| # flake8: noqa: E266, C417, B950 |
| from dataclasses import dataclass |
| from typing import Optional |
| |
| import torch |
| import torch.nn as nn |
| from torch import Tensor |
| from torch.nn import functional as F |
| |
| |
| def find_multiple(n: int, k: int) -> int: |
| 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 |
| intermediate_size: int = None |
| n_local_heads: int = -1 |
| head_dim: int = 64 |
| rope_base: float = 10000 |
| norm_eps: float = 1e-5 |
| |
| 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 |
| |
| @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) |
| ] |
| |
| # We may have two or more configs matched (e.g. "7B" and "Mistral-7B"). Find the best config match, |
| # take longer name (as it have more symbols matched) |
| if len(config) > 1: |
| config.sort(key=len, reverse=True) |
| assert len(config[0]) != len( |
| config[1] |
| ), name # make sure only one 'best' match |
| |
| return cls(**transformer_configs[config[0]]) |
| |
| |
| transformer_configs = { |
| "CodeLlama-7b-Python-hf": dict( |
| block_size=16384, vocab_size=32000, n_layer=32, dim=4096, rope_base=1000000 |
| ), |
| "7B": dict(n_layer=32, n_head=32, dim=4096), |
| "13B": dict(n_layer=40, n_head=40, dim=5120), |
| "30B": dict(n_layer=60, n_head=52, dim=6656), |
| "34B": dict( |
| n_layer=48, |
| n_head=64, |
| dim=8192, |
| vocab_size=32000, |
| n_local_heads=8, |
| intermediate_size=22016, |
| rope_base=1000000, |
| ), # CodeLlama-34B-Python-hf |
| "70B": dict( |
| n_layer=80, n_head=64, dim=8192, n_local_heads=8, intermediate_size=28672 |
| ), |
| "Mistral-7B": dict( |
| n_layer=32, |
| n_head=32, |
| n_local_heads=8, |
| dim=4096, |
| intermediate_size=14336, |
| vocab_size=32000, |
| ), |
| } |
| |
| |
| class KVCache(nn.Module): |
| def __init__( |
| self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16 |
| ): |
| 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.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.freqs_cis: Optional[Tensor] = None |
| self.mask_cache: Optional[Tensor] = None |
| self.max_batch_size = -1 |
| self.max_seq_length = -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 |
| ) |
| |
| self.freqs_cis = precompute_freqs_cis( |
| self.config.block_size, |
| self.config.dim // self.config.n_head, |
| self.config.rope_base, |
| ) |
| self.causal_mask = torch.tril( |
| torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool) |
| ) |
| |
| def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor: |
| assert self.freqs_cis is not None, "Caches must be initialized first" |
| mask = self.causal_mask[None, None, input_pos] |
| freqs_cis = self.freqs_cis[input_pos] |
| x = self.tok_embeddings(idx) |
| |
| for i, layer in enumerate(self.layers): |
| x = layer(x, input_pos, freqs_cis, 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, freqs_cis: Tensor, mask: Tensor |
| ) -> Tensor: |
| h = x + self.attention(self.attention_norm(x), freqs_cis, 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 |
| self._register_load_state_dict_pre_hook(self.load_hook) |
| |
| def load_hook(self, state_dict, prefix, *args): |
| if prefix + "wq.weight" in state_dict: |
| wq = state_dict.pop(prefix + "wq.weight") |
| wk = state_dict.pop(prefix + "wk.weight") |
| wv = state_dict.pop(prefix + "wv.weight") |
| state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv]) |
| |
| def forward( |
| self, |
| x: Tensor, |
| freqs_cis: 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 = apply_rotary_emb(q, freqs_cis) |
| k = apply_rotary_emb(k, freqs_cis) |
| |
| 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 FeedForward(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) |
| self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False) |
| |
| def forward(self, x: Tensor) -> Tensor: |
| return self.w2(F.silu(self.w1(x)) * self.w3(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 |
| |
| |
| def precompute_freqs_cis(seq_len: int, n_elem: int, base: int = 10000) -> Tensor: |
| freqs = 1.0 / ( |
| base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem) |
| ) |
| t = torch.arange(seq_len, device=freqs.device) |
| freqs = torch.outer(t, freqs) |
| freqs_cis = torch.polar(torch.ones_like(freqs), freqs) |
| cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1) |
| return cache.to(dtype=torch.bfloat16) |
| |
| |
| def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor: |
| xshaped = x.float().reshape(*x.shape[:-1], -1, 2) |
| freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2) |
| x_out2 = torch.stack( |
| [ |
| xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1], |
| xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1], |
| ], |
| -1, |
| ) |
| |
| x_out2 = x_out2.flatten(3) |
| return x_out2.type_as(x) |
