Add PunicaWrapperHPU to handle LoRA computations in HPU

Signed-off-by: Sanju C Sudhakaran <[email protected]>

requirements-hpu.txt

@@ -8,4 +8,4 @@ pandas
 tabulate
 setuptools>=61
 setuptools-scm>=8
-vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@e096d6f
+vllm-hpu-extension @ git+https://github.com/HabanaAI/vllm-hpu-extension.git@4312768

vllm/lora/layers.py

@@ -32,9 +32,6 @@
     VocabParallelEmbedding)
 from vllm.platforms import current_platform
 
-if current_platform.is_hpu():
-    from vllm_hpu_extension.punica_hpu import GaudiPunicaWrapper
-
 if TYPE_CHECKING:
     from vllm.lora.punica_wrapper import PunicaWrapperBase
 
@@ -259,7 +256,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
                                                full_lora_a_embeddings,
                                                self.lora_b_stacked,
                                                add_input=True)
-
         return full_output.view_as(full_output_org)
 
     @classmethod

vllm/lora/punica_wrapper/punica_hpu.py

@@ -0,0 +1,87 @@
+from typing import Optional, Tuple, Union, final
+
+import torch
+from vllm_hpu_extension.ops import (dispatch_bgmv_embedding,
+                                    dispatch_bgmv_linear)
+
+from .punica_base import PunicaWrapperBase
+
+
+@final
+class PunicaWrapperHPU(PunicaWrapperBase):
+
+    def __init__(self, max_num_batched_tokens: int, max_batches: int,
+                 device: Union[torch.device, str], **kwargs):
+        # Increasing max_num_batched_tokens by 3x to handle increase in
+        # tensor size due to padding.
+        PunicaWrapperBase.__init__(self, 3 * max_num_batched_tokens,
+                                   max_batches, device)
+
+    def add_lora_embedding(self,
+                           y: torch.Tensor,
+                           x: torch.Tensor,
+                           lora_b_stacked: torch.Tensor,
+                           add_input: bool = True,
+                           **kwargs) -> None:
+        dispatch_bgmv_embedding(y, x, lora_b_stacked, 0)
+
+    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:
+        y_org = y
+        x = x.view(-1, x.shape[-1])
+        y = y.view(-1, y.shape[-1])
+        offset_left = 0
+
+        for slice_idx in range(len(output_slices)):
+            dispatch_bgmv_linear(
+                y[:, offset_left:offset_left + output_slices[slice_idx]], x,
+                lora_a_stacked[slice_idx], lora_b_stacked[slice_idx], 0, scale)
+            offset_left += output_slices[slice_idx]
+        y = y.view_as(y_org)
+
+    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:
+        y_org = y
+        y = y.view(-1, y.shape[-1])
+        x = x.view(-1, x.shape[-1])
+        dispatch_bgmv_linear(y, x, lora_a_stacked, lora_b_stacked, 0, 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,
+    ) -> None:
+        raise NotImplementedError
+
+    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:
+        raise NotImplementedError

vllm/lora/punica_wrapper/punica_selector.py

@@ -10,5 +10,10 @@ def get_punica_wrapper(*args, **kwargs) -> PunicaWrapperBase:
         from vllm.lora.punica_wrapper.punica_gpu import PunicaWrapperGPU
         print_info_once("Using PunicaWrapperGPU.")
         return PunicaWrapperGPU(*args, **kwargs)
+    elif current_platform.is_hpu():
+        # Lazy import to avoid ImportError
+        from vllm.lora.punica_wrapper.punica_hpu import PunicaWrapperHPU
+        print_info_once("Using PunicaWrapperHPU.")
+        return PunicaWrapperHPU(*args, **kwargs)
     else:
         raise NotImplementedError