attention.py 229 KB
Newer Older
1
# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
2
3
4
5
#
# See LICENSE for license information.

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

cyanguwa's avatar
cyanguwa committed
14
import numpy as np
15
from packaging.version import Version as PkgVersion
16
17

import torch
18
import torch.nn.functional as F
19
20

import transformer_engine_extensions as tex
21
22
23
24
from transformer_engine.pytorch.cpp_extensions import (
    cast_to_fp8,
    cast_from_fp8,
)
25
26
27
28
29
from transformer_engine.pytorch.cpp_extensions.fused_attn import (
    fused_attn_fwd_qkvpacked,
    fused_attn_bwd_qkvpacked,
    fused_attn_fwd_kvpacked,
    fused_attn_bwd_kvpacked,
30
31
    fused_attn_fwd,
    fused_attn_bwd,
32
33
34
35
36
    QKVLayout,
    AttnBiasType,
    AttnMaskType,
    FusedAttnBackend,
)
37
38
from transformer_engine.pytorch.fp8 import get_fp8_te_dtype
from transformer_engine.pytorch.float8_tensor import Float8Tensor
39
from transformer_engine.pytorch.module import LayerNormLinear, Linear
40
from transformer_engine.pytorch.module.base import TransformerEngineBaseModule
41
42
43
44
45
from transformer_engine.pytorch.utils import (
    divide,
    attention_mask_func,
    split_tensor_along_dim,
    get_device_compute_capability,
46
    get_default_init_method,
47
48
49
50
)
from transformer_engine.pytorch.constants import (
    AttnMaskTypes,
    AttnTypes,
51
    AttnBiasTypes,
52
    QKVLayouts,
53
    dist_group_type,
54
    TE_DType,
55
56
57
58
)
from transformer_engine.pytorch.softmax import FusedScaleMaskSoftmax
from transformer_engine.pytorch.distributed import (
    get_distributed_world_size,
59
    get_distributed_rank,
60
    checkpoint,
61
62
63
    set_all_rng_states,
    CudaRNGStatesTracker,
    graph_safe_rng_available,
64
65
)
from transformer_engine.pytorch.export import is_in_onnx_export_mode
66
from transformer_engine.pytorch.jit import jit_fuser, no_torch_dynamo
67
68
from transformer_engine.pytorch.graph import is_graph_capturing

69

70
71
72
73
74
75
76
_flash_attn_version = PkgVersion(get_pkg_version("flash-attn"))
_flash_attn_version_required = PkgVersion("2.0.6")
_flash_attn_max_version = PkgVersion("2.5.8")
_flash_attn_2_1_plus = _flash_attn_version >= PkgVersion("2.1")
_flash_attn_2_3_plus = _flash_attn_version >= PkgVersion("2.3")
_flash_attn_2_4_plus = _flash_attn_version >= PkgVersion("2.4")
_flash_attn_2_4_1_plus = _flash_attn_version >= PkgVersion("2.4.1")
77

78
if _flash_attn_version >= _flash_attn_version_required:
79
    from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_forward_func # pylint: disable=no-name-in-module
80
    from flash_attn_2_cuda import varlen_bwd as flash_attn_cuda_bwd # pylint: disable=no-name-in-module
81
82
    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward as _flash_attn_forward # pylint: disable=no-name-in-module,ungrouped-imports
    from flash_attn.flash_attn_interface import _flash_attn_varlen_backward as _flash_attn_backward # pylint: disable=no-name-in-module
83

84
85
86
87
88
89
META_QKV  = tex.FP8FwdTensors.GEMM1_OUTPUT
META_DQKV = tex.FP8BwdTensors.GRAD_OUTPUT1
META_O    = tex.FP8FwdTensors.GEMM2_INPUT
META_DO   = tex.FP8BwdTensors.GRAD_INPUT2
META_S    = tex.FP8FwdTensors.GEMM3_OUTPUT
META_DP   = tex.FP8BwdTensors.GRAD_INPUT3
90

91
_NVTE_DEBUG = int(os.getenv("NVTE_DEBUG", "0"))
92
93
94
95
96
97
98
99
100
_alibi_cache = {
    "_num_heads": None,
    "_alibi_slopes": None,
    "_max_seqlen_q": None,
    "_max_seqlen_kv": None,
    "_alibi_bias": None,
    "_alibi_slopes_require_update": False,
    "_alibi_bias_require_update": False,
    }
101
102


103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
__all__ = ["DotProductAttention", "InferenceParams", "MultiheadAttention"]

class InferenceParams: # pylint: disable=too-few-public-methods
    """
    Inference parameters that are passed to the main model in order
    to efficienly calculate and store the context during inference.

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

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

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

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

        for layer_number, inference_memory in self.key_value_memory_dict.items():
            inference_key_memory, inference_value_memory = inference_memory
            assert (
                len(batch_indices) == inference_key_memory.shape[1]
            )  # make sure batch size is the same
            new_inference_key_memory = inference_key_memory[:, batch_indices]
            new_inference_value_memory = inference_value_memory[:, batch_indices]
            self.key_value_memory_dict[layer_number] = (
                new_inference_key_memory,
                new_inference_value_memory,
            )
149

150
151
152
153
154
@torch.no_grad()
def get_alibi(
    num_heads: int,
    max_seqlen_q: int,
    max_seqlen_kv: int,
155
156
157
    alibi_slopes: Optional[torch.Tensor] = None,
    bias_dtype: Optional[torch.dtype] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
158
    """
159
160
161
162
163
164
165
166
167
168
169
170
    Parameters
    ----------
    num_heads: int
        Number of heads.
    max_seqlen_q: int
        Maximum sequence length for queries.
    max_seqlen_kv: int
        Maximum sequence length for keys and values.
    alibi_slopes: Optional[torch.Tensor], default = `None`
        Custom ALiBi slopes, FP32, CUDA tensor, in shape [num_heads] or [batch_size, num_heads].
    bias_dtype: Optional[torch.dtype], default = `None`
        Dtype of the generated ALiBi bias. If None, use torch.float32.
171

172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
    Returns
    ----------
    alibi_slopes: torch.Tensor
        ALiBi slopes in FP32 and shape [num_heads] or [batch_size, num_heads].
    alibi_bias: torch.Tensor
        ALiBi bias in FP32 or `bias_dtype`. If `alibi_slopes` is in [num_heads] shape,
        then `alibi_bias` is in [1, num_heads, max_seqlen_q, max_seqlen_kv], and if
        `alibi_slopes` is in [batch_size, num_heads], then the bias is in
        [batch_size, num_heads, max_seqlen_q, max_seqlen_kv].
    """
    global _alibi_cache
    if _alibi_cache["_alibi_slopes_require_update"]:
        if alibi_slopes is not None:
            _alibi_cache["_alibi_slopes"] = alibi_slopes
        else:
            n = 2 ** math.floor(math.log2(num_heads))
            m_0 = 2.0 ** (-8.0 / n)
            m = torch.pow(m_0, torch.arange(1, 1 + n))

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

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

    if _alibi_cache["_alibi_bias_require_update"]:
        assert _alibi_cache["_alibi_slopes"] is not None, "ALiBi slopes can not be None!"
        if _alibi_cache["_alibi_slopes"].dim() == 1:
            slopes_shape = torch.Size([1, _alibi_cache["_alibi_slopes"].shape[0], 1, 1])
        if _alibi_cache["_alibi_slopes"].dim() == 2:
            slopes_shape = torch.Size([*_alibi_cache["_alibi_slopes"].shape[:], 1, 1])
        bias = torch.arange(
            1 - max_seqlen_kv, 1, dtype=torch.int32, device="cuda").view(1, 1, 1, max_seqlen_kv)
        bias = bias - torch.arange(
            1 - max_seqlen_q, 1, dtype=torch.int32, device="cuda").view(1, 1, max_seqlen_q, 1)
        bias = bias.abs().mul(-1)
        bias = bias * _alibi_cache["_alibi_slopes"].view(slopes_shape)
        _alibi_cache["_max_seqlen_q"], _alibi_cache["_max_seqlen_kv"] = max_seqlen_q, max_seqlen_kv
        bias_dtype = torch.float32 if bias_dtype is None else bias_dtype
        _alibi_cache["_alibi_bias"] = bias.contiguous().to(dtype=bias_dtype, device="cuda")
        _alibi_cache["_alibi_bias_require_update"] = False

    return _alibi_cache["_alibi_slopes"], _alibi_cache["_alibi_bias"]
218
219
220
221
222
223
224
225
226


def get_cu_seqlens(mask: torch.Tensor) -> torch.Tensor:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch.
    """
    mask = mask.squeeze(1).squeeze(1)
227
    reduced_mask = mask.logical_not().sum(dim=1)
228
229
230
231
232
233
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    return cu_seqlens

234

235
236
237
def get_cu_seqlens_and_indices(mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Given a padding mask of shape [batch_size, 1, 1, max_seqlen], returns an int32
238
239
240
    tensor of shape [batch_size + 1] containing the cumulative sequence lengths of
    the samples in a batch, and another int32 tensor of shape [batch_size * max_seqlen, 1, 1]
    containing the indices for the valid tokens.
241
242
243
244
    """
    mask = mask.squeeze(1).squeeze(1)
    bs, seqlen = mask.shape

245
    reduced_mask = mask.logical_not().sum(dim=1)
246
247
248
249
250
    cu_seqlens = reduced_mask.cumsum(dim=0).to(torch.int32)
    zero = torch.zeros(1, dtype=torch.int32, device="cuda")
    cu_seqlens = torch.cat((zero, cu_seqlens))

    mask = mask.reshape(-1)
251
    indices = mask.logical_not().nonzero()
252
253
254
255
256
257
258
259
260
261
    indices = indices.unsqueeze(-1)

    num_nonzeros = indices.shape[0]
    pad_amount = bs * seqlen - num_nonzeros
    indices = F.pad(input=indices, pad=(0, 0, 0, 0, 0, pad_amount),
                    mode="constant", value=float(bs * seqlen))

    return cu_seqlens, indices


262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
def get_indices(max_seqlen: int, cu_seqlens: torch.Tensor) -> torch.Tensor:
    """
    Given max_seqlen and cu_seqlens of shape [batch_size + 1], returns an int32
    tensor of shape [batch_size * max_seqlen, 1, 1] containing the indices for
    the valid tokens in a batch.
    """
    bs = len(cu_seqlens) - 1
    seqlens = cu_seqlens[1:] - cu_seqlens[:-1]
    indices = [i*max_seqlen + ii for i,j in enumerate(seqlens) for ii in range(j)]
    indices = torch.Tensor(indices).unsqueeze(1).unsqueeze(1).to(
                    dtype=torch.int64, device="cuda")

    num_nonzeros = indices.shape[0]
    pad_amount = bs * max_seqlen - num_nonzeros
    indices = F.pad(input=indices, pad=(0, 0, 0, 0, 0, pad_amount),
                    mode="constant", value=float(bs * max_seqlen))

    return indices

281
_cu_seqlens_cache = {}
282
283
284
285
286
287
288
289
290
291
def _get_full_cu_seqlens(
    batch_size: int,
    max_seqlen: int,
    device: torch.device,
) -> torch.Tensor:
    """Cumulative sequence lengths in full data batch

    All sequences in batch have the maximum sequence length.

    """
292
293
294
295
296
297
298
299
300
301
    global _cu_seqlens_cache
    if (batch_size, max_seqlen) not in _cu_seqlens_cache:
        _cu_seqlens_cache[(batch_size, max_seqlen)] = torch.arange(
            0,
            (batch_size + 1) * max_seqlen,
            step=max_seqlen,
            dtype=torch.int32,
            device=device,
        )
    return _cu_seqlens_cache[(batch_size, max_seqlen)]
302
303


304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
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
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
409
410
@jit_fuser
def pack_tensor(
    indices: torch.Tensor,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Packs the given tensor using the `indices`.
    """
    padding_indice = torch.zeros(
        1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device)
    tensor = torch.cat((tensor, padding_indice), dim=0)

    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    packed = torch.gather(tensor, 0, indices)
    return packed


@jit_fuser
def pack_2_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Packs the given 2 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    return t1_packed, t2_packed


@jit_fuser
def pack_3_tensors(
    indices: torch.Tensor,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Packs the given 3 tensors using the `indices`.
    """
    t1_packed = pack_tensor(indices, t1)
    t2_packed = pack_tensor(indices, t2)
    t3_packed = pack_tensor(indices, t3)
    return t1_packed, t2_packed, t3_packed


@jit_fuser
def unpack_tensor(
    indices: torch.Tensor,
    dim0: int,
    tensor: torch.Tensor,
) -> torch.Tensor:
    """
    Inverse of `pack_tensor`.
    """
    indices = indices.repeat(1, tensor.shape[1], tensor.shape[2])
    unpacked = torch.zeros(
        dim0 + 1, tensor.shape[1], tensor.shape[2], dtype=tensor.dtype, device=tensor.device)
    unpacked.scatter_(0, indices, tensor)
    unpacked = unpacked[0:-1,:,:]
    return unpacked


@jit_fuser
def unpack_2_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_2_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    return t1_unpacked, t2_unpacked


@jit_fuser
def unpack_3_tensors(
    indices: torch.Tensor,
    dim0: int,
    t1: torch.Tensor,
    t2: torch.Tensor,
    t3: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """
    Inverse of `pack_3_tensors`.
    """
    t1_unpacked = unpack_tensor(indices, dim0, t1)
    t2_unpacked = unpack_tensor(indices, dim0, t2)
    t3_unpacked = unpack_tensor(indices, dim0, t3)
    return t1_unpacked, t2_unpacked, t3_unpacked


class PackTensors(torch.autograd.Function):
    """
    Autograd function to pack tensors.
    """
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        *tensors: Tuple[torch.Tensor, ...]
    ) -> Union[Tuple[torch.Tensor, ...], torch.Tensor]:
        assert 1 <= len(tensors) <= 3, f"Packing {len(tensors)} tensors not supported."
411
        ctx.save_for_backward(indices)
412
413
414
415
416
417
418
419
420
        ctx.dim0 = tensors[0].shape[0]
        if len(tensors) == 1:
            return pack_tensor(indices, *tensors)
        if len(tensors) == 2:
            return pack_2_tensors(indices, *tensors)
        return pack_3_tensors(indices, *tensors)

    @staticmethod
    def backward(ctx, *grad_outputs: Tuple[torch.Tensor, ...]):
421
        (indices,) = ctx.saved_tensors
422
        if len(grad_outputs) == 1:
423
            return None, unpack_tensor(indices, ctx.dim0, *grad_outputs)
424
        if len(grad_outputs) == 2:
425
426
            return None, *unpack_2_tensors(indices, ctx.dim0, *grad_outputs)
        return None, *unpack_3_tensors(indices, ctx.dim0, *grad_outputs)
427
428
429
430
431
432
433
434
435
436
437
438
439


class UnpackTensor(torch.autograd.Function):
    """
    Autograd function to unpack a tensor.
    """
    @staticmethod
    def forward(
        ctx,
        indices: torch.Tensor,
        dim0: int,
        tensor: torch.Tensor,
    ) -> torch.Tensor:
440
        ctx.save_for_backward(indices)
441
442
443
444
        return unpack_tensor(indices, dim0, tensor)

    @staticmethod
    def backward(ctx, grad_output):
445
446
        (indices,) = ctx.saved_tensors
        return None, None, pack_tensor(indices, grad_output)
447
448


449
450
451
def flash_attn_p2p_communicate(rank, send_tensor, send_dst,
                               recv_tensor, recv_src,
                               cp_group, batch_p2p_comm):
452
    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
    send_recv_ops = []

    if batch_p2p_comm:
        if rank % 2 == 0:
            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)
            send_recv_ops.append(send_op)
            send_recv_ops.append(recv_op)
        else:
            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)
            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


495
@jit_fuser
496
497
def flash_attn_fwd_out_correction(out, out_per_step, seq_dim,
                                  softmax_lse, softmax_lse_per_step):
498
    """Merge partial outputs of each step in Attention with context parallelism"""
499
    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse).movedim(2, seq_dim)
500
501
502
503
504
    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
    out_corrected = out_per_step*softmax_lse_corrected_exp
    out.add_(out_corrected)


505
@jit_fuser
506
def flash_attn_fwd_softmax_lse_correction(softmax_lse, softmax_lse_per_step):
507
    """Merge softmax stats of each step in Attention with context parallelism"""
508
509
510
511
    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
    new_scale = max_scale + torch.log(1 + torch.exp(min_scale - max_scale))
    softmax_lse.copy_(new_scale)
512
513


514
class AttnFuncWithCP(torch.autograd.Function):
515
    """
516
517
    Attention implementation with context parallelism.
    Split attention compute into multiple steps, and overlap current-step
518
519
520
521
    compute with next-step communication.
    """

    @staticmethod
522
    def forward(ctx, is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
523
524
                dropout_p, cp_group, cp_global_ranks, cp_stream, softmax_scale, qkv_format,
                attn_mask_type, attn_bias_type, attn_bias, deterministic, use_fused_attention):
525
526
527
528
529
530
        if softmax_scale is None:
            softmax_scale = q.shape[-1] ** (-0.5)

        cp_size = get_distributed_world_size(cp_group)
        rank = get_distributed_rank(cp_group)
        send_dst = cp_global_ranks[(rank + 1) % cp_size]
531
        recv_src = cp_global_ranks[(rank - 1) % cp_size]
532
533
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

534
535
        causal = (attn_mask_type == "causal")

536
537
        qkv_layout = qkv_format + "_" + qkv_format + "_" + qkv_format

538
        if causal:
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
            if qkv_format == "bshd":
                # [b, s, np, hn] -> [b, 2, s//2, np, hn]
                q, k, v = [x.view(x.shape[0], 2, x.shape[1]//2, *x.shape[2:]) for x in [q, k, v]]
            elif qkv_format == "sbhd":
                # [s, b, np, hn] -> [2, s//2, b, np, hn]
                q, k, v = [x.view(2, x.shape[0]//2, *x.shape[1:]) for x in [q, k, v]]
        if attn_bias is not None:
            assert (len(attn_bias.shape) == 4), (
                "Only support bias shape of [b, h, sq, sk] for forward, "
                "and [1, h, sq, sk] for backward!"
            )
            # [b, np, sq, sk] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            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) \
            )
            # [b, np, sq, sk] -> [b, np, sq, 2*cp, sk//(2*cp)]
            attn_bias = attn_bias.view( \
                *attn_bias.shape[:-1], \
                2*cp_size, attn_bias.shape[-1]//(2*cp_size) \
            )
561
        assert(q.shape[-1] % 8 == 0), "hidden size per attention head should be multiple of 8"
562
563
564
565
566
        fa_optional_forward_kwargs = {}
        if _flash_attn_2_3_plus:
            fa_optional_forward_kwargs["window_size"] = [-1, 0] if causal else [-1, -1]
        if _flash_attn_2_4_plus:
            fa_optional_forward_kwargs["alibi_slopes"] = None
567

568
569
570
        # Flash Attn inputs
        q_inputs = [None, None]
        kv_inputs = [None, None]
571
        attn_bias_inputs = [None, None]
572
573
574
575
        # 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)]
576
        attn_biases = [None for _ in range(cp_size)]
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606

        # 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)]
        p2p_comm_buffers[0] = torch.cat((k.unsqueeze(0), v.unsqueeze(0)), dim=0)
        send_recv_reqs = [[], []]

