[LingBot World] Eleven bit-identical removals of repeated work in the realtime path

docs/configuration/environment_variables.md

@@ -70,6 +70,7 @@ The lifecycle labels used below are:
 | `OMNI_DIFFUSION_PROMPT_EMBED_CACHE_SIZE` | Positive integer; default `32` entries | Each diffusion runner; resolved during model setup | A valid environment value overrides the explicit cache size. A non-integer logs a warning; a non-positive value is ignored. | Experimental |
 | `OMNI_DIFFUSION_SESSION_STATE_MANAGER` | Boolean with the same accepted spellings as the prompt cache; default disabled | Experimental diffusion session manager; model setup | A recognized environment value overrides the explicit enable setting. An unrecognized value is ignored. | Experimental |
 | `OMNI_DIFFUSION_SESSION_STATE_MANAGER_MAX_SESSIONS` | Positive integer; default `64` | Experimental diffusion session manager; model setup | A valid environment value overrides the explicit maximum. A non-integer logs a warning; a non-positive value is ignored. | Experimental |
+| `VLLM_OMNI_AR_DIFFUSION_KV_GATHER` | `1` enables; default `0` (off) | AR-diffusion KV manager at allocation and attention at dispatch; set before worker startup | Only the exact value `1` enables contiguous KV gathering. History staging additionally requires `ARDiffusionKVConfig.reuse_history_staging`. | Experimental |
 
 Backend names for `DIFFUSION_ATTENTION_BACKEND` are the members of
 `DiffusionAttentionBackendEnum`, such as `FLASH_ATTN`, `TORCH_SDPA`,

tests/config/test_environment_variables.py

@@ -164,7 +164,7 @@ def test_inventory_matches_reviewed_snapshot_counts():
     """Make an inventory expansion an explicit review decision."""
     category_counts = Counter(item.category for item in ENVIRONMENT_VARIABLE_INVENTORY.values())
     assert category_counts == {
-        EnvironmentVariableCategory.PUBLIC_OMNI: 28,
+        EnvironmentVariableCategory.PUBLIC_OMNI: 29,
         EnvironmentVariableCategory.INHERITED_VLLM: 20,
         EnvironmentVariableCategory.PLATFORM_EXTERNAL: 27,
         EnvironmentVariableCategory.MODEL_SPECIFIC: 57,

tests/diffusion/ar_diffusion/test_kv_cache.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Unit tests for the AR-Diffusion KV cache helpers (Phase 1, PR-2).
 
 Covers the request adapter, the chunk-window spec/manager (registration + the
@@ -269,9 +270,14 @@ def _(key_pool, value_pool, value):
     # drives the reinplace pass, so a drift here would silently test nothing.
     real_schema = torch.ops.vllm_omni.ar_diffusion_paged_write_attn.default._schema
     real_mutated = [arg.name for arg in real_schema.arguments if arg.alias_info and arg.alias_info.is_write]
-    assert real_mutated == ["key_pool", "value_pool"], (
+    # The staging buffers are mutated too when reuse_history_staging is on, but they are separate
+    # allocations and not what this test is about; the pools are what must not be cloned.
+    assert real_mutated[:2] == ["key_pool", "value_pool"], (
         f"the real paged-write op now mutates {real_mutated}; this test still models two pools"
     )
+    assert set(real_mutated) - {"key_pool", "value_pool"} <= {"stage_key", "stage_value"}, (
+        f"unexpected mutated argument in the real paged-write op: {real_mutated}"
+    )
 
     num_blocks, heads, dim = 8, 4, 16
     cache_shape = (num_blocks, BLOCK, heads, dim)
@@ -357,6 +363,7 @@ def _make_tiny_capacity_kv(
     available_bytes: int,
     gpu_memory_fraction: float = 1.0,
     model_owned_state_bytes_per_session: int = 0,
+    reuse_history_staging: bool = False,
 ) -> ARDiffusionKVCache:
     return ARDiffusionKVCache(
         ARDiffusionKVConfig(
@@ -365,6 +372,7 @@ def _make_tiny_capacity_kv(
             window_chunks=3,
             sink_chunks=3,
             gpu_memory_fraction=gpu_memory_fraction,
+            reuse_history_staging=reuse_history_staging,
         ),
         num_layers=1,
         num_kv_heads=1,
@@ -382,6 +390,33 @@ def _make_tiny_capacity_kv(
     )
 
