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import math |
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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from typing import Optional |
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from michelangelo.models.modules.checkpoint import checkpoint |
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def init_linear(l, stddev): |
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nn.init.normal_(l.weight, std=stddev) |
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if l.bias is not None: |
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nn.init.constant_(l.bias, 0.0) |
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class MultiheadAttention(nn.Module): |
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def __init__( |
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self, |
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*, |
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device: torch.device, |
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dtype: torch.dtype, |
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n_ctx: int, |
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width: int, |
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heads: int, |
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init_scale: float, |
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qkv_bias: bool, |
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flash: bool = False |
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): |
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super().__init__() |
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self.n_ctx = n_ctx |
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self.width = width |
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self.heads = heads |
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self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias, device=device, dtype=dtype) |
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self.c_proj = nn.Linear(width, width, device=device, dtype=dtype) |
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self.attention = QKVMultiheadAttention(device=device, dtype=dtype, heads=heads, n_ctx=n_ctx, flash=flash) |
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init_linear(self.c_qkv, init_scale) |
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init_linear(self.c_proj, init_scale) |
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def forward(self, x): |
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x = self.c_qkv(x) |
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x = checkpoint(self.attention, (x,), (), True) |
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x = self.c_proj(x) |
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return x |
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class QKVMultiheadAttention(nn.Module): |
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def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, n_ctx: int, flash: bool = False): |
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super().__init__() |
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self.device = device |
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self.dtype = dtype |
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self.heads = heads |
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self.n_ctx = n_ctx |
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self.flash = flash |
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def forward(self, qkv): |
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bs, n_ctx, width = qkv.shape |
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attn_ch = width // self.heads // 3 |
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scale = 1 / math.sqrt(math.sqrt(attn_ch)) |
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qkv = qkv.view(bs, n_ctx, self.heads, -1) |
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q, k, v = torch.split(qkv, attn_ch, dim=-1) |
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if self.flash: |
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out = F.scaled_dot_product_attention(q, k, v) |
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else: |
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weight = torch.einsum( |
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"bthc,bshc->bhts", q * scale, k * scale |
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) |
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wdtype = weight.dtype |
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weight = torch.softmax(weight.float(), dim=-1).type(wdtype) |
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out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1) |
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return out |
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class ResidualAttentionBlock(nn.Module): |
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def __init__( |
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self, |
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*, |
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device: torch.device, |
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dtype: torch.dtype, |
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n_ctx: int, |
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width: int, |
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heads: int, |
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init_scale: float = 1.0, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_checkpoint: bool = False |
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): |
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super().__init__() |
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self.use_checkpoint = use_checkpoint |
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self.attn = MultiheadAttention( |
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device=device, |
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dtype=dtype, |
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n_ctx=n_ctx, |
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width=width, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash |
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) |
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self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype) |
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self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale) |
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self.ln_2 = nn.LayerNorm(width, device=device, dtype=dtype) |
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def _forward(self, x: torch.Tensor): |
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x = x + self.attn(self.ln_1(x)) |
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x = x + self.mlp(self.ln_2(x)) |
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return x |
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def forward(self, x: torch.Tensor): |
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return checkpoint(self._forward, (x,), self.parameters(), self.use_checkpoint) |
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class MultiheadCrossAttention(nn.Module): |
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def __init__( |
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self, |
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*, |
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device: torch.device, |
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dtype: torch.dtype, |
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width: int, |
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heads: int, |
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init_scale: float, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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n_data: Optional[int] = None, |
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data_width: Optional[int] = None, |
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): |
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super().__init__() |
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self.n_data = n_data |
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self.width = width |
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self.heads = heads |
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self.data_width = width if data_width is None else data_width |
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self.c_q = nn.Linear(width, width, bias=qkv_bias, device=device, dtype=dtype) |
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self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias, device=device, dtype=dtype) |
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self.c_proj = nn.Linear(width, width, device=device, dtype=dtype) |
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self.attention = QKVMultiheadCrossAttention( |
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device=device, dtype=dtype, heads=heads, n_data=n_data, flash=flash |
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) |
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init_linear(self.c_q, init_scale) |
