mirror of
https://github.com/deepseek-ai/DeepInfer.git
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fd96b77e53
Four more kernels, and with them the non-speculative path no longer touches TileLang:
- `fused_norm_rope_hadamard_cast_store_kvcache` -- all the forms the engine builds: bf16 out,
bf16 out after dequantising (cast_back), quantised out with scales, and the main/index KV-cache
write with a bf16 stage of `norm`. 18 cases.
- `compress_and_update_state_{prefill,decode}` -- the ring-cache state machine, two launches each.
- `prepare_v4_decode_metadata` -- one program per sequence, every output including the
ratio-bucketed ones.
Details that were not obvious and are now pinned by cases:
RoPE pairs differ per layout: interleaved pairs `(2k, 2k+1)`, Neox pairs `(k, k + rotary/2)`. Both
reduce to "is this the first element of its pair" plus a partner offset, so the lane vector is
gathered once with `tl.gather` instead of two code paths. That also fixed a real bug: the first
version paired adjacent lanes for both layouts, which is only correct for interleaved.
`bf16_stage="norm"` means the post-normalisation *pre-RoPE* row -- the indexer consumes it. It has
to be stored before the rotation overwrites the row; storing it afterwards produced a wrong value
only when RoPE was enabled, which is exactly the case the engine uses.
Scales come in two storages for the same rounded power-of-two value: a ue8m0 byte for the main KV,
an fp32 for the index KV. Both are now covered, in the standalone output and in the cache write.
`compress` needed its tail-copy grid bounded by the *query token* count, not the compressed-row
count; and `_enable_fast_math` mutates `pass_configs` on the factory, a TileLang concept, so the
Triton factories expose an inert dict to keep that call working.
Several case bugs of the same shape were found and fixed along the way -- a sentinel that was never
required to be overwritten, a `total_q` that did not match the actual prefix sum, two tokens
mapped to one cache slot or one state slot so the winner depended on CTA order. Each of them made
the two backends agree for the wrong reason, which is worth more than the kernels themselves: a
comparison that can pass without testing anything is worse than no comparison.
912 lines
44 KiB
Python
912 lines
44 KiB
Python
"""Per-kernel A/B cases: run the public interface function once per backend and compare.
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Each entry maps a kernel name to a builder returning ``(case_name, exact, callable)`` triples.
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``exact=True`` demands bitwise equality, which is right for index and metadata kernels; floating
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point kernels are compared with a tolerance and are labelled ``exact=False``.
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Shapes and dtypes reuse the ones the engine actually passes, taken from the TileLang test matrices
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in ``cuda/*.py`` and from the traced inference run.
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"""
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from __future__ import annotations
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from collections.abc import Callable
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from typing import Any
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import torch
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from deepinfer.model_executor.layers.tilelang_ops import ops
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from deepinfer.model_executor.layers.tilelang_ops.cache_common import (
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FP8_FLOAT32_SF_INDEX_KV_FORMAT,
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V41_FORMAT,
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)
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from deepinfer.model_executor.layers.tilelang_ops.compress import (
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compress_and_update_state_decode,
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compress_and_update_state_prefill,
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)
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from deepinfer.model_executor.layers.tilelang_ops.flash_mla_utils import convert_kv_index
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from deepinfer.model_executor.layers.tilelang_ops.fused_transform import (
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CastArgs,
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KVCacheOutArgs,
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NormArgs,
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RopeArgs,
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fused_norm_rope_hadamard_cast_store_kvcache,
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)
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from deepinfer.model_executor.layers.tilelang_ops.gather import gather
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from deepinfer.model_executor.layers.tilelang_ops.gather_engram_tokens_zos import gather_engram_tokens_zos
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from deepinfer.model_executor.layers.tilelang_ops.gather_input_ids_zos import gather_input_ids_zos
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from deepinfer.model_executor.layers.tilelang_ops.get_kv_index import get_kv_ca_index_decode, get_kv_ca_index_prefill
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from deepinfer.model_executor.layers.tilelang_ops.kv_index_cat import kv_index_cat
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from deepinfer.model_executor.layers.tilelang_ops.load_paged_kv_cache import load_paged_kv_cache
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from deepinfer.model_executor.layers.tilelang_ops.norm import norm
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from deepinfer.model_executor.layers.tilelang_ops.prepare_v4_decode_metadata import prepare_v4_decode_metadata
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from deepinfer.model_executor.layers.tilelang_ops.sampler import map_lookup
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from deepinfer.model_executor.layers.tilelang_ops.store_kv_cache import store_kv_cache
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from deepinfer.model_executor.layers.tilelang_ops.store_mtp_info import store_mtp_info
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from deepinfer.model_executor.layers.tilelang_ops.swa_index import expand_swa_index
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def _cases_gather() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Row gather across the widths the engine uses: metadata rows, heads, and hidden states."""
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def make(n: int, rows: int, d: int, dtype: torch.dtype, index_dtype: torch.dtype, strided: bool):
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def run() -> Any:
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torch.manual_seed(0)
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base = (
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torch.randn(n, d, device="cuda").to(dtype)
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if dtype.is_floating_point
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else torch.randint(0, 100, (n, d), device="cuda", dtype=dtype)
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)
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x = base[:, ::1] if strided else base
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index = torch.randint(-3, n + 3, (rows,), device="cuda", dtype=index_dtype)
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return gather(x, index)
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return run
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return [
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("n1k_m233_d128_int32_idx", True, make(1024, 233, 128, torch.int8, torch.int32, False)),
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("n1k_m1_d4_int8", True, make(1024, 1, 4, torch.int8, torch.int32, False)),
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("n64_m256_d528_bytes", True, make(64, 256, 528, torch.int8, torch.int32, False)),
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("n512_m300_d4096_bytes", True, make(512, 300, 4096, torch.int8, torch.int32, False)),
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("n512_m300_d7168_int64_idx", True, make(512, 300, 7168, torch.int8, torch.int64, False)),
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("n256_m256_d4096_bf16", True, make(256, 256, 4096, torch.bfloat16, torch.int32, False)),
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("n256_m0_d128_empty", True, make(256, 0, 128, torch.int8, torch.int32, False)),
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("n1_m17_d128_single_row", True, make(1, 17, 128, torch.int8, torch.int32, False)),
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("n300_m233_d1025_odd", True, make(300, 233, 1025, torch.int8, torch.int32, False)),
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]
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def _cases_gather_input_ids() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Draft-token replay: remapped requests replay previous ids, fresh ones use their own row."""