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

                    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)

                    kv_inputs[i%2] = p2p_comm_buffers[i]
                    if causal:
                        if i == 0:
607
                            if use_fused_attention:
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
                                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:])
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, k.shape[0], -1, *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, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, -1, *k.shape[-3:])
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = torch.cat(
                                        (attn_bias[..., idx, :], \
                                         attn_bias[..., (2*cp_size-idx-1), :]),
                                        dim=-1
                                    ).contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
628
629
630
631
632
633
                                fused_attn_fwd(
                                    is_training, max_seqlen_q, max_seqlen_k, cu_seqlens_q,
                                    cu_seqlens_k, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                    attn_scale=softmax_scale, dropout=dropout_p,
634
635
                                    qkv_layout=qkv_layout, attn_mask_type="causal",
                                    attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
636
                                )
637
638
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
639
640
641
642
643
644
645
646
647
648
649
650
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                                q_inputs[i%2] = q.view(-1, *q.shape[-2:])
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                                kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                    dropout_p, softmax_scale, causal=True, return_softmax=False,
                                    **fa_optional_forward_kwargs
                                )
651
                        elif i <= rank:
652
                            if use_fused_attention:
653
654
655
656
657
658
659
660
661
662
663
664
665
666
                                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:])
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, :, 0, ...].contiguous()
                                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, 2, sk//2, b, np, hn] -> [2, sk//2, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, 0, ...].contiguous()
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = attn_bias[..., idx, :].contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
667
668
669
670
671
672
                                fused_attn_fwd(
                                    is_training, max_seqlen_q, max_seqlen_k//2, cu_seqlens_q,
                                    cu_seqlens_k//2, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                    attn_scale=softmax_scale, dropout=dropout_p,
673
674
                                    qkv_layout=qkv_layout, attn_mask_type="no_mask",
                                    attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
675
                                )
676
677
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
678
679
680
                            else:
                                # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                                q_inputs[i%2] = q.view(-1, *q.shape[-2:])
681
682
683
684
685
686
687
                                if qkv_format == "thd":
                                    # [2, t, np, hn] -> [2, t/2, np, hn]
                                    kv_inputs[i%2] = tex.thd_read_half_tensor(
                                        kv_inputs[i%2], cu_seqlens_k, 0)
                                else:
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2][:, :, 0, ...].contiguous()
688
689
690
691
692
693
694
695
696
697
698
699
700
                                # [2, b, sk//2, np, hn] -> [2, b*sk//2, np, hn]
                                kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                                if _flash_attn_2_3_plus:
                                    fa_optional_forward_kwargs["window_size"] = [-1, -1]
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q, cu_seqlens_k//2, max_seqlen_q, max_seqlen_k//2,
                                    dropout_p, softmax_scale, causal=False, return_softmax=False,
                                    **fa_optional_forward_kwargs
                                )
                        else:
                            if use_fused_attention:
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
                                if qkv_format == "bshd":
                                    # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                                    q_inputs[i%2] = q[:, 1, ...].contiguous()
                                    # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, k.shape[0], -1, *k.shape[-2:])
                                elif qkv_format == "sbhd":
                                    # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                                    q_inputs[i%2] = q[1].contiguous()
                                    # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                                    kv_inputs[i%2] = kv_inputs[i%2].view(
                                        2, -1, *k.shape[-3:])
                                if attn_bias is not None:
                                    idx = (rank - i) % cp_size
                                    attn_bias_inputs[i%2] = torch.cat(
                                        (attn_bias_[..., 1, :, idx, :], \
                                         attn_bias_[..., 1, :, (2*cp_size-idx-1), :]),
                                        dim=-1
                                    ).contiguous()
                                out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
721
722
723
724
725
726
                                fused_attn_fwd(
                                    is_training, max_seqlen_q//2, max_seqlen_k, cu_seqlens_q//2,
                                    cu_seqlens_k, q_inputs[i%2], kv_inputs[i%2][0],
                                    kv_inputs[i%2][1], TE_DType[q.dtype],
                                    tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                    attn_scale=softmax_scale, dropout=dropout_p,
727
728
                                    qkv_layout=qkv_layout, attn_mask_type="no_mask",
                                    attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
729
                                )
730
731
                                if len(rest) > 0:
                                    attn_biases[i] = rest[0]
732
                            else:
733
734
735
736
737
738
739
                                if qkv_format == "thd":
                                    # [t, np, hn] -> [t/2, np, hn]
                                    q_inputs[i%2] = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
                                else:
                                    # [b, 2, sq//2, np, hn]->[b, sq//2, np, hn]->[b*sq//2, np, hn]
                                    q_inputs[i%2] = \
                                        q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
740
741
742
743
744
745
746
747
748
749
750
751
752
                                # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                                kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
                                if _flash_attn_2_3_plus:
                                    fa_optional_forward_kwargs["window_size"] = [-1, -1]
                                _, _, _, _, out_per_step[i], \
                                softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                    q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
                                    cu_seqlens_q//2, cu_seqlens_k, max_seqlen_q//2, max_seqlen_k,
                                    dropout_p, softmax_scale, causal=False, return_softmax=False,
                                    **fa_optional_forward_kwargs
                                )
                    else:
                        if use_fused_attention:
753
754
755
756
757
758
759
                            if attn_bias is not None:
                                idx = (rank - i) % cp_size
                                attn_bias_inputs[i%2] = torch.cat(
                                    (attn_bias[..., idx, :], attn_bias[..., (2*cp_size-idx-1), :]),
                                    dim=-1
                                ).contiguous()
                            out_per_step[i], [softmax_lse_per_step[i], rng_states[i], *rest] = \
760
761
762
763
764
765
                            fused_attn_fwd(
                                is_training, max_seqlen_q, max_seqlen_k, cu_seqlens_q,
                                cu_seqlens_k, q, kv_inputs[i%2][0],
                                kv_inputs[i%2][1], TE_DType[q.dtype],
                                tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                                attn_scale=softmax_scale, dropout=dropout_p,
766
767
                                qkv_layout=qkv_layout, attn_mask_type="no_mask",
                                attn_bias_type=attn_bias_type, attn_bias=attn_bias_inputs[i%2],
768
                            )
769
770
                            if len(rest) > 0:
                                attn_biases[i] = rest[0]
771
                        else:
772
773
774
                            # [b, sq, np, hn] -> [b*sq, np, hn]
                            q_inputs[i%2] = q.view(-1, *q.shape[-2:])
                            # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
775
                            kv_inputs[i%2] = kv_inputs[i%2].view(2, -1, *k.shape[-2:])
776
777
778
                            _, _, _, _, out_per_step[i], \
                            softmax_lse_per_step[i], _, rng_states[i] = _flash_attn_forward(
                                q_inputs[i%2], kv_inputs[i%2][0], kv_inputs[i%2][1],
779
780
781
                                cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
                                dropout_p, softmax_scale, causal=False, return_softmax=False,
                                **fa_optional_forward_kwargs
782
                            )
783
784
785
786
787
788

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

789
790
791
792
                if use_fused_attention:
                    # [b, np, sq, 1] -> [b, np, sq]
                    softmax_lse_per_step[i-1].squeeze_(-1)

793
                with torch.cuda.stream(flash_attn_streams[(i-1)%2]):
794
795
796
                    if i == 1:
                        out = torch.empty_like(q).zero_()
                        softmax_lse = torch.clone(softmax_lse_per_step[0]).to(torch.double)
797
                        if causal and qkv_format != "thd":
798
799
800
801
                            # [b, np, sq] -> [b, np, 2, sq//2]
                            softmax_lse_ = softmax_lse.view(
                                *softmax_lse.shape[:-1], 2, softmax_lse.shape[-1]//2
                            )
802
803
804
                    elif (i-1) <= rank or not causal:
                        flash_attn_fwd_softmax_lse_correction(softmax_lse,
                                                              softmax_lse_per_step[i-1])
805
                    else:
806
807
808
809
810
811
812
813
                        if qkv_format == "thd":
                            tex.thd_second_half_lse_correction(softmax_lse,
                                                               softmax_lse_per_step[i-1],
                                                               cu_seqlens_q,
                                                               q.size(0))
                        else:
                            flash_attn_fwd_softmax_lse_correction(softmax_lse_[..., 1, :],
                                                                  softmax_lse_per_step[i-1])
814
815
816
817
818
819
820

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

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

        softmax_lse = softmax_lse.to(torch.float)
821
822
        if qkv_format in ["bshd", "sbhd"]:
            seq_dim = qkv_format.index("s")
823
        for i in range(cp_size):
824
825
826
827
828
829
            if qkv_format == "bshd":
                out_per_step[i] = out_per_step[i].view(out.shape[0], -1, *out.shape[-2:])
                out_ = out[:, 1, ...]
            elif qkv_format == "sbhd":
                out_per_step[i] = out_per_step[i].view(-1, *out.shape[-3:])
                out_ = out[1]
830

831
            if i <= rank or not causal:
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
                if qkv_format in ["bshd", "sbhd"]:
                    flash_attn_fwd_out_correction(out.view(*out_per_step[i].shape),
                                                  out_per_step[i],
                                                  seq_dim,
                                                  softmax_lse,
                                                  softmax_lse_per_step[i])
                elif qkv_format == "thd":
                    tex.thd_out_correction(out,
                                           out_per_step[i],
                                           softmax_lse,
                                           softmax_lse_per_step[i],
                                           cu_seqlens_q,
                                           False)
                else:
                    assert False, f"{qkv_format} is an unsupported qkv_format!"
847
            else:
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
                if qkv_format in ["bshd", "sbhd"]:
                    flash_attn_fwd_out_correction(out_,
                                                  out_per_step[i],
                                                  seq_dim,
                                                  softmax_lse_[..., 1, :],
                                                  softmax_lse_per_step[i])
                elif qkv_format == "thd":
                    tex.thd_out_correction(out,
                                           out_per_step[i],
                                           softmax_lse,
                                           softmax_lse_per_step[i],
                                           cu_seqlens_q,
                                           True)
                else:
                    assert False, f"{qkv_format} is an unsupported qkv_format!"
863
864

        kv = p2p_comm_buffers[-1]
865
        if use_fused_attention:
866
867
868
869
            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:])
870
871
        else:
            out = out.view(-1, *out.shape[-2:])
872

873
874
        ctx.save_for_backward(q, kv, out, softmax_lse,
            cu_seqlens_q, cu_seqlens_k, *rng_states, *attn_biases)
875
876
877
878
879
880
881
        ctx.cp_group = cp_group
        ctx.cp_global_ranks = cp_global_ranks
        ctx.dropout_p = dropout_p
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_k = max_seqlen_k
        ctx.softmax_scale = softmax_scale
        ctx.causal = causal
882
883
884
        ctx.qkv_format = qkv_format
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_bias_shape = None if attn_bias is None else attn_bias.shape
885
        ctx.deterministic = deterministic
886
        ctx.use_fused_attention = use_fused_attention
887
888
889
890
        return out

    @staticmethod
    def backward(ctx, dout):
891
        (q, kv, out, softmax_lse, cu_seqlens_q, cu_seqlens_k) = ctx.saved_tensors[:6]
892
        cp_size = get_distributed_world_size(ctx.cp_group)
893
894
895
        rng_states = ctx.saved_tensors[6:6+cp_size]
        attn_biases = ctx.saved_tensors[6+cp_size:6+cp_size*2]

896
        rank = get_distributed_rank(ctx.cp_group)
897
        send_dst = ctx.cp_global_ranks[(rank - 1) % cp_size]
898
899
900
        recv_src = ctx.cp_global_ranks[(rank + 1) % cp_size]
        batch_p2p_comm = int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2)

901
902
        qkv_layout = ctx.qkv_format + "_" + ctx.qkv_format + "_" + ctx.qkv_format

903
        if attn_biases[0] is not None:
904
905
906
            # [b, np, sq, 2*cp, sk//(2*cp)]
            attn_dbias = torch.zeros(
                *ctx.attn_bias_shape,
907
908
                dtype=attn_biases[0].dtype,
                device=attn_biases[0].device
909
910
911
912
913
914
915
916
            )
            # [b, np, sq, 2*cp, sk//(2*cp)] -> [b, np, 2, sq//2, 2*cp, sk//(2*cp)]
            attn_dbias_ = attn_dbias.view(
                *attn_dbias.shape[:-3], 2, attn_dbias.shape[-3]//2, *attn_dbias.shape[-2:]
            )
        else:
            attn_dbias = None

917
        if ctx.causal:
918
919
920
921
922
923
924
925
926
927
928
            if ctx.qkv_format == "thd":
                softmax_lse_ = tex.thd_read_second_half_lse(softmax_lse, cu_seqlens_q, q.size(0))
            else:
                # [b, np, sq] -> [b, np, 2, sq//2]
                softmax_lse_ = \
                    softmax_lse.view(*softmax_lse.shape[:-1], 2, softmax_lse.shape[-1]//2)
                softmax_lse_ = softmax_lse_[..., 1, :].contiguous()
                if ctx.use_fused_attention:
                    # [b, np, sq//2] -> [b, np, sq//2, 1]
                    softmax_lse_.unsqueeze_(-1)

929
930
931
        if ctx.use_fused_attention:
            # [b, np, sq] -> [b, np, sq, 1]
            softmax_lse.unsqueeze_(-1)
932
933
934
935
936
937
938
939
940
941
        out = out.view(*q.shape)
        dout = dout.view(*q.shape)
        # Flash Attn outputs
        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)
        send_recv_reqs = []

942
943
944
945
946
947
        fa_optional_backward_kwargs = {}
        if _flash_attn_2_4_plus:
            fa_optional_backward_kwargs["alibi_slopes"] = None
        if _flash_attn_2_4_1_plus:
            fa_optional_backward_kwargs["deterministic"] = ctx.deterministic

948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
        for i in range(cp_size):
            # wait until KV is received
            for req in send_recv_reqs:
                req.wait()

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

            kv = p2p_comm_buffers[i%2][0]
            # In reversed order of fwd
            if ctx.causal:
                if i == (cp_size-1):
974
                    if ctx.use_fused_attention:
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                            kv_ = kv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
                            # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
991
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
992
                        if attn_dbias is not None:
993
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
994
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
995
996
                            ctx.max_seqlen_q, ctx.max_seqlen_k,
                            cu_seqlens_q, cu_seqlens_k,
997
998
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
999
1000
1001
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1002
                            qkv_layout=qkv_layout,
1003
                            attn_mask_type="causal",
1004
                            attn_bias_type=ctx.attn_bias_type,
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
                        dq_ = torch.empty_like(q_)
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, 0]
                        _flash_attn_backward(
                            dout_, q_, kv_[0], kv_[1], out_, softmax_lse,
                            dq_, dkv_[0], dkv_[1], cu_seqlens_q, cu_seqlens_k,
                            ctx.max_seqlen_q, ctx.max_seqlen_k,
                            ctx.dropout_p, ctx.softmax_scale, True,
1023
                            rng_state=rng_states[cp_size-i-1],
1024
1025
1026
1027
                            **fa_optional_backward_kwargs
                        )
                elif i >= (cp_size-rank-1):
                    if ctx.use_fused_attention:
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            q_ = q.view(q.shape[0], -1, *q.shape[-2:])
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous()
                            # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                            out_ = out.view(out.shape[0], -1, *out.shape[-2:])
                            dout_ = dout.view(dout.shape[0], -1, *dout.shape[-2:])
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            q_ = q.view(-1, *q.shape[-3:])
                            # [2, 2, sk//2, b, np, hn] -> [2, sk//2, b, np, hn]
                            kv_ = kv[:, 0, ...].contiguous()
                            # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                            out_ = out.view(-1, *out.shape[-3:])
                            dout_ = dout.view(-1, *dout.shape[-3:])
1044
                        aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
1045
                        if attn_dbias is not None:
1046
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1047
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1048
1049
                            ctx.max_seqlen_q, ctx.max_seqlen_k//2,
                            cu_seqlens_q, cu_seqlens_k//2,
1050
1051
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1052
1053
1054
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1055
                            qkv_layout=qkv_layout,
1056
                            attn_mask_type="no_mask",
1057
                            attn_bias_type=ctx.attn_bias_type,
1058
1059
1060
1061
1062
                        )
                    else:
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        q_ = q.view(-1, *q.shape[-2:])
                        dq_ = torch.empty_like(q_)
1063
1064
1065
1066
1067
1068
                        if ctx.qkv_format == "thd":
                            # [2, t, np, hn] -> [2, t/2, np, hn]
                            kv_ = tex.thd_read_half_tensor(kv, cu_seqlens_k, 0)
                        else:
                            # [2, b, 2, sk//2, np, hn]->[2, b, sk//2, np, hn]->[2, b*sk//2, np, hn]
                            kv_ = kv[:, :, 0, ...].contiguous().view(2, -1, *kv.shape[-2:])
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
                        dkv_ = torch.empty_like(kv_)
                        # [b, 2, sq//2, np, hn] -> [b*sq, np, hn]
                        out_ = out.view(-1, *out.shape[-2:])
                        dout_ = dout.view(-1, *dout.shape[-2:])
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, -1]
                        _flash_attn_backward(
                            dout_, q_, kv_[0], kv_[1], out_, softmax_lse,
                            dq_, dkv_[0], dkv_[1], cu_seqlens_q, cu_seqlens_k//2,
                            ctx.max_seqlen_q, ctx.max_seqlen_k//2,
                            ctx.dropout_p, ctx.softmax_scale, False,
1080
                            rng_state=rng_states[cp_size-i-1],
1081
1082
1083
1084
                            **fa_optional_backward_kwargs
                        )
                else:
                    if ctx.use_fused_attention:
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
                        if ctx.qkv_format == "bshd":
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous()
                            # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                            kv_ = kv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous()
                            dout_ = dout[:, 1, ...].contiguous()
                        elif ctx.qkv_format == "sbhd":
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            q_ = q[1].contiguous()
                            # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                            kv_ = kv.view(kv.shape[0], -1, *kv.shape[-3:])
                            # [2, sq//2, b, np, hn] -> [sq//2, b, np, hn]
                            out_ = out[1].contiguous()
                            dout_ = dout[1].contiguous()
1101
                        aux_ctx_tensors = [softmax_lse_, rng_states[cp_size-i-1]]
1102
                        if attn_dbias is not None:
1103
                            aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1104
                        dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1105
1106
                            ctx.max_seqlen_q//2, ctx.max_seqlen_k,
                            cu_seqlens_q//2, cu_seqlens_k,
1107
1108
                            q_, kv_[0], kv_[1], out_, dout_,
                            TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1109
1110
1111
                            tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                            attn_scale=ctx.softmax_scale,
                            dropout=ctx.dropout_p,
1112
                            qkv_layout=qkv_layout,
1113
                            attn_mask_type="no_mask",
1114
                            attn_bias_type=ctx.attn_bias_type,
1115
1116
                        )
                    else:
1117
1118
1119
1120
1121
1122
                        if ctx.qkv_format == "thd":
                            # [t, np, hn] -> [t/2, np, hn]
                            q_ = tex.thd_read_half_tensor(q, cu_seqlens_q, 1)
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            q_ = q[:, 1, ...].contiguous().view(-1, *q.shape[-2:])
1123
1124
1125
1126
                        dq_ = torch.empty_like(q_)
                        # [2, b, 2, sk//2, np, hn] -> [2, b*sk, np, hn]
                        kv_ = kv.view(2, -1, *kv.shape[-2:])
                        dkv_ = torch.empty_like(kv_)
1127
1128
1129
1130
1131
1132
1133
                        if ctx.qkv_format == "thd":
                            out_ = tex.thd_read_half_tensor(out, cu_seqlens_q, 1)
                            dout_ = tex.thd_read_half_tensor(dout, cu_seqlens_q, 1)
                        else:
                            # [b, 2, sq//2, np, hn] -> [b, sq//2, np, hn] -> [b*sq//2, np, hn]
                            out_ = out[:, 1, ...].contiguous().view(-1, *out.shape[-2:])
                            dout_ = dout[:, 1, ...].contiguous().view(-1, *dout.shape[-2:])
1134
1135
1136
1137
1138
1139
1140
                        if _flash_attn_2_3_plus:
                            fa_optional_backward_kwargs["window_size"] = [-1, -1]
                        _flash_attn_backward(
                            dout_, q_, kv_[0], kv_[1], out_, softmax_lse_,
                            dq_, dkv_[0], dkv_[1], cu_seqlens_q//2, cu_seqlens_k,
                            ctx.max_seqlen_q//2, ctx.max_seqlen_k,
                            ctx.dropout_p, ctx.softmax_scale, False,
1141
                            rng_state=rng_states[cp_size-i-1],
1142
1143
1144
1145
                            **fa_optional_backward_kwargs
                        )
            else:
                if ctx.use_fused_attention:
1146
                    aux_ctx_tensors = [softmax_lse, rng_states[cp_size-i-1]]
1147
                    if attn_dbias is not None:
1148
                        aux_ctx_tensors += [attn_biases[cp_size-i-1]]
1149
                    dq_, dk_, dv_, dbias_ = fused_attn_bwd(
1150
1151
                        ctx.max_seqlen_q, ctx.max_seqlen_k,
                        cu_seqlens_q, cu_seqlens_k,
1152
1153
                        q, kv[0], kv[1], out, dout,
                        TE_DType[q.dtype], TE_DType[kv.dtype], aux_ctx_tensors,
1154
1155
1156
                        tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen,
                        attn_scale=ctx.softmax_scale,
                        dropout=ctx.dropout_p,
1157
                        qkv_layout=qkv_layout,
1158
                        attn_mask_type="no_mask",
1159
                        attn_bias_type=ctx.attn_bias_type,
1160
1161
1162
                    )
                else:
                    # [b, sq, np, hn] -> [b*sq, np, hn]
1163
1164
                    q_ = q.view(-1, *q.shape[-2:])
                    dq_ = torch.empty_like(q_)
1165
                    # [2, b, sk, np, hn] -> [2, b*sk, np, hn]
1166
1167
                    kv_ = kv.view(2, -1, *kv.shape[-2:])
                    dkv_ = torch.empty_like(kv_)
1168
                    # [b, sq, np, hn] -> [b*sq, np, hn]
1169
1170
                    out_ = out.view(-1, *out.shape[-2:])
                    dout_ = dout.view(-1, *dout.shape[-2:])
1171
1172
                    if _flash_attn_2_3_plus:
                        fa_optional_backward_kwargs["window_size"] = [-1, -1]
1173
1174
1175
1176
1177
                    _flash_attn_backward(
                        dout_, q_, kv_[0], kv_[1], out_, softmax_lse,
                        dq_, dkv_[0], dkv_[1], cu_seqlens_q, cu_seqlens_k,
                        ctx.max_seqlen_q, ctx.max_seqlen_k,
                        ctx.dropout_p, ctx.softmax_scale, False,
1178
                        **fa_optional_backward_kwargs
1179
1180
                    )

