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""" |
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A collection of positional encoding modules. |
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""" |
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import torch |
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import math |
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class LearnedPosEncoding(torch.nn.Module): |
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""" |
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Basic learned positional encoding |
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""" |
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def __init__(self, hidden_dim, context_window): |
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super().__init__() |
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self.pe = torch.nn.Embedding( |
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num_embeddings=context_window, embedding_dim=hidden_dim |
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) |
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def forward(self, x): |
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""" |
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Takes the input tensor and returns it positionally encoded. |
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Args: |
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x: torch.tensor(B, S, H) |
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Returns: |
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x: torch.tensor(B, S, H) |
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""" |
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if len(x.shape) >= 2: |
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return x + (self.pe(torch.arange(x.size(1), device=x.device)).unsqueeze(0)) |
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else: |
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return x + self.pe(torch.arange(x.size(1), device=x.device)) |
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class IdentityEncoding(torch.nn.Module): |
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""" |
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In case RoPE is used, there is no need for an initial positional encoding. |
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""" |
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def __init__(self): |
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super().__init__() |
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def forward(self, x): |
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""" |
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Returns the input tensor as is. |
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""" |
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return x |
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class SinCosPosEncoding( |
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torch.nn.Module |
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): |
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"""SinCos encoding taken from: |
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\\url{https://github.com/pytorch/examples/blob/main/word_language_model/model.py#L65} |
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As used in the Vaiswani et al. paper...""" |
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def __init__(self, hidden_dim, context_window): |
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"""Set up the pe buffer etc.""" |
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super().__init__() |
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pe = torch.zeros(context_window, hidden_dim) |
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position = torch.arange(0, context_window, dtype=torch.float).unsqueeze(1) |
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div_term = torch.exp(torch.arange(0, hidden_dim, 2).float() * (-math.log(10000.0) / hidden_dim)) |
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pe[:, 0::2] = torch.sin(position * div_term) |
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pe[:, 1::2] = torch.cos(position * div_term) |
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pe = pe.unsqueeze(0) |
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self.pe = torch.nn.Parameter(pe) |
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self.pe.requires_grad = False |
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def forward(self, x): |
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"""Add the pe to the input tensor.""" |
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return x + self.pe[:, :x.size(1)] |
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POS_ENCODING_DICT = { |
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"learned": lambda dim, size, **_: LearnedPosEncoding( |
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hidden_dim=dim, context_window=size |
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), |
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"rope": lambda **_: IdentityEncoding(), |
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"none": lambda **_: IdentityEncoding(), |
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"sincos": lambda dim, size, **_: SinCosPosEncoding( |
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hidden_dim=dim, context_window=size |
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), |
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} |
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def build_positional_encodings(model_cfg): |
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""" |
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Given the positional encoding config, build it. |
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Args: |
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cfg: cfg |
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Returns: |
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positional_encodings: positional_encodings_instance |
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""" |
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return POS_ENCODING_DICT[model_cfg["positional_encoding_type"]]( |
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dim=model_cfg["hidden_dim"], size=model_cfg["context_window"] |
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) |
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