[Fix] get_num_blocks_to_allocate provides wrong block count while external cache hit by zqs164 · Pull Request #46892 · vllm-project/vllm

zqs164 · 2026-06-27T03:51:37Z

…to_allocate returns the wrong number of blocks

Purpose

When external cache hit, MambaManager will allocates 1 block for external cache + 1 block for new scheduled tokens, But get_num_blocks_to_allocate says it only needs one extra block besides the speculative blocks, which isn't right

Test Plan

Test Result

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The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
The test plan, such as providing test command.
The test results, such as pasting the results comparison before and after, or e2e results
(Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.

claude

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github-actions · 2026-06-27T03:51:45Z

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MengqingCao

LGTM, @ZJY0516 @ivanium could you take a look at this? This pr fixes the wrong calculation of num_new_blocks when cache hit on the external connectors in hybrid models with mamba cache.

ivanium · 2026-06-30T07:36:08Z

+                    if (
+                        total_computed_tokens
+                        > len(new_computed_blocks) * self.block_size
+                        and num_tokens_main_model > total_computed_tokens
+                    ):


Maybe we can enhance this to mirror the coordinator's own short-circuit to avoid potential issues with preemption and resume.

Suggested change

if (

total_computed_tokens

> len(new_computed_blocks) * self.block_size

and num_tokens_main_model > total_computed_tokens

):

if (

request_id not in self.num_cached_block

and total_computed_tokens > len(new_computed_blocks) * self.block_size

and num_tokens_main_model > total_computed_tokens

):

ivanium

Thanks for spotting the issue and the fix. Overall makes sense to me. Left one comment and open to discuss.

ivanium

LGTM. Thanks again! @zqs164 @MengqingCao

mergify · 2026-06-30T09:51:41Z

Hi @zqs164, the pre-commit checks have failed. Please run:

uv pip install pre-commit>=4.5.1
pre-commit install
pre-commit run --all-files

Then, commit the changes and push to your branch.

For future commits, pre-commit will run automatically on changed files before each commit.

ivanium · 2026-07-01T08:04:06Z

I've enabled auto-merge but pre-commit seems failing

…to_allocate returns the wrong number of blocks Signed-off-by: Qingsong Zhang <1640410765@qq.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com> Signed-off-by: Spicy-Stick <873805887@qq.com>

mergify · 2026-07-12T07:00:15Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @zqs164.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com> Signed-off-by: xqchen7 <chenxueqing7@huawei.com>

