[0.11.0][Perf] Add fused matmul/reduce-scatter kernel for performance optimization. (#3725)

### What this PR does / why we need it?
This PR boosts performance by introducing a fused kernel for the matrix
matmul and reduce scatter operations. It supports both unquantized
(e.g., BFloat16) and W8A8 quantized models.

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

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Signed-off-by: ZYang6263 <zy626375@gmail.com>

vllm_ascend/ops/linear_op.py

@@ -39,11 +39,15 @@ from typing import Optional, Union
 
 import torch
 import torch.distributed as dist
+import torch.nn.functional as F
 import torch_npu
 from torch.distributed import ProcessGroup
 from torch.nn.parameter import Parameter
-from vllm.distributed import split_tensor_along_last_dim
+from vllm.distributed import (split_tensor_along_last_dim,
+                              tensor_model_parallel_all_reduce,
+                              tensor_model_parallel_reduce_scatter)
 from vllm.distributed.parallel_state import get_tp_group
+from vllm.forward_context import get_forward_context
 
 from vllm_ascend.distributed.parallel_state import (get_mlp_tp_group,
                                                     get_otp_group)
@@ -375,12 +379,83 @@ class SequenceRowParallelOp(CustomRowParallelOp):
     def matmul_and_reduce(self, input_parallel: torch.Tensor,
                           bias_: Optional[Parameter]) -> torch.Tensor:
         assert self.quant_method is not None
-        output_parallel = self.quant_method.apply(self.layer,
-                                                  input_parallel,
-                                                  bias=bias_)
-        from vllm_ascend.ops.register_custom_ops import \
-            _maybe_pad_and_reduce_impl
-        output = _maybe_pad_and_reduce_impl(output_parallel)
+        try:
+            forward_context = get_forward_context()
+            sp_enabled = forward_context.sp_enabled
+        except AssertionError:
+            sp_enabled = False
+
+        x = input_parallel
+
+        if not sp_enabled:
+            output_parallel = self.layer.quant_method.apply(self.layer,
+                                                            x,
+                                                            bias=bias_)
+            return tensor_model_parallel_all_reduce(output_parallel)
+
+        pad_size = forward_context.pad_size
+        if pad_size > 0:
+            x = F.pad(x, (0, 0, 0, pad_size))
+
+        world_size = self.layer.tp_size
+        comm_mode = "aiv"
+        hcom_name = get_tp_group().device_group._get_backend(
+            torch.device('npu')).get_hccl_comm_name(self.layer.tp_rank)
+
+        from vllm.model_executor.layers.linear import UnquantizedLinearMethod
+
+        from vllm_ascend.quantization.quant_config import AscendLinearMethod
+        from vllm_ascend.quantization.w8a8 import (AscendW8A8LinearMethod,
+                                                   quant_per_tensor)
+
+        # For unquant
+        if isinstance(self.layer.quant_method, UnquantizedLinearMethod
+                      ) and torch.version.cann.startswith("8.3"):
+            output = torch_npu.npu_mm_reduce_scatter_base(
+                x,
+                self.layer.weight.t(),
+                hcom_name,
+                world_size,
+                reduce_op="sum",
+                bias=None,
+                comm_turn=0,
+                comm_mode=comm_mode)
+            if bias_ is not None:
+                output.add_(bias_)
+        # For w8a8 quant
+        elif (isinstance(self.layer.quant_method, AscendLinearMethod)
+              and isinstance(self.layer.quant_method.quant_method,
+                             AscendW8A8LinearMethod)
+              ) and torch.version.cann.startswith("8.3"):
+            if x.dtype != torch.int8:
+                x_quant = quant_per_tensor(
+                    x, self.layer.aclnn_input_scale_reciprocal,
+                    self.layer.aclnn_input_offset)
+            else:
+                x_quant = x
+            quant_bias = self.layer.quant_bias
+            deq_scale = self.layer.deq_scale
+            output_dtype = torch.bfloat16
+            output = torch_npu.npu_mm_reduce_scatter_base(
+                x_quant,
+                self.layer.weight,
+                hcom_name,
+                world_size,
+                reduce_op="sum",
+                bias=None,
+                comm_turn=0,
+                x2_scale=deq_scale,
+                output_dtype=output_dtype,
+                comm_mode=comm_mode)
+            output = torch.add(
+                output,
+                torch.mul(quant_bias, deq_scale).to(self.layer.params_dtype))
+        else:
+            output_parallel = self.layer.quant_method.apply(self.layer,
+                                                            x,
+                                                            bias=bias_)
+            output = tensor_model_parallel_reduce_scatter(output_parallel, 0)
+
         return output
 
     def update_attrs(self):