[Bugfix][LoRA] Fix forward error and shape mismatch when using LoRA (#3153)

### What this PR does / why we need it?
Relying on #3044, this PR aims to further fix:
1. The forward error occured when `LogitsProcessorWithLoRA` calls
`AscendLogitsProcessor.forward`. Since `LogitsProcessorWithLoRA`
bypasses the MRO to call it, `super().forward(...)` in
`AscendLogitsProcessor.forward` will raise an error. This PR fixes it by
directly invoking `LogitsProcessor.forward(self, ...)`;
2. The shape mismatch in `add_lora_logits` in punica_npu.py. The
`lora_a_stacked` and `lora_b_stacked` are organized as [num_loras, 1,
lora_rank, hidden_size] and [num_loras, 1, vocab_size, lora_rank] shapes
respectively, but they are misunderstood in #1583---the last two
dimensions were assumed in reverse order, which causes errors in
`bgmv_shrink` and `bgmv_expand`. This PR fixes it by reverting it to the
previous version to align with the implementation in punica_cpu.py in
vllm.

### Dependencies
This PR depends on changes introduced by #3044 (LoRA support for
`AscendQKVParallelLinear` and `AscendMergedQKVParallelLinear` layers).

### Does this PR introduce _any_ user-facing change?
N/A

### How was this patch tested?
The LoRA-related tests, e.g., test_ilama_lora.py and
test_ilama_lora_tp2.py, use ilama-3.2-1B, and this model is regarded as
`TransformersForCausalLM`, where `embedding_modules` attribute lacks
`lm_head`. However, `LlamaForCausalLM` and most other models include
both `embed_tokens` and `lm_head` in `embedding_modules`. This attribute
contributes to `supported_lora_modules` when using LoRA in vllm.
Therefore, without `lm_head` in `embedding_modules`, current tests using
ilama-3.2-1B are unable to find the abve errors since
`LogitsProcessorWithLoRA` replacing `lm_head` is skipped. Simply using
Meta-Llama-3.1-8B-Instruct can reproduce the above errors and check
whether these fixes can work. What's more, it's necessary to add more
comprehensive tests for LoRA.

- vLLM version: v0.10.2
- vLLM main:
f225ea7dd9

Signed-off-by: Zetong Li <slippersss@126.com>

vllm_ascend/lora/punica_npu.py

@@ -341,13 +341,7 @@ class PunicaWrapperNPU(PunicaWrapperBase):
         y_org = y
         y = y.view(-1, y.shape[-1])
         x = x.view(-1, x.shape[-1])
-
-        if lora_a_stacked.dim() == 2:
-            lora_a_stacked = lora_a_stacked.unsqueeze(0)
-        if lora_b_stacked.dim() == 2:
-            lora_b_stacked = lora_b_stacked.unsqueeze(0)
-
-        r = lora_a_stacked.size(-1)
+        r = lora_b_stacked.size(-1)
 
         if buffer is None:
             buffer = torch.zeros((x.size(0), r),
@@ -355,13 +349,8 @@ class PunicaWrapperNPU(PunicaWrapperBase):
                                  device=x.device)
 
         indices = self.sampler_indices
-        if indices.max() >= lora_a_stacked.size(0):
-            indices = torch.clamp(indices, 0, lora_a_stacked.size(0) - 1)
 
-        lora_a_reshaped = lora_a_stacked.transpose(1, 2)
-        lora_b_reshaped = lora_b_stacked.transpose(1, 2)
-
-        bgmv_shrink(x, lora_a_reshaped, buffer, indices, scale)
-        bgmv_expand(buffer, lora_b_reshaped, y, indices, add_inputs=True)
+        bgmv_shrink(x, lora_a_stacked, buffer, indices, scale)
+        bgmv_expand(buffer, lora_b_stacked, y, indices, add_inputs=True)
 
         y = y.view_as(y_org)

vllm_ascend/ops/vocab_parallel_embedding.py

@@ -262,6 +262,7 @@ class AscendLogitsProcessor(LogitsProcessor):
         sampling_metadata=None,  # type: ignore
         embedding_bias: Optional[torch.Tensor] = None,
     ) -> Optional[torch.Tensor]:
-        return super().forward(lm_head,
-                               hidden_states,
-                               embedding_bias=embedding_bias)
+        return LogitsProcessor.forward(self,
+                                       lm_head,
+                                       hidden_states,
+                                       embedding_bias=embedding_bias)