Transpose mla weight offline (#1261)
Co-authored-by: Yineng Zhang <me@zhyncs.com>
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@@ -417,12 +417,8 @@ class DeepseekV2AttentionMLA(nn.Module):
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v_head_dim=self.kv_lora_rank,
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)
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kv_b_proj = self.kv_b_proj
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w_kc, w_vc = kv_b_proj.weight.unflatten(
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0, (-1, qk_nope_head_dim + v_head_dim)
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).split([qk_nope_head_dim, v_head_dim], dim=1)
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self.w_kc = w_kc
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self.w_vc = w_vc
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self.w_kc = None
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self.w_vc = None
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def forward(
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self,
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@@ -464,7 +460,7 @@ class DeepseekV2AttentionMLA(nn.Module):
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)
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torch.bmm(
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attn_output.transpose(0, 1),
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self.w_vc.transpose(1, 2).contiguous(),
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self.w_vc,
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out=attn_bmm_output.transpose(0, 1),
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)
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@@ -715,5 +711,15 @@ class DeepseekV2ForCausalLM(nn.Module):
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)
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weight_loader(param, loaded_weight)
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if global_server_args_dict["enable_mla"]:
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for layer_id in range(self.config.num_hidden_layers):
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self_attn = self.model.layers[layer_id].self_attn
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w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(
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0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
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).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
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self_attn.w_kc = w_kc.contiguous()
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self_attn.w_vc = w_vc.transpose(1, 2).contiguous()
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del self_attn.kv_b_proj
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EntryClass = DeepseekV2ForCausalLM
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