_custom_ops.py 93.2 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
from typing import TYPE_CHECKING, Literal
5
6
7

import torch

8
import vllm.envs as envs
9
from vllm.logger import init_logger
10
from vllm.platforms import current_platform
11
from vllm.scalar_type import ScalarType
12
13
14

logger = init_logger(__name__)

15
current_platform.import_kernels()
16

17
if TYPE_CHECKING:
18
19
20
21
22
23
24
25
26

    def register_fake(fn):
        return lambda name: fn
else:
    try:
        from torch.library import register_fake
    except ImportError:
        from torch.library import impl_abstract as register_fake

27
28
29
30
31
32
33
34
35
36

# page attention ops
def paged_attention_v1(
    out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
37
    seq_lens: torch.Tensor,
38
    block_size: int,
39
    max_seq_len: int,
40
    alibi_slopes: torch.Tensor | None,
41
    kv_cache_dtype: str,
42
43
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
44
45
46
47
48
    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
49
) -> None:
50
    torch.ops._C.paged_attention_v1(
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
        out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        tp_rank,
        blocksparse_local_blocks,
        blocksparse_vert_stride,
        blocksparse_block_size,
        blocksparse_head_sliding_step,
    )
71
72
73
74
75
76
77
78
79
80
81
82
83


def paged_attention_v2(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
84
    seq_lens: torch.Tensor,
85
    block_size: int,
86
    max_seq_len: int,
87
    alibi_slopes: torch.Tensor | None,
88
    kv_cache_dtype: str,
89
90
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
91
92
93
94
95
    tp_rank: int = 0,
    blocksparse_local_blocks: int = 0,
    blocksparse_vert_stride: int = 0,
    blocksparse_block_size: int = 64,
    blocksparse_head_sliding_step: int = 0,
96
) -> None:
97
    torch.ops._C.paged_attention_v2(
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
        out,
        exp_sum,
        max_logits,
        tmp_out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        tp_rank,
        blocksparse_local_blocks,
        blocksparse_vert_stride,
        blocksparse_block_size,
        blocksparse_head_sliding_step,
    )
121
122


123
124
125
126
127
128
129
130
131
132
133
134
def paged_attention_rocm(
    out: torch.Tensor,
    exp_sum: torch.Tensor,
    max_logits: torch.Tensor,
    tmp_out: torch.Tensor,
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    num_kv_heads: int,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
135
    query_start_loc: torch.Tensor | None,
136
137
    block_size: int,
    max_seq_len: int,
138
    alibi_slopes: torch.Tensor | None,
139
    kv_cache_dtype: str,
140
141
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
142
    fp8_out_scale: torch.Tensor | None = None,
xiao-llm's avatar
xiao-llm committed
143
    mfma_type: str = "fp8" if envs.VLLM_ROCM_FP8_MFMA_PAGE_ATTN else "f16",
144
) -> None:
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
    torch.ops._rocm_C.paged_attention(
        out,
        exp_sum,
        max_logits,
        tmp_out,
        query,
        key_cache,
        value_cache,
        num_kv_heads,
        scale,
        block_tables,
        seq_lens,
        query_start_loc,
        block_size,
        max_seq_len,
        alibi_slopes,
        kv_cache_dtype,
        k_scale,
        v_scale,
        fp8_out_scale,
        mfma_type,
    )
167
168


Thien Tran's avatar
Thien Tran committed
169
170
171
172
173
174
175
176
def mla_decode_kvcache_cpu(
    out: torch.Tensor,
    query: torch.Tensor,
    kv_cache: torch.Tensor,
    scale: float,
    block_tables: torch.Tensor,
    seq_lens: torch.Tensor,
) -> None:
177
178
179
    torch.ops._C_cpu.mla_decode_kvcache(
        out, query, kv_cache, scale, block_tables, seq_lens
    )
Thien Tran's avatar
Thien Tran committed
180
181


182
# merge attn states ops
183
184
185
186
187
188
def merge_attn_states(
    output: torch.Tensor,
    prefix_output: torch.Tensor,
    prefix_lse: torch.Tensor,
    suffix_output: torch.Tensor,
    suffix_lse: torch.Tensor,
189
    output_lse: torch.Tensor | None = None,
190
191
192
193
) -> None:
    torch.ops._C.merge_attn_states(
        output, output_lse, prefix_output, prefix_lse, suffix_output, suffix_lse
    )
194
195


196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
def convert_vertical_slash_indexes(
    q_seqlens: torch.Tensor,  # [BATCH, ]
    kv_seqlens: torch.Tensor,  # [BATCH, ]
    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
    context_size: int,
    block_size_M: int,
    block_size_N: int,
    causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    batch_size = slash_indexes.size(0)
    num_heads = slash_indexes.size(1)
    nnz_slash = slash_indexes.size(2)
    nnz_vertical = vertical_indexes.size(2)
    num_rows = (context_size + block_size_M - 1) // block_size_M

212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
    block_count = torch.zeros(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    block_offset = torch.zeros(
        batch_size,
        num_heads,
        num_rows,
        nnz_slash,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
    column_count = torch.zeros(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    column_index = torch.zeros(
        batch_size,
        num_heads,
        num_rows,
        nnz_vertical,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
234
235

    torch.ops._C.convert_vertical_slash_indexes(
236
237
238
239
240
241
242
243
244
245
246
247
248
        block_count,
        block_offset,
        column_count,
        column_index,
        q_seqlens,
        kv_seqlens,
        vertical_indexes,
        slash_indexes,
        context_size,
        block_size_M,
        block_size_N,
        causal,
    )
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
    return block_count, block_offset, column_count, column_index


def convert_vertical_slash_indexes_mergehead(
    q_seqlens: torch.Tensor,  # [BATCH, ]
    kv_seqlens: torch.Tensor,  # [BATCH, ]
    vertical_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_V]
    slash_indexes: torch.Tensor,  # [BATCH, N_HEADS, NNZ_S]
    # [N_HEADS] : different head use different number of indices
    vertical_indices_count: torch.Tensor,
    slash_indices_count: torch.Tensor,
    context_size: int,
    block_size_M: int,
    block_size_N: int,
    causal: bool = True,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    batch_size = slash_indexes.size(0)
    num_heads = slash_indexes.size(1)
    nnz_slash = slash_indexes.size(2)
    nnz_vertical = vertical_indexes.size(2)
    num_rows = (context_size + block_size_M - 1) // block_size_M

271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
    block_count = torch.empty(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    block_offset = torch.empty(
        batch_size,
        num_heads,
        num_rows,
        nnz_slash,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
    column_count = torch.empty(
        batch_size, num_heads, num_rows, dtype=q_seqlens.dtype, device=q_seqlens.device
    )
    column_index = torch.empty(
        batch_size,
        num_heads,
        num_rows,
        nnz_vertical,
        dtype=q_seqlens.dtype,
        device=q_seqlens.device,
    )
293
294

    torch.ops._C.convert_vertical_slash_indexes_mergehead(
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
        block_count,
        block_offset,
        column_count,
        column_index,
        q_seqlens,
        kv_seqlens,
        vertical_indexes,
        slash_indexes,
        vertical_indices_count,
        slash_indices_count,
        context_size,
        block_size_M,
        block_size_N,
        causal,
    )
310
311
312
    return block_count, block_offset, column_count, column_index


313
314
315
316
# pos encoding ops
def rotary_embedding(
    positions: torch.Tensor,
    query: torch.Tensor,
317
    key: torch.Tensor | None,
318
319
320
321
    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
) -> None:
322
323
324
    torch.ops._C.rotary_embedding(
        positions, query, key, head_size, cos_sin_cache, is_neox
    )
325
326
327


# layer norm ops
328
329
330
def rms_norm(
    out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
331
    torch.ops._C.rms_norm(out, input, weight, epsilon)
332
333


334
335
336
def fused_add_rms_norm(
    input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, epsilon: float
) -> None:
337
    torch.ops._C.fused_add_rms_norm(input, residual, weight, epsilon)
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
def fused_qk_norm_rope(
    qkv: torch.Tensor,
    num_heads_q: int,
    num_heads_k: int,
    num_heads_v: int,
    head_dim: int,
    eps: float,
    q_weight: torch.Tensor,
    k_weight: torch.Tensor,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    position_ids: torch.Tensor,
) -> None:
    torch.ops._C.fused_qk_norm_rope(
        qkv,
        num_heads_q,
        num_heads_k,
        num_heads_v,
        head_dim,
        eps,
        q_weight,
        k_weight,
        cos_sin_cache,
        is_neox,
        position_ids,
    )


368
def apply_repetition_penalties_torch(
369
370
371
372
373
    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
374
    repetition_penalties = repetition_penalties.unsqueeze(dim=1).repeat(
375
376
        1, logits.size(1)
    )
377
    # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.
378
    penalties = torch.where(prompt_mask | output_mask, repetition_penalties, 1.0)
379
380
381
382
383
384
    # If logits are positive, divide by penalty, otherwise multiply by penalty.
    scaling = torch.where(logits > 0, 1.0 / penalties, penalties)
    logits *= scaling


def apply_repetition_penalties_cuda(
385
386
387
388
389
390
391
392
    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
    torch.ops._C.apply_repetition_penalties_(
        logits, prompt_mask, output_mask, repetition_penalties
    )
393
394


395
396
397
398
399
400
def apply_repetition_penalties(
    logits: torch.Tensor,
    prompt_mask: torch.Tensor,
    output_mask: torch.Tensor,
    repetition_penalties: torch.Tensor,
) -> None:
401
402
403
404
405
406
407
408
    """Apply repetition penalties to logits in-place.

    Args:
        logits: The logits tensor of shape [num_seqs, vocab_size].
        prompt_mask: A boolean tensor indicating which tokens appear in the prompt.
        output_mask: A boolean tensor indicating which tokens appear in the output.
        repetition_penalties: The repetition penalties of shape (num_seqs, ).
    """
409
    if logits.is_cuda and logits.is_contiguous():
410
411
412
        apply_repetition_penalties_cuda(
            logits, prompt_mask, output_mask, repetition_penalties
        )
413
    else:
414
415
416
        apply_repetition_penalties_torch(
            logits, prompt_mask, output_mask, repetition_penalties
        )
417
418


419
420
421
422
423
424
# fused quant layer norm ops
def rms_norm_dynamic_per_token_quant(
    input: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
425
426
    scale_ub: torch.Tensor | None = None,
    residual: torch.Tensor | None = None,
427
) -> tuple[torch.Tensor, torch.Tensor]:
428
    output = torch.empty_like(input, dtype=quant_dtype)
429
430
431
    scales = torch.empty(
        (input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
    )
432

433
434
435
    torch.ops._C.rms_norm_dynamic_per_token_quant(
        output, input, weight, scales, epsilon, scale_ub, residual
    )
436
437
438
    return output, scales


439
440
441
442
443
444
445
446
447
448
449
450
451
452
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
# fused quant layer norm ops blocked
def rms_norm_per_block_quant(
    input: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
    group_size: list[int],
    scale_ub: torch.Tensor | None = None,
    residual: torch.Tensor | None = None,
    is_scale_transposed: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert len(group_size) == 2
    output = torch.empty_like(input, dtype=quant_dtype)
    if is_scale_transposed:
        scales = torch.empty(
            (input.shape[-1] // group_size[1], input.numel() // input.shape[-1]),
            device=input.device,
            dtype=torch.float32,
        ).transpose(0, 1)
    else:
        scales = torch.empty(
            (input.numel() // input.shape[-1], input.shape[-1] // group_size[1]),
            device=input.device,
            dtype=torch.float32,
        )

    torch.ops._C.rms_norm_per_block_quant(
        output,
        input,
        weight,
        scales,
        epsilon,
        scale_ub,
        residual,
        group_size[1],
        is_scale_transposed,
    )
    return output, scales


479
480
# quantization ops
# awq
481
482
483
484
485
486
487
488
def awq_dequantize(
    qweight: torch.Tensor,
    scales: torch.Tensor,
    zeros: torch.Tensor,
    split_k_iters: int,
    thx: int,
    thy: int,
) -> torch.Tensor:
489
490
    if envs.VLLM_USE_TRITON_AWQ:
        from vllm.model_executor.layers.quantization.awq_triton import (
491
492
493
            awq_dequantize_triton,
        )

494
        return awq_dequantize_triton(qweight, scales, zeros)
495
    return torch.ops._C.awq_dequantize(qweight, scales, zeros, split_k_iters, thx, thy)
496
497


498
499
500
501
def awq_gemm(
    input: torch.Tensor,
    qweight: torch.Tensor,
    scales: torch.Tensor,
502
    qzeros: torch.Tensor,
503
504
    split_k_iters: int,
) -> torch.Tensor:
505
    if envs.VLLM_USE_TRITON_AWQ:
506
507
        from vllm.model_executor.layers.quantization.awq_triton import awq_gemm_triton

508
509
        return awq_gemm_triton(input, qweight, scales, qzeros, split_k_iters)
    return torch.ops._C.awq_gemm(input, qweight, scales, qzeros, split_k_iters)
510
511
512


