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159 lines
6.1 KiB
159 lines
6.1 KiB
from typing import List |
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import torch |
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from torch import nn, BoolTensor, FloatTensor, LongTensor |
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class GLUTorch(nn.Module): |
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def __init__(self, count_in_out, count_middle): |
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super().__init__() |
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self.gelu = nn.GELU() |
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self.ln0 = nn.LayerNorm(count_in_out) |
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self.ln1 = nn.LayerNorm(count_middle) |
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self.fc0 = nn.Linear(count_in_out, count_middle, bias=False) |
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self.fc1 = nn.Linear(count_in_out, count_middle, bias=False) |
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self.fc2 = nn.Linear(count_middle, count_in_out, bias=False) |
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def forward(self, z: FloatTensor) -> FloatTensor: |
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z = self.ln0.forward(z) |
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w = self.fc0.forward(z) |
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w = self.gelu.forward(w) |
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v = self.fc1.forward(z) |
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z = self.ln1.forward(w * v) |
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z = self.fc2.forward(z) |
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return z |
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class AttentionTorch(nn.Module): |
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def __init__(self, head_count: int, embed_count: int): |
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super().__init__() |
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self.head_count = head_count |
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self.embed_count = embed_count |
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self.head_dim = embed_count // head_count |
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self.k_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False) |
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self.v_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False) |
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self.q_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False) |
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self.out_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False) |
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def forward(self, |
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keys: FloatTensor, |
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values: FloatTensor, |
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queries: FloatTensor, |
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attention_mask: BoolTensor |
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) -> FloatTensor: |
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batch_count = keys.shape[0] |
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# b(hc)1q -> bqhc |
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# print(keys.shape, "keys", values.shape, "values", queries.shape, "queries") |
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keys = keys.transpose(1, 3) |
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keys = keys.reshape(keys.shape[:2] + (self.head_count, -1)) |
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# b(hc)1q -> bchq |
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shape = (batch_count, self.head_count, self.head_dim, -1) |
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values = values.reshape(shape) |
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values = values.transpose(1, 2) |
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queries = queries.reshape(shape) |
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queries = queries.transpose(1, 2) |
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# print(keys.shape, "keys", values.shape, "values", queries.shape, "queries") |
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attention_bias = torch.where( |
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attention_mask, |
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torch.zeros([1, 1]), |
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torch.ones([1, 1]) * (-torch.inf), |
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) |
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attention_weights: FloatTensor = torch.einsum( |
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'bchq,bkhc->bkhq', |
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queries / self.head_dim ** 0.5, |
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keys |
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) |
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attention_weights += attention_bias[:, :, None, None] |
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attention_weights = torch.softmax(attention_weights, 1) |
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# print(attention_weights.shape, "attention_weights") |
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hidden_state: FloatTensor = torch.einsum( |
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"bkhq,bchk->bchq", |
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attention_weights, |
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values |
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) |
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# bchq -> b(hc)1q |
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# print(hidden_state.shape, "hidden_state") |
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hidden_state = hidden_state.transpose(1, 2) |
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hidden_state = hidden_state.reshape(batch_count, self.embed_count, 1, -1) |
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hidden_state = self.out_proj.forward(hidden_state) |
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# print(hidden_state.shape, "hidden_state") |
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return hidden_state |
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class EncoderSelfAttentionTorch(AttentionTorch): |
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def forward( |
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self, |
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encoder_state: FloatTensor, |
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attention_mask: BoolTensor |
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) -> FloatTensor: |
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encoder_state = encoder_state.transpose(1, 2).unsqueeze(2) |
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# print(encoder_state.shape, "encoder_state") |
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keys = self.k_proj.forward(encoder_state) |
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values = self.v_proj.forward(encoder_state) |
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queries = self.q_proj.forward(encoder_state) |
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return super().forward(keys, values, queries, attention_mask) |
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class EncoderLayerTorch(nn.Module): |
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def __init__(self, embed_count: int, head_count: int, glu_embed_count: int): |
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super().__init__() |
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self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.self_attn = EncoderSelfAttentionTorch(head_count, embed_count) |
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self.self_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.glu = GLUTorch(embed_count, glu_embed_count) |
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def forward( |
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self, |
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encoder_state: FloatTensor, |
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attention_mask: BoolTensor |
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) -> FloatTensor: |
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residual = encoder_state |
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encoder_state = self.pre_self_attn_layer_norm.forward(encoder_state) |
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encoder_state = self.self_attn.forward(encoder_state, attention_mask) |
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encoder_state = encoder_state.transpose(1, 3).squeeze(2) |
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encoder_state = self.self_attn_layer_norm.forward(encoder_state) |
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encoder_state = residual + encoder_state |
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residual = encoder_state |
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encoder_state = self.glu.forward(encoder_state) |
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encoder_state = residual + encoder_state |
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return encoder_state |
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class DalleBartEncoderTorch(nn.Module): |
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def __init__(self, |
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layer_count: int, |
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embed_count: int, |
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attention_head_count: int, |
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text_vocab_count: int, |
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text_token_count: int, |
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glu_embed_count: int |
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): |
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super().__init__() |
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self.embed_tokens = nn.Embedding(text_vocab_count, embed_count) |
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self.embed_positions = nn.Embedding(text_token_count, embed_count) |
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self.layers: List[EncoderLayerTorch] = nn.ModuleList([ |
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EncoderLayerTorch( |
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embed_count = embed_count, |
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head_count = attention_head_count, |
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glu_embed_count = glu_embed_count |
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) |
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for _ in range(layer_count) |
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]) |
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self.layernorm_embedding = nn.LayerNorm(embed_count) |
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self.final_ln = nn.LayerNorm(embed_count) |
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def forward(self, text_tokens: LongTensor) -> FloatTensor: |
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attention_mask = text_tokens.not_equal(1) |
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batch_count, token_count = text_tokens.shape |
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pose_tokens = torch.stack([torch.arange(token_count)] * batch_count) |
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encoder_state = ( |
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self.embed_tokens.forward(text_tokens) + |
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self.embed_positions.forward(pose_tokens) |
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) |
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encoder_state = self.layernorm_embedding.forward(encoder_state) |
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for layer in self.layers: |
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encoder_state = layer.forward(encoder_state, attention_mask) |
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encoder_state = self.final_ln.forward(encoder_state) |
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return encoder_state |