[NPU]LoRA: Adding Torch Native backend

python/sglang/srt/lora/backend/ascend_backend.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import torch
 
 from sglang.srt.lora.backend.base_backend import BaseLoRABackend
@@ -204,24 +202,41 @@ def run_gate_up_lora(
         return output_tensor
 
     def init_cuda_graph_batch_info(
-        self, cuda_graph_batch_info: LoRABatchInfo, max_bs_in_cuda_graph: int
+        self,
+        max_bs_in_cuda_graph: int,
+        num_tokens_per_bs: int,
     ):
-        # Initialize seg_lens and seg_indptr for CUDA graph as they remain constant
-        # across batches.
-        cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph].fill_(1)
-        torch.cumsum(
-            cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph],
-            dim=0,
-            out=cuda_graph_batch_info.seg_indptr[1 : max_bs_in_cuda_graph + 1],
-        )
+        with torch.device("npu"):
+            self.npu_graph_batch_info = LoRABatchInfo(
+                bs=max_bs_in_cuda_graph,
+                use_cuda_graph=True,
+                num_segments=None,
+                seg_lens=torch.full(
+                    (max_bs_in_cuda_graph,), num_tokens_per_bs, dtype=torch.int32
+                ),
+                seg_indptr=torch.empty(max_bs_in_cuda_graph + 1, dtype=torch.int32),
+                max_len=num_tokens_per_bs,
+                weight_indices=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
+                lora_ranks=torch.zeros(self.max_loras_per_batch, dtype=torch.int32),
+                scalings=torch.zeros(self.max_loras_per_batch, dtype=torch.float),
+                permutation=None,
+            )
+
+            # Initialize seg_indptr for NPU graph as they remain constant
+            # across batches.
+            torch.cumsum(
+                self.npu_graph_batch_info.seg_lens[:max_bs_in_cuda_graph],
+                dim=0,
+                out=self.npu_graph_batch_info.seg_indptr[1 : max_bs_in_cuda_graph + 1],
+            )
 
     def prepare_lora_batch(
         self,
         forward_batch: ForwardBatch,
         weight_indices: list[int],
         lora_ranks: list[int],
         scalings: list[float],
-        batch_info: Optional[LoRABatchInfo] = None,
+        use_cuda_graph: bool,
     ):
         # Use pinned memory to avoid synchronizations during host-to-device transfer
         weight_indices_tensor = torch.tensor(
@@ -236,10 +251,11 @@ def prepare_lora_batch(
 
         bs = forward_batch.batch_size
 
-        if batch_info is not None:
+        if use_cuda_graph:
             assert (
-                batch_info.use_cuda_graph
-            ), "batch_info.use_cuda_graph must be True when batch_info is provided"
+                self.npu_graph_batch_info is not None
+            ), "NPU Graph batch info is not initialized."
+            batch_info = self.npu_graph_batch_info
             batch_info.bs = forward_batch.batch_size
             batch_info.num_segments = forward_batch.batch_size
         else:

python/sglang/srt/lora/backend/lora_registry.py

@@ -36,6 +36,13 @@ def create_ascend_backend():
     return AscendLoRABackend
 
 
+@register_lora_backend("torch_native")
+def create_torch_native_backend():
+    from sglang.srt.lora.backend.torch_backend import TorchNativeLoRABackend
+
+    return TorchNativeLoRABackend
+
+
 @register_lora_backend("flashinfer")
 def create_flashinfer_backend():
     raise ValueError(

