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name: "turbomind"
backend: "turbomind"
default_model_filename: "weights"
max_batch_size: 1

model_transaction_policy {
  decoupled: True
}

instance_group [
  {
    # max concurrent instances
    count: 48
    kind: KIND_CPU
  }
]

input [
  {
    name: "input_ids"
    data_type: TYPE_UINT32
    dims: [ -1 ]
    # allow_ragged_batch: true
  },
  {
    name: "input_lengths"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
  },
  {
    name: "request_output_len"
    data_type: TYPE_UINT32
    dims: [ -1 ]
  },
  {
    name: "input_embeddings"
    data_type: TYPE_INT8
    dims: [ -1 ]
    optional: true
  },
  {
    name: "input_embedding_ranges"
    data_type: TYPE_UINT32
    dims: [ -1, 2 ]
    optional: true
  },
  {
    name: "step"
    data_type: TYPE_INT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "session_len"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "runtime_top_k"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "runtime_top_p"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "beam_search_diversity_rate"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "temperature"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "len_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "repetition_penalty"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "random_seed"
    data_type: TYPE_UINT64
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "is_return_log_probs"
    data_type: TYPE_BOOL
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "beam_width"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
    {
    name: "start_id"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "end_id"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "bad_words_list"
    data_type: TYPE_INT32
    dims: [ 2, -1 ]
    optional: true
  },
  {
    name: "stop_words_list"
    data_type: TYPE_INT32
    dims: [ 2, -1 ]
    optional: true
  },
  {
    name: "prompt_learning_task_name_ids"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "top_p_decay"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "top_p_min"
    data_type: TYPE_FP32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "top_p_reset_ids"
    data_type: TYPE_UINT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "START"
    data_type: TYPE_INT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "END"
    data_type: TYPE_INT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "STOP"
    data_type: TYPE_INT32
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  },
  {
    name: "CORRID"
    data_type: TYPE_UINT64
    dims: [ 1 ]
    reshape: { shape: [ ] }
    optional: true
  }
]
output [
  {
    name: "output_ids"
    data_type: TYPE_UINT32
    dims: [ -1, -1 ]
  },
  {
    name: "sequence_length"
    data_type: TYPE_UINT32
    dims: [ -1 ]
  },
  {
    name: "cum_log_probs"
    data_type: TYPE_FP32
    dims: [ -1 ]
  },
  {
    name: "output_log_probs"
    data_type: TYPE_FP32
    dims: [ -1, -1 ]
  }
]

parameters {
  key: "pipeline_para_size"
  value: {
    string_value: "1"
  }
}
parameters {
  key: "data_type"
  value: {
    string_value: "fp16"
  }
}
parameters {
  key: "model_type"
  value: {
    string_value: "Llama"
  }
}

parameters {
  key: "enable_custom_all_reduce"
  value: {
    string_value: "0"
  }
}
parameters {
  key: "tensor_para_size"
  value: {
    string_value: "1"
  }
}
parameters {
  key: "model_name"
  value: {
    string_value: "internlm-chat-7b"
  }
}