# gptq
513
514
515
516
517
518
519
def gptq_gemm(
    a: torch.Tensor,
    b_q_weight: torch.Tensor,
    b_gptq_qzeros: torch.Tensor,
    b_gptq_scales: torch.Tensor,
    b_g_idx: torch.Tensor,
    use_exllama: bool,
520
    use_v2_format: bool,
521
522
523
    bit: int,
) -> torch.Tensor:
    return torch.ops._C.gptq_gemm(
524
525
526
527
528
529
530
531
        a,
        b_q_weight,
        b_gptq_qzeros,
        b_gptq_scales,
        b_g_idx,
        use_exllama,
        use_v2_format,
        bit,
532
    )
533
534


535
if hasattr(torch.ops._C, "gptq_gemm"):
536

537
    @register_fake("_C::gptq_gemm")
538
539
540
541
542
543
544
    def _gptq_gemm_fake(
        a: torch.Tensor,
        b_q_weight: torch.Tensor,
        b_gptq_qzeros: torch.Tensor,
        b_gptq_scales: torch.Tensor,
        b_g_idx: torch.Tensor,
        use_exllama: bool,
545
        use_v2_format: bool,
546
547
548
549
550
        bit: int,
    ) -> torch.Tensor:
        return torch.empty(
            (a.size(0), b_q_weight.size(1)), dtype=a.dtype, device=a.device
        )
551
552


553
def gptq_shuffle(q_weight: torch.Tensor, q_perm: torch.Tensor, bit: int) -> None:
554
    torch.ops._C.gptq_shuffle(q_weight, q_perm, bit)
555
556


557
# marlin_24
558
559
560
561
562
563
564
565
566
567
568
569
570
571
def gptq_marlin_24_gemm(
    a: torch.Tensor,
    b_q_weight: torch.Tensor,
    b_meta: torch.Tensor,
    b_scales: torch.Tensor,
    workspace: torch.Tensor,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_24_gemm(
        a, b_q_weight, b_meta, b_scales, workspace, b_q_type.id, size_m, size_n, size_k
    )
572
573


574
if hasattr(torch.ops._C, "gptq_marlin_24_gemm"):
575

576
    @register_fake("_C::gptq_marlin_24_gemm")
577
578
579
580
581
582
583
584
585
586
587
    def _gptq_marlin_24_gemm_fake(
        a: torch.Tensor,
        b_q_weight: torch.Tensor,
        b_meta: torch.Tensor,
        b_scales: torch.Tensor,
        workspace: torch.Tensor,
        b_q_type: ScalarType,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
    ) -> torch.Tensor:
588
589
        return torch.empty((size_m, size_n), device=a.device, dtype=a.dtype)

590
    @register_fake("_C::gptq_marlin_gemm")
591
592
    def _gptq_marlin_gemm_fake(
        a: torch.Tensor,
593
        c: torch.Tensor | None,
594
        b_q_weight: torch.Tensor,
595
        b_bias: torch.Tensor | None,
596
        b_scales: torch.Tensor,
597
        a_scales: torch.Tensor | None,
598
599
600
601
        global_scale: torch.Tensor | None,
        b_zeros: torch.Tensor | None,
        g_idx: torch.Tensor | None,
        perm: torch.Tensor | None,
602
603
604
605
606
607
608
609
610
611
        workspace: torch.Tensor,
        b_q_type_id: int,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
        is_k_full: bool = True,
        use_atomic_add: bool = False,
        use_fp32_reduce: bool = False,
        is_zp_float: bool = False,
    ) -> torch.Tensor:
612
613
614
615
        dtype = a.dtype
        if dtype not in [torch.half, torch.bfloat16]:
            dtype = b_scales.dtype
        return torch.empty((size_m, size_n), device=a.device, dtype=dtype)
616

617
    @register_fake("_C::awq_dequantize")
618
619
620
621
622
623
624
625
    def _awq_dequantize_fake(
        qweight: torch.Tensor,
        scales: torch.Tensor,
        zeros: torch.Tensor,
        split_k_iters: torch.SymInt,
        thx: int,
        thy: int,
    ) -> torch.Tensor:
626
627
628
        in_c = qweight.size(0)
        qout_c = qweight.size(1)
        out_c = qout_c * 8
629
        return torch.empty((in_c, out_c), dtype=scales.dtype, device=scales.device)
630

631
    @register_fake("_C::awq_gemm")
632
633
634
635
    def _awq_gemm_fake(
        input: torch.Tensor,
        qweight: torch.Tensor,
        scales: torch.Tensor,
636
        qzeros: torch.Tensor,
637
638
        split_k_iters: torch.SymInt,
    ) -> torch.Tensor:
639
        num_in_feats = input.size(0)
640
641
642
643
644
        return torch.empty(
            (split_k_iters, num_in_feats, qweight.size(1) * 8),
            dtype=input.dtype,
            device=input.device,
        ).sum(0)
645

646
647
    @register_fake("_C::machete_mm")
    def machete_mm_fake(
648
        a: torch.Tensor,
649
        # b_q Should be the tensor returned by machete_prepack_B
650
        b_q: torch.Tensor,
651
        b_type: ScalarType,
652
653
654
655
656
657
658
        out_type: torch.dtype | None = None,
        b_group_scales: torch.Tensor | None = None,
        b_group_zeros: torch.Tensor | None = None,
        b_group_size: int | None = None,
        b_channel_scales: torch.Tensor | None = None,
        a_token_scales: torch.Tensor | None = None,
        schedule: str | None = None,
659
660
661
662
663
    ) -> torch.Tensor:
        m = a.size(0)
        n = b_q.size(1)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)

664
    @register_fake("_C::machete_prepack_B")
665
    def machete_prepack_B_fake(
666
667
668
        b_q_weight: torch.Tensor,
        a_type: torch.dtype,
        b_type: ScalarType,
669
        group_scales_type: torch.dtype | None,
670
671
    ) -> torch.Tensor:
        return torch.empty_like(b_q_weight, memory_format=torch.contiguous_format)
672

673
674
    @register_fake("_C::cutlass_w4a8_mm")
    def cutlass_w4a8_mm_fake(
675
676
677
678
679
680
681
        a: torch.Tensor,
        # b_q Should be the tensor returned by cutlass_encode_and_reorder_int4b
        b_q: torch.Tensor,
        b_group_scales: torch.Tensor,
        b_group_size: int,
        b_channel_scales: torch.Tensor,
        a_token_scales: torch.Tensor,
682
683
        out_type: torch.dtype | None = None,
        maybe_schedule: str | None = None,
684
    ) -> torch.Tensor:
685
686
687
688
689
690
691
692
693
694
695
696
697
        m = a.size(0)
        n = b_q.size(1)
        out_dtype = out_type if out_type is not None else torch.bfloat16
        return torch.empty((m, n), device=a.device, dtype=out_dtype)

    @register_fake("_C::cutlass_pack_scale_fp8")
    def cutlass_pack_scale_fp8_fake(scales: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(scales, memory_format=torch.contiguous_format)

    @register_fake("_C::cutlass_encode_and_reorder_int4b")
    def cutlass_encode_and_reorder_int4b_fake(b: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(b, memory_format=torch.contiguous_format)

698
699
700
701
    @register_fake("_C::cutlass_encode_and_reorder_int4b_grouped")
    def cutlass_encode_and_reorder_int4b_grouped_fake(b: torch.Tensor) -> torch.Tensor:
        return torch.empty_like(b, memory_format=torch.contiguous_format)

702

703
704
705
if hasattr(torch.ops._C, "allspark_w8a16_gemm"):

    @register_fake("_C::allspark_w8a16_gemm")
706
707
708
709
    def _allspark_w8a16_gemm_fake(
        a: torch.Tensor,
        b_qweight: torch.Tensor,
        b_scales: torch.Tensor,
710
        b_qzeros: torch.Tensor | None,
711
712
713
714
715
716
717
718
        n: torch.SymInt,
        group_size: torch.SymInt,
        sm_count: torch.SymInt,
        sm_version: torch.SymInt,
        CUBLAS_M_THRESHOLD: torch.SymInt,
        has_zp: bool,
        n32k16_reorder: bool,
    ) -> torch.Tensor:
719
720
721
722
        m = a.size(0)
        return torch.empty((m, n), device=a.device, dtype=a.dtype)


723
724
725
if hasattr(torch.ops._C, "ggml_dequantize"):

    @register_fake("_C::ggml_dequantize")
726
    def _ggml_dequantize_fake(
727
728
729
730
        W: torch.Tensor,
        quant_type: int,
        m: torch.SymInt,
        n: torch.SymInt,
731
        dtype: torch.dtype | None = None,
732
    ) -> torch.Tensor:
733
734
735
736
737
738
739
740
741
        return torch.empty((m, n), dtype=torch.float16, device=W.device)

    @register_fake("_C::ggml_mul_mat_vec_a8")
    def _ggml_mul_mat_vec_a8_fake(
        W: torch.Tensor,
        X: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
    ) -> torch.Tensor:
742
        return torch.empty((X.shape[0], row), dtype=X.dtype, device=W.device)
743
744
745
746
747
748
749
750
751

    @register_fake("_C::ggml_mul_mat_a8")
    def _ggml_mul_mat_a8_fake(
        W: torch.Tensor,
        X: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
    ) -> torch.Tensor:
        batch = X.size(0)
752
        return torch.empty((batch, row), dtype=X.dtype, device=W.device)
753

754
755
756
757
758
759
760
761
762
763
764
765
766
    @register_fake("_C::ggml_moe_a8")
    def _ggml_moe_a8_fake(
        X: torch.Tensor,
        W: torch.Tensor,
        sorted_token_ids: torch.Tensor,
        expert_ids: torch.Tensor,
        num_tokens_post_padded: torch.Tensor,
        quant_type: int,
        row: torch.SymInt,
        top_k: torch.SymInt,
        tokens: torch.SymInt,
    ) -> torch.Tensor:
        tokens = X.size(0)
767
        return torch.empty((tokens * top_k, row), dtype=torch.float16, device=W.device)
768

769

770
771
772
773
774
775
776
777
778
779
780
781
782
if hasattr(torch.ops._C, "ggml_moe_a8_vec"):

    @register_fake("_C::ggml_moe_a8_vec")
    def _ggml_moe_a8_vec_fake(
        X: torch.Tensor,
        W: torch.Tensor,
        topk_ids: torch.Tensor,
        top_k: int,
        quant_type: int,
        row: torch.SymInt,
        tokens: torch.SymInt,
    ) -> torch.Tensor:
        tokens = X.size(0)
783
        return torch.empty((tokens * top_k, row), dtype=X.dtype, device=W.device)
784
785


786
# cutlass
787
788
789
790
def cutlass_scaled_mm_supports_fp4(cuda_device_capability: int) -> bool:
    return torch.ops._C.cutlass_scaled_mm_supports_fp4(cuda_device_capability)


791
792
793
794
795
796
797
798
def cutlass_scaled_fp4_mm(
    a: torch.Tensor,
    b: torch.Tensor,
    block_scale_a: torch.Tensor,
    block_scale_b: torch.Tensor,
    alpha: torch.Tensor,
    out_dtype: torch.dtype,
) -> torch.Tensor:
799
800
801
    assert a.ndim == 2 and b.ndim == 2
    m, n = a.shape[0], b.shape[0]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)
802
    torch.ops._C.cutlass_scaled_fp4_mm(out, a, b, block_scale_a, block_scale_b, alpha)
803
804
805
    return out


806
807
808
809
def cutlass_scaled_mm_supports_fp8(cuda_device_capability: int) -> bool:
    return torch.ops._C.cutlass_scaled_mm_supports_fp8(cuda_device_capability)


810
def cutlass_scaled_mm_supports_block_fp8(cuda_device_capability: int) -> bool:
811
    return torch.ops._C.cutlass_scaled_mm_supports_block_fp8(cuda_device_capability)
812
813


814
815
816
817
818
819
def cutlass_scaled_mm(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
820
    bias: torch.Tensor | None = None,
821
) -> torch.Tensor:
822
    """
823
    `cutlass_scaled_mm` implements a fused version of
824
        `output = torch.mm((scale_a * a), (scale_b * b)).to(out_dtype)`
825
826
827
828
829
830
831
832
    where scale_a * a and scale_b * b are implemented using numpy-style
    broadcasting.