 
+def test_history_staging_is_reserved_in_the_kv_budget(monkeypatch):
+    """The per-layer staging pair is worker-wide memory allocated after admission; the budget must hold it."""
+    monkeypatch.setenv("VLLM_OMNI_AR_DIFFUSION_KV_GATHER", "1")
+    # Same geometry as the capacity-two test: 192 bytes fits two sessions exactly without staging.
+    plain = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192)
+    assert plain.session_capacity == 2 and plain.history_staging_reserved_bytes == 0
+
+    staged = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192, reuse_history_staging=True)
+    # 2 (K, V) x 1 layer x (sink 3 + window 3 + one action-capacity block) tokens x 1 head x 1 dim x 4 bytes.
+    assert staged.history_staging_reserved_bytes == 2 * 1 * 7 * 1 * 1 * 4
+    # It comes out of the same budget, so the second session no longer fits.
+    assert staged.session_capacity == 1
+    assert staged.num_blocks_total * 8 + staged.history_staging_reserved_bytes <= 192
+
+    # Boundary: one session fits at 128 bytes without staging; with staging the same budget is rejected
+    # up front instead of failing on the first forward.
+    _make_tiny_capacity_kv(requested_capacity=1, available_bytes=128)
+    with pytest.raises(ValueError, match="cannot fit one session"):
+        _make_tiny_capacity_kv(requested_capacity=1, available_bytes=128, reuse_history_staging=True)
+
+
+def test_history_staging_is_not_reserved_when_the_gather_path_is_off(monkeypatch):
+    monkeypatch.delenv("VLLM_OMNI_AR_DIFFUSION_KV_GATHER", raising=False)
+    staged = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192, reuse_history_staging=True)
+    assert staged.history_staging_reserved_bytes == 0 and staged.session_capacity == 2
+
+
 def test_capacity_two_retains_both_windows_and_allocates_next_block():
     # LingBot-like single-branch geometry: page=8 bytes; capacity=2 requires
     # 17 managed + 3 scratch blocks, plus two 16-byte cross-attention
@@ -588,3 +623,70 @@ def test_non_contiguous_branch_indices_rejected():
             kv_branches=(ARDiffusionKVBranchSpec("main", 1),),
             session_capacity=1,
         )
+
+
+def test_staged_window_reuse_matches_a_full_gather():
+    """Reusing the staged history must be byte-identical to re-gathering the whole window.
+
+    This is the property the optimisation rests on: the leading blocks were staged by an earlier forward of
+    the same AR block, so skipping them can only be correct if what is already there equals what a fresh
+    gather would write. The test stages once, mutates only the current chunk's blocks in the pool (what a
+    later probe of the same block does), restages the tail alone, and compares against a full gather.
+    """
+    from vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention import _stage_window
+
+    torch.manual_seed(0)
+    num_blocks, block_size, heads, dim = 6, 4, 2, 8
+    current_blocks = 2
+    key_cache = torch.randn(num_blocks, block_size, heads, dim)
+    value_cache = torch.randn(num_blocks, block_size, heads, dim)
+    block_ids = torch.tensor([5, 4, 3, 2, 1, 0])
+    stage_key = torch.zeros(num_blocks * block_size, heads, dim)
+    stage_value = torch.zeros(num_blocks * block_size, heads, dim)
+
+    _stage_window(stage_key, stage_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=0)
+
+    # A later probe rewrites only the current chunk's blocks in the pool.
+    for slot in block_ids[num_blocks - current_blocks :]:
+        key_cache[slot] = torch.randn(block_size, heads, dim)
+        value_cache[slot] = torch.randn(block_size, heads, dim)
+
+    _stage_window(
+        stage_key,
+        stage_value,
+        key_cache,
+        value_cache,
+        block_ids,
+        num_blocks,
+        block_size,
+        first_block=num_blocks - current_blocks,
+    )
+
+    full_key = torch.zeros_like(stage_key)
+    full_value = torch.zeros_like(stage_value)
+    _stage_window(full_key, full_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=0)
+
+    torch.testing.assert_close(stage_key, full_key, rtol=0, atol=0)
+    torch.testing.assert_close(stage_value, full_value, rtol=0, atol=0)
+
+
+def test_staged_window_tail_refresh_leaves_history_untouched():
+    """The tail restage must not write the history rows -- that is what makes it cheaper."""
+    from vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention import _stage_window
+
+    torch.manual_seed(1)
+    num_blocks, block_size, heads, dim = 5, 4, 1, 4
+    key_cache = torch.randn(num_blocks, block_size, heads, dim)
+    value_cache = torch.randn(num_blocks, block_size, heads, dim)
+    block_ids = torch.arange(num_blocks)
+    stage_key = torch.full((num_blocks * block_size, heads, dim), -1.0)
+    stage_value = torch.full((num_blocks * block_size, heads, dim), -1.0)
+
+    _stage_window(
+        stage_key, stage_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=num_blocks - 1
+    )
+
+    history_rows = (num_blocks - 1) * block_size
+    assert (stage_key[:history_rows] == -1.0).all(), "history rows were rewritten by a tail restage"
+    assert (stage_value[:history_rows] == -1.0).all()
+    torch.testing.assert_close(stage_key[history_rows:], key_cache[-1].reshape(block_size, heads, dim), rtol=0, atol=0)

