[Misc][LoRA] Abstract PunicaWrapper (#10955)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>

tests/lora/test_layers.py

@@ -28,7 +28,7 @@ from vllm.lora.layers import (BaseLayerWithLoRA, ColumnParallelLinearWithLoRA,
 # yapf: enable
 from vllm.lora.models import (LongContextLoRAContext, LoRALayerWeights,
                               PackedLoRALayerWeights)
-from vllm.lora.punica import PunicaWrapper
+from vllm.lora.punica_wrapper import get_punica_wrapper
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
                                                QKVParallelLinear,
@@ -48,11 +48,12 @@ TOLERANCES = {
     torch.float32: (5e-3, 5e-3),
     torch.bfloat16: (3e-2, 2e-2),
 }
-CUDA_DEVICES = [
+# TODO: Modify this based on platform
+DEVICES = [
     f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)
 ]
 
-# We will launch different triton kernels between the prefill and decode
+#For GPU, we will launch different triton kernels between the prefill and decode
 # stages, so we need to verify this. prefill stage(True) or decode stage(False)
 STAGES = [True, False]
 
@@ -192,9 +193,18 @@ def create_random_inputs(
     return inputs, index_mapping, prompt_mapping
 
 
+def check_punica_wrapper(punica_wrapper) -> bool:
+    if current_platform.is_cuda_alike():
+        from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
+
+        return type(punica_wrapper) is PunicaWrapperGPU
+    else:
+        return False
+
+
 @torch.inference_mode()
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 128000])
 @pytest.mark.parametrize("stage", STAGES)
 def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
@@ -205,7 +215,8 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
 
     torch.set_default_device(device)
     max_loras = 8
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
                              lora_dtype=torch.float16)
@@ -296,7 +307,7 @@ def test_embeddings(dist_init, num_loras, device, vocab_size, stage) -> None:
 # @pytest.mark.skip(
 #     reason="Fails when loras are in any slot other than the first.")
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 128000])
 @pytest.mark.parametrize("stage", STAGES)
 def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
@@ -305,7 +316,8 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
     torch.cuda.set_device(device)
     torch.set_default_device(device)
     max_loras = 8
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
                              lora_dtype=torch.float16)
@@ -432,7 +444,7 @@ def test_embeddings_with_new_embeddings(dist_init, num_loras, device,
 
 @torch.inference_mode()
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("vocab_size", [512, 32000, 64000, 256512])
 @pytest.mark.parametrize("stage", STAGES)
 def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size,
@@ -441,7 +453,8 @@ def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size,
     torch.cuda.set_device(device)
     torch.set_default_device(device)
     max_loras = 8
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
                              lora_dtype=torch.float16)
@@ -563,7 +576,7 @@ def test_lm_head_logits_processor(dist_init, num_loras, device, vocab_size,
 
 @torch.inference_mode()
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("stage", STAGES)
 @pytest.mark.parametrize("bias_enabled", [True, False])
 def test_linear_replicated(dist_init, num_loras, device, stage,
@@ -571,7 +584,8 @@ def test_linear_replicated(dist_init, num_loras, device, stage,
 
     torch.cuda.set_device(device)
     torch.set_default_device(device)
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     max_loras = 8
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
@@ -675,7 +689,7 @@ def test_linear_replicated(dist_init, num_loras, device, stage,
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
 @pytest.mark.parametrize("orientation", ["row", "column"])
 @pytest.mark.parametrize("fully_shard", [True, False])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("stage", STAGES)
 @pytest.mark.parametrize("bias_enabled", [True, False])
 def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
@@ -683,7 +697,8 @@ def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
 
     torch.cuda.set_device(device)
     torch.set_default_device(device)
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     max_loras = 8
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
@@ -797,7 +812,7 @@ def test_linear_parallel(dist_init, num_loras, orientation, fully_shard,
 @pytest.mark.parametrize("num_loras", [1, 2, 4, 8])
 @pytest.mark.parametrize("repeats", [1, 2, 3])
 @pytest.mark.parametrize("fully_shard", [True, False])
-@pytest.mark.parametrize("device", CUDA_DEVICES)
+@pytest.mark.parametrize("device", DEVICES)
 @pytest.mark.parametrize("stage", STAGES)
 @pytest.mark.parametrize("bias_enabled", [True, False])
 def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard,
@@ -805,7 +820,8 @@ def test_column_parallel_packed(dist_init, num_loras, repeats, fully_shard,
 