1181
1182
1183
1184
1185
            if i >= (cp_size-rank-1) or not ctx.causal:
                # [b*sq, np, hn] -> [b, 2, sq//2, np, hn] if causal
                # [b*sq, np, hn] -> [b, sq, np, hn] if not causal
                dq_ = dq_.view(*dq.shape)
            else:
1186
1187
1188
1189
1190
1191
                if ctx.qkv_format == "bshd":
                    # [b*sq//2, np, hn] -> [b, sq//2, np, hn]
                    dq_ = dq_.view(dq.shape[0], *dq.shape[2:])
                elif ctx.qkv_format == "sbhd":
                    # [b*sq//2, np, hn] -> [sq//2, b, np, hn]
                    dq_ = dq_.view(-1, *dq.shape[-3:])
1192

1193
            if ctx.causal:
1194
1195
1196
1197
1198
1199
                if i > (cp_size-rank-1):
                    dq.add_(dq_)
                elif i == (cp_size-rank-1):
                    if rank == (cp_size-1):
                        dq.copy_(dq_)
                    else:
1200
1201
1202
1203
1204
1205
                        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])
1206
1207
                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "copy", "add")
1208
                elif i > 0:
1209
1210
1211
1212
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].add_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].add_(dq_)
1213
1214
                    elif ctx.qkv_format == "thd":
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "none", "add")
1215
                else:
1216
1217
1218
1219
                    if ctx.qkv_format == "bshd":
                        dq[:, 1, ...].copy_(dq_)
                    elif ctx.qkv_format == "sbhd":
                        dq[1].copy_(dq_)
1220
1221
                    elif ctx.qkv_format == "thd":
                        tex.thd_grad_correction(dq, dq_, cu_seqlens_q, "none", "copy")
1222
1223
1224
1225
1226
            else:
                if i == 0:
                    dq.copy_(dq_)
                else:
                    dq.add_(dq_)
1227

1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
            if attn_dbias is not None:
                idx = (rank+i+1)%cp_size
                if i == (cp_size - 1) or not ctx.causal:
                    # [b, np, sq, sk//cp] -> [b, np, sq, 2, sk//(2*cp)]
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1]//2)
                    attn_dbias[..., idx, :].copy_(dbias_[..., 0, :])
                    attn_dbias[..., (2*cp_size-idx-1), :].copy_(dbias_[..., 1, :])
                elif i >= (cp_size-rank-1):
                    # [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)]
                    dbias_ = dbias_.view(*dbias_.shape[:-1], 2, dbias_.shape[-1]//2)
                    attn_dbias_[..., 1, :, idx, :].copy_(dbias_[..., 0, :])
                    attn_dbias_[..., 1, :, (2*cp_size-idx-1), :].copy_(dbias_[..., 1, :])

1244
1245
1246
            # wait until dKV is received
            for req in send_recv_reqs:
                req.wait()
1247

1248
1249
1250
1251
            dkv = p2p_comm_buffers[(i+1)%2][1]
            if ctx.use_fused_attention:
                dkv_ = torch.cat((dk_.unsqueeze(0), dv_.unsqueeze(0)), dim=0)
            if ctx.causal and i >= (cp_size-rank-1) and i != (cp_size-1):
1252
1253
1254
1255
1256
1257
                if ctx.qkv_format == "bshd":
                    # [2, b*sk//2, np, hn] -> [2, b, sk//2, np, hn]
                    dkv_ = dkv_.view(*dkv.shape[0:2], *dkv.shape[3:])
                elif ctx.qkv_format == "sbhd":
                    # [2, b*sk//2, np, hn] -> [2, sk//2, b, np, hn]
                    dkv_ = dkv_.view(dkv.shape[0], -1, *dkv.shape[-3:])
1258
1259
1260
1261
            else:
                # [2, b*sk, np, hn] -> [2, b, 2, sk//2, np, hn] if causal
                # [2, b*sk, np, hn] -> [2, b, sk, np, hn] if not causal
                dkv_ = dkv_.view(*dkv.shape)
1262

1263
            if ctx.causal:
1264
1265
                if i == (cp_size-1):
                    if rank == 0:
1266
1267
1268
1269
1270
1271
                        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, ...])
1272
1273
                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "add", "copy")
1274
1275
1276
1277
                    else:
                        dkv.add_(dkv_)
                elif i >= (cp_size-rank-1):
                    if i == 0 and rank == (cp_size-1):
1278
1279
1280
1281
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].copy_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].copy_(dkv_)
1282
1283
                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "copy", "none")
1284
                    else:
1285
1286
1287
1288
                        if ctx.qkv_format == "bshd":
                            dkv[:, :, 0, ...].add_(dkv_)
                        elif ctx.qkv_format == "sbhd":
                            dkv[:, 0, ...].add_(dkv_)
1289
1290
                        elif ctx.qkv_format == "thd":
                            tex.thd_grad_correction(dkv, dkv_, cu_seqlens_k, "add", "none")
1291
1292
1293
1294
1295
                elif i > 0:
                    dkv.add_(dkv_)
                else:
                    dkv.copy_(dkv_)
            else:
1296
1297
1298
1299
1300
1301
                if i == 0:
                    dkv.copy_(dkv_)
                else:
                    dkv.add_(dkv_)

        if ctx.causal:
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
            if ctx.qkv_format == "bshd":
                # [b, 2, sq//2, np, hn] -> [b, sq, np, hn]
                dq = dq.view(q.shape[0], -1, *q.shape[-2:])
                # [2, b, 2, sk//2, np, hn] -> [2, b, sk, np, hn]
                dkv = dkv.view(*kv.shape[0:2], -1, *kv.shape[-2:])
            elif ctx.qkv_format == "sbhd":
                # [2, sq//2, b, np, hn] -> [sq, b, np, hn]
                dq = dq.view(-1, *q.shape[-3:])
                # [2, 2, sk//2, b, np, hn] -> [2, sk, b, np, hn]
                dkv = dkv.view(kv.shape[0], -1, *kv.shape[-3:])

        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)

1317
        return None, dq, dkv[0], dkv[1], None, None, None, None, None, None, \
1318
                None, None, None, None, None, None, attn_dbias, None, None
1319
1320
1321


def attn_forward_func_with_cp(
1322
1323
1324
1325
    is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
    dropout_p, cp_group, cp_global_ranks, cp_stream, softmax_scale=None, qkv_format="bshd",
    attn_mask_type="causal", attn_bias_type="no_bias", attn_bias=None, deterministic=False,
    use_fused_attention=False
1326
1327
) -> torch.Tensor:
    """Attention implementation with context parallelism"""
1328
    assert(qkv_format in ["bshd", "sbhd", "thd"]
1329
1330
1331
        ), 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!"
1332
1333
    assert(not(qkv_format == "thd" and use_fused_attention)
        ), "FusedAttention does not support thd format!"
1334
1335
    assert (attn_mask_type in ["causal", "no_mask"]
        ), f"Mask type of {attn_mask_type} is not supported with context parallelism!"
1336
1337
    assert (attn_bias is None or use_fused_attention
        ), "Attention bias is only supported with FusedAttention!"
1338
1339
    out = AttnFuncWithCP.apply(
        is_training, q, k, v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k,
1340
1341
        dropout_p, cp_group, cp_global_ranks, cp_stream, softmax_scale, qkv_format,
        attn_mask_type, attn_bias_type, attn_bias, deterministic, use_fused_attention
1342
1343
1344
1345
    )
    return out


1346
1347
1348
1349
1350
1351
1352
class RotaryPositionEmbedding(torch.nn.Module):
    """
    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
    """
    def __init__(
        self,
        dim: int,
1353
        rotary_percent: float = 1.0,
1354
1355
1356
1357
1358
1359
1360
1361
        seq_len_interpolation_factor: Optional[int] = None,
        pretrained_max_position_embeddings: Optional[int] = None,
    ):
        """
        Parameters
        ----------
        dim: int
            rotary embedding dimension
1362
1363
        rotary_percent: float
            Percent of rotary dimension to use for rotary position embeddings.
1364
1365
1366
1367
1368
1369
1370
        seq_len_interpolation_factor: int
            if not None, discrete positions will be interpolated by this factor via the trick in
            https://arxiv.org/abs/2306.15595
        pretrained_max_position_embeddings: int
            pre-trained max_position_embeddings before position interpolation
        """
        super().__init__()
1371
1372
        if rotary_percent < 1.0:
            dim = int(dim * rotary_percent)
1373
        self.seq_len_interpolation_factor = seq_len_interpolation_factor
1374
1375
1376
1377
1378
1379
1380
        inv_freq = 1.0 / (
            10000
            ** (
                torch.arange(0, dim, 2, dtype=torch.float32, device=torch.cuda.current_device())
                / dim
            )
        )
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
        self.register_buffer('inv_freq', inv_freq)
        self.pretrained_max_position_embeddings = pretrained_max_position_embeddings

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

        Parameters
        ----------
        max_seq_len: int
            sequence length of a sample
        offset: int, default = 0
            fixed offset for freqencies
        """
1395
1396
1397
1398
        seq = (
            torch.arange(max_seq_len, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
            + offset
        )
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416

        if (self.pretrained_max_position_embeddings is not None
            and self.seq_len_interpolation_factor is not None):
            if (max_seq_len >
                self.pretrained_max_position_embeddings * self.seq_len_interpolation_factor):
                # dynamic linear scaling (length > position we have learned)
                seq *= 1 / (max_seq_len / self.pretrained_max_position_embeddings)
            else:
                # fixed linear scaling
                seq *= 1 / self.seq_len_interpolation_factor

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

1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434

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

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

    @staticmethod
    def forward(
        ctx,
        t: torch.Tensor,
        freqs: torch.Tensor,
        tensor_format: str = "sbhd",
        cu_seqlens: Union[torch.Tensor, None] = None,
    ) -> torch.Tensor:
1435
1436
        if freqs.dtype != torch.float32:
            freqs = freqs.float()
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
        if tensor_format == "sbhd":
            output = tex.fused_rope_forward(t, freqs, False)
        elif tensor_format == "bshd":
            output = tex.fused_rope_forward(
                t.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif tensor_format == "thd":
            output = tex.fused_rope_thd_forward(t, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {tensor_format}.")
        ctx.save_for_backward(freqs, cu_seqlens)
        ctx.tensor_format = tensor_format

        return output

    @staticmethod
    def backward(
        ctx, grad_output: torch.Tensor
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        freqs, cu_seqlens = ctx.saved_tensors
        if ctx.tensor_format == "sbhd":
            grad_input = tex.fused_rope_backward(grad_output, freqs, False)
        elif ctx.tensor_format == "bshd":
            grad_input = tex.fused_rope_backward(
                grad_output.transpose(0, 1), freqs, True
            ).transpose(0, 1)
        elif ctx.tensor_format == "thd":
            grad_input = tex.fused_rope_thd_backward(grad_output, cu_seqlens, freqs)
        else:
            raise ValueError(f"Unsupported tensor_format: {ctx.tensor_format}.")

        return grad_input, None, None, None, None


1471
1472
1473
1474
1475
1476
1477
1478
1479
def _rotate_half(x: torch.Tensor) -> torch.Tensor:
    """
    change sign so the last dimension becomes [-odd, +even]
    """
    x = x.view(x.shape[:-1] + torch.Size((2, x.shape[-1] // 2)))
    x1, x2 = x.unbind(dim=-2)
    return torch.cat((-x2, x1), dim=-1)


1480
def apply_rotary_pos_emb(
1481
1482
1483
1484
1485
1486
    t: torch.Tensor,
    freqs: torch.Tensor,
    tensor_format: str = "sbhd",
    fused: bool = False,
    cu_seqlens: Union[torch.Tensor, None] = None,
) -> torch.Tensor:
1487
    """
1488
    Apply rotary positional embedding tensor to the input tensor.
1489

1490
1491
1492
    Parameters
    ----------
    t: torch.Tensor
1493
        Input tensor of shape `[s, b, h, d]`, `[b, s, h, d]` or `[t, h, d]`, on which
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
        rotary positional embedding will be applied.
    freqs: torch.Tensor
        Rotary positional embedding tensor of shape `[s2, 1, 1, d2]` and dtype 'float',
        with `s2 >= s` and `d2 <= d`.
    fused: bool, default = False
        Whether to use a fused applying RoPE implementation.
    tensor_format: {'sbhd', 'bshd', 'thd'}, default = 'sbhd'
        is `bshd` if `t` is of shape `[bs, seq, ...]`, or `sbhd` if `t` is
        of shape `[seq, bs, ...]`. 'thd' is only supported when `fused` is True.
    cu_seqlens: torch.Tensor, default = None.
        Cumulative sum of sequence lengths in a batch for `t`, with shape [b + 1] and
        dtype torch.int32. Only valid when `tensor_format` is 'thd'.
1506
    """
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
    if fused:
        assert (
            tensor_format != "thd" or cu_seqlens is not None
        ), "cu_seqlens must not be None when tensor_format is 'thd'."
        return FusedRoPEFunc.apply(t, freqs, tensor_format, cu_seqlens)

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

1518
1519
1520
1521
1522
    max_seq_len = freqs.shape[0]
    cur_seq_len = t.shape[1] if tensor_format == "bshd" else t.shape[0]

    # Only apply the rotary embeddings up to the sequence length of the running
    # input.
1523
1524
1525
1526
    assert cur_seq_len <= max_seq_len, (
        f"Rotary Embeddings only supported up to {max_seq_len} sequence length!"
    )
    freqs = freqs[:cur_seq_len]
1527
    if tensor_format == "bshd":
1528
1529
1530
1531
        freqs = freqs.transpose(0, 1)  # [seq, 1, 1, dim] -> [1, seq, 1, dim]
    # cos/sin first then dtype conversion for better precision
    cos_ = torch.cos(freqs).to(t.dtype)
    sin_ = torch.sin(freqs).to(t.dtype)
1532

1533
1534
1535
1536
1537
1538
    rot_dim = freqs.shape[-1]
    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]

    # first part is cosine component
    # second part is sine component, need to change signs with _rotate_half method
1539
    t = (t * cos_) + (_rotate_half(t) * sin_)
1540
1541
1542
    return torch.cat((t, t_pass), dim=-1)


cyanguwa's avatar
cyanguwa committed
1543
class _SplitAlongDim(torch.autograd.Function):
1544
1545
1546
1547
1548
    """"""

    @staticmethod
    def forward(ctx,
                mixed_x_layer: torch.Tensor,
cyanguwa's avatar
cyanguwa committed
1549
1550
                split_dim: int,
                split_size_or_sections: Union[int, List[int], Tuple[int]],
1551
    ) -> Tuple[torch.Tensor, ...]:
cyanguwa's avatar
cyanguwa committed
1552
1553
        ctx.split_dim = split_dim
        ctx.split_size_or_sections = split_size_or_sections
1554
1555
1556
1557
1558
1559
1560
1561
        if isinstance(mixed_x_layer, Float8Tensor):
            return tuple(Float8Tensor.make_like(
                mixed_x_layer,
                data=x,
                ) for x in torch.split(
                    mixed_x_layer._data,
                    split_size_or_sections=split_size_or_sections,
                    dim=split_dim))
cyanguwa's avatar
cyanguwa committed
1562
        return torch.split(mixed_x_layer, split_size_or_sections, dim = split_dim)
1563
1564
1565
1566
1567
1568

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

cyanguwa's avatar
cyanguwa committed
1569
1570
1571
1572
1573
1574
1575
1576
1577
        if isinstance(ctx.split_size_or_sections, (list, tuple)):
            split_sizes = ctx.split_size_or_sections
            assert (len(grad_outputs) == len(split_sizes)
                ), "Unequal number of gradients vs split sections for backprop!"
        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

1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
        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]
                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):
                    noop_ok = False
                    break
            if noop_ok:
                ret = torch.Tensor().to(device=grad_outputs[0].device,
                                        dtype=grad_outputs[0]._data.dtype)
                new_shape = list(shape)
                new_shape[split_dim] = sum(split_sizes)
                ret.set_(grad_outputs[0]._data.untyped_storage(),
                         grad_outputs[0]._data.storage_offset(),
                         new_shape,
                         strides
                )
                return Float8Tensor.make_like(grad_outputs[0], data=ret), None, None

            grad_outputs_data = [x._data for x in grad_outputs]
            return Float8Tensor.make_like(
                grad_outputs[0],
                data=torch.cat(grad_outputs_data, dim = split_dim)), None, None
1609
1610
        noop_ok = True
        strides = grad_outputs[0].stride()
1611
        data_ptr = grad_outputs[0].untyped_storage().data_ptr()
cyanguwa's avatar
cyanguwa committed
1612
        shape = list(grad_outputs[0].shape)
1613
        for i, tensor in enumerate(grad_outputs):
cyanguwa's avatar
cyanguwa committed
1614
1615
1616
            shape_i = shape
            shape_i[split_dim] = split_sizes[i]
            offset_size = sum(split_sizes[:i]) * np.prod(shape[split_dim+1:])
1617
            if (tensor.stride() != strides or
cyanguwa's avatar
cyanguwa committed
1618
                list(tensor.shape) != shape_i or
1619
                tensor.untyped_storage().data_ptr() != data_ptr or
cyanguwa's avatar
cyanguwa committed
1620
                tensor.storage_offset() != offset_size):
1621
1622
1623
1624
1625
1626
                noop_ok = False
                break
        if noop_ok:
            ret = torch.Tensor().to(device=grad_outputs[0].device,
                                    dtype=grad_outputs[0].dtype)
            new_shape = list(shape)
cyanguwa's avatar
cyanguwa committed
1627
1628
            new_shape[split_dim] = sum(split_sizes)
            ret.set_(grad_outputs[0].untyped_storage(),
1629
1630
                     grad_outputs[0].storage_offset(),
                     new_shape,
cyanguwa's avatar
cyanguwa committed
1631
                     strides
1632
            )
cyanguwa's avatar
cyanguwa committed
1633
            return ret, None, None
1634

cyanguwa's avatar
cyanguwa committed
1635
        return torch.cat(grad_outputs, dim = split_dim), None, None
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655


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

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        layer_number: Optional[int] = None,
    ) -> None:
        super().__init__()

        self.norm_factor = norm_factor
        self.attention_dropout_ctx = attention_dropout_ctx
        self.layer_number = layer_number

1656
        self.scale_mask_softmax = FusedScaleMaskSoftmax(attention_mask_func)
1657
1658
1659
1660
1661
1662

        # 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)

1663
1664
1665
1666
        # An FP16 training trick required for certain GPT-like models.
        self.apply_qk_layer_scaling = (
            bool(int(os.getenv("NVTE_APPLY_QK_LAYER_SCALING", "0"))) and layer_number is not None)

1667
1668
1669
1670
1671
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
1672
1673
1674
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None, # pylint: disable=unused-argument
        cu_seqlens_kv: Optional[torch.Tensor] = None, # pylint: disable=unused-argument
1675
        attn_mask_type: str = "causal",
1676
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
1677
1678
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
1679
        alibi_slopes: Optional[torch.Tensor] = None,
1680
    ) -> torch.Tensor:
1681
        """Unfused attention fprop"""
1682

1683
1684
1685
1686
1687
1688
1689
        assert (qkv_layout in QKVLayouts
            ), f"UnfusedDotProductAttention does not support qkv_layout = {qkv_layout}!"
        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])
        if qkv_format == 'bshd':
            # convert to sbhd and use sbhd implementation for now
            query_layer, key_layer, value_layer = [x.transpose(0, 1)
                for x in [query_layer, key_layer, value_layer]]
1690

1691
        batch_size, seqlen = query_layer.shape[1], query_layer.shape[0]
1692
        apply_qk_layer_scaling = self.apply_qk_layer_scaling and key_layer.dtype == torch.float16
1693
1694
1695
1696
1697
1698
1699
1700
1701

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

1702
1703
1704
1705
1706
1707
1708
1709
        if key_layer.shape[2] != query_layer.shape[2]:
            assert (query_layer.shape[2]%key_layer.shape[2]==0
                ),"The number of attention heads must be divisible by the number of GQA groups!"
            key_layer = key_layer.repeat_interleave(
                    int(query_layer.shape[2]/key_layer.shape[2]), dim = 2)
            value_layer = value_layer.repeat_interleave(
                    int(query_layer.shape[2]/value_layer.shape[2]), dim = 2)

1710
1711
1712
1713
1714
1715
1716
1717
        # [sq, b, np, hn] -> [sq, b * np, hn]
        query_layer = query_layer.reshape(
            output_size[2], output_size[0] * output_size[1], -1
        )
        # [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]
1718
1719
        # WAR to set dtype to FP32 as ONNX lacks BF16 support for ConstantOfShape operator
        is_bf16 = query_layer.dtype == torch.bfloat16
1720
1721
1722
1723
        matmul_result = torch.empty(
            output_size[0] * output_size[1],
            output_size[2],
            output_size[3],
1724
            dtype=torch.float32 if is_in_onnx_export_mode() and is_bf16 else query_layer.dtype,
1725
1726
1727
            device=torch.cuda.current_device(),
        )

1728
1729
1730
        if is_in_onnx_export_mode() and is_bf16:
            matmul_result = matmul_result.bfloat16()

