Source code for torchtune.models.phi3._component_builders

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import List

from torch import nn

from torchtune.models.phi3._position_embeddings import Phi3RotaryPositionalEmbeddings
from torchtune.modules import (
    MultiHeadAttention,
    FeedForward,
    FrozenNF4Linear,
    RMSNorm,
    TransformerDecoder,
    TransformerSelfAttentionLayer,
)

from torchtune.modules.common_utils import _register_reparametrize_state_dict_hooks

from torchtune.modules.peft import DoRALinear, LORA_ATTN_MODULES, LoRALinear

"""
Component builders for the Phi3 4K Mini Instruct model.

torchtune provides composable building blocks. Builder functions help
stitch these building blocks into higher-level components. This design has
two benefits:
- The building blocks themselves are very flexible. For example, ``MultiHeadAttention``
can take either nn.Linear or nn.LoRALinear for ``q_proj``.
- Builder functions expose a set of configurable params which keep the constructors of
the building blocks simple.
"""

def phi3(
    vocab_size: int,
    num_layers: int,
    num_heads: int,
    num_kv_heads: int,
    embed_dim: int,
    intermediate_dim: int,
    max_seq_len: int,
    attn_dropout: float = 0.0,
    norm_eps: float = 1e-5,
    rope_base: int = 10_000,
) -> TransformerDecoder:
    """
    Args:
        vocab_size (int): number of tokens in vocabulary.
        num_layers (int): number of layers in the transformer decoder.
        num_heads (int): number of query heads. For MHA this is also the
            number of heads for key and value
        num_kv_heads (int): number of key and value heads. User should ensure
            `num_heads` % `num_kv_heads` == 0. For standard MHA set `num_kv_heads` == `num_heads`,
            for GQA `num_kv_heads` < `num_heads`, and for MQA set `num_kv_heads` == 1.
        embed_dim (int): embedding dimension for self-attention
        intermediate_dim (int): intermediate dimension for MLP
        max_seq_len (int): maximum sequence length the model will be run with,
        attn_dropout (float): dropout value passed onto scaled_dot_product_attention.
            Default: 0.0
        norm_eps (float): epsilon in RMS norms
        rope_base (int): base for the rotary positional embeddings. Default: 10_000

    Returns:
        TransformerDecoder: Instantiation of Phi3 Mini 4K Instruct model.
    """
    head_dim = embed_dim // num_heads
    num_kv_heads = num_kv_heads if num_kv_heads else num_heads

    rope = Phi3RotaryPositionalEmbeddings(dim=head_dim, max_seq_len=max_seq_len, base=rope_base)
    self_attn = MultiHeadAttention(
        embed_dim=embed_dim,
        num_heads=num_heads,
        num_kv_heads=num_kv_heads,
        head_dim=head_dim,
        q_proj=nn.Linear(embed_dim, num_heads * head_dim, bias=False),
        k_proj=nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False),
        v_proj=nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False),
        output_proj=nn.Linear(embed_dim, embed_dim, bias=False),
        pos_embeddings=rope,
        kv_cache=None,
        max_seq_len=max_seq_len,
        attn_dropout=attn_dropout,
    )
    mlp = phi3_mlp(dim=embed_dim, hidden_dim=intermediate_dim)
    layer = TransformerSelfAttentionLayer(
        attn=self_attn,
        mlp=mlp,
        sa_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
        mlp_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
    )
    tok_embeddings = nn.Embedding(vocab_size, embed_dim)
    output_proj = nn.Linear(embed_dim, vocab_size, bias=False)
    return TransformerDecoder(
        tok_embeddings=tok_embeddings,
        layers=layer,
        num_layers=num_layers,
        max_seq_len=max_seq_len,
        num_heads=num_heads,
        head_dim=head_dim,
        norm=RMSNorm(embed_dim, eps=norm_eps),
        output=output_proj,
    )

def phi3_mlp(dim: int, hidden_dim: int, quantize_base: bool = False) -> FeedForward:
    """
    Build the MLP layer associated with the Phi3 Mini 4K Instruct model.
    """
    gate_proj = nn.Linear(dim, hidden_dim, bias=False) if not quantize_base else FrozenNF4Linear(dim, hidden_dim, bias=False)
    down_proj = nn.Linear(hidden_dim, dim, bias=False) if not quantize_base else FrozenNF4Linear(hidden_dim, dim, bias=False)
    up_proj = nn.Linear(dim, hidden_dim, bias=False) if not quantize_base else FrozenNF4Linear(dim, hidden_dim, bias=False)
    return FeedForward(gate_proj=gate_proj, down_proj=down_proj, up_proj=up_proj)


