Adds method to read the pooling types from model's files

[flaviabeo]

This adds a method to load the pooling config file from sentence transformer models like sentence-transformers/all-MiniLM-L12-v2.

The pooling types added can be found at the sentence-transformers Pooling

FIX #9388 (link existing issues this PR will resolve)

cc: @maxdebayser

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[robertgshaw2-redhat]

Thanks! This is great! Can you make sure to add some integration tests?

[maxdebayser]

Here we need to add a verification to make sure that the pooling layer is configured correctly

[mgoin]

Could we call this something more general than "bert_config"? Maybe "encoder_config", I just feel bert is quite specific

[mergify]

This pull request has merge conflicts that must be resolved before it can be
merged. @flaviabeo please rebase it. https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

[flaviabeo]

I was able to reproduce the error we are seeing at the CI tests in my environment - it is regarding another change that was included recently. The two environments compared in this test are:

env 1  {'VLLM_TORCH_COMPILE_LEVEL': '0'}
env 2  {'VLLM_TORCH_COMPILE_LEVEL': '3'}

All the vllm engine args and model configuration are the same, but when the test compares the tensors produced with this two different torch compiles, it fails. The vectors are different.

Following are the engine args:

['--enforce-eager', '--task', 'embedding', '-pp', '1', '-tp', '1', '--load-format', 'dummy']

And model config parameters:

model='BAAI/bge-multilingual-gemma2', speculative_config=None, tokenizer='BAAI/bge-multilingual-gemma2', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=LoadFormat.DUMMY, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=True, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=BAAI/bge-multilingual-gemma2, num_scheduler_steps=1, chunked_prefill_enabled=False multi_step_stream_outputs=True, enable_prefix_caching=False, use_async_output_proc=False, use_cached_outputs=True, chat_template_text_format=string, mm_processor_kwargs=None, pooler_config=PoolerConfig(pooling_type='LAST', pooling_norm=False, pooling_softmax=None, pooling_step_tag_id=None, pooling_returned_token_ids=None))

[DarkLight1337]

@youkaichao maybe there is a bug in using torch.compile?

[youkaichao]

the mse is mse=0.0005555402021855116 , is it acceptable? torch.compile can indeed change the model's output slightly.

[DarkLight1337]

It is acceptable. I think we should measure cosine similarity instead of MSE for embedding models though.

[youkaichao]

It is acceptable. I think we should measure cosine similarity instead of MSE for embedding models though.

feel free to update the tests then.

[mergify]

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

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