    In order to support blockwise scaling like found in DeepSeek V3 we also
    support extended "group" broadcast rules. We extend the numpy-style
    broadcasting rules with the following rule:
        "if the extent of a dimension in the source shape is between 1 and
        corresponding extent in the target shape we repeat each element along
833
834
835
836
837
838
839
840
841
842
843
        that dimension  src_shape[dim] // target_shape[dim] times consecutively"
    example if we have:
          a = [[1, 2], and target_shape = (2, 4)
               [3, 4]]
    then we would expand a to:
          a = [[1, 1, 2, 2],
               [3, 3, 4, 4]]
    currently we only support the case:
        scale_a.shape * [1, 128] == a.shape
        scale_b.shape * [128, 128] == b.shape
    """
844
845
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.numel() == b.shape[1] and bias.dtype == out_dtype
846

847
848
849
    # Massage the input to be 2D
    target_shape = (*a.shape[:-1], b.shape[1])
    a = a.view(-1, a.shape[-1])
850

851
    cutlass_compatible_b = b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
852
    if current_platform.is_rocm() or not cutlass_compatible_b:
853
        from vllm.model_executor.layers.quantization.compressed_tensors.triton_scaled_mm import (  # noqa
854
855
856
            triton_scaled_mm,
        )

857
858
        out = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
    else:
859
        out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
860
        torch.ops._C.cutlass_scaled_mm(out, a, b, scale_a, scale_b, bias)
861

862
    return out.view(*target_shape)
863
864


865
866
867
868
869
870
871
def cutlass_scaled_mm_azp(
    a: torch.Tensor,
    b: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
    azp_adj: torch.Tensor,
872
873
    azp: torch.Tensor | None = None,
    bias: torch.Tensor | None = None,
874
) -> torch.Tensor:
875
876
877
878
879
    """
    :param azp_adj: In the per-tensor case, this should include the azp.
    Always per-channel.
    :param azp: Only set in the per-token case. Per-token if set.
    """
880
881
882
    assert b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.numel() == b.shape[1] and bias.dtype == out_dtype
883

884
885
886
887
    # Massage the input to be 2D
    target_shape = (*a.shape[:-1], b.shape[1])
    a = a.view(-1, a.shape[-1])
    assert azp is None or azp.numel() == a.shape[0]
888

889
890
    out = torch.empty((a.shape[0], b.shape[1]), dtype=out_dtype, device=a.device)
    torch.ops._C.cutlass_scaled_mm_azp(out, a, b, scale_a, scale_b, azp_adj, azp, bias)
891
    return out.view(*target_shape)
892
893


894
def cutlass_sparse_scaled_mm_supported(cuda_device_capability: int) -> bool:
895
    return torch.ops._C.cutlass_sparse_scaled_mm_supported(cuda_device_capability)
896
897


898
def cutlass_group_gemm_supported(cuda_device_capability: int) -> bool:
899
900
901
902
903
    try:
        return torch.ops._C.cutlass_group_gemm_supported(cuda_device_capability)
    except AttributeError:
        # Return False on non-CUDA platforms where it is not available
        return False
904

905

906
def cutlass_sparse_compress(a: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
907
908
909
910
911
912
913
914
    """
    Compresses a sparse matrix for use with Cutlass sparse operations.

    This function takes a dense tensor and compresses it into two components:
    non-zero elements and metadata. The compressed representation is compatible
    with Cutlass sparse kernels.

    Args:
915
        a (torch.Tensor):
916
917
918
919
920
921
922
            The input tensor to be compressed. Must have one of the following data types:
            - `torch.int8`
            - `torch.float8_e4m3fn`
            - `torch.bfloat16`
            - `torch.float16`

    Returns:
923
        tuple[torch.Tensor, torch.Tensor]:
924
925
926
927
928
929
930
931
932
933
934
935
936
            A tuple containing:
            - `a_nzs` (torch.Tensor): A tensor containing non-zero elements of `a`.
            - `a_meta` (torch.Tensor): A tensor containing metadata for the sparse representation.

    Raises:
        ValueError: If the compression operation fails.

    Notes:
        - The `a_meta` tensor has a data type of `torch.uint8`.
        - Each metadata element encodes the sparsity of 4 non-zero elements (i.e., `elemsPerMetaElem = 4`).
        - The shape of `a_nzs` is `(m, k // 2)`, where `m` and `k` are the dimensions of the input tensor.
        - The shape of `a_meta` is `(m, k // 2 // elemsPerMetaElem)`.
    """
937
938
    assert a.dtype in [torch.int8, torch.float8_e4m3fn, torch.bfloat16, torch.float16]
    assert a.is_contiguous()
939
940
941

    # a_meta.dtype: torch.uint8 so elemsPerMetaElem = 8b / 2b_per_nz = 4
    elemsPerMetaElem = 4
942
    assert a.shape[1] % (2 * elemsPerMetaElem) == 0
943

944
    return torch.ops._C.cutlass_sparse_compress(a)
945
946
947


def cutlass_scaled_sparse_mm(
948
949
950
951
952
953
    a: torch.Tensor,
    bt_nzs: torch.Tensor,
    bt_meta: torch.Tensor,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    out_dtype: torch.dtype,
954
    bias: torch.Tensor | None = None,
955
) -> torch.Tensor:
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
    """
    Performs a scaled sparse matrix multiplication using Cutlass.

    Steps:
    1. Create a dense matrix `a` of shape (m, k) on the CUDA device:
    `a = torch.randn((m, k), device='cuda')`.

    2. Create a dense matrix `b` of shape (k, n) on the CUDA device:
    `b = torch.randn((k, n), device='cuda')`.

    3. Prune matrix `b` to 2:4 sparsity along the specified dimension:
    `b = prune_to_2_4(b, dim=0)`.

    4. Compress the transposed sparse matrix `b.t()`:
    `bt_nzs, bt_meta = cutlass_sparse_compress(b.t())`.

    5. Perform sparse matrix multiplication using the compressed matrix,
    applying scaling factors for `a` and `b`, and the output data type:
    `out = cutlass_scaled_sparse_mm(a, bt_nzs, bt_meta, scale_a, scale_b, out_dtype)`.

    Returns:
    - The result of the scaled sparse matrix multiplication.
    """
979
980
981
    assert bt_nzs.shape[0] % 16 == 0 and bt_nzs.shape[1] % 16 == 0
    assert out_dtype is torch.bfloat16 or out_dtype is torch.float16
    assert bias is None or bias.shape[0] == bt_nzs.shape[0] and bias.dtype == out_dtype
982
983
984
985
986

    m = a.shape[0]
    n = bt_nzs.shape[0]
    out = torch.empty((m, n), dtype=out_dtype, device=a.device)

987
988
989
    torch.ops._C.cutlass_scaled_sparse_mm(
        out, a, bt_nzs, bt_meta, scale_a, scale_b, bias
    )
990
991
992
993

    return out


994
995
996
997
998
999
1000
1001
1002
1003
def get_cutlass_moe_mm_data(
    topk_ids: torch.Tensor,
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    input_permutation: torch.Tensor,
    output_permutation: torch.Tensor,
    num_experts: int,
    n: int,
    k: int,
1004
    blockscale_offsets: torch.Tensor | None = None,
1005
):
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
    """
    Prepare data necessary to perform CUTLASS grouped matrix multiplications
    used in CUTLASS-based fused MoE.

    The function takes in topk_ids (token-expert mapping) and uses it to
    compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation after the input is sorted with
                      input_permutation. The number of tokens computed with
                      expert E is expert_offsets[E + 1] - expert_offsets[E]
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    - input_permutation: Permutation that must be used to shuffle the input
                         before executing the MMs.
    - output_permutation: Permutation that must be used to shuffle the output
                          after executing the MMs.
1023
1024
1025
1026
1027
    - blockscale_offsets: Optional argument passed for fp4 moe. Indices that
                          mark at which block scale index each expert begins
                          its computation. The number of block scale rows
                          computed with expert E is blockscale_offsets[E + 1] -
                          blockscale_offsets[E]
1028
    """
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
    return torch.ops._C.get_cutlass_moe_mm_data(
        topk_ids,
        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        input_permutation,
        output_permutation,
        num_experts,
        n,
        k,
        blockscale_offsets,
    )
1041
1042


1043
def get_cutlass_moe_mm_problem_sizes(
1044
1045
1046
1047
1048
1049
    topk_ids: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    num_experts: int,
    n: int,
    k: int,
1050
    blockscale_offsets: torch.Tensor | None = None,
1051
    force_swap_ab: bool | None = None,
1052
):
1053
1054
1055
1056
1057
1058
1059
1060
    """
    Compute only the per-expert problem sizes needed by the two grouped matrix
    multiplications used in CUTLASS-based fused MoE.

    The function takes in topk_ids (token→expert mapping) and computes:
    - problem_sizes1, problem_sizes2: M×N×K sizes of each expert's
                                    multiplication for the two grouped MMs
                                    used in the fused MoE operation.
1061
1062
1063
1064
    Optional:
    - force_swap_ab: If set to True or False, explicitly enable or disable the
                     A/B input swap optimization. If None (default), the swap
                     is selected automatically based on tensor sizes.
1065
1066
    """
    return torch.ops._C.get_cutlass_moe_mm_problem_sizes(
1067
1068
1069
1070
1071
1072
1073
1074
        topk_ids,
        problem_sizes1,
        problem_sizes2,
        num_experts,
        n,
        k,
        blockscale_offsets,
        force_swap_ab,
1075
    )
1076
1077


1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
def get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
    expert_first_token_offset: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    n: int,
    k: int,
    swap_ab: bool,
):
    """Compute per-expert (M, N, K) problem sizes from expert_first_token_offset"""
    return torch.ops._C.get_cutlass_moe_mm_problem_sizes_from_expert_offsets(
        expert_first_token_offset,
        problem_sizes1,
        problem_sizes2,
        n,
        k,
        swap_ab,
    )


1097
1098
1099
1100
1101
1102
def shuffle_rows(input_tensor: torch.Tensor, dst2src_map: torch.Tensor):
    """
    Shuffle and expand the input tensor according to the dst2src_map and store the result in output_tensor.
    This is used in MoE to permute the input tensor before performing grouped matrix multiplications.
    """
    num_tokens_permuted = dst2src_map.shape[0]
1103
1104
1105
1106
1107
    output_tensor = torch.empty(
        (num_tokens_permuted, input_tensor.shape[1]),
        device=input_tensor.device,
        dtype=input_tensor.dtype,
    )
1108
1109
    torch.ops._moe_C.shuffle_rows(input_tensor, dst2src_map, output_tensor)
    return output_tensor
1110
1111


1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
def get_cutlass_pplx_moe_mm_data(
    expert_offsets: torch.Tensor,
    problem_sizes1: torch.Tensor,
    problem_sizes2: torch.Tensor,
    expert_num_tokens: torch.Tensor,
    num_local_experts: int,
    padded_m: int,
    n: int,
    k: int,
):
1122
1123
1124
1125
1126
    """
    Prepare data necessary to perform CUTLASS grouped matrix multiplications
    used in CUTLASS-based fused MoE.

    The function takes in expert_num_tokens (token count per expert) and
1127
    non_zero_expert_idxs (consecutive indices of experts with non-zero token
1128
1129
1130
1131
1132
1133
1134
1135
    counts) and uses them to compute:
    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation.
    - problem_sizes1, problem_sizes2: MxNxK sizes of each expert's
                                      multiplication in two grouped MMs used in
                                      the fused MoE operation.
    """
    return torch.ops._C.get_cutlass_pplx_moe_mm_data(
1136
1137
1138
1139
1140
1141
1142
1143
1144
        expert_offsets,
        problem_sizes1,
        problem_sizes2,
        expert_num_tokens,
        num_local_experts,
        padded_m,
        n,
        k,
    )
1145
1146


1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
def cutlass_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    expert_offsets: torch.Tensor,
    problem_sizes: torch.Tensor,
    a_strides: torch.Tensor,
    b_strides: torch.Tensor,
    c_strides: torch.Tensor,
    per_act_token: bool,
    per_out_ch: bool,
):
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
    """
    A single grouped matrix multiplication used in CUTLASS-based fused MoE.
    The function executes fp8-quantized OUT = AB matrix multiplication.

    - expert_offsets: Indices that mark at which token index each expert begins
                      its computation. The number of tokens computed with
                      expert E is expert_offsets[E + 1] - expert_offsets[E]
    - problem_sizes: MxNxK sizes of each expert's multiplication in two grouped
                     MMs used in the fused MoE operation.
    - a/b/c_strides: The data strides passed to grouped matrix multiplication.
    """
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
    return torch.ops._C.cutlass_moe_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        expert_offsets,
        problem_sizes,
        a_strides,
        b_strides,
        c_strides,
        per_act_token,
        per_out_ch,
    )
1186
1187


1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
def cutlass_fp4_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    alphas: torch.Tensor,
    problem_sizes: torch.Tensor,
    expert_offsets: torch.Tensor,
    sf_offsets: torch.Tensor,
):
1199
    """
1200
    An FP4 Blockscaled Group Gemm that takes in  a_tensors, b_tensors and runs
1201
1202
1203
1204
1205
1206
    the gemms for each combination based on the specified problem sizes.

