refactor: simplify attention mask handling
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@@ -76,9 +76,8 @@ class GQA(nn.Module):
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rotary_emb: Tensor,
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attn_mask: Tensor = None,
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paged_cache: Optional[CacheView] = None,
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is_causal: bool = False,
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) -> Tensor:
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is_causal = attn_mask is None
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q = self._split_heads(self.q_proj(x), self.n_heads)
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k = self._split_heads(self.k_proj(x), self.n_kv_heads)
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v = self._split_heads(self.v_proj(x), self.n_kv_heads)
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@@ -163,9 +162,9 @@ class MLA(nn.Module):
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rotary_emb: Tensor,
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attn_mask: Tensor = None,
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paged_cache: Optional[CacheView] = None,
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is_causal: bool = False,
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) -> Tensor:
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bsz, seq_len, _ = x.size()
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is_causal = attn_mask is None
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q = self.q_proj(x)
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q = q.view(bsz, seq_len, self.n_heads, self.head_dim)
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