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Class MultiheadAttentionImpl

Inheritance Relationships

Base Type

Class Documentation

class torch::nn::MultiheadAttentionImpl : public torch::nn::Cloneable<MultiheadAttentionImpl>

Applies the MultiheadAttention function element-wise.

See https://pytorch.org/docs/master/nn.html#torch.nn.MultiheadAttention to learn about the exact behavior of this module.

See the documentation for torch::nn::MultiheadAttentionOptions class to learn what constructor arguments are supported for this module.

Example:

MultiheadAttention model(MultiheadAttentionOptions(20, 10).bias(false));

Public Functions

MultiheadAttentionImpl(int64_t embed_dim, int64_t num_heads)
MultiheadAttentionImpl(const MultiheadAttentionOptions &options_)
std::tuple<Tensor, Tensor> forward(const Tensor &query, const Tensor &key, const Tensor &value, const Tensor &key_padding_mask = {}, bool need_weights = true, const Tensor &attn_mask = {})
void reset() override

reset() must perform initialization of all members with reference semantics, most importantly parameters, buffers and submodules.

void _reset_parameters()

Public Members

MultiheadAttentionOptions options

The options with which this Module was constructed.

bool _qkv_same_embed_dim
Tensor in_proj_weight
Tensor in_proj_bias
Tensor bias_k
Tensor bias_v
Linear out_proj = nullptr
Tensor q_proj_weight
Tensor k_proj_weight
Tensor v_proj_weight
int64_t head_dim

Protected Functions

bool _forward_has_default_args() override

The following three functions allow a module with default arguments in its forward method to be used in a Sequential module.

You should NEVER override these functions manually. Instead, you should use the FORWARD_HAS_DEFAULT_ARGS macro.

unsigned int _forward_num_required_args() override
std::vector<torch::nn::AnyValue> _forward_populate_default_args(std::vector<torch::nn::AnyValue> &&arguments) override

Friends

friend struct torch::nn::AnyModuleHolder

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