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751ad26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | from __future__ import annotations
from typing import Literal, Sequence
import numpy as np
from .attention_reference import softmax
from .backends import (
PreparedPageTorch,
cuda_available,
decode_multi_query_step_cuda,
decode_step_cuda,
decode_multi_query_step_mps,
decode_step_mps,
mix_page_cpu_ref,
mix_page_cuda,
mix_page_mps,
mps_available,
page_supported_cuda,
page_supported_mps,
prepare_page_cuda,
prepare_page_mps,
prepare_pages_cuda,
prepare_pages_mps,
score_pages_cuda,
score_pages_mps,
score_page_cpu_ref,
score_page_cuda,
score_page_mps,
)
from .page_cache import PreparedPageCache
from .tracing import ExecutionTrace
from .types import EncodedPage
BackendName = Literal["cpu_ref", "torch_mps", "torch_cuda", "auto"]
PageLike = EncodedPage | PreparedPageTorch
def _resolve_backend(backend: BackendName, page: PageLike) -> Literal["cpu_ref", "torch_mps", "torch_cuda"]:
if backend == "cpu_ref":
return "cpu_ref"
if backend == "torch_mps":
if not mps_available():
raise RuntimeError("torch_mps is unavailable on this machine")
if not page_supported_mps(page):
raise ValueError("page is unsupported by torch_mps in this phase")
return "torch_mps"
if backend == "torch_cuda":
if not cuda_available():
raise RuntimeError("torch_cuda is unavailable on this machine")
if not page_supported_cuda(page):
raise ValueError("page is unsupported by torch_cuda in this phase")
return "torch_cuda"
if isinstance(page, PreparedPageTorch):
return "torch_cuda" if page.device_type == "cuda" else "torch_mps"
if cuda_available() and page_supported_cuda(page):
return "torch_cuda"
if mps_available() and page_supported_mps(page):
return "torch_mps"
return "cpu_ref"
def _prepared_pages_backend(pages: Sequence[PageLike]) -> Literal["torch_mps", "torch_cuda"] | None:
if not pages or not all(isinstance(page, PreparedPageTorch) for page in pages):
return None
device_type = pages[0].device_type
if any(page.device_type != device_type for page in pages):
raise ValueError("prepared torch pages must all target the same device")
return "torch_cuda" if device_type == "cuda" else "torch_mps"
def prepare_page(
page: PageLike,
*,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> PageLike:
resolved_backend = _resolve_backend(backend, page)
if resolved_backend == "torch_mps":
if cache is not None:
return cache.prepare_page(page, backend="torch_mps", trace=trace)
return prepare_page_mps(page, trace=trace)
if resolved_backend == "torch_cuda":
if cache is not None:
return cache.prepare_page(page, backend="torch_cuda", trace=trace)
return prepare_page_cuda(page, trace=trace)
return page.source_page if isinstance(page, PreparedPageTorch) else page
def prepare_pages(
pages: Sequence[PageLike],
*,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> list[PageLike]:
if pages:
resolved_backend = _resolve_backend(backend, pages[0])
if resolved_backend == "torch_mps":
if cache is not None:
return cache.prepare_pages(list(pages), backend="torch_mps", trace=trace)
return prepare_pages_mps(pages, trace=trace)
if resolved_backend == "torch_cuda":
if cache is not None:
return cache.prepare_pages(list(pages), backend="torch_cuda", trace=trace)
return prepare_pages_cuda(pages, trace=trace)
return [prepare_page(page, backend=backend, cache=cache, trace=trace) for page in pages]
def score_page(
query_slice: np.ndarray,
page: PageLike,
*,
backend: BackendName = "auto",
trace: ExecutionTrace | None = None,
) -> np.ndarray:
resolved_backend = _resolve_backend(backend, page)
if resolved_backend == "torch_mps":
return score_page_mps(query_slice, page, trace=trace)
if resolved_backend == "torch_cuda":
return score_page_cuda(query_slice, page, trace=trace)
return score_page_cpu_ref(query_slice, page, trace=trace)
def score_pages(
query_slice: np.ndarray,
pages: Sequence[PageLike],
*,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> list[np.ndarray]:
if not pages:
return []
prepared_pages = prepare_pages(pages, backend=backend, cache=cache, trace=trace)
prepared_backend = _prepared_pages_backend(prepared_pages)
if prepared_backend == "torch_mps":
return score_pages_mps(query_slice, prepared_pages, trace=trace)
if prepared_backend == "torch_cuda":
return score_pages_cuda(query_slice, prepared_pages, trace=trace)
return [score_page(query_slice, page, backend=backend, trace=trace) for page in prepared_pages]
def mix_page(
attn_weights: np.ndarray,
page: PageLike,
*,
out_acc: np.ndarray | None = None,
backend: BackendName = "auto",
trace: ExecutionTrace | None = None,
) -> np.ndarray:
resolved_backend = _resolve_backend(backend, page)
if resolved_backend == "torch_mps":
return mix_page_mps(attn_weights, page, out_acc=out_acc, trace=trace)
if resolved_backend == "torch_cuda":
return mix_page_cuda(attn_weights, page, out_acc=out_acc, trace=trace)