# ------------------ LoRA Phi3 ------------------


def lora_phi3(
    lora_attn_modules: List[LORA_ATTN_MODULES],
    apply_lora_to_mlp: bool = False,
    apply_lora_to_output: bool = False,
    *,
    # phi3 args
    vocab_size: int,
    num_layers: int,
    num_heads: int,
    num_kv_heads: int,
    embed_dim: int,
    intermediate_dim: int,
    max_seq_len: int,
    attn_dropout: float = 0.0,
    norm_eps: float = 1e-5,
    rope_base: int = 10_000,
    # LoRA args
    lora_rank: int,
    lora_alpha: float,
    lora_dropout: float = 0.0,
    use_dora: bool = False,
    # Quantization args
    quantize_base: bool = False,
) -> TransformerDecoder:
    """
    Return a version of Phi3 (an instance of :func:`~torchtune.modules.TransformerDecoder`)
    with LoRA applied based on the passed in configuration.

    Args:
        lora_attn_modules (List[LORA_ATTN_MODULES]): list of which linear layers
            LoRA should be applied to in each self-attention block. Options are
            ``{"q_proj", "k_proj", "v_proj", "output_proj"}``.
        apply_lora_to_mlp (bool): whether to apply LoRA to the MLP in each transformer layer.
            Default: False
        apply_lora_to_output (bool): whether to apply LoRA to the model's final output projection.
            Default: False
        vocab_size (int): number of tokens in vocabulary.
        num_layers (int): number of layers in the transformer decoder.
        num_heads (int): number of query heads. For MHA this is also the
            number of heads for key and value
        num_kv_heads (int): number of key and value heads. User should ensure
            `num_heads` % `num_kv_heads` == 0. For standard MHA set `num_kv_heads` == `num_heads`,
            for GQA `num_kv_heads` < `num_heads`, and for MQA set `num_kv_heads` == 1.
        embed_dim (int): embedding dimension for self-attention
        intermediate_dim (int): intermediate dimension for MLP
        max_seq_len (int): maximum sequence length the model will be run with, as used
            by :func:`~torchtune.modules.KVCache`
        attn_dropout (float): dropout value passed onto scaled_dot_product_attention.
            Default: 0.0
        norm_eps (float): epsilon in RMS norms.
        rope_base (int): base value for Rotary Position Embeddings.
            Default: 10000
        lora_rank (int): rank of each low-rank approximation
        lora_alpha (float): scaling factor for the low-rank approximation
        lora_dropout (float): LoRA dropout probability. Default: 0.0
        use_dora (bool): Decompose the LoRA weight into magnitude and direction, as
            introduced in "DoRA: Weight-Decomposed Low-Rank Adaptation" (https://arxiv.org/abs/2402.09353).
        quantize_base: (bool): Whether to quantize base model weights or not. Only applied to base
            weights within linear layers LoRA is applied to. The final output linear projection is not
            supported for quantization currently.

    Returns:
        TransformerDecoder: Instantiation of Llama3 model with LoRA applied to
        a subset of the attention projections in each layer.