    This is used as the MoE gemm during NVFP4 Quantized FusedMoE forward.
    - a/b_tensors: the NVFP4 a_ptrs and b_ptrs tensors which are quantized
                     input and expert weights.
    - a_/b_scales: The blockscales in FP8-E4M3 precision
1207
1208
1209
1210
    - expert_offsets/sf_offsets: Indices that mark at which token index
                    each expert begins its computation. The number of tokens
                    computed with expert E is expert_offsets[E + 1] -
                    expert_offsets[E] And the sf_size per expert is
1211
1212
1213
1214
                    sf_offset[E+1] - sf_offset[E]
    - problem_sizes: MxNxK sizes of each expert's multiplication in two grouped
                     MMs used in the fused MoE operation.
    """
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
    return torch.ops._C.cutlass_fp4_group_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        alphas,
        problem_sizes,
        expert_offsets,
        sf_offsets,
    )
1226
1227


1228
# gptq_marlin
1229
1230
1231
1232
1233
1234
def gptq_marlin_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1235
    is_a_8bit: bool = False,
1236
) -> torch.Tensor:
1237
1238
1239
    return torch.ops._C.gptq_marlin_repack(
        b_q_weight, perm, size_k, size_n, num_bits, is_a_8bit
    )
1240
1241


1242
1243
1244
1245
1246
1247
1248
1249
1250
if hasattr(torch.ops._C, "gptq_marlin_repack"):

    @register_fake("_C::gptq_marlin_repack")
    def _gptq_marlin_repack_fake(
        b_q_weight: torch.Tensor,
        perm: torch.Tensor,
        size_k: torch.SymInt,
        size_n: torch.SymInt,
        num_bits: int,
1251
        is_a_8bit: bool = False,
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
    ) -> torch.Tensor:
        pack_factor = 32 // num_bits
        marlin_tile_size = 16
        return torch.empty(
            (size_k // marlin_tile_size, size_n * marlin_tile_size // pack_factor),
            dtype=b_q_weight.dtype,
            device=b_q_weight.device,
        )


# awq_marlin
1263
def awq_marlin_repack(
1264
1265
1266
1267
1268
    b_q_weight: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
    is_a_8bit: bool = False,
1269
) -> torch.Tensor:
1270
1271
1272
    return torch.ops._C.awq_marlin_repack(
        b_q_weight, size_k, size_n, num_bits, is_a_8bit
    )
1273
1274


1275
1276
1277
1278
1279
1280
1281
1282
if hasattr(torch.ops._C, "awq_marlin_repack"):

    @register_fake("_C::awq_marlin_repack")
    def _awq_marlin_repack_fake(
        b_q_weight: torch.Tensor,
        size_k: torch.SymInt,
        size_n: torch.SymInt,
        num_bits: int,
1283
        is_a_8bit: bool = False,
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
    ) -> torch.Tensor:
        pack_factor = 32 // num_bits
        marlin_tile_size = 16
        return torch.empty(
            (size_k // marlin_tile_size, size_n * marlin_tile_size // pack_factor),
            dtype=b_q_weight.dtype,
            device=b_q_weight.device,
        )


1294
1295
1296
1297
1298
1299
def gptq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1300
    is_a_8bit: bool = False,
1301
) -> torch.Tensor:
1302
1303
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
1304
1305
1306
1307
1308
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1309
    for e in range(num_experts):
1310
        output[e] = torch.ops._C.gptq_marlin_repack(
1311
            b_q_weight[e], perm[e], size_k, size_n, num_bits, is_a_8bit
1312
        )
1313
1314
1315
    return output


1316
1317
1318
1319
1320
1321
def awq_marlin_moe_repack(
    b_q_weight: torch.Tensor,
    perm: torch.Tensor,
    size_k: int,
    size_n: int,
    num_bits: int,
1322
    is_a_8bit: bool = False,
1323
) -> torch.Tensor:
1324
1325
    num_experts = b_q_weight.shape[0]
    assert size_k % 16 == 0
1326
1327
1328
1329
1330
    output = torch.empty(
        (num_experts, size_k // 16, size_n * (num_bits // 2)),
        device=b_q_weight.device,
        dtype=b_q_weight.dtype,
    )
1331
    for e in range(num_experts):
1332
        output[e] = torch.ops._C.awq_marlin_repack(
1333
            b_q_weight[e], size_k, size_n, num_bits, is_a_8bit
1334
        )
1335
1336
1337
    return output


1338
1339
1340
1341
1342
1343
1344
1345
def marlin_int4_fp8_preprocess(
    qweight: torch.Tensor,
    qzeros_or_none: torch.Tensor | None = None,
    inplace: bool = False,
):
    return torch.ops._C.marlin_int4_fp8_preprocess(qweight, qzeros_or_none, inplace)


1346
1347
def gptq_marlin_gemm(
    a: torch.Tensor,
1348
    c: torch.Tensor | None,
1349
    b_q_weight: torch.Tensor,
1350
    b_bias: torch.Tensor | None,
1351
    b_scales: torch.Tensor,
1352
    a_scales: torch.Tensor | None,
1353
1354
1355
1356
    global_scale: torch.Tensor | None,
    b_zeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    perm: torch.Tensor | None,
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
    workspace: torch.Tensor,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
    is_k_full: bool = True,
    use_atomic_add: bool = False,
    use_fp32_reduce: bool = False,
    is_zp_float: bool = False,
) -> torch.Tensor:
    return torch.ops._C.gptq_marlin_gemm(
        a,
        c,
        b_q_weight,
        b_bias,
        b_scales,
1373
        a_scales,
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
        global_scale,
        b_zeros,
        g_idx,
        perm,
        workspace,
        b_q_type.id,
        size_m,
        size_n,
        size_k,
        is_k_full,
        use_atomic_add,
        use_fp32_reduce,
        is_zp_float,
    )
1388
1389


1390
# machete
1391
def machete_supported_schedules(
1392
1393
    a_type: torch.dtype,
    b_type: ScalarType,
1394
1395
1396
1397
1398
    group_scales_type: torch.dtype | None,
    group_zeros_type: torch.dtype | None = None,
    channel_scales_type: torch.dtype | None = None,
    token_scales_type: torch.dtype | None = None,
    out_type: torch.dtype | None = None,
1399
) -> list[str]:
1400
    return torch.ops._C.machete_supported_schedules(
1401
1402
1403
1404
1405
1406
1407
1408
        a_type,
        b_type.id,
        group_scales_type,
        group_zeros_type,
        channel_scales_type,
        token_scales_type,
        out_type,
    )
1409
1410
1411


def machete_mm(
1412
1413
1414
1415
    a: torch.Tensor,
    # b_q Should be the tensor returned by machete_prepack_B
    b_q: torch.Tensor,
    b_type: ScalarType,
1416
1417
1418
1419
1420
1421
1422
    out_type: torch.dtype | None = None,
    b_group_scales: torch.Tensor | None = None,
    b_group_zeros: torch.Tensor | None = None,
    b_group_size: int | None = None,
    b_channel_scales: torch.Tensor | None = None,
    a_token_scales: torch.Tensor | None = None,
    schedule: str | None = None,
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
) -> torch.Tensor:
    return torch.ops._C.machete_mm(
        a,
        b_q,
        b_type.id,
        out_type,
        b_group_scales,
        b_group_zeros,
        b_group_size,
        b_channel_scales,
        a_token_scales,
        schedule,
    )
1436
1437
1438


def machete_prepack_B(
1439
1440
1441
    b_q_weight: torch.Tensor,
    a_type: torch.dtype,
    b_type: ScalarType,
1442
    group_scales_type: torch.dtype | None,
1443
1444
1445
1446
) -> torch.Tensor:
    return torch.ops._C.machete_prepack_B(
        b_q_weight, a_type, b_type.id, group_scales_type
    )
1447
1448


1449
1450
# CUTLASS W4A8
def cutlass_w4a8_mm(
1451
1452
1453
1454
1455
1456
1457
    a: torch.Tensor,
    # b_q Should be the tensor returned by cutlass_encode_and_reorder_int4b
    b_q: torch.Tensor,
    b_group_scales: torch.Tensor,
    b_group_size: int,
    b_channel_scales: torch.Tensor,
    a_token_scales: torch.Tensor,
1458
1459
    out_type: torch.dtype | None = None,
    maybe_schedule: str | None = None,
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
) -> torch.Tensor:
    return torch.ops._C.cutlass_w4a8_mm(
        a,
        b_q,
        b_group_scales,
        b_group_size,
        b_channel_scales,
        a_token_scales,
        out_type,
        maybe_schedule,
    )
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480


def cutlass_pack_scale_fp8(scales: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.cutlass_pack_scale_fp8(scales)


def cutlass_encode_and_reorder_int4b(b: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.cutlass_encode_and_reorder_int4b(b)


1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
def cutlass_w4a8_moe_mm(
    out_tensors: torch.Tensor,
    a_tensors: torch.Tensor,
    b_tensors: torch.Tensor,
    a_scales: torch.Tensor,
    b_scales: torch.Tensor,
    b_group_scales: torch.Tensor,
    b_group_size: int,
    expert_offsets: torch.Tensor,
    problem_sizes: torch.Tensor,
    a_strides: torch.Tensor,
    b_strides: torch.Tensor,
    c_strides: torch.Tensor,
    group_scale_strides: torch.Tensor,
    maybe_schedule: str | None = None,
):
    """
    Executes the CUTLASS-based fused-MoE grouped matrix multiplication for the
    W4A8 quantization scheme. Uses group-wise quantization (INT4 -> FP8)
    and both per-channel + per-token scaling in the epilogue.

    Args:
        out_tensors:
            Output buffer for all experts (updated in-place).
        a_tensors:
            FP8 (E4M3FN) activations for all experts.
        b_tensors:
            INT4-packed weight matrix for all experts, packed to INT32
        a_scales:
            Per-token FP8 activation scales, applied in the epilogue.
        b_scales:
            Per-channel FP8 weight scales for each expert, applied in the epilogue.
        b_group_scales:
            FP8 scale values for group-wise INT4 weight blocks.
        b_group_size:
            Number of elements grouped under each entry of b_group_scales.
        expert_offsets:
            Cumulative token offsets
        problem_sizes:
            Per-expert (M, N, K) GEMM sizes used by the grouped GEMM launcher.
        a/b/c/group_scale_strides:
            Strides describing the memory layout of the input tensors.
        maybe_schedule:
            Optional override to choose a specific kernel or epilogue schedule.

    Returns:
        out_tensors updated in-place with the dequantized INT4xFP8 grouped GEMM result.
    """
    return torch.ops._C.cutlass_w4a8_moe_mm(
        out_tensors,
        a_tensors,
        b_tensors,
        a_scales,
        b_scales,
        b_group_scales,
        b_group_size,
        expert_offsets,
        problem_sizes,
        a_strides,
        b_strides,
        c_strides,
        group_scale_strides,
        maybe_schedule,
    )


def cutlass_encode_and_reorder_int4b_grouped(
    b_tensors: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    return torch.ops._C.cutlass_encode_and_reorder_int4b_grouped(b_tensors)


1553
if hasattr(torch.ops._C, "permute_cols"):
1554

1555
    @register_fake("_C::permute_cols")
1556
    def _permute_cols_fake(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
1557
1558
1559
1560
1561
1562
1563
        return torch.empty_like(a)


def permute_cols(a: torch.Tensor, perm: torch.Tensor) -> torch.Tensor:
    return torch.ops._C.permute_cols(a, perm)


1564
1565
# fp4
def scaled_fp4_quant(
1566
1567
    input: torch.Tensor, input_global_scale: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
    """
    Quantize input tensor to FP4 and return quantized tensor and scale.

    This function quantizes the last dimension of the given tensor `input`. For
    every 16 consecutive elements, a single dynamically computed scaling factor
    is shared. This scaling factor is quantized using the `input_global_scale`
    and is stored in a swizzled layout (see
    https://docs.nvidia.com/cuda/parallel-thread-execution/#tcgen05-mma-scale-factor-b-layout-4x).

    Args:
        input: The input tensor to be quantized to FP4
        input_global_scale: A scalar scaling factor for the entire tensor.

    Returns:
1582
        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP4 but every
1583
1584
1585
            two values are packed into a uint8 and float8_e4m3 scaling factors
            in the sizzled layout.
    """
1586
    assert not current_platform.is_rocm()
1587
    assert input.ndim >= 1, f"input.ndim needs to be >= 1, but got {input.ndim}."
1588
1589
1590
1591
1592
1593
    other_dims = 1 if input.ndim == 1 else -1
    input = input.reshape(other_dims, input.shape[-1])
    m, n = input.shape
    block_size = 16
    device = input.device

1594
    assert n % block_size == 0, f"last dim has to be multiple of 16, but got {n}."
1595
    assert input.dtype in (torch.float16, torch.bfloat16), (
1596
1597
        f"input.dtype needs to be fp16 or bf16 but got {input.dtype}."
    )
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610

    # Two fp4 values will be packed into an uint8.
    output = torch.empty((m, n // 2), device=device, dtype=torch.uint8)

    # We use the rounded values to store the swizzled values. Due to the
    # requirement of the Tensor Core, the minimum tile is 128x4 for the scales.
    # So, we first pad the scales to multiples of 128 and 4. Then, the scales
    # (in float8_e4m3fn) are packed into an int32 for every 4 values. More:
    # https://docs.nvidia.com/cuda/parallel-thread-execution/#tcgen05-mma-scale-factor-b-layout-4x
    round_up = lambda x, y: (x + y - 1) // y * y
    rounded_m = round_up(m, 128)
    scale_n = n // block_size
    rounded_n = round_up(scale_n, 4)
1611
    output_scale = torch.empty(
1612
1613
        (rounded_m, rounded_n // 4), device=device, dtype=torch.int32
    )
1614

1615
    torch.ops._C.scaled_fp4_quant(output, input, output_scale, input_global_scale)
1616
1617
1618
1619
    output_scale = output_scale.view(torch.float8_e4m3fn)
    return output, output_scale


