[Fix] Fix accuracy bug and refactor codes for lora (#3413)

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

-from .base_backend import BaseLoraBackend
-from .flashinfer_backend import FlashInferLoraBackend
-from .triton_backend import TritonLoraBackend
+from .base_backend import BaseLoRABackend
+from .flashinfer_backend import FlashInferLoRABackend
+from .triton_backend import TritonLoRABackend
+
+
+def get_backend_from_name(name: str) -> BaseLoRABackend:
+    """
+    Get corresponding backend class from backend's name
+    """
+    backend_mapping = {
+        "triton": TritonLoRABackend,
+        "flashinfer": FlashInferLoRABackend,
+    }
+
+    if name in backend_mapping:
+        return backend_mapping[name]
+
+    raise Exception(
+        f"No supported lora backend called {name}. It should be one of {list(backend_mapping.keys())}"
+    )
+
 
 __all__ = [
-    "FlashInferLoraBackend",
-    "TritonLoraBackend",
+    "BaseLoRABackend",
+    "FlashInferLoRABackend",
+    "TritonLoRABackend",
+    "get_backend_from_name",
 ]

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

@@ -2,7 +2,7 @@ from typing import Tuple, Union
 
 import torch
 
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.utils import LoRABatchInfo
 
 
 def get_fuse_output_scaling_add_from_name(name: str) -> bool:
@@ -13,7 +13,7 @@ def get_fuse_output_scaling_add_from_name(name: str) -> bool:
     return mapping.get(name, False)
 
 
-def get_fuse_qkv_lora_b_from_name(name: str) -> bool:
+def get_fuse_stacked_lora_b_from_name(name: str) -> bool:
     mapping = {
         "triton": True,
         "flashinfer": False,
@@ -21,7 +21,7 @@ def get_fuse_qkv_lora_b_from_name(name: str) -> bool:
     return mapping.get(name, False)
 
 
-class BaseLoraBackend:
+class BaseLoRABackend:
     """Base class for different Lora backends.
        Each backend has its own implementation of Lora kernels.
 
@@ -32,11 +32,11 @@ class BaseLoraBackend:
                                  and the operation of scaling and adding will be fused into kernel
     """
 
-    def __init__(self, name: str, batch_info: LoraBatchInfo = None):
+    def __init__(self, name: str, batch_info: LoRABatchInfo = None):
         self.name = name
         self.batch_info = batch_info
         self.fuse_output_scaling_add = get_fuse_output_scaling_add_from_name(name)
-        self.fuse_qkv_lora_b = get_fuse_qkv_lora_b_from_name(name)
+        self.fuse_stacked_lora_b = get_fuse_stacked_lora_b_from_name(name)
 
     def run_lora_a_sgemm(
         self, x: torch.Tensor, weights: torch.Tensor, *args, **kwargs
@@ -46,10 +46,11 @@ class BaseLoraBackend:
 
         Args:
              x: input matrix with shape (s, input_dim), here s is the sum of all sequence lengths
-             weights: a set of lora weights with shape (num_lora, r, input_dim), here r is lora rank
+             weights: a set of lora weights with shape (num_lora, c * r, input_dim),
+                      here r is lora rank, c is a multiplier for stacked modules (e.g., c=3 for qkv_proj, c=2 for gate_up_proj)
                       usually input_dim is much larger than r
         Returns:
-             result with shape (s, r)
+             result with shape (s, c * r)
         """
         pass
 
@@ -83,7 +84,7 @@ class BaseLoraBackend:
             qkv_lora_a: lora_a module for qkv, with shape (num_lora, 3 * r, input_dim)
             qkv_lora_b: lora_b module for qkv.
                         If passed in as a tensor, its shape should be (num_lora,output_dim_q + 2 * output_dim_kv, r)
-                        If passed in as a tuple of two tensors containing:
+                        If passed in as a tuple of two tensors, it should contain:
                            a lora_b module for q, with shape (1, num_lora, output_dim_q, r)
                            and a combined lora_b module for kv, with shape (2, num_lora, output_dim_kv, r)
         Returns:
@@ -91,5 +92,26 @@ class BaseLoraBackend:
         """
         pass
 
-    def set_batch_info(self, batch_info: LoraBatchInfo):
+    def run_gate_up_lora(
+        self,
+        x: torch.Tensor,
+        gate_up_lora_a: torch.Tensor,
+        gate_up_lora_b: Union[torch.Tensor, Tuple[torch.Tensor]],
+        *args,
+        **kwargs
+    ) -> torch.Tensor:
+        """Run the lora pass for gate_up_proj, usually attached to MergedColumnParallelLayer.
+
+        Args:
+            x: input matrix with shape (s, input_dim), here s is the sum of all sequence lengths
+            gate_up_lora_a: lora_a module for gate_up_proj, with shape (num_lora, 2 * r, input_dim)
+            gate_up_lora_b: lora_b module for qkv.
+                        If passed in as a tensor, its shape should be (num_lora, 2 * output_dim, r)
+                        If passed in as a tuple, it should contain two tensors with shape (num_lora, output_dim, r)
+        Returns:
+            result with shape (s, 2 * output_dim)
+        """
+        pass
+
+    def set_batch_info(self, batch_info: LoRABatchInfo):
         self.batch_info = batch_info

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

@@ -2,17 +2,17 @@ from typing import Tuple
 
 import torch
 
-from sglang.srt.lora.backend import BaseLoraBackend
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.utils import LoRABatchInfo
 from sglang.srt.utils import is_flashinfer_available
 
 if is_flashinfer_available():
     from flashinfer import SegmentGEMMWrapper
 
 
-class FlashInferLoraBackend(BaseLoraBackend):
+class FlashInferLoRABackend(BaseLoRABackend):
 
-    def __init__(self, name: str, batch_info: LoraBatchInfo = None):
+    def __init__(self, name: str, batch_info: LoRABatchInfo = None):
         super().__init__(name, batch_info)
 
         # Set up SGemm Wrapper from flashinfer
@@ -55,6 +55,8 @@ class FlashInferLoraBackend(BaseLoraBackend):
         **kwargs,
     ) -> torch.Tensor:
 
+        assert isinstance(qkv_lora_b, tuple) and len(qkv_lora_b) == 2
+
         # Shape of lora_a_output: (s, 3 * r)
         lora_a_output = self.run_lora_a_sgemm(x=x, weights=qkv_lora_a)
 
@@ -89,3 +91,38 @@ class FlashInferLoraBackend(BaseLoraBackend):
         )
 
         return lora_output
+
+    def run_gate_up_lora(
+        self,
+        x: torch.Tensor,
+        gate_up_lora_a: torch.Tensor,
+        gate_up_lora_b: Tuple[torch.Tensor],
+        *args,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        assert isinstance(gate_up_lora_b, tuple) and len(gate_up_lora_b) == 2
+        lora_rank = gate_up_lora_b[0].shape[-1]
+        output_dim = gate_up_lora_b[0].shape[-2]
+
+        # Shape of lora_a_output: (s, 2 * r)
+        lora_a_output = self.run_lora_a_sgemm(x=x, weights=gate_up_lora_a)
+
+        lora_output = torch.empty(
+            (x.shape[0], 2 * output_dim),
+            device=x.device,
+            dtype=x.dtype,
+        )
+
+        # Compute lora for gate and up proj respectively
+        lora_output[:, :output_dim] = self.run_lora_b_sgemm(
+            x=lora_a_output[:, :lora_rank].contiguous(),
+            weights=gate_up_lora_b[0],
+        )
+
+        lora_output[:, output_dim:] = self.run_lora_b_sgemm(
+            x=lora_a_output[:, lora_rank:].contiguous(),
+            weights=gate_up_lora_b[1],
+        )
+
+        return lora_output

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

 import torch
 
-from sglang.srt.lora.backend import BaseLoraBackend
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.backend import BaseLoRABackend
 from sglang.srt.lora.triton_ops import (
+    gate_up_lora_b_fwd,
     qkv_lora_b_fwd,
     sgemm_lora_a_fwd,
     sgemm_lora_b_fwd,
 )
+from sglang.srt.lora.utils import LoRABatchInfo
 
 
-class TritonLoraBackend(BaseLoraBackend):
+class TritonLoRABackend(BaseLoRABackend):
 