tests/diffusion/ar_diffusion/test_paged_attention.py

@@ -1,4 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Tests for AR-Diffusion paged self-attention contexts."""
 
 from __future__ import annotations
@@ -20,6 +21,8 @@
     ar_diffusion_paged_attention,
     paged_write_attn,
 )
+from vllm_omni.experimental.ar_diffusion.kv_cache import paged_attention as paged_attention_module
+from vllm_omni.experimental.ar_diffusion.kv_cache.config import KV_GATHER_ENV
 from vllm_omni.experimental.ar_diffusion.kv_cache.state import ARDiffusionKVState
 
 pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]
@@ -32,8 +35,12 @@
 NEG = "negative"
 
 
-def make_state(*, num_layers=1, window_chunks=2, dtype=torch.float32, device=torch.device("cpu")):
-    cfg = ARDiffusionKVConfig(enable=True, chunk_size=BLOCK, window_chunks=window_chunks)
+def make_state(
+    *, num_layers=1, window_chunks=2, dtype=torch.float32, device=torch.device("cpu"), reuse_history_staging=False
+):
+    cfg = ARDiffusionKVConfig(
+        enable=True, chunk_size=BLOCK, window_chunks=window_chunks, reuse_history_staging=reuse_history_staging
+    )
     kv = ARDiffusionKVCache(
         cfg,
         num_layers=num_layers,
@@ -236,6 +243,86 @@ def test_paged_attention_matches_dense_reference_cpu(history_chunks, action_len,
     assert st.adapter(POS).completed_chunks == before + (1 if commit_current else 0)
 
