[Text Generation][V2] NonKVCachePipeline

[dbogunowicz]

Feature Description

Added the TestGenerationPipelineNoKVCache. This pipeline processes the prompt and returns the new token. That's it.
Its main functionality is mapping prompt tokens to logits, instrumental for computing the perplexity of the model given a dataset

Testing

Updated the integration tests to cover the case of non-kv-cache inference.

Example Use

from deepsparse.v2.text_generation import TextGenerationPipelineNoCache

prompt = ["Some funny prompt", "Why are you so"]

pipeline = TextGenerationPipelineNoCache(model_path="hf:mgoin/TinyStories-1M-ds",
                                         onnx_model_name="model-orig.onnx",
                                         sequence_length=20)

out = pipeline(prompt=prompt,
               include_prompt_logits=True,
               generation_kwargs=dict(output_scores=True))

for gen in out.generations:
    print(gen)

text='.' score=array([[ 2.9344807 , -0.03345669, -4.11256   , ..., -6.9316325 ,
        -4.6005425 ,  1.1827914 ],
       [ 7.008805  , -0.11603884, -7.1837015 , ..., -7.0405912 ,
        -2.386351  , -2.2007818 ],
       [ 6.348213  , -2.2960157 , -6.433192  , ..., -6.5930486 ,
        -5.8315077 , -0.58804405],
       [ 0.        ,  0.        ,  0.        , ...,  0.        ,
         0.        ,  0.        ]], dtype=float32) finished=True finished_reason='length' # notice that logits get zero padding from the end, this is because all logits need to have the same shape (the length of the longest prompt in the input +1)
text=' sad' score=array([[ 2.560934 ,  1.1993233, -6.670935 , ..., -7.3002615, -3.823823 ,
         1.8125833],
       [-1.1050931, -2.4256568, -7.3015127, ..., -6.1500154, -4.074909 ,
         1.8155754],
       [ 6.172593 , -2.2252593, -9.146653 , ..., -7.70834  , -4.810748 ,
         0.3985293],
       [ 1.4988875,  1.0973434, -4.4714937, ..., -4.8026247, -1.1791464,
         1.6924176]], dtype=float32) finished=True finished_reason='length'

Next steps

• Create a parentTextGenerationPipeline operator that can either choose to use the kv-cache or non-kv cache version of the pipeline, depending on the topology of the ONNX model
• Move the overwriting of the transformer inputs to some high-level function
• Use the V2 pipeline for Perplexity calculation
• swap GraphRouter for LinearRouter in TextGenerationPipelineNoKVCache

[dsikka]

Thank you for the changes - this looks great.
I think the only thing that we're missing is the logic check which runs the appropriate pipeline by checking if kv_cache is present. We should do this in a follow-up PR.