-    def __init__(self, name: str, batch_info: LoraBatchInfo = None):
+    def __init__(self, name: str, batch_info: LoRABatchInfo = None):
         super().__init__(name, batch_info)
 
     def run_lora_a_sgemm(
@@ -59,3 +60,32 @@ class TritonLoraBackend(BaseLoraBackend):
             scaling,
         )
         return lora_output
+
+    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,
+        scaling: float = 1.0,
+        *args,
+        **kwargs
+    ) -> torch.Tensor:
+
+        # x: (s, input_dim)
+        # gate_up_lora_a: (num_lora, 2 * r, input_dim)
+        # gate_up_lora_b: (num_lora, 2 * output_dim, r)
+        assert isinstance(gate_up_lora_b, torch.Tensor)
+        output_dim = gate_up_lora_b.shape[-2] // 2
+
+        # lora_a_output: (s, 2 * r)
+        lora_a_output = sgemm_lora_a_fwd(x, gate_up_lora_a, self.batch_info)
+        lora_output = gate_up_lora_b_fwd(
+            lora_a_output,
+            gate_up_lora_b,
+            self.batch_info,
+            output_dim,
+            base_output,
+            scaling,
+        )
+        return lora_output

python/sglang/srt/lora/layers.py

+import torch
+from torch import nn
+
+from sglang.srt.distributed import (
+    get_tensor_model_parallel_rank,
+    split_tensor_along_last_dim,
+    tensor_model_parallel_all_gather,
+    tensor_model_parallel_all_reduce,
+)
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.lora.backend import BaseLoRABackend
+
+
+class BaseLayerWithLoRA(nn.Module):
+    def __init__(
+        self,
+        base_layer: nn.Module,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ):
+        super().__init__()
+        self.base_layer: nn.Module = base_layer
+        self.lora_rank: int = lora_rank
+        self.scaling: float = scaling
+        self.set_lora: bool = False
+        self.lora_backend: BaseLoRABackend = lora_backend
+
+    def forward(self, x: torch.Tensor):
+        return self.base_layer.forward(x)
+
+    def set_lora_info(self, *args):
+        pass
+
+
+class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
+    def __init__(
+        self,
+        base_layer: VocabParallelEmbedding,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ) -> None:
+        super().__init__(base_layer, lora_rank, scaling, lora_backend)
+        self.weight = base_layer.weight
+
+
+class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
+    def __init__(
+        self,
+        base_layer: ColumnParallelLinear,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ) -> None:
+        super().__init__(base_layer, lora_rank, scaling, lora_backend)
+
+    def set_lora_info(
+        self,
+        A_buffer: torch.Tensor,
+        B_buffer: torch.Tensor,
+    ):
+        self.set_lora = True
+        self.A_buffer = A_buffer
+        self.B_buffer = B_buffer
+
+    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+        backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
+        lora_a_output = self.lora_backend.run_lora_a_sgemm(x, self.A_buffer)
+        lora_output = self.lora_backend.run_lora_b_sgemm(
+            lora_a_output,
+            self.B_buffer[0],
+            **backend_kwargs,
+        )
+        return (
+            lora_output
+            if self.lora_backend.fuse_output_scaling_add
+            else base_output + lora_output * self.scaling
+        )
+
+    def forward(self, input_: torch.Tensor):
+        # duplicate the logic in ColumnParallelLinear
+        bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None
+        output_parallel = self.base_layer.quant_method.apply(
+            self.base_layer, input_, bias
+        )
+
+        if self.set_lora:
+            output_parallel = self.apply_lora(output_parallel, input_)
+
+        if self.base_layer.gather_output:
+            output = tensor_model_parallel_all_gather(output_parallel)
+        else:
+            output = output_parallel
+        output_bias = self.base_layer.bias if self.base_layer.skip_bias_add else None
+        return output, output_bias
+
+
+class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
+    def __init__(
+        self,
+        base_layer: MergedColumnParallelLinear,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ) -> None:
+        super().__init__(base_layer, lora_rank, scaling, lora_backend)
+
+    def set_lora_info(
+        self,
+        A_buffer: torch.Tensor,
+        B_buffer: torch.Tensor,
+    ):
+        self.set_lora = True
+        self.A_buffer_gate_up = A_buffer
+        if self.lora_backend.fuse_stacked_lora_b:
+            # B_buffer_gate_up: (num_lora, 2 * output_dim, r)
+            self.B_buffer_gate_up = torch.cat(
+                (B_buffer[0], B_buffer[1]), dim=-2
+            ).contiguous()
+        else:
+            self.B_buffer_gate_up = (B_buffer[0], B_buffer[1])
+
+    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+        backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
+
+        lora_output = self.lora_backend.run_gate_up_lora(
+            x,
+            self.A_buffer_gate_up,
+            self.B_buffer_gate_up,
+            **backend_kwargs,
+        )
+        return (
+            lora_output
+            if self.lora_backend.fuse_output_scaling_add
+            else base_output + lora_output * self.scaling
+        )
+
+
+class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
+    def init__(
+        self,
+        base_layer: QKVParallelLinear,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ) -> None:
+        super().__init__(base_layer, lora_rank, scaling, lora_backend)
+
+    def set_lora_info(
+        self,
+        A_buffer_qkv: torch.Tensor,
+        B_buffer_q: torch.Tensor,
+        B_buffer_kv: torch.Tensor,
+    ):
+        self.set_lora = True
+        self.A_buffer_qkv = A_buffer_qkv
+
+        if self.lora_backend.fuse_stacked_lora_b:
+            assert (
+                B_buffer_q.shape[-1] == B_buffer_kv.shape[-1]
+            ), "The lora rank of q and kv should be the same when enabling fusion of qkv lora_b"
+            output_dim_q, output_dim_kv = B_buffer_q.shape[-2], B_buffer_kv.shape[-2]
+
+            # B_buffer_qkv: (num_lora, output_dim_q + 2 * output_dim_kv, r)
+            self.B_buffer_qkv = torch.cat(
+                (B_buffer_q[0], B_buffer_kv[0], B_buffer_kv[1]), dim=-2
+            ).contiguous()
+
+            # Offsets of q/k/v in output dimension
+            self.output_offset = torch.tensor(
+                [
+                    0,
+                    output_dim_q,
+                    output_dim_q + output_dim_kv,
+                    output_dim_q + 2 * output_dim_kv,
+                ],
+                dtype=torch.int32,
+                device=B_buffer_q.device,
+            )
+            # For computing number of launched blocks
+            self.max_qkv_out_dim = max(output_dim_q, output_dim_kv)
+        else:
+            self.B_buffer_qkv = (
+                B_buffer_q,
+                B_buffer_kv,
+            )
+
+    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+        backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
+        if self.lora_backend.fuse_stacked_lora_b:
+            backend_kwargs["output_offset"] = self.output_offset
+            backend_kwargs["max_qkv_out_dim"] = self.max_qkv_out_dim
+
+        lora_output = self.lora_backend.run_qkv_lora(
+            x,
+            self.A_buffer_qkv,
+            self.B_buffer_qkv,
+            **backend_kwargs,
+        )
+        return (
+            lora_output
+            if self.lora_backend.fuse_output_scaling_add
+            else base_output + lora_output * self.scaling
+        )
+
+
+class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
+    def __init__(
+        self,
+        base_layer: RowParallelLinear,
+        lora_rank: int,
+        scaling: float,
+        lora_backend: BaseLoRABackend,
+    ) -> None:
+        super().__init__(base_layer, lora_rank, scaling, lora_backend)
+
+    def set_lora_info(self, A_buffer: torch.Tensor, B_buffer: torch.Tensor):
+        self.set_lora = True
+        self.A_buffer = A_buffer
+        self.B_buffer = B_buffer
+
+    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
+        backend_kwargs = {"base_output": base_output, "scaling": self.scaling}
+        lora_a_output = self.lora_backend.run_lora_a_sgemm(x, self.A_buffer)
+        lora_output = self.lora_backend.run_lora_b_sgemm(
+            lora_a_output,
+            self.B_buffer[0],
+            **backend_kwargs,
+        )
+        return (
+            lora_output
+            if self.lora_backend.fuse_output_scaling_add
+            else base_output + lora_output * self.scaling
+        )
+
+    def forward(self, input_: torch.Tensor):
+        # duplicate the logic in RowParallelLinear
+        if self.base_layer.input_is_parallel:
+            input_parallel = input_
+        else:
+            tp_rank = get_tensor_model_parallel_rank()
+            splitted_input = split_tensor_along_last_dim(
+                input_, num_partitions=self.base_layer.tp_size
+            )
+            input_parallel = splitted_input[tp_rank].contiguous()
+        output_parallel = self.base_layer.quant_method.apply(
+            self.base_layer, input_parallel
+        )
+
+        if self.set_lora:
+            output_parallel = self.apply_lora(output_parallel, input_parallel)
+
+        if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
+            output_ = tensor_model_parallel_all_reduce(output_parallel)
+        else:
+            output_ = output_parallel
+
+        if not self.base_layer.skip_bias_add:
+            output = (
+                output_ + self.base_layer.bias
+                if self.base_layer.bias is not None
+                else output_
+            )
+            output_bias = None
+        else:
+            output = output_
+            output_bias = self.base_layer.bias
+        return output, output_bias
+
+
+def get_lora_layer(
+    layer: nn.Module, lora_rank: int, scaling: int, lora_backend: BaseLoRABackend
+) -> BaseLayerWithLoRA:
+    supported_layer_types = {
+        # the order matters
+        VocabParallelEmbedding: VocabParallelEmbeddingWithLoRA,
+        QKVParallelLinear: QKVParallelLinearWithLoRA,
+        MergedColumnParallelLinear: MergedColumnParallelLinearWithLoRA,
+        ColumnParallelLinear: ColumnParallelLinearWithLoRA,
+        RowParallelLinear: RowParallelLinearWithLoRA,
+    }
+    for src_layer_type, lora_layer_type in supported_layer_types.items():
+        if isinstance(layer, src_layer_type):  # pylint: disable=unidiomatic-typecheck
+            ret = lora_layer_type(layer, lora_rank, scaling, lora_backend)
+            return ret
+    raise Exception(f"No corresponding LoRA layer supported for {type(layer)}.")

python/sglang/srt/lora/lora.py

@@ -19,282 +19,25 @@
 # https://github.com/vllm-project/vllm/blob/4abf6336ec65c270343eb895e7b18786e9274176/vllm/lora/layers.py
 
 import re
-from dataclasses import dataclass
+from typing import Dict, List
 
 import torch
 from torch import nn
 
-from sglang.srt.layers.linear import (
-    ColumnParallelLinear,
-    MergedColumnParallelLinear,
-    QKVParallelLinear,
-    RowParallelLinear,
-)
-from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from sglang.srt.configs.load_config import LoadConfig
+from sglang.srt.hf_transformers_utils import AutoConfig
+from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.lora_config import LoRAConfig
 from sglang.srt.model_loader.loader import DefaultModelLoader
 
