| 1 | # SPDX-License-Identifier: Apache-2.0 |
| 2 | # SPDX-FileCopyrightText: Copyright contributors to the vLLM project |
| 3 | """b12x paged causal attention backend for SM12x.""" |
| 4 | |
| 5 | from __future__ import annotations |
| 6 | |
| 7 | import copy |
| 8 | import math |
| 9 | from dataclasses import dataclass, replace |
| 10 | from functools import partial |
| 11 | from typing import Any, ClassVar |
| 12 | |
| 13 | import torch |
| 14 | |
| 15 | from vllm.config import VllmConfig, get_current_vllm_config |
| 16 | from vllm.config.cache import CacheDType |
| 17 | from vllm.logger import init_logger |
| 18 | from vllm.model_executor.warmup.cutedsl_warmup import ( |
| 19 | CuTeDSLCompileUnit, |
| 20 | register_cutedsl_warmup_provider, |
| 21 | ) |
| 22 | from vllm.platforms import current_platform |
| 23 | from vllm.platforms.interface import DeviceCapability |
| 24 | from vllm.utils.b12x import get_b12x_paged_attention |
| 25 | from vllm.utils.math_utils import cdiv |
| 26 | from vllm.utils.torch_utils import ( |
| 27 | canonicalize_singleton_dim_strides, |
| 28 | get_dtype_size, |
| 29 | is_quantized_kv_cache, |
| 30 | ) |
| 31 | from vllm.v1.attention.backend import ( |
| 32 | AttentionBackend, |
| 33 | AttentionCGSupport, |
| 34 | AttentionImpl, |
| 35 | AttentionLayer, |
| 36 | AttentionMetadata, |
| 37 | AttentionMetadataBuilder, |
| 38 | AttentionType, |
| 39 | CommonAttentionMetadata, |
| 40 | MultipleOf, |
| 41 | ) |
| 42 | from vllm.v1.kv_cache_interface import AttentionSpec, KVCacheLayout, KVCacheSpec |
| 43 | from vllm.v1.worker.workspace import current_workspace_manager |
| 44 | |
| 45 | logger = init_logger(__name__) |
| 46 | |
| 47 | _B12X_SUPPORTED_PAGE_SIZES = (64, 128) |
| 48 | _B12X_PREFERRED_PAGE_SIZE = 128 |
| 49 | _MIN_PAGED_TILE_Q = 16 |
| 50 | _B12X_FP8_KV_CACHE_DTYPES: tuple[CacheDType, ...] = ("fp8", "fp8_e4m3") |
| 51 | _B12X_SUPPORTED_KV_CACHE_DTYPES: tuple[CacheDType, ...] = ( |
| 52 | "auto", |
| 53 | "bfloat16", |
| 54 | *_B12X_FP8_KV_CACHE_DTYPES, |
| 55 | ) |
| 56 | |
| 57 | |
| 58 | def _max_page_table_width( |
| 59 | max_model_len: int, |
| 60 | block_size: int, |
| 61 | max_num_batched_tokens: int, |
| 62 | is_hybrid: bool, |
| 63 | ) -> int: |
| 64 | width = max(cdiv(max(max_model_len, 1), block_size), 1) |
| 65 | if is_hybrid: |
| 66 | # Hybrid cache setup can enlarge the storage block after attention |
| 67 | # layers are initialized. Its expansion into kernel-sized blocks adds |
| 68 | # at most one storage block of trailing page-table capacity. |
| 69 | width += cdiv(max_num_batched_tokens, block_size) |
| 70 | return width |
| 71 | |
| 72 | |
| 73 | def _kv_page_size(key_cache: torch.Tensor, value_cache: torch.Tensor) -> int: |
| 74 | """Return the static kernel page geometry negotiated by vLLM. |
| 75 | |
| 76 | The KV manager can split the configured storage block into a smaller |
| 77 | kernel page when another backend shares its cache group. Cache shapes are |
| 78 | fixed before graph capture, so this is not a live-length policy decision. |
| 79 | """ |
| 80 | if key_cache.ndim < 2 or value_cache.ndim < 2: |
| 81 | raise ValueError( |
| 82 | "b12x expects paged K/V caches with a page dimension, got " |
| 83 | f"{tuple(key_cache.shape)} and {tuple(value_cache.shape)}." |
| 84 | ) |
| 85 | key_page_size = int(key_cache.shape[1]) |
| 86 | value_page_size = int(value_cache.shape[1]) |
| 87 | if key_page_size != value_page_size: |
| 88 | raise ValueError( |
| 89 | "b12x requires matching K/V page sizes, got " |
| 90 | f"{key_page_size} and {value_page_size}." |
| 91 | ) |
| 92 | if key_page_size not in _B12X_SUPPORTED_PAGE_SIZES: |
| 93 | raise ValueError( |
| 94 | "b12x requires runtime page size in " |
| 95 | f"{_B12X_SUPPORTED_PAGE_SIZES}, got {key_page_size}." |
| 96 | ) |
| 97 | return key_page_size |
| 98 | |
| 99 | |
| 100 | def _capture_alloc_forbidden() -> bool: |
| 101 | if not torch.cuda.is_available(): |
| 102 | return False |
| 103 | try: |
| 104 | return bool(torch.cuda.is_current_stream_capturing()) |
| 105 | except RuntimeError: |
| 106 | return False |
| 107 | |
| 108 | |
| 109 | def _ensure_i32_contiguous(tensor: torch.Tensor, name: str) -> torch.Tensor: |
| 110 | if tensor.dtype != torch.int32: |
| 111 | if _capture_alloc_forbidden(): |
| 112 | raise RuntimeError( |
| 113 | f"b12x would convert {name} to int32 during CUDA graph " |
| 114 | "capture. Prepare int32 metadata before capture." |
| 115 | ) |
| 116 | tensor = tensor.to(torch.int32) |
| 117 | if not tensor.is_contiguous(): |
| 118 | if _capture_alloc_forbidden(): |
| 119 | raise RuntimeError( |
| 120 | f"b12x would make {name} contiguous during CUDA graph " |
| 121 | "capture. Prepare contiguous metadata before capture." |
| 122 | ) |
| 123 | tensor = tensor.contiguous() |
| 124 | return tensor |
| 125 | |
| 126 | |
| 127 | def _dtype_from_cache_config( |
| 128 | kv_cache_dtype: str, |
| 129 | vllm_config: VllmConfig, |
| 130 | ) -> torch.dtype: |
| 131 | if kv_cache_dtype == "bfloat16": |
| 132 | return torch.bfloat16 |
| 133 | if kv_cache_dtype in _B12X_FP8_KV_CACHE_DTYPES: |
| 134 | return current_platform.fp8_dtype() |
| 135 | if kv_cache_dtype != "auto": |
