_quantizer,
4871
4872
4873
4874
4875
4876
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4877
4878
                        ctx.window_size,
                        ctx.deterministic,
4879
                    )
4880

4881
4882
                    # is_input_fp8 = False: dq, dk, dv: torch.float16 or torch.bfloat16
                    # is_input_fp8 = True:  dq, dk, dv: torch.float8_e5m2
4883
                    if not ctx.is_input_fp8:
4884
                        qkv_group = len(ctx.qkv_layout.replace("paged_kv_", "").split("_"))
4885
                        if qkv_group == 1:
4886
                            dim = ctx.qkv_layout.find("3")
4887
4888
                            dqkv_fp8_data = _combine_tensors(
                                [dq_fp8._data, dk_fp8._data, dv_fp8._data], dim
4889
                            )
4890
4891
4892
4893
4894
                            dqkv_fp8 = dq_fp8.make_like(
                                tensor=dq_fp8, data=dqkv_fp8_data, shape=dqkv_fp8_data.shape
                            )
                            dqkv = dqkv_fp8.dequantize()
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1, 1, 1], True)
4895
                        if qkv_group == 2:
4896
                            dq = dq_fp8.dequantize()
4897
4898
4899
4900
4901
                            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]
                            )
4902
4903
                            dkv = dkv_c_fp8.dequantize()
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1, 1], True)
4904
                        if qkv_group == 3:
4905
4906
4907
4908
4909
                            dq = dq_fp8.dequantize()
                            dk = dk_fp8.dequantize()
                            dv = dv_fp8.dequantize()
                    else:
                        dq, dk, dv = dq_fp8, dk_fp8, dv_fp8
4910
                else:
4911
4912
                    if isinstance(d_out, QuantizedTensor):
                        d_out = d_out.dequantize()
4913
4914
                    dqkv_dtype = TE_DType[d_out.dtype]
                    # q, k, v, out, d_out, dq, dk, dv: torch.float16 or torch.bfloat16
4915
                    dq, dk, dv, *rest = fused_attn_bwd(
4916
4917
4918
4919
4920
4921
4922
4923
4924
                        ctx.max_seqlen_q,
                        ctx.max_seqlen_kv,
                        cu_seqlens_q,
                        cu_seqlens_kv,
                        q,
                        k,
                        v,
                        out,
                        d_out,
4925
4926
                        fake_dtype,
                        dqkv_dtype,
4927
                        aux_ctx_tensors,
4928
                        ctx.fused_attention_backend,
4929
4930
                        cu_seqlens_q_padded,
                        cu_seqlens_kv_padded,
4931
4932
4933
4934
4935
4936
4937
4938
4939
                        None,
                        None,
                        None,
                        ctx.attn_scale,
                        ctx.dropout_p,
                        ctx.fast_zero_fill,
                        ctx.qkv_layout,
                        ctx.attn_bias_type,
                        ctx.attn_mask_type,
4940
4941
                        ctx.window_size,
                        ctx.deterministic,
4942
                    )
4943

4944
4945
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
4946
4947
4948
4949
4950
4951
4952
4953
            return (
                None,
                None,
                None,
                None,
                None,
                None,
                None,
4954
4955
                None,
                None,
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
                dq,
                dk,
                dv,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
                None,
            )
4975
        # else, return (dqkv, dbias)
4976
4977
4978
4979
4980
4981
4982
4983
        return (
            None,
            None,
            None,
            None,
            None,
            None,
            None,
4984
4985
            None,
            None,
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
5001
5002
            dq,
            dk,
            dv,
            rest[0],
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
            None,
5003
            None,
5004
        )
5005

5006

5007
class FusedAttention(torch.nn.Module):
5008
5009
5010
5011
5012
5013
5014
5015
5016
    """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:

5017
5018
5019
5020
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
5021
    | attn_type     | self/cross              | self/cross                     |
5022
    | qkv_layout    |                         |                                |
5023
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
5024
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
5025
5026
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
5027
5028
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
5029
    | dropout       | yes                     | yes                            |
5030
5031
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
5032
    | output dtype  | fp16/bf16               | fp16/bf16                      |
5033
5034
5035
5036
    """

    def __init__(
        self,
5037
        softmax_scale: float,
5038
5039
5040
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
5041
5042
        layer_number: Optional[int] = None,
        deterministic: bool = False,
5043
5044
5045
    ) -> None:
        super().__init__()

5046
        self.softmax_scale = softmax_scale
5047
5048
5049
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
5050
5051
5052
        self.use_FAv2_bwd = os.getenv(
            "NVTE_FUSED_ATTN_USE_FAv2_BWD", "0"
        ) == "1" and get_device_compute_capability() == (9, 0)
5053
        self.layer_number = 1 if layer_number is None else layer_number
5054
        self.deterministic = deterministic
5055

5056
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
5057
5058
            """
            Temporarily remove fused_attention._extra_state as a missing key
5059
            or an unexpected key when loading Transformer Engine checkpoints.
5060
5061
            Please store FP8 metadata as DotProductAttention's _extra_state,
            rather than FusedAttention's _extra_state. This hook will be
5062
            phased out in Transformer Engine 2.0.
5063
5064
            """
            for key in incompatible_keys.missing_keys:
5065
                if "fused_attention._extra_state" in key:
5066
                    incompatible_keys.missing_keys.remove(key)
5067
5068
5069
5070
5071
5072
5073
            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."
                    )
5074

5075
5076
        self.register_load_state_dict_post_hook(remove_extra_states_check)

5077
    @no_torch_dynamo()
5078
5079
5080
5081
5082
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5083
5084
5085
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
5086
5087
        cu_seqlens_q_padded: Optional[torch.Tensor] = None,
        cu_seqlens_kv_padded: Optional[torch.Tensor] = None,
5088
5089
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
5090
        attn_mask_type: str = "causal",
5091
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
5092
        window_size: Optional[Tuple[int, int]] = None,
5093
        fused_attention_backend: tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
5094
5095
5096
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
5097
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5098
5099
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
5100
        cp_comm_type: str = "p2p",
5101
5102
        fp8: bool = False,
        fp8_meta: Optional[Dict[str, Any]] = None,
5103
        quantizers=None,
5104
        pad_between_seqs: bool = False,
5105
        inference_params: Optional[InferenceParams] = None,
5106
5107
    ) -> torch.Tensor:
        """fused attention fprop"""
5108
5109
5110
        assert (
            fused_attention_backend != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
        ), "No fused attention backend supports this input combination!"
5111
5112
5113
5114
        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."
5115
5116
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
5117
        ), "FusedAttention only supports CUDA tensors."
5118
5119
        assert (
            qkv_layout in QKVLayouts
5120
        ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"
5121

5122
5123
5124
5125
5126
5127
        cp_size = 1
        if isinstance(cp_group, dist_group_type):
            cp_size = get_distributed_world_size(cp_group)
        elif isinstance(cp_group, list):
            for group in cp_group:
                cp_size *= get_distributed_world_size(group)
5128
        context_parallel = cp_size > 1
5129

5130
5131
        # get q_format and kv_format for training and inference
        qkv_format, q_format, kv_format = dpa_utils.get_qkv_format(qkv_layout, inference_params)
5132

