[Ascend] LoRA: adding Ascend LoRA backend with using kernels from sgl_kernel_npu

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

@@ -0,0 +1,287 @@
+from typing import Optional
+
+import torch
+
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
+from sglang.srt.lora.utils import LoRABatchInfo
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch
+from sglang.srt.utils import is_npu
+
+if is_npu():
+    import sgl_kernel_npu  # noqa: F401
+    import torch_npu  # noqa: F401
+
+
+class AscendLoRABackend(BaseLoRABackend):
+    name = "ascend"
+
+    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
+        )
+        torch.ops.npu.sgmv_shrink(
+            x,
+            weights,
+            self.batch_info.weight_indices,
+            self.batch_info.seg_lens,
+            output_tensor,
+            1.0,
+        )
+        scaling = (
+            self.batch_info.scalings.gather(0, self.batch_info.weight_indices)
+            .repeat_interleave(self.batch_info.seg_lens, output_size=total_seq_len)
+            .unsqueeze(-1)
+        )
+        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
+
+        torch.ops.npu.sgmv_expand(
+            x,
+            weights,
+            self.batch_info.weight_indices,
+            self.batch_info.seg_lens,
+            output_tensor,
+            0,
+            weight_out_dim,
+        )
+
+        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
+        )
+        torch.ops.npu.sgmv_shrink(
+            x,
+            qkv_lora_a,
+            self.batch_info.weight_indices,
+            self.batch_info.seg_lens,
+            lora_a_output,
+            1.0,
+        )
+
+        scaling = (
+            self.batch_info.scalings.gather(0, self.batch_info.weight_indices)
+            .repeat_interleave(self.batch_info.seg_lens, output_size=total_seq_len)
+            .unsqueeze(-1)
+        )
+        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
+            torch.ops.npu.sgmv_expand(
+                lora_a_output[:, (max_rank * slice_id) : (max_rank * (slice_id + 1))],
+                qkv_lora_b[:, slice_offset:slice_offset_next],
+                self.batch_info.weight_indices,
+                self.batch_info.seg_lens,
+                output_tensor,
+                slice_offset,
+                slice_size,
+            )
+
+        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
+        )
+
+        torch.ops.npu.sgmv_shrink(
+            x,
+            gate_up_lora_a,
+            self.batch_info.weight_indices,
+            self.batch_info.seg_lens,
+            lora_a_output,
+            1.0,
+        )
+
+        scaling = (
+            self.batch_info.scalings.gather(0, self.batch_info.weight_indices)
+            .repeat_interleave(self.batch_info.seg_lens, output_size=total_seq_len)
+            .unsqueeze(-1)
+        )
+        lora_a_output *= scaling
+
+        slice_offset = 0
+        for slice_id in range(num_slices):
+            torch.ops.npu.sgmv_expand(
+                lora_a_output[:, (max_rank * slice_id) : (max_rank * (slice_id + 1))],
+                gate_up_lora_b[:, slice_offset : slice_offset + slice_size],
+                self.batch_info.weight_indices,
+                self.batch_info.seg_lens,
+                output_tensor,
+                slice_offset,
+                slice_size,
+            )
+            slice_offset += slice_size
+
+        return output_tensor
+
+    def init_cuda_graph_batch_info(
+        self, cuda_graph_batch_info: LoRABatchInfo, max_bs_in_cuda_graph: 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],
+        )
+
+    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 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 batch_info is not None:
+            assert (
+                batch_info.use_cuda_graph
+            ), "batch_info.use_cuda_graph must be True when batch_info is provided"
+            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/backend/base_backend.py

@@ -133,23 +133,3 @@ def prepare_lora_batch(
                         internal batch info (e.g., self.cuda_graph_batch_info for CUDA Graph mode)
         """
         pass
-
-
-def get_backend_from_name(name: str) -> BaseLoRABackend:
-    """
-    Get corresponding backend class from backend's name
-    """
-    if name == "triton":
-        from sglang.srt.lora.backend.triton_backend import TritonLoRABackend
-
-        return TritonLoRABackend
-    elif name == "csgmv":
-        from sglang.srt.lora.backend.chunked_backend import ChunkedSgmvLoRABackend
-
-        return ChunkedSgmvLoRABackend
-    elif name == "flashinfer":
-        raise ValueError(
-            "FlashInfer LoRA backend has been deprecated, please use `triton` instead."
-        )
-    else:
-        raise ValueError(f"Invalid backend: {name}")

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

@@ -0,0 +1,53 @@
+import logging
+
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
+
+logger = logging.getLogger(__name__)
+
+LORA_SUPPORTED_BACKENDS = {}
+
+
+def register_lora_backend(name):
+    def decorator(fn):
+        LORA_SUPPORTED_BACKENDS[name] = fn
+        return fn
+
+    return decorator
+
+
+@register_lora_backend("triton")
+def create_triton_backend():
+    from sglang.srt.lora.backend.triton_backend import TritonLoRABackend
+
+    return TritonLoRABackend
+
+
+@register_lora_backend("csgmv")
+def create_triton_csgmv_backend():
+    from sglang.srt.lora.backend.chunked_backend import ChunkedSgmvLoRABackend
+
+    return ChunkedSgmvLoRABackend
+
+
+@register_lora_backend("ascend")
+def create_ascend_backend():
+    from sglang.srt.lora.backend.ascend_backend import AscendLoRABackend
+
+    return AscendLoRABackend
+
+
+@register_lora_backend("flashinfer")
+def create_flashinfer_backend():
+    raise ValueError(
+        "FlashInfer LoRA backend has been deprecated, please use `triton` instead."
+    )
+
+
+def get_backend_from_name(name: str) -> BaseLoRABackend:
+    """
+    Get corresponding backend class from backend's name
+    """
+    if name not in LORA_SUPPORTED_BACKENDS:
+        raise ValueError(f"Invalid backend: {name}")
+    lora_backend = LORA_SUPPORTED_BACKENDS[name]()
+    return lora_backend

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

@@ -97,7 +97,9 @@ def run_gate_up_lora(
         return lora_output
 
     def init_cuda_graph_batch_info(
-        self, cuda_graph_batch_info: LoRABatchInfo, max_bs_in_cuda_graph: int
+        self,
+        cuda_graph_batch_info: LoRABatchInfo,
+        max_bs_in_cuda_graph: int,
     ):
         # Initialize seg_lens and seg_indptr for CUDA graph as they remain constant
         # across batches.