This PR changes the initial value of blocksize back to 128 and adds hash
value of request id list in model runner for implementing sampling param
cache in sampler.
Signed-off-by: hw_whx <wanghexiang7@huawei.com>
Co-authored-by: hw_whx <wanghexiang7@huawei.com>
This PR changes the shape of kv cache to avoid the view of k_cache and
v_cache.
What's more, cache the metadata of k_cache and v_cache to avoid
duplicative slice operations to improve performance.
Signed-off-by: hw_whx <wanghexiang7@huawei.com>
### What this PR does / why we need it?
Remove redundant `profile_run()` in model runner.
### Does this PR introduce _any_ user-facing change?
no.
### How was this patch tested?
no.
---------
Signed-off-by: Shanshan Shen <467638484@qq.com>
This PR added pooling support for vllm-ascend
Tested with `bge-base-en-v1.5` by encode:
```
from vllm import LLM
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create an LLM.
model = LLM(model="./bge-base-en-v1.5", enforce_eager=True)
# Generate embedding. The output is a list of EmbeddingRequestOutputs.
outputs = model.encode(prompts)
# Print the outputs.
for output in outputs:
print(output.outputs.embedding) # list of 4096 floats
```
Tested by embedding:
```
from vllm import LLM, SamplingParams
llm = LLM(model="./bge-base-en-v1.5", task="embed")
(output,) = llm.embed("Hello, my name is")
embeds = output.outputs.embedding
print(f"Embeddings: {embeds!r} (size={len(embeds)})")
```
Related: https://github.com/vllm-project/vllm-ascend/issues/200
## Known issue
The accuracy is not correct since this feature rely on `enc-dec`
support. It'll be done in the following PR by @MengqingCao
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
