aphrodite-engine/aphrodite/modeling/models/decilm.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
# Copyright 2023 The PygmalionAI team.
# Copyright 2023 DeciAI Research Team. All rights reserved.
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on MistralAI GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Inference-only DeciLM model compatible with HuggingFace weights."""

from typing import Optional

import torch
from transformers import PretrainedConfig

from aphrodite.common.config import LoRAConfig
from aphrodite.modeling.layers.linear import LinearMethodBase
from aphrodite.modeling.models.llama import LlamaForCausalLM
from aphrodite.modeling.hf_downloader import (default_weight_loader,
                                              hf_model_weights_iterator)


class DeciLMForCausalLM(LlamaForCausalLM):
    """
    Implementation for https://huggingface.co/Deci/DeciLM-7b-instruct.
    Based on the llama executor.

    The main difference is that DeciLM uses Variable Grouped Query Attention.
    The constant number of GQA heads in the decoder is overridden with a value
    per layer.

    Usually, in the HuggingFace implementation, instead of
    "config.num_key_value_heads", we use
    "config.num_key_value_heads_per_layer[i]" which varies.

    Currently, PagedAttention does not work well with variable GQA, so we
    normalize the weights upon loading, and use uniform GQA with the max value
    instead.
    """

    def __init__(
        self,
        config: Optional[PretrainedConfig] = None,
        linear_method: Optional[LinearMethodBase] = None,
        lora_config: Optional[LoRAConfig] = None,
    ) -> None:
        config.num_key_value_heads = max(config.num_key_value_heads_per_layer)
        delattr(config, "num_key_value_heads_per_layer")
        super().__init__(config=config,
                         linear_method=linear_method,
                         lora_config=lora_config)

    def load_weights(self,
                     model_name_or_path: str,
                     cache_dir: Optional[str] = None,
                     load_format: str = "auto",
                     revision: Optional[str] = None):
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("gate_up_proj", "gate_proj", 0),
            ("gate_up_proj", "up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in hf_model_weights_iterator(
                model_name_or_path, cache_dir, load_format, revision,
                self.config):
            if "rotary_emb.inv_freq" in name:
                continue

            if "k_proj" in name or "v_proj" in name:
                loaded_weight = self._degroup_weight(loaded_weight)

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)

    def _degroup_weight(self, loaded_weight: torch.Tensor) -> torch.Tensor:
        hidden_size = self.config.hidden_size
        head_size = self.config.hidden_size // self.config.num_attention_heads
        target_num_kv_heads = self.config.num_key_value_heads
        num_kv_heads = loaded_weight.shape[0] // head_size
        n_repeats = target_num_kv_heads / num_kv_heads
        assert n_repeats == int(n_repeats)

        n_repeats = int(n_repeats)
        loaded_weight = loaded_weight.view(num_kv_heads, head_size,
                                           hidden_size)
        loaded_weight = torch.repeat_interleave(loaded_weight,
                                                repeats=n_repeats,
                                                dim=0)
        loaded_weight = loaded_weight.reshape(target_num_kv_heads * head_size,
                                              hidden_size)

        return loaded_weight