Cheng Wan
aaf9a95763
Fifth of five; stacked on #38049. The split gave every namespace a file, but only for the half an operator types. This is the other half. ## The parallel quotients are declared, not written out `attn_tp_size` and its five siblings were sixty lines of near-identical properties in the runtime context, a file away from the leaves they are quotients of, so reading `parallel.py` told you what you could set and nothing about what that decides. They are declared in `Parallel` now, in the same class as those leaves. They carry no annotation, so they are not dataclass fields and `collect_input_fields` never puts them on the record -- the same mechanism that already keeps `_NS_PATH` off it. That is the right exclusion: a quotient has no operator input to preserve, and the record is what crosses a process boundary, where a stamped width is one an elastic scale-up will not refresh. ## A quotient is a value in the bag, like every other derived one `_derived_width` answered from a stamp or, failing that, a live process group. The group read could never disagree with the stamp: - `initialize_model_parallel` stamps all six as its last statement, unconditionally; - an elastic scale-up restamps `attn_dp_size` through `update_dp_attention_post_scale` -- the comment claiming it does *not* was wrong; - no hardware backend builds groups of its own; - `multimodal_gen`, which has its own `initialize_model_parallel` and does not stamp, never reads a quotient. So a built group was always already stamped, and the group read goes -- and with it the last reason for a quotient to be resolved on every read. Every input to `derive_parallel_widths` is a record field. `dcp_enabled` is `decode_context_parallel_size > 1`, not a fact about a built group; it was spelled `_DCP is not None`, which is a longer way to say the same thing. So the six are fixed once the configuration is fixed -- the same test every other `Derived(fn=...)` in this PR passes. They are declared the same way and computed the same way: once, at publish, into ordinary bag leaves. What remains is override -> stamp -> published leaf. The stamp stays above the leaf because an elastic scale-up restamps `attn_dp_size`; the override stays on top because that is how a test names a width. ## One answer for the config-derived predicates `enable_mamba_extra_buffer` and its lazy variant, `is_ep_joiner`, `is_ep_scale_joiner`, `is_startup_weight_load_overlap`: each existed as a `ServerArgs` member for the resolution pipeline and, for most of them, again as a `runtime_context` function for readers after publish. Three places to keep saying the same thing. A `Derived(fn=...)` is a pure function of the published configuration, so `publish` computes it once and stores it as an ordinary bag leaf -- a plain attribute load, which is what a read inside compiled model code needs. The function is handed the whole resolved config rather than the bag it lands in, because a derivation is free to span namespaces and the mamba one does: it reads `memory.disable_radix_cache` alongside its own `exec.mamba` strategy, which is why it could never have been a method on either bag. The pre-publish helpers stay -- resolution needs the predicate before there is a bag to read -- and three readers keep them, because they run before their own process publishes: `initialize_dp_attention`, which the weight-cache daemon calls while building its groups thirty lines before its `publish`, and `PortArgs.init_new`, a factory handed the record that already reads eighteen other fields off it. ## Notes for a reviewer **Overriding a leaf does not move its quotient.** `override(tp_size=2)` leaves `attn_tp_size` where the published config put it, because nothing is recomputed on read. A test states a topology by publishing a config -- which is what a real process does -- or by naming the width it wants, `override(attn_tp_size=2)`. Six tests say it that way now. This is the price of having one answer computed once, and it is the same price every other derived value in the config already carries. A caller that reads a quotient without publishing or overriding now gets an explicit error naming the field, instead of a default that an uninitialised group happened to supply. One fixture was in that state -- `TestMlaWriteDoorsUnderDcp` built a bare pool and asked whether DCP was on -- and it publishes a config now, which is what the process it stands in for does. Eighteen sites read these predicates without calling them. That is correct -- they are properties -- but it is worth saying they were checked, because a census that assumes otherwise reports eighteen always-true conditions. ## The skill that documents this subsystem is updated with it `.claude/rules/modify-component-must-read.md` points at `.claude/skills/sglang-runtime-context/SKILL.md` before anyone touches these files, so a stale sentence there is a wrong instruction rather than a stale note. Four of its load-bearing statements stopped being true across this series and are corrected here: `NS(...)` is no longer how a field states its namespace (the declaring class is); the DCP degrade rule is gone, because the quotients are not live reads; `mamba_extra_buffer_enabled()` and the other predicate functions it named as the shape to copy no longer exist; and the namespace-coverage ratchet is described in terms of the marker. The docstring of `test_server_args_namespaces.py` said the same thing and is fixed too. The consequence a test author actually trips over is stated there as well: overriding a leaf no longer moves its quotient, so a topology is stated by publishing a config or by naming the width. ## Verification A full registered-unit sweep (648 files) against this stack's merge-base: 19 failures on both sides, the same 19 -- AMD `gfx950`, `modelopt`, `cuda_vmm`, `weight_checker` and friends, none of them config. The narrower 139-file config sweep used earlier in this series does not contain the files this change reaches -- `test_kv_index_translator` never names `get_parallel()`, it constructs an object that does -- which is why the baseline differential over everything is what is quoted here.