5133
5134
5135
5136
5137
5138
5139
5140
5141
5142
        # cuDNN can work with 0-length sequences in the batch for both bshd/sbhd and thd formats
        # however, for bshd/sbhd, q/k/v tensors need to have the same batch size as indicated by
        # cu_seqlens, whereas thd does not have this requirement
        # e.g. if q_format = bshd, and q.shape = [3, 1, 16, 64], we should have k.shape[0] =
        # v.shape[0] = q.shape[0], and cu_seqlens_q.shape = cu_seqlens_kv.shape = [4]
        if q_format in ["bshd", "sbhd"] or kv_format in ["bshd", "sbhd"]:
            batch_size = query_layer.shape[0] if q_format == "bshd" else query_layer.shape[1]
            cu_seqlens_q = cu_seqlens_q[: batch_size + 1]
            cu_seqlens_kv = cu_seqlens_kv[: batch_size + 1]

5143
5144
5145
5146
5147
5148
5149
5150
5151
5152
5153
5154
5155
5156
5157
5158
5159
5160
5161
5162
5163
5164
5165
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
        page_table = None
        if inference_params is None:
            if qkv_format in ["sbhd", "bshd"]:
                if qkv_format == "sbhd":
                    batch_size = query_layer.shape[1]
                    max_seqlen_q = query_layer.shape[0]
                    max_seqlen_kv = key_layer.shape[0]
                if qkv_format == "bshd":
                    batch_size = query_layer.shape[0]
                    max_seqlen_q = query_layer.shape[1]
                    max_seqlen_kv = key_layer.shape[1]
                max_seqlen_q *= cp_size
                max_seqlen_kv *= cp_size
                if "padding" in attn_mask_type:
                    assert (
                        not context_parallel
                    ), "Padding mask not supported with context parallelism!"
                    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!"
                            )
                        if self.attention_type == "self":
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
                            cu_seqlens_kv = cu_seqlens_q
                        else:
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
                else:
                    if cu_seqlens_q is None:
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
5177
                        )
5178
5179
5180
5181
5182
5183
5184
5185
5186
5187
5188
5189
5190
5191
5192
5193
5194
                    if cu_seqlens_kv is None:
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
            if qkv_format == "thd":
                assert (
                    max_seqlen_q is not None
                    and max_seqlen_kv is not None
                    and cu_seqlens_q is not None
                    and cu_seqlens_kv is not None
                ), "max_seqlen_q/kv and cu_seqlens_q/kv can not be None when qkv_format is thd!"
        elif inference_params.is_paged:
            page_table = inference_params.cache_manager.page_table

        if (q_format == "thd" or "padding" in attn_mask_type) and cu_seqlens_q_padded is None:
5195
            cu_seqlens_q_padded = cu_seqlens_q
5196
        if (kv_format == "thd" or "padding" in attn_mask_type) and cu_seqlens_kv_padded is None:
5197
            cu_seqlens_kv_padded = cu_seqlens_kv
5198

5199
5200
5201
5202
5203
        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)
        )
5204

5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
        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!"
            )

5216
        if context_parallel:
5217
            assert (
5218
5219
                fp8
                or fused_attention_backend == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
5220
5221
5222
5223
5224
5225
5226
            ), 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)
            ]
5227
5228
5229
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
5230
5231
5232
5233
5234
5235
5236
                    query_layer,
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_q,
                    max_seqlen_kv,
5237
5238
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5239
                    self.attention_dropout if self.training else 0.0,
5240
5241
5242
                    cp_group,
                    cp_global_ranks,
                    cp_stream,
5243
                    cp_comm_type,
5244
                    softmax_scale=self.softmax_scale,
5245
                    qkv_format=qkv_format,
5246
                    attn_mask_type=attn_mask_type,
5247
5248
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
5249
                    deterministic=self.deterministic,
5250
                    use_fused_attention=True,
5251
                    window_size=window_size,
5252
5253
                    fp8=fp8,
                    fp8_meta=fp8_meta,
5254
                    quantizers=quantizers,
5255
                    pad_between_seqs=pad_between_seqs,
5256
5257
                )
        else:
5258
5259
5260
5261
5262
5263
5264
            with self.attention_dropout_ctx():
                output = FusedAttnFunc.apply(
                    self.training,
                    max_seqlen_q,
                    max_seqlen_kv,
                    cu_seqlens_q,
                    cu_seqlens_kv,
5265
5266
                    cu_seqlens_q_padded,
                    cu_seqlens_kv_padded,
5267
5268
                    page_table,
                    page_table,
5269
5270
5271
5272
5273
5274
5275
5276
5277
5278
                    query_layer,
                    key_layer,
                    value_layer,
                    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,
5279
                    window_size,
5280
5281
5282
5283
5284
                    None,  # rng_gen
                    fused_attention_backend,
                    use_FAv2_bwd,
                    fp8,
                    fp8_meta,
5285
                    quantizers,
5286
                    self.deterministic,
5287
                )
5288

5289
5290
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
5291
5292


5293
class DotProductAttention(TransformerEngineBaseModule):
5294
5295
5296
5297
5298
5299
    """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::

5300
        Argument :attr:`attention_mask` in the `forward` call is only used when
5301
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
5302
5303
5304

    .. warning::

5305
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
5306
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
5307
5308
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
5309

5310
5311
5312
5313
5314
5315
5316
    .. note::

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


5317
5318
5319
5320
    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
5321
5322
5323
    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.
5324
5325
5326
5327
5328
5329
5330
5331
    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`.
5332
5333
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
5334
    attn_mask_type: str, default = `causal`
5335
                   type of attention mask passed into softmax operation, options are "`no_mask`",
5336
5337
5338
5339
5340
5341
5342
5343
5344
                   "`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
5345
                   "`padding_causal`" and "`padding_causal_bottom_right`", Transformer Engine
5346
5347
5348
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
                   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].
5360
5361
5362
5363
    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
5364
5365
5366
                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
5367
                be overridden by :attr:`window_size` in `forward` as well.
5368
5369
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
5370
5371
5372
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
5373
5374
5375
    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,
5376
               `h` the number of heads, `d` head size, and `t` the total number of tokens
5377
5378
5379
5380
5381
               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.
5382
               For that, please use `get_qkv_layout` to gain the layout information.
5383
5384
    softmax_scale: Optional[float], default = `None`
                softmax scale for the attention scores. If `None`, defaults to
5385
                `1.0/math.sqrt(kv_channels if isinstance(kv_channels, int) else kv_channels[0])`.
5386
5387
5388
5389
5390
5391
5392
5393
5394