1620
1621
1622
1623
1624
1625
1626
1627
def scaled_fp4_experts_quant(
    input_tensor: torch.Tensor,
    input_global_scale: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
1628
    Quantize input tensor to NVFP4 and return quantized tensor and scale, for
1629
1630
    packed MoE Inputs.
    Args:
1631
        input_tensor: The input tensor to be quantized to NVFP4
1632
1633
1634
1635
        input_global_scale: A scalar scaling factor for the entire tensor.
        expert_offsets: The expert offsets tensor
        blockscale_offsets: The blockscale offsets tensor
    Outputs:
1636
        output: The quantized tensor in NVFP4
1637
1638
1639
1640
        output_scales: The blockscale tensor in FP8-E4M3
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2, (
1641
1642
        f"input.ndim needs to be == 2, but got {input_tensor.ndim}."
    )
1643

1644
1645
1646
1647
1648
    # Control the maximum number of tokens per expert supported by the
    # NVFP4 MoE Expert Quantization. This is used to prevent the kernel
    # from running out of memory. This value can also be increased to support
    # larger models.
    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
1649
1650
    m_numtopk, k = input_tensor.shape

1651
    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk, (
1652
1653
1654
        f"m_numtopk must be less than MAX_TOKENS_PER_EXPERT("
        f"{MAX_TOKENS_PER_EXPERT})"
        f" for cutlass_moe_fp4, observed m_numtopk = {m_numtopk}. Use"
1655
1656
        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
    )
1657
1658
1659
1660
    scales_k = k // 16
    padded_k = (scales_k + (4 - 1)) // 4

    # output is uint8 and packed fp4 values
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
    output = torch.empty(
        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
    )
    output_scales = torch.empty(
        MAX_TOKENS_PER_EXPERT * topk,
        padded_k,
        dtype=torch.int32,
        device=input_tensor.device,
    )
    torch.ops._C.scaled_fp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        input_global_scale,
        expert_offsets,
        blockscale_offsets,
    )
1678
1679
1680
1681
    output_scales = output_scales.view(torch.float8_e4m3fn)
    return output, output_scales


1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
def silu_and_mul_scaled_fp4_experts_quant(
    input_tensor: torch.Tensor,
    input_global_scale: torch.Tensor,
    expert_offsets: torch.Tensor,
    blockscale_offsets: torch.Tensor,
    topk: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Fused SiLU+Mul+NVFP4 quantization for MoE intermediate activations.

    Args:
        input_tensor: The input tensor with gate || up layout [m_topk, k*2]
        input_global_scale: A per-expert scaling factor [n_experts]
        expert_offsets: The expert offsets tensor [n_experts+1]
        blockscale_offsets: The blockscale offsets tensor [n_experts+1]
        topk: Number of top-k experts selected
    Outputs:
        output: The quantized tensor in NVFP4 [m_topk, k/2]
        output_scales: The blockscale tensor in FP8-E4M3
    """
    assert not current_platform.is_rocm()
    assert input_tensor.ndim == 2, (
        f"input.ndim needs to be == 2, but got {input_tensor.ndim}."
    )

    # Control the maximum number of tokens per expert supported by the
    # NVFP4 MoE Expert Quantization. This is used to prevent the kernel
    # from running out of memory. This value can also be increased to support
    # larger models.
    MAX_TOKENS_PER_EXPERT = envs.VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE
    m_numtopk, k_times_2 = input_tensor.shape
    assert k_times_2 % 2 == 0, "input width must be even (gate || up layout)"
    k = k_times_2 // 2

    assert m_numtopk <= MAX_TOKENS_PER_EXPERT * topk, (
        f"m_numtopk must be less than MAX_TOKENS_PER_EXPERT("
        f"{MAX_TOKENS_PER_EXPERT})"
        f" for cutlass_moe_fp4, observed m_numtopk = {m_numtopk}. Use"
        f" VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE to set this value."
    )
    scales_k = k // 16
    padded_k = (scales_k + (4 - 1)) // 4

    # output is uint8 and packed fp4 values
    output = torch.empty(
        m_numtopk, k // 2, device=input_tensor.device, dtype=torch.uint8
    )
    output_scales = torch.empty(
        MAX_TOKENS_PER_EXPERT * topk,
        padded_k,
        dtype=torch.int32,
        device=input_tensor.device,
    )
    torch.ops._C.silu_and_mul_scaled_fp4_experts_quant(
        output,
        output_scales,
        input_tensor,
        input_global_scale,
        expert_offsets,
        blockscale_offsets,
    )
    output_scales = output_scales.view(torch.float8_e4m3fn)
    return output, output_scales


1747
# fp8
1748
1749
def scaled_fp8_quant(
    input: torch.Tensor,
1750
1751
1752
    scale: torch.Tensor | None = None,
    num_token_padding: int | None = None,
    scale_ub: torch.Tensor | None = None,
1753
    use_per_token_if_dynamic: bool = False,
1754
    output: torch.Tensor | None = None,
1755
    group_shape: tuple[int, int] | None = None,
1756
) -> tuple[torch.Tensor, torch.Tensor]:
1757
1758
1759
1760
1761
1762
    """
    Quantize input tensor to FP8 and return quantized tensor and scale.

    This function supports both static and dynamic quantization: If you
    provide the scale, it will use static scaling and if you omit it,
    the scale will be determined dynamically. The function also allows
1763
    optional padding of the output tensors for downstream kernels that
1764
1765
1766
    will benefit from padding.

    Args:
1767
1768
1769
1770
1771
1772
1773
        input: The input tensor to be quantized to FP8 (must be 2D: [M, N])
        scale: Optional scaling factor for the FP8 quantization. Supports:
            - 0D or [1]: per-tensor scaling
            - 1D: requires explicit group_shape to disambiguate per-channel
              vs per-token (use (-1, 1) for per-channel, (1, -1) for per-token)
            - 2D [M/group_m, N/group_n]: group scaling (e.g. [M, N/128] for
              DeepSeek-style (1,128) groups, or [M/128, N/128] for (128,128))
1774
        scale_ub: Optional upper bound for scaling factor in dynamic
1775
            per token case
1776
        num_token_padding: If specified, pad the first dimension
1777
            of the output to at least this value.
1778
        use_per_token_if_dynamic: Whether to do per_tensor or per_token
1779
            in the dynamic quantization case.
1780
1781
1782
1783
        group_shape: Optional tuple (group_m, group_n) specifying the group
            shape for static quantization. Use -1 for "full extent" (e.g.,
            (-1, -1) for per-tensor, (-1, 1) for per-channel, etc.)
            Required for 1D scales; optional for 2D scales.
1784
1785

    Returns:
1786
        tuple[torch.Tensor, torch.Tensor]: The output tensor in FP8 and
1787
1788
            scaling factor.
    """
1789
    # This code assumes batch_dim and num_tokens are flattened
1790
    assert input.ndim == 2
1791
    shape: tuple[int, int] | torch.Size = input.shape
1792
1793
    # For ROCm on MI300, the output fp8 dtype is torch.float_e3m3fnuz
    out_dtype: torch.dtype = current_platform.fp8_dtype()
1794
1795
    if num_token_padding:
        shape = (max(num_token_padding, input.shape[0]), shape[1])
1796
1797
1798
    if output is None:
        output = torch.empty(shape, device=input.device, dtype=out_dtype)
    else:
1799
        assert num_token_padding is None, "padding not supported if output passed in"
1800
        assert output.dtype == out_dtype
1801

1802
    if scale is None:
1803
        if use_per_token_if_dynamic:
1804
            scale = torch.empty((shape[0], 1), device=input.device, dtype=torch.float32)
1805
            torch.ops._C.dynamic_per_token_scaled_fp8_quant(
1806
1807
                output, input, scale, scale_ub
            )
1808
        else:
1809
            scale = torch.empty(1, device=input.device, dtype=torch.float32)
1810
            torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
1811
    else:
1812
        torch.ops._C.static_scaled_fp8_quant(output, input, scale, group_shape)
1813

1814
    return output, scale
1815
1816


1817
1818
# gptq allspark
def allspark_repack_weight(
1819
1820
    qweight: torch.Tensor,
    scale: torch.Tensor,
1821
    zero_point: torch.Tensor | None = None,
1822
    has_zp: bool = False,
1823
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
1824
    """
1825
    Rearrange qweight, scale, and zero_point(if asymmetric) to n32k16 format
1826
1827
1828
1829
1830
1831
1832
1833
    for Ampere W8A16 Fused Gemm kernel

    Args:
        qweight: uint8 weight tensor, original k x n format.
        scale: fp16/bf16 weight scale tensor, 1 x n format.
        zero_point: fp16/bf16 weight zero_point tensor, 1 x n format.
            Must be provided for asymmetric quantization.
        has_zp: if use symmetric quantization, has_zp = False.
1834
1835
            if use asymmetric quantization, has_zp = True.

1836
    Returns:
1837
        tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] :
1838
1839
1840
1841
1842
1843
            rearranged weight, scale, and optionally zero_point.
    """
    K = qweight.shape[0]
    N = qweight.shape[1]
    N_32align = (N + 32 - 1) // 32 * 32

1844
1845
1846
1847
    qweight_reorder = torch.empty(
        (N_32align, K), device=qweight.device, dtype=qweight.dtype
    )
    scale_reorder = torch.empty((1, N_32align), device=scale.device, dtype=scale.dtype)
1848
1849
1850
    zero_point_reorder = None
    if has_zp:
        assert zero_point is not None, (
1851
1852
1853
1854
1855
            "zero_point must be provided for asymmetric quantization."
        )
        zero_point_reorder = torch.empty(
            (1, N_32align), device=zero_point.device, dtype=zero_point.dtype
        )
1856
1857

    torch.ops._C.rearrange_kn_weight_as_n32k16_order(
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
        qweight,
        scale,
        zero_point,
        has_zp,
        qweight_reorder,
        scale_reorder,
        zero_point_reorder,
        K,
        N,
        N_32align,
    )
1869
1870
1871
1872

    return qweight_reorder, scale_reorder, zero_point_reorder


1873
1874
1875
1876
def allspark_w8a16_gemm(
    a: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
1877
    b_qzeros: torch.Tensor | None,
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
    n: int,
    group_size: int,
    sm_count: int,
    sm_version: int,
    CUBLAS_M_THRESHOLD: int,
    has_zp: bool,
    n32k16_reorder: bool,
) -> torch.Tensor:
    return torch.ops._C.allspark_w8a16_gemm(
        a,
        b_qweight,
        b_scales,
        b_qzeros,
        n,
        group_size,
        sm_count,
        sm_version,
        CUBLAS_M_THRESHOLD,
        has_zp,
        n32k16_reorder,
    )
1899
1900


1901
# int8
1902
def scaled_int8_quant(
1903
    input: torch.Tensor,
1904
1905
    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
1906
    symmetric: bool = True,
1907
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]:
1908
    """
1909
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.
1910
1911
1912

    Args:
        input: The input tensor to be quantized to int8.
1913
1914
        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
1915
1916
1917
        azp: Optional zero-point for the int8 quantization.
            Must be provided for asymmetric quantization if `scale` is provided.
        symmetric: Whether to use symmetric quantization (scale only, azp ignored).
1918
1919

    Returns:
1920
      tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] : Output int8 tensor, scales, and optionally azp.
1921
    """
1922
1923
1924
    output = torch.empty_like(input, dtype=torch.int8)
    if scale is not None:
        # static-per-tensor quantization.
1925
1926
1927
        assert symmetric == (azp is None), (
            "azp must only be provided for asymmetric quantization."
        )
1928
        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
1929
        return output, scale, azp
1930
1931

    # dynamic-per-token quantization.
1932
1933
1934
1935
1936
1937
1938
    input_scales = torch.empty(
        (input.numel() // input.shape[-1], 1), device=input.device, dtype=torch.float32
    )
    input_azp = None if symmetric else torch.empty_like(input_scales, dtype=torch.int32)
    torch.ops._C.dynamic_scaled_int8_quant(
        output, input.contiguous(), input_scales, input_azp
    )
1939
    return output, input_scales, input_azp
1940
1941


1942
# gguf
1943
def ggml_dequantize(
1944
    W: torch.Tensor, quant_type: int, m: int, n: int, dtype: torch.dtype | None
1945
) -> torch.Tensor:
1946
    return torch.ops._C.ggml_dequantize(W, quant_type, m, n, dtype)
1947
1948
1949
1950
1951
1952
1953


def ggml_mul_mat_vec_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
1954
) -> torch.Tensor:
1955
1956
1957
1958
1959
1960
1961
1962
    return torch.ops._C.ggml_mul_mat_vec_a8(W, X, quant_type, row)


def ggml_mul_mat_a8(
    W: torch.Tensor,
    X: torch.Tensor,
    quant_type: int,
    row: int,
1963
) -> torch.Tensor:
1964
1965
1966
    return torch.ops._C.ggml_mul_mat_a8(W, X, quant_type, row)


1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
def ggml_moe_a8(
    X: torch.Tensor,
    W: torch.Tensor,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_padded: torch.Tensor,
    quant_type: int,
    row: int,
    top_k: int,
    tokens: int,
) -> torch.Tensor:
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
    return torch.ops._C.ggml_moe_a8(
        X,
        W,
        sorted_token_ids,
        expert_ids,
        num_tokens_post_padded,
        quant_type,
        row,
        top_k,
        tokens,
    )
1989
1990