### What this PR does / why we need it? #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py - Conditionally implement start_weight_update() and finish_weight_update() as no-op methods for non-0.23.0 releases. - Keep the NPU IPC weight transfer engine compatible with the updated WeightTransferEngine interface. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/platform/patch_torch_accelerator.py - Redirect torch.accelerator.get_memory_info() to torch.npu.mem_get_info() on non-0.23.0. - Avoid crashes caused by the unsupported C10 DeviceAllocator path when constructing MemorySnapshot. - Align with the existing NPU-specific memory API patches. - Upstream source: commit 747b068 (v0.24.0+ MemorySnapshot(device=device) path). --- #### vllm_ascend/patch/worker/patch_qwen3_dflash.py - Wrap DFlashQwen3ForCausalLM._read_mask_embedding() to ignore optional mask embedding download failures. - Preserve the expected "mask embedding not present" behavior when the file is unavailable. - Upstream source: vllm#46104 (vllm-project/vllm#46104). --- #### vllm_ascend/worker/v2/model_runner.py #### vllm_ascend/patch/worker/patch_v2/patch_input_batch.py - Forward is_padding and prompt_lens when constructing AscendInputBatch. - Match the updated upstream InputBatch interface and avoid initialization failures on newer releases. - Upstream source: vllm#40654 (vllm-project/vllm#40654). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Add the reduce_results argument to DeepseekV2MLAAttention. - Forward the parameter to RowParallelLinear to stay compatible with the updated upstream attention initialization. - Keep the implementation compatible across all supported vLLM versions. - Upstream source: vllm#45895 (vllm-project/vllm#45895). --- #### vllm_ascend/distributed/device_communicators/npu_communicator.py - Register a no-op all2all_manager for NPUCommunicator. - Bypass the upstream MoE fault-tolerance check (which queries all2all_manager when data_parallel_size > 1 and is_moe) while preserving the existing MC2 communication path. - Keep compatibility with the updated distributed initialization. - Related upstream changes: - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Share routed expert parameters through direct nn.Parameter aliasing instead of creating wrapper parameters. - Ensure both legacy and routed_experts parameter paths reference the same underlying weights. - Apply the aliasing strategy to all routed-expert MoE models on newer vLLM releases. - Related upstream changes: - vllm#40996 (vllm-project/vllm#40996) - vllm#46892 (vllm-project/vllm#46892) --- #### vllm_ascend/worker/worker.py #### vllm_ascend/distributed/weight_transfer/npu_ipc_engine.py #### vllm_ascend/distributed/weight_transfer/hccl_engine.py #### vllm_ascend/patch/platform/patch_weight_transfer_engine.py - Adapt WeightTransferEngineFactory.create_engine() and WeightTransferEngine.__init__() to support both legacy and current upstream signatures. - Keep weight transfer compatible across v0.23.0, v0.24.0, and newer upstream releases. - Upstream source: vllm#44353 (vllm-project/vllm#44353). --- #### vllm_ascend/patch/worker/patch_deepseek_v2.py - Remove the upstream model-level all-gather path for DeepSeek-V2 on non-0.23.0. - Keep the implementation compatible with the Ascend MC2 dispatch flow. - Avoid tensor shape mismatches and residual concatenation failures introduced by the upstream refactor. - Related upstream changes: - vllm#41184 (vllm-project/vllm#41184) --- #### vllm_ascend/ops/fused_moe/fused_moe.py - Remove the unnecessary .contiguous() call after weight transposition on non-0.23.0. - Reduce transient NPU peak memory during MoE weight loading. - Prevent OOM caused by duplicate temporary tensor allocations. - Related upstream changes: - vllm#44589 (vllm-project/vllm#44589) - vLLM version: v0.23.0 - vLLM main: vllm-project/vllm@ee0da84 --------- Signed-off-by: hfadzxy <starmoon_zhang@163.com>

zqs164 requested review from ApostaC, WoosukKwon, alexm-redhat, heheda12345, njhill, orozery, robertgshaw2-redhat and ywang96 as code owners June 27, 2026 03:51

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MengqingCao approved these changes Jun 30, 2026

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ivanium enabled auto-merge (squash) June 30, 2026 09:29

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Head branch was pushed to by a user without write access

zqs164 force-pushed the main_mamba_pooling branch from 68b0957 to 2ceca6f Compare June 30, 2026 09:39

MengqingCao mentioned this pull request Jun 30, 2026

[BugFix]Block count calculation error in MambaManager when hitting the external cache vllm-project/vllm-ascend#11129

Merged

zqs164 added 3 commits July 1, 2026 16:49

[Fix] When an external cache hit occurs, MambaManager#get_num_blocks_…

e864865

…to_allocate returns the wrong number of blocks Signed-off-by: Qingsong Zhang <1640410765@qq.com>

[Fix] When an external cache hit occurs, MambaManager#get_num_blocks_…

14c0480

…to_allocate returns the wrong number of blocks Signed-off-by: Qingsong Zhang <1640410765@qq.com>

[Fix] When an external cache hit occurs, MambaManager#get_num_blocks_…

854ed9e

…to_allocate returns the wrong number of blocks Signed-off-by: Qingsong Zhang <1640410765@qq.com>

zqs164 force-pushed the main_mamba_pooling branch from 9ae26f2 to 854ed9e Compare July 1, 2026 08:49

ivanium enabled auto-merge (squash) July 1, 2026 09:14

zhangxinyuehfad mentioned this pull request Jul 5, 2026

[CI] main2main 0703 commit: 1f486d9 vllm-project/vllm-ascend#10454

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mergify Bot added the needs-rebase label Jul 12, 2026

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[Fix] get_num_blocks_to_allocate provides wrong block count while external cache hit - #46892

[Fix] get_num_blocks_to_allocate provides wrong block count while external cache hit#46892
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