 
-@dataclass
-class LoraBatchInfo:
-    # Batch size
-    bs: int
-
-    # Lengths of each sequence in shape (bs,)
-    seg_lens: torch.Tensor
-
-    # Indice pointers of each sequence in shape (bs + 1, )
-    seg_indptr: torch.Tensor
-
-    # Maximum sequence length of current batch
-    max_len: int
-
-    # The index of lora adapter used by each sequence, in shape (bs,)
-    weight_indices: torch.Tensor
-
-
-class BaseLayerWithLoRA(nn.Module):
-    def __init__(self, base_layer, lora_rank, scaling, lora_backend):
-        super().__init__()
-        self.base_layer = base_layer
-        self.lora_rank = lora_rank
-        self.scaling = scaling
-        self.set_lora = False
-        self.lora_backend = lora_backend
-
-    def forward(self, x: torch.Tensor):
-        return self.base_layer.forward(x)
-
-    def set_lora_info(self, *args):
-        pass
-
-
-class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):
-    def __init__(
-        self, base_layer: VocabParallelEmbedding, lora_rank, scaling, lora_backend
-    ) -> None:
-        super().__init__(base_layer, lora_rank, scaling, lora_backend)
-        self.weight = base_layer.weight
-
-
-class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
-    def __init__(
-        self, base_layer: ColumnParallelLinear, lora_rank, scaling, lora_backend
-    ) -> None:
-        super().__init__(base_layer, lora_rank, scaling, lora_backend)
-
-    def apply_lora(self, output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
-        # TODO
-        return output
-
-    def forward(self, input_: torch.Tensor):
-        # duplicate the logic in ColumnParallelLinear
-        bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None
-        output_parallel = self.base_layer.quant_method.apply(
-            self.base_layer, input_, bias
-        )
-
-        if self.set_lora:
-            output_parallel = self.apply_lora(output_parallel, input_)
-
-        if self.base_layer.gather_output:
-            output = tensor_model_parallel_all_gather(output_parallel)
-        else:
-            output = output_parallel
-        output_bias = self.base_layer.bias if self.base_layer.skip_bias_add else None
-        return output, output_bias
-
-
-class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
-    def __init__(
-        self, base_layer: MergedColumnParallelLinear, lora_rank, scaling, lora_backend
-    ) -> None:
-        super().__init__(base_layer, lora_rank, scaling, lora_backend)
-
-    def set_lora_info(
-        self,
-        A_buffer,
-        B_buffer,
-    ):
-        self.set_lora = True
-        self.A_buffer = A_buffer
-        self.B_buffer = B_buffer
-
-    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
-        lora_a_output = self.lora_backend.run_lora_a_sgemm(x=x, weights=self.A_buffer)
-
-        output_dim = base_output.shape[-1]
-        lora_output = torch.empty_like(base_output)
-        lora_output[:, :output_dim] = self.lora_backend.run_lora_b_sgemm(
-            x=lora_a_output[:, 0 : self.lora_rank].contiguous(),
-            weights=self.B_buffer[0],
-        )
-
-        lora_output[:, output_dim : 2 * output_dim] = (
-            self.lora_backend.run_lora_b_sgemm(
-                x=lora_a_output[:, self.lora_rank : 2 * self.lora_rank].contiguous(),
-                weights=self.B_buffer[1],
-            )
-        )
-
-        return base_output + lora_output * self.scaling
-
-
-class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
-    def init__(
-        self, base_layer: QKVParallelLinear, lora_rank, scaling, lora_backend
-    ) -> None:
-        super().__init__(base_layer, lora_rank, scaling, lora_backend)
-
-    def set_lora_info(
-        self,
-        A_buffer_qkv,
-        B_buffer_q,
-        B_buffer_kv,
-    ):
-        self.set_lora = True
-        self.A_buffer_qkv = A_buffer_qkv
-
-        if self.lora_backend.fuse_qkv_lora_b:
-            assert (
-                B_buffer_q.shape[-1] == B_buffer_kv.shape[-1]
-            ), "The lora rank of q and kv should be the same when enabling fusion of qkv lora_b"
-            output_dim_q, output_dim_kv = B_buffer_q.shape[-2], B_buffer_kv.shape[-2]
-
-            # B_buffer_qkv: (num_lora, output_dim_q + 2 * output_dim_kv, r)
-            self.B_buffer_qkv = torch.cat(
-                (B_buffer_q[0], B_buffer_kv[0], B_buffer_kv[1]), dim=-2
-            ).contiguous()
-
-            # Offsets of q/k/v in output dimension
-            self.output_offset = torch.tensor(
-                [
-                    0,
-                    output_dim_q,
-                    output_dim_q + output_dim_kv,
-                    output_dim_q + 2 * output_dim_kv,
-                ],
-                dtype=torch.int32,
-                device=B_buffer_q.device,
-            )
-            # For computing number of launched blocks
-            self.max_qkv_out_dim = max(output_dim_q, output_dim_kv)
-        else:
-            self.B_buffer_qkv = (
-                B_buffer_q,
-                B_buffer_kv,
-            )
-            self.output_offset = None
-            self.max_qkv_out_dim = None
-
-    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
-        lora_output = self.lora_backend.run_qkv_lora(
-            x,
-            self.A_buffer_qkv,
-            self.B_buffer_qkv,
-            output_offset=self.output_offset,
-            max_qkv_out_dim=self.max_qkv_out_dim,
-            base_output=base_output,
-            scaling=self.scaling,
-        )
-        return (
-            lora_output
-            if self.lora_backend.fuse_output_scaling_add
-            else base_output + lora_output * self.scaling
-        )
-
-
-class RowParallelLinearWithLoRA(BaseLayerWithLoRA):
-    def __init__(
-        self, base_layer: RowParallelLinear, lora_rank, scaling, lora_backend
-    ) -> None:
-        super().__init__(base_layer, lora_rank, scaling, lora_backend)
-
-    def set_lora_info(self, A_buffer, B_buffer):
-        self.set_lora = True
-        self.A_buffer = A_buffer
-        self.B_buffer = B_buffer
-
-    def apply_lora(self, base_output: torch.Tensor, x: torch.Tensor) -> torch.Tensor:
-        lora_a_output = self.lora_backend.run_lora_a_sgemm(x, self.A_buffer)
-        lora_output = self.lora_backend.run_lora_b_sgemm(
-            lora_a_output,
-            self.B_buffer[0],
-            base_output=base_output,
-            scaling=self.scaling,
-        )
-        return (
-            lora_output
-            if self.lora_backend.fuse_output_scaling_add
-            else base_output + lora_output * self.scaling
-        )
-
-    def forward(self, input_):
-        # duplicate the logic in RowParallelLinear
-        if self.base_layer.input_is_parallel:
-            input_parallel = input_
-        else:
-            tp_rank = get_tensor_model_parallel_rank()
-            splitted_input = split_tensor_along_last_dim(
-                input_, num_partitions=self.base_layer.tp_size
-            )
-            input_parallel = splitted_input[tp_rank].contiguous()
-        output_parallel = self.base_layer.quant_method.apply(
-            self.base_layer, input_parallel
-        )
-
-        if self.set_lora:
-            output_parallel = self.apply_lora(output_parallel, input_parallel)
-
-        if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
-            output_ = tensor_model_parallel_all_reduce(output_parallel)
-        else:
-            output_ = output_parallel
-
-        if not self.base_layer.skip_bias_add:
-            output = (
-                output_ + self.base_layer.bias
-                if self.base_layer.bias is not None
-                else output_
-            )
-            output_bias = None
-        else:
-            output = output_
-            output_bias = self.base_layer.bias
-        return output, output_bias
-
-
-def get_lora_layer(
-    layer: nn.Module, lora_rank, scaling, lora_backend
-) -> BaseLayerWithLoRA:
-    supported_layer_types = {
-        # the order matters
-        VocabParallelEmbedding: VocabParallelEmbeddingWithLoRA,
-        QKVParallelLinear: QKVParallelLinearWithLoRA,
-        MergedColumnParallelLinear: MergedColumnParallelLinearWithLoRA,
-        ColumnParallelLinear: ColumnParallelLinearWithLoRA,
-        RowParallelLinear: RowParallelLinearWithLoRA,
-    }
-    for src_layer_type, lora_layer_type in supported_layer_types.items():
-        if isinstance(layer, src_layer_type):  # pylint: disable=unidiomatic-typecheck
-            ret = lora_layer_type(layer, lora_rank, scaling, lora_backend)
-            return ret
-    raise Exception(f"No corresponding LoRA layer supported for {type(layer)}.")
-
-
-def get_mapped_params(module_names):
-    ret = set()
-    for module_name in module_names:
-        ret.add(params_mapping(module_name))
-    return list(ret)
-
-
 class LoRALayer(nn.Module):
-    def __init__(self, config, base_hf_config):
+    def __init__(self, config: LoRAConfig, base_hf_config: AutoConfig):
         super().__init__()
-        self.config = config
-        self.base_hf_config = base_hf_config
-        self.weights = {}
-        self.weight_gpu = {}
+        self.config: LoRAConfig = config
+        self.base_hf_config: AutoConfig = base_hf_config
+        self.weights: Dict[str, torch.Tensor] = {}
+        self.weight_gpu: Dict[str, torch.Tensor] = {}
 
     def load_to_gpu(self):
         for name, weight in self.weights.items():
@@ -306,33 +49,32 @@ class LoRALayer(nn.Module):
 
 
 class LoRAAdapter(nn.Module):
-    def __init__(self, uid, config, base_hf_config, load_config, lora_backend):
+    def __init__(
+        self,
+        uid: str,
+        config: LoRAConfig,
+        base_hf_config: AutoConfig,
+        load_config: LoadConfig,
+        lora_backend: BaseLoRABackend,
+    ):
         super().__init__()
-        self.uid = uid
-        self.config = config
+        self.uid: str = uid
+        self.config: LoRAConfig = config
         assert self.config.hf_config["peft_type"].lower() == "lora"
-        self.base_hf_config = base_hf_config
-        self.load_config = load_config
-        self.lora_backend = lora_backend
-        self.scaling = self.config.lora_alpha / self.config.r
+        self.base_hf_config: AutoConfig = base_hf_config
+        self.load_config: LoadConfig = load_config
+        self.lora_backend: BaseLoRABackend = lora_backend
+        self.scaling: float = self.config.lora_alpha / self.config.r
 