1991
1992
1993
1994
1995
1996
1997
1998
1999
def ggml_moe_a8_vec(
    X: torch.Tensor,
    W: torch.Tensor,
    topk_ids: torch.Tensor,
    top_k: int,
    quant_type: int,
    row: torch.SymInt,
    tokens: torch.SymInt,
) -> torch.Tensor:
2000
    return torch.ops._C.ggml_moe_a8_vec(X, W, topk_ids, top_k, quant_type, row, tokens)
2001
2002


2003
2004
2005
2006
def ggml_moe_get_block_size(quant_type: int) -> int:
    return torch.ops._C.ggml_moe_get_block_size(quant_type)


2007
# mamba
2008
2009
2010
2011
2012
2013
def selective_scan_fwd(
    u: torch.Tensor,
    delta: torch.Tensor,
    A: torch.Tensor,
    B: torch.Tensor,
    C: torch.Tensor,
2014
2015
2016
    D_: torch.Tensor | None,
    z_: torch.Tensor | None,
    delta_bias_: torch.Tensor | None,
2017
    delta_softplus: bool,
2018
2019
2020
    query_start_loc: torch.Tensor | None,
    cache_indices: torch.Tensor | None,
    has_initial_state: torch.Tensor | None,
2021
2022
    ssm_states: torch.Tensor,
    pad_slot_id: int,
2023
2024
2025
2026
    block_size: int = 1024,
    block_idx_first_scheduled_token: torch.Tensor | None = None,
    block_idx_last_scheduled_token: torch.Tensor | None = None,
    initial_state_idx: torch.Tensor | None = None,
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
):
    torch.ops._C.selective_scan_fwd(
        u,
        delta,
        A,
        B,
        C,
        D_,
        z_,
        delta_bias_,
        delta_softplus,
        query_start_loc,
        cache_indices,
        has_initial_state,
        ssm_states,
        pad_slot_id,
2043
2044
2045
2046
        block_size,
        block_idx_first_scheduled_token,
        block_idx_last_scheduled_token,
        initial_state_idx,
2047
    )
2048
2049


2050
# ROCm skinny gemms
2051
def LLMM1(a: torch.Tensor, b: torch.Tensor, rows_per_block: int) -> torch.Tensor:
2052
2053
2054
    return torch.ops._rocm_C.LLMM1(a, b, rows_per_block)


2055
2056
2057
def wvSplitK(
    a: torch.Tensor, b: torch.Tensor, cu_count: int, bias: torch.Tensor = None
) -> torch.Tensor:
2058
2059
2060
    return torch.ops._rocm_C.wvSplitK(a, b, bias, cu_count)


2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
def wvSplitKQ(
    a: torch.Tensor,
    b: torch.Tensor,
    out_dtype: torch.dtype,
    scale_a: torch.Tensor,
    scale_b: torch.Tensor,
    cu_count: int,
    bias: torch.Tensor = None,
) -> torch.Tensor:
    out = torch.empty((b.shape[0], a.shape[0]), dtype=out_dtype, device=b.device)
2071
    torch.ops._rocm_C.wvSplitKQ(a, b, bias, out, scale_a, scale_b, cu_count)
2072
2073
2074
    return out


2075
# moe
2076
2077
2078
2079
def moe_sum(input: torch.Tensor, output: torch.Tensor):
    torch.ops._moe_C.moe_sum(input, output)


2080
2081
2082
2083
2084
2085
2086
def moe_align_block_size(
    topk_ids: torch.Tensor,
    num_experts: int,
    block_size: int,
    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
2087
    expert_map: torch.Tensor | None = None,
2088
2089
2090
2091
2092
2093
2094
2095
) -> None:
    torch.ops._moe_C.moe_align_block_size(
        topk_ids,
        num_experts,
        block_size,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
2096
        expert_map,
2097
    )
2098
2099


2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
def batched_moe_align_block_size(
    max_tokens_per_batch: int,
    block_size: int,
    expert_num_tokens: torch.Tensor,
    sorted_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
) -> None:
    torch.ops._moe_C.batched_moe_align_block_size(
        max_tokens_per_batch,
        block_size,
        expert_num_tokens,
        sorted_ids,
        expert_ids,
        num_tokens_post_pad,
    )


2118
2119
2120
2121
2122
2123
def moe_lora_align_block_size(
    topk_ids: torch.Tensor,
    token_lora_mapping: torch.Tensor,
    num_experts: int,
    block_size: int,
    max_loras: int,
2124
2125
    max_num_tokens_padded: int,
    max_num_m_blocks: int,
2126
2127
2128
    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
2129
2130
    adapter_enabled: torch.Tensor,
    lora_ids: torch.Tensor,
gnovack's avatar
gnovack committed
2131
    expert_map: torch.Tensor | None = None,
2132
2133
2134
2135
2136
2137
2138
) -> None:
    torch.ops._moe_C.moe_lora_align_block_size(
        topk_ids,
        token_lora_mapping,
        num_experts,
        block_size,
        max_loras,
2139
2140
        max_num_tokens_padded,
        max_num_m_blocks,
2141
2142
2143
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
2144
2145
        adapter_enabled,
        lora_ids,
gnovack's avatar
gnovack committed
2146
        expert_map,
2147
2148
2149
    )


2150
2151
2152
2153
2154
def moe_wna16_gemm(
    input: torch.Tensor,
    output: torch.Tensor,
    b_qweight: torch.Tensor,
    b_scales: torch.Tensor,
2155
2156
    b_qzeros: torch.Tensor | None,
    topk_weights: torch.Tensor | None,
2157
2158
2159
2160
2161
2162
2163
2164
2165
    sorted_token_ids: torch.Tensor,
    experts_ids: torch.Tensor,
    num_tokens_post_pad: torch.Tensor,
    top_k: int,
    BLOCK_SIZE_M: int,
    BLOCK_SIZE_N: int,
    BLOCK_SIZE_K: int,
    bit: int,
) -> torch.Tensor:
2166
2167
    if not current_platform.is_cuda():
        raise NotImplementedError(
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
            "The optimized moe_wna16_gemm kernel is only available on CUDA platforms"
        )
    torch.ops._moe_C.moe_wna16_gemm(
        input,
        output,
        b_qweight,
        b_scales,
        b_qzeros,
        topk_weights,
        sorted_token_ids,
        experts_ids,
        num_tokens_post_pad,
        top_k,
        BLOCK_SIZE_M,
        BLOCK_SIZE_N,
        BLOCK_SIZE_K,
        bit,
    )
2186
2187


2188
2189
2190
2191
2192
def topk_softmax(
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    token_expert_indices: torch.Tensor,
    gating_output: torch.Tensor,
2193
    renormalize: bool = False,
2194
2195
) -> None:
    torch.ops._moe_C.topk_softmax(
2196
        topk_weights, topk_ids, token_expert_indices, gating_output, renormalize
2197
    )
2198
2199


2200
2201
2202
2203
2204
2205
2206
def grouped_topk(
    scores: torch.Tensor,
    num_expert_group: int,
    topk_group: int,
    topk: int,
    renormalize: bool,
    routed_scaling_factor: float,
2207
2208
    bias: torch.Tensor,
    scoring_func: int = 0,
2209
):
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
    """
    Perform grouped top-k routing for mixture of experts.

    Args:
        scores: Raw inputs (logits if scoring_func=1, scores if scoring_func=0)
        num_expert_group: Number of expert groups
        topk_group: Number of groups to select
        topk: Number of experts to select per token
        renormalize: Whether to renormalize the output weights
        routed_scaling_factor: Scaling factor for routing weights
        bias: Bias tensor (e_score_correction_bias). Always fused in kernel.
        scoring_func: 0=none (no activation), 1=sigmoid
    """
2223
    if not current_platform.is_cuda():
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
        raise NotImplementedError(
            "The fused grouped_topk kernel is only available on CUDA platforms"
        )
    return torch.ops._moe_C.grouped_topk(
        scores,
        num_expert_group,
        topk_group,
        topk,
        renormalize,
        routed_scaling_factor,
2234
2235
        bias,
        scoring_func,
2236
2237
2238
2239
2240
    )


def moe_wna16_marlin_gemm(
    input: torch.Tensor,
2241
    output: torch.Tensor | None,
2242
    b_qweight: torch.Tensor,
2243
    b_bias: torch.Tensor | None,
2244
    b_scales: torch.Tensor,
2245
    a_scales: torch.Tensor | None,
2246
2247
2248
2249
    global_scale: torch.Tensor | None,
    b_qzeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    perm: torch.Tensor | None,
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
    workspace: torch.Tensor,
    sorted_token_ids: torch.Tensor,
    expert_ids: torch.Tensor,
    num_tokens_past_padded: torch.Tensor,
    topk_weights: torch.Tensor,
    moe_block_size: int,
    top_k: int,
    mul_topk_weights: bool,
    b_q_type: ScalarType,
    size_m: int,
    size_n: int,
    size_k: int,
    is_k_full: bool,
    use_atomic_add: bool,
    use_fp32_reduce: bool,
    is_zp_float: bool,
2266
2267
2268
    thread_k: int = -1,
    thread_n: int = -1,
    blocks_per_sm: int = -1,
2269
) -> torch.Tensor:
2270
    return torch.ops._moe_C.moe_wna16_marlin_gemm(
2271
2272
2273
2274
2275
        input,
        output,
        b_qweight,
        b_bias,
        b_scales,
2276
        a_scales,
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
        global_scale,
        b_qzeros,
        g_idx,
        perm,
        workspace,
        sorted_token_ids,
        expert_ids,
        num_tokens_past_padded,
        topk_weights,
        moe_block_size,
        top_k,
        mul_topk_weights,
        b_q_type.id,
        size_m,
        size_n,
        size_k,
        is_k_full,
        use_atomic_add,
        use_fp32_reduce,
        is_zp_float,
2297
2298
2299
        thread_k,
        thread_n,
        blocks_per_sm,
2300
    )
2301
2302


2303
if hasattr(torch.ops, "_moe_C") and hasattr(torch.ops._moe_C, "marlin_gemm_moe"):
2304

2305
    @register_fake("_moe_C::marlin_gemm_moe")
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
    def marlin_gemm_moe_fake(
        a: torch.Tensor,
        b_q_weights: torch.Tensor,
        sorted_ids: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        b_scales: torch.Tensor,
        b_zero_points: torch.Tensor,
        g_idx: torch.Tensor,
        perm: torch.Tensor,
        workspace: torch.Tensor,
        b_q_type: ScalarType,
        size_m: torch.SymInt,
        size_n: torch.SymInt,
        size_k: torch.SymInt,
        is_k_full: bool,
        num_experts: int,
        topk: int,
        moe_block_size: int,
        replicate_input: bool,
        apply_weights: bool,
    ) -> torch.Tensor:
        return torch.empty((size_m, topk, size_n), dtype=a.dtype, device=a.device)
2329

2330
    @register_fake("_moe_C::moe_wna16_marlin_gemm")
2331
2332
    def moe_wna16_marlin_gemm_fake(
        input: torch.Tensor,
2333
        output: torch.Tensor | None,
2334
        b_qweight: torch.Tensor,
2335
        b_bias: torch.Tensor | None,
2336
        b_scales: torch.Tensor,
2337
2338
        a_scales: torch.Tensor | None,
        global_scale: torch.Tensor | None,
2339
2340
2341
        b_qzeros: torch.Tensor | None,
        g_idx: torch.Tensor | None,
        perm: torch.Tensor | None,
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
        workspace: torch.Tensor,
        sorted_token_ids: torch.Tensor,
        expert_ids: torch.Tensor,
        num_tokens_past_padded: torch.Tensor,
        topk_weights: torch.Tensor,
        moe_block_size: int,
        top_k: int,
        mul_topk_weights: bool,
        b_q_type: ScalarType,
        size_m: int,
        size_n: int,
        size_k: int,
        is_k_full: bool,
        use_atomic_add: bool,
        use_fp32_reduce: bool,
        is_zp_float: bool,
2358
    ):
2359
2360
2361
        return torch.empty(
            (size_m * top_k, size_n), dtype=input.dtype, device=input.device
        )
2362

2363

2364
2365
2366
2367
2368
2369
2370
def reshape_and_cache(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
2371
2372
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
2373
) -> None:
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
    torch.ops._C_cache_ops.reshape_and_cache(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        kv_cache_dtype,
        k_scale,
        v_scale,
    )
2384
2385


2386
2387
2388
2389
2390
2391
2392
def reshape_and_cache_flash(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
2393
2394
    k_scale: torch.Tensor,
    v_scale: torch.Tensor,
2395
) -> None:
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
    torch.ops._C_cache_ops.reshape_and_cache_flash(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        kv_cache_dtype,
        k_scale,
        v_scale,
    )
2406
2407


2408
2409
2410
2411
2412
2413
2414
2415
def concat_and_cache_mla(
    kv_c: torch.Tensor,
    k_pe: torch.Tensor,
    kv_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    kv_cache_dtype: str,
    scale: torch.Tensor,
) -> None:
2416
2417
2418
    torch.ops._C_cache_ops.concat_and_cache_mla(
        kv_c, k_pe, kv_cache, slot_mapping, kv_cache_dtype, scale
    )
2419
2420