| 136 | raise NotImplementedError( |
| 137 | "b12x currently supports only auto, bfloat16, " |
| 138 | "fp8, and fp8_e4m3 " |
| 139 | f"KV cache dtypes; got {kv_cache_dtype!r}." |
| 140 | ) |
| 141 | return vllm_config.model_config.dtype |
| 142 | |
| 143 | |
| 144 | def _is_b12x_fp8_kv_cache(kv_cache_dtype: str) -> bool: |
| 145 | return kv_cache_dtype in _B12X_FP8_KV_CACHE_DTYPES |
| 146 | |
| 147 | |
| 148 | class B12xPagedAttentionBackend(AttentionBackend): |
| 149 | """b12x paged attention backend for regular/GQA decoder layers.""" |
| 150 | |
| 151 | @classmethod |
| 152 | def customize_spec(cls, spec: AttentionSpec) -> AttentionSpec: |
| 153 | if spec.state_content_bytes is not None: |
| 154 | return spec |
| 155 | assert spec.head_size == spec.head_size_v, ( |
| 156 | "Separate K/V planes require symmetric K/V head sizes." |
| 157 | ) |
| 158 | return replace( |
| 159 | spec, |
| 160 | num_head_slots=2, |
| 161 | state_content_bytes=spec.num_kv_heads |
| 162 | * spec.head_size |
| 163 | * get_dtype_size(spec.dtype), |
| 164 | ) |
| 165 | |
| 166 | supported_dtypes: ClassVar[list[torch.dtype]] = [torch.bfloat16] |
| 167 | supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = list( |
| 168 | _B12X_SUPPORTED_KV_CACHE_DTYPES |
| 169 | ) |
| 170 | |
| 171 | forward_includes_kv_cache_update: bool = False |
| 172 | |
| 173 | @staticmethod |
| 174 | def get_name() -> str: |
| 175 | return "B12X" |
| 176 | |
| 177 | @classmethod |
| 178 | def get_impl_cls(cls) -> type[B12xPagedAttentionImpl]: |
| 179 | return B12xPagedAttentionImpl |
| 180 | |
| 181 | @staticmethod |
| 182 | def get_builder_cls() -> type[B12xPagedMetadataBuilder]: |
| 183 | return B12xPagedMetadataBuilder |
| 184 | |
| 185 | @staticmethod |
| 186 | def get_supported_kernel_block_sizes(kv_cache_spec=None) -> list[int | MultipleOf]: |
| 187 | return list(_B12X_SUPPORTED_PAGE_SIZES) |
| 188 | |
| 189 | @classmethod |
| 190 | def supports_block_size(cls, block_size: int | None) -> bool: |
| 191 | return block_size is None or int(block_size) in _B12X_SUPPORTED_PAGE_SIZES |
| 192 | |
| 193 | @classmethod |
| 194 | def get_preferred_block_size(cls, default_block_size: int) -> int: |
| 195 | if int(default_block_size) in _B12X_SUPPORTED_PAGE_SIZES: |
| 196 | return int(default_block_size) |
| 197 | return _B12X_PREFERRED_PAGE_SIZE |
| 198 | |
| 199 | @classmethod |
| 200 | def get_supported_head_sizes(cls) -> list[int]: |
| 201 | return [64, 128, 192, 256] |
| 202 | |
| 203 | @classmethod |
| 204 | def supports_sink(cls) -> bool: |
| 205 | return True |
| 206 | |
| 207 | @classmethod |
| 208 | def supports_sliding_window(cls) -> bool: |
| 209 | return True |
| 210 | |
| 211 | @classmethod |
| 212 | def supports_compute_capability(cls, capability: DeviceCapability) -> bool: |
| 213 | # Consumer Blackwell SM120 / SM121. The b12x paged kernels also gate |
| 214 | # internally, but keep vLLM selection fail-fast and explicit. |
| 215 | return (capability.major, capability.minor) in ((12, 0), (12, 1)) |
| 216 | |
| 217 | @classmethod |
| 218 | def supports_combination( |
| 219 | cls, |
| 220 | head_size: int, |
| 221 | dtype: torch.dtype, |
| 222 | kv_cache_dtype: CacheDType | None, |
| 223 | block_size: int | None, |
| 224 | use_mla: bool, |
| 225 | has_sink: bool, |
| 226 | use_sparse: bool, |
| 227 | use_mm_prefix: bool, |
| 228 | device_capability: DeviceCapability, |
| 229 | ) -> str | None: |
| 230 | if dtype != torch.bfloat16: |
| 231 | return "b12x currently requires bfloat16 queries" |
| 232 | if kv_cache_dtype == "float16": |
| 233 | return "b12x does not support float16 KV cache" |
| 234 | if ( |
| 235 | kv_cache_dtype is not None |
| 236 | and is_quantized_kv_cache(kv_cache_dtype) |
| 237 | and not _is_b12x_fp8_kv_cache(kv_cache_dtype) |
| 238 | ): |
| 239 | return "b12x currently supports only fp8/fp8_e4m3 quantized KV cache dtypes" |
| 240 | paged_attention = get_b12x_paged_attention() |
| 241 | if paged_attention is None: |
| 242 | return "Install the b12x backend with `pip install vllm[b12x]`" |
| 243 | if not paged_attention.is_supported(): |
| 244 | return "b12x paged attention is not supported on the current device" |
| 245 | return None |
| 246 | |
| 247 | @classmethod |
| 248 | def supported_kv_cache_layouts(cls) -> tuple[KVCacheLayout, ...]: |
| 249 | return (KVCacheLayout.LBHNC, KVCacheLayout.BLHNC) |
| 250 | |
| 251 | |
| 252 | @dataclass |
| 253 | class B12xPagedMetadata(AttentionMetadata): |
| 254 | num_actual_tokens: int |
| 255 | max_query_len: int |
| 256 | query_start_loc: torch.Tensor |
| 257 | max_seq_len: int |
| 258 | seq_lens: torch.Tensor |
| 259 | block_table: torch.Tensor |
| 260 | slot_mapping: torch.Tensor |
| 261 | causal: bool = True |
| 262 | |
| 263 | |
| 264 | class B12xPagedMetadataBuilder(AttentionMetadataBuilder[B12xPagedMetadata]): |
| 265 | """Metadata builder for b12x. |
| 266 | |
| 267 | Decode and uniform speculative-verifier batches use preplanned graph |
| 268 | buckets. Extend/prefill remains eager and does not affect uniform decode |
| 269 | graph eligibility. |
| 270 | """ |
| 271 | |
| 272 | _cudagraph_support: ClassVar[AttentionCGSupport] = AttentionCGSupport.UNIFORM_BATCH |