     torch.cuda.set_device(device)
     torch.set_default_device(device)
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     max_loras = 8
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,
@@ -963,7 +979,8 @@ def test_rotary_embedding_long_context(dist_init, num_loras, device,
     seed = 0
     current_platform.seed_everything(seed)
     torch.set_default_device(device)
-    punica_wrapper = PunicaWrapper(8192, 256, device)
+    punica_wrapper = get_punica_wrapper(8192, 256, device)
+    assert check_punica_wrapper(punica_wrapper)
     max_loras = 8
     lora_config = LoRAConfig(max_loras=max_loras,
                              max_lora_rank=8,

vllm/lora/layers.py

@@ -17,7 +17,6 @@ from vllm.distributed import (get_tensor_model_parallel_rank,
                               tensor_model_parallel_all_reduce,
                               tensor_model_parallel_gather)
 from vllm.distributed.utils import divide
-from vllm.lora.punica import PunicaWrapper
 # yapf: disable
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                LinearBase,
@@ -33,7 +32,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
     VocabParallelEmbedding)
 
 if TYPE_CHECKING:
-    pass
+    from vllm.lora.punica_wrapper import PunicaWrapperBase
 
 
 def _get_lora_device(base_layer: nn.Module) -> torch.device:
@@ -115,9 +114,9 @@ class BaseLayerWithLoRA(nn.Module):
 
     def set_mapping(
         self,
-        punica_wrapper: PunicaWrapper,
+        punica_wrapper,
     ):
-        self.punica_wrapper: PunicaWrapper = punica_wrapper
+        self.punica_wrapper: PunicaWrapperBase = punica_wrapper
 
     @classmethod
     def can_replace_layer(

vllm/lora/models.py

@@ -21,7 +21,7 @@ from vllm.lora.layers import (BaseLayerWithLoRA,
                               LinearScalingRotaryEmbeddingWithLora,
                               LoRAMapping)
 from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
-from vllm.lora.punica import PunicaWrapper
+from vllm.lora.punica_wrapper import get_punica_wrapper
 from vllm.lora.utils import (from_layer, from_layer_logits_processor,
                              is_regex_target_modules,
                              parse_fine_tuned_lora_name, replace_submodule)
@@ -331,7 +331,7 @@ class LoRAModelManager(AdapterModelManager):
         self.lora_index_to_id: List[Optional[int]] = [None] * self.lora_slots
         self.vocab_size = vocab_size
         self.long_lora_context: Optional[LongContextLoRAContext] = None
-        self.punica_wrapper = PunicaWrapper(max_num_batched_tokens,
+        self.punica_wrapper = get_punica_wrapper(max_num_batched_tokens,
                                                  max_batches=self.max_num_seqs,
                                                  device=self.device)
         # Scaling factor -> offset to the sin_cos_cache to it.

vllm/lora/punica_wrapper/__init__.py

+from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
+from vllm.lora.punica_wrapper.punica_selector import get_punica_wrapper
+
+__all__ = [
+    "PunicaWrapperBase",
+    "get_punica_wrapper",
+]