2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
def concat_and_cache_mla_rope_fused(
    positions: torch.Tensor,
    q_pe: torch.Tensor,
    k_pe: torch.Tensor,
    kv_c: torch.Tensor,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    slot_mapping: torch.Tensor,
    kv_cache: torch.Tensor,
    kv_cache_dtype: str,
    kv_cache_scale: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.concat_and_cache_mla_rope_fused(
        positions,
        q_pe,
        k_pe,
        kv_c,
        cos_sin_cache,
        is_neox,
        slot_mapping,
        kv_cache,
        kv_cache_dtype,
        kv_cache_scale,
    )


2447
2448
2449
def swap_blocks(
    src: torch.Tensor, dst: torch.Tensor, block_mapping: torch.Tensor
) -> None:
2450
    torch.ops._C_cache_ops.swap_blocks(src, dst, block_mapping)
2451
2452


2453
2454
2455
def convert_fp8(
    output: torch.Tensor, input: torch.Tensor, scale: float = 1.0, kv_dtype: str = "fp8"
) -> None:
2456
2457
2458
    torch.ops._C_cache_ops.convert_fp8(output, input, scale, kv_dtype)


2459
def gather_and_maybe_dequant_cache(
2460
2461
2462
2463
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
2464
2465
    token_to_seq: torch.Tensor,
    num_tokens: int,
2466
2467
    kv_cache_dtype: str,
    scale: torch.Tensor,
2468
    seq_starts: torch.Tensor | None = None,
2469
) -> None:
2470
    torch.ops._C_cache_ops.gather_and_maybe_dequant_cache(
2471
2472
2473
2474
        src_cache,
        dst,
        block_table,
        cu_seq_lens,
2475
2476
        token_to_seq,
        num_tokens,
2477
2478
2479
2480
        kv_cache_dtype,
        scale,
        seq_starts,
    )
2481
2482


2483
2484
2485
2486
2487
2488
def cp_gather_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    batch_size: int,
2489
    seq_starts: torch.Tensor | None = None,
2490
2491
2492
2493
) -> None:
    torch.ops._C_cache_ops.cp_gather_cache(
        src_cache, dst, block_table, cu_seq_lens, batch_size, seq_starts
    )
2494
2495


2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
def cp_gather_and_upconvert_fp8_kv_cache(
    src_cache: torch.Tensor,
    dst: torch.Tensor,
    block_table: torch.Tensor,
    seq_lens: torch.Tensor,
    workspace_starts: torch.Tensor,
    batch_size: int,
) -> None:
    """Gather and upconvert FP8 KV cache to BF16 workspace.

    Args:
        src_cache: FP8 KV cache [num_blocks, block_size, 656]
        dst: BF16 output workspace [total_tokens, 576]
        block_table: Block indices [num_reqs, max_blocks]
        seq_lens: Sequence lengths [num_reqs]
        workspace_starts: Workspace start offsets [num_reqs]
        batch_size: Number of requests
    """
    torch.ops._C_cache_ops.cp_gather_and_upconvert_fp8_kv_cache(
        src_cache, dst, block_table, seq_lens, workspace_starts, batch_size
    )


2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
def indexer_k_quant_and_cache(
    k: torch.Tensor,
    kv_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    quant_block_size: int,
    kv_cache_dtype: str,
) -> None:
    torch.ops._C_cache_ops.indexer_k_quant_and_cache(
        k, kv_cache, slot_mapping, quant_block_size, kv_cache_dtype
    )
2529
2530


2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
def cp_gather_indexer_k_quant_cache(
    kv_cache: torch.Tensor,
    dst_k: torch.Tensor,
    dst_scale: torch.Tensor,
    block_table: torch.Tensor,
    cu_seq_lens: torch.Tensor,
) -> None:
    torch.ops._C_cache_ops.cp_gather_indexer_k_quant_cache(
        kv_cache, dst_k, dst_scale, block_table, cu_seq_lens
    )


2543
2544
2545
2546
2547
2548
2549
def get_device_attribute(attribute: int, device: int) -> int:
    return torch.ops._C_cuda_utils.get_device_attribute(attribute, device)


def get_max_shared_memory_per_block_device_attribute(device: int) -> int:
    # ruff: noqa: E501
    return torch.ops._C_cuda_utils.get_max_shared_memory_per_block_device_attribute(
2550
2551
        device
    )
2552
2553
2554


# custom ar
2555
2556
2557
2558
2559
2560
2561
2562
2563
def init_custom_ar(
    ipc_tensors: list[torch.Tensor],
    rank_data: torch.Tensor,
    rank: int,
    fully_connected: bool,
) -> int:
    return torch.ops._C_custom_ar.init_custom_ar(
        ipc_tensors, rank_data, rank, fully_connected
    )
2564
2565


2566
2567
2568
2569
2570
2571
2572
2573
def all_reduce(
    fa: int,
    inp: torch.Tensor,
    out: torch.Tensor,
    reg_buffer: int,
    reg_buffer_sz_bytes: int,
) -> None:
    torch.ops._C_custom_ar.all_reduce(fa, inp, out, reg_buffer, reg_buffer_sz_bytes)
2574

2575
2576
2577
2578
2579
2580
2581
2582
2583

def dispose(fa: int) -> None:
    torch.ops._C_custom_ar.dispose(fa)


def meta_size() -> int:
    return torch.ops._C_custom_ar.meta_size()


2584
def register_buffer(fa: int, ipc_tensors: list[int]) -> None:
2585
    return torch.ops._C_custom_ar.register_buffer(fa, ipc_tensors)
2586
2587


2588
def get_graph_buffer_ipc_meta(fa: int) -> tuple[list[int], list[int]]:
2589
2590
2591
    return torch.ops._C_custom_ar.get_graph_buffer_ipc_meta(fa)


2592
2593
2594
def register_graph_buffers(
    fa: int, handles: list[list[int]], offsets: list[list[int]]
) -> None:
2595
    torch.ops._C_custom_ar.register_graph_buffers(fa, handles, offsets)
2596
2597


2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
def allocate_shared_buffer_and_handle(size: int) -> tuple[int, torch.Tensor]:
    return torch.ops._C_custom_ar.allocate_shared_buffer_and_handle(size)


def open_mem_handle(mem_handle: torch.Tensor):
    return torch.ops._C_custom_ar.open_mem_handle(mem_handle)


def free_shared_buffer(ptr: int) -> None:
    torch.ops._C_custom_ar.free_shared_buffer(ptr)


2610
# quick all reduce
2611
def init_custom_qr(rank: int, world_size: int, qr_max_size: int | None = None) -> int:
2612
2613
2614
2615
2616
2617
2618
    return torch.ops._C_custom_ar.init_custom_qr(rank, world_size, qr_max_size)


def qr_destroy(fa: int) -> None:
    torch.ops._C_custom_ar.qr_destroy(fa)


2619
2620
2621
2622
2623
2624
2625
2626
def qr_all_reduce(
    fa: int,
    inp: torch.Tensor,
    out: torch.Tensor,
    quant_level: int,
    cast_bf2half: bool = False,
) -> None:
    torch.ops._C_custom_ar.qr_all_reduce(fa, inp, out, quant_level, cast_bf2half)
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640


def qr_get_handle(fa: int) -> torch.Tensor:
    return torch.ops._C_custom_ar.qr_get_handle(fa)


def qr_open_handles(fa: int, handles: list[torch.Tensor]) -> None:
    return torch.ops._C_custom_ar.qr_open_handles(fa, handles)


def qr_max_size() -> int:
    return torch.ops._C_custom_ar.qr_max_size()


2641
2642
2643
2644
def get_flash_mla_metadata(
    cache_seqlens: torch.Tensor,
    num_heads_per_head_k: int,
    num_heads_k: int,
2645
) -> tuple[torch.Tensor, torch.Tensor]:
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
    """
    Arguments:
        cache_seqlens: (batch_size), dtype torch.int32.
        num_heads_per_head_k: Equals to seq_len_q * num_heads_q // num_heads_k.
        num_heads_k: num_heads_k.

    Return:
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), dtype torch.int32.
        num_splits: (batch_size + 1), dtype torch.int32.
    """
2656
2657
2658
    return torch.ops._C.get_flash_mla_metadata(
        cache_seqlens, num_heads_per_head_k, num_heads_k
    )
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668


def flash_mla_with_kvcache(
    q: torch.Tensor,
    k_cache: torch.Tensor,
    block_table: torch.Tensor,
    cache_seqlens: torch.Tensor,
    head_dim_v: int,
    tile_scheduler_metadata: torch.Tensor,
    num_splits: torch.Tensor,
2669
    softmax_scale: float | None = None,
2670
    causal: bool = False,
2671
) -> tuple[torch.Tensor, torch.Tensor]:
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
    """
    Arguments:
        q: (batch_size, seq_len_q, num_heads_q, head_dim).
        k_cache: (num_blocks, page_block_size, num_heads_k, head_dim).
        block_table: (batch_size, max_num_blocks_per_seq), torch.int32.
        cache_seqlens: (batch_size), torch.int32.
        head_dim_v: Head_dim of v.
        tile_scheduler_metadata: (num_sm_parts, TileSchedulerMetaDataSize), torch.int32, return by get_mla_metadata.
        num_splits: (batch_size + 1), torch.int32, return by get_mla_metadata.
        softmax_scale: float. The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim).
        causal: bool. Whether to apply causal attention mask.

    Return:
        out: (batch_size, seq_len_q, num_heads_q, head_dim_v).
        softmax_lse: (batch_size, num_heads_q, seq_len_q), torch.float32.
    """
    if softmax_scale is None:
2689
        softmax_scale = q.shape[-1] ** (-0.5)
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
    out, softmax_lse = torch.ops._C.flash_mla_fwd_kvcache(
        q,
        k_cache,
        None,
        head_dim_v,
        cache_seqlens,
        block_table,
        softmax_scale,
        causal,
        tile_scheduler_metadata,
        num_splits,
    )
    return out, softmax_lse
2703
2704


2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
def sm100_cutlass_mla_decode(
    out: torch.Tensor,
    lse: torch.Tensor,
    q_nope: torch.Tensor,
    q_pe: torch.Tensor,
    kv_c_and_k_pe_cache: torch.Tensor,
    seq_lens: torch.Tensor,
    page_table: torch.Tensor,
    workspace: torch.Tensor,
    scale: float,
    num_kv_splits: int,
) -> torch.Tensor:
    torch.ops._C.sm100_cutlass_mla_decode(
        out,
        lse,
        q_nope,
        q_pe,
        kv_c_and_k_pe_cache,
        seq_lens,
        page_table,
        workspace,
        scale,
        num_kv_splits,
    )
2729
2730
2731
    return out


2732
2733
2734
def sm100_cutlass_mla_get_workspace_size(
    max_seq_len: int, num_batches: int, sm_count: int, num_kv_splits: int
) -> int:
2735
    return torch.ops._C.sm100_cutlass_mla_get_workspace_size(
2736
2737
        max_seq_len, num_batches, sm_count, num_kv_splits
    )
2738
2739


2740
2741
2742
if hasattr(torch.ops._C, "weight_packed_linear"):

    @register_fake("_C::weight_packed_linear")
2743
2744
2745
    def weight_packed_linear_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
2746
        bias: torch.Tensor | None,
2747
2748
2749
2750
2751
        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty(
            (mat1.size(0), mat2.size(0)), dtype=mat1.dtype, device=mat2.device
        )
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765


if hasattr(torch.ops._C, "fused_experts_cpu"):

    @register_fake("_C::fused_experts_cpu")
    def fused_experts_cpu_fake(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        inplace: bool,
        use_int8_w8a8: bool,
        use_fp8_w8a16: bool,
2766
2767
2768
2769
2770
        w1_scale: torch.Tensor | None,
        w2_scale: torch.Tensor | None,
        block_size: list[int] | None,
        a1_scale: torch.Tensor | None,
        a2_scale: torch.Tensor | None,
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
        is_vnni: bool,
    ) -> torch.Tensor:
        return torch.empty_like(hidden_states)


if hasattr(torch.ops._C, "int8_scaled_mm_with_quant"):

    @register_fake("_C::int8_scaled_mm_with_quant")
    def int8_scaled_mm_with_quant_fake(
        mat1: torch.Tensor,
        mat2: torch.Tensor,
        scales2: torch.Tensor,
2783
        bias: torch.Tensor | None,
2784
2785
2786
2787
2788
2789
        out_dtype: torch.dtype,
        is_vnni: bool,
    ) -> torch.Tensor:
        M = mat1.size(0)
        N = mat2.size(0)
        return torch.empty((M, N), dtype=out_dtype)
2790
2791
2792
2793


class CPUDNNLGEMMHandler:
    def __init__(self) -> None:
2794
        self.handler: int | None = None
2795
2796
2797
2798
2799
2800
2801
2802
        self.n = -1
        self.k = -1

    def __del__(self):
        if self.handler is not None:
            torch.ops._C.release_dnnl_matmul_handler(self.handler)


2803
_supports_onednn = bool(hasattr(torch.ops._C, "create_onednn_mm_handler"))
2804
2805


2806
2807
2808
2809
def is_onednn_acl_supported():
    return torch.ops._C.is_onednn_acl_supported()


2810
2811
2812
2813
2814
2815
2816
def create_onednn_mm(
    weight: torch.Tensor,  # [K, N]
    primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
    handler = CPUDNNLGEMMHandler()
    handler.k, handler.n = weight.size()
    handler.handler = torch.ops._C.create_onednn_mm_handler(
2817
2818
        weight, primitive_cache_size
    )
2819
2820
2821
2822
2823
2824
    return handler


def onednn_mm(
    dnnl_handler: CPUDNNLGEMMHandler,
    x: torch.Tensor,
2825
    bias: torch.Tensor | None,
2826
2827
) -> torch.Tensor:
    output = torch.empty((*x.shape[0:-1], dnnl_handler.n), dtype=x.dtype)
2828
2829
2830
    torch.ops._C.onednn_mm(
        output, x.reshape(-1, dnnl_handler.k), bias, dnnl_handler.handler
    )
2831
2832
2833
2834

    return output


2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
def create_onednn_scaled_mm(
    weight: torch.Tensor,  # [K, N]
    weight_scales: torch.Tensor,
    output_type: torch.dtype,
    dynamic_quant: bool,
    use_azp: bool,
    primitive_cache_size: int = 128,
) -> CPUDNNLGEMMHandler:
    handler = CPUDNNLGEMMHandler()
    handler.k, handler.n = weight.size()
    handler.handler = torch.ops._C.create_onednn_scaled_mm_handler(
2846
2847
        weight, weight_scales, output_type, dynamic_quant, use_azp, primitive_cache_size
    )
2848
2849
2850
    return handler


2851
2852
def onednn_scaled_int8_quant(
    input: torch.Tensor,
2853
2854
    scale: torch.Tensor | None = None,
    azp: torch.Tensor | None = None,
2855
2856
    symmetric: bool = True,
):
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
    """
    Quantize the input tensor to int8 and return the quantized tensor and scale, and maybe azp.