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def make(n_seq: int, max_seqlen_q: int, prev_n_seq: int, mix: bool):
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def run() -> Any:
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torch.manual_seed(0)
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nextn = max_seqlen_q - 1
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fresh = torch.randint(0, 1000, (n_seq, max_seqlen_q), device="cuda", dtype=torch.int32)
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prev_sampled = torch.randint(0, 1000, (prev_n_seq,), device="cuda", dtype=torch.int32)
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prev_predict = torch.randint(0, 1000, (prev_n_seq, nextn), device="cuda", dtype=torch.int32)
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if mix:
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remap = torch.tensor([-1 if i % 2 == 0 else i % prev_n_seq for i in range(n_seq)], device="cuda", dtype=torch.int32)
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else:
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remap = torch.full((n_seq,), -1, device="cuda", dtype=torch.int32)
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seqlens = torch.randint(1, max_seqlen_q + 1, (n_seq,), device="cuda", dtype=torch.int32)
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cu = torch.cumsum(seqlens, 0, dtype=torch.int32)
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out = torch.full((int(cu[-1].item()),), -7, device="cuda", dtype=torch.int32)
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gather_input_ids_zos(fresh, prev_sampled, prev_predict, remap, seqlens, cu, out, max_seqlen_q=max_seqlen_q)
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return out
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return run
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return [
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("n8_nextn5_mixed", True, make(8, 6, 8, True)),
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("n8_nextn5_all_fresh", True, make(8, 6, 8, False)),
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("n1_nextn1_mixed", True, make(1, 2, 3, True)),
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("n64_nextn5_mixed", True, make(64, 6, 64, True)),
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("n5_nextn2_full_len", True, make(5, 3, 5, True)),
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]
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def _cases_gather_engram() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Engram n-gram windows, including the image-sentinel padding rule."""
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def make(n_seq: int, max_seqlen_q: int, max_ngram: int, vocab: int, with_image: bool):
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def run() -> Any:
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torch.manual_seed(0)
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nextn = max_seqlen_q - 1
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window = nextn + max_ngram - 1
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pad_token = 7
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sentinel = -1
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win = torch.randint(0, 50, (n_seq, window), device="cuda", dtype=torch.int32)
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if with_image:
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win[:, window // 2] = sentinel
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fresh = torch.randint(0, 50, (n_seq, max_seqlen_q), device="cuda", dtype=torch.int32)
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if with_image:
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fresh[:, -1] = sentinel
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prev_sampled = torch.randint(0, vocab, (n_seq,), device="cuda", dtype=torch.int32)
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prev_predict = torch.randint(0, vocab, (n_seq, nextn), device="cuda", dtype=torch.int32)
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prev_accepted = torch.randint(0, nextn + 1, (n_seq,), device="cuda", dtype=torch.int32)
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remap = torch.tensor([-1 if i % 2 == 0 else i for i in range(n_seq)], device="cuda", dtype=torch.int32)
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lookup = torch.arange(vocab, device="cuda", dtype=torch.int32) % 13
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seqlens = torch.randint(1, max_seqlen_q + 1, (n_seq,), device="cuda", dtype=torch.int32)
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cu = torch.cumsum(seqlens, 0, dtype=torch.int32)
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out = torch.full((int(cu[-1].item()), max_ngram), -7, device="cuda", dtype=torch.int32)
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gather_engram_tokens_zos(
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win,
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fresh,
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prev_sampled,
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prev_predict,
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prev_accepted,
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remap,
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lookup,
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seqlens,
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cu,
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out,
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max_seqlen_q=max_seqlen_q,
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max_ngram_size=max_ngram,
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engram_compressed_pad_token=pad_token,
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image_sentinel=sentinel,
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)
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return out
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return run
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return [
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("n8_nextn5_ngram3", True, make(8, 6, 3, 64, False)),
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("n8_nextn5_ngram1", True, make(8, 6, 1, 64, False)),
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("n8_nextn5_ngram3_image", True, make(8, 6, 3, 64, True)),
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("n1_nextn1_ngram2", True, make(1, 2, 2, 64, False)),
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]
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def _cases_swa_index() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Sliding-window index expansion for the widths the engine uses."""
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def make(n: int, width: int, overflow: bool):
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def run() -> Any:
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torch.manual_seed(0)
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pivots = torch.zeros((n, 4), device="cuda", dtype=torch.int32)
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pivots[:, 0] = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
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if overflow:
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# len1 + len2 can exceed width; the third branch must still win past len1 + len2.
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pivots[:, 1] = torch.randint(0, width + 1, (n,), device="cuda", dtype=torch.int32)
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pivots[:, 2] = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
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pivots[:, 3] = torch.randint(0, width + 1, (n,), device="cuda", dtype=torch.int32)
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else:
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total = torch.randint(0, width + 1, (n,), device="cuda", dtype=torch.int32)
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pivots[:, 1] = total // 2
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pivots[:, 2] = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
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pivots[:, 3] = total - total // 2
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return expand_swa_index(pivots, width)
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return run
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return [
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("n32_w512", True, make(32, 512, False)),
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("n32_w513_overflow", True, make(32, 513, True)),
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("n1_w1", True, make(1, 1, False)),
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("n37_w2048_overflow", True, make(37, 2048, True)),
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("n8_w64", True, make(8, 64, False)),
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]
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def _cases_kv_index_cat() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Window + compressed index concatenation with the -1 padding preserved."""
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def make(total_q: int, window: int, topk: int):
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def run() -> Any:
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torch.manual_seed(0)
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kv_index = torch.randint(-1, 4000, (total_q, window), device="cuda", dtype=torch.int32)
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kv_ca = torch.randint(-1, 300, (total_q, topk), device="cuda", dtype=torch.int32)
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return kv_index_cat(kv_index, kv_ca, 512, 2000)
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return run
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return [
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("q8_w512_k128", True, make(8, 512, 128)),
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("q1_w64_k32", True, make(1, 64, 32)),
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("q5_w2048_k1024", True, make(5, 2048, 1024)),
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]
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def _cases_convert_kv_index() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Logical to physical index translation through a page table."""