vllm/lora/punica_wrapper/punica_gpu.py

+"""
+Based on:
+Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
+Punica: Multi-Tenant LoRA Serving. 
+https://arxiv.org/abs/2310.18547
+"""
+
+from typing import Callable, Optional, Tuple, Union, final
+
+import torch
+
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.lora.ops.bgmv_expand import bgmv_expand
+    from vllm.lora.ops.bgmv_expand_slice import bgmv_expand_slice
+    from vllm.lora.ops.bgmv_shrink import bgmv_shrink
+    from vllm.lora.ops.sgmv_expand import sgmv_expand
+    from vllm.lora.ops.sgmv_expand_slice import sgmv_expand_slice
+    from vllm.lora.ops.sgmv_shrink import sgmv_shrink
+
+from .punica_base import PunicaWrapperBase
+
+
+@final
+class PunicaWrapperGPU(PunicaWrapperBase):
+    """
+    PunicaWrapperGPU is designed to manage and provide metadata for the punica 
+    kernel. The main function is to maintain the state information for 
+    Multi-LoRA, and to provide the interface for the punica triton kernel.
+    """
+
+    def __init__(self, max_num_batched_tokens: int, max_batches: int,
+                 device: Union[torch.device, str], **kwargs):
+        PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
+                                   device)
+
+    def _shrink_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        scale: float,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_shrink(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            scale,
+        )
+
+    def _shrink_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        scale: float,
+    ):
+        bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale)
+
+    def _expand_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        add_input: bool,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_expand(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            add_input,
+        )
+
+    def _expand_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        add_input: bool,
+    ):
+        bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_input)
+
+    def _expand_slice_prefill(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: Optional[int],
+        y_slice_size: Optional[int],
+        add_input: bool,
+    ):
+        #No LoRA request, so return directly
+        if self.no_lora:
+            return
+        sgmv_expand_slice(
+            x,
+            w_t_all,
+            y,
+            *self.prefill_metadata,
+            y_offset,
+            y_slice_size,
+            add_input,
+        )
+
+    def _expand_slice_decode(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: Optional[int],
+        y_slice_size: Optional[int],
+        add_input: bool,
+    ):
+        bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset,
+                          y_slice_size, add_input)
+
+    def _apply_expand(
+        self,
+        y: torch.Tensor,
+        x: torch.Tensor,
+        w_t_all: torch.Tensor,
+        y_offset: Optional[int],
+        y_slice_size: Optional[int],
+        add_input: bool = True,
+    ):
+        """
+        Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all` 
+        computation, which is suitable for the
+        GEMM of lora'b.
+        """
+
+        expand_slice_fun: Callable = (self._expand_slice_prefill
+                                      if self.is_prefill else
+                                      self._expand_slice_decode)
+        expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_input)
+
+    def _apply_shrink(self, y: torch.Tensor, x: torch.Tensor,
+                      w_t_all: torch.Tensor, scale: float):
+        """
+        Perform the ` y+=x@w_t_all` computation, which is suitable for the
+        GEMM of lora'a.
+        When `is_prefill is` true, it indicates that it is currently the
+        prefill stage, and the `_shrink_prefill` function should be called.
+        Otherwise, it is the decode stage, and the _shrink_decode function
+        should be called.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        shrink_fun: Callable = (self._shrink_prefill
+                                if self.is_prefill else self._shrink_decode)
+        shrink_fun(y, x, w_t_all, scale)
+        y = y.view_as(y_org)
+
+    def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
+                   scale: float, **kwargs):
+        """
+        Performs GEMM  for multiple slices of lora_a.
+        When `is_prefill is` true, it indicates that it is currently the
+        prefill stage, and the `_shrink_prefill` function should be called.
+        Otherwise, it is the decode stage, and the _shrink_decode function
+        should be called.
+            
+        Semantics:
+        for i in range(len(lora_a_stacked)):
+            y[i] += (x @ lora_a_stacked[i]) * scale
+        
+        Args:
+            y (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weights
+            scale (float): Scaling factor for the operation
+        """
+
+        x = x.view(-1, x.shape[-1])
+        # TODO fuse these kernels
+        for slice_idx in range(len(lora_a_stacked)):
+            self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx],
+                               scale)
+
+    def add_expand(self,
+                   y: torch.Tensor,
+                   x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
+                   lora_b_stacked: Tuple[torch.Tensor, ...],
+                   lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
+                   output_slices: Tuple[int, ...],
+                   offset_start: int = 0,
+                   add_input=True,
+                   **kwargs) -> None:
+        """
+        Performs GEMM and bias addition for multiple slices of lora_b.
+      
+        Semantics:
+            for i in range(len(lora_b_stacked)):
+                slice = output_slices[i]
+                y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + 
+                    lora_bias_stacked[i] 
+                offset += slice