    """

    self_attn = lora_phi3_self_attention(
        lora_modules=lora_attn_modules,
        embed_dim=embed_dim,
        num_heads=num_heads,
        num_kv_heads=num_kv_heads,
        max_seq_len=max_seq_len,
        attn_dropout=attn_dropout,
        rope_base=rope_base,
        lora_rank=lora_rank,
        lora_alpha=lora_alpha,
        lora_dropout=lora_dropout,
        use_dora=use_dora,
        quantize_base=quantize_base,
    )

    if apply_lora_to_mlp:
        mlp = lora_phi3_mlp(
            dim=embed_dim,
            hidden_dim=intermediate_dim,
            lora_rank=lora_rank,
            lora_alpha=lora_alpha,
            lora_dropout=lora_dropout,
            use_dora=use_dora,
            quantize_base=quantize_base,
        )
    else:
        mlp = phi3_mlp(dim=embed_dim, hidden_dim=intermediate_dim, quantize_base=quantize_base)

    layer = TransformerSelfAttentionLayer(
        attn=self_attn,
        mlp=mlp,
        sa_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
        mlp_norm=RMSNorm(dim=embed_dim, eps=norm_eps),
    )

    tok_embeddings = nn.Embedding(vocab_size, embed_dim)

    # TODO: quantize_base is not applied to final output_proj currently.
    adapter_cls = DoRALinear if use_dora else LoRALinear
    output_proj = (
        adapter_cls(embed_dim, vocab_size, rank=lora_rank, alpha=lora_alpha, dropout=lora_dropout)
        if apply_lora_to_output
        else nn.Linear(embed_dim, vocab_size, bias=False)
    )
    model = TransformerDecoder(
        tok_embeddings=tok_embeddings,
        layers=layer,
        num_layers=num_layers,
        max_seq_len=max_seq_len,
        num_heads=num_heads,
        head_dim=(embed_dim // num_heads),
        norm=RMSNorm(embed_dim, eps=norm_eps),
        output=output_proj,
    )

    if quantize_base:
        # For QLoRA, we reparametrize 4-bit tensors to bf16, and offload to CPU on the fly
        # so as to not increase peak memory
        _register_reparametrize_state_dict_hooks(model, dtype=tok_embeddings.weight.dtype)

    return model


def lora_phi3_self_attention(
    lora_modules: List[LORA_ATTN_MODULES],
    *,
    # MultiHeadAttention args
    embed_dim: int,
    num_heads: int,
    num_kv_heads: int,
    max_seq_len: int,
    attn_dropout: float = 0.0,
    rope_base: int = 10_000,
    # LoRA args
    lora_rank: int,
    lora_alpha: float,
    lora_dropout: float = 0.0,
    use_dora: bool = False,
    quantize_base: bool = False,
) -> MultiHeadAttention:
    """
    Return an instance of :func:`~torchtune.modules.MultiHeadAttention` with LoRA
    applied to a subset of its linear layers

    Args:
        lora_modules (List[LORA_ATTN_MODULES]): list of which linear layers
            LoRA should be applied to. Options are ``{"q_proj", "k_proj", "v_proj",
            "output_proj"}``.
        embed_dim (int): embedding dimension for self-attention
        num_heads (int): number of query heads. For MHA this is also the
            number of heads for key and value
        num_kv_heads (int): number of key and value heads. User should ensure
            `num_heads` % `num_kv_heads` == 0. For standard MHA set `num_kv_heads` == `num_heads`,
            for GQA `num_kv_heads` < `num_heads`, and for MQA set `num_kv_heads` == 1.
        max_seq_len (int): maximum sequence length the model will be run with, as used
            by :func:`~torchtune.modules.KVCache`
        attn_dropout (float): dropout value passed onto scaled_dot_product_attention.
            Default: 0.0
        rope_base (int): base value for Rotary Position Embeddings.
            Default: 10000
        lora_rank (int): rank of each low-rank approximation
        lora_alpha (float): scaling factor for the low-rank approximation
        lora_dropout (float): LoRA dropout probability. Default: 0.0
        use_dora (bool): Decompose the LoRA weight into magnitude and direction, as
            introduced in "DoRA: Weight-Decomposed Low-Rank Adaptation" (https://arxiv.org/abs/2402.09353).
        quantize_base (bool): Whether to quantize base model parameters for linear layers
            LoRA is being applied to. Default is ``False``.

    Returns:
        MultiHeadAttention: instantiation of self-attention module with LoRA
        applied to a subset of Q, K, V, output projections.