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def make(b: int, s: int, k: int, block_size: int, max_blocks: int):
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def run() -> Any:
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torch.manual_seed(0)
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topk = torch.randint(0, max_blocks * block_size, (b, s, k), device="cuda", dtype=torch.int32)
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ctx = torch.randint(0, k + 1, (b, s), device="cuda", dtype=torch.int32)
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table = torch.randint(0, 50, (b, max_blocks), device="cuda", dtype=torch.int32)
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convert_kv_index(topk, block_size, ctx, table)
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return topk
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return run
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return [
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("b2_s4_k64_bs64", True, make(2, 4, 64, 64, 32)),
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("b1_s1_k128_bs128", True, make(1, 1, 128, 128, 16)),
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("b3_s2_k16_bs8", True, make(3, 2, 16, 8, 64)),
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]
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def _cases_load_kv_index() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Prefill (contiguous) and decode (paged) index expansion."""
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def make_prefill(n: int, length: int):
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def run() -> Any:
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torch.manual_seed(0)
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off_len = torch.zeros((n, 2), device="cuda", dtype=torch.int32)
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off_len[:, 0] = torch.randint(0, 500, (n,), device="cuda", dtype=torch.int32)
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off_len[:, 1] = torch.randint(0, length + 1, (n,), device="cuda", dtype=torch.int32)
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return get_kv_ca_index_prefill(off_len, length)
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return run
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def make_decode(n: int, length: int, tpb: int, num_blocks: int):
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def run() -> Any:
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torch.manual_seed(0)
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off_len = torch.zeros((n, 2), device="cuda", dtype=torch.int32)
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off_len[:, 0] = torch.randint(0, 500, (n,), device="cuda", dtype=torch.int32)
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off_len[:, 1] = torch.randint(0, length + 1, (n,), device="cuda", dtype=torch.int32)
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table = torch.randint(0, num_blocks, (num_blocks,), device="cuda", dtype=torch.int32)
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return get_kv_ca_index_decode(off_len, length, tpb, table)
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return run
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return [
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("prefill_n8_len512", True, make_prefill(8, 512)),
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("prefill_n1_len1024", True, make_prefill(1, 1024)),
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("prefill_n33_len100", True, make_prefill(33, 100)),
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("decode_n8_len512_tpb64", True, make_decode(8, 512, 64, 128)),
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("decode_n5_len128_tpb128", True, make_decode(5, 128, 128, 64)),
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]
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def _cases_store_mtp_info() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Draft logprob pooling, including the slot-0 no-write path."""
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def make(req_cnt: int, nextn: int, vocab: int, has_index: bool, has_confidence: bool, zero_slots: bool):
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def run() -> Any:
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torch.manual_seed(0)
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# The pool is addressed as slot * nextn + step and slots run 0..req_cnt, so it needs
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# room for every slot including the reserved one.
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pool_rows = (req_cnt + 1) * nextn
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write_slots = torch.arange(1, req_cnt + 1, device="cuda", dtype=torch.int32)
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if zero_slots:
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write_slots[0] = 0
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token_ids = torch.randint(0, vocab, (req_cnt, nextn), device="cuda", dtype=torch.int32)
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logprobs = torch.randn((req_cnt, nextn, vocab), device="cuda", dtype=torch.float32)
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logprobs_index = torch.randint(0, vocab, (req_cnt, nextn, vocab), device="cuda", dtype=torch.int32)
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confidence = torch.rand((req_cnt, nextn), device="cuda", dtype=torch.float32)
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token_ids_pool = torch.full((pool_rows,), -7, device="cuda", dtype=torch.int32)
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logprobs_pool = torch.full((pool_rows, vocab), -7.0, device="cuda", dtype=torch.float32)
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logprobs_index_pool = torch.full((pool_rows, vocab), -7, device="cuda", dtype=torch.int32)
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confidence_pool = torch.full((pool_rows,), -7.0, device="cuda", dtype=torch.float32)
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store_mtp_info(
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write_slots,
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token_ids,
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logprobs,
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logprobs_index,
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confidence,
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token_ids_pool,
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logprobs_pool,
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logprobs_index_pool,
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confidence_pool,
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)
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return (
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token_ids_pool.clone(),
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logprobs_pool.clone(),
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logprobs_index_pool.clone(),
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confidence_pool.clone(),
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)
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return run
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return [
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("n4_nextn5_v1k_full", True, make(4, 5, 1024, True, True, False)),
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("n4_nextn5_v1k_no_index", True, make(4, 5, 1024, False, True, False)),
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("n4_nextn5_v1k_no_conf", True, make(4, 5, 1024, True, False, False)),
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("n4_nextn5_v1k_slot0", True, make(4, 5, 1024, True, True, True)),
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("n2_nextn1_v129280", True, make(2, 1, 129280, True, True, False)),
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]
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def _cases_norm() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""RMSNorm in the forms the engine uses: bf16 out, fp8 with fp32 scales, and both at once."""
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def make(rows: int, d: int, with_residual: bool, as_fp8: bool, return_bf16: bool, round_sf: bool):
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def run() -> Any:
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torch.manual_seed(0)
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x = torch.randn((rows, d), device="cuda", dtype=torch.bfloat16)
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w = torch.randn((d,), device="cuda", dtype=torch.bfloat16)
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residual = torch.randn((rows, d), device="cuda", dtype=torch.bfloat16) if with_residual else None
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kwargs: dict[str, Any] = {"eps": 1e-20, "out_scale": 1.5}
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if as_fp8:
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kwargs.update(
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fmt="e4m3",
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num_per_channels=128,
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round_sf=round_sf,
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use_tma_aligned_col_major_sf=True,
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)
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return norm(x, w, residual=residual, return_bf16=return_bf16, **kwargs)
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return run
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return [
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("r128_d5120_bf16", False, make(128, 5120, False, False, False, False)),
|
|
("r128_d5120_bf16_residual", False, make(128, 5120, True, False, False, False)),
|
|
("r64_d5120_fp8_round", False, make(64, 5120, False, True, False, True)),
|
|
("r64_d5120_fp8_no_round", False, make(64, 5120, False, True, False, False)),
|
|
("r64_d5120_fp8_round_bf16", False, make(64, 5120, False, True, True, True)),
|
|
("r33_d5120_fp8_round_bf16", False, make(33, 5120, False, True, True, True)),
|
|
("r16_d7168_fp8_round", False, make(16, 7168, False, True, False, True)),
|
|
("r8_d512_fp8_round", False, make(8, 512, False, True, False, True)),
|
|
]
|
|
|
|
|
|
def _cases_map_lookup() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Both lookup modes, including out-of-range and missing keys."""