    Args:
        input: The input tensor to be quantized to int8.
        scale: Optional scaling factor for the int8 quantization.
            When not provided, we invoke dynamic-per-token quantization.
        azp: Optional zero-point for the int8 quantization.
            Must be provided for asymmetric quantization if `scale` is provided.
        symmetric: Whether to use symmetric quantization (scale only, azp ignored).

    Returns:
2869
      tuple[torch.Tensor, torch.Tensor, torch.Tensor | None] : Output int8 tensor, scales, and optionally azp.
2870
2871
2872
2873
2874
2875
    """
    output = torch.empty_like(input, dtype=torch.int8)
    token_num = input.numel() // input.shape[-1]
    input = input.view((token_num, input.shape[-1]))
    if scale is not None:
        # static-per-tensor quantization.
2876
2877
2878
        assert symmetric == (azp is None), (
            "azp must only be provided for asymmetric quantization."
        )
2879
2880
2881
2882
        torch.ops._C.static_scaled_int8_quant(output, input, scale, azp)
        return output, scale, azp

    # dynamic-per-token quantization.
2883
2884
2885
    input_scales = torch.empty((token_num, 1), device=input.device, dtype=torch.float32)
    input_azp = None if symmetric else torch.empty_like(input_scales, dtype=torch.int32)
    torch.ops._C.dynamic_scaled_int8_quant(output, input, input_scales, input_azp)
2886
2887
2888
2889
2890
2891
2892
    return output, input_scales, input_azp


def onednn_scaled_mm(
    dnnl_handler: CPUDNNLGEMMHandler,
    x: torch.Tensor,
    output: torch.Tensor,
2893
2894
2895
2896
    input_scale: torch.Tensor | None,
    input_zp: torch.Tensor | None,
    input_zp_adj: torch.Tensor | None,
    bias: torch.Tensor | None,
2897
) -> torch.Tensor:
2898
2899
2900
    torch.ops._C.onednn_scaled_mm(
        output, x, input_scale, input_zp, input_zp_adj, bias, dnnl_handler.handler
    )
2901
2902

    return output
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
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
def cpu_attn_get_scheduler_metadata(
    num_reqs: int,
    num_heads: int,
    num_kv_heads: int,
    head_dim: int,
    seq_lens: torch.Tensor,
    dtype: torch.dtype,
    query_start_loc: torch.Tensor,
    causal: bool,
    sliding_window_size: int,
    isa: str,
    enable_kv_split: bool,
) -> torch.Tensor:
    sheduler_metadata = torch.ops._C.get_scheduler_metadata(
        num_reqs,
        num_heads,
        num_kv_heads,
        head_dim,
        seq_lens,
        dtype,
        query_start_loc,
        causal,
        sliding_window_size,
        isa,
        enable_kv_split,
    )
    return sheduler_metadata


def cpu_attn_reshape_and_cache(
    key: torch.Tensor,
    value: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    slot_mapping: torch.Tensor,
    isa: str,
) -> None:
    torch.ops._C.cpu_attn_reshape_and_cache(
        key,
        value,
        key_cache,
        value_cache,
        slot_mapping,
        isa,
    )


def cpu_attention_with_kv_cache(
    query: torch.Tensor,
    key_cache: torch.Tensor,
    value_cache: torch.Tensor,
    output: torch.Tensor,
    query_start_loc: torch.Tensor,
    seq_lens: torch.Tensor,
    scale: float,
    causal: bool,
    alibi_slopes: torch.Tensor | None,
    sliding_window: tuple[int, int],
    block_table: torch.Tensor,
    softcap: float,
    scheduler_metadata: torch.Tensor,
    s_aux: torch.Tensor | None,
) -> None:
    torch.ops._C.cpu_attention_with_kv_cache(
        query,
        key_cache,
        value_cache,
        output,
        query_start_loc,
        seq_lens,
        scale,
        causal,
        alibi_slopes,
        sliding_window[0],
        sliding_window[1],
        block_table,
        softcap,
        scheduler_metadata,
        s_aux,
    )


Li, Jiang's avatar
Li, Jiang committed
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
def cpu_gemm_wna16(
    input: torch.Tensor,
    q_weight: torch.Tensor,
    scales: torch.Tensor,
    zeros: torch.Tensor | None,
    g_idx: torch.Tensor | None,
    bias: torch.Tensor | None,
    pack_factor: int,
    isa_hint: str,
) -> torch.Tensor:
    output = torch.empty((input.size(0), scales.size(1)), dtype=input.dtype)
    torch.ops._C.cpu_gemm_wna16(
        input,
        q_weight,
        output,
        scales,
        zeros,
        g_idx,
        bias,
        pack_factor,
        isa_hint,
    )
    return output


3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
def cpu_prepack_moe_weight(
    weight: torch.Tensor,
    isa: str,
) -> torch.Tensor:
    output = torch.empty_like(weight)
    torch.ops._C.prepack_moe_weight(weight, output, isa)
    return output


def cpu_fused_moe(
    input: torch.Tensor,
    w13: torch.Tensor,
    w2: torch.Tensor,
    w13_bias: torch.Tensor | None,
    w2_bias: torch.Tensor | None,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    act: str,
    isa: str,
) -> torch.Tensor:
    output = torch.empty_like(input)
    torch.ops._C.cpu_fused_moe(
        output,
        input,
        w13,
        w2,
        w13_bias,
        w2_bias,
        topk_weights,
        topk_ids,
        act,
        isa,
    )
    return output


3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
if hasattr(torch.ops._qutlass_C, "matmul_mxf4_bf16_tn"):

    @register_fake("_qutlass_C::matmul_mxf4_bf16_tn")
    def _fake_matmul_mxf4_bf16_tn(
        a: torch.Tensor,
        b: torch.Tensor,
        a_sf: torch.Tensor,
        b_sf: torch.Tensor,
        alpha: torch.Tensor,
    ):
        return a.new_empty(*a.shape[:-1], b.shape[0], dtype=torch.bfloat16)


def matmul_mxf4_bf16_tn(
    a: torch.Tensor,
    b: torch.Tensor,
    a_sf: torch.Tensor,
    b_sf: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._qutlass_C.matmul_mxf4_bf16_tn(a, b, a_sf, b_sf, alpha)


if hasattr(torch.ops._qutlass_C, "matmul_ada_mxf4_bf16_tn"):

    @register_fake("_qutlass_C::matmul_ada_mxf4_bf16_tn")
    def _fake_matmul_ada_mxf4_bf16_tn(
        a: torch.Tensor,
        b: torch.Tensor,
        a_sf: torch.Tensor,
        b_sf: torch.Tensor,
        alpha: torch.Tensor,
    ):
        return a.new_empty(*a.shape[:-1], b.shape[0], dtype=torch.bfloat16)


def matmul_ada_mxf4_bf16_tn(
    a: torch.Tensor,
    b: torch.Tensor,
    a_sf: torch.Tensor,
    b_sf: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return torch.ops._qutlass_C.matmul_ada_mxf4_bf16_tn(a, b, a_sf, b_sf, alpha)


def ceil_div(a, b):
    return (a + b - 1) // b


if hasattr(torch.ops._qutlass_C, "fusedQuantizeMxQuest"):

    @register_fake("_qutlass_C::fusedQuantizeMxQuest")
    def _fake_fused_quantize_mx_quest(
        a: torch.Tensor, b: torch.Tensor, xh_e2m1: torch.Tensor, xh_e8m0: torch.Tensor
    ):
        return xh_e2m1, xh_e8m0


if hasattr(torch.ops._qutlass_C, "fusedQuantizeMxAbsMax"):

    @register_fake("_qutlass_C::fusedQuantizeMxAbsMax")
    def _fake_fused_quantize_mx_absmax(
        a: torch.Tensor, b: torch.Tensor, xh_e2m1: torch.Tensor, xh_e8m0: torch.Tensor
    ):
        return xh_e2m1, xh_e8m0


def fusedQuantizeMx(
    a: torch.Tensor, b: torch.Tensor, *, method: Literal["quest", "abs_max"] = "quest"
) -> tuple[torch.Tensor, torch.Tensor]:
    if a.dim() == 0:
        raise ValueError("`a` must have at least 1 dimension.")
    if a.size(-1) % 32 != 0:
        raise ValueError(f"last dim of `a` must be divisible by 32, got {a.size(-1)}.")
    if b.device != a.device:
        raise ValueError("`a` and `b` must be on the same device.")

    xh_e2m1 = torch.empty(
        *a.shape[:-1], a.size(-1) // 2, dtype=torch.uint8, device=a.device
    )

    rows, cols = a.numel() // a.size(-1), a.size(-1) // 32
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)
    padded_rows = n_row_blocks * 128
    padded_cols = n_col_blocks * 4

    xh_e8m0 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e8m0fnu, device=a.device
    )

    if not hasattr(torch.ops, "_qutlass_C"):
        raise RuntimeError(
            "The `_qutlass_C` extension is not loaded. "
            "Make sure your custom op library is imported before calling fusedQuantizeMx."
        )

    if method == "quest":
        return torch.ops._qutlass_C.fusedQuantizeMxQuest(a, b, xh_e2m1, xh_e8m0)
    elif method == "abs_max":
        return torch.ops._qutlass_C.fusedQuantizeMxAbsMax(a, b, xh_e2m1, xh_e8m0)
    else:
        raise ValueError(f"invalid method {method!r}, must be 'quest' or 'abs_max'")


if hasattr(torch.ops._qutlass_C, "fusedQuantizeNv"):

    @register_fake("_qutlass_C::fusedQuantizeNv")
    def _fake_fused_quantize_nv(
        a: torch.Tensor,
        b: torch.Tensor,
        xh_e2m1: torch.Tensor,
        xh_e4m3: torch.Tensor,
        global_scale: torch.Tensor,
    ):
        return xh_e2m1, xh_e4m3


def fusedQuantizeNv(
    a: torch.Tensor, b: torch.Tensor, global_scale: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    xh_e2m1 = torch.empty(
        *a.shape[:-1], a.size(-1) // 2, dtype=torch.uint8, device=a.device
    )

    rows, cols = a.numel() // a.size(-1), a.size(-1) // 16
    n_row_blocks = ceil_div(rows, 128)
    n_col_blocks = ceil_div(cols, 4)
    padded_rows = n_row_blocks * 128
    padded_cols = n_col_blocks * 4
    xh_e4m3 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e4m3fn, device=a.device
    )

    return torch.ops._qutlass_C.fusedQuantizeNv(a, b, xh_e2m1, xh_e4m3, global_scale)


3186
3187
3188
3189
3190
3191
3192
3193
def hadacore_transform(x: torch.Tensor, inplace: bool = True) -> torch.Tensor:
    """
    Perform Hadamard transforms using [Hadacore](https://arxiv.org/abs/2412.08832)
    kernels. Note that these kernels exploit the recursive properties of
    Sylvester Hadamards, and therefore do not require transform weight data

    Note that sylvester hadamard transforms are also symmetric, which means that
    this function is also applies the (transpose <=> inverse) transform.
3194

3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
    :param x: value to be transformed inplace
    :param inplace: modify value in place
    :return: value after transformation
    """
    return torch.ops._C.hadacore_transform(x, inplace)


if hasattr(torch.ops._C, "hadacore_transform"):

    @register_fake("_C::hadacore_transform")
3205
    def _hadacore_transform_fake(x: torch.Tensor, inplace: bool) -> torch.Tensor:
3206
        return torch.empty_like(x) if not inplace else x