| 273 | supports_update_block_table: bool = True |
| 274 | |
| 275 | @classmethod |
| 276 | def get_cudagraph_support( |
| 277 | cls, |
| 278 | vllm_config: VllmConfig, |
| 279 | kv_cache_spec: KVCacheSpec, |
| 280 | ) -> AttentionCGSupport: |
| 281 | del vllm_config, kv_cache_spec |
| 282 | return cls._cudagraph_support |
| 283 | |
| 284 | def __init__( |
| 285 | self, |
| 286 | kv_cache_spec: AttentionSpec, |
| 287 | layer_names: list[str], |
| 288 | vllm_config: VllmConfig, |
| 289 | device: torch.device, |
| 290 | ) -> None: |
| 291 | super().__init__(kv_cache_spec, layer_names, vllm_config, device) |
| 292 | |
| 293 | def build( |
| 294 | self, |
| 295 | common_prefix_len: int, |
| 296 | common_attn_metadata: CommonAttentionMetadata, |
| 297 | fast_build: bool = False, |
| 298 | ) -> B12xPagedMetadata: |
| 299 | del common_prefix_len, fast_build |
| 300 | cm = common_attn_metadata |
| 301 | return B12xPagedMetadata( |
| 302 | num_actual_tokens=cm.num_actual_tokens, |
| 303 | max_query_len=cm.max_query_len, |
| 304 | query_start_loc=cm.query_start_loc, |
| 305 | max_seq_len=cm.max_seq_len, |
| 306 | seq_lens=cm.seq_lens, |
| 307 | block_table=cm.block_table_tensor, |
| 308 | slot_mapping=cm.slot_mapping, |
| 309 | causal=cm.causal, |
| 310 | ) |
| 311 | |
| 312 | def update_block_table( |
| 313 | self, |
| 314 | metadata: B12xPagedMetadata, |
| 315 | blk_table: torch.Tensor, |
| 316 | slot_mapping: torch.Tensor, |
| 317 | ) -> B12xPagedMetadata: |
| 318 | new_metadata = copy.copy(metadata) |
| 319 | new_metadata.block_table = blk_table |
| 320 | new_metadata.slot_mapping = slot_mapping |
| 321 | return new_metadata |
| 322 | |
| 323 | |
| 324 | class B12xPagedAttentionImpl(AttentionImpl[B12xPagedMetadata]): |
| 325 | """b12x paged GQA attention implementation.""" |
| 326 | |
| 327 | can_return_lse_for_decode: bool = False |
| 328 | supports_dcp: bool = False |
| 329 | |
| 330 | def __init__( |
| 331 | self, |
| 332 | num_heads: int, |
| 333 | head_size: int, |
| 334 | scale: float, |
| 335 | num_kv_heads: int, |
| 336 | alibi_slopes: list[float] | None, |
| 337 | sliding_window: int | None, |
| 338 | kv_cache_dtype: str, |
| 339 | logits_soft_cap: float | None = None, |
| 340 | attn_type: AttentionType = AttentionType.DECODER, |
| 341 | kv_sharing_target_layer_name: str | None = None, |
| 342 | sinks: torch.Tensor | None = None, |
| 343 | ) -> None: |
| 344 | if alibi_slopes is not None: |
| 345 | raise NotImplementedError("b12x does not support ALiBi.") |
| 346 | if logits_soft_cap not in (None, 0): |
| 347 | raise NotImplementedError("b12x does not support logits soft cap.") |
| 348 | if attn_type != AttentionType.DECODER: |
| 349 | raise NotImplementedError( |
| 350 | "b12x currently supports decoder self-attention only." |
| 351 | ) |
| 352 | if is_quantized_kv_cache(kv_cache_dtype) and not _is_b12x_fp8_kv_cache( |
| 353 | kv_cache_dtype |
| 354 | ): |
| 355 | raise NotImplementedError( |
| 356 | "b12x currently supports only fp8/fp8_e4m3 quantized KV cache dtypes." |
| 357 | ) |
| 358 | if num_heads % num_kv_heads != 0: |
| 359 | raise ValueError("b12x requires q heads divisible by kv heads.") |
| 360 | |
| 361 | expected_scale = head_size**-0.5 |
| 362 | if not math.isclose(float(scale), expected_scale, rel_tol=1e-5, abs_tol=1e-7): |
| 363 | raise NotImplementedError( |
| 364 | "b12x currently requires canonical softmax scale " |
| 365 | f"head_dim**-0.5={expected_scale}, got {scale}." |
| 366 | ) |
| 367 | if self.total_cp_world_size > 1: |
| 368 | raise NotImplementedError( |
| 369 | "b12x does not yet support decode/prefill context parallelism." |
| 370 | ) |
| 371 | |
| 372 | self.num_heads = int(num_heads) |
| 373 | self.head_size = int(head_size) |
| 374 | self.output_head_size = self.head_size |
| 375 | self.scale = float(scale) |
| 376 | self.num_kv_heads = int(num_kv_heads) |
| 377 | self.num_queries_per_kv = self.num_heads // self.num_kv_heads |
| 378 | self.kv_cache_dtype = kv_cache_dtype |
| 379 | self.attn_type = attn_type |
| 380 | self.kv_sharing_target_layer_name = kv_sharing_target_layer_name |
| 381 | self.window_left = -1 if sliding_window is None else int(sliding_window) - 1 |
| 382 | |
| 383 | self._sinks_source = sinks |
| 384 | if sinks is not None and ( |
| 385 | sinks.ndim != 1 or int(sinks.shape[0]) != self.num_heads |
| 386 | ): |
| 387 | raise ValueError( |
| 388 | "b12x sinks must have shape " |
| 389 | f"[{self.num_heads}], got {tuple(sinks.shape)}." |
| 390 | ) |
| 391 | self.sinks = sinks if sinks is None or sinks.dtype == torch.float32 else None |
| 392 | |
| 393 | vllm_config = get_current_vllm_config() |
| 394 | scheduler_config = vllm_config.scheduler_config |
| 395 | model_config = vllm_config.model_config |
| 396 | cache_config = vllm_config.cache_config |
| 397 | spec_config = vllm_config.speculative_config |
| 398 | default_block_size = int(cache_config.block_size) |
| 399 | if default_block_size not in _B12X_SUPPORTED_PAGE_SIZES: |
| 400 | raise ValueError( |
| 401 | "b12x requires --block-size in " |
| 402 | f"{_B12X_SUPPORTED_PAGE_SIZES}, got " |
| 403 | f"{cache_config.block_size}." |
| 404 | ) |
| 405 | |