|
|
|
|
def make_index(rows: int, width: int, dtype: torch.dtype):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
value = (
|
|
torch.randn((rows, width), device="cuda", dtype=dtype)
|
|
if dtype.is_floating_point
|
|
else torch.randint(0, 1000, (rows, width), device="cuda", dtype=dtype)
|
|
)
|
|
query = torch.randint(-width, 2 * width, (rows, 1), device="cuda", dtype=torch.int32)
|
|
return map_lookup(None, value, query, mode="by_index")
|
|
|
|
return run
|
|
|
|
def make_key(rows: int, width: int):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
# Distinct keys per row: that is what the indexer produces, and it is also the only
|
|
# case where the kernel (last match wins) and the Python reference (max) agree.
|
|
key = torch.randperm(width, device="cuda").to(torch.int32).unsqueeze(0).repeat(rows, 1)
|
|
value = torch.randn((rows, width), device="cuda", dtype=torch.float32)
|
|
# Half the queries exist in the row, half do not.
|
|
present = key.gather(-1, torch.randint(0, width, (rows, 1), device="cuda").long())
|
|
missing = torch.full((rows, 1), 10_000, device="cuda", dtype=torch.int32)
|
|
keep = torch.randint(0, 2, (rows, 1), device="cuda") == 1
|
|
query = torch.where(keep, present, missing)
|
|
return map_lookup(key, value, query, mode="by_key")
|
|
|
|
return run
|
|
|
|
return [
|
|
("index_f32", True, make_index(256, 4096, torch.float32)),
|
|
("index_i32", True, make_index(256, 4096, torch.int32)),
|
|
("key_f32", False, make_key(128, 4096)),
|
|
]
|
|
|
|
|
|
def _cases_expand_sampling_params() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Per-row parameter expansion for the draft rows."""
|
|
|
|
def make(req_cnt: int, total_q: int):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
temps = torch.rand((req_cnt,), device="cuda", dtype=torch.float32)
|
|
top_ps = torch.rand((req_cnt,), device="cuda", dtype=torch.float32)
|
|
top_ks = torch.randint(0, 4096, (req_cnt,), device="cuda", dtype=torch.int32)
|
|
positions = torch.randint(0, 1000, (req_cnt,), device="cuda", dtype=torch.int32)
|
|
seeds = torch.randint(0, 2**62, (req_cnt,), device="cuda", dtype=torch.uint64)
|
|
row_to_req = torch.randint(0, req_cnt, (total_q,), device="cuda", dtype=torch.int32)
|
|
local_step = torch.randint(0, 6, (total_q,), device="cuda", dtype=torch.int32)
|
|
outs = [
|
|
torch.empty((total_q,), device="cuda", dtype=torch.float32),
|
|
torch.empty((total_q,), device="cuda", dtype=torch.float32),
|
|
torch.empty((total_q,), device="cuda", dtype=torch.int32),
|
|
torch.empty((total_q,), device="cuda", dtype=torch.int32),
|
|
torch.empty((total_q,), device="cuda", dtype=torch.uint64),
|
|
]
|
|
kernel = ops.expand_sampling_params_tl()
|
|
kernel(temps, top_ps, top_ks, positions, seeds, row_to_req, local_step, *outs)
|
|
return tuple(outs)
|
|
|
|
return run
|
|
|
|
return [("n4_q32", True, make(4, 32)), ("n1_q5", True, make(1, 5)), ("n8_q100", True, make(8, 100))]
|
|
|
|
|
|
def _cases_prepare_verification() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Draft-row metadata: request index, local step and the verify flag."""
|
|
|
|
def make(req_cnt: int, max_len: int, build_steps: int):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
seqlen_q = torch.randint(1, max_len + 1, (req_cnt,), device="cuda", dtype=torch.int32)
|
|
cu = torch.cumsum(seqlen_q, 0, dtype=torch.int32)
|
|
total_q = int(cu[-1].item())
|
|
ftp = torch.zeros((req_cnt,), device="cuda", dtype=torch.int32)
|
|
ftp[1:] = cu[:-1]
|
|
# The sentinel makes "wrote nothing" visible. `row_to_req` is a `torch.empty` buffer in
|
|
# the engine, so a kernel that fills fewer than `total_q` rows leaves garbage behind and
|
|
# every consumer then indexes with it -- and both backends would still agree with each
|
|
# other here, which is why this check has to be explicit rather than a comparison.
|
|
row_to_req = torch.full((total_q,), -7, device="cuda", dtype=torch.int32)
|
|
local_step = torch.full((total_q,), -7, device="cuda", dtype=torch.int32)
|
|
is_verify = torch.zeros((total_q,), device="cuda", dtype=torch.bool)
|
|
# Driven through the factory: the interface wrapper allocates its own outputs, which
|
|
# would leave nothing to compare.
|
|
kernel = ops.prepare_verification_metadata_tl(build_steps)
|
|
kernel(ftp, seqlen_q, row_to_req, local_step, is_verify)
|
|
assert int((row_to_req == -7).sum()) == 0, "kernel left rows unwritten"
|
|
assert int((local_step == -7).sum()) == 0, "kernel left rows unwritten"
|
|
return row_to_req, local_step, is_verify
|
|
|
|
return run
|
|
|
|
return [
|
|
("n4_maxlen6", True, make(4, 6, 6)),
|
|
("n1_maxlen1", True, make(1, 1, 1)),
|
|
("n8_maxlen6", True, make(8, 6, 6)),
|
|
# `num_steps=0` is what the engine builds when speculative decoding is off, so it must
|
|
# still write every row.
|
|
("n8_zero_steps", True, make(8, 6, 0)),
|
|
# A request longer than one tile (32 lanes) exercises the runtime tile loop.
|
|
("n2_maxlen130", True, make(2, 130, 130)),
|
|
("n64_maxlen6", True, make(64, 6, 6)),
|
|
]
|
|
|
|
|
|
def _cases_copy_block_tables() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Compact 1D block table expanded to one row per token."""
