### What this PR does / why we need it?
A refactoring of forward_context and model_runner_v1, add some context
which is necessary in model inference into forward_context, and refactor
dummy_run logic, make it more reasonable.
Some details for this PR:
Add `ascend_forward_context`;
Update mc2_v2 op, and support `active_mask` param;
Update scripts in examples dir;
refactor `dummy_run` logic;
Add soc_version for A2 and A3;
### Does this PR introduce _any_ user-facing change?
No change at user-facing.
### How was this patch tested?
- vLLM version: v0.10.0
- vLLM main:
57c22e57f9
Signed-off-by: zzzzwwjj <1183291235@qq.com>
Before do attention module refactor, we can do some code cleanup to make
the next step easier.
What this PR does:
1. remove uesless `common_prefix_len` for attention builder
2. remove uesless `is_only_prefill` and `num_input_tokens` in attention
metadata.
3. remove `CommonAttentionMetadata` and ues `query_start_loc` instead,
`CommonAttentionMetadata` is over designed and uesless
4. update the attention backend input parameters to keep the same as
vLLM.
5. Rename attention name to the same style with `ASCEND` prefix
- vLLM version: v0.9.2
- vLLM main:
107111a859
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
### What this PR does / why we need it?
This PR implements the Eagle Pososer feature for vLLM v1, which enables
more efficient speculative decoding by using a draft model to predict
potential future tokens.
- The implementation includes the core Eagle algorithm integration with
vLLM's existing architecture, allowing for faster inference while
maintaining output quality.
- This is needed to significantly improve the generation speed of large
language models without compromising on the quality of generated text.
### Does this PR introduce any user-facing change?
Yes, this PR introduces a new speculative decoding mode that can be
enabled via configuration.
- Users can now choose to use Eagle Pososer by setting appropriate flags
in the inference configuration.
- The API remains backward compatible, with the new functionality being
opt-in.
### How was this patch tested?
CI passed with new unit tests added for the Eagle Pososer functionality.
- Benchmark tests were conducted comparing generation speed and quality
with and without Eagle Pososer.
- Integration tests were performed with various model architectures to
ensure compatibility.
- Manual testing was done using different prompt scenarios to verify
output quality remains consistent.
- we test accept rate on one Ascend 910B npu, The acceptance rate
results are basically consistent with those shown here:
https://github.com/vllm-project/vllm/pull/16937
- Currently, we support scenarios where num_spec_tokens <= 2. When
num_spec_tokens > 2, issues such as insufficient GPU memory and operator
computation errors may occur. We will address this in subsequent
updates.
- We will add support for Eagle v1 in future updates.
### Acceptance Test Script
```bash
SCRIPT="/offline/eagle.py"
DATASET="ShareGpt"
MODEL=Meta-Llama-3.1-8B-Instruct
DRAFT=EAGLE3-LLaMA3.1-Instruct-8B
CUDA_VISIBLE_DEVICES="0" VLLM_USE_V1=1 $PYTHON $SCRIPT \
--dataset $DATASET \
--num_spec_tokens 2 \
--max_num_seqs 1 \
--model_dir $MODEL \
--eagle_dir $DRAFT \
--tp 1 \
--num_prompts 80
```
### Acceptance Test Results
```bash
██████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [21:22<00:00, 16.03s/it, est. speed input: 4.72 toks/s, output: 13.56 toks/s]
-------------------------------------------------------------------------------------
mean acceptance length: 1.63
-------------------------------------------------------------------------------------
total_counts: 8062
acceptance at token 0: 1.00 (8062 times)
acceptance at token 1: 0.70 (5612 times)
acceptance at token 2: 0.47 (3765 times)
```
Closes: https://github.com/vllm-project/vllm-ascend/issues/1004
---------
Signed-off-by: yuancaoyaoHW <a2749322671@gmail.com>
