forked from jiuyuan/CPM-9G-8B
101 lines
3.7 KiB
Python
101 lines
3.7 KiB
Python
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import math
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from .position_embedding import RotaryEmbedding
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class Embedding(torch.nn.Module):
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def __init__(
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self,
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vocab_size: int,
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embedding_size: int,
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dtype: torch.dtype = torch.half,
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scale: bool = True,
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init_mean: float = 0.0,
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init_std: float = 1,
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):
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super().__init__()
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self.dim_model = embedding_size
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self.weight = torch.nn.parameter.Parameter(torch.empty(vocab_size, embedding_size, dtype=dtype))
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self.scale = scale
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def forward(self, ids: torch.Tensor):
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"""
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Args:
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ids (:obj:`torch.Tensor` of shape ``(batch_size, seq_len)``): Indices of input sequence tokens.
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Return:
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:obj:`torch.Tensor` of shape ``(batch_size, seq_len, embedding_size)``: The embedding output.
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""" # noqa: E501
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if self.scale:
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embeds = F.embedding(ids, self.weight) / math.sqrt(self.dim_model)
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else:
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embeds = F.embedding(ids, self.weight)
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return embeds.clone()
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def projection(self, x: torch.Tensor):
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"""
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Projection based on embedding's weight. For example, embedding map vocab_size to embed_size, than projection map embed_size back to vocab_size.
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Args:
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x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_model)``): Input of projection
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Returns:
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:obj:`torch.Tensor` of shape ``(batch, seq_len, vocab_output_size)``: The projection output.
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""" # noqa: E501
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if self.scale:
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logits = F.linear(x / math.sqrt(self.dim_model), self.weight)
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else:
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logits = F.linear(x, self.weight)
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return logits
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class EmbeddingExt(torch.nn.Module):
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def __init__(
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self,
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vocab_size: int,
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embedding_size: int,
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dtype: torch.dtype = torch.half,
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init_mean: float = 0.0,
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init_std: float = 1,
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distance_scale: int = 16,
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):
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super().__init__()
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self.dim_model = embedding_size
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self.rotary_emb = RotaryEmbedding(dim=embedding_size, distance_scale=distance_scale, dtype=dtype)
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self.weight = torch.nn.parameter.Parameter(
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torch.empty(vocab_size, embedding_size, dtype=dtype),
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)
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def forward(self, ids: torch.Tensor, ids_sub: torch.Tensor):
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"""
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Args:
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ids (:obj:`torch.Tensor` of shape ``(batch_size, seq_len)``): Indices of input sequence tokens.
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ids (:obj:`torch.Tensor` of shape ``(batch_size)``): Subscript of input sequence tokens.
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Return:
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:obj:`torch.Tensor` of shape ``(batch_size, seq_len, embedding_size)``: The embedding output.
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""" # noqa: E501
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embeds = F.embedding(ids, self.weight) / math.sqrt(self.dim_model)
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return self.rotary_emb(embeds, ids_sub)
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def projection(self, x: torch.Tensor, ext_table: Optional[torch.Tensor] = None):
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"""
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Projection based on embedding's weight. For example, embedding map vocab_size to embed_size, than projection map embed_size back to vocab_size.
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Args:
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x (:obj:`torch.Tensor` of shape ``(batch, seq_len, dim_model)``): Input of projection
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ext_table (:obj:`torch.Tensor` of shape ``(ext_table_size, dim_model)``): Ext vocab table.
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Returns:
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:obj:`torch.Tensor` of shape ``(batch, seq_len, vocab_size + ext_table_size)``: The projection output.
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""" # noqa: E501
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logits = F.linear(x / math.sqrt(self.dim_model), self.weight)
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if ext_table is not None:
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logits_ext = F.linear(x, ext_table)
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logits = torch.cat([logits, logits_ext], dim=-1)
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return logits
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