| 406 | self.device = torch.device("cuda", torch.accelerator.current_device_index()) |
| 407 | self.dtype = model_config.dtype |
| 408 | self.kv_torch_dtype = _dtype_from_cache_config(kv_cache_dtype, vllm_config) |
| 409 | if self.dtype != torch.bfloat16: |
| 410 | raise NotImplementedError("b12x currently requires bfloat16 queries.") |
| 411 | max_batched = int(scheduler_config.max_num_batched_tokens) |
| 412 | max_num_seqs = int(scheduler_config.max_num_seqs) |
| 413 | max_model_len = int(model_config.max_model_len) |
| 414 | self._max_num_seqs = max_num_seqs |
| 415 | max_page_table_widths = { |
| 416 | page_size: _max_page_table_width( |
| 417 | max_model_len, |
| 418 | page_size, |
| 419 | max_batched, |
| 420 | model_config.is_hybrid, |
| 421 | ) |
| 422 | for page_size in _B12X_SUPPORTED_PAGE_SIZES |
| 423 | } |
| 424 | |
| 425 | # Extend dispatch may depend on the static Q tensor capacity, but never |
| 426 | # on live per-request lengths. Keep a small set of capacity buckets so |
| 427 | # short/tail prefills do not replay the maximum 8K CTA grid. |
| 428 | self._extend_q_capacities = tuple( |
| 429 | sorted( |
| 430 | { |
| 431 | min(max_batched, q_capacity) |
| 432 | for q_capacity in (128, 512, 1024, 2048, 4096, max_batched) |
| 433 | if q_capacity > 0 |
| 434 | } |
| 435 | ) |
| 436 | ) |
| 437 | |
| 438 | paged_attention = get_b12x_paged_attention() |
| 439 | assert paged_attention is not None and paged_attention.is_supported() |
| 440 | |
| 441 | def _extend_work_items( |
| 442 | page_size: int, |
| 443 | q_capacity: int, |
| 444 | batch_size: int, |
| 445 | ) -> int: |
| 446 | capacity = paged_attention.extend_graph_capacity( |
| 447 | device=self.device, |
| 448 | q_dtype=self.dtype, |
| 449 | kv_dtype=self.kv_torch_dtype, |
| 450 | num_q_heads=self.num_heads, |
| 451 | num_kv_heads=self.num_kv_heads, |
| 452 | head_dim_qk=self.head_size, |
| 453 | head_dim_vo=self.output_head_size, |
| 454 | page_size=page_size, |
| 455 | batch=batch_size, |
| 456 | total_q_capacity=q_capacity, |
| 457 | max_cache_page_count=max_page_table_widths[page_size], |
| 458 | window_left=self.window_left, |
| 459 | ) |
| 460 | return capacity.max_work_items |
| 461 | |
| 462 | self._paged_attention = paged_attention |
| 463 | |
| 464 | def _make_plan( |
| 465 | page_size: int, |
| 466 | mode: str, |
| 467 | max_total_q: int, |
| 468 | max_batch: int, |
| 469 | max_work_items: int, |
| 470 | max_partial_rows: int, |
| 471 | use_cuda_graph: bool, |
| 472 | num_cache_pages: int, |
| 473 | copy_runtime_metadata: bool, |
| 474 | ) -> Any: |
| 475 | return paged_attention.plan( |
| 476 | paged_attention.Caps( |
| 477 | device=self.device, |
| 478 | mode=mode, |
| 479 | dtype=self.dtype, |
| 480 | kv_dtype=self.kv_torch_dtype, |
| 481 | num_q_heads=self.num_heads, |
| 482 | num_kv_heads=self.num_kv_heads, |
| 483 | head_dim_qk=self.head_size, |
| 484 | head_dim_vo=self.output_head_size, |
| 485 | page_size=page_size, |
| 486 | max_total_q=max_total_q, |
| 487 | max_batch=max_batch, |
| 488 | max_page_table_width=max_page_table_widths[page_size], |
| 489 | max_work_items=max_work_items, |
| 490 | max_partial_rows=max_partial_rows, |
| 491 | # Shape-only planning tensor; runtime cache shape is |
| 492 | # validated by head/page geometry, not page count. |
| 493 | num_cache_pages=num_cache_pages, |
| 494 | use_cuda_graph=use_cuda_graph, |
| 495 | copy_runtime_metadata=copy_runtime_metadata, |
| 496 | ) |
| 497 | ) |
| 498 | |
| 499 | capture_sizes = vllm_config.compilation_config.cudagraph_capture_sizes or [] |
| 500 | decode_plan_sizes = { |
| 501 | int(size) for size in capture_sizes if 0 < int(size) <= max_num_seqs |
| 502 | } |
| 503 | decode_plan_sizes.add(max_num_seqs) |
| 504 | |
| 505 | def _create_decode_plan(page_size: int, batch_size: int) -> Any: |
| 506 | max_page_table_width = max_page_table_widths[page_size] |
| 507 | capacity = paged_attention.decode_graph_capacity( |
| 508 | device=self.device, |
| 509 | q_dtype=self.dtype, |
| 510 | kv_dtype=self.kv_torch_dtype, |
| 511 | num_q_heads=self.num_heads, |
| 512 | num_kv_heads=self.num_kv_heads, |
| 513 | head_dim_qk=self.head_size, |
| 514 | head_dim_vo=self.output_head_size, |
| 515 | page_size=page_size, |
| 516 | batch=batch_size, |
| 517 | max_cache_page_count=max_page_table_width, |
| 518 | window_left=self.window_left, |
| 519 | ) |
| 520 | plan = _make_plan( |
| 521 | page_size, |
| 522 | "decode", |
| 523 | batch_size, |
| 524 | batch_size, |
| 525 | capacity.max_work_items, |
| 526 | capacity.max_partial_rows, |
| 527 | True, |
| 528 | max_page_table_width, |
| 529 | True, |
| 530 | ) |
| 531 | plan.prepare_decode_graph_replay_state( |
| 532 | batch=batch_size, |
| 533 | total_q_capacity=batch_size, |
| 534 | max_page_table_width=max_page_table_width, |
| 535 | max_cache_page_count=max_page_table_width, |
| 536 | window_left=self.window_left, |
| 537 | ) |
| 538 | return plan |
| 539 | |
| 540 | self._create_decode_plan = _create_decode_plan |
| 541 | self._verify_q_per_req = 0 |
| 542 | if spec_config is not None: |
| 543 | self._verify_q_per_req = 1 + int( |
| 544 | getattr(spec_config, "num_speculative_tokens", None) or 0 |
| 545 | ) |
| 546 | if self._verify_q_per_req <= 1: |