    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.
5395
    cp_group : Union[ProcessGroup, List[ProcessGroup]], default = `None`
5396
              context parallel process group.
5397
5398
5399
              ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
              List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
              and cp_group[1] are for a2a and p2p communications respectively.
5400
5401
5402
5403
5404
5405
5406
    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.
5407
    cp_comm_type : str, default = `p2p`
5408
                  inter-gpu communication type for context parallelism.
5409
                  Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5410
5411
5412
5413
5414
5415
                  "p2p": Exchange KV chunks with P2P communications in ring topology.
                         P2P is async and can be overlapped with attention compute.
                  "all_gather": All-gather to get full sequence of KV before attention.
                                The all-gather is not async, and cannot be overlapped.
                  "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                         group, and gather to get full sequence of QKV.
5416
5417
5418
                  "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                  across each CP sub-group (e.g., via NVLink), then exchanging KV with
                  p2p between sub-groups (e.g., via IBLink).
5419
5420
5421
5422
5423
    """

    def __init__(
        self,
        num_attention_heads: int,
5424
        kv_channels: Union[int, Tuple[int, int]],
5425
        num_gqa_groups: Optional[int] = None,
5426
        attention_dropout: float = 0.0,
5427
        qkv_format: str = "sbhd",
5428
        attn_mask_type: str = "causal",
5429
        window_size: Optional[Tuple[int, int]] = None,
5430
5431
5432
5433
5434
        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,
5435
        attention_type: str = "self",
5436
        cp_group: Optional[Union[dist_group_type, List[dist_group_type]]] = None,
5437
        cp_global_ranks: List[int] = None,
5438
        cp_stream: torch.cuda.Stream = None,
5439
        cp_comm_type: str = "p2p",
5440
        softmax_scale: Optional[float] = None,
5441
5442
5443
    ) -> None:
        super().__init__()

5444
        self.logger = logging.getLogger("DotProductAttention")
5445
        self.logger.setLevel(attn_log._log_level)
5446
        if not self.logger.hasHandlers():
5447
            self.logger.addHandler(attn_log._stream_handler)
5448
        self.qkv_format = qkv_format
5449
        attn_mask_type = attn_mask_type.replace(",", "_")
5450
5451
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
5452
        self.attn_mask_type = attn_mask_type
5453
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5454
5455
5456
5457
5458
5459
5460
        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)
5461
        self.get_rng_state_tracker = get_rng_state_tracker
5462
        self.num_attention_heads = num_attention_heads
5463
        self.layer_number = 1 if layer_number is None else layer_number
5464
5465
5466
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5467
        self.cp_comm_type = cp_comm_type
5468

5469
5470
5471
5472
5473
5474
        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]
        )
5475

5476
        self.num_gqa_groups = num_attention_heads if num_gqa_groups is None else num_gqa_groups
5477
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // self.tp_size)
5478

5479
5480
5481
        assert (
            num_attention_heads % self.num_gqa_groups == 0
        ), "The number of attention heads must be divisible by the number of GQA groups!"
5482

5483
        self.rng_states_tracker = None
5484
5485
5486
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
5487
5488
5489
            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
5490

5491
        if softmax_scale is None:
5492
5493
5494
            softmax_scale = 1.0 / math.sqrt(
                kv_channels if isinstance(kv_channels, int) else kv_channels[0]
            )
5495

5496
5497
5498
        self.deterministic = (
            not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1")))
            or torch.are_deterministic_algorithms_enabled()
5499
        )
5500
5501
5502
5503
5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
        # 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"
5519

5520
        assert attention_type in AttnTypes, f"attention_type {attention_type} not supported"
5521
5522
5523
5524

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

5525
5526
5527
5528
5529
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

5530
5531
5532
5533
5534
5535
5536
        self.flash_attention = FlashAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5537

5538
        # Instantiating three types since use of flash-attn and FusedAttention
5539
        # might be ruled out due to forward inputs.
5540
5541
5542
5543
5544
5545
5546
        self.fused_attention = FusedAttention(
            softmax_scale,
            attention_type=attention_type,
            layer_number=layer_number,
            deterministic=self.deterministic,
            **attn_kwargs,
        )
5547

5548
        self.unfused_attention = UnfusedDotProductAttention(
5549
5550
5551
5552
            softmax_scale,
            attention_type=attention_type,
            **attn_kwargs,
            layer_number=layer_number,
5553
        )
5554

5555
5556
5557
        def remove_extra_states_check(self, incompatible_keys):  # pylint: disable=unused-argument
            """
            Temporarily remove core_attention._extra_state as a missing key
5558
5559
            when loading older Transformer Engine checkpoints. Will phase out
            this hook in Transformer Engine 2.0.
5560
5561
5562
5563
5564
5565
5566
            """
            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)

5567
5568
5569
5570
5571
5572
5573
5574
5575
5576
5577
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588
    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        """
        This function helps to load Transformer Engine 1.6 and 1.7 checkpoints, where FP8 attention
        metadata is stored under the `core_attention.fused_attention._extra_state` key and not the
        `core_attention._extra_state` key. Please see `FP8 checkpoint compatibility
        <https://docs.nvidia.com/deeplearning/transformer-engine/user-guide/faq.html#fp8-checkpoint-compatibility>`_ for more details.
        """
        fused_attn_key = False
        dot_product_attn_key = False
        for k in state_dict.keys():
            if "core_attention.fused_attention._extra_state" in k:
                fused_attn_key = True
            if "core_attention._extra_state" in k:
                dot_product_attn_key = True
        if fused_attn_key and not dot_product_attn_key:
            prefix = prefix + "fused_attention."
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

5589
5590
5591
5592
    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
5593
        **forward_kwargs: Dict[str, Any],
5594
5595
5596
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

5597
5598
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
5599
5600
5601

        hidden_states = checkpoint(
            custom_forward,
5602
5603
5604
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
5605
            *forward_args,
5606
            **forward_kwargs,
5607
5608
5609
5610
        )

        return hidden_states

5611
5612
    def set_context_parallel_group(
        self,
5613
        cp_group: Union[dist_group_type, List[dist_group_type], None],
5614
5615
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
5616
        cp_comm_type: str = "p2p",
5617
    ) -> None:
5618
5619
5620
5621
5622
5623
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
5624
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
5625
                  context parallel process group.
5626
5627
5628
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
5629
5630
5631
5632
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
5633
        cp_comm_type : str, default = `p2p`
5634
                      inter-gpu communication type for context parallelism.
5635
                      Can be "p2p" or "all_gather" or "a2a" or "a2a+p2p".
5636
5637
5638
5639
5640
5641
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
5642
5643
5644
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
5645
        """
5646
5647
5648
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
5649
        self.cp_comm_type = cp_comm_type
5650

5651
    @no_torch_dynamo(recursive=False)
5652
5653
5654
5655
5656
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
5657
5658
5659
5660
5661
5662
5663
5664
        attention_mask: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]] = None,
        qkv_format: str = None,
        cu_seqlens_q: torch.Tensor = None,
        cu_seqlens_kv: torch.Tensor = None,
        cu_seqlens_q_padded: torch.Tensor = None,
        cu_seqlens_kv_padded: torch.Tensor = None,
        max_seqlen_q: int = None,
        max_seqlen_kv: int = None,
5665
        attn_mask_type: Optional[str] = None,
5666
        window_size: Optional[Tuple[int, int]] = None,
5667
        checkpoint_core_attention: bool = False,
5668
5669
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
5670
        alibi_slopes: Optional[torch.Tensor] = None,
5671
        fast_zero_fill: bool = True,
5672
        inference_params: Optional[InferenceParams] = None,
5673
        pad_between_seqs: Optional[bool] = None,
5674
5675
5676
5677
5678
5679
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

5680
5681
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
5682

5683
5684
        .. note::

5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
5695
5696
5697
            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,
5698
            and FusedAttention backend if applicable, to use. Transformer Engine prioritizes
5699
5700
5701
5702
            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
5703
5704
            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
5705
            optimizations in FusedAttention. When unset, Transformer Engine determines the code path
5706
5707
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
5708

