MixtralMlpMixin()这个函数里面moe只是计算专家的logits但是没看到分发逻辑

[AlenjandroWang]

MixtralMlpMixin()这个函数里面moe只是计算专家的logits但是没看到分发逻辑

[1049451037]

在这里：

SwissArmyTransformer/sat/transformer_defaults.py

Lines 156 to 202 in eb4fac9

	def mlp_forward_default(self, hidden_states, expert_id=-1, **kw_args):
	if self.transformer.num_experts == 1 or expert_id > -1:
	self = self.transformer.layers[kw_args['layer_id']].mlp
	suffix = f"_{expert_id}" if expert_id > 0 else ""
	if self.is_gated_mlp:
	intermediate_parallel = getattr(self, "dense_h_to_4h"+suffix)(hidden_states)
	gated_intermediate_parallel = getattr(self, "dense_h_to_4h_gate"+suffix)(hidden_states)
	intermediate_parallel = self.activation_func(gated_intermediate_parallel) * intermediate_parallel
	output = getattr(self, "dense_4h_to_h"+suffix)(intermediate_parallel)
	else:
	intermediate_parallel = getattr(self, "dense_h_to_4h"+suffix)(hidden_states)
	intermediate_parallel = self.activation_func(intermediate_parallel)
	output = getattr(self, "dense_4h_to_h"+suffix)(intermediate_parallel)
	return output
	else:
	mlp_forward = self.hooks.get('mlp_forward', partial(mlp_forward_default, self))
	routing_forward = self.hooks.get('routing_forward', partial(routing_forward_default, self))
	self = self.transformer.layers[kw_args['layer_id']].mlp
	fwd_weight, fwd_idx = routing_forward(hidden_states, **kw_args)
	# Adapted from mixtral-8x7b https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/modeling_mixtral.py
	batch_size, sequence_length, hidden_dim = hidden_states.shape
	hidden_states = hidden_states.view(-1, hidden_dim)
	final_hidden_states = torch.zeros(
	(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
	)
	# One hot encode the selected experts to create an expert mask
	# this will be used to easily index which expert is going to be sollicitated
	expert_mask = torch.nn.functional.one_hot(fwd_idx, num_classes=self.num_experts).permute(2, 1, 0)
	# Loop over all available experts in the model and perform the computation on each expert
	for expert_idx in range(self.num_experts):
	idx, top_x = torch.where(expert_mask[expert_idx])
	if top_x.shape[0] == 0:
	continue
	# in torch it is faster to index using lists than torch tensors
	top_x_list = top_x.tolist()
	idx_list = idx.tolist()
	# Index the correct hidden states and compute the expert hidden state for
	# the current expert. We need to make sure to multiply the output hidden
	# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
	current_state = hidden_states[top_x_list] # I don't know why using hidden_states[None, top_x_list].reshape(-1, hidden_dim)
	current_hidden_states = mlp_forward(current_state, expert_id=expert_idx, **kw_args) * fwd_weight[top_x_list, idx_list, None]
	# However `index_add_` only support torch tensors for indexing so we'll use
	# the `top_x` tensor here.
	final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
	output = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
	return output