|
|
|
|
def make(n_seq: int, max_seqlen_q: int, max_blocks: int):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
seqlens = torch.randint(1, max_seqlen_q + 1, (n_seq,), device="cuda", dtype=torch.int32)
|
|
cu = torch.cumsum(seqlens, 0, dtype=torch.int32)
|
|
total_q = int(cu[-1].item())
|
|
counts = torch.randint(1, max_blocks + 1, (n_seq,), device="cuda", dtype=torch.int32)
|
|
offsets = torch.zeros((n_seq + 1,), device="cuda", dtype=torch.int32)
|
|
offsets[1:] = torch.cumsum(counts, 0, dtype=torch.int32)
|
|
flat = torch.randint(0, 512, (int(offsets[-1].item()),), device="cuda", dtype=torch.int32)
|
|
out = torch.full((total_q, max_blocks), -7, device="cuda", dtype=torch.int32)
|
|
# No standalone interface function: this kernel is only reached from
|
|
# `prepare_v4_decode_metadata`, so the case drives the factory directly. The harness
|
|
# swaps `ops.copy_block_tables_tl` between backends, which is all that is needed.
|
|
kernel = ops.copy_block_tables_tl(max_seqlen_q, max_blocks)
|
|
kernel(seqlens, cu, flat, offsets, out)
|
|
return out
|
|
|
|
return run
|
|
|
|
return [
|
|
("n4_q6_b64", True, make(4, 6, 64)),
|
|
("n1_q1_b32", True, make(1, 1, 32)),
|
|
("n8_q6_b1400_multi_chunk", True, make(8, 6, 1400)),
|
|
]
|
|
|
|
|
|
def _cases_store_kv_cache() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Quantised cache writes: the whole cache buffer must match byte for byte."""
|
|
|
|
def make(layout: Any, block_size: int, n: int, with_invalid: bool):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
num_blocks = (n + block_size - 1) // block_size + 2
|
|
kv = torch.randn((n, layout.num_elements), device="cuda", dtype=torch.bfloat16)
|
|
slots = torch.arange(n, dtype=torch.int32, device="cuda") + block_size
|
|
if with_invalid:
|
|
slots[::7] = -1
|
|
cache = torch.zeros((num_blocks, block_size, 1, layout.bytes_per_token), dtype=torch.uint8, device="cuda")
|
|
store_kv_cache(kv, cache, slots, layout=layout)
|
|
return cache.clone()
|
|
|
|
return run
|
|
|
|
return [
|
|
("main_fp8_b32", True, make(V41_FORMAT, 32, 100, False)),
|
|
("main_fp8_b64", True, make(V41_FORMAT, 64, 233, True)),
|
|
("main_fp8_b134", True, make(V41_FORMAT, 134, 500, False)),
|
|
("index_fp32sf_b32", True, make(FP8_FLOAT32_SF_INDEX_KV_FORMAT, 32, 64, False)),
|
|
("index_fp32sf_b64", True, make(FP8_FLOAT32_SF_INDEX_KV_FORMAT, 64, 200, True)),
|
|
]
|
|
|
|
|
|
def _cases_load_paged_kv_cache() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Paged reads, including holes (src_block_id == -1) and partial segments.
|
|
|
|
The cache is filled by `store_kv_cache` rather than with random bytes: random bytes produce
|
|
fp8 NaN patterns and NaN scales, and those compare unequal to themselves, which would report a
|
|
mismatch that says nothing about the load kernel.
|
|
"""
|
|
|
|
def make(layout: Any, block_size: int, num_blocks: int, num_segments: int, with_hole: bool):
|
|
torch.manual_seed(0)
|
|
total = num_blocks * block_size
|
|
kv = torch.randn((total, layout.num_elements), device="cuda", dtype=torch.bfloat16)
|
|
cache = torch.zeros((num_blocks, block_size, 1, layout.bytes_per_token), dtype=torch.uint8, device="cuda")
|
|
store_kv_cache(kv, cache, torch.arange(total, dtype=torch.int32, device="cuda"), layout=layout)
|
|
|
|
segments = []
|
|
dst = 0
|
|
for i in range(num_segments):
|
|
begin = i % block_size
|
|
end = min(block_size, begin + 1 + (i * 7) % block_size)
|
|
src = -1 if (with_hole and i == 1) else i % num_blocks
|
|
segments.append([src, begin, end, dst])
|
|
dst += end - begin
|
|
seg = torch.tensor(segments, dtype=torch.int32, device="cuda")
|
|
|
|
def run() -> Any:
|
|
result = load_paged_kv_cache(cache, seg, dst, layout=layout)
|
|
if result.bf16_data is not None:
|
|
return result.bf16_data
|
|
# Byte views for the index layout: both payload and scales are passthrough copies, so
|
|
# exact bytes are the right comparison and NaN bit patterns are harmless.
|
|
return result.quantized_data.view(torch.uint8), result.sf_data.view(torch.uint8)
|
|
|
|
return run
|
|
|
|
return [
|
|
("main_fp8_b32", True, make(V41_FORMAT, 32, 8, 6, False)),
|
|
("main_fp8_b64_holes", True, make(V41_FORMAT, 64, 8, 6, True)),
|
|
("index_fp32sf_b32", True, make(FP8_FLOAT32_SF_INDEX_KV_FORMAT, 32, 8, 6, False)),
|
|
("index_fp32sf_b64_holes", True, make(FP8_FLOAT32_SF_INDEX_KV_FORMAT, 64, 8, 6, True)),
|
|
]
|
|
|
|
|
|
def _cases_fused_transform() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""The norm -> RoPE -> fp8 pipeline, in every form the engine can ask for."""