5709
5710
5711
5712
5713
5714
5715
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
5732
5733
5734
5735
5736
5737
5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
        .. note::
            .. _cu_seqlens note:

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

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

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

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

        .. note::
            .. _max_seqlen note:

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

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

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

5763
5764
5765
5766
5767
5768
5769
5770
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
5771
5772
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
5773
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
5774
5775
             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]
5776
5777
5778
5779
             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.
5780
5781
5782
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
5783
                   Cumulative sum of sequence lengths (without offset) in a batch for `query_layer`,
5784
                   with shape [batch_size + 1] and dtype torch.int32.
5785
                   See :ref:`note<cu_seqlens note>` for more details.
5786
        cu_seqlens_kv: Optional[torch.Tensor], default = `None`
5787
5788
                   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.
5789
                   See :ref:`note<cu_seqlens note>` for more details.
5790
5791
5792
5793
5794
        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`.
5795
                   See :ref:`note<cu_seqlens note>` for more details.
5796
5797
5798
5799
5800
        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`.
5801
                   See :ref:`note<cu_seqlens note>` for more details.
5802
5803
        max_seqlen_q: Optional[int], default = `None`
                      Maximum sequence length in `query_layer`.
5804
                      See :ref:`note<max_seqlen note>` for more details.
5805
5806
        max_seqlen_kv: Optional[int], default = `None`
                       Maximum sequence length in `key_layer` and `value_layer`.
5807
                       See :ref:`note<max_seqlen note>` for more details.
5808
5809
5810
5811
5812
5813
5814
        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.
5815
        window_size: Optional[Tuple[int, int]], default = `None`
5816
                    Sliding window size for local attention.
5817
5818
5819
5820
5821
        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.
5822
        core_attention_bias_type: str, default = `no_bias`
5823
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
5824
        core_attention_bias: Optional[torch.Tensor], default = `None`
5825
5826
                    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.
5827
5828
5829
5830
        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.
5831
        fast_zero_fill: bool, default = `True`
5832
                    Whether to use the fast path to set output tensors to 0 or not.
5833
5834
5835
5836
5837
5838
5839
5840
5841
5842
        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.
5843
5844
5845
        pad_between_seqs: Optional[bool], default = `None`
            If None, inferred from qkv_format, cu_seqlens and cu_seqlens_padded.
            If true, there are padding tokens between individual sequences in a packed batch.
5846
        """
5847

5848
5849
5850
5851
5852
        with self.prepare_forward(
            query_layer,
            num_gemms=3,
            allow_non_contiguous=True,
        ) as query_layer:
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
            # checks for RNG
            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."

            # checks for FP8
5863
5864
5865
5866
            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
5867
                        self.logger.warning(
5868
5869
5870
                            """Forcing fp8_meta["recipe"].fp8_dpa=True due to """
                            """fp8_meta["recipe"].fp8_mha=True"""
                        )
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
            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."""
5881

5882
            # checks for q/k/v shapes
5883
5884
5885
            assert (
                query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), "DotProductAttention only supports CUDA tensors."
5886
5887
5888
            assert (
                query_layer.dtype == key_layer.dtype and query_layer.dtype == value_layer.dtype
            ), "Queries, keys and values must have the same data type!"
5889
5890
5891
            assert (
                key_layer.shape[:-1] == value_layer.shape[:-1]
            ), "Keys and values must have the same batch size, sequence length and number of heads!"
5892
5893
            num_attention_heads = query_layer.shape[-2]
            num_gqa_groups = key_layer.shape[-2]
5894
            assert (
5895
5896
5897
                query_layer.shape[-1] == key_layer.shape[-1]
            ), "Queries and keys must have the same head dimension!"
            head_dim_qk, head_dim_v = query_layer.shape[-1], value_layer.shape[-1]
5898
            assert (
5899
5900
                head_dim_qk == self.hidden_size_per_attention_head_k
            ), f"Keys have head_dim = {head_dim_qk}, "
5901
5902
            "but expected head_dim = {self.hidden_size_per_attention_head_k}!"
            assert (
5903
5904
                head_dim_v == self.hidden_size_per_attention_head_v
            ), f"Values have head_dim = {head_dim_v}, "
5905
            "but expected head_dim = {self.hidden_size_per_attention_head_v}!"
5906
5907
5908
5909
            assert num_gqa_groups == self.num_gqa_groups_per_partition, (
                "Keys and values must have num_gqa_group ="
                f" {self.num_gqa_groups_per_partition} heads! Found {num_gqa_groups}."
            )
5910

5911
            # checks for attention mask
5912
5913
5914
5915
5916
5917
            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"
5918
            assert (
5919
5920
                attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
5921

5922
            # checks for sliding window
5923
5924
            if window_size is None:
                window_size = self.window_size
5925
            window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
5926

5927
5928
5929
            # checks for qkv_format
            if qkv_format is None:
                qkv_format = self.qkv_format
5930
5931
5932
5933
5934
            assert qkv_format in [
                "sbhd",
                "bshd",
                "thd",
            ], "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
            batch_size = None
            if qkv_format in ["sbhd", "bshd"]:
                assert all(
                    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=}!"
                if qkv_format == "sbhd":
                    batch_size = query_layer.shape[1]
                    max_seqlen_q = query_layer.shape[0] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[0] if max_seqlen_kv is None else max_seqlen_kv
                else:
                    batch_size = query_layer.shape[0]
                    max_seqlen_q = query_layer.shape[1] if max_seqlen_q is None else max_seqlen_q
                    max_seqlen_kv = key_layer.shape[1] if max_seqlen_kv is None else max_seqlen_kv
5948
            if qkv_format == "thd":
5949
                assert all(
5950
5951
                    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!"
5952
5953
5954
                assert (
                    "padding" in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
                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!"
5966
                batch_size = len(cu_seqlens_q) - 1
5967
                if max_seqlen_q is None:
5968
5969
5970
5971
                    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]
5972
                    max_seqlen_q = int((seqlens_q.max().item() + 63) // 64 * 64)
5973
                if max_seqlen_kv is None:
5974
5975
5976
5977
                    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]
5978
                    max_seqlen_kv = int((seqlens_kv.max().item() + 63) // 64 * 64)
5979

5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
6020
6021
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
6034
6035
6036
6037
6038
6039
6040
6041
6042
6043
6044
6045
6046
6047
6048
6049
            # update KV cache and retrieve saved tokens from cache for inference
            if inference_params is not None:
                assert self.layer_number is not None, "Layer number must be set!"