    Raises:
        ValueError: If lora_modules arg is an empty list
    """
    if not lora_modules:
        raise ValueError(
            f"Must pass one or more of {LORA_ATTN_MODULES} as lora_modules"
        )

    head_dim = embed_dim // num_heads
    num_kv_heads = num_kv_heads if num_kv_heads else num_heads
    adapter_cls = DoRALinear if use_dora else LoRALinear
    q_proj = (
        adapter_cls(
            embed_dim,
            num_heads * head_dim,
            rank=lora_rank,
            alpha=lora_alpha,
            dropout=lora_dropout,
            quantize_base=quantize_base,
        )
        if "q_proj" in lora_modules
        else (
            nn.Linear(embed_dim, num_heads * head_dim, bias=False)
            if not quantize_base
            else FrozenNF4Linear(embed_dim, num_heads * head_dim, bias=False)
        )
    )
    k_proj = (
        adapter_cls(
            embed_dim,
            num_kv_heads * head_dim,
            rank=lora_rank,
            alpha=lora_alpha,
            dropout=lora_dropout,
            quantize_base=quantize_base,
        )
        if "k_proj" in lora_modules
        else (
            nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False)
            if not quantize_base
            else FrozenNF4Linear(embed_dim, num_kv_heads * head_dim, bias=False)
        )
    )
    v_proj = (
        adapter_cls(
            embed_dim,
            num_kv_heads * head_dim,
            rank=lora_rank,
            alpha=lora_alpha,
            dropout=lora_dropout,
            quantize_base=quantize_base,
        )
        if "v_proj" in lora_modules
        else (
            nn.Linear(embed_dim, num_kv_heads * head_dim, bias=False)
            if not quantize_base
            else FrozenNF4Linear(embed_dim, num_kv_heads * head_dim, bias=False)
        )
    )
    output_proj = (
        adapter_cls(
            embed_dim,
            embed_dim,
            rank=lora_rank,
            alpha=lora_alpha,
            dropout=lora_dropout,
            quantize_base=quantize_base,
        )
        if "output_proj" in lora_modules
        else (
            nn.Linear(embed_dim, embed_dim, bias=False)
            if not quantize_base
            else FrozenNF4Linear(embed_dim, embed_dim, bias=False)
        )
    )
    rope = Phi3RotaryPositionalEmbeddings(dim=head_dim, max_seq_len=max_seq_len, base=rope_base)
    self_attn = MultiHeadAttention(
        embed_dim=embed_dim,
        num_heads=num_heads,
        num_kv_heads=num_kv_heads,
        head_dim=head_dim,
        q_proj=q_proj,
        k_proj=k_proj,
        v_proj=v_proj,
        output_proj=output_proj,
        pos_embeddings=rope,
        max_seq_len=max_seq_len,
        attn_dropout=attn_dropout,
    )
    return self_attn


def lora_phi3_mlp(
    *,
    dim: int,
    hidden_dim: int,
    lora_rank: int,
    lora_alpha: float,
    lora_dropout: float = 0.0,
    use_dora: bool = False,
    quantize_base: bool = False,
) -> FeedForward:
    adapter_cls = DoRALinear if use_dora else LoRALinear
    gate_proj = adapter_cls(
        in_dim=dim,
        out_dim=hidden_dim,
        rank=lora_rank,
        alpha=lora_alpha,
        dropout=lora_dropout,
        quantize_base=quantize_base,
    )
    down_proj = adapter_cls(
        in_dim=hidden_dim,
        out_dim=dim,
        rank=lora_rank,
        alpha=lora_alpha,
        dropout=lora_dropout,
        quantize_base=quantize_base,
    )
    up_proj = adapter_cls(
        in_dim=dim,
        out_dim=hidden_dim,
        rank=lora_rank,
        alpha=lora_alpha,
        dropout=lora_dropout,
        quantize_base=quantize_base,
    )
    return FeedForward(
        gate_proj=gate_proj,
        down_proj=down_proj,
        up_proj=up_proj,
    )