|
|
|
|
def make(
|
|
n: int,
|
|
num_heads: int,
|
|
head_dim: int,
|
|
rotary: int,
|
|
*,
|
|
norm: bool,
|
|
rope: bool,
|
|
cast: bool,
|
|
cast_back: bool,
|
|
is_neox: bool = False,
|
|
conjugate: bool = False,
|
|
npc: int = 32,
|
|
sf_format: str = "ue8m0",
|
|
):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
x = torch.randn((n, num_heads, head_dim), device="cuda", dtype=torch.bfloat16)
|
|
norm_args = NormArgs(weight=torch.randn((head_dim,), device="cuda", dtype=torch.bfloat16), eps=1e-20) if norm else None
|
|
rope_args = None
|
|
if rope:
|
|
rotary_dim = rotary
|
|
half = rotary_dim // 2
|
|
angles = torch.randn((4096, half), device="cuda", dtype=torch.float32).cumsum(-1) * 0.01
|
|
cache = torch.cat([angles.cos(), angles.sin()], dim=-1)
|
|
positions = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
|
|
rope_args = RopeArgs(
|
|
positions=positions,
|
|
cos_sin_cache=cache,
|
|
rotary_dim=rotary_dim,
|
|
is_neox=is_neox,
|
|
offset=head_dim - rotary_dim,
|
|
conjugate=conjugate,
|
|
)
|
|
cast_args = CastArgs("e4m3", npc, round_sf=True, sf_format=sf_format, cast_back=cast_back) if cast else None
|
|
return fused_norm_rope_hadamard_cast_store_kvcache(x, norm_args, rope_args, None, cast_args, None)
|
|
|
|
return run
|
|
|
|
def make_index_cache(n: int, num_heads: int, head_dim: int, rotary: int, num_blocks: int):
|
|
"""Index-K path: fp32 scales, per-128 groups, into the index KV cache."""
|
|
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
layout = FP8_FLOAT32_SF_INDEX_KV_FORMAT
|
|
block_size = 64
|
|
x = torch.randn((n, num_heads, head_dim), device="cuda", dtype=torch.bfloat16)
|
|
half = rotary // 2
|
|
angles = torch.randn((4096, half), device="cuda", dtype=torch.float32).cumsum(-1) * 0.01
|
|
cos_sin = torch.cat([angles.cos(), angles.sin()], dim=-1)
|
|
positions = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
|
|
kv_cache = torch.zeros((num_blocks, block_size, 1, layout.bytes_per_token), dtype=torch.uint8, device="cuda")
|
|
# Distinct slots: two tokens landing on the same slot would race, and the winner
|
|
# would depend on CTA scheduling rather than on the kernel under test.
|
|
slots = torch.randperm(num_blocks * block_size, device="cuda")[:n].to(torch.int32)
|
|
fused_norm_rope_hadamard_cast_store_kvcache(
|
|
x,
|
|
None,
|
|
RopeArgs(positions, cos_sin, rotary, False, offset=head_dim - rotary),
|
|
None,
|
|
CastArgs.from_cache_format(layout),
|
|
KVCacheOutArgs(kv_cache, slots, layout=layout),
|
|
)
|
|
return kv_cache
|
|
|
|
return run
|
|
|
|
def make_cache(n: int, block_size: int, num_blocks: int, with_bf16: bool, with_rope: bool = True):
|
|
"""The main-KV path: quantise and write straight into the paged cache."""
|
|
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
head_dim, rotary = 512, 64
|
|
layout = V41_FORMAT
|
|
x = torch.randn((n, 1, head_dim), device="cuda", dtype=torch.bfloat16)
|
|
weight = torch.randn((head_dim,), device="cuda", dtype=torch.bfloat16)
|
|
half = rotary // 2
|
|
angles = torch.randn((4096, half), device="cuda", dtype=torch.float32).cumsum(-1) * 0.01
|
|
cache_cs = torch.cat([angles.cos(), angles.sin()], dim=-1)
|
|
positions = torch.randint(0, 4096, (n,), device="cuda", dtype=torch.int32)
|
|
rope_args = RopeArgs(positions, cache_cs, rotary, False, offset=head_dim - rotary) if with_rope else None
|
|
kv_cache = torch.zeros((num_blocks, block_size, 1, layout.bytes_per_token), dtype=torch.uint8, device="cuda")
|
|
# Distinct slots: two tokens landing on the same slot would race, and the winner
|
|
# would depend on CTA scheduling rather than on the kernel under test.
|
|
slots = torch.randperm(num_blocks * block_size, device="cuda")[:n].to(torch.int32)
|
|
out_bf16 = torch.empty((n, 1, head_dim), device="cuda", dtype=torch.bfloat16) if with_bf16 else None
|
|
fused_norm_rope_hadamard_cast_store_kvcache(
|
|
x,
|
|
NormArgs(weight, 1e-20),
|
|
rope_args,
|
|
None,
|
|
CastArgs.from_cache_format(layout),
|
|
KVCacheOutArgs(kv_cache, slots, out=out_bf16, out_stage="norm", layout=layout),
|
|
)
|
|
return (kv_cache, out_bf16) if with_bf16 else kv_cache
|
|
|
|
return run
|
|
|
|
return [
|
|
# The configuration the engine builds, from DEEPINFER_TRACE_KERNEL_ARGS on a full run.
|
|
("engine_cfg_n1_h512", False, make(16, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=True)),
|
|
# The engine's other call site: norm + rope + quantise into the paged KV cache, with the
|
|
# post-norm row also returned for the indexer.
|
|
("engine_cache", False, make_cache(16, 134, 4, True)),
|
|
("engine_cache_no_bf16", False, make_cache(16, 134, 4, False)),
|
|
("engine_cache_b32", False, make_cache(9, 32, 8, True)),
|
|
("engine_cache_no_rope", False, make_cache(8, 134, 2, True, with_rope=False)),
|
|
("engine_cfg_n128", False, make(128, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=True)),
|
|
("engine_cfg_n1_token", False, make(1, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=True)),
|
|
("neox", False, make(16, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=True, is_neox=True)),
|
|
("conjugate", False, make(16, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=True, conjugate=True)),
|
|
("no_rope", False, make(64, 1, 512, 64, norm=True, rope=False, cast=True, cast_back=True)),
|
|
("no_norm", False, make(64, 1, 512, 64, norm=False, rope=True, cast=True, cast_back=True)),
|
|
("bf16_only", False, make(64, 1, 512, 64, norm=True, rope=True, cast=False, cast_back=False)),
|
|
("return_quant", False, make(64, 1, 512, 64, norm=True, rope=True, cast=True, cast_back=False)),
|
|
("multi_head_h128", False, make(32, 4, 128, 128, norm=True, rope=True, cast=True, cast_back=True)),
|
|
("h128_rope64", False, make(32, 2, 128, 64, norm=True, rope=True, cast=True, cast_back=True)),
|
|
# The indexer's Q quantisation: no norm, per-128 groups, fp32 scales, quantised output.
|
|
("index_q_fp32sf", False, make(64, 4, 128, 64, norm=False, rope=True, cast=True, cast_back=False, npc=128, sf_format="fp32")),
|
|
(
|
|
"index_q_fp32sf_many",
|
|
False,
|
|
make(256, 32, 128, 64, norm=False, rope=True, cast=True, cast_back=False, npc=128, sf_format="fp32"),
|
|
),
|
|
# Same, but written straight into the index KV cache.
|
|
# The index KV cache holds a single KV head (the indexer is MQA), which the baseline
|
|
# asserts too.
|
|
("index_k_cache", False, make_index_cache(48, 1, 128, 64, 16)),
|
|
]
|
|
|
|
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def _cases_prepare_v4_decode_metadata() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Every output of the decode-metadata kernel, including the ratio-bucketed ones."""