| 547 | self._verify_q_per_req = 0 |
| 548 | |
| 549 | def _create_verify_plan(page_size: int, batch_size: int) -> Any: |
| 550 | if self._verify_q_per_req <= 1: |
| 551 | raise RuntimeError("b12x verifier plan requested without speculation") |
| 552 | max_page_table_width = max_page_table_widths[page_size] |
| 553 | total_q = batch_size * self._verify_q_per_req |
| 554 | capacity = paged_attention.verify_graph_capacity( |
| 555 | device=self.device, |
| 556 | q_dtype=self.dtype, |
| 557 | kv_dtype=self.kv_torch_dtype, |
| 558 | num_q_heads=self.num_heads, |
| 559 | num_kv_heads=self.num_kv_heads, |
| 560 | head_dim_qk=self.head_size, |
| 561 | head_dim_vo=self.output_head_size, |
| 562 | page_size=page_size, |
| 563 | batch=batch_size, |
| 564 | query_len=self._verify_q_per_req, |
| 565 | max_cache_page_count=max_page_table_width, |
| 566 | window_left=self.window_left, |
| 567 | ) |
| 568 | plan = _make_plan( |
| 569 | page_size, |
| 570 | "verify", |
| 571 | total_q, |
| 572 | batch_size, |
| 573 | capacity.max_work_items, |
| 574 | capacity.max_partial_rows, |
| 575 | True, |
| 576 | max_page_table_width, |
| 577 | True, |
| 578 | ) |
| 579 | page_ids = torch.arange( |
| 580 | max_page_table_width, |
| 581 | dtype=torch.int32, |
| 582 | device=self.device, |
| 583 | ) |
| 584 | max_page_table = page_ids.unsqueeze(0).expand(batch_size, -1).contiguous() |
| 585 | max_cache_seqlens = torch.full( |
| 586 | (batch_size,), |
| 587 | capacity.representative_cache_seqlen, |
| 588 | dtype=torch.int32, |
| 589 | device=self.device, |
| 590 | ) |
| 591 | max_cu_seqlens_q = torch.arange( |
| 592 | 0, |
| 593 | total_q + 1, |
| 594 | self._verify_q_per_req, |
| 595 | dtype=torch.int32, |
| 596 | device=self.device, |
| 597 | ) |
| 598 | plan.prepare_graph_replay_state( |
| 599 | page_table=max_page_table, |
| 600 | cache_seqlens=max_cache_seqlens, |
| 601 | cu_seqlens_q=max_cu_seqlens_q, |
| 602 | active_total_q=total_q, |
| 603 | window_left=self.window_left, |
| 604 | ) |
| 605 | return plan |
| 606 | |
| 607 | self._create_verify_plan = _create_verify_plan |
| 608 | |
| 609 | def _create_extend_plan( |
| 610 | page_size: int, |
| 611 | batch_size: int, |
| 612 | q_capacity: int, |
| 613 | ) -> Any: |
| 614 | """Prepare a fixed-capacity extend plan without reading live lengths.""" |
| 615 | max_page_table_width = max_page_table_widths[page_size] |
| 616 | plan = _make_plan( |
| 617 | page_size, |
| 618 | "extend", |
| 619 | q_capacity, |
| 620 | batch_size, |
| 621 | _extend_work_items(page_size, q_capacity, batch_size), |
| 622 | 0, |
| 623 | True, |
| 624 | max_page_table_width, |
| 625 | False, |
| 626 | ) |
| 627 | page_ids = torch.arange( |
| 628 | max_page_table_width, |
| 629 | dtype=torch.int32, |
| 630 | device=self.device, |
| 631 | ) |
| 632 | max_page_table = page_ids.unsqueeze(0).expand(batch_size, -1).contiguous() |
| 633 | max_cache_seqlens = torch.full( |
| 634 | (batch_size,), |
| 635 | min(max_model_len, max_page_table_width * page_size), |
| 636 | dtype=torch.int32, |
| 637 | device=self.device, |
| 638 | ) |
| 639 | # Put one row in every request except the last, which owns the |
| 640 | # remainder. This represents the full total-Q capacity while the |
| 641 | # replay kernel remains responsible for packing arbitrary live |
| 642 | # per-request lengths from device cu_seqlens_q. |
| 643 | max_cu_seqlens_q = torch.arange( |
| 644 | 0, |
| 645 | batch_size + 1, |
| 646 | dtype=torch.int32, |
| 647 | device=self.device, |
| 648 | ) |
| 649 | max_cu_seqlens_q[-1] = q_capacity |
| 650 | plan.prepare_graph_replay_state( |
| 651 | page_table=max_page_table, |
| 652 | cache_seqlens=max_cache_seqlens, |
| 653 | cu_seqlens_q=max_cu_seqlens_q, |
| 654 | active_total_q=q_capacity, |
| 655 | window_left=self.window_left, |
| 656 | ) |
| 657 | return plan |
| 658 | |
| 659 | self._create_extend_plan = _create_extend_plan |
| 660 | decode_scratch_envelopes = { |
| 661 | page_size: paged_attention.decode_graph_scratch_envelope( |
| 662 | device=self.device, |
| 663 | q_dtype=self.dtype, |
| 664 | kv_dtype=self.kv_torch_dtype, |
| 665 | num_q_heads=self.num_heads, |
| 666 | num_kv_heads=self.num_kv_heads, |
| 667 | head_dim_qk=self.head_size, |
| 668 | head_dim_vo=self.output_head_size, |
| 669 | page_size=page_size, |
| 670 | max_batch=max_num_seqs, |
| 671 | max_page_table_width=max_page_table_widths[page_size], |
| 672 | max_cache_page_count=max_page_table_widths[page_size], |
| 673 | window_left=self.window_left, |
| 674 | copy_runtime_metadata=True, |
| 675 | ) |
| 676 | for page_size in _B12X_SUPPORTED_PAGE_SIZES |
| 677 | } |
| 678 | self._decode_plans: dict[tuple[int, int], Any] = {} |
| 679 | self._verify_plans: dict[tuple[int, int], Any] = {} |
| 680 | self._extend_plans: dict[tuple[int, int, int], Any] = {} |
| 681 | for page_size in _B12X_SUPPORTED_PAGE_SIZES: |
| 682 | for batch_size in sorted(decode_plan_sizes): |
| 683 | self._decode_plans[page_size, batch_size] = self._create_decode_plan( |
| 684 | page_size, batch_size |
| 685 | ) |
| 686 | if self._verify_q_per_req > 1: |
| 687 | for batch_size in range(1, max_num_seqs + 1): |
| 688 | self._verify_plans[page_size, batch_size] = ( |