+            
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (Union[Tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
+            lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight
+            lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): 
+                bias's weight
+            output_slices (Tuple[int, ...]): Every slice's size
+            add_input (bool):  Defaults to True.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        offset_left = offset_start
+        if lora_bias_stacked is not None:
+            self._apply_bias(self.token_lora_indices, y, output_slices,
+                             lora_bias_stacked)
+        for slice_idx in range(len(lora_b_stacked)):
+            self._apply_expand(
+                y,
+                x[slice_idx],
+                lora_b_stacked[slice_idx],
+                offset_left,
+                output_slices[slice_idx],
+                add_input=add_input,
+            )
+            offset_left += output_slices[slice_idx]
+        y = y.view_as(y_org)
+
+    def add_lora_embedding(self,
+                           y: torch.Tensor,
+                           x: torch.Tensor,
+                           lora_b_stacked: torch.Tensor,
+                           add_input: bool = True,
+                           **kwargs) -> None:
+        """
+        Applies lora  specifically for VocabParallelEmbeddingWithLoRA.
+
+        Semantics:
+            y += x @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_b_stacked (torch.Tensor): lora_b's weights.
+            add_input (bool): Default to True.
+        """
+
+        # Embedding layer only need expand op
+        expand_fun: Callable = (self._expand_prefill
+                                if self.is_prefill else self._expand_decode)
+        expand_fun(y, x, lora_b_stacked, add_input)
+
+    def add_lora_linear(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: Tuple[torch.Tensor, ...],
+                        lora_b_stacked: Tuple[torch.Tensor, ...],
+                        lora_bias_stacked: Optional[Tuple[torch.Tensor, ...]],
+                        scale: float,
+                        output_slices: Tuple[int, ...],
+                        *,
+                        buffer: Optional[Tuple[torch.Tensor, ...]] = None,
+                        **kwargs) -> None:
+        """
+        Applicable to linear-related lora. 
+
+        Semantics:
+            for i in range(len(lora_a_stacked)):
+                y[i] += (
+                    x[i].unsqueeze(0)
+                    @ lora_a_stacked[indices[i], layer_idx, :, :]
+                    @ lora_b_stacked[indices[i], layer_idx, :, :]
+                    * scale
+                    ).squeeze(0)+lora_bias_stacked[i]
+
+        Args:
+            y (torch.Tensor): Output tensor. Will be changed in-place.
+            x (torch.Tensor): Input tensor
+            lora_a_stacked (Tuple[torch.Tensor, ...]): lora_a's weight.
+            lora_b_stacked (Tuple[torch.Tensor, ...]): lora_b's weight.
+            lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
+            scale (float): Scaling factor.
+            output_slices (Tuple[int, ...]): Every slice's size.
+            buffer (Optional[Tuple[torch.Tensor, ...]]): Defaults to None.
+        """
+
+        assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
+        if lora_bias_stacked is not None:
+            assert len(lora_bias_stacked) == len(output_slices)
+            y = self._apply_bias(self.token_lora_indices, y, output_slices,
+                                 lora_bias_stacked)
+
+        if buffer is None:
+            r = lora_b_stacked[0].size(-1)
+            # We set the buffer to be float32 by default ,refer to:
+            # https://github.com/triton-lang/triton/issues/1387
+            buffer = tuple(
+                torch.zeros(
+                    (x.size(0), r), dtype=torch.float32, device=x.device)
+                for _ in range(len(output_slices)))
+        self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
+        self.add_expand(y,
+                        buffer,
+                        lora_b_stacked,
+                        None,
+                        output_slices,
+                        add_input=True,
+                        **kwargs)
+
+    def add_lora_logits(self,
+                        y: torch.Tensor,
+                        x: torch.Tensor,
+                        lora_a_stacked: torch.Tensor,
+                        lora_b_stacked: torch.Tensor,
+                        scale,
+                        *,
+                        buffer: Optional[torch.Tensor] = None,
+                        **kwargs) -> None:
+        """
+        Applies lora  specifically for LogitsProcessorWithLoRA.
+        
+        Semantics:
+            buffer = (x @ lora_a_stacked) * scale
+            y += buffer @ lora_b_stacked
+
+        Args:
+            y (torch.Tensor): Output tensor.
+            x (torch.Tensor): Input tensor.
+            lora_a_stacked (torch.Tensor): lora_a's weights.
+            lora_b_stacked (torch.Tensor):lora_b's weights.
+            scale (float): Scaling factor.
+            buffer (Optional[torch.Tensor]):Default to None.
+        """
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        x = x.view(-1, x.shape[-1])
+        r = lora_b_stacked.size(-1)
+        if buffer is None:
+            # We set the buffer to be float32 by default ,refer to:
+            # https://github.com/triton-lang/triton/issues/1387
+            buffer = torch.zeros((x.size(0), r),
+                                 dtype=torch.float32,
+                                 device=x.device)
+        # LogitsProcessorWithLoRA always using bgmv.
+        bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale)
+        bgmv_expand(buffer,
+                    lora_b_stacked,
+                    y,
+                    self.sampler_indices,
+                    add_inputs=True)
+        y = y.view_as(y_org)