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def make(
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n_seq: int,
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max_seqlen_q: int,
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max_num_blocks: int,
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window_size: int,
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swa_block_size: int,
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block_size: int,
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ca_ratios: tuple[int, ...],
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dense_ratios: tuple[int, ...],
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sparse_ratios: tuple[int, ...],
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placeholders: bool,
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):
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def run() -> Any:
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torch.manual_seed(0)
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seqlens_q = torch.randint(1, max_seqlen_q + 1, (n_seq,), device="cuda", dtype=torch.int32)
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seqlens_k = seqlens_q + torch.randint(0, 40, (n_seq,), device="cuda", dtype=torch.int32)
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cu = torch.cumsum(seqlens_q, 0, dtype=torch.int32)
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total_q = int(cu[-1].item())
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per_seq_blocks = torch.randint(1, max_num_blocks + 1, (n_seq,), device="cuda", dtype=torch.int32)
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offsets = torch.zeros((n_seq + 1,), device="cuda", dtype=torch.int32)
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offsets[1:] = torch.cumsum(per_seq_blocks, 0, dtype=torch.int32)
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total_blocks = int(offsets[-1].item())
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block_tables_in = torch.randint(0, 40, (total_blocks,), device="cuda", dtype=torch.int32)
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state_block_idx = torch.randint(0, 8, (n_seq,), device="cuda", dtype=torch.int32)
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is_placeholder = torch.zeros((n_seq,), device="cuda", dtype=torch.bool)
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if placeholders:
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is_placeholder[::2] = True
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n_ca = max(len(ca_ratios), 1)
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n_dense = max(len(dense_ratios), 1)
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n_sparse = max(len(sparse_ratios), 1)
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i32 = {"device": "cuda", "dtype": torch.int32}
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outs = [
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torch.full((total_q,), -7, **i32), # positions
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torch.zeros((total_q,), device="cuda", dtype=torch.bool), # valid_token_mask
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torch.zeros((total_q,), device="cuda", dtype=torch.bool), # gate_force_random
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torch.ones((total_q,), device="cuda", dtype=torch.bool), # fix_routing_mask
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torch.full((total_q,), -7, **i32), # context_lens
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torch.full((n_sparse, total_q), -7, **i32), # klen
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torch.full((n_dense, total_q, 2), -7, **i32), # kv_ca_index_offset_len
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torch.full((total_q, 2), -7, **i32), # kv_swa_index_offset_len
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torch.full((total_q,), -7, **i32), # slot_mapping_store_swa
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torch.full((total_q,), -7, **i32), # query_indices
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torch.full((total_q,), -7, **i32), # state_block_idx_out
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torch.full((total_q, max_num_blocks), -7, **i32), # block_tables_out
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torch.full((n_ca, total_q), -7, **i32), # positions_c
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torch.full((n_ca, total_q), -7, **i32), # slot_mapping_store_ca
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]
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prepare_v4_decode_metadata(
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seqlens_q,
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seqlens_k,
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cu,
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block_tables_in,
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offsets,
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state_block_idx,
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is_placeholder,
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*outs,
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max_seqlen_q=max_seqlen_q,
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max_num_blocks=max_num_blocks,
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window_size=window_size,
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swa_block_size=swa_block_size,
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block_size=block_size,
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ca_ratios=ca_ratios,
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dense_ratios=dense_ratios,
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sparse_ratios=sparse_ratios,
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force_load_balance=False,
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)
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# Nothing should be left at the sentinel: a partially written output is a bug even when
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# both backends agree on it.
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for name, tensor in (
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("positions", outs[0]),
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("context_lens", outs[4]),
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("kv_swa_index_offset_len", outs[7]),
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("slot_mapping_store_swa", outs[8]),
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("query_indices", outs[9]),
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("positions_c", outs[12]),
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("slot_mapping_store_ca", outs[13]),
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):
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assert int((tensor == -7).sum()) == 0, f"{name} left unwritten rows"
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return tuple(outs)
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return run
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return [
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# The engine's own configuration: window 128, SWA block 134, ratios {1, 2} all compressed.
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("engine_cfg", False, make(8, 6, 64, 128, 134, 128, (1, 2), (), (1, 2), False)),
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("engine_cfg_placeholders", False, make(8, 6, 64, 128, 134, 128, (1, 2), (), (1, 2), True)),
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("with_dense", False, make(6, 6, 32, 64, 128, 128, (1, 2, 4), (1, 2), (4,), False)),
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("single_seq", False, make(1, 1, 8, 32, 32, 32, (1,), (), (1,), False)),
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("prefill_len8", False, make(5, 8, 16, 512, 512, 64, (1, 2, 4), (), (1, 2, 4), False)),
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]
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def _cases_compress_prefill() -> list[tuple[str, bool, Callable[[], Any]]]:
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"""Block compression plus the ring-cache tail write, including a window that spans chunks."""
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def make(dim: int, ratio: int, n_seq: int, cache_size: int, slots: int, spans_chunks: bool):
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def run() -> Any:
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torch.manual_seed(0)
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qlens = torch.randint(3, 7, (n_seq,), device="cuda", dtype=torch.int32)
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# qstart > 0 means the first compression window reaches into the previous chunk, which
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# is the only way the cache-read branch is exercised.
