大改

vllm_npu/attention/attention_mask.py

@@ -0,0 +1,96 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+
+
+def _generate_attn_mask(max_seq_len, dtype):
+    # Construct lower triangle matrix.
+    mask_flag = torch.ones((max_seq_len, max_seq_len),
+                           dtype=torch.bool).tril_()
+    # Create upper triangle matrix used to mark mask positions.
+    mask_flag = ~mask_flag
+    # Currently for fp16 dtype, the mask value should be set to -inf.
+    # TODO: Eliminate this part in the future.
+    mask_value = float('-inf') if dtype == torch.float16 else 1
+    attn_mask = torch.zeros(size=(max_seq_len, max_seq_len), dtype=dtype) \
+        .masked_fill_(mask_flag, mask_value)
+    return attn_mask
+
+
+class AttentionMaskBuilder:
+
+    def __init__(
+        self,
+        max_seq_len: int,
+        dtype: torch.dtype,
+        device: torch.device = None,
+    ):
+        # NOTE: The device argument specifies the target NPU
+        # to be used for the newly added FIA operator.
+        # Only pass this parameter when using the new FIA operator.
+
+        attn_mask = _generate_attn_mask(max_seq_len, dtype)
+
+        self._seq_len_cached = attn_mask.shape[0]
+        self.attn_mask_cache = attn_mask
+        self.device = device
+        self.pooling_mask = None
+        assigned_mask_dim = 2048
+        self.chunked_prefill_attn_mask = torch.triu(
+            torch.ones(assigned_mask_dim, assigned_mask_dim),
+            diagonal=1).to(torch.int8).to(device)
+
+    @staticmethod
+    def get_mask_scale_factor(dtype: torch.dtype = torch.float16):
+        if dtype == torch.float16:
+            mask_scale_factor = 1
+        elif dtype == torch.bfloat16:
+            mask_scale_factor = -10000
+        else:
+            raise ValueError(
+                "The current operation now only supports data types: torch.float16 and "
+                "torch.bfloat16. Please ensure the input is of one of these types."
+            )
+        return mask_scale_factor
+
+    def get_attn_mask(self, max_seq_len: int, dtype: torch.dtype,
+                      device: torch.device):
+        self._update_attn_cache(max_seq_len, dtype)
+        return self.attn_mask_cache[:max_seq_len, :max_seq_len].contiguous(
+        ).to(device, non_blocking=True)
+
+    def get_pooling_mask(self, device):
+        if self.pooling_mask is None:
+            # the compressed attention mask for npu_fusion_attention sparse mode 4
+            self.pooling_mask = torch.triu(torch.ones(
+                2048, 2048), diagonal=1).to(torch.bool).to(device,
+                                                           non_blocking=True)
+        return self.pooling_mask
+
+    def get_splitfuse_attn_mask(
+        self,
+        seq_lens: torch.Tensor = None,
+        position: torch.Tensor = None,
+        dtype: torch.dtype = None,
+        device: torch.device = None,
+    ) -> torch.Tensor:
+        return self.chunked_prefill_attn_mask
+
+    def _update_attn_cache(self, seqlen: int, dtype: torch.dtype):
+        if seqlen > self._seq_len_cached:
+            self._seq_len_cached = seqlen
+            self.attn_mask_cache = _generate_attn_mask(seqlen, dtype)
+        if self.attn_mask_cache.dtype != dtype:
+            self.attn_mask_cache = self.attn_mask_cache.to(dtype)