-        self.layers = nn.ModuleList(
+        self.layers: List[LoRALayer] = nn.ModuleList(
             [
                 LoRALayer(config, base_hf_config)
                 for i in range(base_hf_config.num_hidden_layers)
             ]
         )
 
-        self.weights = {}
-        self.weights_gpu = {}
-
-    def get_stacked_multiply(self, module_name):
-        stacked_rank = {
-            "qkv_proj": 3,
-            "kv_proj": 2,
-            "gate_up_proj": 2,
-        }
-        return stacked_rank[module_name] if module_name in stacked_rank else 1
+        self.weights: Dict[str, torch.Tensor] = {}
+        self.weights_gpu: Dict[str, torch.Tensor] = {}
 
     def load_to_gpu(self):
         for name, weight in self.weights.items():
@@ -367,44 +109,77 @@ class LoRAAdapter(nn.Module):
         for i in range(self.base_hf_config.num_hidden_layers):
             layer = self.layers[i]
             weight_names = [name for name, _ in layer.weights.items()]
-            for weight_name in weight_names:
-                if "k_proj" in weight_name:
-                    q_name = weight_name.replace("k_proj", "q_proj")
-                    v_name = weight_name.replace("k_proj", "v_proj")
-                    kv_name = weight_name.replace("k_proj", "kv_proj")
-                    qkv_name = weight_name.replace("k_proj", "qkv_proj")
-                    if "lora_A" in weight_name:
-                        layer.weights[qkv_name] = torch.cat(
-                            (
-                                layer.weights[q_name],
-                                layer.weights[weight_name],
-                                layer.weights[v_name],
-                            ),
-                            0,
-                        )
-                        layer.weights.pop(q_name)
-                        layer.weights.pop(weight_name)
-                        layer.weights.pop(v_name)
-                    else:
-                        layer.weights[kv_name] = torch.stack(
-                            [
-                                layer.weights[weight_name],
-                                layer.weights[v_name],
-                            ],
-                            dim=0,
-                        )
-                        layer.weights.pop(weight_name)
-                        layer.weights.pop(v_name)
-                elif "gate_proj" in weight_name:
-                    up_name = weight_name.replace("gate_proj", "up_proj")
-                    gate_up_name = weight_name.replace("gate_proj", "gate_up_proj")
-                    if "lora_A" in weight_name:
-                        layer.weights[gate_up_name] = torch.cat(
-                            (layer.weights[weight_name], layer.weights[up_name]), 0
-                        )
-                    else:
-                        layer.weights[gate_up_name] = torch.stack(
-                            [layer.weights[weight_name], layer.weights[up_name]], dim=0
-                        )
-                    layer.weights.pop(weight_name)
-                    layer.weights.pop(up_name)
+            self.stack_qkv_proj(weight_names, layer.weights)
+            self.stack_gate_up_proj(weight_names, layer.weights)
+
+    def stack_qkv_proj(self, weight_names: List[str], weights: Dict[str, torch.Tensor]):
+
+        # Collect target q/k/v modules. This process is necessary since there might be no lora attached to k_proj
+        target_module = set()
+        for weight_name in weight_names:
+            if "k_proj" in weight_name:
+                target_module.add("k_proj")
+            if "q_proj" in weight_name:
+                target_module.add("q_proj")
+            if "v_proj" in weight_name:
+                target_module.add("v_proj")
+        if len(target_module) == 0:
+            return
+
+        for weight_name in weight_names:
+            # We assume every lora adaptor should contain lora modules for q_proj
+            if "q_proj" in weight_name:
+                q_name = weight_name
+                k_name = weight_name.replace("q_proj", "k_proj")
+                v_name = weight_name.replace("q_proj", "v_proj")
+                kv_name = weight_name.replace("q_proj", "kv_proj")
+                qkv_name = weight_name.replace("q_proj", "qkv_proj")
+
+                # If k_proj doesn't have lora, initialize it to zero
+                k_proj_weight = (
+                    weights[k_name]
+                    if "k_proj" in target_module
+                    else torch.zeros_like(weights[v_name])
+                )
+                if "lora_A" in weight_name:
+                    weights[qkv_name] = torch.cat(
+                        (
+                            weights[q_name],
+                            k_proj_weight,
+                            weights[v_name],
+                        ),
+                        0,
+                    )
+                    weights.pop(q_name)
+                    if "k_proj" in target_module:
+                        weights.pop(k_name)
+                    weights.pop(v_name)
+                else:
+                    weights[kv_name] = torch.stack(
+                        [
+                            k_proj_weight,
+                            weights[v_name],
+                        ],
+                        dim=0,
+                    )
+                    if "k_proj" in target_module:
+                        weights.pop(k_name)
+                    weights.pop(v_name)
+
+    def stack_gate_up_proj(
+        self, weight_names: List[str], weights: Dict[str, torch.Tensor]
+    ):
+        for weight_name in weight_names:
+            if "gate_proj" in weight_name:
+                up_name = weight_name.replace("gate_proj", "up_proj")
+                gate_up_name = weight_name.replace("gate_proj", "gate_up_proj")
+                if "lora_A" in weight_name:
+                    weights[gate_up_name] = torch.cat(
+                        (weights[weight_name], weights[up_name]), 0
+                    )
+                else:
+                    weights[gate_up_name] = torch.stack(
+                        [weights[weight_name], weights[up_name]], dim=0
+                    )
+                weights.pop(weight_name)
+                weights.pop(up_name)

python/sglang/srt/lora/lora_manager.py

@@ -16,307 +16,115 @@
 # and "Punica: Multi-Tenant LoRA Serving"
 
 import logging
-import re
+from typing import Dict, List, Set, Tuple
 
 import torch
 
-from sglang.srt.lora.backend import FlashInferLoraBackend, TritonLoraBackend
-from sglang.srt.lora.lora import LoRAAdapter, LoraBatchInfo, get_lora_layer
+from sglang.srt.configs.load_config import LoadConfig
+from sglang.srt.hf_transformers_utils import AutoConfig
+from sglang.srt.lora.backend import BaseLoRABackend, get_backend_from_name
+from sglang.srt.lora.layers import get_lora_layer
+from sglang.srt.lora.lora import LoRAAdapter
 from sglang.srt.lora.lora_config import LoRAConfig
+from sglang.srt.lora.mem_pool import LoRAMemoryPool
+from sglang.srt.lora.utils import (
+    LoRABatchInfo,
+    LoRAType,
+    get_customized_names_from_hf_names,
+    get_layer_id,
+    get_stacked_name,
+    get_weight_name,
+)
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
-from sglang.srt.utils import is_flashinfer_available, replace_submodule
+from sglang.srt.utils import replace_submodule
 
 logger = logging.getLogger(__name__)
 
