fam/llm/fast_model.py

# Copyright (c) Kotoba Technologies, Inc. and affiliates.
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from dataclasses import dataclass
from functools import reduce
from math import gcd
from typing import Optional, Tuple

import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F

from fam.llm.utils import get_default_dtype

import logging

# Adjust the logging level
logger = logging.getLogger("torch")
logger.setLevel(logging.ERROR)


def find_multiple(n: int, *args: Tuple[int]) -> int:
    k = reduce(lambda x, y: x * y // gcd(x, y), args + (1,))
    if n % k == 0:
        return n
    return n + k - (n % k)


@dataclass
class ModelArgs:
    block_size: int = 2048
    vocab_size: int = 32000
    n_layer: int = 32
    n_head: int = 32
    dim: int = 4096
    speaker_emb_dim: int = 256
    intermediate_size: int = None
    n_local_heads: int = -1
    head_dim: int = 64
    norm_eps: float = 1e-5
    dtype: torch.dtype = torch.bfloat16

    def __post_init__(self):
        if self.n_local_heads == -1:
            self.n_local_heads = self.n_head
        if self.intermediate_size is None:
            hidden_dim = 4 * self.dim
            n_hidden = int(2 * hidden_dim / 3)
            self.intermediate_size = find_multiple(n_hidden, 256)
        self.head_dim = self.dim // self.n_head

        self.dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[get_default_dtype()]

    @classmethod
    def from_name(cls, name: str):
        if name in transformer_configs:
            return cls(**transformer_configs[name])
        # fuzzy search
        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
        assert len(config) == 1, name
        return cls(**transformer_configs[config[0]])


transformer_configs = {
    "kotoba-speech-v0.1": dict(
        n_layer=24,
        n_head=16,
        dim=2048,
        vocab_size=2562,
    ),
}


class KVCache(nn.Module):
    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype):
        super().__init__()
        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
        self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype))
        self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype))

    def update(self, input_pos, k_val, v_val):
        # input_pos: [S], k_val: [B, H, S, D]
        assert input_pos.shape[0] == k_val.shape[2]

        k_out = self.k_cache
        v_out = self.v_cache
        k_out[:, :, input_pos] = k_val
        v_out[:, :, input_pos] = v_val

        return k_out, v_out


class Transformer(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.config = config

        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
        self.pos_embeddings = nn.Embedding(config.block_size, config.dim)
        self.speaker_cond_pos = nn.Linear(config.speaker_emb_dim, config.dim, bias=False)
        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)

        self.mask_cache: Optional[Tensor] = None
        self.max_batch_size = -1
        self.max_seq_length = -1

    def setup_spk_cond_mask(self):
        self.spk_cond_mask = torch.zeros((2, 1, self.config.dim), dtype=torch.bool)
        self.spk_cond_mask[0] = 1

    def setup_caches(self, max_batch_size, max_seq_length):
        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
            return
        head_dim = self.config.dim // self.config.n_head
        max_seq_length = find_multiple(max_seq_length, 8)
        self.max_seq_length = max_seq_length
        self.max_batch_size = max_batch_size
        for b in self.layers:
            b.attention.kv_cache = KVCache(
                max_batch_size, max_seq_length, self.config.n_local_heads, head_dim, dtype=self.config.dtype
            )

        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))

    def forward(self, idx: Tensor, spk_emb: Tensor, input_pos: Tensor) -> Tensor:
        mask = self.causal_mask[None, None, input_pos]
        x = (
            self.tok_embeddings(idx)
            + self.pos_embeddings(input_pos)
            # masking for speaker condition free guidance
            + self.speaker_cond_pos(spk_emb) * self.spk_cond_mask
        )

        for i, layer in enumerate(self.layers):
            x = layer(x, input_pos, mask)
        x = self.norm(x)
        logits = self.output(x)
        return logits

    @classmethod
    def from_name(cls, name: str):
        return cls(ModelArgs.from_name(name))


class TransformerBlock(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.attention = Attention(config)
        self.feed_forward = FeedForward(config)
        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
        self.attention_norm = RMSNorm(config.dim, config.norm_eps)

    def forward(self, x: Tensor, input_pos: Tensor, mask: Tensor) -> Tensor:
        h = x + self.attention(self.attention_norm(x), mask, input_pos)
        out = h + self.feed_forward(self.ffn_norm(h))
        return out


class Attention(nn.Module):
    def __init__(self, config: ModelArgs):
        super().__init__()
        assert config.dim % config.n_head == 0

        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
        # key, query, value projections for all heads, but in a batch
        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
        self.wo = nn.Linear(config.dim, config.dim, bias=False)
        self.kv_cache = None

        self.n_head = config.n_head
        self.head_dim = config.head_dim
        self.n_local_heads = config.n_local_heads
        self.dim = config.dim

    def forward(
        self,
        x: Tensor,
        mask: Tensor,
        input_pos: Optional[Tensor] = None,
    ) -> Tensor:
        bsz, seqlen, _ = x.shape

        kv_size = self.n_local_heads * self.head_dim
        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)

        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)

        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))

        if self.kv_cache is not None:
            k, v = self.kv_cache.update(input_pos, k, v)

        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)

        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)

        y = self.wo(y)
        return y


class SwiGLU(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
        self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)

    def forward(self, x: Tensor) -> Tensor:
        return F.silu(self.w1(x)) * self.w3(x)


class FeedForward(nn.Module):
    def __init__(self, config: ModelArgs) -> None:
        super().__init__()
        self.swiglu = SwiGLU(config)
        self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)

    def forward(self, x: Tensor) -> Tensor:
        return self.w2(self.swiglu(x))


class RMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-5):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)

    def forward(self, x: Tensor) -> Tensor:
        output = self._norm(x.float()).type_as(x)
        return output * self.weight