1731
1732
1733
1734
1735
        scale = self.norm_factor
        if apply_qk_layer_scaling:
            scale *= self.layer_number

        # Raw attention scores. [b * np, sq, sk]
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
        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,
                alpha=(1.0 / scale),
            )

        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]
            )
            matmul_result = (matmul_result.view(
                output_size[0], output_size[1], output_size[2], output_size[3])
                + core_attention_bias).view(-1, output_size[2], output_size[3])
            matmul_result /= scale

1756
1757
1758
1759
        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":
1760
1761
                _, core_attention_bias = get_alibi(
                    output_size[1], output_size[2], output_size[3], alibi_slopes=alibi_slopes)
1762
1763
1764
1765
1766
1767
1768
1769
1770
            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,
                alpha=(1.0 / scale),
            )
            matmul_result = (matmul_result.view(
                output_size[0], output_size[1], output_size[2], output_size[3])
1771
1772
                + core_attention_bias).view(-1, output_size[2], output_size[3]).to(
                dtype=query_layer.dtype)
1773
1774
1775
1776
1777
1778

        # change view to [b, np, sq, sk]
        attention_scores = matmul_result.view(*output_size)

        # attention scores and attention mask [b, np, sq, sk]
        softmax_scale = self.layer_number if apply_qk_layer_scaling else None
1779
1780
        attention_probs = self.scale_mask_softmax(
            attention_scores, attention_mask, attn_mask_type, softmax_scale)
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811

        # 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]
        value_layer = value_layer.reshape(
            value_layer.size(0), output_size[0] * output_size[1], -1
        )

        # change view [b * np, sq, sk]
        attention_probs = attention_probs.view(
            output_size[0] * output_size[1], output_size[2], -1
        )

        # 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)

1812
1813
1814
        if qkv_format == 'sbhd':
            # [b, np, sq, hn] --> [sq, b, np, hn]
            context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
1815

1816
1817
1818
1819
1820
1821
1822
1823
1824
            # [sq, b, np, hn] --> [sq, b, hp]
            context_layer = context_layer.view(seqlen, batch_size, -1)

        if qkv_format == 'bshd':
            # [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)
1825
1826
1827
1828
1829
1830
1831
1832
1833

        return context_layer


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

    @staticmethod
1834
1835
1836
1837
1838
1839
    def forward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
        # 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
1851
1852
1853
1854
1855
    def backward(
        _ctx: torch.autograd.function.FunctionCtx,  # unused
        dq: torch.Tensor,
        dk: torch.Tensor,
        dv: torch.Tensor
1856
1857
1858
1859
1860
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        dqkv = tex.fa_prepare_bwd(dq, dk, dv)
        dq, dk, dv = split_tensor_along_dim(dqkv, -1, 3)
        return dq, dk, dv

1861

1862
1863
1864
1865
1866
1867
1868
def _get_qkv_layout(
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        qkv_format: str = 'sbhd',
    ) -> str:
    """Get qkv layout.
1869

1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
    Parameters
    ----------
    q: torch.Tensor
        Query tensor.
    k: torch.Tensor
        Key tensor.
    v: torch.Tensor
        Value tensor.
    qkv_format: str, default = `sbhd`
        Dimension format for `q`, `k` and `v`, {`sbhd`, `bshd`, `thd`}. `s` stands for
        the sequence length dimension, `b` batch size, `h` the number of attention heads,
        `d` head size, and `t` the total number of sequences in a batch, i.e.
        `t = sum(s_i) for i = 0...b-1`.

    Returns
    ----------
    qkv_layout: str
       Memory layout of `q`, `k` and `v`. Each `qkv_format` can be mapped to one of five
       memory layouts. For example, `sb3hd` means `q`, `k`, `v` are created as one chunk
       of memory and that they are interleaved in the `2`nd dimension. `sbhd_sbh2d` means
       `q` and `kv` are created in two chunks and that `q` itself is contiguous and `k`, `v`
       are interleaved with each other in the `3`rd dimension, `k = kv[:,:,:,0,:]` and
       `v = kv[:,:,:,1,:]`.
       Mapping:
       `sbhd`: {`sb3hd`, `sbh3d`, `sbhd_sb2hd`, `sbhd_sbh2d`, `sbhd_sbhd_sbhd`}
       `bshd`: {`bs3hd`, `bsh3d`, `bshd_bs2hd`, `bshd_bsh2d`, `bshd_bshd_bshd`}
       `thd` : {`t3hd`, `th3d`, `thd_t2hd`, `thd_th2d`, `thd_thd_thd`}
    """
1898

1899
1900
    check_last_dim_contiguous = all(x.stride(-1) == 1 for x in [q, k, v])
    assert check_last_dim_contiguous, "q, k and v must have stride 1 in their last dimension!"
1901

1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
    def run_iteratively(q, k, v):
        data_ptr = q.untyped_storage().data_ptr()
        check_ptrs_qkv = all(x.untyped_storage().data_ptr() == data_ptr for x in [q, k, v])
        data_ptr = k.untyped_storage().data_ptr()
        check_ptrs_kv = all(x.untyped_storage().data_ptr() == data_ptr for x in [k, v])

        stride = q.stride()
        check_strides_qkv = all(stride == x.stride() for x in [q, k, v])
        stride = k.stride()
        check_strides_kv = all(stride == x.stride() for x in [k, v])

        shape = q.shape
        check_shapes_qkv = all(shape == x.shape for x in [q, k, v])
        shape = k.shape
        check_shapes_kv = all(shape == x.shape for x in [k, v])

        last_dim_size = q.shape[-1]
        check_last_dim_offsets_qkv = all(i * last_dim_size == x.storage_offset()
                            for i, x in enumerate([q, k, v]))
        last_dim_size = k.shape[-1]
        check_last_dim_offsets_kv = all(i * last_dim_size == x.storage_offset()
                            for i, x in enumerate([k, v]))

        last_two_dims_size = q.shape[-1] * q.shape[-2]
        check_last_two_dims_offsets_qkv = all(i * last_two_dims_size == x.storage_offset()
                            for i, x in enumerate([q, k, v]))
        last_two_dims_size = k.shape[-1] * k.shape[-2]
        check_last_two_dims_offsets_kv = all(i * last_two_dims_size == x.storage_offset()
                            for i, x in enumerate([k, v]))

        if (check_ptrs_qkv and check_strides_qkv and check_shapes_qkv
            and check_last_two_dims_offsets_qkv
            and not check_last_dim_offsets_qkv):
            # sb3hd, bs3hd, t3hd
            qkv_layout = qkv_format[:-2] + '3' + qkv_format[-2:]
        elif (check_ptrs_qkv and check_strides_qkv and check_shapes_qkv
            and check_last_dim_offsets_qkv):
            # sbh3d, bsh3d, th3d
            qkv_layout = qkv_format[:-1] + '3' + qkv_format[-1:]
        elif (check_ptrs_kv and check_strides_kv and check_shapes_kv
            and check_last_two_dims_offsets_kv
            and not check_last_dim_offsets_kv):
            # sbhd_sb2hd, bshd_bs2hd, thd_t2hd
            qkv_layout = qkv_format + '_' + qkv_format[:-2] + '2' + qkv_format[-2:]
        elif (check_ptrs_kv and check_strides_kv and check_shapes_kv
            and check_last_dim_offsets_kv):
            # sbhd_sbh2d, bshd_bsh2d, thd_th2d
            qkv_layout = qkv_format + '_' + qkv_format[:-1] + '2' + qkv_format[-1:]
        elif check_strides_kv and check_shapes_kv:
            # sbhd_sbhd_sbhd, bshd_bshd_bshd, thd_thd_thd
            qkv_layout = '_'.join(list([qkv_format])*3)
        else:
            qkv_layout = 'not_supported'

        return qkv_layout

    qkv_layout = run_iteratively(q, k, v)
    if qkv_layout == 'not_supported':
        # force q,k,v to be contiguous and run get_layout again
        q, k, v = [x.contiguous() for x in [q, k, v]]
        qkv_layout = run_iteratively(q, k, v)
    if qkv_layout == 'not_supported':
1964
1965
        raise Exception("The provided qkv memory layout is not supported!")

1966
    return qkv_layout, q, k, v
1967

1968

1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
def check_set_window_size(
        attn_mask_type: str,
        window_size: Tuple[int, int] = None,
    ):
    """Check if sliding window size is compliant with mask type and if not,
    assert or set it to the appropriate size
    """
    if "causal" in attn_mask_type:
        if window_size is None:
            window_size = (-1, 0)
        else:
            assert (
                window_size[1] == 0
            ), "window_size[1] should be 0 when self_attn_mask_type includes 'causal'!"
    else:
        if window_size is None:
            window_size = (-1, -1)
    return window_size
1987

1988

1989
class FlashAttention(torch.nn.Module):
1990
    """Dot product attention, using HazyResearch flash-attn package:
1991
    https://github.com/Dao-AILab/flash-attention
1992
1993
1994
1995
1996
1997
1998
    """

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
1999
2000
        attention_type: str = "self",
        layer_number: Optional[int] = None,
2001
        deterministic: bool = False,
2002
2003
2004
2005
2006
2007
    ) -> None:
        super().__init__()

        assert (
            _flash_attn_version >= _flash_attn_version_required
        ), f"FlashAttention minimum version {_flash_attn_version_required} is required."
2008
2009
2010
        assert (
            _flash_attn_version <= _flash_attn_max_version
        ), f"FlashAttention maximum version {_flash_attn_max_version} is supported."
2011
2012
2013
2014

        self.norm_factor = norm_factor
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_dropout = attention_dropout
2015
2016
        self.attention_type = attention_type
        self.layer_number = 1 if layer_number is None else layer_number
2017
        self.deterministic = deterministic
2018
2019
2020
2021
2022
2023

    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
2024
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
2025
2026
2027
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
2028
2029
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
2030
        attn_mask_type: str = "causal",
2031
        window_size: Optional[Tuple[int, int]] = None,
2032
        alibi_slopes: Optional[torch.Tensor] = None,
2033
        cp_group: Optional[dist_group_type] = None,
2034
        cp_global_ranks: List[int] = None,
2035
        cp_stream: torch.cuda.Stream = None,
2036
2037
2038
    ) -> torch.Tensor:
        """flash-attn fprop"""

2039
2040
        window_size = check_set_window_size(attn_mask_type, window_size)

2041
        assert (
2042
2043
2044
            query_layer.dtype in [torch.float16, torch.bfloat16]
            and key_layer.dtype in [torch.float16, torch.bfloat16]
            and value_layer.dtype in [torch.float16, torch.bfloat16]
2045
            ), "FlashAttention currently only supports FP16 and BF16."
2046
2047
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
2048
2049
2050
2051
2052
            ), "FlashAttention currently only supports CUDA tensors."
        assert (
            qkv_layout in QKVLayouts
            ), f"FlashAttention does not support qkv_layout = {qkv_layout}!"

2053
2054
        context_parallel = (cp_group is not None) and (get_distributed_world_size(cp_group) != 1)

2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])

        if qkv_format == 'sbhd':
            # For now just 128, will make it more general in the future
            if (query_layer.shape[-1] == 128 and
                query_layer.shape[0] * query_layer.shape[1] >= 512 and
                qkv_layout == "sbh3d"):
                query_layer, key_layer, value_layer = _PrepareQKVForFA.apply(query_layer,
                                                                             key_layer,
                                                                             value_layer)
            else:
                query_layer, key_layer, value_layer = [x.transpose(0,1).contiguous()
                    for x in (query_layer, key_layer, value_layer)]
2068
        elif qkv_format in ['bshd', 'thd']:
2069
2070
2071
            query_layer, key_layer, value_layer = [x.contiguous()
                for x in (query_layer, key_layer, value_layer)]

2072
        batch_size = query_layer.shape[0]
2073

2074
        if qkv_format in ['sbhd', 'bshd']:
2075
            max_seqlen_q, max_seqlen_kv = query_layer.shape[1], key_layer.shape[1]
2076
2077
2078
2079
2080
2081
2082
            if not context_parallel:
                # [b * s, h, d]
                query_layer, key_layer, value_layer = [
                    x.view(x.shape[0] * x.shape[1], *x.shape[2:])
                    for x in [query_layer, key_layer, value_layer]
                ]

2083
            if 'padding' in attn_mask_type:
2084
                assert not context_parallel, "Padding mask not supported with context parallelism!"
2085
2086
2087
2088
2089

                if self.attention_type == "self":
                    assert (
                        max_seqlen_q == max_seqlen_kv
                    ), "Maximum sequence length for Q and KV should be the same."
2090
2091
                    if cu_seqlens_q is None:
                        assert (attention_mask is not None
2092
                                ), "Please provide attention_mask for padding!"
2093
2094
2095
2096
2097
2098
                        cu_seqlens_q, indices_q = get_cu_seqlens_and_indices(attention_mask)
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                    cu_seqlens_kv = cu_seqlens_q
                    query_layer, key_layer, value_layer = PackTensors.apply(
                        indices_q, query_layer, key_layer, value_layer
2099
2100
                    )
                else:
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
                    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 = get_cu_seqlens_and_indices(
                            attention_mask[0])
                        cu_seqlens_kv, indices_kv = get_cu_seqlens_and_indices(
                            attention_mask[1])
                    else:
                        indices_q = get_indices(max_seqlen_q, cu_seqlens_q)
                        indices_kv = get_indices(max_seqlen_kv, cu_seqlens_kv)
                    query_layer = PackTensors.apply(indices_q, query_layer)
                    key_layer, value_layer = PackTensors.apply(
                        indices_kv, key_layer, value_layer
2114
2115
                    )
            else:
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
                # Cumulative sequence lengths for unpadded data
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
2129
        elif qkv_format == 'thd':
2130
2131
            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!"
2132
2133
2134
2135
2136
2137
            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()
2138

2139
        if context_parallel:
2140
2141
2142
            assert (
                window_size in ((-1, -1), (-1, 0))
                ), "Sliding window attention is not supported with context parallelism."
2143
2144
2145
            assert (
                alibi_slopes is None
            ), "Alibi slope bias addition is not supported with context parallelism."
2146
            with self.attention_dropout_ctx():
2147
2148
                output = attn_forward_func_with_cp(
                    self.training, query_layer, key_layer, value_layer,
2149
                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
2150
                    self.attention_dropout if self.training else 0.0,
2151
                    cp_group, cp_global_ranks, cp_stream,
2152
                    softmax_scale=1.0/self.norm_factor,
2153
                    qkv_format="bshd" if qkv_format=="sbhd" else qkv_format,
2154
                    attn_mask_type=attn_mask_type,
2155
                    deterministic=self.deterministic
2156
2157
                )
        else:
2158
2159
2160
2161
2162
2163
2164
2165

            from .cpu_offload import CPUOffloadEnabled
            if CPUOffloadEnabled:
                tensor_list = [query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_kv]
                for tensor in tensor_list:
                    if tensor is not None:
                        tensor.activation_offloading = True

2166
            with self.attention_dropout_ctx():
2167
                fa_optional_forward_kwargs = {}
2168
2169
                if _flash_attn_2_3_plus:
                    fa_optional_forward_kwargs["window_size"] = window_size
2170
2171
2172
2173
                if _flash_attn_2_4_plus:
                    fa_optional_forward_kwargs["alibi_slopes"] = alibi_slopes
                if _flash_attn_2_4_1_plus:
                    fa_optional_forward_kwargs["deterministic"] = self.deterministic
2174
                output = flash_attn_forward_func(
2175
                    query_layer, key_layer, value_layer,
2176
                    cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv,
2177
                    self.attention_dropout if self.training else 0.0,
2178
                    softmax_scale=1.0/self.norm_factor, causal="causal" in attn_mask_type,
2179
                    **fa_optional_forward_kwargs,
2180
                )
2181

2182
        if qkv_format in ['sbhd', 'bshd'] and 'padding' in attn_mask_type:
2183
            output = UnpackTensor.apply(indices_q, batch_size * max_seqlen_q, output)
2184

2185
2186
2187
        if qkv_format == 'sbhd':
            # (bs)hd -> bs(hd) -> sb(hd)
            output = output.view(batch_size, max_seqlen_q, -1).transpose(0, 1).contiguous()
2188
        elif qkv_format == 'bshd':
2189
2190
            # (bs)hd -> bs(hd)
            output = output.view(batch_size, max_seqlen_q, -1).contiguous()
2191
2192
2193
        elif qkv_format == 'thd':
            # thd -> t(hd)
            output = output.view(output.shape[0], -1).contiguous()
2194
2195

        return output
2196

2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
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)
    new_stride = list(tensors[0].stride())
    new_stride.insert(dim, int(new_stride[dim-1]/num_tensors))
    if isinstance(tensors[0], Float8Tensor):
        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)
    else:
        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)

    return combined_tensor
2226

2227
2228
2229
2230
class FusedAttnFunc_qkvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed QKV input"""

    @staticmethod
2231
2232
2233
    def forward(ctx, is_training, max_seqlen, cu_seqlens,
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
                qkv, qkv_dtype, attn_bias, attn_scale,
2234
                dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
2235
                rng_gen, fused_attention_backend, use_FAv2_bwd,
2236
                fp8, fp8_meta):
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
        if fp8:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using FP8 forward')
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(qkv, Float8Tensor)), "qkv must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = qkv._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            # 1: qkv packed, 2: kv packed, 3: qkv separate
            qkv_group = len(qkv_layout.split('_'))
            assert (qkv_group == 1
                ), f"qkv layout should conform to 3hd or h3d, e.g. sb3hd, \
                but found {qkv_layout}."
            if fp8_meta["recipe"].fp8_mha:
                qkv_fp8 = qkv._data
            else:
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                qkv_fp8 = cast_to_fp8(qkv_c,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(qkv.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
                is_training, max_seqlen, cu_seqlens,
                qkv_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2260
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=qkv.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                qkv = cast_from_fp8(qkv_c._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[qkv.dtype]).view(qkv.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            fp8_tensors = (qkv_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using non-FP8 forward')
            out_ret, aux_ctx_tensors = fused_attn_fwd_qkvpacked(
                is_training, max_seqlen, cu_seqlens, qkv, qkv_dtype,
                fused_attention_backend, attn_bias,
2301
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2302
2303
2304
2305
2306
2307
2308
2309
                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            fp8_tensors = (None, None, None, None)
            out_save = out_ret

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (qkv, out_save) if not ctx.fp8 else (None, None)
2310
2311
2312
        ctx.save_for_backward(*qkvo_tensors, cu_seqlens,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            *fp8_tensors, *aux_ctx_tensors)
2313
        ctx.fp8_meta = fp8_meta
2314
2315
2316
2317
2318
2319
2320
2321
        ctx.max_seqlen = max_seqlen
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
2322
2323
        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
2324
        ctx.use_FAv2_bwd = use_FAv2_bwd
2325

2326
        return out_ret
2327
2328
2329

    @staticmethod
    def backward(ctx, d_out):
2330
2331
2332
2333
2334
2335
        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

2336
        d_out = d_out.contiguous()
2337
2338
2339
        (qkv, out, cu_seqlens,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            qkv_fp8, out_fp8,
2340
2341
2342
            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
2343
        if ctx.use_FAv2_bwd:
2344
            softmax_lse, rng_state = aux_ctx_tensors
2345
2346
2347
2348
2349
2350
2351
2352
            dqkv = torch.empty_like(qkv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, qkv[:,0], qkv[:,1], qkv[:,2], out)]
            flash_attn_cuda_bwd(
                d_out, q, k, v, out, softmax_lse, dqkv[:,0], dqkv[:,1], dqkv[:,2],
                cu_seqlens, cu_seqlens, ctx.max_seqlen, ctx.max_seqlen,
                ctx.dropout_p, ctx.attn_scale, False,
2353
                "causal" in ctx.attn_mask_type, None, rng_state
2354
2355
2356
            )
            dqkv = dqkv[..., :d_out.shape[-1]]
        else:
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
            with torch.cuda.nvtx.range("_FusedAttn_qkvpacked"):
                if ctx.fp8:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using FP8 backward')
                    fp8_dtype_forward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dqkv_fp8, *rest = fused_attn_bwd_qkvpacked(
                        ctx.max_seqlen, cu_seqlens,
                        qkv_fp8, out_fp8, d_out_fp8,
2376
                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
2377
                        ctx.fused_attention_backend,
2378
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dqkv = Float8Tensor(data=dqkv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        dqkv_c_fp8 = dqkv_fp8.view(-1,
                            dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1])
                        dqkv = cast_from_fp8(dqkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dqkv_fp8.shape)
                else:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using non-FP8 backward')
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(qkv.dtype)
                    dqkv, *rest = fused_attn_bwd_qkvpacked(
                        ctx.max_seqlen, cu_seqlens, qkv, out, d_out,
2412
                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
2413
                        ctx.fused_attention_backend,
2414
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2415
2416
2417
                        None, None, None, None, None, None, None, None, None, None,
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
2418

2419
2420
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
2421
            return (None, None, None, None, None, None, None, dqkv, None, None, None,
2422
2423
2424
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
2425
        return (None, None, None, None, None, None, None, dqkv, None, rest[0], None,
2426
2427
2428
                None, None, None, None, None, None,
                None, None, None, None, None, None)

