234 lines
9.2 KiB
Python
234 lines
9.2 KiB
Python
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"""
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Much of this code is adapted from Andrej Karpathy's NanoGPT
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(https://github.com/karpathy/nanoGPT)
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"""
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import math
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from dataclasses import dataclass
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import torch
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from coqpit import Coqpit
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from torch import nn
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from torch.nn import functional as F
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class LayerNorm(nn.Module):
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"""LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False"""
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def __init__(self, ndim, bias):
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super().__init__()
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self.weight = nn.Parameter(torch.ones(ndim))
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self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
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def forward(self, x):
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return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
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class CausalSelfAttention(nn.Module):
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def __init__(self, config):
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super().__init__()
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assert config.n_embd % config.n_head == 0
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# key, query, value projections for all heads, but in a batch
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self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
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# output projection
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self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
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# regularization
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self.attn_dropout = nn.Dropout(config.dropout)
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self.resid_dropout = nn.Dropout(config.dropout)
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self.n_head = config.n_head
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self.n_embd = config.n_embd
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self.dropout = config.dropout
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# flash attention make GPU go brrrrr but support is only in PyTorch nightly and still a bit scary
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self.flash = hasattr(torch.nn.functional, "scaled_dot_product_attention")
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if not self.flash:
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# print("WARNING: using slow attention. Flash Attention atm needs PyTorch nightly and dropout=0.0")
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# causal mask to ensure that attention is only applied to the left in the input sequence
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self.register_buffer(
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"bias",
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torch.tril(torch.ones(config.block_size, config.block_size)).view(
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1, 1, config.block_size, config.block_size
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),
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)
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def forward(self, x, past_kv=None, use_cache=False):
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B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
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# calculate query, key, values for all heads in batch and move head forward to be the batch dim
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q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
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k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
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q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
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v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
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if past_kv is not None:
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past_key = past_kv[0]
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past_value = past_kv[1]
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k = torch.cat((past_key, k), dim=-2)
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v = torch.cat((past_value, v), dim=-2)
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FULL_T = k.shape[-2]
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if use_cache is True:
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present = (k, v)
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else:
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present = None
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# causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
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if self.flash:
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# efficient attention using Flash Attention CUDA kernels
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if past_kv is not None:
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# When `past_kv` is provided, we're doing incremental decoding and `q.shape[2] == 1`: q only contains
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# the query for the last token. scaled_dot_product_attention interprets this as the first token in the
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# sequence, so if is_causal=True it will mask out all attention from it. This is not what we want, so
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# to work around this we set is_causal=False.
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is_causal = False
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else:
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is_causal = True
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# efficient attention using Flash Attention CUDA kernels
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y = torch.nn.functional.scaled_dot_product_attention(q, k, v, dropout_p=self.dropout, is_causal=is_causal)
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else:
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# manual implementation of attention
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att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
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att = att.masked_fill(self.bias[:, :, FULL_T - T : FULL_T, :FULL_T] == 0, float("-inf"))
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att = F.softmax(att, dim=-1)
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att = self.attn_dropout(att)
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y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
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y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
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# output projection
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y = self.resid_dropout(self.c_proj(y))
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return (y, present)
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class MLP(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
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self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
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self.dropout = nn.Dropout(config.dropout)
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self.gelu = nn.GELU()
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def forward(self, x):
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x = self.c_fc(x)
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x = self.gelu(x)
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x = self.c_proj(x)
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x = self.dropout(x)
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return x
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class Block(nn.Module):
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def __init__(self, config, layer_idx):
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super().__init__()
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self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
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self.attn = CausalSelfAttention(config)
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self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
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self.mlp = MLP(config)
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self.layer_idx = layer_idx
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def forward(self, x, past_kv=None, use_cache=False):
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attn_output, prev_kvs = self.attn(self.ln_1(x), past_kv=past_kv, use_cache=use_cache)
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x = x + attn_output
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x = x + self.mlp(self.ln_2(x))
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return (x, prev_kvs)
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@dataclass
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class GPTConfig(Coqpit):
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block_size: int = 1024
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input_vocab_size: int = 10_048
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output_vocab_size: int = 10_048
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n_layer: int = 12
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n_head: int = 12
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n_embd: int = 768
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dropout: float = 0.0
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bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
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class GPT(nn.Module):
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def __init__(self, config):
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super().__init__()
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assert config.input_vocab_size is not None
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assert config.output_vocab_size is not None
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assert config.block_size is not None
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self.config = config
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self.transformer = nn.ModuleDict(
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dict(
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wte=nn.Embedding(config.input_vocab_size, config.n_embd),
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wpe=nn.Embedding(config.block_size, config.n_embd),
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drop=nn.Dropout(config.dropout),
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h=nn.ModuleList([Block(config, idx) for idx in range(config.n_layer)]),
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ln_f=LayerNorm(config.n_embd, bias=config.bias),
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)
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)
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self.lm_head = nn.Linear(config.n_embd, config.output_vocab_size, bias=False)
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def get_num_params(self, non_embedding=True):
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"""
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Return the number of parameters in the model.
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For non-embedding count (default), the position embeddings get subtracted.
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The token embeddings would too, except due to the parameter sharing these
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params are actually used as weights in the final layer, so we include them.
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"""
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n_params = sum(p.numel() for p in self.parameters())
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if non_embedding:
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n_params -= self.transformer.wte.weight.numel()
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n_params -= self.transformer.wpe.weight.numel()
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return n_params
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def forward(self, idx, merge_context=False, past_kv=None, position_ids=None, use_cache=False):
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device = idx.device
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_, t = idx.size()
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if past_kv is not None:
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assert t == 1
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tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
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else:
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if merge_context:
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assert idx.shape[1] >= 256 + 256 + 1
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t = idx.shape[1] - 256
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else:
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assert (
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t <= self.config.block_size
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), f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
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# forward the GPT model itself
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if merge_context:
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tok_emb = torch.cat(
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[
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self.transformer.wte(idx[:, :256]) + self.transformer.wte(idx[:, 256 : 256 + 256]),
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self.transformer.wte(idx[:, 256 + 256 :]),
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],
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dim=1,
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)
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else:
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tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
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if past_kv is None:
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past_length = 0
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past_kv = tuple([None] * len(self.transformer.h))
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else:
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past_length = past_kv[0][0].size(-2)
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if position_ids is None:
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position_ids = torch.arange(past_length, t + past_length, dtype=torch.long, device=device)
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position_ids = position_ids.unsqueeze(0) # shape (1, t)
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assert position_ids.shape == (1, t)
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pos_emb = self.transformer.wpe(position_ids) # position embeddings of shape (1, t, n_embd)
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x = self.transformer.drop(tok_emb + pos_emb)
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new_kv = () if use_cache else None
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for _, (block, past_layer_kv) in enumerate(zip(self.transformer.h, past_kv)):
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x, kv = block(x, past_kv=past_layer_kv, use_cache=use_cache)
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if use_cache:
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new_kv = new_kv + (kv,)
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x = self.transformer.ln_f(x)
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# inference-time mini-optimization: only forward the lm_head on the very last position
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logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
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return (logits, new_kv)
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