[docs] Fix the docs build (#48008)

Signed-off-by: Martin Hickey <martin.hickey@ie.ibm.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
(cherry picked from commit 6cf7b26bd4)

requirements/cuda.txt

@@ -9,7 +9,7 @@ torchaudio==2.11.0
 # These must be updated alongside torch
 torchvision==0.26.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 torchcodec >= 0.14
-PyNvVideoCodec==2.1.0
+PyNvVideoCodec==2.0.4
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.6.13
 flashinfer-cubin==0.6.13

vllm/model_executor/layers/fused_moe/layer.py

@@ -186,9 +186,10 @@ def FusedMoE(
         has_bias: Whether expert layers have bias terms
         is_sequence_parallel: Whether sequence parallelism is enabled
         reduce_results: Whether to all-reduce the final output. Setting this
-        to False (to fuse the all-reduce downstream) is only honored on the
-        late-AR path.
-        expert_mapping: Expert parameter mapping for weight loading
+            to False (to fuse the all-reduce downstream) is only honored on
+            the late-AR path.
+        ckpt_names: Checkpoint parameter name tuple (gate_proj, down_proj,
+            up_proj) used for weight loading
         n_shared_experts: Number of shared experts to fuse into the routed
             grouped GEMM (ROCm; requires aiter FSE or the router-append path)
         router_logits_dtype: Data type for router logits buffers

vllm/v1/core/kv_cache_manager.py

@@ -8,10 +8,14 @@ from typing import Literal, overload
 
 from vllm.distributed.kv_events import BlockStored, KVCacheEvent
 from vllm.logger import init_logger
+from vllm.utils.math_utils import cdiv
 from vllm.v1.core.kv_cache_coordinator import get_kv_cache_coordinator
 from vllm.v1.core.kv_cache_metrics import KVCacheMetricsCollector
 from vllm.v1.core.kv_cache_utils import KVCacheBlock
 from vllm.v1.kv_cache_interface import (
+    AttentionSpec,
+    CrossAttentionSpec,
+    EncoderOnlyAttentionSpec,
     KVCacheConfig,
     get_kv_cache_spec_kind,
     get_kv_cache_spec_sliding_window,
@@ -593,6 +597,26 @@ class KVCacheManager:
         """Get the block ids of a request."""
         return self.get_blocks(request_id).get_block_ids()
 
+    def get_block_ids_for_computed_tokens(
+        self,
+        request_id: str,
+        num_computed_tokens: int,
+    ) -> tuple[list[int], ...]:
+        """Get block ids covering the request's computed tokens."""
+        block_ids = self.get_block_ids(request_id)
+        clipped_block_ids: list[list[int]] = []
+        for group, ids in zip(self.kv_cache_config.kv_cache_groups, block_ids):
+            spec = group.kv_cache_spec
+            if not isinstance(spec, AttentionSpec) or isinstance(
+                spec, (CrossAttentionSpec, EncoderOnlyAttentionSpec)
+            ):
+                clipped_block_ids.append(ids)
+                continue
+
+            num_valid_blocks = cdiv(num_computed_tokens, spec.block_size)
+            clipped_block_ids.append(ids[:num_valid_blocks])
+        return tuple(clipped_block_ids)
+
     def cache_blocks(self, request: Request, num_computed_tokens: int) -> None:
         """Cache the blocks for the request, if enabled.
 

vllm/v1/core/sched/scheduler.py

@@ -872,12 +872,10 @@ class Scheduler(SchedulerInterface):
                     if num_new_tokens == 0:
                         break
 
-                # Handles an edge case when P/D Disaggregation
-                # is used with Spec Decoding where an
-                # extra block gets allocated which
-                # creates a mismatch between the number
-                # of local and remote blocks.
-                limit_lookahead_tokens = load_kv_async and self.use_eagle
+                # During async KV load, no forward pass is run yet.
+                # Allocate speculative lookahead slots later to avoid
+                # mismatching local and remote block counts.
+                limit_lookahead_tokens = load_kv_async and self.num_lookahead_tokens > 0
                 effective_lookahead_tokens = (
                     0 if limit_lookahead_tokens else self.num_lookahead_tokens
                 )
@@ -2371,7 +2369,10 @@ class Scheduler(SchedulerInterface):
             num_prompt_tokens=request.num_prompt_tokens,
         )
 
-        block_ids = self.kv_cache_manager.get_block_ids(request.request_id)
+        block_ids = self.kv_cache_manager.get_block_ids_for_computed_tokens(
+            request_id=request.request_id,
+            num_computed_tokens=request.num_computed_tokens,
+        )
 
         if not isinstance(self.connector, SupportsHMA):
             # NOTE(Kuntai): We should deprecate this code path after we enforce

vllm/v1/kv_offload/tiering/p2p/manager.py

@@ -11,7 +11,7 @@ from __future__ import annotations
 import time
 from collections.abc import Iterable, Sequence
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 from typing_extensions import override
 
@@ -115,7 +115,7 @@ class P2PSecondaryTierManager(SecondaryTierManager):
         port: int = 7777,
         backends: list[str] | None = None,
         num_threads: int = 4,
-        **kwargs,
+        **kwargs: Any,
     ) -> None:
         """Initialize the P2P secondary tier manager.
 

vllm/v1/worker/xpu_model_runner.py

@@ -49,7 +49,13 @@ def _torch_cuda_wrapper():
     torch.cuda.current_stream = partial(torch.xpu.current_stream)
     torch.cuda.stream = partial(torch.xpu.stream)
     torch.cuda.set_stream = partial(torch.xpu.set_stream)
-    torch.cuda.Event = partial(torch.xpu.Event)
+
+    # torch.xpu.Event does not accept the ``blocking`` kwarg that
+    # torch.cuda.Event supports, so drop it here.
+    def _xpu_event(*args, blocking=None, **kwargs):
+        return torch.xpu.Event(*args, **kwargs)
+
+    torch.cuda.Event = _xpu_event
     if supports_xpu_graph():
         torch.cuda.graph = partial(torch.xpu.graph)
         torch.cuda.CUDAGraph = torch.xpu.XPUGraph