2429

2430
2431
2432
2433
2434
class FusedAttnFunc_kvpacked(torch.autograd.Function):
    """Function for FusedAttention with packed KV input"""

    @staticmethod
    def forward(ctx, is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
2435
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2436
                q, kv, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
2437
                qkv_layout, attn_bias_type, attn_mask_type, rng_gen, fused_attention_backend,
2438
                use_FAv2_bwd, fp8, fp8_meta):
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
        if fp8:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using FP8 forward')
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(q, Float8Tensor)
                    and isinstance(kv, Float8Tensor)), "q/kv must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
                q_fp8, kv_fp8 = q._data, kv._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                assert (qkv_group == 2
                    ), f"qkv layout should conform to hd_2hd or hd_h2d, e.g. sbhd_sb2hd, \
                    but found {qkv_layout}."
                q_fp8 = cast_to_fp8(q,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(q.shape)
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                kv_fp8 = cast_to_fp8(kv_c,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward).view(kv.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd_kvpacked(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q_fp8, kv_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2466
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret
            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                q = cast_from_fp8(q._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                kv = cast_from_fp8(kv_c._data,
                    fp8_meta["scaling_fwd"],
                    META_QKV, fp8_dtype_forward, TE_DType[kv.dtype]).view(kv.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            fp8_tensors = (q_fp8, kv_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using non-FP8 forward')
            out_ret, aux_ctx_tensors = fused_attn_fwd_kvpacked(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, kv, qkv_dtype, fused_attention_backend, attn_bias,
2510
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2511
2512
2513
2514
2515
2516
2517
2518
                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None)

        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, kv, out_save) if not ctx.fp8 else (None, None, None)
2519
        ctx.save_for_backward(*qkvo_tensors, cu_seqlens_q, cu_seqlens_kv,
2520
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2521
            *fp8_tensors, *aux_ctx_tensors)
2522
        ctx.fp8_meta = fp8_meta
2523
2524
2525
2526
2527
2528
2529
2530
2531
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
2532
2533
        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
2534
        ctx.use_FAv2_bwd = use_FAv2_bwd
2535

2536
        return out_ret
2537
2538
2539

    @staticmethod
    def backward(ctx, d_out):
2540
2541
2542
2543
2544
2545
        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

2546
        d_out = d_out.contiguous()
2547
2548
2549
        (q, kv, out, cu_seqlens_q, cu_seqlens_kv,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            q_fp8, kv_fp8, out_fp8,
2550
2551
2552
            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
2553
        if ctx.use_FAv2_bwd:
2554
            softmax_lse, rng_state = aux_ctx_tensors
2555
2556
2557
2558
2559
2560
2561
2562
2563
            dq = torch.empty_like(q)
            dkv = torch.empty_like(kv)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, q, kv[:,0], kv[:,1], out)]
            flash_attn_cuda_bwd(
                d_out, q, k, v, out, softmax_lse, dq, dkv[:,0], dkv[:,1],
                cu_seqlens_q, cu_seqlens_kv, ctx.max_seqlen_q, ctx.max_seqlen_kv,
                ctx.dropout_p, ctx.attn_scale, False,
2564
                "causal" in ctx.attn_mask_type, None, rng_state
2565
2566
2567
2568
            )
            dq = dq[..., :d_out.shape[-1]]
            dkv = dkv[..., :d_out.shape[-1]]
        else:
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
            with torch.cuda.nvtx.range("_FusedAttn_kvpacked"):
                if ctx.fp8:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using FP8 backward')
                    fp8_dtype_forward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dq_fp8, dkv_fp8, *rest = fused_attn_bwd_kvpacked(
                        ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                        q_fp8, kv_fp8, out_fp8, d_out_fp8,
2588
                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
2589
                        ctx.fused_attention_backend,
2590
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dq = Float8Tensor(data=dq_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dkv = Float8Tensor(data=dkv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        dq = cast_from_fp8(
                            dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                        dkv_c_fp8 = dkv_fp8.view(-1,
                            dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1])
                        dkv = cast_from_fp8(dkv_c_fp8,
                            ctx.fp8_meta["scaling_bwd"], META_DQKV,
                            fp8_dtype_backward, ctx.qkv_dtype).view(dkv_fp8.shape)
                else:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using non-FP8 backward')
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dkv, *rest = fused_attn_bwd_kvpacked(
                        ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                        q, kv, out, d_out,
2636
                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
2637
                        ctx.fused_attention_backend,
2638
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2639
2640
2641
                        None, None, None, None, None, None, None, None, None, None,
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
2642

2643
2644
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
2645
            return (None, None, None, None, None, None, None, None, None, dq, dkv, None, None, None,
2646
2647
2648
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
2649
        return (None, None, None, None, None, None, None, None, None, dq, dkv, None, rest[0], None,
2650
2651
2652
                None, None, None, None, None, None,
                None, None, None, None, None, None)

2653
2654
2655
2656
2657
class FusedAttnFunc(torch.autograd.Function):
    """Function for FusedAttention with separate Q, K, V tensors"""

    @staticmethod
    def forward(ctx, is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
2658
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2659
                q, k, v, qkv_dtype, attn_bias, attn_scale, dropout_p, fast_zero_fill,
2660
                qkv_layout, attn_bias_type, attn_mask_type, rng_gen, fused_attention_backend,
2661
                use_FAv2_bwd, fp8, fp8_meta):
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
        if fp8:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using FP8 forward')
            fused_attention_backend = FusedAttnBackend["FP8"]
            fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)
            if fp8_meta["recipe"].fp8_mha:
                assert (isinstance(q, Float8Tensor)
                    and isinstance(k, Float8Tensor)
                    and isinstance(v, Float8Tensor)), "q/k/v must be Float8Tensors for FP8 MHA."
                fp8_meta["scaling_fwd"].scale_inv[META_QKV] = q._scale_inv
                q_fp8, k_fp8, v_fp8 = q._data, k._data, v._data
            else:
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                if qkv_group == 1:
                    dim = qkv_layout.find('3')
                    qkv = _combine_tensors([q,k,v], dim)
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv_fp8 = cast_to_fp8(qkv_c,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(qkv.shape)
                    q_fp8, k_fp8, v_fp8 = _SplitAlongDim.apply(qkv_fp8, dim, [1,1,1])
                    q_fp8, k_fp8, v_fp8 = [x.squeeze(dim) for x in [q_fp8, k_fp8, v_fp8]]
                if qkv_group == 2:
                    q_fp8 = cast_to_fp8(q,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(q.shape)
                    dim = qkv_layout.split('_')[1].find('2')
                    kv = _combine_tensors([k,v], dim)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv_fp8 = cast_to_fp8(kv_c,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(kv.shape)
                    k_fp8, v_fp8 = _SplitAlongDim.apply(kv_fp8, dim, [1,1])
                    k_fp8, v_fp8 = [x.squeeze(dim) for x in [k_fp8, v_fp8]]
                if qkv_group == 3:
                    q_fp8 = cast_to_fp8(q,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(q.shape)
                    k_fp8 = cast_to_fp8(k,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(k.shape)
                    v_fp8 = cast_to_fp8(v,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward).view(v.shape)
            out_fp8, aux_ctx_tensors = fused_attn_fwd(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q_fp8, k_fp8, v_fp8, fp8_dtype_forward, fused_attention_backend, attn_bias,
2710
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
                fp8_meta["scaling_fwd"].scale_inv[META_QKV],
                fp8_meta["scaling_fwd"].scale_inv[META_S],
                fp8_meta["scaling_fwd"].scale[META_S],
                fp8_meta["scaling_fwd"].scale[META_O],
                fp8_meta["scaling_fwd"].amax_history[0][META_S],
                fp8_meta["scaling_fwd"].amax_history[0][META_O],
                attn_scale, dropout_p, fast_zero_fill, qkv_layout,
                attn_bias_type, attn_mask_type, rng_gen)
            if fp8_meta["recipe"].fp8_mha:
                out_ret = Float8Tensor(data=out_fp8,
                    fp8_meta=fp8_meta,
                    fp8_meta_forward=True,
                    fp8_meta_index=META_O,
                    fp8_dtype=fp8_dtype_forward,
                    dtype=q.dtype,
                )
            else:
                out_ret = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)
            out_save = out_ret

            if fp8_meta["recipe"].fp8_mha and not int(os.getenv("NVTE_FP8_DPA_BWD", "1")):
                # 1: qkv packed, 2: kv packed, 3: qkv separate
                qkv_group = len(qkv_layout.split('_'))
                if qkv_group == 1:
                    dim = qkv_layout.find('3')
                    qkv = _combine_tensors([q,k,v], dim)
                    qkv_c = qkv.view(-1, qkv.shape[-3] * qkv.shape[-2] * qkv.shape[-1])
                    qkv_no_fp8 = cast_from_fp8(qkv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[qkv.dtype]).view(qkv.shape)
                    q, k, v = _SplitAlongDim.apply(qkv_no_fp8, dim, [1,1,1])
                    q, k, v = [x.squeeze(dim) for x in [q, k, v]]
                if qkv_group == 2:
                    q = cast_from_fp8(q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                    dim = qkv_layout.split('_')[1].find('2')
                    kv = _combine_tensors([k,v], dim)
                    kv_c = kv.view(-1, kv.shape[-3] * kv.shape[-2] * kv.shape[-1])
                    kv_no_fp8 = cast_from_fp8(kv_c._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[kv.dtype]).view(kv.shape)
                    k, v = _SplitAlongDim.apply(kv_no_fp8, dim, [1,1])
                    k, v = [x.squeeze(dim) for x in [k, v]]
                if qkv_group == 3:
                    q = cast_from_fp8(q._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[q.dtype]).view(q.shape)
                    k = cast_from_fp8(k._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[k.dtype]).view(k.shape)
                    v = cast_from_fp8(v._data,
                        fp8_meta["scaling_fwd"],
                        META_QKV, fp8_dtype_forward, TE_DType[v.dtype]).view(v.shape)
                out_save = cast_from_fp8(
                    out_fp8.view(-1, out_fp8.shape[-2] * out_fp8.shape[-1]),
                    fp8_meta["scaling_fwd"], META_O,
                    fp8_dtype_forward, qkv_dtype).view(out_fp8.shape)

            fp8_tensors = (q_fp8, k_fp8, v_fp8, out_fp8,
                fp8_meta["scaling_fwd"].scale.clone(),
                fp8_meta["scaling_fwd"].scale_inv.clone())
        else:
            if _NVTE_DEBUG:
                print('[DotProductAttention]: using non-FP8 forward')
            out_ret, aux_ctx_tensors = fused_attn_fwd(
                is_training, max_seqlen_q, max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                q, k, v, qkv_dtype, fused_attention_backend, attn_bias,
2782
                seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2783
2784
2785
2786
2787
                None, None, None, None, None, None,
                attn_scale, dropout_p, fast_zero_fill, qkv_layout, attn_bias_type, attn_mask_type,
                rng_gen)
            out_save = out_ret
            fp8_tensors = (None, None, None, None, None, None)
2788

2789
2790
        from .cpu_offload import CPUOffloadEnabled
        if CPUOffloadEnabled:
2791
            tensor_list = [q, k, v, out_save, cu_seqlens_q, cu_seqlens_kv]
2792
2793
2794
2795
2796
            qkv_layout = 'sbhd_sbhd_sbhd'
            for tensor in tensor_list:
                if tensor is not None:
                    tensor.activation_offloading = True

2797
2798
        ctx.fp8 = fp8 and int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
        qkvo_tensors = (q, k, v, out_save) if not ctx.fp8 else (None, None, None, None)
2799
        ctx.save_for_backward(*qkvo_tensors, cu_seqlens_q, cu_seqlens_kv,
2800
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2801
            *fp8_tensors, *aux_ctx_tensors)
2802
        ctx.fp8_meta = fp8_meta
2803
2804
2805
2806
2807
2808
2809
2810
2811
        ctx.max_seqlen_q = max_seqlen_q
        ctx.max_seqlen_kv = max_seqlen_kv
        ctx.qkv_dtype = qkv_dtype
        ctx.attn_scale = attn_scale
        ctx.dropout_p = dropout_p
        ctx.fast_zero_fill = fast_zero_fill
        ctx.qkv_layout = qkv_layout
        ctx.attn_bias_type = attn_bias_type
        ctx.attn_mask_type = attn_mask_type
2812
2813
        ctx.fused_attention_backend = \
            fused_attention_backend if ctx.fp8 else FusedAttnBackend["F16_arbitrary_seqlen"]
2814
2815
        ctx.use_FAv2_bwd = use_FAv2_bwd

2816
        return out_ret
2817
2818
2819

    @staticmethod
    def backward(ctx, d_out):
2820
2821
2822
2823
2824
2825
        if ctx.fp8_meta["recipe"].fp8_mha:
            assert (isinstance(d_out, Float8Tensor)
                ), "Gradient of the DPA output must be in Float8Tensor type for FP8 MHA."
            d_out_f8tensor = d_out
            d_out = d_out._data

2826
        d_out = d_out.contiguous()
2827
2828
2829
        (q, k, v, out, cu_seqlens_q, cu_seqlens_kv,
            seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
            q_fp8, k_fp8, v_fp8, out_fp8,
2830
2831
2832
            fwd_scales, fwd_scale_invs, *aux_ctx_tensors) = ctx.saved_tensors
        if not aux_ctx_tensors[0].is_contiguous():
            aux_ctx_tensors[0] = aux_ctx_tensors[0].contiguous()
2833
        if ctx.use_FAv2_bwd:
2834
            softmax_lse, rng_state = aux_ctx_tensors
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
            dq = torch.empty_like(q)
            dk = torch.empty_like(k)
            dv = torch.empty_like(v)
            maybe_contiguous = lambda x: x.contiguous() if x.stride(-1) != 1 else x
            d_out, q, k, v, out = [maybe_contiguous(x)
                for x in (d_out, q, k, v, out)]
            flash_attn_cuda_bwd(
                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,
2845
                "causal" in ctx.attn_mask_type, None, rng_state
2846
2847
2848
2849
2850
            )
            dq = dq[..., :d_out.shape[-1]]
            dk = dk[..., :d_out.shape[-1]]
            dv = dv[..., :d_out.shape[-1]]
        else:
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
            with torch.cuda.nvtx.range("_FusedAttn"):
                if ctx.fp8:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using FP8 backward')
                    fp8_dtype_forward = get_fp8_te_dtype(ctx.fp8_meta["recipe"], fprop_tensor=True)
                    fp8_dtype_backward = get_fp8_te_dtype(
                        ctx.fp8_meta["recipe"], fprop_tensor=False)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        d_out_fp8 = d_out
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO] = d_out_f8tensor._scale_inv
                    else:
                        d_out_fp8 = cast_to_fp8(
                            d_out.view(-1, d_out.shape[-2] * d_out.shape[-1]),
                            ctx.fp8_meta["scaling_bwd"], META_DO, fp8_dtype_backward
                            ).view(d_out.shape)
                    dq_fp8, dk_fp8, dv_fp8, *rest = fused_attn_bwd(
                        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,
2869
                        fp8_dtype_forward, fp8_dtype_backward, aux_ctx_tensors,
2870
                        ctx.fused_attention_backend,
2871
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
                        fwd_scale_invs[META_QKV], # d_scale_qkv,
                        fwd_scale_invs[META_S], # d_scale_s,
                        fwd_scale_invs[META_O], # d_scale_o,
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DO], # d_scale_do
                        ctx.fp8_meta['scaling_bwd'].scale_inv[META_DP], # d_scale_dp
                        fwd_scales[META_S], # q_scale_s
                        ctx.fp8_meta['scaling_bwd'].scale[META_DP], # q_scale_dp
                        ctx.fp8_meta['scaling_bwd'].scale[META_DQKV], # q_scale_dqkv
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DP], # amax_dp
                        ctx.fp8_meta['scaling_bwd'].amax_history[0][META_DQKV], # amax_dqkv
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
                    if ctx.fp8_meta["recipe"].fp8_mha:
                        dq = Float8Tensor(data=dq_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dk = Float8Tensor(data=dk_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                        dv = Float8Tensor(data=dv_fp8,
                            fp8_meta=ctx.fp8_meta,
                            fp8_meta_forward=False,
                            fp8_meta_index=META_DQKV,
                            fp8_dtype=fp8_dtype_backward,
                            dtype=d_out_f8tensor.dtype,
                            )
                    else:
                        qkv_group = len(ctx.qkv_layout.split('_'))
                        if qkv_group == 1:
                            dim = ctx.qkv_layout.find('3')
                            dqkv_fp8 = _combine_tensors([dq_fp8,dk_fp8,dv_fp8], dim)
                            dqkv_c_fp8 = dqkv_fp8.view(-1,
                                dqkv_fp8.shape[-3] * dqkv_fp8.shape[-2] * dqkv_fp8.shape[-1])
                            dqkv = cast_from_fp8(dqkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dqkv_fp8.shape)
                            dq, dk, dv = _SplitAlongDim.apply(dqkv, dim, [1,1,1])
                            dq, dk, dv = [x.squeeze(dim) for x in [dq, dk, dv]]
                        if qkv_group == 2:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                            dim = ctx.qkv_layout.split('_')[1].find('2')
                            dkv_fp8 = _combine_tensors([dk_fp8,dv_fp8], dim)
                            dkv_c_fp8 = dkv_fp8.view(-1,
                                dkv_fp8.shape[-3] * dkv_fp8.shape[-2] * dkv_fp8.shape[-1])
                            dkv = cast_from_fp8(dkv_c_fp8,
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dkv_fp8.shape)
                            dk, dv = _SplitAlongDim.apply(dkv, dim, [1,1])
                            dk, dv = [x.squeeze(dim) for x in [dk, dv]]
                        if qkv_group == 3:
                            dq = cast_from_fp8(
                                dq_fp8.view(-1, dq_fp8.shape[-2] * dq_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dq_fp8.shape)
                            dk = cast_from_fp8(
                                dk_fp8.view(-1, dk_fp8.shape[-2] * dk_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dk_fp8.shape)
                            dv = cast_from_fp8(
                                dv_fp8.view(-1, dv_fp8.shape[-2] * dv_fp8.shape[-1]),
                                ctx.fp8_meta["scaling_bwd"], META_DQKV,
                                fp8_dtype_backward, ctx.qkv_dtype).view(dv_fp8.shape)
                else:
                    if _NVTE_DEBUG:
                        print('[DotProductAttention]: using non-FP8 backward')
                    if d_out.dtype == torch.uint8:
                        d_out = d_out_f8tensor.from_float8(q.dtype)
                    dq, dk, dv, *rest = fused_attn_bwd(
                        ctx.max_seqlen_q, ctx.max_seqlen_kv, cu_seqlens_q, cu_seqlens_kv,
                        q, k, v, out, d_out,
2953
                        ctx.qkv_dtype, ctx.qkv_dtype, aux_ctx_tensors,
2954
                        ctx.fused_attention_backend,
2955
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
2956
2957
2958
                        None, None, None, None, None, None, None, None, None, None,
                        ctx.attn_scale, ctx.dropout_p, ctx.fast_zero_fill,
                        ctx.qkv_layout, ctx.attn_bias_type, ctx.attn_mask_type)
2959

2960
2961
        # if no_bias or alibi, return dqkv
        if ctx.attn_bias_type in ["no_bias", "alibi"]:
2962
2963
            return (None, None, None, None, None, None,
                    None, None, None, dq, dk, dv, None, None, None,
2964
2965
2966
                    None, None, None, None, None, None,
                    None, None, None, None, None, None)
        # else, return (dqkv, dbias)
2967
2968
        return (None, None, None, None, None, None,
                None, None, None, dq, dk, dv, None, rest[0], None,
2969
2970
2971
                None, None, None, None, None, None,
                None, None, None, None, None, None)

2972

2973
class FusedAttention(TransformerEngineBaseModule):
2974
2975
2976
2977
2978
2979
2980
2981
2982
    """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:

2983
2984
2985
2986
    | backend       | 1                       | 2                              |
    | flash based   | no                      | yes                            |
    | cuDNN based   | yes                     | yes                            |
    | qkv dtype     | fp16/bf16               | fp16/bf16                      |
2987
    | attn_type     | self/cross              | self/cross                     |
2988
    | qkv_layout    |                         |                                |
2989
    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
2990
    |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
2991
2992
    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
2993
2994
    | mask_type     | causal/padding/no_mask  | causal/padding/no_mask         |
    | bias_type     | post_scale_bias/no_bias | post_scale_bias/alibi/no_bias  |
2995
    | dropout       | yes                     | yes                            |
2996
2997
    | max_seqlen    | <=512, multiple of 64   | any, multiple of 64            |
    | head_dim      | 64                      | <=128, multiple of 8           |
2998
    | output dtype  | fp16/bf16               | fp16/bf16                      |
2999
3000
3001
3002
3003
3004
3005
3006
    """

    def __init__(
        self,
        norm_factor: float,
        attention_dropout: float = 0.0,
        attention_dropout_ctx: Optional[Callable] = nullcontext,
        attention_type: str = "self",
3007
3008
        layer_number: Optional[int] = None,
        deterministic: bool = False,
3009
3010
3011
3012
3013
3014
3015
    ) -> None:
        super().__init__()

        self.norm_factor = norm_factor
        self.attention_dropout = attention_dropout
        self.attention_dropout_ctx = attention_dropout_ctx
        self.attention_type = attention_type
3016
        self.use_FAv2_bwd = (os.getenv("NVTE_FUSED_ATTN_USE_FAv2_BWD", "0") == "1"
Tim Moon's avatar
Tim Moon committed
3017
                        and get_device_compute_capability() == (9, 0))
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
        self.layer_number = 1 if layer_number is None else layer_number
        if deterministic:
            # workspace optimization path is deterministic
            os.environ["CUDNN_FRONTEND_ATTN_DP_WORKSPACE_LIMIT"] = "-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"
3034

3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
        def remove_extra_states_check(self, incompatible_keys): # pylint: disable=unused-argument
            """
            Temporarily remove fused_attention._extra_state as a missing key
            when loading older TransformerEngine checkpoints. Will phase out
            this hook in TransformerEngine 2.0.
            """
            for key in incompatible_keys.missing_keys:
                if 'fused_attention._extra_state' in key:
                    incompatible_keys.missing_keys.remove(key)
        self.register_load_state_dict_post_hook(remove_extra_states_check)

3046
3047
3048
3049
3050
3051
    def get_fp8_weights_scratchpad(
        self,
        is_first_microbatch: Union[bool, None],
    ) -> List[Float8Tensor]:
        """Needs override."""

