[gpt-oss] Add gpt-oss bf16 support

vllm/lora/ops/torch_ops/__init__.py

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.lora.ops.torch_ops.lora_ops import bgmv_expand  # noqa: F401
+from vllm.lora.ops.torch_ops.lora_ops import (bgmv_expand_slice, bgmv_shrink,
+                                              sgmv_expand, sgmv_expand_slice,
+                                              sgmv_shrink)
+
+__all__ = [
+    "bgmv_expand",
+    "bgmv_expand_slice",
+    "bgmv_shrink",
+    "sgmv_expand",
+    "sgmv_expand_slice",
+    "sgmv_shrink",
+]

vllm/lora/ops/torch_ops/lora_ops.py

@@ -0,0 +1,119 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+
+def sgmv_expand(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                b_seq_start_loc: torch.Tensor,
+                seq_len_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                batches: int,
+                max_seq_length: int,
+                token_nums: int,
+                add_inputs: bool = False):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_expand(inputs, lora_b_weights, output_tensor, exploded_indices,
+                add_inputs)
+
+
+def bgmv_expand(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                add_inputs: bool = True):
+    selected_loras = lora_b_weights[lora_indices_tensor].to(
+        dtype=output_tensor.dtype)
+    if len(selected_loras.shape) == 4:
+        selected_loras = selected_loras.squeeze(dim=1)
+    inputs = inputs.to(dtype=output_tensor.dtype)
+    outputs = torch.einsum("bi, boi -> bo", inputs, selected_loras)
+
+    limit = output_tensor.shape[0]
+    if outputs.shape[0] == 1 and output_tensor.shape[0] != 1:
+        limit = 1
+
+    # LoRA adapter and model may add different amounts of padding to output
+    common_len = min(outputs.shape[1], output_tensor.shape[1])
+
+    if add_inputs:
+        output_tensor[:, :common_len] += outputs[:limit, :common_len]
+    else:
+        output_tensor[:, :common_len] = outputs[:limit, :common_len]
+
+
+def sgmv_shrink(
+    inputs: torch.Tensor,
+    lora_a_weights: torch.Tensor,
+    output_tensor: torch.Tensor,
+    b_seq_start_loc: torch.Tensor,
+    seq_len_tensor: torch.Tensor,
+    lora_indices_tensor: torch.Tensor,
+    batches: int,
+    max_seq_length: int,
+    token_nums: int,
+    scaling: float,
+):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_shrink(inputs, lora_a_weights, output_tensor, exploded_indices,
+                scaling)
+
+
+def bgmv_shrink(inputs: torch.Tensor,
+                lora_b_weights: torch.Tensor,
+                output_tensor: torch.Tensor,
+                lora_indices_tensor: torch.Tensor,
+                scaling: float = 1.0):
+    selected_loras = lora_b_weights[lora_indices_tensor].to(
+        dtype=output_tensor.dtype)
+    if len(selected_loras.shape) == 4:
+        selected_loras = selected_loras.squeeze(dim=1)
+    inputs = inputs.to(dtype=output_tensor.dtype)
+    outputs = torch.einsum("bi, boi -> bo", inputs, selected_loras)
+
+    output_tensor[:, :outputs.shape[1]] = scaling * outputs[:]
+
+
+def sgmv_expand_slice(inputs: torch.Tensor,
+                      lora_b_weights: torch.Tensor,
+                      output_tensor: torch.Tensor,
+                      b_seq_start_loc: torch.Tensor,
+                      seq_len_tensor: torch.Tensor,
+                      lora_indices_tensor: torch.Tensor,
+                      batches: int,
+                      max_seq_length: int,
+                      token_nums: int,
+                      slice_offset: int,
+                      slice_size: int,
+                      add_inputs: bool = False):
+    exploded_indices = torch.repeat_interleave(lora_indices_tensor,
+                                               seq_len_tensor)
+
+    bgmv_expand_slice(inputs, lora_b_weights, output_tensor, exploded_indices,
+                      slice_offset, slice_size, add_inputs)
+
+
+def bgmv_expand_slice(inputs: torch.Tensor,
+                      lora_b_weights: torch.Tensor,
+                      output_tensor: torch.Tensor,
+                      lora_indices_tensor: torch.Tensor,
+                      slice_offset: int,
+                      slice_size: int,
+                      add_inputs: bool = True):
+    selected_loras = lora_b_weights[lora_indices_tensor].to(
+        dtype=output_tensor.dtype)
+    inputs = inputs.to(dtype=output_tensor.dtype)
+    if len(selected_loras.shape) == 4:
+        selected_loras = selected_loras.squeeze(dim=1)
+    outputs = torch.einsum("bi, boi -> bo", inputs, selected_loras)
+
+    if add_inputs:
+        output_tensor[:, slice_offset:slice_offset + slice_size] += outputs[:]
+    else:
+        output_tensor[:, slice_offset:slice_offset + slice_size] = outputs[:]