lit_llama/model.py

"""Full definition of a LLaMA Language Model, all of it in this single file.

Based on the nanoGPT implementation: https://github.com/karpathy/nanoGPT.
"""
# mypy: ignore-errors
import math
from dataclasses import dataclass

import torch
import torch.nn as nn
from torch.nn import functional as F
from typing_extensions import Self


@dataclass
class LLaMAConfig:
    block_size: int = 4096
    vocab_size: int = 32000
    n_layer: int = 32
    n_head: int = 32
    n_embd: int = 4096

    @classmethod
    def from_name(cls, name: str) -> Self:
        return cls(**llama_configs[name])


llama_configs = {
    "7B": dict(n_layer=32, n_head=32, n_embd=4096),
    "13B": dict(n_layer=40, n_head=40, n_embd=5120),
    "30B": dict(n_layer=60, n_head=52, n_embd=6656),
    "65B": dict(n_layer=80, n_head=64, n_embd=8192),
}


class LLaMA(nn.Module):
    def __init__(self, config: LLaMAConfig) -> None:
        super().__init__()
        assert config.vocab_size is not None
        assert config.block_size is not None
        self.config = config

        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer = nn.ModuleDict(
            dict(
                wte=nn.Embedding(config.vocab_size, config.n_embd),
                h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
                ln_f=RMSNorm(config.n_embd),
            )
        )
        # self.llama_proj = nn.Sequential(
        #     nn.Linear(256, 1024),
        #     nn.ReLU(),
        #     nn.Linear(1024, config.n_embd)
        # )
        self.llama_proj = nn.Linear(512, config.n_embd)
        # self.motion_proj = nn.Sequential(
        #     nn.Linear(config.n_embd, 1024),
        #     nn.ReLU(),
        #     nn.Linear(1024, 256)
        # )
        self.motion_proj = nn.Linear(config.n_embd, 512)

    def _init_weights(self, module: nn.Module) -> None:
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02 / math.sqrt(2 * self.config.n_layer))

    def forward(self, idx: torch.Tensor) -> torch.Tensor:
        # import pdb; pdb.set_trace()
        _, t = idx.size()
        assert (
            t <= self.config.block_size
        ), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"

        # forward the LLaMA model itself
        x = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)

        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)

        logits = self.lm_head(x)  # (b, t, vocab_size)

        return logits

    @classmethod
    def from_name(cls, name: str) -> Self:
        return cls(LLaMAConfig.from_name(name))


class Block(nn.Module):
    def __init__(self, config: LLaMAConfig) -> None:
        super().__init__()
        self.rms_1 = RMSNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.rms_2 = RMSNorm(config.n_embd)
        self.mlp = MLP(config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.rms_1(x))
        x = x + self.mlp(self.rms_2(x))
        return x


class CausalSelfAttention(nn.Module):
    def __init__(self, config: LLaMAConfig) -> None:
        super().__init__()
        assert config.n_embd % config.n_head == 0

        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
        # output projection
        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)

        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.block_size = config.block_size
        self.rope_cache = None

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)

        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)

        head_size = C // self.n_head
        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)
        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)  # (B, nh, T, hs)

        if self.rope_cache is None:
            # cache for future forward calls
            self.rope_cache = build_rope_cache(
                seq_len=self.block_size,
                n_elem=self.n_embd // self.n_head, 
                dtype=x.dtype,
                device=x.device,
            )

        q = apply_rope(q, self.rope_cache)
        k = apply_rope(k, self.rope_cache)

        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
        #  att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
        #  att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
        #  att = F.softmax(att, dim=-1)
        #  y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)

        # efficient attention using Flash Attention CUDA kernels
        y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)

        y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side

        # output projection
        y = self.c_proj(y)

        return y


class MLP(nn.Module):
    def __init__(self, config: LLaMAConfig) -> None:
        super().__init__()
        hidden_dim = 4 * config.n_embd
        n_hidden = int(2 * hidden_dim / 3)
        N = 256
        # ensure n_hidden is multiple of N
        n_hidden = ((n_hidden - 1) // N) * N + N

        self.c_fc1 = nn.Linear(config.n_embd, n_hidden, bias=False)
        self.c_fc2 = nn.Linear(config.n_embd, n_hidden, bias=False)
        self.c_proj = nn.Linear(n_hidden, config.n_embd, bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
        x = self.c_proj(x)
        return x


class RMSNorm(nn.Module):
    """Root Mean Square Layer Normalization.

    Derived from https://github.com/bzhangGo/rmsnorm/blob/master/rmsnorm_torch.py. BSD 3-Clause License:
    https://github.com/bzhangGo/rmsnorm/blob/master/LICENSE.
    """

    def __init__(self, size: int, dim: int = -1, eps: float = 1e-5) -> None:
        super().__init__()
        self.scale = nn.Parameter(torch.ones(size))
        self.eps = eps
        self.dim = dim

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # NOTE: the original RMSNorm paper implementation is not equivalent
        # norm_x = x.norm(2, dim=self.dim, keepdim=True)
        # rms_x = norm_x * d_x ** (-1. / 2)
        # x_normed = x / (rms_x + self.eps)
        norm_x = torch.mean(x * x, dim=self.dim, keepdim=True)
        x_normed = x * torch.rsqrt(norm_x + self.eps)
        return self.scale * x_normed


def build_rope_cache(seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000) -> torch.Tensor:
    """Enhanced Transformer with Rotary Position Embedding.

    Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
    transformers/rope/__init__.py. MIT License:
    https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
    """
    # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
    theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=dtype, device=device) / n_elem))

    # Create position indexes `[0, 1, ..., seq_len - 1]`
    seq_idx = torch.arange(seq_len, dtype=dtype, device=device)

    # Calculate the product of position index and $\theta_i$
    idx_theta = torch.outer(seq_idx, theta)

    # Compute cache. Because polar only takes float32 or float64, we need to cast
    # when working with 16 bit floats (float16 or bfloat16)
    dtypes_requiring_casting = [torch.float16, torch.bfloat16, torch.int8]
    working_dtype = (
        torch.float32 if dtype in dtypes_requiring_casting else dtype
    )
    complex_dtype = (
        torch.complex32 if dtype in dtypes_requiring_casting else torch.complex64
    )
    cache = torch.polar(
        torch.ones_like(idx_theta).to(working_dtype), idx_theta.to(working_dtype)
    ).to(complex_dtype)
    return cache


def apply_rope(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
    x = x.transpose(1, 2)

    # truncate to support variable sizes
    T = x.size(1)
    rope_cache = rope_cache[:T]

    # cast because `view_as_complex` does not support 16 bit tensors
    xc = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2))
    rope_cache = rope_cache.view(1, xc.size(1), 1, xc.size(3))
    x_out = torch.view_as_real(xc * rope_cache).flatten(3)
    return x_out.transpose(1, 2).type_as(x)