3052
    @no_torch_dynamo()
3053
3054
3055
3056
3057
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3058
3059
3060
        qkv_layout: str = "sbh3d",
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
3061
3062
3063
3064
        seq_offsets_q: Optional[torch.Tensor] = None,
        seq_offsets_k: Optional[torch.Tensor] = None,
        seq_offsets_v: Optional[torch.Tensor] = None,
        seq_offsets_o: Optional[torch.Tensor] = None,
3065
3066
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
3067
        attn_mask_type: str = "causal",
3068
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3069
3070
        fused_attention_backend:
            tex.NVTE_Fused_Attn_Backend = tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend,
3071
3072
3073
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
        fast_zero_fill: bool = True,
3074
3075
3076
        cp_group: Optional[dist_group_type] = None,
        cp_global_ranks: List[int] = None,
        cp_stream: torch.cuda.Stream = None,
3077
        is_first_microbatch: Optional[bool] = None,
3078
3079
    ) -> torch.Tensor:
        """fused attention fprop"""
3080
        assert (fused_attention_backend
3081
3082
            != tex.NVTE_Fused_Attn_Backend.NVTE_No_Backend
            ), 'No fused attention backend supports this input combination!'
3083
        assert (
3084
3085
3086
            (query_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (key_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
            and (value_layer.dtype in [torch.float16, torch.bfloat16, torch.uint8])
3087
3088
3089
3090
            ), 'FusedAttention only supports FP16 and BF16 data types.'
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), 'FusedAttention only supports CUDA tensors.'
3091
3092
3093
3094
        assert (
            qkv_layout in QKVLayouts
            ), f"FusedAttention does not support qkv_layout = {qkv_layout}!"

3095
3096
        context_parallel = (cp_group is not None) and (get_distributed_world_size(cp_group) != 1)

3097
        qkv_format = ''.join([i for i in qkv_layout.split('_')[0] if i.isalpha()])
3098

3099
3100
3101
3102
3103
3104
3105
        if qkv_format in ['sbhd', 'bshd']:
            if qkv_format == 'sbhd':
                batch_size, max_seqlen_q, max_seqlen_kv = (
                    query_layer.shape[1], query_layer.shape[0], key_layer.shape[0])
            if qkv_format == 'bshd':
                batch_size, max_seqlen_q, max_seqlen_kv = (
                    query_layer.shape[0], query_layer.shape[1], key_layer.shape[1])
3106
            if 'padding' in attn_mask_type:
3107
3108
                assert not context_parallel, "Padding mask not supported with context parallelism!"

3109
3110
3111
3112
3113
                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!"
                        )
3114
                    if self.attention_type == "self":
3115
3116
                        cu_seqlens_q = get_cu_seqlens(attention_mask)
                        cu_seqlens_kv = cu_seqlens_q
3117
                    else:
3118
3119
                        cu_seqlens_q = get_cu_seqlens(attention_mask[0])
                        cu_seqlens_kv = get_cu_seqlens(attention_mask[1])
3120
            else:
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
                if cu_seqlens_q is None:
                    cu_seqlens_q = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_q,
                        query_layer.device,
                    )
                if cu_seqlens_kv is None:
                    cu_seqlens_kv = _get_full_cu_seqlens(
                        batch_size,
                        max_seqlen_kv,
                        key_layer.device,
                    )
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
        if qkv_format == 'thd':
            assert not context_parallel, "thd format not supported with context parallelism!"
            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!"
            if (seq_offsets_q is None
                or seq_offsets_k is None
                or seq_offsets_v is None
                or seq_offsets_o is None):
                qkv_group = ''.join([x for x in qkv_layout if x not in 'bst'])
                num_heads = query_layer.shape[-2]
                num_gqa_groups = key_layer.shape[-2]
                head_dim = query_layer.shape[-1]
                seq_offsets_o = num_heads * head_dim * cu_seqlens_q
                if qkv_group == 'hd_hd_hd':
                    seq_offsets_q = num_heads * head_dim * cu_seqlens_q
                    seq_offsets_k = num_gqa_groups * head_dim * cu_seqlens_kv
                    seq_offsets_v = num_gqa_groups * head_dim * cu_seqlens_kv
                if qkv_group in ['3hd', 'h3d']:
                    seq_offsets_q = num_heads * head_dim * 3 * cu_seqlens_q
                    seq_offsets_k = num_heads * head_dim * 3 * cu_seqlens_q
                    seq_offsets_v = num_heads * head_dim * 3 * cu_seqlens_q
                if qkv_group in ['hd_2hd', 'hd_h2d']:
                    seq_offsets_q = num_heads * head_dim * cu_seqlens_q
                    seq_offsets_k = num_gqa_groups * head_dim * 2 * cu_seqlens_kv
                    seq_offsets_v = num_gqa_groups * head_dim * 2 * cu_seqlens_kv
3161
3162
3163

        qkv_dtype = TE_DType[query_layer.dtype]

3164
        use_FAv2_bwd = (self.use_FAv2_bwd
3165
                and (core_attention_bias_type == "no_bias")
3166
3167
                and (fused_attention_backend
                    == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen))
3168
3169
3170
3171
3172

        if context_parallel:
            assert (fused_attention_backend
                == tex.NVTE_Fused_Attn_Backend.NVTE_F16_arbitrary_seqlen
                ), f"{fused_attention_backend} does not work with context parallelism!"
3173
3174
3175
3176
3177
            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)]
3178
3179
3180
3181
3182
3183
3184
3185
3186
            with self.attention_dropout_ctx():
                output = attn_forward_func_with_cp(
                    self.training,
                    query_layer, key_layer, value_layer,
                    cu_seqlens_q, cu_seqlens_kv,
                    max_seqlen_q, max_seqlen_kv,
                    self.attention_dropout if self.training else 0.0,
                    cp_group, cp_global_ranks, cp_stream,
                    softmax_scale=1.0/self.norm_factor,
3187
                    qkv_format=qkv_format,
3188
                    attn_mask_type=attn_mask_type,
3189
3190
                    attn_bias_type=core_attention_bias_type,
                    attn_bias=core_attention_bias,
3191
3192
3193
                    use_fused_attention=True,
                )
        else:
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
            with self.prepare_forward(query_layer,
                is_first_microbatch,
                num_gemms=3,
                allow_non_contiguous=True) as query_layer:
                with self.attention_dropout_ctx():
                    forced_fp8_dpa = ""
                    if self.fp8_meta["recipe"].fp8_mha:
                        if not self.fp8_meta["recipe"].fp8_dpa:
                            self.fp8_meta["recipe"].fp8_dpa = True
                            forced_fp8_dpa = " (forced)"
                    if _NVTE_DEBUG:
                        print("[DotProductAttention]: "
                            f"""using fp8_recipe.fp8_mha={self.fp8_meta["recipe"].fp8_mha}, """
                            f"""fp8_recipe.fp8_dpa={self.fp8_meta["recipe"].fp8_dpa}"""
                            f"""{forced_fp8_dpa} and """
                            f"""NVTE_FP8_DPA_BWD={int(os.getenv("NVTE_FP8_DPA_BWD", "1"))}""")
                    output = FusedAttnFunc.apply(
                        self.training,
                        max_seqlen_q, max_seqlen_kv,
                        cu_seqlens_q, cu_seqlens_kv,
3214
                        seq_offsets_q, seq_offsets_k, seq_offsets_v, seq_offsets_o,
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
                        query_layer, key_layer, value_layer,
                        qkv_dtype,
                        core_attention_bias,
                        1.0/self.norm_factor,
                        self.attention_dropout if self.training else 0.0,
                        fast_zero_fill,
                        qkv_layout,
                        core_attention_bias_type,
                        attn_mask_type,
                        None, # rng_gen
                        fused_attention_backend,
                        use_FAv2_bwd,
                        self.fp8 and self.fp8_meta["recipe"].fp8_dpa,
                        self.fp8_meta,
                    )
3230

3231
3232
        # ...hd -> ...(hd)
        return output.view(*output.shape[:-2], -1)
3233
3234


3235
3236
3237
3238
3239
3240
3241
class DotProductAttention(torch.nn.Module):
    """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::

3242
        Argument :attr:`attention_mask` in the `forward` call is only used when
3243
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
3244
3245
3246

    .. warning::

3247
        FlashAttention uses a non-deterministic algorithm for optimal performance. To observe
3248
        deterministic behavior at the cost of performance, use FlashAttention version >= `2.4.1`
3249
3250
        and set the environment variable :attr:`NVTE_ALLOW_NONDETERMINISTIC_ALGO=0`. In order
        to disable`flash-attn` entirely, set :attr:`NVTE_FLASH_ATTN=0`.
3251
3252
3253
3254
3255
3256

    Parameters
    ----------
    num_attention_heads : int
                         number of attention heads in the transformer layer.
    kv_channels : int
3257
                number of key-query-value channels per attention head.
3258
3259
3260
3261
3262
3263
3264
3265
    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`.
3266
3267
    attention_dropout: float, default = 0.0
                      dropout probability for the dropout op during multi-head attention.
3268
    attn_mask_type: str, default = `causal`
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
                   type of attention mask passed into softmax operation, options are "`no_mask`",
                   "`padding`", "`causal`", "`padding,causal`", "`causal,padding`", and
                   "`arbitrary`", where "`padding,causal`" and "`causal,padding`" 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. For "`no_mask`", no attention mask is applied. For
                   "`causal`" or the causal mask in "`padding,causal`", TransformerEngine calculates
                   and applies an upper triangular mask to the softmax input. No user input is
                   needed. For "`padding`" or the padding mask in "`padding,causal`", users need to
                   provide the locations of padded tokens either via :attr:`cu_seqlens_q` and
                   :attr:`cu_seqlens_kv` in the shape of [batch_size + 1] or :attr:`attention_mask`
                   in the shape [batch_size, 1, 1, max_seq_len]. For the "`arbitrary`" mask, users
                   need to provide a mask that is broadcastable to the shape of softmax input.
3283
3284
3285
3286
3287
3288
    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
                window and causal mask specifically. Similar to :attr:`attn_mask_type`, it can
                be overridden by :attr:`window_size` in `forward` as well.
3289
3290
    attention_type: str, default = `self`
                   type of attention, either "`self`" and "`cross`".
3291
3292
3293
    layer_number: int, default = `None`
                 layer number of the current `DotProductAttention` when multiple such modules
                 are concatenated, for instance in consecutive transformer blocks.
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
    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,
               `h` the number of heads, `d` head size, and `t` the total number of sequences
               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.
               For that, please use `_get_qkv_layout` to gain the layout information.
3304
3305
3306
3307
3308
3309
3310
3311
3312

    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.
3313
3314
3315
3316
3317
3318
3319
3320
3321
    cp_group : ProcessGroup, default = `None`
              context parallel process group.
    cp_global_ranks : list of global rank IDs, default = `None`
                     global rank IDs of GPUs that are in cp_group.
    cp_stream : CUDA stream, default = `None`
               context parallelism splits flash attention into multiple steps for
               compute and communication overlapping. To address the wave quantization
               issue of each split step, we add an additional CUDA stream so that we
               can overlap two flash attention kernels.
3322
3323
3324
3325
3326
3327
    """

    def __init__(
        self,
        num_attention_heads: int,
        kv_channels: int,
3328
        num_gqa_groups: Optional[int] = None,
3329
        attention_dropout: float = 0.0,
3330
        qkv_format: str = "sbhd",
3331
        attn_mask_type: str = "causal",
3332
        window_size: Optional[Tuple[int, int]] = None,
3333
3334
3335
3336
3337
        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,
3338
        attention_type: str = "self",
3339
        cp_group: Optional[dist_group_type] = None,
3340
        cp_global_ranks: List[int] = None,
3341
        cp_stream: torch.cuda.Stream = None,
3342
3343
3344
    ) -> None:
        super().__init__()

3345
        self.qkv_format = qkv_format
3346
3347
3348
        attn_mask_type = attn_mask_type.replace(",","_")
        if attn_mask_type == "causal_padding":
            attn_mask_type = "padding_causal"
3349
        self.attn_mask_type = attn_mask_type
3350
3351
        self.window_size = window_size
        self.window_size = check_set_window_size(attn_mask_type, self.window_size)
3352
        self.tp_size = tp_size if tp_group is None else get_distributed_world_size(tp_group)
3353
3354
        self.tp_group = tp_group
        self.get_rng_state_tracker = get_rng_state_tracker
3355
        self.num_attention_heads = num_attention_heads
3356
        self.layer_number = 1 if layer_number is None else layer_number
3357
3358
3359
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream
3360

3361
        self.hidden_size_per_attention_head = kv_channels
3362

3363
3364
        self.num_gqa_groups = (
            num_attention_heads if num_gqa_groups is None else num_gqa_groups
3365
        )
3366
3367
3368
3369
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)

        assert (num_attention_heads % self.num_gqa_groups == 0
                ), "The number of attention heads must be divisible by the number of GQA groups!"
3370

3371
        self.rng_states_tracker = None
3372
3373
3374
        if sequence_parallel or get_rng_state_tracker is None:
            attention_dropout_ctx = nullcontext
        else:
3375
3376
3377
            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
3378

3379
        norm_factor = math.sqrt(kv_channels)
3380
3381

        self.device_compute_capability = get_device_compute_capability()
3382
3383
        self.deterministic = not bool(int(os.getenv("NVTE_ALLOW_NONDETERMINISTIC_ALGO", "1"))) \
                             or torch.are_deterministic_algorithms_enabled()
3384

3385
3386
        self.use_flash_attention = (
            int(os.getenv("NVTE_FLASH_ATTN", "1"))
Tim Moon's avatar
Tim Moon committed
3387
            and self.device_compute_capability >= (8, 0)
3388
        )
3389
        if not _flash_attn_2_4_1_plus and self.deterministic:
3390
3391
            self.use_flash_attention = False
            warnings.warn(
3392
3393
3394
                "Disabling usage of FlashAttention since version <2.4.1 does not support "
                "deterministic execution. In order to use FA with deterministic behavior,"
                " please install FlashAttention version >=2.4.1."
3395
3396
            )

3397
3398
        self.use_fused_attention = (
            int(os.getenv("NVTE_FUSED_ATTN", "1"))
Tim Moon's avatar
Tim Moon committed
3399
            and self.device_compute_capability >= (8, 0)
3400
        )
3401

3402
3403
3404
3405
3406
3407
3408
        assert (
            attention_type in AttnTypes
        ), f"attention_type {attention_type} not supported"

        self.attention_type = attention_type
        self.attention_dropout = attention_dropout

3409
3410
3411
3412
3413
3414
        attn_kwargs = {
            "attention_dropout": attention_dropout,
            "attention_dropout_ctx": attention_dropout_ctx,
        }

        if self.use_flash_attention:
3415
3416
3417
3418
3419
3420
            self.flash_attention = FlashAttention(norm_factor,
                                                  attention_type=attention_type,
                                                  layer_number=layer_number,
                                                  deterministic=self.deterministic,
                                                  **attn_kwargs)

3421
        # Instantiating three types since use of flash-attn and FusedAttention
3422
        # might be ruled out due to forward inputs.
3423
        if self.use_fused_attention:
3424
3425
3426
3427
            self.fused_attention = FusedAttention(norm_factor,
                                                  attention_type=attention_type,
                                                  layer_number=layer_number,
                                                  deterministic=self.deterministic,
3428
                                                  **attn_kwargs)
3429

3430
3431
3432
3433
3434
3435
3436
        self.unfused_attention = UnfusedDotProductAttention(
            norm_factor, **attn_kwargs, layer_number=layer_number)

    def _checkpointed_attention_forward(
        self,
        attention_func: Callable,
        *forward_args: Tuple[torch.Tensor, ...],
3437
        **forward_kwargs: Dict[str, Any],
3438
3439
3440
    ) -> torch.Tensor:
        """Forward method with activation checkpointing."""

3441
3442
        def custom_forward(*input_args, **input_kwargs):
            return attention_func(*input_args, **input_kwargs)
3443
3444
3445

        hidden_states = checkpoint(
            custom_forward,
3446
3447
3448
            distribute_saved_activations=False,
            get_rng_state_tracker=self.get_rng_state_tracker,
            tp_group=self.tp_group,
3449
            *forward_args,
3450
            **forward_kwargs,
3451
3452
3453
3454
        )

        return hidden_states

3455
3456
3457
3458
3459
3460
    def set_context_parallel_group(
        self,
        cp_group: Union[dist_group_type, None],
        cp_global_ranks: List[int],
        cp_stream: torch.cuda.Stream,
    ) -> None:
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
        """
3474
3475
3476
3477
        self.cp_group = cp_group
        self.cp_global_ranks = cp_global_ranks
        self.cp_stream = cp_stream

3478
    @no_torch_dynamo(recursive=False)
3479
3480
3481
3482
3483
    def forward(
        self,
        query_layer: torch.Tensor,
        key_layer: torch.Tensor,
        value_layer: torch.Tensor,
3484
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
3485
3486
3487
        qkv_format: Optional[str] = None,
        cu_seqlens_q: Optional[torch.Tensor] = None,
        cu_seqlens_kv: Optional[torch.Tensor] = None,
3488
3489
3490
3491
        seq_offsets_q: Optional[torch.Tensor] = None,
        seq_offsets_k: Optional[torch.Tensor] = None,
        seq_offsets_v: Optional[torch.Tensor] = None,
        seq_offsets_o: Optional[torch.Tensor] = None,
3492
3493
        max_seqlen_q: Optional[int] = None,
        max_seqlen_kv: Optional[int] = None,
3494
        attn_mask_type: Optional[str] = None,
3495
        window_size: Optional[Tuple[int, int]] = None,
3496
        checkpoint_core_attention: bool = False,
3497
3498
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
3499
        alibi_slopes: Optional[torch.Tensor] = None,
3500
        fast_zero_fill: bool = True,
3501
        inference_params: Optional[InferenceParams] = None,
3502
        is_first_microbatch: Optional[bool] = None,
3503
3504
3505
3506
3507
3508
    ) -> torch.Tensor:
        """
        Dot Product Attention Layer.

        .. note::

3509
3510
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes '"padding"' or `"arbitrary"`.
3511
3512
3513

        .. note::

3514
3515
3516
            Input tensor :attr:`query_layer` must be of shape
            (:attr:`sequence_length`, :attr:`batch_size`, :attr:`num_attention_heads`,
            :attr:`kv_channels`) and the tensors :attr:`key_layer` and :attr:`value_layer`
3517
            must each be of shape (:attr:`sequence_length`, :attr:`batch_size`,
3518
            :attr:`num_gqa_groups`, :attr:`kv_channels`). Output of shape
3519
3520
3521
            (:attr:`sequence_length`, :attr:`batch_size`, :attr:`num_attention_heads`
            * :attr:`kv_channels`) is returned.

3522
3523
        .. note::

3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
            DotProductAttention supports three backends: 1) FlashAttention which calls
            HazyResearch/Dao-AILab's `flash-attn <https://arxiv.org/pdf/2305.13245.pdf>`_
            PyTorch API, 2) FusedAttention which has multiple fused attention implementations
            based on `cuDNN Graph API
            <https://docs.nvidia.com/deeplearning/cudnn/developer-guide/index.html#op-fusion>`_
            (see :attr:`FusedAttention` for more details on FusedAttention backends), and 3)
            UnfusedDotProductAttention which is the native PyTorch implementation
            with fused scaled masked softmax.