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|
qstart = (
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torch.randint(1, 4, (n_seq,), device="cuda", dtype=torch.int32)
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if spans_chunks
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|
else torch.zeros((n_seq,), device="cuda", dtype=torch.int32)
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)
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klen = qstart + qlens
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# All four prefix sums are exclusive with a leading zero (`n_seq + 1` entries); the
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# kernel's ABI rejects the inclusive form.
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zero = torch.zeros((1,), device="cuda", dtype=torch.int32)
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cu_q = torch.cat([zero, torch.cumsum(qlens, 0, dtype=torch.int32)])
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cu_k = torch.cat([zero, torch.cumsum(klen, 0, dtype=torch.int32)])
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total_q = int(cu_q[-1].item())
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|
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new_blocks = (klen // ratio - qstart // ratio).tolist()
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reserved = [max(1, b) for b in new_blocks]
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mapping = torch.cat([torch.full((r,), i, device="cuda", dtype=torch.int32) for i, r in enumerate(reserved)])
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# Exclusive prefix sum with a leading zero, i.e. `n_seq + 1` entries: the kernel binds
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# its `num_seqs` symbol from this tensor, so a shorter one fails the ABI check.
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|
cu_c = torch.zeros((n_seq + 1,), device="cuda", dtype=torch.int32)
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|
acc = 0
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|
for i, r in enumerate(reserved):
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|
cu_c[i] = acc
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|
acc += r
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|
cu_c[n_seq] = acc
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# Distinct slots per sequence, as the engine assigns them. Sharing a slot would let one
|
|
# sequence's tail copy overwrite the other's cache, which makes the result depend on
|
|
# kernel interleaving rather than on the code under test.
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|
assert n_seq <= slots
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|
state_block_idx = torch.randperm(slots, device="cuda")[:n_seq].to(torch.int32)
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|
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|
score = torch.randn((total_q, dim), device="cuda", dtype=torch.float32)
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latent = torch.randn((total_q, dim), device="cuda", dtype=torch.float32)
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|
state_cache = torch.randn((slots, cache_size, 2, dim), device="cuda", dtype=torch.float32)
|
|
|
|
kv = compress_and_update_state_prefill(
|
|
score,
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|
latent,
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|
state_cache,
|
|
state_block_idx,
|
|
mapping,
|
|
cu_q,
|
|
cu_k,
|
|
cu_c,
|
|
ratio,
|
|
None,
|
|
)
|
|
return kv, state_cache
|
|
|
|
return run
|
|
|
|
return [
|
|
("engine_r2", False, make(512, 2, 3, 16, 4, True)),
|
|
("engine_r2_no_prev", False, make(512, 2, 3, 16, 4, False)),
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|
("r1", False, make(512, 1, 3, 16, 4, True)),
|
|
("single_seq", False, make(512, 2, 1, 8, 1, True)),
|
|
# Tightest legal ring: at least one chunk's worth of tokens, so the tail copy cannot wrap
|
|
# onto a slot the compression step is reading. The interface asserts this relation too.
|
|
("tight_cache", False, make(512, 2, 2, 6, 2, True)),
|
|
]
|
|
|
|
|
|
def _cases_compress_decode() -> list[tuple[str, bool, Callable[[], Any]]]:
|
|
"""Per-token cache update and the block-closing compression."""
|
|
|
|
def make(dim: int, ratio: int, total_q: int, max_seqlen_q: int, slots: int, cache_size: int):
|
|
def run() -> Any:
|
|
torch.manual_seed(0)
|
|
score = torch.randn((total_q, dim), device="cuda", dtype=torch.float32)
|
|
latent = torch.randn((total_q, dim), device="cuda", dtype=torch.float32)
|
|
state_cache = torch.randn((slots, cache_size, 2, dim), device="cuda", dtype=torch.float32)
|
|
# One distinct state slot per token: the engine gives every sequence its own slot, and
|
|
# sharing one would make the cache writes overlap and the result order-dependent.
|
|
assert total_q <= slots
|
|
state_block_idx = torch.randperm(slots, device="cuda")[:total_q].to(torch.int32)
|
|
context_lens = torch.randint(1, 40, (total_q,), device="cuda", dtype=torch.int32)
|
|
kv = compress_and_update_state_decode(
|
|
score,
|
|
latent,
|
|
state_cache,
|
|
state_block_idx,
|
|
context_lens,
|
|
max_seqlen_q,
|
|
ratio,
|
|
)
|
|
return kv, state_cache
|
|
|
|
return run
|
|
|
|
return [
|
|
("engine_r2", False, make(512, 2, 8, 6, 8, 8)),
|
|
("r1", False, make(512, 1, 8, 6, 8, 8)),
|
|
("single_token", False, make(512, 2, 1, 1, 1, 4)),
|
|
("many_slots", False, make(512, 2, 16, 6, 16, 16)),
|
|
]
|
|
|
|
|
|
CASES: dict[str, Callable[[], list[tuple[str, bool, Callable[[], Any]]]]] = {
|
|
"store_kv_cache_tl": _cases_store_kv_cache,
|
|
"load_paged_kv_cache_tl": _cases_load_paged_kv_cache,
|
|
"norm_tl": _cases_norm,
|
|
"map_lookup_tl": _cases_map_lookup,
|
|
"expand_sampling_params_tl": _cases_expand_sampling_params,
|
|
"prepare_verification_metadata_tl": _cases_prepare_verification,
|
|
"fused_norm_rope_hadamard_cast_store_kvcache_tl": _cases_fused_transform,
|
|
"prepare_v4_decode_metadata_tl": _cases_prepare_v4_decode_metadata,
|
|
"compress_and_update_state_prefill_tl": _cases_compress_prefill,
|
|
"compress_and_update_state_decode_tl": _cases_compress_decode,
|
|
"copy_block_tables_tl": _cases_copy_block_tables,
|
|
"gather_tl": _cases_gather,
|
|
"gather_input_ids_zos_tl": _cases_gather_input_ids,
|
|
"gather_engram_tokens_zos_tl": _cases_gather_engram,
|
|
"expand_swa_index_tl": _cases_swa_index,
|
|
"kv_index_cat_tl": _cases_kv_index_cat,
|
|
"convert_kv_index_tl": _cases_convert_kv_index,
|
|
"load_kv_index_tl": _cases_load_kv_index,
|
|
"store_mtp_info_tl": _cases_store_mtp_info,
|
|
}
|