### What this PR does / why we need it?
The ds3.2 model adaptation supports the PCP feature.
The solution is as follows: When saving the KV cache, first perform an
allgather operation on the KVs, and then each node saves its own copy.
When the attention or indexer performs calculations, they all gather the
KV cache and then perform the calculations.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
02/12 23:05:10 - AISBench - INFO - Running 1-th replica of evaluation
02/12 23:05:10 - AISBench - INFO - Task [vllm-api-general-chat/gsm8k]:
{'accuracy': 96.35416666666667, 'type': 'GEN'}
02/12 23:05:10 - AISBench - INFO - time elapsed: 2.87s
02/12 23:05:12 - AISBench - INFO - Evaluation tasks completed.
02/12 23:05:12 - AISBench - INFO - Summarizing evaluation results...
dataset version metric mode vllm-api-general-chat
gsm8kdataset - accuracy gen 96.35
- vLLM version: v0.15.0
- vLLM main:
9562912cea
---------
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
mooncake layerwise support pcp function
PCP (Prefill Context Parallelism) Support: Introduced explicit support
for Prefill Context Parallelism (PCP) and Decode Context Parallelism
(DCP) in the Mooncake layerwise KV cache transfer mechanism, allowing
for more granular control and awareness of parallel configurations
during data transfer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
### What this PR does / why we need it?
We only do restore and recover for pcp, so we should set
`kv_inverse_idx_for_chunk` and `cp_kv_recover_idx_for_chunk` to `None`
when only using dcp.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
PR #5672 attempted to remove the -1 padding for duplicate tokens in the
decode slot_mapping when adapting PCP for MLAPO, and adopted a simpler
slicing approach. However, in the single-ops logic and mixed PD batches,
the decode slot_mapping did not eliminate the -1 and also shared the
slicing method, resulting in incorrect slot_mapping. This PR resolves
this issue, and the logic will be further consolidated in subsequent
refactoring PRs.
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
Fix the MTP test failure caused by accessing non-existent attribute
`forward_context.draft_attn_metadatas`.
**Root cause:**
In `AscendAttentionBackendImpl.update_graph_params`, the code
incorrectly accessed `forward_context.draft_attn_metadatas`, but
`ForwardContext` class doesn't have this attribute. The original code
passed this value via function parameter.
**Fix:**
Add `draft_attn_metadatas` parameter to the entire call chain:
- `update_full_graph_params` function in `acl_graph.py`
- All `update_graph_params` methods in attention backends
- Pass the parameter correctly in `eagle_proposer.py`
Also applied Gemini's suggestion to make `vllm_config=None` in
`AscendAttentionCPImpl.update_graph_params` for API consistency.
Related to item 9 in #5463
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
This fixes the CI test failure:
`test_deepseek_mtp_correctness[True-FULL_DECODE_ONLY-2-wemaster/deepseek_mtp_main_random_bf16]`
Signed-off-by: lico67373 <918688502@qq.com>
This reverts commit 8966a99710.
It breaks the test
`tests/e2e/singlecard/spec_decode/test_mtp_eagle_correctness.py::test_deepseek_mtp_correctness[True-FULL_DECODE_ONLY-2-wemaster/deepseek_mtp_main_random_bf16]`
- vLLM version: v0.14.0
- vLLM main:
d68209402d
### What this PR does / why we need it?
**Refactor: Unify full-graph parameter update logic**
This PR consolidates the scattered full-graph parameter update logic
into a unified approach, improving code architecture and eliminating
duplication.
**Key improvements:**
1. **Unified interface**
- Create `update_full_graph_params` as the single entry point for all
full-graph updates
- Replace multiple scattered update calls with one unified function
- Remove ~50 lines of duplicated if-else logic across
`model_runner_v1.py` and `eagle_proposer.py`
2. **Better architecture**
- Move update logic to respective Backend classes
(`AscendAttentionBackend`, `AscendMLABackend`)
- Each Backend manages its own parameter update logic internally
- Simplify caller code to just dispatch to the appropriate Backend
3. **Cleaner parameter handling**
- Remove unnecessary `pcp_size` and `dcp_size` parameter passing
- Get parallel configuration directly from distributed groups
- Consistent with how other parts of the codebase obtain these values
**Why we need it:**
- **Maintainability**: Future changes only need to be made in one place
per Backend
- **Code quality**: Follows DRY principle and Single Responsibility
Principle
- **Readability**: Cleaner, more intuitive code structure
### Does this PR introduce _any_ user-facing change?
**No.** This is a pure refactoring with no functional changes - same
behavior, cleaner code.
### How was this patch tested?
- All existing unit tests pass with updated mocks
- No new tests needed (pure refactoring, no behavior changes)
- CI validates correctness
---
- vLLM version: v0.13.0
Signed-off-by: lico67373 <918688502@qq.com>
Co-authored-by: drslark <slarksblood@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
### What this PR does / why we need it?
PCP/DCP splits the kv-cache onto different cards. After introducing the
parameter cp-kv-cache-interleave-size, the first size tokens will be
cached at Card 0, and so on.