                # convert top-left causal to bottom-right causal due to KV caching
                # users can still use the same attention mask for inference as for training
                assert "padding" in attn_mask_type, "KV caching requires padding mask!"
                if attn_mask_type == "padding_causal":
                    attn_mask_type = attn_mask_type + "_bottom_right"

                self.attention_type = "cross"
                self.flash_attention.attention_type = self.attention_type
                self.fused_attention.attention_type = self.attention_type
                self.unfused_attention.attention_type = self.attention_type

                query_layer, key_layer, value_layer = [
                    x.contiguous() if not x.is_contiguous() else x
                    for x in [query_layer, key_layer, value_layer]
                ]

                # get full K/V tensors from cache and adjust cu_seqlens, qkv_format based on the cache
                (
                    key_layer,
                    value_layer,
                    cu_seqlens_q,
                    cu_seqlens_kv,
                    max_seqlen_kv,
                    qkv_format,
                ) = inference_params.step(
                    self.layer_number,
                    key_layer,
                    value_layer,
                    qkv_format,
                )
                cu_seqlens_q_padded = None
                cu_seqlens_kv_padded = None

            # get qkv's memory layout
            if all(isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer]):
                (
                    qkv_layout,
                    query_layer._data,
                    key_layer._data,
                    value_layer._data,
                    q_format,
                    kv_format,
                ) = dpa_utils.get_qkv_layout(
                    query_layer._data,
                    key_layer._data,
                    value_layer._data,
                    qkv_format=qkv_format,
                    inference_params=inference_params,
                )
            else:
                (
                    qkv_layout,
                    query_layer,
                    key_layer,
                    value_layer,
                    q_format,
                    kv_format,
                ) = dpa_utils.get_qkv_layout(
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_format=qkv_format,
                    inference_params=inference_params,
                )

            # adjust max_seqlen and cu_seqlens for CP
6050
6051
6052
6053
6054
6055
            cp_size = 1
            if isinstance(self.cp_group, dist_group_type):
                cp_size = get_distributed_world_size(self.cp_group)
            elif isinstance(self.cp_group, list):
                for group in self.cp_group:
                    cp_size *= get_distributed_world_size(group)
6056
            context_parallel = cp_size > 1
6057
            if q_format in ["sbhd", "bshd"]:
6058
                max_seqlen_q *= cp_size
6059
                if cu_seqlens_q is None:
6060
6061
6062
6063
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
6064
                        if self.attention_type == "self":
6065
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask)
6066
                        else:
6067
                            cu_seqlens_q = dpa_utils.get_cu_seqlens(attention_mask[0])
6068
                    else:
6069
                        cu_seqlens_q = dpa_utils.get_full_cu_seqlens(
6070
6071
6072
6073
                            batch_size,
                            max_seqlen_q,
                            query_layer.device,
                        )
6074
6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
            if kv_format in ["sbhd", "bshd"]:
                max_seqlen_kv *= cp_size
                if cu_seqlens_kv is None:
                    if "padding" in attn_mask_type:
                        assert (
                            attention_mask is not None
                        ), "Please provide attention_mask for padding!"
                        if self.attention_type == "self":
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask)
                        else:
                            cu_seqlens_kv = dpa_utils.get_cu_seqlens(attention_mask[1])
                    else:
6086
                        cu_seqlens_kv = dpa_utils.get_full_cu_seqlens(
6087
6088
6089
6090
                            batch_size,
                            max_seqlen_kv,
                            key_layer.device,
                        )
6091

6092
            # set ALiBi attributes
6093
6094
6095
6096
6097
6098
6099
6100
            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
6101
            bottom_right_alignment = (attn_mask_type not in ["causal", "padding_causal"],)
6102
6103
6104
6105
6106
6107
6108
6109
            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
6110
                    or _alibi_cache["_bottom_right_alignment"] != bottom_right_alignment
6111
6112
6113
6114
6115
                    or _alibi_cache["_alibi_slopes"] is None
                ):
                    _alibi_cache["_alibi_slopes_require_update"] = True
                    _alibi_cache["_alibi_bias_require_update"] = True

6116
            # detect bias shape
6117
6118
            core_attention_bias_shape = None
            if core_attention_bias is not None:
6119
                if (
6120
6121
                    core_attention_bias.shape[0] == batch_size
                    and core_attention_bias.shape[1] == query_layer.shape[-2]
6122
                ):
6123
6124
6125
6126
6127
6128
6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
6139
                    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"

6140
6141
6142
6143
6144
6145
6146
6147
6148
6149
6150
            if pad_between_seqs is None:
                if qkv_format == "thd":
                    pad_between_seqs = (
                        cu_seqlens_q_padded is not None
                        and not torch.equal(cu_seqlens_q_padded[:-1], cu_seqlens_q[:-1])
                    ) or (
                        cu_seqlens_kv_padded is not None
                        and not torch.equal(cu_seqlens_kv_padded[:-1], cu_seqlens_kv[:-1])
                    )
                else:
                    pad_between_seqs = False
6151

6152
            # gather attention params for get_attention_backend
6153
            attention_params = dpa_utils.AttentionParams(
6154
6155
6156
6157
                qkv_type=type(query_layer),
                qkv_dtype=query_layer.dtype,
                qkv_layout=qkv_layout,
                batch_size=batch_size,
6158
6159
                num_heads=num_attention_heads,
                num_gqa_groups=num_gqa_groups,
6160
6161
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
6162
6163
                head_dim_qk=head_dim_qk,
                head_dim_v=head_dim_v,
6164
6165
6166
6167
6168
6169
6170
6171
6172
6173
6174
                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,
6175
6176
                deterministic=self.deterministic,
                is_training=self.training,
6177
6178
                fp8=self.fp8,
                fp8_meta=self.fp8_meta,
6179
                inference_params=inference_params,
6180
            )
6181
            global _attention_backends
6182
6183
6184
6185
6186
6187
6188
6189
6190
            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"]:
                (
                    use_flash_attention,
6191
                    flash_attention_backend,
6192
6193
6194
6195
                    use_fused_attention,
                    fused_attention_backend,
                    use_unfused_attention,
                    _,
6196
6197
6198
6199
                ) = dpa_utils.get_attention_backend(attention_params)
                # Set global _attention_backends var using return value
                # from get_attention_backend()
                _attention_backends["use_flash_attention"] = use_flash_attention
6200
                _attention_backends["flash_attention_backend"] = flash_attention_backend
6201
6202
6203
6204
                _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
6205
                if use_flash_attention:
6206
6207
                    self.logger.info(
                        "Running with FlashAttention backend (version %s)",
6208
                        flash_attention_backend,
6209
                    )
6210
6211
6212
6213
                elif use_fused_attention:
                    self.logger.info(
                        "Running with FusedAttention backend (sub-backend %s)",
                        int(fused_attention_backend),
6214
                    )
6215
6216
6217
6218
                elif use_unfused_attention:
                    self.logger.info("Running with UnfusedDotProductAttention backend")
            else:
                use_flash_attention = _attention_backends["use_flash_attention"]
6219
                flash_attention_backend = _attention_backends["flash_attention_backend"]
6220
6221
6222
                use_fused_attention = _attention_backends["use_fused_attention"]
                fused_attention_backend = _attention_backends["fused_attention_backend"]
                use_unfused_attention = _attention_backends["use_unfused_attention"]
6223

6224
6225
            # raise exception if no backend is available
            if sum([use_flash_attention, use_fused_attention, use_unfused_attention]) == 0:
6226
6227
6228
6229
6230
                raise ValueError(
                    "No dot product attention backend is available for the provided inputs. Please"
                    " run with NVTE_DEBUG=1 NVTE_DEBUG_LEVEL=2 to find out the reasons for"
                    " disabling all backends."
                )
6231
6232

