45 lines
2.0 KiB
Python
45 lines
2.0 KiB
Python
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# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mixtral/modeling_mixtral.py
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import torch
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from transformers import MixtralModel, MixtralConfig
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from transformers.models.mixtral.modeling_mixtral import load_balancing_loss_func, MixtralSparseMoeBlock
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# This is required because batch sizes > 1 throws errors
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def Fixed_MixtralSparseMoeBlock_forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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""" """
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batch_size, sequence_length, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.reshape(-1, hidden_dim) # was view()
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# router_logits: (batch * sequence_length, n_experts)
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router_logits = self.gate(hidden_states)
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routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
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routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
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routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
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# we cast back to the input dtype
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routing_weights = routing_weights.to(hidden_states.dtype)
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final_hidden_states = torch.zeros(
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(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
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)
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expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
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for expert_idx in range(self.num_experts):
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expert_layer = self.experts[expert_idx]
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idx, top_x = torch.where(expert_mask[expert_idx])
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if top_x.shape[0] == 0:
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continue
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top_x_list = top_x.tolist()
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idx_list = idx.tolist()
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current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
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current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
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final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
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final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
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return final_hidden_states, router_logits
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Original_MixtralSparseMoeBlock_forward = MixtralSparseMoeBlock.forward
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MixtralSparseMoeBlock.forward = Fixed_MixtralSparseMoeBlock_forward
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