vllm/lora/punica_wrapper/punica_selector.py

+from vllm.platforms import current_platform
+from vllm.utils import print_info_once
+
+from .punica_base import PunicaWrapperBase
+
+
+def get_punica_wrapper(*args, **kwargs) -> PunicaWrapperBase:
+    if current_platform.is_cuda_alike():
+        # Lazy import to avoid ImportError
+        from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
+        print_info_once("Using PunicaWrapperGPU.")
+        return PunicaWrapperGPU(*args, **kwargs)
+    else:
+        raise NotImplementedError

vllm/lora/punica_wrapper/utils.py

+from typing import TYPE_CHECKING, List, Optional, Tuple, Union
+
+import torch
+
+if TYPE_CHECKING:
+    # avoid circuit import
+    from vllm.lora.layers import LoRAMapping
+    from vllm.lora.models import LongContextLoRAContext
+
+
+def compute_meta(
+    token_lora_tensor: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int, bool]:
+    """
+    Get the information required for the sgmv kernel. With the  features:
+    1. If consecutive requests in the batch use the same LoRA, this function
+    will combine them into a single request, improving sgmv kernel inference
+    performance.
+    2. At the beginning of each prefill stage inference, recalculations are
+    needed based on the input, but only once.
+    """
+
+    lora_indices_tensor, seq_length_tensor = torch.unique_consecutive(
+        token_lora_tensor, return_counts=True)
+    cum_result = torch.cumsum(seq_length_tensor, dim=0)
+    b_seq_start_tensor = torch.zeros_like(seq_length_tensor)
+    b_seq_start_tensor[1:].copy_(cum_result[:-1])
+    max_length = seq_length_tensor.max().item()
+    token_nums = seq_length_tensor.sum().item()
+    batch_size = lora_indices_tensor.size(0)
+    no_lora = False
+    # -1 means no lora should be applied. Use `no_lora` to determine whether
+    # the current step requires LoRA. If LoRA is not needed, the prefill stage
+    # does not need to launch the triton kernel, which can improve performance
+    if batch_size == 1 and lora_indices_tensor == -1:
+        no_lora = True
+    return (b_seq_start_tensor, seq_length_tensor, lora_indices_tensor,
+            batch_size, max_length, token_nums, no_lora)
+
+
+# TODO see if this can be vectorized
+def convert_mapping(
+    mapping: "LoRAMapping",
+    lora_index_to_id: List[Optional[int]],
+    max_loras: int,
+    vocab_size: int,
+    extra_vocab_size: int,
+    device: torch.device,
+    long_lora_context: Optional["LongContextLoRAContext"] = None,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
+           Optional[torch.Tensor], List[int]]:
+    """Converts LoRAMapping to index tensors.
+
+    Args:
+        mapping: LoRAMapping mapping rows in a batch to LoRA ids.
+        lora_index_to_id: List mapping LoRA ids to LoRA indices.
+        max_loras: Maximum number of LoRAs.
+        vocab_size: Model vocab size.
+        extra_vocab_size: Extra vocab size each LoRA can have.
+        long_lora_context: Passed if there are long context lora in a batch.
+
+    Returns:
+        A tuple of tensors:
+            base_indices: Tensor of shape [batch_size] mapping batch rows to
+                LoRA indices.
+            sampler_indices: Tensor of shape [batch_size] mapping requests to
+                LoRA indices for sampler. For generation, this will be the
+                same as base_indicies. For prefill, this will map requests
+                to LoRA indices.
+            sampler_indices_padded: Tensor of shape [batch_size] mapping
+                requests to LoRA indices for sampler with padding.
+                Same as sampler_indicies, but -1 is replaced with
+                max_loras.
+            embeddings_indices: Tensor of shape [2, batch_size] mapping
