[Model] Support DeepSeek-V4

vllm_mlu/lora/layers/base_linear.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
+
+import torch
+
+from vllm.lora.layers.base_linear import BaseLinearLayerWithLoRA
+from vllm.platforms import current_platform
+
+from vllm_mlu.mlu_hijack_utils import MluHijackObject
+
+
+def vllm__lora__layers__row_parallel_linear__BaseLinearLayerWithLoRA__apply(
+    self,
+    x: torch.Tensor,
+    bias: torch.Tensor | None,
+    residual: torch.Tensor | None = None,
+) -> torch.Tensor:
+    '''
+    =============================
+    Modify by vllm_mlu
+    =============================
+    @brief: add residual in matmul
+    '''
+    output = self.base_layer.quant_method.apply(self.base_layer, x, bias, residual)
+    '''
+    ==================
+    End of MLU Hijack
+    ==================
+    '''
+    # In transformers backend, x and output have extra batch dimension like
+    # (1, seq_len, hidden_dim), while punica expects (seq_len, hidden_dim),
+    # therefore we need to flatten the batch dimensions.
+    if x.ndim == 3 and output.ndim == 3:
+        output = output.flatten(0, 1)
+        x = x.flatten(0, 1)
+
+    lora_output: torch.Tensor | None = self.punica_wrapper.add_lora_linear(
+        output, x, self.lora_a_stacked, self.lora_b_stacked, 1.0, self.output_slices
+    )
+    if not current_platform.can_update_inplace():
+        output = lora_output
+
+    return output
+
+
+MluHijackObject.apply_hijack(
+    BaseLinearLayerWithLoRA,
+    BaseLinearLayerWithLoRA.apply,
+    vllm__lora__layers__row_parallel_linear__BaseLinearLayerWithLoRA__apply,
+)