[cherry-pick]Upgrade CANN to 8.3.rc1 (#3945) (#3962)

This PR upgrade CANN from 8.2rc1 to 8.3rc1 and remove the CANN version
check logic.

TODO: we notice that UT runs failed with CANN 8.3 image. So the base
image for UT is still 8.2. We'll fix it later.

- vLLM version: v0.11.0
- vLLM main:
83f478bb19

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/ops/linear.py

@@ -45,8 +45,8 @@ class AscendUnquantizedLinearMethod(UnquantizedLinearMethod):
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         super().process_weights_after_loading(layer)
-        if (is_enable_nz() and torch.version.cann.startswith("8.3") and
-                layer.weight.data.dtype in [torch.float16, torch.bfloat16]):
+        if (is_enable_nz() and layer.weight.data.dtype
+                in [torch.float16, torch.bfloat16]):
             layer.weight.data = torch_npu.npu_format_cast(
                 layer.weight.data, ACL_FORMAT_FRACTAL_NZ)
 

vllm_ascend/ops/linear_op.py

@@ -411,9 +411,8 @@ class SequenceRowParallelOp(CustomRowParallelOp):
                                                    quant_per_tensor)
 
         # For unquant
-        if mmrs_fusion and isinstance(
-                self.layer.quant_method, UnquantizedLinearMethod
-        ) and torch.version.cann.startswith("8.3"):
+        if mmrs_fusion and isinstance(self.layer.quant_method,
+                                      UnquantizedLinearMethod):
             output = torch_npu.npu_mm_reduce_scatter_base(
                 x,
                 self.layer.weight.t(),
@@ -429,8 +428,7 @@ class SequenceRowParallelOp(CustomRowParallelOp):
         elif mmrs_fusion and (
                 isinstance(self.layer.quant_method, AscendLinearMethod)
                 and isinstance(self.layer.quant_method.quant_method,
-                               AscendW8A8LinearMethod)
-        ) and torch.version.cann.startswith("8.3"):
+                               AscendW8A8LinearMethod)):
             if x.dtype != torch.int8:
                 x_quant = quant_per_tensor(
                     x, self.layer.aclnn_input_scale_reciprocal,