padding attention mask on torch side

python/llm/src/ipex_llm/transformers/models/common.py

@@ -184,3 +184,80 @@ def layer_norm_forward(self, hidden_states: torch.Tensor):
             hidden_states, self.normalized_shape,
             self.weight, self.bias, self.eps
         )
+
+
+def prepare_mask(mask, bsz, n_heads, seq_length, kv_length, is_causal, dtype, device):
+    max_kvs = 128
+    padding_kv_length = (kv_length + max_kvs - 1) // max_kvs * max_kvs
+    if mask is None:
+        if is_causal:
+            mask = torch.full([1, 1, seq_length, padding_kv_length], torch.finfo(dtype).min,
+                              dtype=dtype, device=device)
+            mask.triu_(1)
+            mask = mask.expand([bsz, n_heads, seq_length, padding_kv_length])
+        elif seq_length != kv_length and seq_length <= 32:
+            mask = None
+        else:
+            mask = torch.zeros([1, 1, 1, padding_kv_length], torch.finfo(dtype).min,
+                               dtype=dtype, device=device)
+            mask = mask.expand([bsz, n_heads, seq_length, padding_kv_length])
+    else:
+        if seq_length != kv_length and seq_length <= 32:
+            mask = mask[..., :seq_length, :kv_length]
+            mask = mask.expand([bsz, n_heads, seq_length, kv_length])
+        elif mask.size(3) != padding_kv_length:
+            new_mask = torch.empty([bsz, 1, seq_length, padding_kv_length],
+                                   dtype=dtype, device=device)
+            new_mask[:, :, :, :kv_length] = mask[:, 0:1, :seq_length, :kv_length]
+            new_mask[:, :, :, kv_length:] = torch.finfo(dtype).min
+            new_mask = new_mask.expand([bsz, n_heads, seq_length, padding_kv_length])
+            mask.set_(new_mask)     # modify `mask` inplaced
+        else:
+            mask = mask.expand([bsz, n_heads, seq_length, padding_kv_length])
+    return mask
+
+
+def scaled_dot_product_attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor,
+                                 mask: torch.Tensor = None,
+                                 is_causal: bool = False, scale: float = None) -> torch.Tensor:
+    bsz, n_heads, seq_length, head_dim = query.shape
+    _, n_kv_heads, kv_length, _ = key.shape
+
+    dtype, device = query.dtype, query.device
+
+    if (
+        device.type == "xpu"
+        and dtype in [torch.float, torch.half]
+        and head_dim in [64, 80, 96, 128]
+    ):
+        # prepare scale
+        scale = 1 / math.sqrt(head_dim) if scale is None else scale
+
+        # prepare mask
+        mask = prepare_mask(mask, bsz, n_heads, seq_length, kv_length, is_causal, dtype, device)
+
+        # compute
+        import xe_addons
+        if is_causal:
+            if key.dtype == torch.uint8:
+                attn_output = xe_addons.sdp_fp8_causal(query, key, value, mask, scale)
+            else:
+                attn_output = xe_addons.sdp_causal(query, key, value, mask, scale)
+        elif seq_length != kv_length and seq_length <= 32:
+            # todo: add scale support
+            if key.dtype == torch.uint8:
+                attn_output = xe_addons.sdp_fp8(query, key, value, mask)
+            else:
+                attn_output = xe_addons.sdp(query, key, value, mask)
+        else:
+            if key.dtype == torch.uint8:
+                attn_output = xe_addons.sdp_fp8(query, key, value, mask, scale)
+            else:
+                attn_output = xe_addons.sdp_non_causal(query, key, value, mask, scale)
+
+        return attn_output
+    else:
+        mask = mask[..., :seq_length, :kv_length] if mask is not None else None
+        return torch.nn.functional.scaled_dot_product_attention(
+            query, key, value, mask, is_causal=is_causal, scale=scale
+        )