 
-def get_module_name(name):
-    # Fallback solution of mapping from config module name to module name in model class.
-    # Please check if it aligns with your base model.
-    # Please implement the function in the model class if it is not.
-    # You can reference this function in llama.py.
-    params_mapping = {
-        "q_proj": "qkv_proj",
-        "k_proj": "qkv_proj",
-        "v_proj": "qkv_proj",
-        "gate_proj": "gate_up_proj",
-        "up_proj": "gate_up_proj",
-    }
-    return params_mapping.get(name, name)
-
-
-def get_hidden_dim(module_name, config):
-    # Fallback solution of get_hidden_dim for different modules
-    # Please check if it aligns with your base model.
-    # Please implement the function in the model class if it is not.
-    # You can reference this function in llama.py.
-    if module_name in ["q_proj", "o_proj", "qkv_proj"]:
-        return config.hidden_size, config.hidden_size
-    elif module_name in ["kv_proj"]:
-        return config.hidden_size, config.hidden_size // (
-            config.num_attention_heads // config.num_key_value_heads
-        )
-    elif module_name == "gate_up_proj":
-        return config.hidden_size, config.intermediate_size
-    elif module_name == "down_proj":
-        return config.intermediate_size, config.hidden_size
-    else:
-        raise NotImplementedError()
-
-
-def get_stacked_name(name):
-    # origin name -> (name for A, name for B)
-    params_mapping = {
-        "q_proj": ("qkv_proj", "q_proj"),
-        "k_proj": ("qkv_proj", "kv_proj"),
-        "v_proj": ("qkv_proj", "kv_proj"),
-        "gate_proj": ("gate_up_proj", "gate_up_proj"),
-        "up_proj": ("gate_up_proj", "gate_up_proj"),
-    }
-    return params_mapping.get(name, (name, name))
-
-
-def get_backend_from_name(name):
-    backend_mapping = {
-        "triton": TritonLoraBackend,
-        "flashinfer": FlashInferLoraBackend,
-    }
-
-    if name in backend_mapping:
-        return backend_mapping[name]
-
-    raise Exception(
-        f"No supported lora backend called {name}. It should be one of {list(backend_mapping.keys())}"
-    )
-
-
-def get_layer_id(name):
-    match = re.search(r"layers\.(\d+)\.", name)
-    if match is None:
-        return None
-    return int(match.group(1))
-
-
 class LoRAManager:
     def __init__(
         self,
-        base_model,
-        lora_paths,
-        base_hf_config,
-        max_loras_per_batch,
-        load_config,
-        dtype,
-        lora_backend,
+        base_model: torch.nn.Module,
+        lora_paths: Dict[str, str],
+        base_hf_config: AutoConfig,
+        max_loras_per_batch: int,
+        load_config: LoadConfig,
+        dtype: torch.dtype,
+        lora_backend: str = "triton",
     ):
-        self.base_model = base_model
-        self.lora_paths = lora_paths
-        self.base_hf_config = base_hf_config
-        self.max_loras_per_batch = max_loras_per_batch
-        self.load_config = load_config
-        self.dtype = dtype
-
-        logger.info(f"Using {lora_backend} as backend of Lora kernels.")
+        self.base_model: torch.nn.Module = base_model
+        self.lora_paths: Dict[str, str] = lora_paths
+        self.base_hf_config: AutoConfig = base_hf_config
+        self.max_loras_per_batch: int = max_loras_per_batch
+        self.load_config: LoadConfig = load_config
+        self.dtype: torch.dtype = dtype
+
+        # LoRA backend for running sgemm kernels
+        logger.info(f"Using {lora_backend} as backend of LoRA kernels.")
         backend_type = get_backend_from_name(lora_backend)
-        self.lora_backend = backend_type(lora_backend)
+        self.lora_backend: BaseLoRABackend = backend_type(lora_backend)
 
         self.init_loras()
         self.init_lora_memory_pool()
-        self.init_lora_batch()
-
-    def match_target_modules(self, module_name):
-        for target_module in self.target_modules:
-            if module_name.split(".")[-1] == target_module:
-                return True
-        return False
-
-    def get_target_modules(self):
-        modules = []
-        for module_name, module in self.base_model.named_modules():
-            if self.match_target_modules(module_name):
-                modules.append((module_name, module))
-        return modules
-
-    def set_lora_module(self, module_name, module):
-        lora_module = get_lora_layer(
-            module, self.max_lora_dim, self.scaling, self.lora_backend
-        )
-        replace_submodule(self.base_model, module_name, lora_module)
-        return lora_module
 
     def init_loras(self):
-        # get configs and target modules
-        self.configs = {}
-        self.origin_target_modules = set()
+        # Config of each LoRA adapter
+        self.configs: Dict[str, LoRAConfig] = {}
+
+        # Target module names in huggingface lora configs.
+        # e.g., {"k_proj", "q_proj", "v_proj", "o_proj"}
+        self.hf_target_names: Set[str] = set()
         for name, path in self.lora_paths.items():
             self.configs[name] = LoRAConfig(path)
-            self.origin_target_modules = set(self.origin_target_modules) | set(
+            self.hf_target_names = set(self.hf_target_names) | set(
                 self.configs[name].target_modules
             )
-        if hasattr(self.base_model, "get_module_name"):
-            self.target_modules = {
-                self.base_model.get_module_name(module)
-                for module in self.origin_target_modules
-            }
-        else:
-            logger.warning(
-                "WARNING: get_module_name() is not defined, "
-                "which is used to map config module name to model implementation module name."
-                "Use the default one, but please check if it is correct for your model."
-            )
-            self.target_modules = {
-                get_module_name(module) for module in self.origin_target_modules
-            }
-        self.target_weights = set(
-            [get_stacked_name(module) for module in self.origin_target_modules]
+
+        # Target lora weight names for lora_a and lora_b modules repectively.
+        # e.g., {("qkv_proj", "q_proj"), ("qkv_proj", "kv_proj")}
+        self.lora_weight_names: Set[Tuple[str]] = set(
+            [get_stacked_name(module) for module in self.hf_target_names]
         )
 
         # load all weights to cpu
-        self.loras = []
-        self.lora_id = {}
+        self.loras: Dict[str, LoRAAdapter] = {}
         for name in self.lora_paths.keys():
-            self.lora_id[name] = len(self.loras)
-            self.loras.append(
-                LoRAAdapter(
-                    name,
-                    self.configs[name],
-                    self.base_hf_config,
-                    self.load_config,
-                    self.lora_backend,
-                )
+            lora_adapter = LoRAAdapter(
+                name,
+                self.configs[name],
+                self.base_hf_config,
+                self.load_config,
+                self.lora_backend,
             )
-            self.loras[-1].initialize_weights()
+            lora_adapter.initialize_weights()
+            self.loras[name] = lora_adapter
 
         # misc lora configs
-        self.max_lora_dim = max([x.hf_config["r"] for x in self.configs.values()])
-        self.scaling = self.loras[0].scaling
-        # FIXME remove the restrictions
+        # FIXME remove the restrictions after implementing unified paging
+        self.max_lora_dim: int = max([x.hf_config["r"] for x in self.configs.values()])
+        self.scaling: float = list(self.loras.values())[0].scaling
         assert all(x.hf_config["r"] == self.max_lora_dim for x in self.configs.values())
-        assert all(x.scaling == self.scaling for x in self.loras)
+        assert all(x.scaling == self.scaling for x in self.loras.values())
 
-        # monkey patch to use the LoRA version
-        self.lora_modules = []
-        for module_name, module in self.get_target_modules():
-            self.lora_modules.append(
-                (module_name, self.set_lora_module(module_name, module))
-            )
+        # Convert original model layers to layers with LoRA
+        self.convert_to_lora_layers()
 
     def init_lora_memory_pool(self):
-        # preallocate lora memory pool
-        self.A_buffer = {}
-        self.B_buffer = {}
-        num_layer = self.base_hf_config.num_hidden_layers
-        for module_A, module_B in self.target_weights:
-            # init A tensor, column_major=True
-            if hasattr(self.base_model, "get_hidden_dim"):
-                hidden_dim_A, _ = self.base_model.get_hidden_dim(module_A)
-            else:
-                logger.warning(
-                    "WARNING: get_hidden_dim() is not defined, "
-                    "which is used to get the hidden dim for different lora modules"
-                    "Use the default one, but please check if it is correct for your model."
-                )
-                hidden_dim_A, _ = get_hidden_dim(module_A, self.base_hf_config)
-            c = self.loras[-1].get_stacked_multiply(module_A)
-            if module_A not in self.A_buffer:
-                self.A_buffer[module_A] = [
-                    torch.empty(
-                        (
-                            self.max_loras_per_batch,
-                            self.max_lora_dim * c,
-                            hidden_dim_A,
-                        ),
-                        dtype=self.dtype,
-                        device="cuda",
-                    )
-                    for i in range(num_layer)
-                ]
-            # init B tensor, column_major=True
-            if hasattr(self.base_model, "get_hidden_dim"):
-                _, hidden_dim_B = self.base_model.get_hidden_dim(module_B)
-            else:
-                logger.warning(
-                    "WARNING: get_hidden_dim() is not defined, "
-                    "which is used to get the hidden dim for different lora modules"
-                    "Use the default one, but please check if it is correct for your model."
-                )
-                _, hidden_dim_B = get_hidden_dim(module_B, self.base_hf_config)
-            c = self.loras[-1].get_stacked_multiply(module_B)
-            if module_B not in self.B_buffer:
-                self.B_buffer[module_B] = [
-                    torch.empty(
-                        (
-                            c,
-                            self.max_loras_per_batch,
-                            hidden_dim_B,
-                            self.max_lora_dim,
-                        ),
-                        dtype=self.dtype,
-                        device="cuda",
-                    )
-                    for i in range(num_layer)
-                ]
-
-    def init_lora_batch(self):
-        self.active_uids = set()  # set of active loras
-        self.buffer_id = {}  # lora uid -> idx in memory pool
-
-    def get_weight_name(self, name, idx):
-        for target_weight_name in self.target_weights:
-            if target_weight_name[idx] in name:
-                return target_weight_name[idx]
-
-    def load_lora(self, uid, buffer_id):
-        num_layer = self.base_hf_config.num_hidden_layers
-        if uid is None:
-            for i in range(num_layer):
-                for k in self.A_buffer.keys():
-                    self.A_buffer[k][i][buffer_id] *= 0
-            return
+        # Initialize memory pool
+        self.memory_pool = LoRAMemoryPool(
+            self.base_hf_config, self.max_loras_per_batch, self.max_lora_dim, self.dtype
+        )
 