python/sglang/srt/lora/backend/torch_backend.py

@@ -0,0 +1,297 @@
+import torch
+
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
+from sglang.srt.lora.torch_ops import sgmv_expand, sgmv_expand_slice, sgmv_shrink
+from sglang.srt.lora.utils import LoRABatchInfo
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+
+
+class TorchNativeLoRABackend(BaseLoRABackend):
+    name = "torch_native"
+
+    def __init__(
+        self,
+        max_loras_per_batch: int,
+        device: torch.device,
+        **kwargs,
+    ):
+        super().__init__(max_loras_per_batch, device)
+
+    def run_lora_a_sgemm(
+        self, x: torch.Tensor, weights: torch.Tensor, *args, **kwargs
+    ) -> torch.Tensor:
+
+        total_seq_len, _ = x.shape
+        _, weight_out_dim, _ = weights.shape
+
+        output_tensor = torch.zeros(
+            (total_seq_len, weight_out_dim), dtype=x.dtype, device=x.device
+        )
+        sgmv_shrink(
+            x,
+            weights,
+            output_tensor,
+            self.batch_info.seg_lens,
+            self.batch_info.weight_indices,
+            1.0,
+        )
+        scaling = torch.repeat_interleave(
+            self.batch_info.scalings[self.batch_info.weight_indices],
+            self.batch_info.seg_lens,
+            output_size=total_seq_len,
+        ).unsqueeze(-1)
+        output_tensor = output_tensor * scaling
+
+        return output_tensor
+
+    def run_lora_b_sgemm(
+        self,
+        x: torch.Tensor,
+        weights: torch.Tensor,
+        base_output: torch.Tensor = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        total_seq_len, _ = x.shape
+        _, weight_out_dim, _ = weights.shape
+
+        if base_output is None:
+            output_tensor = torch.zeros(
+                (total_seq_len, weight_out_dim), device=x.device, dtype=x.dtype
+            )
+        else:
+            output_tensor = base_output
+
+        sgmv_expand(
+            x,
+            weights,
+            output_tensor,
+            self.batch_info.seg_lens,
+            self.batch_info.weight_indices,
+            True,
+        )
+
+        return output_tensor
+
+    def run_qkv_lora(
+        self,
+        x: torch.Tensor,
+        qkv_lora_a: torch.Tensor,
+        qkv_lora_b: torch.Tensor,
+        output_offset: torch.Tensor,
+        output_offset_cpu: torch.Tensor,
+        max_qkv_out_dim: int,
+        base_output: torch.Tensor = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+        num_slices = 3
+        assert isinstance(qkv_lora_b, torch.Tensor)
+
+        total_seq_len, _ = x.shape
+        _, weight_intermediate_dim, _ = qkv_lora_a.shape
+        _, weight_out_dim, _ = qkv_lora_b.shape
+        max_rank = weight_intermediate_dim // num_slices
+
+        if base_output is None:
+            output_tensor = torch.zeros(
+                (total_seq_len, weight_out_dim), device=x.device, dtype=x.dtype
+            )
+        else:
+            output_tensor = base_output
+
+        lora_a_output = torch.zeros(
+            total_seq_len, weight_intermediate_dim, dtype=x.dtype, device=x.device
+        )
+        sgmv_shrink(
+            x,
+            qkv_lora_a,
+            lora_a_output,
+            self.batch_info.seg_lens,
+            self.batch_info.weight_indices,
+            1.0,
+        )
+        scaling = torch.repeat_interleave(
+            self.batch_info.scalings[self.batch_info.weight_indices],
+            self.batch_info.seg_lens,
+            output_size=total_seq_len,
+        ).unsqueeze(-1)
+        lora_a_output = lora_a_output * scaling
+
+        for slice_id in range(num_slices):
+            slice_offset = output_offset_cpu[slice_id]
+            slice_offset_next = output_offset_cpu[slice_id + 1]
+            slice_size = slice_offset_next - slice_offset
+            sgmv_expand_slice(
+                lora_a_output[:, (max_rank * slice_id) : (max_rank * (slice_id + 1))],
+                qkv_lora_b[:, slice_offset:slice_offset_next],
+                output_tensor,
+                self.batch_info.seg_lens,
+                self.batch_info.weight_indices,
+                slice_offset,
+                slice_size,
+                True,
+            )
+
+        return output_tensor
+
+    def run_gate_up_lora(
+        self,
+        x: torch.Tensor,
+        gate_up_lora_a: torch.Tensor,
+        gate_up_lora_b: torch.Tensor,
+        base_output: torch.Tensor = None,
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        num_slices = 2
+        assert isinstance(gate_up_lora_b, torch.Tensor)
+
+        total_seq_len, _ = x.shape
+        _, weight_intermediate_dim, _ = gate_up_lora_a.shape
+        _, weight_out_dim, _ = gate_up_lora_b.shape
+        slice_size = weight_out_dim // num_slices
+        max_rank = weight_intermediate_dim // num_slices
+
+        if base_output is None:
+            output_tensor = torch.zeros(
+                (total_seq_len, weight_out_dim), device=x.device, dtype=x.dtype
+            )
+        else:
+            output_tensor = base_output
+
+        lora_a_output = torch.zeros(
+            total_seq_len, weight_intermediate_dim, dtype=x.dtype, device=x.device
+        )
+        sgmv_shrink(
+            x,
+            gate_up_lora_a,
+            lora_a_output,