 
+@pytest.mark.parametrize("history_chunks", [0, 1, 2, 3])
+def test_staged_reuse_refreshes_the_current_blocks_at_their_live_offset(monkeypatch, history_chunks):
+    """A second probe of the same AR block restages its current K/V where the table actually holds it.
+
+    The padded table always ends in at least one action-capacity block, and while the window is still
+    growing in unused window capacity too, so "the last current_blocks entries" is padding. The refresh
+    has to start right after the visible history: empty (window growing), partial, full, and after a slide.
+    """
+    monkeypatch.setenv(KV_GATHER_ENV, "1")
+    device = torch.device("cpu")
+    dtype = torch.float32
+    kv, st = make_state(dtype=dtype, device=device, window_chunks=2, reuse_history_staging=True)
+    if history_chunks:
+        _commit_video_span(kv, st, kv_branch=POS, n_chunks=history_chunks, dtype=dtype, device=device)
+
+    ctx = st.get_kv_caches(POS, seq_len=BLOCK, commit_current=False)[0].forward_ctx
+    ctx.ensure_video_slots(device)
+    key_cache, value_cache = kv.key_cache(0), kv.value_cache(0)
+
+    def probe(step: int):
+        # Each probe of the block writes new current K/V, then stages.
+        kv._k_pools[0][ctx.current_video_slot_mapping] = torch.full((BLOCK, N_HEADS, HEAD_DIM), float(step))
+        kv._v_pools[0][ctx.current_video_slot_mapping] = torch.full((BLOCK, N_HEADS, HEAD_DIM), -float(step))
+        # Store the metadata on the context the way prepare() does.
+        (ctx.block_table, ctx.query_start_loc, ctx.seq_lens, ctx.max_query_len, ctx.max_seq_len) = (
+            ctx.build_block_table(action_len=0, query_len=BLOCK, device=device)
+        )
+        block_table, max_seq_len = ctx.block_table, ctx.max_seq_len
+        ctx._prepare_history_staging(0)
+        n_blocks = max_seq_len // BLOCK
+        block_ids = block_table[0, :n_blocks].to(torch.long)
+        stage_k, stage_v = ctx.history_staging(0)
+        paged_attention_module._stage_window(
+            stage_k,
+            stage_v,
+            key_cache,
+            value_cache,
+            block_ids,
+            n_blocks,
+            BLOCK,
+            first_block=ctx.stage_first_block if ctx.reuse_history else 0,
+        )
+        full_k = key_cache.index_select(0, block_ids).reshape(n_blocks * BLOCK, N_HEADS, HEAD_DIM)
+        full_v = value_cache.index_select(0, block_ids).reshape(n_blocks * BLOCK, N_HEADS, HEAD_DIM)
+        return stage_k, stage_v, full_k, full_v, n_blocks
+
+    stage_k, stage_v, full_k, full_v, n_blocks = probe(1)
+    assert ctx.reuse_history is False
+    assert torch.equal(stage_k, full_k) and torch.equal(stage_v, full_v)
+
+    stage_k, stage_v, full_k, full_v, n_blocks = probe(2)
+    assert ctx.reuse_history is True
+    visible_history_blocks = min(history_chunks, 2 - 1)  # window of 2 blocks minus the current one
+    assert ctx.stage_first_block == visible_history_blocks
+    # The padded table is wider than the live window, so the end-of-table guess is not the offset.
+    assert n_blocks - 1 != ctx.stage_first_block
+    assert torch.equal(stage_k, full_k) and torch.equal(stage_v, full_v)
+
+
+@pytest.mark.parametrize("gather_enabled", [False, True])
+def test_staging_is_only_allocated_for_the_gather_path(monkeypatch, gather_enabled):
+    if gather_enabled:
+        monkeypatch.setenv(KV_GATHER_ENV, "1")
+    else:
+        monkeypatch.delenv(KV_GATHER_ENV, raising=False)
+    device = torch.device("cpu")
+    kv, st = make_state(device=device, reuse_history_staging=True)
+    # The manager owns the pairs and allocates them only when their consumer is on.
+    assert bool(kv.history_staging) is gather_enabled
+    ctx = st.get_kv_caches(POS, seq_len=BLOCK, commit_current=False)[0].forward_ctx
+    ctx.ensure_video_slots(device)
+    (ctx.block_table, ctx.query_start_loc, ctx.seq_lens, ctx.max_query_len, ctx.max_seq_len) = ctx.build_block_table(
+        action_len=0, query_len=BLOCK, device=device
+    )
+    ctx._prepare_history_staging(0)
+    assert ctx.staging_enabled is gather_enabled
+    stage_k, _ = ctx.history_staging(0)
+    assert (stage_k is not None) is gather_enabled
+
+
 @pytest.mark.skipif(not _gpu_flash_attn_usable(), reason="usable GPU FlashAttention is required")
 @pytest.mark.parametrize("history_chunks", [1, 3])
 @pytest.mark.parametrize("action_len", [0, 3])
@@ -376,13 +463,27 @@ def test_custom_op_registration_idempotent():
     assert hasattr(torch.ops.vllm_omni, "ar_diffusion_paged_write_attn")
 
 
-def test_custom_op_compiles_fullgraph_without_recompile_on_value_change():
+def test_custom_op_mutable_arguments_cannot_be_elided_as_defaults():
+    # Older PyTorch ADInplaceOrView handlers index positional mutable inputs
+    # directly. Default-valued trailing inputs can disappear before that handler.
+    schema = torch.ops.vllm_omni.ar_diffusion_paged_write_attn.default._schema
+    mutable = [arg for arg in schema.arguments if arg.alias_info is not None and arg.alias_info.is_write]
+    assert {arg.name for arg in mutable} == {"key_pool", "value_pool", "stage_key", "stage_value"}
+    assert all(not arg.has_default_value() for arg in mutable)
+
+
+@pytest.mark.parametrize("reuse_history_staging", [False, True])
+def test_custom_op_compiles_fullgraph_without_recompile_on_value_change(monkeypatch, reuse_history_staging):
     """The op must trace as one opaque node: fullgraph OK, and changed tensor
     VALUES (new slots / block ids) must not trigger recompilation."""
     import torch._dynamo
 