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init_linear(self.c_kv, init_scale) |
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init_linear(self.c_proj, init_scale) |
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def forward(self, x, data): |
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x = self.c_q(x) |
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data = self.c_kv(data) |
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x = checkpoint(self.attention, (x, data), (), True) |
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x = self.c_proj(x) |
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return x |
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class QKVMultiheadCrossAttention(nn.Module): |
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def __init__(self, *, device: torch.device, dtype: torch.dtype, heads: int, |
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flash: bool = False, n_data: Optional[int] = None): |
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super().__init__() |
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self.device = device |
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self.dtype = dtype |
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self.heads = heads |
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self.n_data = n_data |
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self.flash = flash |
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def forward(self, q, kv): |
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_, n_ctx, _ = q.shape |
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bs, n_data, width = kv.shape |
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attn_ch = width // self.heads // 2 |
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scale = 1 / math.sqrt(math.sqrt(attn_ch)) |
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q = q.view(bs, n_ctx, self.heads, -1) |
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kv = kv.view(bs, n_data, self.heads, -1) |
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k, v = torch.split(kv, attn_ch, dim=-1) |
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if self.flash: |
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out = F.scaled_dot_product_attention(q, k, v) |
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else: |
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weight = torch.einsum( |
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"bthc,bshc->bhts", q * scale, k * scale |
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) |
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wdtype = weight.dtype |
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weight = torch.softmax(weight.float(), dim=-1).type(wdtype) |
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out = torch.einsum("bhts,bshc->bthc", weight, v).reshape(bs, n_ctx, -1) |
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return out |
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class ResidualCrossAttentionBlock(nn.Module): |
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def __init__( |
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self, |
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*, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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n_data: Optional[int] = None, |
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width: int, |
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heads: int, |
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data_width: Optional[int] = None, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False |
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): |
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super().__init__() |
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if data_width is None: |
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data_width = width |
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self.attn = MultiheadCrossAttention( |
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device=device, |
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dtype=dtype, |
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n_data=n_data, |
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width=width, |
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heads=heads, |
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data_width=data_width, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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) |
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self.ln_1 = nn.LayerNorm(width, device=device, dtype=dtype) |
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self.ln_2 = nn.LayerNorm(data_width, device=device, dtype=dtype) |
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self.mlp = MLP(device=device, dtype=dtype, width=width, init_scale=init_scale) |
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self.ln_3 = nn.LayerNorm(width, device=device, dtype=dtype) |
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def forward(self, x: torch.Tensor, data: torch.Tensor): |
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x = x + self.attn(self.ln_1(x), self.ln_2(data)) |
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x = x + self.mlp(self.ln_3(x)) |
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return x |
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class MLP(nn.Module): |
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def __init__(self, *, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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width: int, |
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init_scale: float): |
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super().__init__() |
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self.width = width |
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self.c_fc = nn.Linear(width, width * 4, device=device, dtype=dtype) |
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self.c_proj = nn.Linear(width * 4, width, device=device, dtype=dtype) |
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self.gelu = nn.GELU() |
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init_linear(self.c_fc, init_scale) |
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init_linear(self.c_proj, init_scale) |
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def forward(self, x): |
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return self.c_proj(self.gelu(self.c_fc(x))) |
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class Transformer(nn.Module): |
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def __init__( |
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self, |
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*, |
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device: Optional[torch.device], |
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dtype: Optional[torch.dtype], |
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n_ctx: int, |
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width: int, |
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layers: int, |
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heads: int, |
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init_scale: float = 0.25, |
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qkv_bias: bool = True, |
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flash: bool = False, |
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use_checkpoint: bool = False |
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): |
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super().__init__() |
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self.n_ctx = n_ctx |
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self.width = width |
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self.layers = layers |
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self.resblocks = nn.ModuleList( |
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[ |
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ResidualAttentionBlock( |
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device=device, |
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dtype=dtype, |
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n_ctx=n_ctx, |
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width=width, |
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heads=heads, |
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init_scale=init_scale, |
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qkv_bias=qkv_bias, |
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flash=flash, |
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use_checkpoint=use_checkpoint |
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) |
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for _ in range(layers) |
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] |
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) |
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def forward(self, x: torch.Tensor): |
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for block in self.resblocks: |
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x = block(x) |
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return x |
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