+            self.batch_info.seg_lens,
+            self.batch_info.weight_indices,
+            1.0,
+        )
+        scaling = torch.repeat_interleave(
+            self.batch_info.scalings[self.batch_info.weight_indices],
+            self.batch_info.seg_lens,
+            output_size=total_seq_len,
+        ).unsqueeze(-1)
+        lora_a_output = lora_a_output * scaling
+
+        slice_offset = 0
+        for slice_id in range(num_slices):
+            sgmv_expand_slice(
+                lora_a_output[:, (max_rank * slice_id) : (max_rank * (slice_id + 1))],
+                gate_up_lora_b[:, slice_offset : slice_offset + slice_size],
+                output_tensor,
+                self.batch_info.seg_lens,
+                self.batch_info.weight_indices,
+                slice_offset,
+                slice_size,
+                True,
+            )
+            slice_offset += slice_size
+
+        return output_tensor
+
+    def init_cuda_graph_batch_info(
+        self,
+        max_bs_in_cuda_graph: int,
+        num_tokens_per_bs: int,
+    ):
+        with torch.device("cuda"):
+            self.cuda_graph_batch_info = LoRABatchInfo(
+                bs=max_bs_in_cuda_graph,
+                use_cuda_graph=True,
+                num_segments=None,
+                seg_lens=torch.full(
+                    (max_bs_in_cuda_graph,), num_tokens_per_bs, dtype=torch.int32
+                ),
+                seg_indptr=torch.empty(max_bs_in_cuda_graph + 1, dtype=torch.int32),
+                max_len=num_tokens_per_bs,
+                weight_indices=torch.zeros(max_bs_in_cuda_graph, dtype=torch.int32),
+                lora_ranks=torch.zeros(self.max_loras_per_batch, dtype=torch.int32),
+                scalings=torch.zeros(self.max_loras_per_batch, dtype=torch.float),
+                permutation=None,
+            )
+
+            # Initialize seg_indptr for CUDA graph as they remain constant
+            # across batches.
+            torch.cumsum(
+                self.cuda_graph_batch_info.seg_lens[:max_bs_in_cuda_graph],
+                dim=0,
+                out=self.cuda_graph_batch_info.seg_indptr[1 : max_bs_in_cuda_graph + 1],
+            )
+
+    def prepare_lora_batch(
+        self,
+        forward_batch: ForwardBatch,
+        weight_indices: list[int],
+        lora_ranks: list[int],
+        scalings: list[float],
+        use_cuda_graph: bool,
+    ):
+        # Use pinned memory to avoid synchronizations during host-to-device transfer
+        weight_indices_tensor = torch.tensor(
+            weight_indices, dtype=torch.int32, pin_memory=True, device="cpu"
+        )
+        lora_ranks_tensor = torch.tensor(
+            lora_ranks, dtype=torch.int32, pin_memory=True, device="cpu"
+        )
+        scalings_tensor = torch.tensor(
+            scalings, dtype=torch.float, pin_memory=True, device="cpu"
+        )
+
+        bs = forward_batch.batch_size
+
+        if use_cuda_graph:
+            assert (
+                self.cuda_graph_batch_info is not None
+            ), "CUDA Graph batch info is not initialized."
+            batch_info = self.cuda_graph_batch_info
+            batch_info.bs = forward_batch.batch_size
+            batch_info.num_segments = forward_batch.batch_size
+        else:
+            max_len = (
+                # Calculate max_len from the CPU copy to avoid D2H transfer.
+                max(forward_batch.extend_seq_lens_cpu)
+                if forward_batch.forward_mode.is_extend()
+                else 1
+            )
+            seg_lens = (
+                forward_batch.extend_seq_lens
+                if forward_batch.forward_mode.is_extend()
+                else torch.ones(bs, dtype=torch.int32, device=self.device)
+            )
+            seg_indptr = torch.zeros((bs + 1,), dtype=torch.int32, device=self.device)
+            seg_indptr[1:] = torch.cumsum(seg_lens, dim=0)
+
+            batch_info = LoRABatchInfo(
+                bs=forward_batch.batch_size,
+                num_segments=forward_batch.batch_size,
+                max_len=max_len,
+                use_cuda_graph=False,
+                seg_lens=seg_lens,
+                seg_indptr=seg_indptr,
+                weight_indices=torch.empty(
+                    (bs,), dtype=torch.int32, device=self.device
+                ),
+                lora_ranks=torch.empty(
+                    (self.max_loras_per_batch,), dtype=torch.int32, device=self.device
+                ),
+                scalings=torch.empty(
+                    (self.max_loras_per_batch,), dtype=torch.float, device=self.device
+                ),
+                permutation=None,
+            )
+
+        # Copy to device asynchronously
+        batch_info.lora_ranks[: self.max_loras_per_batch].copy_(
+            lora_ranks_tensor, non_blocking=True
+        )
+        batch_info.scalings[: self.max_loras_per_batch].copy_(
+            scalings_tensor, non_blocking=True
+        )
+        batch_info.weight_indices[:bs].copy_(weight_indices_tensor, non_blocking=True)
+        self.batch_info = batch_info

python/sglang/srt/lora/torch_ops/__init__.py

@@ -0,0 +1,7 @@
+from .lora_ops import sgmv_expand, sgmv_expand_slice, sgmv_shrink
+
+__all__ = [
+    "sgmv_expand",
+    "sgmv_expand_slice",
+    "sgmv_shrink",
+]