| 689 | self._create_verify_plan(page_size, batch_size) |
| 690 | ) |
| 691 | for batch_size in range(1, max_num_seqs + 1): |
| 692 | for q_capacity in self._extend_q_capacities: |
| 693 | # Equal capacity is necessarily one query token per |
| 694 | # request, which is handled by the decode plan. |
| 695 | if batch_size >= q_capacity: |
| 696 | continue |
| 697 | self._extend_plans[page_size, batch_size, q_capacity] = ( |
| 698 | self._create_extend_plan( |
| 699 | page_size, |
| 700 | batch_size, |
| 701 | q_capacity, |
| 702 | ) |
| 703 | ) |
| 704 | self._scratch_nbytes = max( |
| 705 | *(int(envelope.nbytes) for envelope in decode_scratch_envelopes.values()), |
| 706 | *(int(plan.layout.nbytes) for plan in self._verify_plans.values()), |
| 707 | *(int(plan.layout.nbytes) for plan in self._extend_plans.values()), |
| 708 | ) |
| 709 | |
| 710 | current_workspace_manager().get_simultaneous( |
| 711 | ((self._scratch_nbytes,), torch.uint8), |
| 712 | ) |
| 713 | |
| 714 | self.supports_quant_query_input = False |
| 715 | register_cutedsl_warmup_provider(self) |
| 716 | |
| 717 | logger.info_once( |
| 718 | "Using b12x with q_heads=%d kv_heads=%d head_dim_qk=%d " |
| 719 | "head_dim_vo=%d window_left=%d planned_page_sizes=%s " |
| 720 | "verify_q_per_req=%d extend_q_capacities=%s scratch=%d bytes.", |
| 721 | self.num_heads, |
| 722 | self.num_kv_heads, |
| 723 | self.head_size, |
| 724 | self.output_head_size, |
| 725 | self.window_left, |
| 726 | _B12X_SUPPORTED_PAGE_SIZES, |
| 727 | self._verify_q_per_req, |
| 728 | self._extend_q_capacities, |
| 729 | self._scratch_nbytes, |
| 730 | ) |
| 731 | |
| 732 | def _compile_paged_extend_entry(self, page_size: int) -> None: |
| 733 | """Compile fixed-capacity paged-prefill entries without a live plan.""" |
| 734 | warmup_plans: list[tuple[int, int, Any, bool]] = [] |
| 735 | for batch_size in range(1, self._max_num_seqs + 1): |
| 736 | candidates = sorted( |
| 737 | (q_capacity, plan) |
| 738 | for (plan_page_size, plan_batch, q_capacity), plan in ( |
| 739 | self._extend_plans.items() |
| 740 | ) |
| 741 | if plan_page_size == page_size and plan_batch == batch_size |
| 742 | ) |
| 743 | for index, (q_capacity, plan) in enumerate(candidates): |
| 744 | warmup_plans.append((batch_size, q_capacity, plan, index == 0)) |
| 745 | if not warmup_plans: |
| 746 | return |
| 747 | |
| 748 | max_q_rows = max( |
| 749 | min(q_capacity, max(64, batch_size + 1)) |
| 750 | for batch_size, q_capacity, _, _ in warmup_plans |
| 751 | ) |
| 752 | q = torch.zeros( |
| 753 | (max_q_rows, self.num_heads, self.head_size), |
| 754 | dtype=self.dtype, |
| 755 | device=self.device, |
| 756 | ) |
| 757 | output = torch.zeros( |
| 758 | (max_q_rows, self.num_heads, self.output_head_size), |
| 759 | dtype=self.dtype, |
| 760 | device=self.device, |
| 761 | ) |
| 762 | kv_cache = torch.zeros( |
| 763 | (1, 2, page_size, self.num_kv_heads * self.head_size), |
| 764 | dtype=self.kv_torch_dtype, |
| 765 | device=self.device, |
| 766 | ) |
| 767 | key_cache, value_cache = self._kv_cache_views(kv_cache) |
| 768 | (scratch_storage,) = current_workspace_manager().get_simultaneous( |
| 769 | ((self._scratch_nbytes,), torch.uint8), |
| 770 | ) |
| 771 | for batch_size, q_capacity, plan, execute in warmup_plans: |
| 772 | q_rows = min(q_capacity, max(64, batch_size + 1)) |
| 773 | page_table = torch.zeros( |
| 774 | (batch_size, plan.caps.max_page_table_width), |
| 775 | dtype=torch.int32, |
| 776 | device=self.device, |
| 777 | ) |
| 778 | cache_seqlens = torch.full( |
| 779 | (batch_size,), page_size, dtype=torch.int32, device=self.device |
| 780 | ) |
| 781 | cu_seqlens_q = torch.arange( |
| 782 | 0, |
| 783 | batch_size + 1, |
| 784 | dtype=torch.int32, |
| 785 | device=self.device, |
| 786 | ) |
| 787 | cu_seqlens_q[-1] = q_rows |
| 788 | k_descale = None |
| 789 | v_descale = None |
| 790 | if _is_b12x_fp8_kv_cache(self.kv_cache_dtype): |
| 791 | k_descale = torch.ones( |
| 792 | (), dtype=torch.float32, device=self.device |
| 793 | ).expand(batch_size) |
| 794 | v_descale = torch.ones( |
| 795 | (), dtype=torch.float32, device=self.device |
| 796 | ).expand(batch_size) |
| 797 | binding = plan.bind( |
| 798 | scratch=scratch_storage, |
| 799 | q=q[:q_rows], |
| 800 | k_cache=key_cache, |
| 801 | v_cache=value_cache, |
| 802 | output=output[:q_rows], |
| 803 | page_table=page_table, |
| 804 | cache_seqlens=cache_seqlens, |
| 805 | cu_seqlens_q=cu_seqlens_q, |
| 806 | window_left=self.window_left, |
| 807 | attention_sink_bias=self.sinks, |
| 808 | k_descale=k_descale, |
| 809 | v_descale=v_descale, |
| 810 | ) |
| 811 | self._paged_attention.compile(binding=binding) |
| 812 | if execute: |
| 813 | # Compile-only warmup does not launch the device-side compact |
| 814 | # scheduler. One execution per batch covers the capture-static |
| 815 | # metadata variant shared by its Q-capacity plans. |
| 816 | self._paged_attention.run(binding=binding) |
| 817 | |
| 818 | def get_cutedsl_warmup_compile_units(self) -> tuple[CuTeDSLCompileUnit, ...]: |
| 819 | common_key = ( |
| 820 | "b12x_paged_extend", |
| 821 | str(self.device), |
| 822 | str(self.dtype), |
| 823 | str(self.kv_torch_dtype), |