367 lines
11 KiB
Python
367 lines
11 KiB
Python
import sys
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from types import SimpleNamespace
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import pytest
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import torch
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from sglang.srt.layers.attention.minicpm.cache import (
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attach_compressed_cache,
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)
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from sglang.srt.managers.schedule_batch import ReqKvInfo
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from sglang.srt.managers.scheduler_components.invariant_checker import (
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SchedulerInvariantChecker,
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)
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from sglang.srt.managers.scheduler_components.pool_stats_observer import (
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SchedulerPoolStatsObserver,
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)
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from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
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from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
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from sglang.srt.runtime_context import publish, reset_context
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.session.streaming_session import SessionSlot, StreamingSession
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from sglang.test.ci.ci_register import register_cpu_ci
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register_cpu_ci(est_time=12, suite="base-a-test-cpu")
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class RecordingAllocator(BaseTokenToKVPoolAllocator):
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"""Single-pool double. Subclassing the base routes free_full / free_segment /
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free_segments into free(), so a new free API cannot slip past the recorder."""
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def __init__(self, capacity: int):
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super().__init__(
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size=capacity,
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page_size=1,
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dtype=torch.bfloat16,
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device="cpu",
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kvcache=None,
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need_sort=False,
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)
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self.capacity = capacity
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self.next_slot = 1
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self.live: set[int] = set()
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def alloc(self, size: int):
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if size > self.available_size():
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return None
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slots = torch.arange(self.next_slot, self.next_slot + size, dtype=torch.int64)
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self.next_slot += size
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self.live.update(slots.tolist())
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return slots
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def free(self, slots: torch.Tensor):
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self.live.difference_update(slots.tolist())
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def available_size(self):
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return self.capacity - len(self.live)
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def get_all_free_pages(self):
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return self.free_pages
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def clear(self):
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self.next_slot = 1
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self.live.clear()
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def make_pool_and_req(capacity: int = 64):
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allocator = RecordingAllocator(capacity)
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pool = ReqToTokenPool(
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size=2,
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max_context_len=64,
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device="cpu",
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enable_memory_saver=False,
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)
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attach_compressed_cache(
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pool,
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allocator,
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kernel_size=4,
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kernel_stride=2,
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enable_memory_saver=False,
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)
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req = SimpleNamespace(
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inflight_middle_chunks=0,
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kv=ReqKvInfo(),
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)
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req_pool_idx = pool.alloc([req])[0]
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return pool, req, req_pool_idx, allocator
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def alloc_extend(pool, req_pool_idx: int, seq_len: int):
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pool.alloc_aux_to_lengths(
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req_pool_indices_cpu=torch.tensor([req_pool_idx], dtype=torch.int64),
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target_seq_lens_cpu=torch.tensor([seq_len], dtype=torch.int64),
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)
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def setup_function(_):
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# The cache reads its parallel topology from the context.