+                requests to embedding indices. First row is for embeddings
+                added by the LoRAs, second row is for the LoRA.lora_a
+                embeddings.
+            long_lora_indices: Tensor of shape [batch_size] mapping
+                requests to RoPE offsets and rot dims for long LoRAs.
+                None if long context lora doesn't exist.
+            indices_len: List of lengths of the above tensors. It contains
+                (base_indices, sampler_indices, sampler_indices_padded,
+                embeddings_indices, long_lora_indices).
+    """
+    index_mapping_indices: List[int] = list(mapping.index_mapping).copy()
+    embedding_indices = index_mapping_indices.copy()
+    lora_indices = index_mapping_indices.copy()
+    long_lora_offsets: Optional[torch.Tensor] = None
+    if long_lora_context:
+        long_lora_offsets = torch.zeros(len(index_mapping_indices),
+                                        device=device,
+                                        dtype=torch.long)
+    prompt_mapping: List[int] = [
+        lora_index_to_id.index(x) if x > 0 else -1
+        for x in mapping.prompt_mapping
+    ]
+    lora_idx = None
+    for i in range(len(index_mapping_indices)):
+        # TODO index can be slow. optimize
+        lora_idx = (lora_index_to_id.index(index_mapping_indices[i])
+                    if index_mapping_indices[i] > 0 else -1)
+        embedding_indices[i] = lora_idx if index_mapping_indices[i] > 0 else 0
+        lora_indices[i] = lora_idx
+        if long_lora_context:
+            assert long_lora_offsets is not None
+            lora_offset: int = long_lora_context.offsets_by_lora_id.get(
+                index_mapping_indices[i], 0)
+            long_lora_offsets[i] = lora_offset
+
+    indices_list: List[Union[List[int], torch.Tensor]] = [
+        index_mapping_indices,
+        lora_indices,
+        embedding_indices,
+    ]
+    if long_lora_context:
+        assert long_lora_offsets is not None
+        indices_list.append(long_lora_offsets)
+    indices = torch.tensor(indices_list, dtype=torch.long, device=device)
+    prompt_mapping_tensor = torch.tensor(prompt_mapping,
+                                         dtype=torch.long,
+                                         device=device)
+    embeddings_indices = torch.stack([
+        indices[2] * extra_vocab_size,
+        indices[2] * (vocab_size + extra_vocab_size),
+    ])
+    embeddings_indices[embeddings_indices == -1] = max_loras - 1
+    base_indices = indices[1]
+    sampler_indices = prompt_mapping_tensor
+    sampler_indices_padded = sampler_indices.clone()
+    sampler_indices_padded[sampler_indices_padded == -1] = max_loras - 1
+    sampler_indices_padded = torch.arange(
+        0, len(sampler_indices_padded), device=device, dtype=torch.long) + (
+            sampler_indices_padded * len(sampler_indices_padded))
+    long_lora_indices = None
+    long_lora_indices_len: Optional[int] = None
+    if long_lora_context:
+        long_lora_indices = indices[3]
+        long_lora_indices_len = long_lora_indices.shape[-1]
+    # Contain length of indices tensors. Used to index into each tensor.
+    indices_len = [
+        base_indices.shape[-1],
+        sampler_indices.shape[-1],
+        sampler_indices_padded.shape[-1],
+        embeddings_indices.shape[-1],
+    ]
+    if long_lora_indices_len is not None:
+        indices_len.append(long_lora_indices_len)
+    else:
+        # If long_lora doesn't exist,append None
+        indices_len.append(None)
+
+    return (
+        base_indices,
+        sampler_indices,
+        sampler_indices_padded,
+        embeddings_indices,
+        long_lora_indices,
+        indices_len,
+    )