-        for i in range(num_layer):
-            layer_weights = self.loras[self.lora_id[uid]].layers[i].weights
-            for name, weights in layer_weights.items():
-                if "lora_A" in name:
-                    lora_weight_name = self.get_weight_name(name, 0)
-                    if lora_weight_name:
-                        self.A_buffer[lora_weight_name][i][buffer_id].copy_(weights)
-                else:
-                    lora_weight_name = self.get_weight_name(name, 1)
-                    if lora_weight_name:
-                        c = self.loras[-1].get_stacked_multiply(lora_weight_name)
-                        if c > 1:
-                            for j in range(c):
-                                self.B_buffer[lora_weight_name][i][j][buffer_id].copy_(
-                                    weights[j]
-                                )
-                        else:
-                            self.B_buffer[lora_weight_name][i][0][buffer_id].copy_(
-                                weights
-                            )
+        # Initialize target lora modules in memory pool
+        self.memory_pool.init_buffers(self.lora_weight_names, self.base_model)
 
     def prepare_lora_batch(self, forward_batch: ForwardBatch):
         # load active loras into lora memory pool
         cur_uids = set(forward_batch.lora_paths)
         assert len(cur_uids) <= self.max_loras_per_batch
-        i = 0
-        j = len(self.active_uids)
-        evictable_uids = list(self.active_uids)
-        for uid in cur_uids:
-            if uid not in self.active_uids:
-                if j < self.max_loras_per_batch:
-                    index = j
-                    j += 1
-                else:
-                    while i < len(evictable_uids) and evictable_uids[i] in cur_uids:
-                        i += 1
-                    assert i < len(evictable_uids)
-                    self.active_uids.remove(evictable_uids[i])
-                    self.buffer_id.pop(evictable_uids[i])
-                    index = i
-                    i += 1
-                self.load_lora(uid, index)
-                self.active_uids.add(uid)
-                self.buffer_id[uid] = index
+        self.memory_pool.prepare_lora_batch(cur_uids, self.loras)
 
+        # FIXME: Handle lora uid with None more safely
         if cur_uids == set([None]):
             return
 
@@ -332,9 +140,9 @@ class LoRAManager:
         max_len = int(torch.max(seg_lens))
         weight_indices = torch.empty((bs,), dtype=torch.int64, device="cuda")
         for i, lora_path in enumerate(forward_batch.lora_paths):
-            weight_indices[i] = self.buffer_id[lora_path]
+            weight_indices[i] = self.memory_pool.get_buffer_id(lora_path)
 
-        batch_info = LoraBatchInfo(
+        batch_info = LoRABatchInfo(
             bs=bs,
             seg_lens=seg_lens,
             seg_indptr=seg_indptr,
@@ -346,16 +154,40 @@ class LoRAManager:
         # call set_lora_info for each lora modules
         for module_name, module in self.lora_modules:
             layer_id = get_layer_id(module_name)
-
             if "qkv_proj" not in module_name:
-                weight_name = self.get_weight_name(module_name, 0)
+                weight_name = get_weight_name(
+                    module_name, self.lora_weight_names, LoRAType.LORA_A
+                )
                 module.set_lora_info(
-                    self.A_buffer[weight_name][layer_id],
-                    self.B_buffer[weight_name][layer_id],
+                    self.memory_pool.get_tensor(weight_name, layer_id, LoRAType.LORA_A),
+                    self.memory_pool.get_tensor(weight_name, layer_id, LoRAType.LORA_B),
                 )
             else:
                 module.set_lora_info(
-                    self.A_buffer["qkv_proj"][layer_id],
-                    self.B_buffer["q_proj"][layer_id],
-                    self.B_buffer["kv_proj"][layer_id],
+                    self.memory_pool.get_tensor("qkv_proj", layer_id, LoRAType.LORA_A),
+                    self.memory_pool.get_tensor("q_proj", layer_id, LoRAType.LORA_B),
+                    self.memory_pool.get_tensor("kv_proj", layer_id, LoRAType.LORA_B),
+                )
+
+    def set_lora_module(self, module_name, module):
+        lora_module = get_lora_layer(
+            module, self.max_lora_dim, self.scaling, self.lora_backend
+        )
+        replace_submodule(self.base_model, module_name, lora_module)
+        return lora_module
+
+    def convert_to_lora_layers(self):
+        # Target module names of customized layers defined in python/sglang/srt/layers
+        # e.g., {"qkv_proj", "o_proj"}
+        customized_target_names = get_customized_names_from_hf_names(
+            self.hf_target_names, self.base_model
+        )
+
+        # Monkey patch to use the LoRA version layers
+        self.lora_modules: List[Tuple[str, torch.nn.Module]] = []
+        for module_name, module in self.base_model.named_modules():
+            # The module should be converted if it is included in target_names
+            if module_name.split(".")[-1] in customized_target_names:
+                self.lora_modules.append(
+                    (module_name, self.set_lora_module(module_name, module))
                 )

python/sglang/srt/lora/mem_pool.py

+from typing import Dict, List, Optional, Set, Tuple
+
+import torch
+
+from sglang.srt.hf_transformers_utils import AutoConfig
+from sglang.srt.lora.lora import LoRAAdapter
+from sglang.srt.lora.utils import (
+    LoRAType,
+    get_hidden_dim,
+    get_stacked_multiply,
+    get_weight_name,
+)
+
+
+class LoRAMemoryPool:
+    """Class for memory pool management of lora modules"""
+
+    def __init__(
+        self,
+        base_hf_config: AutoConfig,
+        max_loras_per_batch: int,
+        max_lora_dim: int,
+        dtype: torch.dtype,
+    ):
+
+        self.base_hf_config: AutoConfig = base_hf_config
+        self.num_layer: int = base_hf_config.num_hidden_layers
+        self.max_loras_per_batch: int = max_loras_per_batch
+        self.max_lora_dim: int = max_lora_dim
+        self.dtype: torch.dtype = dtype
+
+        # Both A_buffer and B_buffer maps lora weight names to its buffer space.
+        # A_buffer contains num_layer number of row-major tensors with shape
+        #   (max_loras_per_batch, stacked_num * max_lora_dim, input_dim)
+        # B_buffer contains num_layer number of column-major tensors with shape
+        #   (stacked_num, max_loras_per_batch, output_dim, max_lora_dim)
+        self.A_buffer: Dict[str, List[torch.Tensor]] = {}
+        self.B_buffer: Dict[str, List[torch.Tensor]] = {}
+
+        # Lora uid -> buffer idx in memory pool
+        self.uid_to_buffer_id: Dict[Optional[str], int] = {}
+
+        # Buffer idx -> lora uid in memory pool
+        # All uids are initalized as empty strings for empty buffer slots
+        # Here we don't initalize to None since None is a valid uid
+        self.buffer_id_to_uid: List[Optional[str]] = [""] * self.max_loras_per_batch
+
+    def init_buffers(
+        self,
+        lora_weight_names: Set[Tuple[str]],
+        base_model: torch.nn.Module,
+    ):
+
+        # lora_weight_names is a set of name pairs indicating each pair of lora modules to load
+        #   e.g., {("qkv_proj", "q_proj"), ("qkv_proj", "kv_proj"), ("o_proj", "o_proj")}
+        self.lora_weight_names: Set[Tuple[str]] = lora_weight_names
+
+        for module_A, module_B in lora_weight_names:
+            # Init A tensor, column_major=False
+            input_dim, _ = get_hidden_dim(module_A, self.base_hf_config, base_model)
+            c = get_stacked_multiply(module_A)
+            if module_A not in self.A_buffer:
+                self.A_buffer[module_A] = [
+                    torch.empty(
+                        (
+                            self.max_loras_per_batch,
+                            self.max_lora_dim * c,
+                            input_dim,
+                        ),
+                        dtype=self.dtype,
+                        device="cuda",
+                    )
+                    for i in range(self.num_layer)
+                ]
+
+            # Init B tensor, column_major=True
+            _, output_dim = get_hidden_dim(module_B, self.base_hf_config, base_model)
+            c = get_stacked_multiply(module_B)
+            if module_B not in self.B_buffer:
+                self.B_buffer[module_B] = [
+                    torch.empty(
+                        (
+                            c,  # stacked lora_b modules might need separation
+                            self.max_loras_per_batch,
+                            output_dim,
+                            self.max_lora_dim,
+                        ),
+                        dtype=self.dtype,
+                        device="cuda",
+                    )
+                    for i in range(self.num_layer)
+                ]
+
+    def prepare_lora_batch(
+        self,
+        cur_uids: Set[Optional[str]],
+        lora_adapters: Dict[str, LoRAAdapter],
+    ):
+
+        def get_available_buffer_slot():
+            for buffer_id in range(self.max_loras_per_batch):
+                # Prioritize empty slots
+                if self.buffer_id_to_uid[buffer_id] == "":
+                    return buffer_id, ""
+
+            for buffer_id in range(self.max_loras_per_batch):
+                # Evict unneeded lora
+                if self.buffer_id_to_uid[buffer_id] not in cur_uids:
+                    return buffer_id, self.buffer_id_to_uid[buffer_id]
+
+            raise ValueError(
+                "No available buffer slots found. Please ensure the number of active loras is less than max_loras_per_batch."
+            )
+
+        for uid in cur_uids:
+            if uid not in self.uid_to_buffer_id:
+                buffer_id, evicted_lora_uid = get_available_buffer_slot()
+                if evicted_lora_uid != "":
+                    self.uid_to_buffer_id.pop(evicted_lora_uid)
+                self.load_lora_weight_to_buffer(
+                    uid, buffer_id, lora_adapters.get(uid, None)
+                )
+                self.uid_to_buffer_id[uid] = buffer_id
+                self.buffer_id_to_uid[buffer_id] = uid
+
+    def load_lora_weight_to_buffer(
+        self, uid: str, buffer_id: int, lora_adapter: LoRAAdapter = None
+    ):
+
+        if uid is None:
+            for i in range(self.num_layer):
+                for k in self.A_buffer.keys():
+                    self.A_buffer[k][i][buffer_id] *= 0
+            return
+
+        assert lora_adapter is not None
+        for layer_id in range(self.num_layer):
+            layer_weights = lora_adapter.layers[layer_id].weights
+            for name, weights in layer_weights.items():
+                if "lora_A" in name:
+                    lora_weight_name = get_weight_name(
+                        name, self.lora_weight_names, LoRAType.LORA_A
+                    )
+                    if lora_weight_name:
+                        self.A_buffer[lora_weight_name][layer_id][buffer_id].copy_(
+                            weights
+                        )
+                else:
+                    lora_weight_name = get_weight_name(
+                        name, self.lora_weight_names, LoRAType.LORA_B
+                    )
+                    if lora_weight_name:
+                        c = get_stacked_multiply(lora_weight_name)
+                        if c > 1:
+                            for stacked_id in range(c):
+                                self.B_buffer[lora_weight_name][layer_id][stacked_id][
+                                    buffer_id
+                                ].copy_(weights[stacked_id])
+                        else:
+                            self.B_buffer[lora_weight_name][layer_id][0][
+                                buffer_id
+                            ].copy_(weights)
+
+    def get_tensor(
+        self, weight_name: str, layer_id: int, lora_type: LoRAType
+    ) -> torch.Tensor:
+
+        if lora_type == LoRAType.LORA_A:
+            return self.A_buffer[weight_name][layer_id]
+
+        return self.B_buffer[weight_name][layer_id]
+
+    def get_buffer_id(self, lora_uid: str):
+        return self.uid_to_buffer_id[lora_uid]