        .. note::

            Users can use environment variables :attr:`NVTE_FLASH_ATTN`, :attr:`NVTE_FUSED_ATTN`,
            and :attr:`NVTE_FUSED_ATTN_BACKEND` to control which DotProductAttention backend,
            and FusedAttention backend if applicable, to use. TransformerEngine prioritizes
            FlashAttention over FusedAttention and over UnfusedDotProductAttention.
            If FusedAttention is being used, users can also choose to switch to flash-attn's
            implementation for backward by setting :attr:`NVTE_FUSED_ATTN_USE_FAv2_BWD=1`
            (default: 0), because of the performance differences between various versions of
3542
3543
3544
3545
3546
            flash-attn and FusedAttention. Further, :attr:`NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT`
            can be used to enable (:attr:`1`) or disable (:attr:`0`) the workspace related
            optimizations in FusedAttention. When unset, TransformerEngine determines the code path
            based on its internal logic. These optimizations trade memory for performance
            and should be used with care.
3547

3548
3549
3550
3551
3552
3553
3554
3555
        Parameters
        ----------
        query_layer : torch.Tensor
                     Query tensor.
        key_layer : torch.Tensor
                   Key tensor.
        value_layer : torch.Tensor
                     Value tensor.
3556
3557
3558
3559
3560
3561
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
             It should be 'None' for 'causal' and 'no_mask' types. For 'padding' masks, it should be
             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]
             for cross-attention. For the 'arbitrary' mask type, it should be in a shape that is
3562
3563
3564
             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.
3565
3566
3567
3568
3569
3570
3571
3572
        qkv_format: str, default = `None`
                   If provided, overrides :attr:`qkv_format` from initialization.
        cu_seqlens_q: Optional[torch.Tensor], default = `None`
                   Cumulative sum of sequence lengths 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 in a batch for `key_layer` and `value_layer`,
                   with shape [batch_size + 1] and dtype torch.int32.
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
        seq_offsets_q: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `query_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_k: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `key_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_v: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for `value_layer`,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
        seq_offsets_o: Optional[torch.Tensor], default = `None`
                   Cumulative offset of different sequences in a batch for forward output,
                   with shape [batch_size + 1] and dtype torch.int32. Required for `thd` layouts.
3585
3586
3587
3588
3589
3590
        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.
3591
3592
3593
        attn_mask_type: {`no_mask`, `padding`, `causal`, `padding,causal`, `causal,padding`,
                       `arbitrary`}, default = `None`. Type of attention mask passed into
                       softmax operation. 'padding,causal' and 'causal,padding' are equivalent.
3594
        window_size: Optional[Tuple[int, int]], default = `None`
3595
                    Sliding window size for local attention.
3596
3597
3598
3599
3600
        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.
3601
        core_attention_bias_type: str, default = `no_bias`
3602
                    Bias type, {`no_bias`, `pre_scale_bias`, `post_scale_bias`, `alibi`}
3603
        core_attention_bias: Optional[torch.Tensor], default = `None`
3604
3605
                    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.
3606
3607
3608
3609
        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.
3610
        fast_zero_fill: bool, default = `True`
3611
                    Whether to use the fast path to set output tensors to 0 or not.
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
        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.
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
        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)
3635
3636
        """

3637
3638
3639
3640
        assert (
            query_layer.is_cuda and key_layer.is_cuda and value_layer.is_cuda
            ), 'DotProductAttention only supports CUDA tensors.'

3641
3642
3643
        assert (key_layer.shape == value_layer.shape
            ), "Keys and values must have the same shape!"

3644
3645
        if attn_mask_type is not None:
            window_size = check_set_window_size(attn_mask_type, window_size)
3646
        if attn_mask_type is None:
3647
            attn_mask_type = self.attn_mask_type
3648
3649
3650
3651
3652
3653
3654
        else:
            attn_mask_type = attn_mask_type.replace(",","_")
            if attn_mask_type == "causal_padding":
                attn_mask_type = "padding_causal"

        assert (attn_mask_type in AttnMaskTypes
            ), f"Attention mask type {attn_mask_type} is not supported!"
3655
3656
3657
        if qkv_format == 'thd':
            assert ('padding' in attn_mask_type
                ), "Attention mask type must be padding or padding_causal for qkv_format=thd!"
3658

3659
3660
3661
3662
3663
3664
3665
3666
        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."

3667
3668
3669
        if window_size is None:
            window_size = self.window_size

3670
3671
        if qkv_format is None:
            qkv_format = self.qkv_format
3672

3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
        if inference_params is not None:
            assert self.layer_number is not None, "Layer number must be set!"

            if qkv_format == "bshd":
                key_layer = key_layer.transpose(0, 1)
                value_layer = value_layer.transpose(0, 1)

            (inference_key_memory, inference_value_memory,
            ) = inference_params.key_value_memory_dict[self.layer_number]

            batch_start = inference_params.batch_size_offset
            batch_end = batch_start + key_layer.size(1)
            assert batch_end <= inference_key_memory.size(1)

            sequence_start = inference_params.sequence_len_offset
            sequence_end = sequence_start + key_layer.size(0)
            assert sequence_end <= inference_key_memory.size(0)

            # Copy keys and values into KV-cache
            inference_key_memory[
                sequence_start:sequence_end, batch_start:batch_end, ...] = key_layer
            inference_value_memory[
                sequence_start:sequence_end, batch_start:batch_end, ...] = value_layer
            key_layer = inference_key_memory[:sequence_end, batch_start:batch_end, ...]
            value_layer = inference_value_memory[:sequence_end, batch_start:batch_end, ...]

            if qkv_format == "bshd":
                key_layer = key_layer.transpose(0, 1)
                value_layer = value_layer.transpose(0, 1)

            key_layer = key_layer.contiguous()
            value_layer = value_layer.contiguous()

3706
        assert (key_layer.shape[-2] == self.num_gqa_groups_per_partition
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
            and value_layer.shape[-2] == self.num_gqa_groups_per_partition
            ), f"Keys and values must have num_gqa_group = {self.num_gqa_groups} heads!"
        assert (qkv_format in ['sbhd', 'bshd', 'thd']
            ), "DotProductAttention only supports qkv_format = {'sbhd', 'bshd', 'thd'}!"

        if qkv_format == 'thd':
            assert (all(len(x.shape) == 3 for x in (query_layer, key_layer, value_layer))
                ), "Queries, keys and values must be 3D tensors when qkv_format = thd!"
            assert (cu_seqlens_q is not None and cu_seqlens_kv is not None
                ), "cu_seqlens_q and cu_seqlens_kv can not be None when qkv_format = thd!"
            assert (cu_seqlens_q.shape == cu_seqlens_kv.shape
                and len(cu_seqlens_q.shape) == 1
                and len(cu_seqlens_kv.shape) == 1
                ), "cu_seqlens_q and cu_seqlens_q must both have shape [batch_size + 1]!"
            assert (cu_seqlens_q.dtype == torch.int32
                and cu_seqlens_kv.dtype == torch.int32
                ), "cu_seqlens_q and cu_seqlens_q must both be in dtype torch.int32!"
3724
3725
            if max_seqlen_q is None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
3726
                max_seqlen_q = pow(2, math.ceil(math.log2(seqlens_q.max().item())))
3727
3728
            if max_seqlen_kv is None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
3729
                max_seqlen_kv = pow(2, math.ceil(math.log2(seqlens_kv.max().item())))
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748

        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 = {qkv_format}!"
            if qkv_format == 'sbhd':
                max_seqlen_q, max_seqlen_kv = (query_layer.shape[0], key_layer.shape[0])
            if qkv_format == 'bshd':
                max_seqlen_q, max_seqlen_kv = (query_layer.shape[1], key_layer.shape[1])
            if cu_seqlens_q is not None:
                seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1]
                assert (all(seqlens_q <= max_seqlen_q)
                    ), """Sequence lengths indicated by cu_seqlens_q must be no greater than
                    the sequence dimention in 'query_layer'!"""
            if cu_seqlens_kv is not None:
                seqlens_kv = cu_seqlens_kv[1:] - cu_seqlens_kv[:-1]
                assert (all(seqlens_kv <= max_seqlen_kv)
                    ), """Sequence lengths indicated by cu_seqlens_kv must be no greater than
                    the sequence dimention in 'key_layer' and 'value_layer'!"""

3749
3750
3751
3752
3753
3754
3755
3756
        if (isinstance(query_layer, Float8Tensor)
            and isinstance(key_layer, Float8Tensor)
            and isinstance(value_layer, Float8Tensor)):
            qkv_layout, query_layer._data, key_layer._data, value_layer._data = _get_qkv_layout(
                query_layer._data, key_layer._data, value_layer._data, qkv_format = qkv_format)
        else:
            qkv_layout, query_layer, key_layer, value_layer = _get_qkv_layout(
                query_layer, key_layer, value_layer, qkv_format = qkv_format)
3757

3758
3759
        # The priority for attention backends (subject to availability and clearing the filters)
        # is: FlashAttention > FusedAttention (cuDNN) > UnfusedDotProductAttention.
3760
        use_flash_attention = self.use_flash_attention
3761
        use_fused_attention = self.use_fused_attention
3762
        use_unfused_attention = True
3763

3764
3765
3766
        # The following section filters out some backends based on
        # certain asserts before executing the forward pass.

3767
3768
3769
3770
        # Filter: QKV layout.
        if qkv_format == 'thd':
            use_unfused_attention = False

3771
3772
3773
3774
3775
        # Filter: ONNX export.
        if is_in_onnx_export_mode():
            use_flash_attention = False
            use_fused_attention = False

3776
        # Filter: Input type.
3777
3778
3779
        if (query_layer.dtype not in [torch.bfloat16, torch.float16]
            or key_layer.dtype not in [torch.bfloat16, torch.float16]
            or value_layer.dtype not in [torch.bfloat16, torch.float16]
3780
            or any(isinstance(x, Float8Tensor) for x in [query_layer, key_layer, value_layer])
3781
3782
        ):
            use_flash_attention = False
3783
3784
3785
3786
        if (query_layer.dtype not in [torch.bfloat16, torch.float16]
            or key_layer.dtype not in [torch.bfloat16, torch.float16]
            or value_layer.dtype not in [torch.bfloat16, torch.float16]
        ):
3787
            use_fused_attention = False
3788

3789
        # Filter: Device and dimensions.
3790
        # FAv2 supports head_dim <= 256, and for >192 requires sm80/sm90
3791
3792
3793
3794
3795
        # FAv2 requires head_dim % 8 == 0
        if (key_layer.shape[-1] > 256
            or key_layer.shape[-1] % 8 != 0
            or (key_layer.shape[-1] > 192
                and self.device_compute_capability not in ((8, 0), (9, 0)))):
3796
3797
            use_flash_attention = False

3798
        # Filter: cross attention + causal mask.
3799
3800
3801
        # (in training mode)
        if (inference_params is None
            and _flash_attn_2_1_plus
3802
            and "causal" in attn_mask_type
3803
3804
            and max_seqlen_q != max_seqlen_kv
        ):
3805
            warnings.warn(
3806
3807
                "In training mode, disable the use of FlashAttention since version 2.1+ has "
                "changed its behavior for causal mask in cross attention. See "
3808
3809
3810
3811
                "https://github.com/Dao-AILab/flash-attention#21-change-behavior-of-causal-flag"
            )
            use_flash_attention = False

3812
3813
3814
        context_parallel = (self.cp_group is not None and \
            get_distributed_world_size(self.cp_group) != 1)

3815
3816
3817
3818
3819
3820
3821
        # Filter: sliding window attention.
        # UnfusedDotProductAttention can support SWA via arbitrary attention mask.
        if window_size not in ((-1, -1), (-1, 0)):
            use_fused_attention = False
            if (not _flash_attn_2_3_plus) or context_parallel:
                use_flash_attention = False

3822
        # Filter: Attention mask type.
3823
        #   attn_mask_type(s)    |     supported backends
3824
        # ------------------------------------------------
3825
3826
        #   no_mask              |     All
        #   padding              |     UnfusedDotProductAttention, FlashAttention, FusedAttention
3827
        #   causal               |     All
3828
        #   padding + causal     |     FlashAttention, FusedAttention
3829
3830
3831
3832
3833
        #   arbitrary            |     UnfusedDotProductAttention
        #
        if attn_mask_type == "arbitrary":
            use_flash_attention = False
            use_fused_attention = False
3834
3835
3836
3837
3838

        if (inference_params is None
            and "causal" in attn_mask_type
            and max_seqlen_q != max_seqlen_kv
        ):
3839
            use_unfused_attention = False
3840

3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
        # Filter: bias.
        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
        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
                or _alibi_cache["_alibi_slopes"] is None):
                _alibi_cache["_alibi_slopes_require_update"] = True
                _alibi_cache["_alibi_bias_require_update"] = True

        if core_attention_bias_type not in ["no_bias", "alibi"] or core_attention_bias is not None:
            use_flash_attention = False

        fu_core_attention_bias_type = core_attention_bias_type
        fu_core_attention_bias = core_attention_bias
        if core_attention_bias_type == "alibi" and use_fused_attention and alibi_slopes is not None:
            fu_core_attention_bias_type = "post_scale_bias"
            _, fu_core_attention_bias = get_alibi(
                query_layer.shape[-2], max_seqlen_q, max_seqlen_kv, alibi_slopes=alibi_slopes,
                bias_dtype=query_layer.dtype)
3869
3870
3871
3872
3873
3874
3875
        if (use_fused_attention
            and fu_core_attention_bias_type == "post_scale_bias"
            and (fu_core_attention_bias.shape[0] != 1
            or fu_core_attention_bias.shape[1] != query_layer.shape[-2])):
            if fu_core_attention_bias.requires_grad:
                # remove this line when cuDNN adds bwd support for
                # [1, 1, s, s], [b, 1, s, s] and [b, h, s, s]
3876
                use_fused_attention = False
3877
            else:
3878
3879
3880
                # max512 backend will only support [1, h, s, s]
                os.environ["NVTE_FUSED_ATTN_BACKEND"] = "1"

3881
3882
        if use_fused_attention:
            fused_attention_backend = tex.get_fused_attn_backend(
3883
3884
3885
3886
                TE_DType[query_layer.dtype]
                if not isinstance(query_layer, Float8Tensor) else query_layer._fp8_dtype,
                TE_DType[key_layer.dtype]
                if not isinstance(key_layer, Float8Tensor) else key_layer._fp8_dtype,
3887
                QKVLayout[qkv_layout],
3888
                AttnBiasType[fu_core_attention_bias_type],
3889
                AttnMaskType[attn_mask_type],
3890
                self.attention_dropout,
3891
3892
3893
3894
3895
3896
                query_layer.shape[-2], # num_attn_heads
                key_layer.shape[-2], # num_gqa_groups
                max_seqlen_q,
                max_seqlen_kv,
                query_layer.shape[-1], # head_dim
            )
3897
3898
            # DPA does not support FP8; for FP8, use cpp_extensions modules directly
            is_backend_avail = (fused_attention_backend in
3899
3900
3901
                [FusedAttnBackend["F16_max512_seqlen"],
                FusedAttnBackend["F16_arbitrary_seqlen"],
                FusedAttnBackend["FP8"]])
3902
3903
3904
3905
            use_fused_attention = ( \
                use_fused_attention and is_backend_avail and \
                (not context_parallel or \
                 fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]))
3906
3907
3908
3909
3910
            if (fused_attention_backend == FusedAttnBackend["F16_max512_seqlen"]
                and fu_core_attention_bias_type == "post_scale_bias"
                and (fu_core_attention_bias.shape[0] != 1
                or fu_core_attention_bias.shape[1] != query_layer.shape[-2])):
                use_fused_attention = False
3911

3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
        # Filter: determinism.
        # backend                                  | deterministic
        # ---------------------------------------------------------
        # flash-attn v1                            | yes
        # flash-attn v2                            | no
        # FusedAttnBackend["F16_max512_seqlen"]    | yes
        # FusedAttnBackend["F16_arbitrary_seqlen"] | workspace optimization path: yes; otherwise: no
        # UnfusedDotProductAttention               | yes
        #
        # Note that FusedAttnBackend["F16_arbitrary_seqlen"] only has workspace optimization path
        # on sm90 architectures.
        #
        if (use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]
            and self.deterministic
            and self.device_compute_capability != (9, 0)):
            use_fused_attention = False

3930
3931
3932
3933
3934
3935
        # Select FusedAttention on sm90 and FlashAttention on others for performance
        if (use_flash_attention
            and use_fused_attention
            and fused_attention_backend == FusedAttnBackend["F16_arbitrary_seqlen"]):
            if self.device_compute_capability == (9, 0):
                use_flash_attention = False
3936
3937

        if use_flash_attention:
3938
3939
            if _NVTE_DEBUG:
                print("[DotProductAttention]: using flash-attn",_flash_attn_version)
3940
3941
3942
            if core_attention_bias_type == "alibi":
                alibi_slopes, _ = get_alibi(
                    query_layer.shape[-2], max_seqlen_q, max_seqlen_kv, alibi_slopes=alibi_slopes)
3943
3944
3945
3946
3947
3948
3949
3950
            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,
3951
                                        window_size=window_size,
3952
                                        alibi_slopes=alibi_slopes,
3953
3954
                                        cp_group=self.cp_group,
                                        cp_global_ranks=self.cp_global_ranks,
3955
3956
3957
                                        cp_stream=self.cp_stream,
                                        max_seqlen_q=max_seqlen_q,
                                        max_seqlen_kv=max_seqlen_kv)
3958

3959
        if use_fused_attention:
3960
3961
3962
            if _NVTE_DEBUG:
                print("[DotProductAttention]: using cuDNN fused attention (backend "
                    + str(int(fused_attention_backend)) + ")")
3963
            if checkpoint_core_attention:
3964
3965
3966
3967
3968
3969
3970
3971
                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,
3972
3973
3974
3975
                    seq_offsets_q=seq_offsets_q,
                    seq_offsets_k=seq_offsets_k,
                    seq_offsets_v=seq_offsets_v,
                    seq_offsets_o=seq_offsets_o,
3976
3977
                    max_seqlen_q=max_seqlen_q,
                    max_seqlen_kv=max_seqlen_kv,
3978
3979
3980
                    attn_mask_type=attn_mask_type,
                    attention_mask=attention_mask,
                    fused_attention_backend=fused_attention_backend,
3981
3982
                    core_attention_bias_type=fu_core_attention_bias_type,
                    core_attention_bias=fu_core_attention_bias,
3983
3984
3985
3986
                    fast_zero_fill=fast_zero_fill,
                    cp_group=self.cp_group,
                    cp_global_ranks=self.cp_global_ranks,
                    cp_stream=self.cp_stream,
3987
                    is_first_microbatch=is_first_microbatch)
3988
3989
3990
3991
3992
3993
3994
            return 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,
3995
3996
3997
3998
                seq_offsets_q=seq_offsets_q,
                seq_offsets_k=seq_offsets_k,
                seq_offsets_v=seq_offsets_v,
                seq_offsets_o=seq_offsets_o,
3999
4000
                max_seqlen_q=max_seqlen_q,
                max_seqlen_kv=max_seqlen_kv,
4001
4002
4003
                attn_mask_type=attn_mask_type,
                attention_mask=attention_mask,
                fused_attention_backend=fused_attention_backend,
4004
4005
                core_attention_bias_type=fu_core_attention_bias_type,
                core_attention_bias=fu_core_attention_bias,
4006
4007
4008
4009
                fast_zero_fill=fast_zero_fill,
                cp_group=self.cp_group,
                cp_global_ranks=self.cp_global_ranks,
                cp_stream=self.cp_stream,
4010
                is_first_microbatch=is_first_microbatch)
4011
4012
4013

        assert (not context_parallel), \
            "Context parallelism is only implemented with Flash Attention and Fused Attention!"
4014

4015
4016
4017
4018
4019
4020
4021
        from .cpu_offload import CPUOffloadEnabled
        if CPUOffloadEnabled:
            warnings.warn(
                           "Attention activation Offloading is only implemented"
                           "with Flash Attention and Fused Attention!"
                         )

4022
4023
        if _NVTE_DEBUG:
            print("[DotProductAttention]: using unfused DPA")
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
        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,
                    core_attention_bias_type = core_attention_bias_type,
4037
4038
                    core_attention_bias = core_attention_bias,
                    alibi_slopes = alibi_slopes)
4039
4040
4041
4042
4043
4044
4045
4046
4047
            return self.unfused_attention(query_layer,
                    key_layer,
                    value_layer,
                    qkv_layout = qkv_layout,
                    cu_seqlens_q = cu_seqlens_q,
                    cu_seqlens_kv = cu_seqlens_kv,
                    attn_mask_type = attn_mask_type,
                    attention_mask = attention_mask,
                    core_attention_bias_type = core_attention_bias_type,
4048
4049
                    core_attention_bias = core_attention_bias,
                    alibi_slopes = alibi_slopes)
4050
4051

        raise Exception("No dot product attention support for the provided inputs!")
4052
4053


4054
4055
4056
4057
4058
4059
4060
class MultiheadAttention(torch.nn.Module):
    r"""
    Multi-head Attention (MHA), including Query,
    Key, Value and Output projection.

