[XPU] [Bugfix] process ragged weights in xpu linear backend

vllm/model_executor/kernels/linear/scaled_mm/xpu.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import math
 from collections.abc import Sequence
 
 import torch
@@ -204,7 +205,34 @@ def process_weights_after_loading(self, layer: torch.nn.Module):
         )
         scale = getattr(layer, scale_attr)
 
-        # Checkpoint scale is [n_blocks, k_blocks] (one value per 128x128 tile).
+        # Ragged N (N % block_n != 0): oneDNN needs n_blocks to divide N.
+        # Weight untouched; only repeat scale rows to a finer N-group gn that
+        # divides both N and block_n (gn = gcd(N, block_n)):
+        #   scale [ceil(N/block_n), K/block_k] --> [N/gn, K/block_k]
+        # oneDNN only accepts gn that is a multiple of 16, and gcd(N, block_n)
+        # is a power of two (block_n=128), so gn must be >= 16. No-op when
+        # N % block_n == 0.
+        block_n, block_k = self.weight_group_shape
+        N, K = layer.weight.shape
+        if N % block_n != 0:
+            gn = math.gcd(N, block_n)
+            assert gn % 16 == 0, (
+                f"XPU block-scaled FP8: N ({N}) yields group width {gn}, but "
+                f"oneDNN only supports multiples of 16; this weight shape is "
+                f"unsupported."
+            )
+            col_start = torch.arange(N // gn, device=scale.device) * gn
+            src_idx = torch.div(col_start, block_n, rounding_mode="floor")
+            scale = scale.index_select(0, src_idx).contiguous()
+
+        # Ragged K needs the runtime activation scale expanded too, which we
+        # don't handle; DeepSeek/GLM keep K block-aligned, so fail loudly.
+        assert K % block_k == 0, (
+            f"XPU block-scaled FP8 requires K ({K}) to be a multiple of the "
+            f"weight block size ({block_k}); ragged-K weights are unsupported."
+        )
+
+        # Checkpoint scale is [n_blocks, k_blocks] (one value per block tile).
         # oneDNN fp8_gemm requires contiguous [k_blocks, n_blocks] layout.
         # We store the transposed contiguous buffer as a .t() view so that:
         #   - MLA's scaled_dequantize still sees [n_blocks, k_blocks] shape