            # run attention
6233
6234
            if use_flash_attention:
                if core_attention_bias_type == "alibi":
6235
6236
                    alibi_slopes, _ = dpa_utils.get_alibi(
                        _alibi_cache,
6237
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
                        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,
6256
                    cp_comm_type=self.cp_comm_type,
6257
6258
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6259
6260
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6261
                    quantizers=self.quantizers,
6262
6263
                    inference_params=inference_params,
                    flash_attention_backend=flash_attention_backend,
6264
                )
6265

6266
            if use_fused_attention:
6267
6268
                fu_core_attention_bias_type = core_attention_bias_type
                fu_core_attention_bias = core_attention_bias
6269
6270
6271
                if core_attention_bias_type == "alibi" and (
                    alibi_slopes is not None or max_seqlen_q != max_seqlen_kv
                ):
6272
                    fu_core_attention_bias_type = "post_scale_bias"
6273
6274
                    _, fu_core_attention_bias = dpa_utils.get_alibi(
                        _alibi_cache,
6275
6276
6277
6278
6279
                        query_layer.shape[-2],
                        max_seqlen_q,
                        max_seqlen_kv,
                        alibi_slopes=alibi_slopes,
                        bias_dtype=query_layer.dtype,
6280
                        bottom_right_alignment=attn_mask_type not in ["causal", "padding_causal"],
6281
                    )
6282
                # checkpoint_core_attention=False
6283
6284
6285
6286
6287
6288
6289
6290
6291
                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,
6292
6293
                        cu_seqlens_q_padded=cu_seqlens_q_padded,
                        cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6294
6295
6296
6297
                        max_seqlen_q=max_seqlen_q,
                        max_seqlen_kv=max_seqlen_kv,
                        attn_mask_type=attn_mask_type,
                        attention_mask=attention_mask,
6298
                        window_size=window_size,
6299
6300
6301
6302
6303
6304
6305
                        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,
6306
                        cp_comm_type=self.cp_comm_type,
6307
6308
                        fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        fp8_meta=self.fp8_meta,
6309
                        quantizers=self.quantizers,
6310
                        pad_between_seqs=pad_between_seqs,
6311
                        inference_params=inference_params,
6312
6313
                    )
                return self.fused_attention(
6314
6315
6316
6317
6318
6319
                    query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout=qkv_layout,
                    cu_seqlens_q=cu_seqlens_q,
                    cu_seqlens_kv=cu_seqlens_kv,
6320
6321
                    cu_seqlens_q_padded=cu_seqlens_q_padded,
                    cu_seqlens_kv_padded=cu_seqlens_kv_padded,
6322
6323
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
6324
6325
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
6326
                    window_size=window_size,
6327
                    fused_attention_backend=fused_attention_backend,
6328
6329
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
6330
6331
6332
6333
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
6334
                    cp_comm_type=self.cp_comm_type,
6335
6336
                    fp8=self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                    fp8_meta=self.fp8_meta,
6337
                    quantizers=self.quantizers,
6338
                    pad_between_seqs=pad_between_seqs,
6339
                    inference_params=inference_params,
6340
                )
6341

6342
            from .cpu_offload import CPUOffloadEnabled
6343

6344
6345
6346
6347
6348
            if CPUOffloadEnabled:
                warnings.warn(
                    "Attention activation Offloading is only implemented"
                    "with Flash Attention and Fused Attention!"
                )
6349

6350
6351
6352
6353
6354
6355
6356
6357
6358
6359
6360
6361
            if use_unfused_attention:
                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,
6362
                        window_size=window_size,
6363
6364
6365
                        core_attention_bias_type=core_attention_bias_type,
                        core_attention_bias=core_attention_bias,
                        alibi_slopes=alibi_slopes,
6366
                        inference_params=inference_params,
6367
6368
                    )
                return self.unfused_attention(
6369
6370
6371
                    query_layer,
                    key_layer,
                    value_layer,
6372
6373
6374
6375
6376
                    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,
6377
                    window_size=window_size,
6378
6379
6380
                    core_attention_bias_type=core_attention_bias_type,
                    core_attention_bias=core_attention_bias,
                    alibi_slopes=alibi_slopes,
6381
                    inference_params=inference_params,
6382
                )
6383
            return None
6384
6385


6386
6387
6388
6389
6390
6391
6392
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

6393
6394
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
6395

6396
6397
6398
6399
6400
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
6416
6417
6418
6419
6420
    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.
6421
6422
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'causal_bottom_right',
                   'padding_causal_bottom_right','arbitrary'},
6423
                   default = `causal`
6424
6425
6426
6427
6428
                   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.
6429
6430
6431
6432
    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
6433
6434
6435
                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
6436
                be overridden by :attr:`window_size` in `forward` as well.
6437
6438
6439
6440
6441
6442
6443
6444
6445
6446
6447
6448
6449
    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.
6450
6451
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
6452
6453
6454
6455
6456
6457
6458
6459
6460
6461
6462
6463
6464
6465
6466
6467
6468
6469
6470
6471
    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"
6472
          The device on which the parameters of the model will be allocated. It is the user's
6473
6474
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
6475
6476
6477
6478
6479
6480
6481
    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.
6482
            For that, please use `get_qkv_layout` to gain the layout information.
6483
6484
    name: str, default = `None`
        name of the module, currently used for debugging purposes.
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
6497
6498
6499
6500
6501
6502
6503
6504
6505
6506
6507
6508
6509
6510
6511
6512
6513
6514
6515
6516
6517
6518
6519
6520
6521
6522
6523
6524

    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`.
6525
6526
6527
6528
6529
6530
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
6531
6532
6533
6534
6535
        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,
6536
        layer_number: Optional[int] = None,
6537
        attn_mask_type: str = "causal",
6538
        window_size: Optional[Tuple[int, int]] = None,
6539
6540
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
6541
        num_gqa_groups: Optional[int] = None,
6542
6543
6544
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
6545
        params_dtype: Optional[torch.dtype] = None,
6546
        return_bias: bool = False,
6547
6548
6549
6550
6551
6552
6553
        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,
6554
        ub_overlap_ag: bool = False,
6555
6556
6557
6558
        ub_overlap_rs: bool = False,
        ub_overlap_rs_dgrad: bool = False,
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: bool = False,
6559
        bias: bool = True,
6560
        normalization: str = "LayerNorm",
6561
        device: Union[torch.device, str] = "cuda",
6562
        qkv_format: str = "sbhd",
6563
        name: str = None,
6564
6565
    ) -> None:
        super().__init__()
6566

6567
        self.qkv_format = qkv_format
6568
        self.attn_mask_type = attn_mask_type
6569
        self.window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
6570
        self.layer_number = 1 if layer_number is None else layer_number
6571
6572
6573
6574
6575
        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
6576
        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
6577
        self.num_attention_heads = num_attention_heads
6578
        self.return_bias = return_bias
6579
6580
        self.cp_size = 1
        self.cp_rank = 0
6581
6582
6583
6584
6585
6586
6587

        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()
6588
6589
6590
6591
6592

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

6593
6594
6595
        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"
6596
6597
6598
6599
6600
6601

        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)
6602
6603
6604
6605
6606
6607
6608
        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!"
6609
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
6610
6611
6612
6613

