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274 lines
9.1 KiB
Python
274 lines
9.1 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# This software may be used and distributed according to the terms of the GNU General Public License version 3.
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# Copyright by Steve Manuatu
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# https://github.com/venuatu
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from typing import Optional, Tuple
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from dataclasses import dataclass
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import math
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import torch
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from torch import nn
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import torch.nn.functional as F
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from torch.nn.utils import skip_init
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from tqdm import tqdm
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@dataclass
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class ModelArgs:
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dim: int = 512
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n_layers: int = 8
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n_heads: int = 8
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vocab_size: int = -1 # defined later by tokenizer
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multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2
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norm_eps: float = 1e-5
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max_batch_size: int = 32
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max_seq_len: int = 1024
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x):
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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output = self._norm(x.float()).type_as(x)
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return output * self.weight
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def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
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freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
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t = torch.arange(end, device=freqs.device) # type: ignore
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freqs = torch.outer(t, freqs).float() # type: ignore
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freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
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return freqs_cis
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def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
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ndim = x.ndim
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assert 0 <= 1 < ndim
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assert freqs_cis.shape == (x.shape[1], x.shape[-1])
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shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
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return freqs_cis.view(*shape)
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def apply_rotary_emb(
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xq: torch.Tensor,
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xk: torch.Tensor,
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freqs_cis: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
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xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
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freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
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xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
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xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
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return xq_out.type_as(xq), xk_out.type_as(xk)
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.n_local_heads = args.n_heads # // fs_init.get_model_parallel_world_size()
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self.head_dim = args.dim // args.n_heads
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self.wq = skip_init(nn.Linear,
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args.dim,
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args.n_heads * self.head_dim,
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bias=False,
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)
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self.wk = skip_init(nn.Linear,
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args.dim,
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args.n_heads * self.head_dim,
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bias=False,
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)
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self.wv = skip_init(nn.Linear,
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args.dim,
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args.n_heads * self.head_dim,
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bias=False,
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)
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self.wo = skip_init(nn.Linear,
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args.n_heads * self.head_dim,
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args.dim,
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bias=False,
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)
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self.cache_k = torch.zeros(
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(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.head_dim)
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).cuda()
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self.cache_v = torch.zeros(
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(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.head_dim)
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).cuda()
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def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
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bsz, seqlen, _ = x.shape
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xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
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xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xv = xv.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis)
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self.cache_k = self.cache_k.to(xq)
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self.cache_v = self.cache_v.to(xq)
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self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk
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self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv
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keys = self.cache_k[:bsz, : start_pos + seqlen]
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values = self.cache_v[:bsz, : start_pos + seqlen]
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xq = xq.transpose(1, 2)
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keys = keys.transpose(1, 2)
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values = values.transpose(1, 2)
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scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim)
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if mask is not None:
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scores = scores + mask # (bs, n_local_heads, slen, cache_len + slen)
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scores = F.softmax(scores.float(), dim=-1).type_as(xq)
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output = torch.matmul(scores, values) # (bs, n_local_heads, slen, head_dim)
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output = output.transpose(
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1, 2
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).contiguous().view(bsz, seqlen, -1)
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return self.wo(output)
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class FeedForward(nn.Module):
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def __init__(
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self,
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dim: int,
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hidden_dim: int,
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multiple_of: int,
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):
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super().__init__()
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hidden_dim = int(2 * hidden_dim / 3)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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self.w1 = skip_init(nn.Linear,
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dim,
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hidden_dim,
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bias=False,
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)
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self.w2 = skip_init(nn.Linear,
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hidden_dim,
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dim,
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bias=False,
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)
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self.w3 = skip_init(nn.Linear,
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dim,
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hidden_dim,
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bias=False,
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)
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def forward(self, x):
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class TransformerBlock(nn.Module):
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def __init__(self, layer_id: int, args: ModelArgs):
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super().__init__()
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self.n_heads = args.n_heads
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self.dim = args.dim
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self.head_dim = args.dim // args.n_heads
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self.attention = Attention(args)
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self.feed_forward = FeedForward(
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dim=args.dim, hidden_dim=4 * args.dim, multiple_of=args.multiple_of
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)
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self.layer_id = layer_id
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self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
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self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
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def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
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h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_cis, mask)
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out = h + self.feed_forward.forward(self.ffn_norm(h))
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return out
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# https://github.com/gmorenz/llama/commit/4daf7f1a2f2bb22208b5d464bc2a18511d54408d
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def move_parameters_to_gpu(module):
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if not hasattr(module, "saved"):
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module.saved = module._parameters.copy()
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for k, param in module.saved.items():
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if param is not None:
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module._parameters[k] = param.to("cuda", non_blocking=True)
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for child in module.children():
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move_parameters_to_gpu(child)
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def move_parameters_to_cpu(module):
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for k, param in module.saved.items():
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del module._parameters[k]
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module._parameters[k] = param
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for child in module.children():
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move_parameters_to_cpu(child)
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class Transformer(nn.Module):
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def __init__(self, params: ModelArgs):
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super().__init__()
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self.params = params
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self.vocab_size = params.vocab_size
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self.n_layers = params.n_layers
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self.tok_embeddings = skip_init(nn.Embedding,
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params.vocab_size,
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params.dim,
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)
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self.layers = torch.nn.ModuleList()
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for layer_id in range(params.n_layers):
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self.layers.append(TransformerBlock(layer_id, params))
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self.layer_locations = [None] * len(self.layers)
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self.norm = RMSNorm(params.dim, eps=params.norm_eps).cuda()
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self.output = skip_init(nn.Linear,
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params.dim,
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params.vocab_size,
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bias=False,
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).cuda()
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self.freqs_cis = precompute_freqs_cis(
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self.params.dim // self.params.n_heads, self.params.max_seq_len * 2
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).cuda()
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@torch.inference_mode()
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def forward(self, tokens: torch.Tensor, start_pos: int):
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use_gpu = True # start_pos == 0
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_bsz, seqlen = tokens.shape
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h = self.tok_embeddings(tokens)
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self.freqs_cis = self.freqs_cis
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freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen]
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if use_gpu:
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h = h.cuda()
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mask = None
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if seqlen > 1:
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mask = torch.full(
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(1, 1, seqlen, seqlen), float("-inf"), device=tokens.device
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)
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mask = torch.triu(mask, diagonal=start_pos + 1).type_as(h)
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if use_gpu and mask is not None:
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mask = mask.cuda()
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for layer in tqdm(self.layers, desc="flayers", leave=True):
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if use_gpu:
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move_parameters_to_gpu(layer)
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h = layer(h, start_pos, freqs_cis, mask)
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if use_gpu:
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move_parameters_to_cpu(layer)
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h = self.norm(h)
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if use_gpu:
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del mask
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torch.cuda.empty_cache()
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output = self.output(h[:, -1, :]) # only compute last logits
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return output.float()
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