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reset_context()
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publish(ServerArgs(model_path="dummy"), role="test")
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def teardown_function(_):
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reset_context()
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def test_extend_allocates_at_sparse_boundaries():
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pool, _, req_pool_idx, allocator = make_pool_and_req()
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cache = pool._aux_cache
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alloc_extend(pool, req_pool_idx, seq_len=3)
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assert allocator.available_size() == 39
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assert len(cache.free_slots) == 25
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alloc_extend(pool, req_pool_idx, seq_len=4)
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assert allocator.available_size() == 39
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assert len(cache.free_slots) == 24
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alloc_extend(pool, req_pool_idx, seq_len=16)
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assert allocator.available_size() == 39
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assert len(cache.free_slots) == 17
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def test_chunk_reuse_only_allocates_new_sparse_slots():
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pool, _, req_pool_idx, _ = make_pool_and_req()
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cache = pool._aux_cache
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alloc_extend(pool, req_pool_idx, seq_len=8)
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assert len(cache.free_slots) == 22
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alloc_extend(pool, req_pool_idx, seq_len=12)
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assert len(cache.free_slots) == 20
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alloc_extend(pool, req_pool_idx, seq_len=12)
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assert len(cache.free_slots) == 20
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def test_decode_does_not_duplicate_sparse_slots():
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"""Retrying the same decode position must not allocate duplicate cache slots."""
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pool, _, req_pool_idx, _ = make_pool_and_req()
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cache = pool._aux_cache
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alloc_extend(pool, req_pool_idx, seq_len=15)
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pool.alloc_aux_to_lengths(
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req_pool_indices_cpu=torch.tensor([req_pool_idx], dtype=torch.int64),
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target_seq_lens_cpu=torch.tensor([16], dtype=torch.int64),
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)
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available_after_first_decode = len(cache.free_slots)
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pool.alloc_aux_to_lengths(
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req_pool_indices_cpu=torch.tensor([req_pool_idx], dtype=torch.int64),
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target_seq_lens_cpu=torch.tensor([16], dtype=torch.int64),
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)
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assert available_after_first_decode == 17
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assert len(cache.free_slots) == available_after_first_decode
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def test_reserve_leaves_only_dense_capacity_visible():
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allocator = RecordingAllocator(capacity=69)
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pool = ReqToTokenPool(
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size=2,
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max_context_len=64,
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device="cpu",
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enable_memory_saver=False,
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)
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attach_compressed_cache(
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pool,
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allocator,
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kernel_size=32,
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kernel_stride=16,
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enable_memory_saver=False,
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)
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assert allocator.available_size() == 64
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assert len(pool._aux_cache.reserved_slots) == 5
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assert pool.schedulable_token_capacity(69) == 64
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def test_reserved_slots_are_excluded_from_full_pool_invariant():
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pool, _, _, allocator = make_pool_and_req(capacity=69)
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checker = SchedulerInvariantChecker(
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is_hybrid_swa=False,
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is_hybrid_ssm=True,
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disaggregation_mode=None,
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page_size=1,
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full_tokens_per_layer=None,
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swa_tokens_per_layer=None,
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max_total_num_tokens=64,
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tree_cache=SimpleNamespace(
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supports_mamba=lambda: False,
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protected_size=lambda: 0,
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),
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token_to_kv_pool_allocator=allocator,
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req_to_token_pool=pool,
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pool_stats_observer=SimpleNamespace(session_held_tokens=lambda: 0),
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get_last_batch=lambda: None,
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get_running_batch=lambda: None,
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scheduler_stage_metrics=None,
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)
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leak, message = checker._check_full_pool(
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SimpleNamespace(
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full_available_size=allocator.available_size(), full_evictable_size=0
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)
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)
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assert not leak, message
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def test_hybrid_pool_stats_exclude_reserved_slots():
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pool, _, _, allocator = make_pool_and_req(capacity=69)
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pool.mamba_allocator = SimpleNamespace(available_size=lambda: 1)
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pool.mamba_pool = SimpleNamespace(size=1)
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observer = SchedulerPoolStatsObserver(
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tree_cache=SimpleNamespace(supports_mamba=lambda: False),
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token_to_kv_pool_allocator=allocator,
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req_to_token_pool=pool,
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session_controller=None,
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hisparse_coordinator=None,
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is_hybrid_swa=False,
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is_hybrid_ssm=True,
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enable_hisparse=False,
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full_tokens_per_layer=None,
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swa_tokens_per_layer=None,
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max_total_num_tokens=42,
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get_last_batch=lambda: None,
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get_running_batch=lambda: None,
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)
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stats = observer._get_mamba_token_info()
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assert stats.full_num_used == 0
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assert stats.full_token_usage == 0
