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dont always dropout!
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model.py
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model.py
@ -69,7 +69,7 @@ class CausalSelfAttention(nn.Module):
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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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y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout, is_causal=True)
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y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
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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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