    .. note::

4061
4062
        Argument :attr:`attention_mask` in the `forward` call is only used when
        :attr:`attn_mask_type` includes '"padding"' or `"arbitrary"`.
4063

4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
    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.
4089
4090
    attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal' 'arbitrary'},
                   default = `causal`
4091
4092
4093
4094
4095
                   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.
4096
4097
4098
4099
4100
4101
    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
                window and causal mask specifically. Similar to :attr:`attn_mask_type`, it can
                be overridden by :attr:`window_size` in `forward` as well.
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
    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.
4115
4116
    input_layernorm: bool, default = `False`
                     if set to `True`, layer normalization to the input is applied.
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
    attention_type: { 'self', 'cross' }, default = 'self'
                   type of attention applied.
    zero_centered_gamma : bool, default = 'False'
                         if set to 'True', gamma parameter in LayerNorm is initialized to 0 and
                         the LayerNorm formula changes to

                         .. math::
                            y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \varepsilon}} *
                            (1 + \gamma) + \beta
    normalization : { 'LayerNorm', 'RMSNorm' }, default = 'LayerNorm'
                   type of normalization applied.
    qkv_weight_interleaved : bool, default = `True`
                            if set to `False`, the QKV weight is interpreted as a concatenation of
                            query, key, and value weights along the `0th` dimension. The default
                            interpretation is that the individual `q`, `k`, and `v` weights for each
                            attention head are interleaved. This parameter is set to `False` when
                            using :attr:`fuse_qkv_params=False`.
    bias : bool, default = `True`
          if set to `False`, the transformer layer will not learn any additive biases.
    device : Union[torch.device, str], default = "cuda"
          The device on which the parameters of the model will allocated. It is the user's
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
4140
4141
4142
4143
4144
4145
4146
4147
    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.
            For that, please use `_get_qkv_layout` to gain the layout information.
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187

    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`.
4188
4189
4190
4191
4192
4193
    """

    def __init__(
        self,
        hidden_size: int,
        num_attention_heads: int,
4194
4195
4196
4197
4198
        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,
4199
        layer_number: Optional[int] = None,
4200
        attn_mask_type: str = "causal",
4201
        window_size: Optional[Tuple[int, int]] = None,
4202
4203
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
4204
        num_gqa_groups: Optional[int] = None,
4205
4206
4207
        fuse_wgrad_accumulation: bool = False,
        get_rng_state_tracker: Optional[Callable] = None,
        sequence_parallel: bool = False,
4208
        params_dtype: Optional[torch.dtype] = None,
4209
        return_bias: bool = False,
4210
4211
4212
4213
4214
4215
4216
4217
4218
        return_layernorm_output: bool = False,
        input_layernorm: bool = False,
        attention_type: str = "self",
        set_parallel_mode: bool = False,
        fuse_qkv_params: bool = False,
        zero_centered_gamma: bool = False,
        qkv_weight_interleaved: bool = True,
        ub_bulk_wgrad: bool = False,
        ub_bulk_dgrad: bool = False,
Jaemin Choi's avatar
Jaemin Choi committed
4219
        ub_overlap_rs_dgrad: bool = False,
4220
4221
        ub_overlap_rs: bool = False,
        ub_overlap_ag: bool = False,
4222
        bias: bool = True,
4223
        normalization: str = "LayerNorm",
4224
        device: Union[torch.device, str] = "cuda",
4225
        qkv_format: str = "sbhd"
4226
4227
    ) -> None:
        super().__init__()
4228

4229
        self.qkv_format = qkv_format
4230
        self.attn_mask_type = attn_mask_type
4231
4232
        self.window_size = window_size
        self.window_size = check_set_window_size(attn_mask_type, self.window_size)
4233
        self.layer_number = layer_number
4234
4235
4236
4237
4238
        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
4239
        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
4240
        self.num_attention_heads = num_attention_heads
4241
4242
4243
4244
4245
4246
4247
4248
        self.return_bias = return_bias

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

        if init_method is None:
            init_method = get_default_init_method()
        if output_layer_init_method is None:
            output_layer_init_method = get_default_init_method()
4249
4250
4251
4252
4253

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

4254
4255
4256
        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"
4257
4258
4259
4260
4261
4262

        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)
4263
4264
4265
4266
        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
cyanguwa's avatar
cyanguwa committed
4267
4268
                ), "The number of attention heads must be divisible by the number of GQA groups!"
        assert (self.num_gqa_groups % tp_size == 0
4269
4270
                ), "The number of GQA groups must be divisible by tensor parallel size!"
        self.num_gqa_groups_per_partition = int(self.num_gqa_groups // tp_size)
4271
4272
4273
4274

        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
4275
4276
4277
4278
4279
4280
4281

        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,
4282
            "params_dtype": self.params_dtype,
4283
            "device": device,
4284
4285
4286
4287
        }

        qkv_parallel_mode = "column" if set_parallel_mode else None

cyanguwa's avatar
cyanguwa committed
4288
        if self.attention_type == "self":
4289
4290
4291
            parameters_split = None
            if not fuse_qkv_params:
                parameters_split = collections.OrderedDict([
4292
                    ("query", self.hidden_size_q),
4293
4294
4295
                    ("key", self.hidden_size_kv),
                    ("value", self.hidden_size_kv),
                ])
4296
4297
4298
            if self.input_layernorm:
                self.layernorm_qkv = LayerNormLinear(
                    hidden_size,
4299
                    self.hidden_size_q + 2 * self.hidden_size_kv,
4300
4301
4302
4303
4304
4305
                    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
4306
                    parameters_split=parameters_split,
4307
4308
4309
                    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
4310
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
4311
                    ub_overlap_ag=ub_overlap_ag,
4312
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
4313
                    ub_name="qkv",
4314
4315
4316
4317
4318
                    **common_gemm_kwargs,
                )
            else:
                self.qkv = Linear(
                    hidden_size,
4319
                    self.hidden_size_q + 2 * self.hidden_size_kv,
4320
4321
4322
4323
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
cyanguwa's avatar
cyanguwa committed
4324
                    parameters_split=parameters_split,
4325
4326
                    **common_gemm_kwargs,
                )
cyanguwa's avatar
cyanguwa committed
4327
        elif self.attention_type == "cross":
4328
4329
4330
            if self.input_layernorm:
                self.layernorm_query = LayerNormLinear(
                    hidden_size,
4331
                    self.hidden_size_q,
4332
4333
4334
4335
4336
                    eps=layernorm_epsilon,
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
4337
                    parameters_split=("query",) if not fuse_qkv_params else None,
4338
4339
4340
4341
                    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
4342
                    ub_overlap_rs_dgrad=ub_overlap_rs_dgrad,
4343
                    ub_overlap_ag=ub_overlap_ag,
4344
                    normalization=normalization,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
4345
                    ub_name="qkv",
4346
4347
4348
4349
4350
                    **common_gemm_kwargs,
                )
            else:
                self.query_layer = Linear(
                    hidden_size,
4351
                    self.hidden_size_q,
4352
4353
4354
4355
4356
4357
4358
4359
                    init_method=init_method,
                    bias=bias,
                    return_bias=False,
                    parallel_mode=qkv_parallel_mode,
                    **common_gemm_kwargs,
                )
            self.key_value = Linear(
                hidden_size,
4360
                2 * self.hidden_size_kv,
4361
4362
4363
4364
                init_method=init_method,
                bias=bias,
                return_bias=False,
                parallel_mode=qkv_parallel_mode,
4365
                parameters_split=("key", "value") if not fuse_qkv_params else None,
4366
4367
4368
4369
4370
4371
                **common_gemm_kwargs,
            )

        # Attention.
        self.core_attention = DotProductAttention(
            num_attention_heads,
4372
            self.hidden_size_per_attention_head,
4373
4374
            num_gqa_groups=self.num_gqa_groups,
            attention_dropout=attention_dropout,
4375
            qkv_format=self.qkv_format,
4376
4377
4378
4379
            tp_size=tp_size,
            get_rng_state_tracker=get_rng_state_tracker,
            sequence_parallel=sequence_parallel,
            tp_group=tp_group,
4380
            layer_number=self.layer_number,
4381
            attention_type=self.attention_type,
4382
4383
4384
4385
        )

        # Linear
        self.proj = Linear(
4386
            self.hidden_size_q,
4387
4388
4389
            hidden_size,
            init_method=output_layer_init_method,
            bias=bias,
4390
            return_bias=return_bias,
4391
            parallel_mode="row" if set_parallel_mode else None,
4392
4393
            ub_overlap_rs=ub_overlap_rs,
            ub_overlap_ag=ub_overlap_ag,
Przemyslaw Tredak's avatar
Przemyslaw Tredak committed
4394
            ub_name="proj",
4395
4396
4397
4398
4399
            **common_gemm_kwargs,
        )


    def _allocate_memory(
4400
        self, inference_max_sequence_len: int, batch_size: int, dtype: torch.dtype
4401
4402
4403
4404
    ) -> torch.Tensor:
        return torch.empty(
            inference_max_sequence_len,
            batch_size,
4405
            self.num_gqa_groups_per_partition,
4406
            self.hidden_size_per_attention_head,
4407
            dtype=dtype,
4408
4409
4410
4411
            device=torch.cuda.current_device(),
        )

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
4412
4413
4414
4415
4416
4417
4418
4419
4420
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
        tp_group : ProcessGroup, default = `None`
                  tensor parallel process group.
        """
4421
4422
        self.tp_group = tp_group

4423
    def set_context_parallel_group(
4424
4425
        self,
        cp_group: Union[dist_group_type, None],
4426
        cp_global_ranks: List[int],
4427
4428
        cp_stream: torch.cuda.Stream,
    ) -> None:
4429
4430
4431
4432
4433
4434
4435
4436
4437
4438
4439
4440
4441
        """
        Set the context parallel attributes for the given
        module before executing the forward pass.

        Parameters
        ----------
        cp_group : ProcessGroup
                  context parallel process group.
        cp_global_ranks : List[int]
                         list of global ranks in the context group.
        cp_stream : torch.cuda.Stream
                   cuda stream for context parallel execution.
        """
4442
4443
4444
4445
4446
4447
        # 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"):
                child.set_context_parallel_group(cp_group, cp_global_ranks, cp_stream)
4448

4449
4450
4451
    def forward(
        self,
        hidden_states: torch.Tensor,
4452
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4453
        encoder_output: Optional[torch.Tensor] = None,
4454
        attn_mask_type: Optional[str] = None,
4455
        window_size: Optional[Tuple[int, int]] = None,
4456
4457
        is_first_microbatch: Optional[bool] = None,
        checkpoint_core_attention: bool = False,
4458
        inference_params: Optional[InferenceParams] = None,
4459
        rotary_pos_emb: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]] = None,
4460
4461
        core_attention_bias_type: str = "no_bias",
        core_attention_bias: Optional[torch.Tensor] = None,
4462
        alibi_slopes: Optional[torch.Tensor] = None,
4463
        fast_zero_fill: bool = True,
4464
    ) -> Tuple[Union[torch.Tensor, None], ...]:
4465
4466
4467
4468
4469
        """
        Forward propagation for MultiheadAttention layer.

        .. note::

4470
4471
            Argument :attr:`attention_mask` is only used when :attr:`attn_mask_type`
            includes `"padding"` or `"arbitrary"`.
4472
4473
4474
4475
4476

        Parameters
        ----------
        hidden_states : torch.Tensor
             Input tensor.
4477
4478
4479
4480
4481
4482
        attention_mask: Optional[Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]],
             default = `None`. Boolean tensor(s) used to mask out attention softmax input.
             It should be 'None' for 'causal' and 'no_mask' types. For 'padding' masks, it should be
             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]
             for cross-attention. For the 'arbitrary' mask type, it should be in a shape that is
4483
4484
4485
             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.
4486
4487
        attn_mask_type: {'no_mask', 'padding', 'causal', 'padding_causal', 'arbitrary'},
                       default = `None`
4488
                       type of attention mask passed into softmax operation.
4489
4490
        window_size: Optional[Tuple[int, int]], default = `None`
                    sliding window size for local attention.
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
        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`
4516
                    Bias type, {`no_bias`, `pre_scale_bias`, 'post_scale_bias`, `alibi`}
4517
        core_attention_bias: Optional[torch.Tensor], default = `None`
4518
4519
                    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.
4520
4521
4522
4523
        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.
4524
4525
4526
        fast_zero_fill: bool, default = `True`
                    Whether to set output tensors to 0 or not before use.
        """
4527
4528
        # hidden_states: [sq, b, h]

4529
4530
        if attn_mask_type is not None:
            window_size = check_set_window_size(attn_mask_type, window_size)
4531
        if attn_mask_type is None:
4532
            attn_mask_type = self.attn_mask_type
4533
4534
        if window_size is None:
            window_size = self.window_size
4535

4536
4537
4538
4539
4540
        if "padding" in attn_mask_type and attention_mask is not None:
            for i,_ in enumerate(attention_mask):
                assert (
                    attention_mask[i].dtype == torch.bool
                ), "Attention mask must be in boolean type!"
4541

4542
4543
        assert (core_attention_bias_type in AttnBiasTypes
                ), f"core_attention_bias_type {core_attention_bias_type} is not supported!"
4544

4545
        # =================================================
4546
        # Pre-allocate memory for key-values for inference
4547
4548
4549
4550
        # =================================================

        if inference_params and self.layer_number is not None:
            if self.layer_number not in inference_params.key_value_memory_dict:
4551
                inf_max_seq_len = inference_params.max_sequence_length
4552
4553
                inf_max_batch_size = inference_params.max_batch_size
                inference_key_memory = self._allocate_memory(
4554
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
4555
4556
                )
                inference_value_memory = self._allocate_memory(
4557
                    inf_max_seq_len, inf_max_batch_size, hidden_states.dtype
4558
4559
4560
4561
4562
4563
4564
4565
4566
4567
4568
                )
                inference_params.key_value_memory_dict[self.layer_number] = (
                    inference_key_memory,
                    inference_value_memory,
                )
            else:
                (
                    inference_key_memory,
                    inference_value_memory,
                ) = inference_params.key_value_memory_dict[self.layer_number]

4569
        # ======================
4570
        # Query, Key, and Value
4571
        # ======================
4572

cyanguwa's avatar
cyanguwa committed
4573
4574
        if self.attention_type == "self":
            # Attention heads [sq, b, h] --> [sq, b, ng * (np/ng + 2) * hn]
4575
4576
4577
4578
4579
4580
4581
4582
4583
4584
4585
4586
4587
            if self.input_layernorm:
                layernorm_qkv_outputs = self.layernorm_qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                if self.return_layernorm_output:
                    mixed_x_layer, layernorm_output = layernorm_qkv_outputs
                else:
                    mixed_x_layer = layernorm_qkv_outputs
            else:
                mixed_x_layer = self.qkv(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
4588
                    is_first_module_in_mha=True, # specific to FP8 MHA
4589
4590
                )

cyanguwa's avatar
cyanguwa committed
4591
4592
            num_queries_per_key_value = (self.num_attention_heads_per_partition //
                                         self.num_gqa_groups_per_partition)
4593
            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
4594
                # [sq, b, ng * (np/ng + 2) * hn] --> [sq, b, ng, (np/ng + 2), hn]
4595
                new_tensor_shape = mixed_x_layer.size()[:-1] + (
cyanguwa's avatar
cyanguwa committed
4596
4597
                    self.num_gqa_groups_per_partition,
                    (num_queries_per_key_value + 2),
4598
4599
4600
4601
                    self.hidden_size_per_attention_head,
                )
                # split along second last dimension
                split_dim = -2
cyanguwa's avatar
cyanguwa committed
4602
4603
4604
4605
4606
4607
4608
4609
4610
            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,
                    self.hidden_size_per_attention_head
                )
                # split along third last dimension
                split_dim = -3
4611
4612
4613

            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)

cyanguwa's avatar
cyanguwa committed
4614
4615
4616
4617
4618
4619
4620
4621
4622
            # qkv_weight_interleaved:
            #  [sq, b, ng, (np/ng + 2), hn]
            #  --> [sq, b, ng, np/ng, hn], [sq, b, ng, 1, hn], [sq, b, ng, 1, hn]
            # not qkv_weight_interleaved:
            #  [sq, b, (np/ng + 2), ng, hn]
            #  --> [sq, b, np/ng, np, hn], [sq, b, 1, ng, hn], [sq, b, 1, ng, hn]
            if not is_in_onnx_export_mode():
                query_layer, key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_x_layer, split_dim, (num_queries_per_key_value, 1, 1)
4623
                )
4624
            else:
cyanguwa's avatar
cyanguwa committed
4625
4626
4627
4628
4629
4630
4631
4632
4633
4634
4635
4636
                query_layer, key_layer, value_layer = torch.split(
                    mixed_x_layer, (num_queries_per_key_value, 1, 1), dim = split_dim,
                 )

            # 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))

        elif self.attention_type == "cross":
            # Attention heads [sk, b, h] --> [sk, b, (ng * 2 * hn)]
4637
            mixed_kv_layer = self.key_value(
cyanguwa's avatar
cyanguwa committed
4638
                encoder_output,
4639
                is_first_microbatch=is_first_microbatch,
4640
                is_first_module_in_mha=True, # specific to FP8 MHA
4641
4642
4643
            )

            if self.qkv_weight_interleaved:
cyanguwa's avatar
cyanguwa committed
4644
                # [sq, b, (ng * 2 * hn)] --> [sq, b, ng, 2 * hn]
4645
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
4646
                    self.num_gqa_groups_per_partition,
4647
4648
4649
4650
4651
                    2 * self.hidden_size_per_attention_head,
                )
                # split along last dimension
                split_dim = -1
            else:
cyanguwa's avatar
cyanguwa committed
4652
                # [sq, b, (ng * 2 * hn)] --> [sq, b, 2 * ng, hn]
4653
                new_tensor_shape = mixed_kv_layer.size()[:-1] + (
4654
                    2 * self.num_gqa_groups_per_partition,
4655
4656
4657
4658
4659
4660
4661
                    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
4662
4663
4664
4665
4666
            # mixed_kv_layer --> 2 [sk, b, ng, hn]
            if not is_in_onnx_export_mode():
                key_layer, value_layer = _SplitAlongDim.apply(
                    mixed_kv_layer, split_dim, mixed_kv_layer.shape[split_dim] // 2,
                )
4667
            else:
cyanguwa's avatar
cyanguwa committed
4668
4669
4670
                key_layer, value_layer = torch.split(
                    mixed_kv_layer, mixed_kv_layer.shape[split_dim] // 2, dim = split_dim,
                )
4671
4672
4673
            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))
4674
4675
4676
4677
4678
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688

            # Attention head [sq, b, h] --> [sq, b, hp]
            if self.input_layernorm:
                layernorm_query_outputs = self.layernorm_query(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
                )
                if self.return_layernorm_output:
                    query_layer, layernorm_output = layernorm_query_outputs
                else:
                    query_layer = layernorm_query_outputs
            else:
                query_layer = self.query_layer(
                    hidden_states,
                    is_first_microbatch=is_first_microbatch,
4689
                    is_first_module_in_mha=True, # specific to FP8 MHA
4690
4691
4692
4693
4694
4695
4696
4697
4698
                )

            # [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)

4699
4700
4701
        # ======================================================
        # Apply relative positional encoding (rotary embedding)
        # ======================================================
4702

4703
        if rotary_pos_emb is not None:
4704
4705
4706
            assert (not isinstance(query_layer, Float8Tensor)
                and not isinstance(key_layer, Float8Tensor)
                ), "RoPE is not supported for Float8Tensors!"
4707
            # duplicate the pos_emb for self attention
4708
4709
4710
4711
            if not isinstance(rotary_pos_emb, tuple):
                rotary_pos_emb = ((rotary_pos_emb,) * 2)

            q_pos_emb, k_pos_emb = rotary_pos_emb
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725

            # adjust key and value for inference
            if inference_params is not None:
                if self.qkv_format == "sbhd":
                    sequence_length = key_layer.size(0)
                elif self.qkv_format == "bshd":
                    sequence_length = key_layer.size(1)

                sequence_start = inference_params.sequence_len_offset
                sequence_end = sequence_start + sequence_length

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

4726
4727
            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb, self.qkv_format, fused=True)
            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb, self.qkv_format, fused=True)
4728

4729
4730
4731
4732
        # ===========================
        # Core attention computation
        # ===========================

4733
4734
4735
4736
        context_layer = self.core_attention(
            query_layer,
            key_layer,
            value_layer,
4737
            qkv_format=self.qkv_format,
4738
4739
            cu_seqlens_q=None,
            cu_seqlens_kv=None,
4740
4741
            attention_mask=attention_mask,
            attn_mask_type=attn_mask_type,
4742
            window_size=window_size,
4743
4744
4745
            checkpoint_core_attention=checkpoint_core_attention,
            core_attention_bias_type=core_attention_bias_type,
            core_attention_bias=core_attention_bias,
4746
            alibi_slopes=alibi_slopes,
4747
            fast_zero_fill=fast_zero_fill,
4748
            inference_params=inference_params,
4749
4750
        )

4751
        # ===================
4752
        # Output. [sq, b, h]
4753
        # ===================
4754

4755
        projection_output = self.proj(
4756
4757
            context_layer,
            is_first_microbatch=is_first_microbatch,
4758
4759
        )

4760
4761
4762
4763
4764
4765
4766
4767
        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,)
4768
        if self.input_layernorm and self.return_layernorm_output:
4769
4770
            outputs += (layernorm_output,)
        return outputs if len(outputs) > 1 else outputs[0]