lib.linux-x86_64-cpython-310/tensorrt_llm/models/phi3/convert.py

import torch

from tensorrt_llm.quantization import QuantAlgo

from ..._utils import str_dtype_to_torch
from .split_weights import shuffle_qkv_weights, split_weights_tp


def convert_hf_weights(hf_model, dtype, config, small_variant, args, rank):
    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
        orig_key = key
        if "model.layers." in key:
            key = key.replace("model.layers.", "transformer.layers.")
            #Attention
            key = key.replace("self_attn.", "attention.")
            key = key.replace("query_key_value.", "qkv.")  # small
            key = key.replace("Wqkv.weight", "qkv.weight")
            key = key.replace("qkv_proj.", "qkv.")  #128k
            #MLP
            key = key.replace("mlp.fc1.", "mlp.fc.")
            key = key.replace("mlp.fc2.", "mlp.proj.")
            key = key.replace("mlp.gate_up_proj.", "mlp.fc.")
            key = key.replace(
                "mlp.up_proj.",
                "mlp.fc." if small_variant else "mlp.gate.")  #128k
            key = key.replace("mlp.down_proj.", "mlp.proj.")  #128k
            key = key.replace("mlp.gate_proj.", "mlp.fc.")  #128k
            key = key.replace("o_proj.", "dense.")  #128k
            #Layer norm
            key = key.replace("post_attention_layernorm.",
                              "post_layernorm.")  #128k

        # Embedding
        key = key.replace("model.embed_tokens.weight",
                          "transformer.vocab_embedding.weight")
        # Final Layer norm
        key = key.replace("model.final_layernorm.", "transformer.ln_f.")
        key = key.replace("model.norm.", "transformer.ln_f.")  #128k

        if "mlp.gate_up_proj." in orig_key:  #4k
            original_weights = value.contiguous().clone()
            half_split = original_weights.shape[0] // 2
            first_half, second_half = original_weights[:
                                                       half_split, :], original_weights[
                                                           half_split:, :]
            # Swap the halves
            value = torch.cat((second_half, first_half), dim=0)

        if "q_proj" in key:  #128k
            q_param = value
            k_param = hf_state_dict[orig_key.replace("q_proj", "k_proj")]
            v_param = hf_state_dict[orig_key.replace("q_proj", "v_proj")]
            value = torch.cat([q_param, k_param, v_param], dim=0)
            key = key.replace("q_proj.weight", "qkv.weight")
        elif "k_proj" in key or "v_proj" in key:
            continue

        weights[key] = value.to(torch_dtype).cpu()

    if small_variant:
        weights['lm_head.weight'] = weights[
            'transformer.vocab_embedding.weight'].clone()

        # Transform QKV weights from custom Phi3Small format to TRT-LLM format
        for key, value in weights.items():
            if "qkv." in key:
                weights[key] = shuffle_qkv_weights(weights[key], config)

        weights = split_weights_tp(config, weights, args, rank, torch_dtype)

    return weights


def convert_small_hf_config(hf_config):
    return {
        'architecture': "Phi3SmallForCausalLM",
        'rotary_base': hf_config.rope_embedding_base,
        'gegelu_limit': hf_config.gegelu_limit,
        'mup_attn_multiplier': hf_config.mup_attn_multiplier,
        'mup_embedding_multiplier': hf_config.mup_embedding_multiplier,
        'mup_use_scaling': hf_config.mup_use_scaling,
        'mup_width_multiplier': hf_config.mup_width_multiplier,
        'blocksparse_block_size': hf_config.blocksparse_block_size,
        'blocksparse_homo_head_pattern':
        hf_config.blocksparse_homo_head_pattern,
        'blocksparse_num_local_blocks': hf_config.blocksparse_num_local_blocks,
        'blocksparse_vertical_stride': hf_config.blocksparse_vert_stride,
        'dense_attention_every_n_layers':
        hf_config.dense_attention_every_n_layers,
    }


def convert_hf_config(hf_config, dtype, args):
    config = {
        'architecture': "Phi3ForCausalLM",
        'dtype': dtype,
        'num_hidden_layers': hf_config.num_hidden_layers,
        'num_attention_heads': hf_config.num_attention_heads,
        'num_key_value_heads': hf_config.num_key_value_heads,
        '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,
    }

    small_variant = hf_config.architectures[0] == "Phi3SmallForCausalLM"
    if small_variant:
        config.update(convert_small_hf_config(hf_config))
    else:
        config.update({
            'rotary_base': hf_config.rope_theta,
            'norm_epsilon': hf_config.rms_norm_eps,
        })

    # Long-context variants
    if hf_config.max_position_embeddings >= 128000:
        config.update({
            'original_max_position_embeddings':
            hf_config.original_max_position_embeddings,
            'longrope_scaling_short_factors':
            hf_config.rope_scaling["short_factor"],
            'longrope_scaling_long_factors':
            hf_config.rope_scaling["long_factor"]
        })

        if small_variant:
            config.update({
                'longrope_long_mscale':
                hf_config.rope_scaling["long_mscale"],
                'longrope_short_mscale':
                hf_config.rope_scaling["short_mscale"]
            })

    if config["hidden_act"] == "silu":
        config["hidden_act"] = "swiglu"

    # Tensor parallelism and weight-only quantization
    if args is not None:
        config.update({
            'mapping': {
                'world_size': args.tp_size * args.pp_size,
                'tp_size': args.tp_size,
                'pp_size': args.pp_size,
            }
        })

        if args.use_weight_only and args.weight_only_precision == 'int8':
            config.update({'quantization': {'quant_algo': QuantAlgo.W8A16}})
        elif args.use_weight_only and args.weight_only_precision == 'int4':
            config.update({'quantization': {'quant_algo': QuantAlgo.W4A16}})

    return config