| 824 | self.num_heads, |
| 825 | self.num_kv_heads, |
| 826 | self.head_size, |
| 827 | self.output_head_size, |
| 828 | self.window_left, |
| 829 | self.sinks is not None, |
| 830 | ) |
| 831 | return tuple( |
| 832 | CuTeDSLCompileUnit( |
| 833 | name="b12x_paged_extend", |
| 834 | key=(*common_key, page_size), |
| 835 | compile=partial(self._compile_paged_extend_entry, page_size), |
| 836 | ) |
| 837 | for page_size in _B12X_SUPPORTED_PAGE_SIZES |
| 838 | ) |
| 839 | |
| 840 | def process_weights_after_loading(self, act_dtype: torch.dtype) -> None: |
| 841 | del act_dtype |
| 842 | source_sinks = self._sinks_source |
| 843 | if source_sinks is None: |
| 844 | return |
| 845 | if source_sinks.dtype == torch.float32: |
| 846 | self.sinks = source_sinks |
| 847 | elif self.sinks is None or self.sinks.dtype != torch.float32: |
| 848 | self.sinks = source_sinks.to(torch.float32) |
| 849 | else: |
| 850 | self.sinks.copy_(source_sinks) |
| 851 | |
| 852 | def _prepare_fp8_descales( |
| 853 | self, |
| 854 | layer: AttentionLayer, |
| 855 | num_reqs: int, |
| 856 | device: torch.device, |
| 857 | ) -> tuple[torch.Tensor | None, torch.Tensor | None]: |
| 858 | if not _is_b12x_fp8_kv_cache(self.kv_cache_dtype): |
| 859 | return None, None |
| 860 | if num_reqs <= 0: |
| 861 | raise ValueError("b12x fp8 KV descale request count must be positive.") |
| 862 | |
| 863 | def _prepare(scale: torch.Tensor, name: str) -> torch.Tensor: |
| 864 | if scale.device != device: |
| 865 | raise RuntimeError(f"b12x {name} must be on the query device.") |
| 866 | if scale.dtype != torch.float32: |
| 867 | raise RuntimeError(f"b12x {name} must be float32.") |
| 868 | if scale.ndim == 0: |
| 869 | return scale.expand(num_reqs) |
| 870 | if scale.ndim == 1: |
| 871 | if int(scale.shape[0]) == 1: |
| 872 | return scale.expand(num_reqs) |
| 873 | if int(scale.shape[0]) >= num_reqs: |
| 874 | return scale[:num_reqs] |
| 875 | raise ValueError( |
| 876 | f"b12x {name} must be scalar or rank-1 with at least " |
| 877 | f"{num_reqs} values; got shape {tuple(scale.shape)}." |
| 878 | ) |
| 879 | |
| 880 | return _prepare(layer._k_scale, "k_scale"), _prepare(layer._v_scale, "v_scale") |
| 881 | |
| 882 | def _kv_cache_views( |
| 883 | self, |
| 884 | kv_cache: torch.Tensor, |
| 885 | ) -> tuple[torch.Tensor, torch.Tensor]: |
| 886 | key_cache, value_cache = kv_cache.unbind(1) |
| 887 | key_cache = key_cache.unflatten(-1, (self.num_kv_heads, self.head_size)) |
| 888 | value_cache = value_cache.unflatten(-1, (self.num_kv_heads, self.head_size)) |
| 889 | key_cache = canonicalize_singleton_dim_strides(key_cache) |
| 890 | value_cache = canonicalize_singleton_dim_strides(value_cache) |
| 891 | if _is_b12x_fp8_kv_cache(self.kv_cache_dtype): |
| 892 | fp8_dtype = current_platform.fp8_dtype() |
| 893 | if key_cache.dtype == torch.uint8: |
| 894 | key_cache = key_cache.view(fp8_dtype) |
| 895 | if value_cache.dtype == torch.uint8: |
| 896 | value_cache = value_cache.view(fp8_dtype) |
| 897 | if ( |
| 898 | key_cache.dtype != self.kv_torch_dtype |
| 899 | or value_cache.dtype != self.kv_torch_dtype |
| 900 | ): |
| 901 | raise TypeError( |
| 902 | f"b12x plan expects KV dtype {self.kv_torch_dtype}, got " |
| 903 | f"{key_cache.dtype}/{value_cache.dtype}." |
| 904 | ) |
| 905 | return key_cache, value_cache |
| 906 | |
| 907 | def _select_plan( |
| 908 | self, |
| 909 | attn_metadata: B12xPagedMetadata, |
| 910 | total_q: int, |
| 911 | q_capacity: int, |
| 912 | num_reqs: int, |
| 913 | page_size: int, |
| 914 | ) -> Any: |
| 915 | if attn_metadata.max_query_len <= 1 and int(total_q) == int(num_reqs): |
| 916 | batch_size = int(total_q) |
| 917 | plan_key = (page_size, batch_size) |
| 918 | plan = self._decode_plans.get(plan_key) |
| 919 | if plan is None: |
| 920 | if _capture_alloc_forbidden(): |
| 921 | raise RuntimeError( |
| 922 | "b12x decode plan was not prepared before CUDA graph " |
| 923 | f"capture for page size {page_size}, batch size " |
| 924 | f"{batch_size}." |
| 925 | ) |
| 926 | plan = self._create_decode_plan(page_size, batch_size) |
| 927 | if int(plan.layout.nbytes) > self._scratch_nbytes: |
| 928 | raise RuntimeError( |
| 929 | "b12x lazily created decode plan exceeds reserved " |
| 930 | f"scratch: {int(plan.layout.nbytes)} > " |
| 931 | f"{self._scratch_nbytes} bytes." |
| 932 | ) |
| 933 | self._decode_plans[plan_key] = plan |
| 934 | return plan |
| 935 | elif ( |
| 936 | self._verify_q_per_req > 1 |
| 937 | and attn_metadata.max_query_len == self._verify_q_per_req |
| 938 | and int(total_q) == int(num_reqs) * self._verify_q_per_req |
| 939 | ): |
| 940 | plan_key = (page_size, int(num_reqs)) |
| 941 | plan = self._verify_plans.get(plan_key) |
| 942 | if plan is None: |
| 943 | if _capture_alloc_forbidden(): |
| 944 | raise RuntimeError( |
| 945 | "b12x verifier plan was not prepared before CUDA " |
| 946 | f"graph capture for page size {page_size}, batch size " |
| 947 | f"{num_reqs}." |
| 948 | ) |
| 949 | plan = self._create_verify_plan(page_size, int(num_reqs)) |
| 950 | if int(plan.layout.nbytes) > self._scratch_nbytes: |
| 951 | raise RuntimeError( |
| 952 | "b12x lazily created verifier plan exceeds reserved " |