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

+from .gate_up_lora_b import gate_up_lora_b_fwd
 from .qkv_lora_b import qkv_lora_b_fwd
 from .sgemm_lora_a import sgemm_lora_a_fwd
 from .sgemm_lora_b import sgemm_lora_b_fwd
 
-__all__ = ["qkv_lora_b_fwd", "sgemm_lora_a_fwd", "sgemm_lora_b_fwd"]
+__all__ = [
+    "gate_up_lora_b_fwd",
+    "qkv_lora_b_fwd",
+    "sgemm_lora_a_fwd",
+    "sgemm_lora_b_fwd",
+]

python/sglang/srt/lora/triton_ops/gate_up_lora_b.py

+import torch
+import triton
+import triton.language as tl
+
+from sglang.srt.lora.utils import LoRABatchInfo
+
+
+@triton.jit
+def _gate_up_lora_b_kernel(
+    # Pointers to matrices
+    x,
+    weights,
+    output,
+    # Parameters of size
+    K,  # K = R
+    output_dim,
+    # Strides
+    x_stride_0,
+    x_stride_1,
+    w_stride_0,
+    w_stride_1,
+    w_stride_2,
+    output_stride_0,
+    output_stride_1,
+    # Information on sequence lengths and weight id
+    seg_lens,
+    seg_indptr,
+    weight_indices,
+    # Meta parameters
+    BLOCK_S: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    # For fused output scaling and adding
+    fuse_scaling_add,
+    scaling,
+):
+    # This kernel packs 2 sgemms (gate/up) into a single kernel.
+
+    # x: (s, 2 * K), s is the sum of sequence lengths, K equals to lora rank
+    # weights: (num_lora, 2 * output_dim, K)
+    # output: (s, 2 * output_dim)
+    # output_dim >> K
+
+    # Current block computes sequence with batch_id,
+    # which starts from row seg_start of x with length seg_len.
+    # gate_up_id decides which of gate or up (0: gate, 1: up)
+    batch_id = tl.program_id(axis=2)
+    gate_up_id = tl.program_id(axis=1)
+    pid = tl.program_id(axis=0)
+    seg_len = tl.load(seg_lens + batch_id)
+    w_index = tl.load(weight_indices + batch_id)
+    seg_start = tl.load(seg_indptr + batch_id)
+    n_start = gate_up_id * output_dim  # offset on output dim
+
+    # The tile in output matrix will have (pid_s, pid_n) as id
+    num_pid_n = tl.cdiv(output_dim, BLOCK_N)
+    pid_s = pid // num_pid_n
+    pid_n = pid % num_pid_n
+
+    # Create pointers for the first block of x and weights
+    # The pointers will be advanced as we move in the K direction
+    # and accumulate
+    s_offset = tl.arange(0, BLOCK_S) + pid_s * BLOCK_S
+    n_offset = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
+    k_offset = tl.arange(0, BLOCK_K)
+
+    x_ptrs = (x + seg_start * x_stride_0 + (gate_up_id * K) * x_stride_1) + (
+        s_offset[:, None] * x_stride_0 + k_offset[None, :] * x_stride_1
+    )
+    w_ptrs = (weights + w_index * w_stride_0 + n_start * w_stride_1) + (
+        k_offset[:, None] * w_stride_2 + n_offset[None, :] * w_stride_1
+    )
+
+    # Iteate to compute the block in output matrix
+    partial_sum = tl.zeros((BLOCK_S, BLOCK_N), dtype=tl.float32)
+    for k in range(0, tl.cdiv(K, BLOCK_K)):
+        x_tile = tl.load(
+            x_ptrs,
+            mask=(s_offset[:, None] < seg_len)
+            and (k_offset[None, :] < K - k * BLOCK_K),
+            other=0.0,
+        )
+        w_tile = tl.load(
+            w_ptrs,
+            mask=(k_offset[:, None] < K - k * BLOCK_K)
+            and (n_offset[None, :] < output_dim),
+            other=0.0,
+        )
+        partial_sum += tl.dot(x_tile, w_tile)
+
+        x_ptrs += BLOCK_K * x_stride_1
+        w_ptrs += BLOCK_K * w_stride_2
+
+    # Store result to output matrix
+    partial_sum *= scaling
+    partial_sum = partial_sum.to(x.dtype.element_ty)
+    output_ptr = (output + seg_start * output_stride_0 + n_start * output_stride_1) + (
+        s_offset[:, None] * output_stride_0 + n_offset[None, :] * output_stride_1
+    )
+    output_mask = (s_offset[:, None] < seg_len) and (n_offset[None, :] < output_dim)
+    if fuse_scaling_add:
+        partial_sum += tl.load(output_ptr, mask=output_mask)
+    tl.store(output_ptr, partial_sum, mask=output_mask)
+
+
+def gate_up_lora_b_fwd(
+    x: torch.Tensor,
+    gate_up_lora_b: torch.Tensor,
+    batch_info: LoRABatchInfo,
+    output_dim: int,
+    base_output: torch.Tensor = None,
+    scaling: float = 1.0,
+) -> torch.Tensor:
+
+    # x: (s, 2 * r)
+    # gate_up_lora_b: (num_lora, 2 * output_dim, r)
+    # output: (s, 2 * output_dim)
+
+    # Compute lora_output with shape (s, output_dim) as follows:
+    # lora_output[:, :output_dim] = sgemm(x[:, :r], gate_up_lora_b[:, :output_dim, :])
+    # lora_output[:, output_dim:]
+    #      = sgemm(x[:, r:], gate_up_lora_b[:, output_dim:, :])
+
+    # Get dims
+    s = x.shape[0]
+    input_dim = x.shape[1]
+    r = gate_up_lora_b.shape[-1]
+    assert input_dim == 2 * r
+
+    BLOCK_S = 16
+    BLOCK_R = 16
+    BLOCK_OUT = 64
+
+    grid_b = (
+        triton.cdiv(batch_info.max_len, BLOCK_S) * triton.cdiv(output_dim, BLOCK_OUT),
+        2,  # this dimension decides current block computes on gate or up proj
+        batch_info.bs,
+    )
+
+    if base_output is None:
+        output = torch.empty((s, 2 * output_dim), device=x.device, dtype=x.dtype)
+        fuse_scaling_add = False
+    else:
+        output = base_output
+        fuse_scaling_add = True
+
+    _gate_up_lora_b_kernel[grid_b](
+        x,
+        gate_up_lora_b,
+        output,
+        r,
+        output_dim,
+        x.stride(0),
+        x.stride(1),
+        gate_up_lora_b.stride(0),
+        gate_up_lora_b.stride(1),
+        gate_up_lora_b.stride(2),
+        output.stride(0),
+        output.stride(1),
+        batch_info.seg_lens,
+        batch_info.seg_indptr,
+        batch_info.weight_indices,
+        BLOCK_S,
+        BLOCK_OUT,
+        BLOCK_R,
+        fuse_scaling_add,
+        scaling,
+    )
+
+    return output

python/sglang/srt/lora/triton_ops/qkv_lora_b.py

@@ -2,7 +2,7 @@ import torch
 import triton
 import triton.language as tl
 
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.utils import LoRABatchInfo
 
 
 @triton.jit
@@ -108,7 +108,7 @@ def _qkv_lora_b_kernel(
 def qkv_lora_b_fwd(
     x: torch.Tensor,
     qkv_lora_b: torch.Tensor,
-    batch_info: LoraBatchInfo,
+    batch_info: LoRABatchInfo,
     output_offset: torch.Tensor,
     max_qkv_out_dim: int,
     base_output: torch.Tensor = None,
@@ -123,11 +123,11 @@ def qkv_lora_b_fwd(
     # output: (s, output_dim_q + 2 * output_dim_kv)
 