        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
6614

6615
6616
        self.name = name

6617
6618
6619
6620
6621
6622
        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,
6623
            "params_dtype": self.params_dtype,
6624
            "device": device,
6625
6626
6627
6628
        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

cyanguwa's avatar
cyanguwa committed
6629
        if self.attention_type == "self":
6630
6631
            parameters_split = None
            if not fuse_qkv_params:
6632
6633
6634
6635
6636
6637
6638
                parameters_split = collections.OrderedDict(
                    [
                        ("query", self.hidden_size_q),
                        ("key", self.hidden_size_kv),
                        ("value", self.hidden_size_kv),
                    ]
                )
6639
6640
6641
            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
6642
                    self.hidden_size_q + 2 * self.hidden_size_kv,
6643
6644
6645
6646
6647
6648
                    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
6649
                    parameters_split=parameters_split,
6650
6651
6652
                    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
6653
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
6654
                    ub_overlap_ag=ub_overlap_ag,
6655
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
6656
                    ub_name="qkv",
6657
                    name=name + ".layernorm_linear_qkv" if name is not None else None,
6658
6659
6660
6661
6662
                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
6663
                    self.hidden_size_q + 2 * self.hidden_size_kv,
6664
6665
6666
6667
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
cyanguwa's avatar
cyanguwa committed
6668
                    parameters_split=parameters_split,
6669
                    name=name + ".linear_qkv" if name is not None else None,
6670
6671
                    **common_gemm_kwargs,
                )
cyanguwa's avatar
cyanguwa committed
6672
        elif self.attention_type == "cross":
6673
6674
6675
            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
6676
                    self.hidden_size_q,
6677
6678
6679
6680
6681
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
6682
                    parameters_split=("query",) if not fuse_qkv_params else None,
6683
6684
6685
6686
                    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
6687
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
6688
                    ub_overlap_ag=ub_overlap_ag,
6689
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
6690
                    ub_name="qkv",
6691
                    name=name + ".layernorm_linear_q" if name is not None else None,
6692
6693
6694
6695
6696
                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
6697
                    self.hidden_size_q,
6698
6699
6700
6701
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
6702
                    name=name + ".linear_q" if name is not None else None,
6703
6704
6705
6706
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
6707
                2 * self.hidden_size_kv,
6708
6709
6710
6711
                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
6712
                parameters_split=("key", "value") if not fuse_qkv_params else None,
6713
                name=name + ".linear_kv" if name is not None else None,
6714
6715
6716
6717
6718
6719
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
6720
            self.hidden_size_per_attention_head,
6721
6722
            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
6723
            qkv_format=self.qkv_format,
6724
6725
6726
6727
            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
6728
            layer_number=self.layer_number,
6729
            attention_type=self.attention_type,
6730
6731
6732
6733
        )

        # Linear
        self.proj = Linear(
6734
            self.hidden_size_q,
6735
6736
6737
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
6738
            return_bias=return_bias,
6739
            parallel_mode="row" if set_parallel_mode else None,
6740
6741
            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
6742
            ub_name="proj",
6743
            name=name + ".proj" if name is not None else None,
6744
6745
6746
6747
            **common_gemm_kwargs,
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
6748
6749
6750
6751
6752
6753
6754
6755
6756
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
6757
6758
        self.tp_group = tp_group

6759
    def set_context_parallel_group(
6760
        self,
6761
        cp_group: Union[dist_group_type, List[dist_group_type], None],
6762
        cp_global_ranks: List[int],
6763
        cp_stream: torch.cuda.Stream,
6764
        cp_comm_type: str = "p2p",
6765
    ) -> None:
6766
6767
6768
6769
6770
6771
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
6772
        cp_group : Union[ProcessGroup, List[ProcessGroup]]
6773
                  context parallel process group.
6774
6775
6776
                  ProcessGroup is for cp_comm_type of "p2p", "all_gather", and "a2a".
                  List[ProcessGroup] is for cp_comm_type of "a2a+p2p", where cp_group[0]
                  and cp_group[1] are for a2a and p2p communications respectively.
6777
6778
6779
6780
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
6781
        cp_comm_type : str, default = `p2p`
6782
                      inter-gpu communication type for context parallelism.
6783
                      Can be "p2p" or "all_gather" or "a2a", "a2a+p2p".
6784
6785
6786
6787
6788
6789
                      "p2p": Exchange KV chunks with P2P communications in ring topology.
                             P2P is async and can be overlapped with attention compute.
                      "all_gather": All-gather to get full sequence of KV before attention.
                                    The all-gather is not async, and cannot be overlapped.
                      "a2a": Like DeepSpeed Ulysses, scatter attention heads across the CP
                             group, and gather to get full sequence of QKV.
6790
6791
6792
                      "a2a+p2p": hierarchical CP implementation. First applying a2a to QKV
                      across each CP sub-group (e.g., via NVLink), then exchanging KV with
                      p2p between sub-groups (e.g., via IBLink).
6793
        """
6794
6795
6796
6797
6798
6799
6800
6801
6802
6803
6804
6805
6806
6807
6808
        if isinstance(cp_group, dist_group_type):
            self.cp_size = get_distributed_world_size(cp_group)
            self.cp_rank = get_distributed_rank(cp_group)
        elif isinstance(cp_group, list):
            assert len(cp_group) == 2, "Current implementation only supports two-level CP groups!"
            assert (
                cp_comm_type == "a2a+p2p"
            ), "Only cp_comm_type of a2a+p2p requires hierarchical CP groups!"
            cp_size_a2a = get_distributed_world_size(cp_group[0])
            cp_rank_a2a = get_distributed_rank(cp_group[0])
            cp_size_p2p = get_distributed_world_size(cp_group[1])
            cp_rank_p2p = get_distributed_rank(cp_group[1])
            self.cp_size = cp_size_a2a * cp_size_p2p
            self.cp_rank = cp_size_a2a * cp_rank_p2p + cp_rank_a2a

6809
6810
6811
6812
6813
        # 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"):
6814
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream, cp_comm_type)
6815

6816
6817
6818
    def forward(
        self,
        hidden_states: torch.Tensor,
6819
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6820
        encoder_output: Optional[torch.Tensor] = None,
6821
        attn_mask_type: Optional[str] = None,
6822
        window_size: Optional[Tuple[int, int]] = None,
6823
6824
        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
6825
        inference_params: Optional[InferenceParams] = None,
6826
        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
6827
6828
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
6829
        alibi_slopes: Optional[torch.Tensor] = None,
6830
6831
6832
6833
        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,
6834
        fast_zero_fill: bool = True,
6835
        pad_between_seqs: Optional[bool] = None,
6836
    ) -> Tuple[Union[torch.Tensor, None], ...]:
6837
6838
6839
6840
6841
        """
        Forward propagation for MultiheadAttention layer.