| 953 | f"scratch: {int(plan.layout.nbytes)} > " |
| 954 | f"{self._scratch_nbytes} bytes." |
| 955 | ) |
| 956 | self._verify_plans[plan_key] = plan |
| 957 | return plan |
| 958 | extend_q_capacity = next( |
| 959 | ( |
| 960 | capacity |
| 961 | for capacity in self._extend_q_capacities |
| 962 | if q_capacity <= capacity |
| 963 | ), |
| 964 | None, |
| 965 | ) |
| 966 | if extend_q_capacity is None: |
| 967 | raise ValueError( |
| 968 | f"b12x extend Q capacity {q_capacity} exceeds prepared " |
| 969 | f"maximum {self._extend_q_capacities[-1]}." |
| 970 | ) |
| 971 | return self._extend_plans[ |
| 972 | page_size, |
| 973 | int(num_reqs), |
| 974 | extend_q_capacity, |
| 975 | ] |
| 976 | |
| 977 | def forward( |
| 978 | self, |
| 979 | layer: AttentionLayer, |
| 980 | query: torch.Tensor, |
| 981 | key: torch.Tensor, |
| 982 | value: torch.Tensor, |
| 983 | kv_cache: torch.Tensor, |
| 984 | attn_metadata: B12xPagedMetadata, |
| 985 | output: torch.Tensor, |
| 986 | output_scale: torch.Tensor | None = None, |
| 987 | output_block_scale: torch.Tensor | None = None, |
| 988 | ) -> torch.Tensor: |
| 989 | del key, value |
| 990 | if output_scale is not None or output_block_scale is not None: |
| 991 | raise NotImplementedError( |
| 992 | "b12x does not support fused output quantization." |
| 993 | ) |
| 994 | if attn_metadata is None: |
| 995 | return output.fill_(0) |
| 996 | if output.shape[-1] != self.output_head_size: |
| 997 | raise ValueError( |
| 998 | f"b12x expected output head dim {self.output_head_size}, got " |
| 999 | f"{output.shape[-1]}." |
| 1000 | ) |
| 1001 | if kv_cache.numel() == 0: |
| 1002 | return output.fill_(0) |
| 1003 | |
| 1004 | # In FULL cudagraph mode vLLM may pad attention metadata to the graph |
| 1005 | # bucket while still passing per-layer Q/output tensors with only the |
| 1006 | # real rows. Use tensor capacity as the launch contract and avoid |
| 1007 | # selecting decode graph replay for padded virtual requests. |
| 1008 | q_capacity = min( |
| 1009 | int(query.shape[0]), |
| 1010 | int(output.shape[0]), |
| 1011 | ) |
| 1012 | num_actual_tokens = min( |
| 1013 | int(attn_metadata.num_actual_tokens), |
| 1014 | q_capacity, |
| 1015 | ) |
| 1016 | if num_actual_tokens <= 0: |
| 1017 | return output |
| 1018 | q = query[:num_actual_tokens] |
| 1019 | out = output[:num_actual_tokens] |
| 1020 | if q.dtype != self.dtype or out.dtype != self.dtype: |
| 1021 | raise TypeError( |
| 1022 | f"b12x plan expects dtype {self.dtype}, got " |
| 1023 | f"q={q.dtype}, output={out.dtype}." |
| 1024 | ) |
| 1025 | |
| 1026 | key_cache, value_cache = self._kv_cache_views(kv_cache) |
| 1027 | page_size = _kv_page_size(key_cache, value_cache) |
| 1028 | if not attn_metadata.causal: |
| 1029 | raise NotImplementedError("b12x supports causal attention only.") |
| 1030 | |
| 1031 | page_table = _ensure_i32_contiguous(attn_metadata.block_table, "block_table") |
| 1032 | cache_seqlens = _ensure_i32_contiguous(attn_metadata.seq_lens, "seq_lens") |
| 1033 | cu_seqlens_q = _ensure_i32_contiguous( |
| 1034 | attn_metadata.query_start_loc, |
| 1035 | "query_start_loc", |
| 1036 | ) |
| 1037 | num_reqs = int(cache_seqlens.shape[0]) |
| 1038 | if attn_metadata.max_query_len <= 1 and num_actual_tokens < num_reqs: |
| 1039 | num_reqs = num_actual_tokens |
| 1040 | page_table = page_table[:num_reqs] |
| 1041 | cache_seqlens = cache_seqlens[:num_reqs] |
| 1042 | cu_seqlens_q = cu_seqlens_q[: num_reqs + 1] |
| 1043 | k_descale, v_descale = self._prepare_fp8_descales( |
| 1044 | layer, |
| 1045 | num_reqs, |
| 1046 | q.device, |
| 1047 | ) |
| 1048 | plan = self._select_plan( |
| 1049 | attn_metadata, |
| 1050 | num_actual_tokens, |
| 1051 | q_capacity, |
| 1052 | num_reqs, |
| 1053 | page_size, |
| 1054 | ) |
| 1055 | (scratch_storage,) = current_workspace_manager().get_simultaneous( |
| 1056 | ((self._scratch_nbytes,), torch.uint8), |
| 1057 | ) |
| 1058 | binding = plan.bind( |
| 1059 | scratch=scratch_storage, |
| 1060 | q=q, |
| 1061 | k_cache=key_cache, |
| 1062 | v_cache=value_cache, |
| 1063 | output=out, |
| 1064 | page_table=page_table, |
| 1065 | cache_seqlens=cache_seqlens, |
| 1066 | cu_seqlens_q=cu_seqlens_q, |
| 1067 | window_left=self.window_left, |
| 1068 | active_total_q=(None if plan.caps.mode == "extend" else num_actual_tokens), |
| 1069 | attention_sink_bias=self.sinks, |
| 1070 | k_descale=k_descale, |
| 1071 | v_descale=v_descale, |
| 1072 | ) |
| 1073 | self._paged_attention.run(binding=binding) |
| 1074 | return output |
| 1075 | |
| 1076 | def do_kv_cache_update( |
| 1077 | self, |
| 1078 | layer: AttentionLayer, |
| 1079 | key: torch.Tensor, |
| 1080 | value: torch.Tensor, |
| 1081 | kv_cache: torch.Tensor, |
| 1082 | slot_mapping: torch.Tensor, |
| 1083 | ) -> None: |
| 1084 | if kv_cache.numel() == 0: |
| 1085 | return |
| 1086 | key_cache, value_cache = self._kv_cache_views(kv_cache) |
| 1087 | torch.ops._C_cache_ops.reshape_and_cache_flash( |
| 1088 | key, |
| 1089 | value, |
| 1090 | key_cache, |
| 1091 | value_cache, |
| 1092 | slot_mapping, |
| 1093 | self.kv_cache_dtype, |
| 1094 | layer._k_scale, |
| 1095 | layer._v_scale, |
| 1096 | ) |