tensorrt_llm_bls/config.pbtxt

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name: "tensorrt_llm_bls"
backend: "python"
max_batch_size: 32

model_transaction_policy {
  decoupled: True
}

input [
  {
    name: "text_input"
    data_type: TYPE_STRING
    dims: [ 1 ]
  },
  {
    name: "decoder_text_input"
    data_type: TYPE_STRING
    dims: [ 1 ]
    optional: true
  },
  {
    name: "image_input"
    data_type: TYPE_FP16
    dims: [ 3, -1, -1 ]
    optional: true
  },
  {
    name: "max_tokens"
    data_type: TYPE_INT32
    dims: [ 1 ]
  },
  {
   name: "bad_words"
   data_type: TYPE_STRING
   dims: [ -1 ]
   optional: true
  },
  {
   name: "stop_words"
   data_type: TYPE_STRING
   dims: [ -1 ]
   optional: true
  },
  {
    name: "end_id"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "pad_id"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "top_k"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "top_p"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "temperature"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "length_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "repetition_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "min_length"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "presence_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "frequency_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "random_seed"
    data_type: TYPE_UINT64
    dims: [ 1 ]
    optional: true
  },
  {
    name: "return_log_probs"
    data_type: TYPE_BOOL
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "return_context_logits"
    data_type: TYPE_BOOL
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "return_generation_logits"
    data_type: TYPE_BOOL
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "beam_width"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
    name: "stream"
    data_type: TYPE_BOOL
    dims: [ 1 ]
    optional: true
  },
  {
    name: "prompt_embedding_table"
    data_type: TYPE_FP16
    dims: [ -1, -1 ]
    optional: true
  },
  {
    name: "prompt_vocab_size"
    data_type: TYPE_INT32
    dims: [ 1 ]
    optional: true
  },
  {
      name: "embedding_bias_words"
      data_type: TYPE_STRING
      dims: [ -1 ]
      optional: true
  },
  {
      name: "embedding_bias_weights"
      data_type: TYPE_FP32
      dims: [ -1 ]
      optional: true
  },
  {
      name: "num_draft_tokens",
      data_type: TYPE_INT32,
      dims: [ 1 ]
      optional: true
  },
  {
      name: "use_draft_logits",
      data_type: TYPE_BOOL,
      dims: [ 1 ]
      reshape: { shape: [ ] }
      optional: true
  }
]
output [
  {
    name: "text_output"
    data_type: TYPE_STRING
    dims: [ -1 ]
  },
  {
    name: "cum_log_probs"
    data_type: TYPE_FP32
    dims: [ -1 ]
  },
  {
    name: "output_log_probs"
    data_type: TYPE_FP32
    dims: [ -1, -1 ]
  },
  {
    name: "context_logits"
    data_type: TYPE_FP32
    dims: [ -1, -1 ]
  },
  {
    name: "generation_logits"
    data_type: TYPE_FP32
    dims: [ -1, -1, -1 ]
  },
  {
    name: "batch_index"
    data_type: TYPE_INT32
    dims: [ 1 ]
  }
]

parameters: {
  key: "accumulate_tokens"
  value: {
    string_value: "${accumulate_tokens}"
  }
}
parameters: {
  key: "tensorrt_llm_model_name"
  value: {
    string_value: "tensorrt_llm"
  }
}
parameters: {
  key: "tensorrt_llm_draft_model_name"
  value: {
    string_value: "${tensorrt_llm_draft_model_name}"
  }
}
parameters: {
  key: "multimodal_encoders_name"
  value: {
    string_value: "${multimodal_encoders_name}"
  }
}

instance_group [
  {
    count: 1
    kind : KIND_CPU
  }
]