However, if there are too few tokens, some cards will not store the
key-value pairs, resulting in values of 0, corrupted values, and
precision issues. Currently, additional operations are introduced to
avoid this precision problem.
After we integrate FIA operator in mla_cp._forward_decode and CANN
updates to 8.5.0, we now can remove these additional operations.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
passed all CI by CANN 8.5.0
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: dsxsteven <dsxsteven@sina.com>
Signed-off-by: dsxsteven <36877507+dsxsteven@users.noreply.github.com>
### What this PR does / why we need it?
Drop vLLM 0.13.0 support, upgrade to 0.14.0
- vLLM version: v0.13.0
- vLLM main:
d68209402d
---------
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
### What this PR does / why we need it?
Replace the npu_multi_head_latent_attention with FIA operator in
mla_cp.py _forward_decode.
Adjust mla_attn_dpc_pcp in acl_graph.py
### Does this PR introduce _any_ user-facing change?
no
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: 白永斌 <baiyongbin3@h-partners.com>
Signed-off-by: Bai Yongbin <845473182@qq.com>
Signed-off-by: tongyuzhou <t00886357@china.huawei.com>
Co-authored-by: 白永斌 <baiyongbin3@h-partners.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
Co-authored-by: tongyuzhou <t00886357@china.huawei.com>
### What this PR does / why we need it?
In long-sequence scenarios, the chunked-prefill component may encounter
dimension misalignment issues, which previously occurred during
precision testing on the code_generate_lite dataset. This PR removes
redundant computations and instead derives the value using existing
results and straightforward calculations.
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
When there is no kv cache in some devices, the `_compute_prefill_context
func` will return `None`, which is unexecpted. This PR replaces None
with full zeros/-inf tensors to avoid TypeError.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```bash
pytest tests/e2e/multicard/4-cards/long_sequence/test_chunked_prefill.py -k test_models_chunked_prefill_with_empty_kvcache
```
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
Add docstrings for Metadata and MetadataBuilder classes in the attention
module to improve code readability.
Related to #5463 (Item 11: Add some comments for CommonMetadata and
others)
**Modified files:**
- `vllm_ascend/attention/context_parallel/common_cp.py`: Added comments
for `AscendPCPMetadata`, `CPChunkedContextMetadata`,
`AscendMetadataForPrefill`, `AscendMetadataForDecode`
- `vllm_ascend/attention/utils.py`: Added comments for
`AscendPrefillContextParallelMetadata`
- `vllm_ascend/attention/mla_v1.py`: Added comments for
`ChunkedContextMetadata`, `AscendMLADecodeMetadata`
- `vllm_ascend/attention/attention_v1.py`: Added comments for
`AscendMetadata`, `AscendAttentionMetadataBuilder`
- `vllm_ascend/attention/context_parallel/attention_cp.py`: Added
comments for `AscendAttentionCPMetadataBuilder`
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
Documentation only, no functional changes.
Signed-off-by: lico67373 <918688502@qq.com>
### What this PR does / why we need it?
In the chunked prefill scenario, CP needs to align the
`max_context_chunk` to the `cp_virtual_block_size`, but the current
implementation only aligns it to the `block_size`. For
PD-disaggregation, `cp_kv_cache_interleave_size` is typically set equal
to `block_size`, in which case `cp_virtual_block_size=block_size *
dcp_size * pcp_size`. Under specific conditions, this can lead to
misalignment of certain chunks, subsequently triggering assertion check
errors.
### Does this PR introduce _any_ user-facing change?
No
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: QiuChunshuo <qiuchunshuo@huawei.com>
### What this PR does / why we need it?
mlapo in deepseek is a huge performance improvement in decode, this pr
support pcp & dcp with mlapo
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: zhenwenqi2024 <zhenwenqi_2022@qq.com>
## What this PR does / why we need it?
This PR fixes the `AttentionMaskBuilder` singleton initialization issue
introduced in PR #4779 and removes the unused `pcp_prefill_mask` field.
### Background
After PR #4779 made `AttentionMaskBuilder` a singleton with `@singleton`
decorator, the class constructor now requires a `device` parameter.
However, two initialization sites were still using the old parameterless
constructor, causing failures.
### Changes
1. **Fix singleton initialization**
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendMLAMetadataBuilder.__init__()`
- Fixed `AttentionMaskBuilder()` → `AttentionMaskBuilder(self.device)`
in `AscendAttentionMetadataBuilder.__init__()`
2. **Remove unused field**
- Removed `pcp_prefill_mask` field from
`AscendPrefillContextParallelMetadata` (never used in codebase)
- Updated related test assertions
### Related
- Issue #5463
- PR #4779 (Unify all mask generation methods)
- PR #5389 (Make AttentionMaskBuilder singleton)
## Does this PR introduce _any_ user-facing change?
No. This is an internal refactoring.
## How was this patch tested?
- ✅ Local testing: No linter errors
- ✅ Unit tests for attention modules verified
- ⏳ CI pipeline
Signed-off-by: lico67373 <918688502@qq.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