     device = torch.device("cpu")
-    kv, st = make_state(num_layers=2, window_chunks=2)
+    monkeypatch.setenv(KV_GATHER_ENV, "1")
+    kv, st = make_state(num_layers=2, window_chunks=2, reuse_history_staging=reuse_history_staging)
+    if reuse_history_staging:
+        # Hold the host-side staging offset fixed while table/slot values change.
+        _commit_video_span(kv, st, kv_branch=POS, n_chunks=2, dtype=torch.float32, device=device)
 
     def run_one_forward(commit):
         contexts = st.get_kv_caches(POS, seq_len=BLOCK, commit_current=commit)
@@ -391,9 +492,8 @@ def run_one_forward(commit):
         q = torch.randn(BLOCK, N_HEADS, HEAD_DIM)
         k = torch.randn(BLOCK, N_HEADS, HEAD_DIM)
         v = torch.randn(BLOCK, N_HEADS, HEAD_DIM)
-        # Both layers through ONE compiled fn: layer_idx is a tensor, so a
-        # different layer must NOT recompile (all 40 DiT blocks share the
-        # block-forward code object in production).
+        # Both layers use one compiled function; the tensor-valued layer index
+        # must not specialize it.
         for layer_ctx in contexts:
             out = compiled(layer_ctx.to_layer_inputs(), q, k, v)
         st.commit_paged_context(POS)
@@ -419,3 +519,52 @@ def fn(inputs, q, k, v):
         # Leave a clean dynamo state for later suites in the same pytest process
         # (e.g. model_executor transformers models).
         torch._dynamo.reset()
+
+
+@pytest.mark.skipif(not _gpu_flash_attn_usable(), reason="usable GPU FlashAttention is required")
+@pytest.mark.parametrize("history_chunks", [0, 1, 3])
+@pytest.mark.parametrize("action_len", [0, 3])
+def test_contiguous_kv_gather_path_matches_paged_path_gpu(monkeypatch, history_chunks, action_len):
+    """VLLM_OMNI_AR_DIFFUSION_KV_GATHER=1 gathers the visible blocks and runs varlen FA3 without a block table.
+
+    Covers an empty, partial and full window plus a partially filled action block:
+    the gather reads the tail-padding null block, so its (zeroed) rows must be masked.
+    """
+
+    torch.manual_seed(0)
+    device = torch.device("cuda")
+    dtype = torch.float16
+    kv, st = make_state(dtype=dtype, device=device, window_chunks=2)
+    if history_chunks:
+        _commit_video_span(kv, st, kv_branch=POS, n_chunks=history_chunks, dtype=dtype, device=device)
+
+    layer_ctx = st.get_kv_caches(POS, seq_len=BLOCK, commit_current=False)[0]
+    ctx = layer_ctx.forward_ctx
+    current_k = torch.randn(1, BLOCK, N_HEADS, HEAD_DIM, dtype=dtype, device=device)
+    current_v = torch.randn(1, BLOCK, N_HEADS, HEAD_DIM, dtype=dtype, device=device)
+    action_k = action_v = None
+    if action_len:
+        action_k = torch.randn(1, action_len, N_HEADS, HEAD_DIM, dtype=dtype, device=device)
+        action_v = torch.randn(1, action_len, N_HEADS, HEAD_DIM, dtype=dtype, device=device)
+    query = torch.randn(1, BLOCK + action_len, N_HEADS, HEAD_DIM, dtype=dtype, device=device)
+    ctx.prepare(device=device, action_len=action_len, query_len=query.shape[1])
+    inputs = layer_ctx.to_layer_inputs()
+
+    def run() -> torch.Tensor:
+        return paged_write_attn(
+            inputs,
+            query[0],
+            current_k[0],
+            current_v[0],
+            action_k[0] if action_len else None,
+            action_v[0] if action_len else None,
+            HEAD_DIM**-0.5,
+        ).unsqueeze(0)
+
+    monkeypatch.delenv(KV_GATHER_ENV, raising=False)
+    paged = run()
+    monkeypatch.setenv(KV_GATHER_ENV, "1")
+    gathered = run()
+    assert torch.isfinite(gathered).all()
+    # Same kernel family on the same K/V: only accumulation order differs.
+    torch.testing.assert_close(gathered, paged, rtol=2e-3, atol=2e-3)