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def test_streaming_session_release_frees_compressed_slots():
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pool, _, req_pool_idx, allocator = make_pool_and_req()
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alloc_extend(pool, req_pool_idx, seq_len=16)
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dense_slots = allocator.alloc(16)
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pool.req_to_token[req_pool_idx, :16] = dense_slots.to(torch.int32)
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compressed_cache = pool._aux_cache
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assert len(compressed_cache.free_slots) < len(compressed_cache.reserved_slots)
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session = StreamingSession(
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SimpleNamespace(
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req_to_token_pool=pool,
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token_to_kv_pool_allocator=allocator,
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page_size=1,
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)
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)
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session.slots["session-a"] = SessionSlot(
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kv=ReqKvInfo(req_pool_idx=req_pool_idx, kv_allocated_len=16),
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)
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session.release_session("session-a")
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assert req_pool_idx in pool.free_slots
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assert len(compressed_cache.free_slots) == len(compressed_cache.reserved_slots)
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def test_mamba_leak_diagnostic_does_not_report_reserved_slots():
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pool, _, _, allocator = make_pool_and_req(capacity=69)
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allocator.free_pages = torch.arange(6, 70, dtype=torch.int64)
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pool.mamba_pool = SimpleNamespace(size=1)
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pool.mamba_allocator = SimpleNamespace(
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size=1,
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free_slots=torch.empty(0, dtype=torch.int64),
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)
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checker = SchedulerInvariantChecker(
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is_hybrid_swa=False,
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is_hybrid_ssm=True,
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disaggregation_mode=None,
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page_size=1,
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full_tokens_per_layer=None,
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swa_tokens_per_layer=None,
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max_total_num_tokens=64,
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tree_cache=SimpleNamespace(
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mamba_protected_size=lambda: 0,
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all_values_flatten=lambda: torch.empty(0, dtype=torch.int64),
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all_mamba_values_flatten=lambda: torch.empty(0, dtype=torch.int64),
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),
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token_to_kv_pool_allocator=allocator,
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req_to_token_pool=pool,
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pool_stats_observer=SimpleNamespace(
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session_held_mamba_slots=lambda: 0,
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),
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get_last_batch=lambda: None,
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get_running_batch=lambda: None,
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scheduler_stage_metrics=None,
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)
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leak, message = checker._check_mamba_pool(
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SimpleNamespace(mamba_available_size=0, mamba_evictable_size=0)
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)
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assert leak
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assert "leaked_full_pages" not in message
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assert "leaked_mamba_pages={1}" in message
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def test_partial_failure_rolls_back_and_free_releases_every_slot():
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"""A failed cache-level allocation must release slots allocated for other levels."""
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pool, req, req_pool_idx, allocator = make_pool_and_req(capacity=18)
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cache = pool._aux_cache
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with pytest.raises(RuntimeError, match="out of reserved slots"):
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alloc_extend(pool, req_pool_idx, seq_len=16)
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assert allocator.available_size() == 11
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assert len(cache.free_slots) == 7
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allocator.capacity = 20
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allocator.clear()
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pool.reset_aux_cache_allocator()
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alloc_extend(pool, req_pool_idx, seq_len=16)
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assert allocator.available_size() == 12
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assert len(cache.free_slots) == 0
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pool.free(req)
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assert req.kv.req_pool_idx is None
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assert allocator.available_size() == 12
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assert len(cache.free_slots) == 8
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def test_allocator_reset_rebuilds_reserve():
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pool, _, _, allocator = make_pool_and_req()
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allocator.clear()
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assert allocator.available_size() == 64
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pool.reset_aux_cache_allocator()
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assert allocator.available_size() == 39
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assert len(pool._aux_cache.free_slots) == 25
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def test_attach_compressed_cache_is_idempotent():
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allocator = RecordingAllocator(capacity=64)
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pool = ReqToTokenPool(
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size=2,
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max_context_len=64,
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device="cpu",
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enable_memory_saver=False,
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)
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attach_compressed_cache(
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pool,
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allocator,
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kernel_size=4,
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kernel_stride=2,
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enable_memory_saver=False,
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)
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cache = pool._aux_cache
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k1_table = pool.req_to_sparse_k1_token
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attach_compressed_cache(
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pool,
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allocator,
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kernel_size=4,
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kernel_stride=2,
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enable_memory_saver=False,
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)
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assert pool._aux_cache is cache
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assert pool.req_to_sparse_k1_token is k1_table
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if __name__ == "__main__":
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sys.exit(pytest.main([__file__, "-v"]))
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