95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import Tensor
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from astrai.factory import BaseFactory
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from astrai.model.components.linear import Linear
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class FFNFactory(BaseFactory[nn.Module]):
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@classmethod
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def create(cls, ffn_type: str, dim: int, dim_ffn: int, **kwargs) -> nn.Module:
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return super().create(ffn_type, dim, dim_ffn, **kwargs)
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@FFNFactory.register("mlp")
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class MLP(nn.Module):
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def __init__(self, dim: int, dim_feed_forward: int, **kwargs):
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super().__init__()
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self.up = Linear(dim, dim_feed_forward)
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self.gate = Linear(dim, dim_feed_forward)
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self.down = Linear(dim_feed_forward, dim)
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def forward(self, x: Tensor) -> Tensor:
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gated = self.up(x) * F.silu(self.gate(x))
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out = self.down(gated)
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return out
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@FFNFactory.register("moe")
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class DeepSeekMoE(nn.Module):
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def __init__(
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self,
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dim: int,
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dim_feed_forward: int,
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n_routed_experts: int,
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n_shared_experts: int = 1,
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n_activated_experts: int = 2,
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topk_method: str = "greedy",
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**kwargs,
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):
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super().__init__()
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self.dim = dim
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self.n_routed_experts = n_routed_experts
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self.n_shared_experts = n_shared_experts
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self.n_activated_experts = n_activated_experts
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self.topk_method = topk_method
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self.router = Linear(dim, n_routed_experts, bias=False)
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self.shared_experts = nn.ModuleList(
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[MLP(dim, dim_feed_forward) for _ in range(n_shared_experts)]
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)
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self.routed_experts = nn.ModuleList(
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[MLP(dim, dim_feed_forward) for _ in range(n_routed_experts)]
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)
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def forward(self, x: Tensor) -> Tensor:
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bsz, seq_len, dim = x.shape
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x_flat = x.view(-1, dim)
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shared_out = self._shared_forward(x_flat)
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routed_out = self._routed_forward(x_flat)
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out = (shared_out + routed_out).view(bsz, seq_len, dim)
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return out
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def _shared_forward(self, x: Tensor) -> Tensor:
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if self.n_shared_experts == 0:
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return torch.zeros_like(x)
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return sum(e(x) for e in self.shared_experts) / self.n_shared_experts
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def _routed_forward(self, x: Tensor) -> Tensor:
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N, D = x.shape
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K = self.n_activated_experts
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router_logits = self.router(x)
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router_probs = torch.softmax(router_logits.float(), dim=-1).to(x.dtype)
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topk_weights, topk_indices = torch.topk(router_probs, K, dim=-1)
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topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
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output = torch.zeros(N, D, device=x.device, dtype=x.dtype)
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for expert_idx in range(self.n_routed_experts):
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expert_mask = topk_indices == expert_idx
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token_idx, k_idx = expert_mask.nonzero(as_tuple=True)
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if token_idx.numel() == 0:
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continue
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expert_input = x[token_idx]
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expert_output = self.routed_experts[expert_idx](expert_input)
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weights = topk_weights[token_idx, k_idx].unsqueeze(-1)
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output.index_add_(0, token_idx, expert_output * weights)
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return output
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