        .. note::

6842
6843
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
6844
6845
6846
6847
6848

        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
6849
6850
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
6851
             It should be `None` for causal masks and "`no_mask`". For padding masks, it should be
6852
6853
             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]
6854
6855
6856
6857
6858
6859
             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'},
6860
                       default = `None`
6861
6862
6863
6864
                       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.
6865
6866
        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
6867
6868
6869
6870
6871
6872
6873
6874
6875
6876
6877
6878
6879
6880
6881
6882
6883
6884
6885
6886
6887
6888
6889
6890
6891
        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`
6892
                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
6893
        core_attention_bias: Optional[torch.Tensor], default = `None`
6894
6895
                    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.
6896
6897
6898
6899
        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.
6900
6901
6902
6903
6904
6905
6906
6907
6908
6909
6910
6911
        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.
6912
6913
        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
6914
6915
6916
        pad_between_seqs: Optional[bool], default = `None`
            If None, inferred from qkv_format, cu_seqlens and cu_seqlens_padded.
            If true, there are padding tokens between individual sequences in a packed batch.
6917
        """
6918
6919
        # hidden_states: [sq, b, h]

6920
        if attn_mask_type is None:
6921
            attn_mask_type = self.attn_mask_type
6922
6923
        if window_size is None:
            window_size = self.window_size
6924
        window_size = dpa_utils.check_set_window_size(attn_mask_type, window_size)
6925

6926
        if "padding" in attn_mask_type and attention_mask is not None:
6927
6928
            for mask in attention_mask:
                assert mask.dtype == torch.bool, "Attention mask must be in boolean type!"
6929

6930
6931
6932
        assert (
            core_attention_bias_type in AttnBiasTypes
        ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
6933

6934
6935
6936
        if TEDebugState.debug_enabled:
            TransformerEngineBaseModule._validate_name(self)

6937
        # =================================================
6938
        # Pre-allocate memory for key-value cache for inference
6939
6940
        # =================================================

6941
6942
6943
6944
6945
        if (
            inference_params is not None
            and self.layer_number not in inference_params.cache_manager.cache
        ):
            inference_params.allocate_memory(self.layer_number)
6946

6947
        # ======================
6948
        # Query, Key, and Value
6949
        # ======================
6950

6951
6952
6953
6954
6955
        fp8_mha = (
            FP8GlobalStateManager.is_fp8_enabled()
            and FP8GlobalStateManager.get_fp8_recipe().fp8_mha
        )

6956
        layernorm_output = None
cyanguwa's avatar
cyanguwa committed
6957
6958
        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
6959
6960
6961
6962
            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
6963
                    fp8_output=fp8_mha and rotary_pos_emb is None,
6964
6965
6966
6967
6968
6969
6970
6971
6972
                )
                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,
6973
                    fp8_output=fp8_mha and rotary_pos_emb is None,
6974
6975
                )

6976
6977
6978
            num_queries_per_key_value = (
                self.num_attention_heads_per_partition // self.num_gqa_groups_per_partition
            )
6979
            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
6980
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
6981
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
cyanguwa's avatar
cyanguwa committed
6982
6983
                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
6984
6985
6986
6987
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
cyanguwa's avatar
cyanguwa committed
6988
6989
6990
6991
6992
            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,
6993
                    self.hidden_size_per_attention_head,
cyanguwa's avatar
cyanguwa committed
6994
6995
6996
                )
                # split along third last dimension
                split_dim = -3
6997
6998
6999

            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
7000
7001
7002
7003
7004
7005
            # 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]
7006
7007
7008
            query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
            )
cyanguwa's avatar
cyanguwa committed
7009

7010
7011
7012
7013
7014
7015
7016
7017
7018
7019
7020
7021
            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
7022
7023
        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
7024
            mixed_kv_layer = self.key_value(
cyanguwa's avatar
cyanguwa committed
7025
                encoder_output,
7026
                is_first_microbatch=is_first_microbatch,
7027
                fp8_output=fp8_mha and rotary_pos_emb is None,
7028
7029
7030
            )

            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
7031
                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
7032
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
7033
                    self.num_gqa_groups_per_partition,
7034
7035
7036
7037
7038
                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
cyanguwa's avatar
cyanguwa committed
7039
                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
7040
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
7041
                    2 * self.num_gqa_groups_per_partition,
7042
7043
7044
7045
7046
7047
7048
                    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
7049
            # mixed_kv_layer --> 2 [sk, b, ng, hn]
7050
7051
7052
7053
7054
            key_layer, value_layer = _SplitAlongDim.apply(
                mixed_kv_layer,
                split_dim,
                mixed_kv_layer.shape[split_dim] // 2,
            )
7055
7056
7057
7058
7059
7060
7061
7062
7063
            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)
            )
7064
7065
7066
7067
7068
7069

            # 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,
7070
                    fp8_output=fp8_mha and rotary_pos_emb is None,
7071
7072
7073
7074
7075
7076
7077
7078
7079
                )
                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,
7080
                    fp8_output=fp8_mha and rotary_pos_emb is None,
7081
7082
7083
7084
7085
7086
7087
7088
7089
                )

            # [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)

7090
7091
7092
        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
7093

7094
        if rotary_pos_emb is not None:
7095
7096
7097
            assert not isinstance(query_layer, Float8Tensor) and not isinstance(
                key_layer, Float8Tensor
            ), "RoPE is not supported for Float8Tensors!"
7098
            # duplicate the pos_emb for self attention
7099
            if not isinstance(rotary_pos_emb, tuple):
7100
                rotary_pos_emb = (rotary_pos_emb,) * 2
7101
7102

            q_pos_emb, k_pos_emb = rotary_pos_emb
7103
7104
7105
7106
7107
7108
7109

            # 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)
7110
                else:
7111
7112
7113
                    raise ValueError(
                        f"qkv_format={self.qkv_format} not supported for KV caching and RoPE."
                    )
7114

7115
7116
                sequence_start = inference_params.get_seqlens_pre_step()
                # sequence_start = inference_params.seqlens[0]
7117
7118
7119
7120
7121
                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, ...]

7122
7123
7124
7125
7126
7127
7128
7129
7130
7131
7132
7133
7134
7135
7136
7137
7138
7139
            query_layer = apply_rotary_pos_emb(
                query_layer,
                q_pos_emb,
                self.qkv_format,
                fused=True,
                cu_seqlens=cu_seqlens_q,
                cp_size=self.cp_size,
                cp_rank=self.cp_rank,
            )
            key_layer = apply_rotary_pos_emb(
                key_layer,
                k_pos_emb,
                self.qkv_format,
                fused=True,
                cu_seqlens=cu_seqlens_kv,
                cp_size=self.cp_size,
                cp_rank=self.cp_rank,
            )
7140

7141
7142
7143
7144
        # ===========================
        # Core attention computation
        # ===========================

7145
7146
7147
7148
        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
7149
            qkv_format=self.qkv_format,
7150
7151
7152
7153
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_kv=cu_seqlens_kv,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_kv=max_seqlen_kv,
7154
7155
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
7156
            window_size=window_size,
7157
7158
7159
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
7160
            alibi_slopes=alibi_slopes,
7161
            fast_zero_fill=fast_zero_fill,
7162
            inference_params=inference_params,
7163
            pad_between_seqs=pad_between_seqs,
7164
7165
        )

7166
        # ===================
7167
        # Output. [sq, b, h]
7168
        # ===================
7169
        projection_output = self.proj(
7170
7171
            context_layer,
            is_first_microbatch=is_first_microbatch,
7172
            fp8_grad=isinstance(context_layer, QuantizedTensor),
7173
7174
        )

7175
7176
7177
7178
7179
7180
7181
7182
        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,)
7183
        if self.input_layernorm and self.return_layernorm_output:
7184
7185
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]