     # Compute lora_output with shape (s, output_dim) as follows:
-    # lora_output[:, :output_dim_q] = sgemm(lora_output_a[:, :r], )
+    # lora_output[:, :output_dim_q] = sgemm(x[:, :r], qkv_lora_b[:, :outptu_dim_q, :])
     # lora_output[:, output_dim_q: output_dim_q + output_dim_kv]
-    #      = sgemm(lora_output_a[:, r: 2 * r], kv_lora_b[0])
+    #      = sgemm(x[:, r: 2 * r], qkv_lora_b[:, outptu_dim_q: output_dim_q + output_dim_kv, :])
     # lora_output[:, output_dim_q + output_dim_kv: ]
-    #      = sgemm(lora_output_a[:, 2 * r: 3 * r], kv_lora_b[1])
+    #      = sgemm(x[:, 2 * r: , qkv_lora_b[:, output_dim_q + output_dim_kv: , :])
 
     # Get dims
     s = x.shape[0]

python/sglang/srt/lora/triton_ops/sgemm_lora_a.py

@@ -2,7 +2,7 @@ import torch
 import triton
 import triton.language as tl
 
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.utils import LoRABatchInfo
 
 
 @triton.jit
@@ -91,7 +91,7 @@ def _sgemm_lora_a_kernel(
 
 
 def sgemm_lora_a_fwd(
-    x: torch.Tensor, weights: torch.Tensor, batch_info: LoraBatchInfo
+    x: torch.Tensor, weights: torch.Tensor, batch_info: LoRABatchInfo
 ) -> torch.Tensor:
     # x: (s, input_dim)
     # weights: (num_lora, r, input_dim)

python/sglang/srt/lora/triton_ops/sgemm_lora_b.py

@@ -2,7 +2,7 @@ import torch
 import triton
 import triton.language as tl
 
-from sglang.srt.lora.lora import LoraBatchInfo
+from sglang.srt.lora.utils import LoRABatchInfo
 
 
 @triton.jit
@@ -98,7 +98,7 @@ def _sgemm_lora_b_kernel(
 def sgemm_lora_b_fwd(
     x: torch.Tensor,
     weights: torch.Tensor,
-    batch_info: LoraBatchInfo,
+    batch_info: LoRABatchInfo,
     base_output: torch.Tensor = None,
     scaling: float = 1.0,
 ) -> torch.Tensor:

python/sglang/srt/lora/utils.py

+import re
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional, Set, Tuple
+
+import torch
+
+from sglang.srt.hf_transformers_utils import AutoConfig
+
+
+@dataclass
+class LoRABatchInfo:
+    # Batch size
+    bs: int
+
+    # Lengths of each sequence in shape (bs,)
+    seg_lens: torch.Tensor
+
+    # Indice pointers of each sequence in shape (bs + 1, )
+    seg_indptr: torch.Tensor
+
+    # Maximum sequence length of current batch
+    max_len: int
+
+    # The index of lora adapter used by each sequence, in shape (bs,)
+    weight_indices: torch.Tensor
+
+
+class LoRAType(Enum):
+    LORA_A = 0
+    LORA_B = 1
+
+
+def get_layer_id(name: str) -> int:
+    """
+    Extract integer id of layer from its name in string.
+    """
+    match = re.search(r"layers\.(\d+)\.", name)
+    if match is None:
+        return None
+    return int(match.group(1))
+
+
+def get_customized_names_from_hf_names(
+    hf_module_names: Set[str], base_model: torch.nn.Module
+) -> Set[str]:
+    """
+    This function takes in a set of huggingface style module names:
+         e.g., {"k_proj", "q_proj", "v_proj", "o_proj"}
+    and outputs a set of module names of customized sglang layers:
+         e.g., {"qkv_proj", "o_proj"}
+    """
+    if hasattr(base_model, "get_module_name"):
+        return {base_model.get_module_name(name) for name in hf_module_names}
+    else:
+        """
+        Fallback solution of mapping from config module name to module name in model class.
+        Please check if it aligns with your base model.
+        Please implement the function in the model class if it is not.
+        You can reference this function in llama.py.
+        """
+        params_mapping = {
+            "q_proj": "qkv_proj",
+            "k_proj": "qkv_proj",
+            "v_proj": "qkv_proj",
+            "gate_proj": "gate_up_proj",
+            "up_proj": "gate_up_proj",
+        }
+        return {params_mapping.get(name, name) for name in hf_module_names}
+
+
+def get_hidden_dim(
+    module_name: str, config: AutoConfig, base_model: torch.nn.Module
+) -> Tuple[int]:
+    """
+    Given a module_name (might be a stacked name), return the hidden dims of modules's input and output.
+    """
+
+    if hasattr(base_model, "get_hidden_dim"):
+        return base_model.get_hidden_dim(module_name)
+    else:
+        """
+        WARNING: get_hidden_dim() is not defined,
+        which is used to get the hidden dim for different lora modules
+        Use the default one, but please check if it is correct for your model.
+        Please implement the function in the model class if it is not.
+        You can reference this function in llama.py.
+        """
+        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
+            return config.hidden_size, config.hidden_size
+        elif module_name in ["kv_proj"]:
+            return config.hidden_size, config.hidden_size // (
+                config.num_attention_heads // config.num_key_value_heads
+            )
+        elif module_name == "gate_up_proj":
+            return config.hidden_size, config.intermediate_size
+        elif module_name == "down_proj":
+            return config.intermediate_size, config.hidden_size
+        else:
+            raise NotImplementedError()
+
+
+def get_stacked_name(name: str) -> Tuple[str]:
+    """
+    Mapping a target module name to (stacked name for Lora A, stacked name for Lora B)
+    """
+    params_mapping = {
+        "q_proj": ("qkv_proj", "q_proj"),
+        "k_proj": ("qkv_proj", "kv_proj"),
+        "v_proj": ("qkv_proj", "kv_proj"),
+        "gate_proj": ("gate_up_proj", "gate_up_proj"),
+        "up_proj": ("gate_up_proj", "gate_up_proj"),
+    }
+    return params_mapping.get(name, (name, name))
+
+
+def get_stacked_multiply(module_name: str) -> int:
+    """
+    Mapping a lora module name to its magnification at output dimension
+    """
+    stacked_rank = {
+        "qkv_proj": 3,
+        "kv_proj": 2,
+        "gate_up_proj": 2,
+    }
+    return stacked_rank[module_name] if module_name in stacked_rank else 1
+
+
+def get_weight_name(
+    target_name: str, lora_weight_names: Set[Tuple[str]], lora_type: LoRAType
+) -> Optional[str]:
+    """
+    target_name is name of a given module,
+    lora_weight_names is a set of lora stacked name pairs (see get_stacked_name method above)
+    If there is a weight name in lora_weight_names that can match target_name, return this name
+    Else return None
+    """
+    idx = 0 if lora_type == LoRAType.LORA_A else 1
+    for weight_name_pair in lora_weight_names:
+        if weight_name_pair[idx] in target_name:
+            return weight_name_pair[idx]

test/srt/models/test_lora_backend.py

@@ -22,7 +22,11 @@ from sglang.test.test_utils import calculate_rouge_l
 
 LORA_SETS = [
     {"base": "meta-llama/Llama-2-7b-hf", "loras": ["winddude/wizardLM-LlaMA-LoRA-7B"]},
-    # {"base": "meta-llama/Llama-2-7b-hf", "loras": ["RuterNorway/Llama-2-7b-chat-norwegian-LoRa"]}
+    {
+        "base": "meta-llama/Llama-3.1-8B-Instruct",
+        "loras": ["reissbaker/llama-3.1-8b-abliterated-lora"],
+        "decode_tolerance": 8e-2,
+    },
 ]
 TORCH_DTYPES = [torch.float16]
 
@@ -128,7 +132,8 @@ class TestLoRABackend(unittest.TestCase):
                 torch.max(abs(hf_logprobs - hf_no_lora_logprobs)),
             )
             if hf_logprobs.shape[0] <= 100:
-                assert torch.all(abs(hf_logprobs - srt_logprobs) < prefill_tolerance), (
+                tol = lora_set.get("prefill_tolerance", prefill_tolerance)
+                assert torch.all(abs(hf_logprobs - srt_logprobs) < tol), (
                     f"prefill logprobs are not all close with model_path={base_path},"
                     f"lora_path={batch_lora_paths[i]}, backend={backend}, prompt={prompts[i]}"
                     f"prefill_tolerance={prefill_tolerance}."
@@ -144,7 +149,8 @@ class TestLoRABackend(unittest.TestCase):
                 "\n",
             )
             if hf_logprobs.shape[0] <= 100:
-                assert torch.all(abs(hf_logprobs - srt_logprobs) < decode_tolerance), (
+                tol = lora_set.get("decode_tolerance", decode_tolerance)
+                assert torch.all(abs(hf_logprobs - srt_logprobs) < tol), (
                     f"decode logprobs are not all close with model_path={base_path},"
                     f"lora_path={batch_lora_paths[i]}, backend={backend}, prompt={prompts[i]}"
                     f"decode_tolerance={decode_tolerance}."
@@ -153,7 +159,7 @@ class TestLoRABackend(unittest.TestCase):
 
             # compare output strings
             srt_output_str = srt_outputs.output_strs[i].strip(" ")
-            hf_output_str = hf_outputs.output_strs[i]
+            hf_output_str = hf_outputs.output_strs[i].strip(" ")
             print(f"srt_output_str={srt_output_str}")
             print(f"hf_output_str={hf_output_str}")
             rouge_l_scores = calculate_rouge_l([srt_output_str], [hf_output_str])