return mix_page_cpu_ref(attn_weights, page, out_acc=out_acc, trace=trace)
def attention_step(
query_slice: np.ndarray,
key_page: PageLike,
value_page: PageLike,
*,
backend: BackendName = "cpu_ref",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
prepared_key_page = prepare_page(key_page, backend=backend, cache=cache, trace=trace)
prepared_value_page = prepare_page(value_page, backend=backend, cache=cache, trace=trace)
logits = score_page(query_slice, prepared_key_page, backend=backend, trace=trace)
weights = softmax(logits)
output = mix_page(weights, prepared_value_page, backend=backend, trace=trace)
return logits, weights, output
def decode_step(
query_slice: np.ndarray,
key_pages: Sequence[PageLike],
value_pages: Sequence[PageLike],
*,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
if len(key_pages) != len(value_pages):
raise ValueError("key_pages and value_pages must contain the same number of pages")
if not key_pages:
raise ValueError("decode_step requires at least one page")
return decode_step_with_page_logits(
query_slice,
key_pages,
value_pages,
backend=backend,
cache=cache,
trace=trace,
)
def decode_multi_query_step(
query_slices: np.ndarray,
key_pages: Sequence[PageLike],
value_pages: Sequence[PageLike],
*,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
queries = np.asarray(query_slices, dtype=np.float32)
if queries.ndim != 2:
raise ValueError("query_slices must have shape [query_count, head_dim]")
if len(key_pages) != len(value_pages):
raise ValueError("key_pages and value_pages must contain the same number of pages")
if not key_pages:
raise ValueError("decode_multi_query_step requires at least one page")
prepared_key_pages = prepare_pages(key_pages, backend=backend, cache=cache, trace=trace)
prepared_value_pages = prepare_pages(value_pages, backend=backend, cache=cache, trace=trace)
prepared_backend = _prepared_pages_backend(prepared_key_pages)
if prepared_backend is not None and prepared_backend == _prepared_pages_backend(prepared_value_pages):
if prepared_backend == "torch_cuda":
return decode_multi_query_step_cuda(
queries,
prepared_key_pages,
prepared_value_pages,
trace=trace,
)
return decode_multi_query_step_mps(
queries,
prepared_key_pages,
prepared_value_pages,
trace=trace,
)
logits_list = []
weights_list = []
output_list = []
for query_slice in queries:
logits, weights, output = decode_step(
query_slice,
prepared_key_pages,
prepared_value_pages,
backend=backend,
trace=trace,
)
logits_list.append(logits)
weights_list.append(weights)
output_list.append(output)
return (
np.stack(logits_list, axis=0).astype(np.float32, copy=False),
np.stack(weights_list, axis=0).astype(np.float32, copy=False),
np.stack(output_list, axis=0).astype(np.float32, copy=False),
)
def decode_step_with_page_logits(
query_slice: np.ndarray,
key_pages: Sequence[PageLike],
value_pages: Sequence[PageLike],
*,
page_logits: Sequence[np.ndarray | None] | None = None,
backend: BackendName = "auto",
cache: PreparedPageCache | None = None,
trace: ExecutionTrace | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
if len(key_pages) != len(value_pages):
raise ValueError("key_pages and value_pages must contain the same number of pages")
if not key_pages:
raise ValueError("decode_step requires at least one page")
if page_logits is not None and len(page_logits) != len(key_pages):
raise ValueError("page_logits must align with key_pages")
prepared_key_pages = prepare_pages(key_pages, backend=backend, cache=cache, trace=trace)
prepared_value_pages = prepare_pages(value_pages, backend=backend, cache=cache, trace=trace)
prepared_backend = _prepared_pages_backend(prepared_key_pages)
if prepared_backend is not None and prepared_backend == _prepared_pages_backend(prepared_value_pages):
if prepared_backend == "torch_cuda":
return decode_step_cuda(
query_slice,
prepared_key_pages,
prepared_value_pages,
precomputed_page_logits=page_logits,
trace=trace,
)
return decode_step_mps(
query_slice,
prepared_key_pages,
prepared_value_pages,
precomputed_page_logits=page_logits,
trace=trace,
)
resolved_page_logits = []
for index, page in enumerate(prepared_key_pages):
cached_logits = None if page_logits is None else page_logits[index]
if cached_logits is None:
cached_logits = score_page(query_slice, page, backend=backend, trace=trace)
resolved_page_logits.append(np.asarray(cached_logits, dtype=np.float32))
logits = np.concatenate(resolved_page_logits).astype(np.float32, copy=False)
weights = softmax(logits)
output = np.zeros(prepared_key_pages[0].header.head_dim, dtype=np.float32)
offset = 0
for value_page in prepared_value_pages:
token_count = value_page.header.token_count
page_weights = weights[offset : offset + token_count]
output = mix_page(page_weights, value_page, out_acc=output, backend=backend, trace=trace)
offset += token_count
return logits, weights, output
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