Align LoRA handling in HPU with PunicaWrapper class (#614)

This PR adds `PunicaWrapperHPU` class to handle LoRA computations in
HPU. These changes are to align LoRA flow refactoring done in the
upstream branch.

vllm/lora/layers.py

@@ -1070,8 +1070,11 @@ def _get_logits(
         ).index_select(0, indices_padded).nan_to_num_(nan=float("-inf"),
                                                       posinf=float("inf"),
                                                       neginf=float("-inf")))
+
+        # HPU needs special handling to prune out dummy samples
         if current_platform.is_hpu():
             lora_logits = lora_logits[:logits.shape[0], :]
+
         logits[:,
                self.base_layer.org_vocab_size:self.base_layer.org_vocab_size +
                lora_logits.shape[1]] = lora_logits

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

@@ -10,11 +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)
-    if current_platform.is_hpu():
+    elif current_platform.is_hpu():
         # Lazy import to avoid ImportError
-        from vllm_hpu_extension.punica_hpu import GaudiPunicaWrapper
-        print_info_once("Using GaudiPunicaWrapper.")
-        return GaudiPunicaWrapper(*args, **kwargs)
-
+        from vllm.lora.punica_wrapper.punica_hpu import PunicaWrapperHPU
+        print_info_once("Using PunicaWrapperHPU.")
+        return PunicaWrapperHPU(*args, **kwargs)
     else:
         raise NotImplementedError

vllm/worker/hpu_model_runner.py

@@ -687,7 +687,10 @@ def load_model(self) -> None:
                 assert hasattr(
                     self.model, "embedding_padding_modules"
                 ), "Model does not have embedding_padding_modules"
-
+                assert not self.lora_config.bias_enabled, \
+                    "Bias support in LoRA is not enabled in HPU yet."
+                assert not self.lora_config.fully_sharded_loras, \
+                    "Fully sharded LoRAs is not enabled in HPU yet."
                 if supports_multimodal(self.model):
                     logger.warning(
                         "Regarding multimodal models, vLLM currently "