case class LanguageModelModule(tokenEmbedding: Embedding, positionEmbedding: Embedding, encoder: TransformerEncoder, finalNorm: LayerNorm) extends GenericModule[LanguageModelInput, LanguageModelOutput] with Product with Serializable
Transformer based language model module
Initial embedding is the sum of token and position embedding. Token embedding is a learned embedding. Position embedding is also a learned embedding (not sinusoidal etc).
Initial embeddings are fed into layers of transformer blocks. Attention masking is governed by the input similarly as described in chapter 11.3.2.1 in d2l v1.0.0-beta0.
Selected sequence positions in the output of the transformer chain are linearly mapped back into the desired vocabulary size.
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- new LanguageModelModule(tokenEmbedding: Embedding, positionEmbedding: Embedding, encoder: TransformerEncoder, finalNorm: LayerNorm)
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- def apply[S](a: LanguageModelInput)(implicit arg0: Sc[S]): LanguageModelOutput
Alias of forward
Alias of forward
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- val encoder: TransformerEncoder
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- val finalNorm: LayerNorm
- def forward[S](x: LanguageModelInput)(implicit arg0: Sc[S]): LanguageModelOutput
The implementation of the function.
The implementation of the function.
In addition of
xit can also use all thestate to compute its value.- Definition Classes
- LanguageModelModule → GenericModule
- final def getClass(): Class[_ <: AnyRef]
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- final def gradients(loss: Variable, zeroGrad: Boolean = true): Seq[Option[STen]]
Computes the gradient of loss with respect to the parameters.
Computes the gradient of loss with respect to the parameters.
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- final def learnableParameters: Long
Returns the total number of optimizable parameters.
Returns the total number of optimizable parameters.
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- final def parameters: Seq[(Constant, PTag)]
Returns the state variables which need gradient computation.
Returns the state variables which need gradient computation.
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- GenericModule
- val positionEmbedding: Embedding
- def productElementNames: Iterator[String]
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- def state: List[(Constant, PTag)]
List of optimizable, or non-optimizable, but stateful parameters
List of optimizable, or non-optimizable, but stateful parameters
Stateful means that the state is carried over the repeated forward calls.
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- LanguageModelModule → GenericModule
- final def synchronized[T0](arg0: => T0): T0
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- val tokenEmbedding: Embedding
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- Deprecated
(Since version 9)