lib.linux-x86_64-cpython-310/tensorrt_llm/models/phi/convert.py

import torch

from ..._utils import str_dtype_to_torch


def convert_hf_weights(hf_model, dtype, args=None):
    torch_dtype = str_dtype_to_torch(dtype)
    hf_state_dict = hf_model.state_dict()
    weights = {}

    # replace key name
    for key, value in hf_state_dict.items():
        # Decoder Layers
        if "model.layers." in key:
            key = key.replace("model.layers.", "transformer.layers.")
            key = key.replace("self_attn.", "attention.")
            key = key.replace("mlp.fc1.", "mlp.fc.")
            key = key.replace("mlp.fc2.", "mlp.proj.")
        # Embedding
        key = key.replace("model.embed_tokens.weight",
                          "transformer.vocab_embedding.weight")
        # Final Layer norm
        key = key.replace("model.final_layernorm.", "transformer.ln_f.")
        weights[key] = value.to(torch_dtype).cpu()

    # merge qkv weights
    qkv_keys = ["q_proj", "k_proj", "v_proj"]

    for key in hf_state_dict.keys():
        if 'self_attn.q_proj.weight' in key:
            prefix = key.split('self_attn')[0].replace("model.layers.",
                                                       "transformer.layers.")

            # [(num_heads x q)|(num_heads x k)|(num_heads x v), hidden_size]
            qkv_weights = []
            qkv_bias = []
            for k in qkv_keys:
                qkv_weights.append(weights.pop(f"{prefix}attention.{k}.weight"))
                qkv_bias.append(weights.pop(f"{prefix}attention.{k}.bias"))

            weights[f"{prefix}attention.qkv.weight"] = torch.cat(qkv_weights,
                                                                 dim=0)
            weights[f"{prefix}attention.qkv.bias"] = torch.cat(qkv_bias, dim=0)

    return weights


def convert_hf_config(hf_config, dtype, args):
    config = {
        'architecture': hf_config.architectures[0],
        'dtype': dtype,
        'num_hidden_layers': hf_config.num_hidden_layers,
        'num_attention_heads': hf_config.num_key_value_heads,
        'partial_rotary_factor': hf_config.partial_rotary_factor,
        'rope_theta': hf_config.rope_theta,
        'hidden_size': hf_config.hidden_size,
        'intermediate_size': hf_config.intermediate_size,
        'vocab_size': hf_config.vocab_size,
        'max_position_embeddings': hf_config.max_position_embeddings,
        'hidden_act': hf_config.hidden_act,
        'share_embedding_table': False,
        'mapping': {
            'world_size': args.tp_size * args.pp_size,
            'tp_size': args.tp_size,
            'pp_size': args.pp_size,
        }
    }
    return config