add qwen3

vllm-v0.6.2/vllm/lora/ops/bgmv_expand_slice.py

@@ -0,0 +1,181 @@
+"""
+Based on:
+Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). 
+Punica: Multi-Tenant LoRA Serving. 
+https://arxiv.org/abs/2310.18547
+"""
+
+import torch
+import triton
+import triton.language as tl
+
+from .utils import get_lora_op_configs
+
+
+@triton.jit
+def _bgmv_expand_slice_kernel(
+    input_ptr,
+    lora_ptr,
+    out_ptr,
+    N,
+    K,
+    lora_indices,
+    xm_stride,
+    xk_stride,
+    l0_stride,
+    lora_k_stride,
+    lora_n_stride,
+    cm_stride,
+    cn_stride,
+    slice_offset,
+    BLOCK_N: tl.constexpr,
+    BLOCK_K: tl.constexpr,
+    SPLIT_N: tl.constexpr,
+    EVEN_K: tl.constexpr,
+    ADD_INPUTS: tl.constexpr,
+    CAST_TYPE: tl.constexpr,
+):
+    """
+    GroupGEMV, additionally, introducing SPLIT_N can improve large hidden_size's
+    performance
+    """
+    pid_sn = tl.program_id(axis=0)
+    cur_batch = tl.program_id(axis=1)
+    lora_index = tl.load(lora_indices + cur_batch)
+    if lora_index == -1:
+        return
+    offset_k = tl.arange(0, BLOCK_K)
+    offset_n = tl.arange(0, BLOCK_N)
+    if EVEN_K:
+        tiled_a = tl.load(input_ptr + cur_batch * xm_stride +
+                          offset_k * xk_stride, )  # [BLOCK_K]
+    else:
+        tiled_a = tl.load(
+            input_ptr + cur_batch * xm_stride + offset_k * xk_stride,
+            mask=offset_k < K,
+            other=0,
+        )  # [BLOCK_K]
+    # N must be divisible by SPLIT_N
+    split_n_length = tl.cdiv(N, SPLIT_N)
+    if CAST_TYPE:
+        tiled_a = tiled_a.to(lora_ptr.dtype.element_ty)
+    # sliding  to  next row-block
+    b_ptr = (lora_ptr + l0_stride * lora_index +
+             pid_sn * split_n_length * lora_k_stride)
+    c_ptr = (out_ptr + cur_batch * cm_stride + pid_sn * split_n_length +
+             slice_offset * cn_stride)
+
+    for n in range(0, split_n_length, BLOCK_N):
+        current_n = n + offset_n
+        b_ptr_mask = (current_n[:, None] < split_n_length) & (offset_k[None, :]
+                                                              < K)
+        c_mask = current_n < split_n_length
+        tiled_b = tl.load(
+            b_ptr + current_n[:, None] * lora_k_stride +
+            offset_k[None, :] * lora_n_stride,
+            mask=b_ptr_mask,
+            other=0.0,
+        )  # [BLOCK_N,BLOCK_K]
+
+        if ADD_INPUTS:
+            tiled_out = tl.load(c_ptr + current_n * cn_stride, mask=c_mask)
+            accumulator = tl.sum(tiled_a * tiled_b, 1) + tiled_out
+        else:
+            accumulator = tl.sum(tiled_a * tiled_b, 1)
+
+        tl.store(c_ptr + current_n * cn_stride, accumulator, mask=c_mask)
+
+
+@torch.inference_mode()
+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,
+) -> None:
+    """
+    Args:
+        inputs (torch.Tensor): input tensor
+        lora_b_weights (torch.Tensor): lora'b weight
+        output_tensor (torch.Tensor): output tensor
+        lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
+            corresponding to each batch, An index of -1 means no lora should be
+            applied.
+        slice_offset (int): output_tensor's offset
+        slice_size (int): current output_tensor's size
+        batches (int): batch size
+        add_inputs (bool, optional): Defaults to False.
+    """
+    assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
+    assert lora_b_weights.dtype in [
+        torch.float16,
+        torch.bfloat16,
+    ]
+    assert inputs.size(1) == lora_b_weights.size(-1)
+
+    assert slice_size == lora_b_weights.size(-2)
+    assert inputs.is_contiguous()
+    assert output_tensor.is_contiguous()
+
+    if lora_b_weights.ndim == 4:  # shape:(lora_num,1,size,rank)
+        assert lora_b_weights.size(1) == 1
+        lora_b_weights = lora_b_weights.squeeze(dim=1)
+    else:
+        assert lora_b_weights.ndim == 3  # shape:(lora_num,size,rank)
+
+    assert lora_b_weights.is_contiguous()
+
+    # TODO tuning this config
+
+    N, K = lora_b_weights.shape[-2:]  # K= rank,N=hidden_size
+    BLOCK_K = triton.next_power_of_2(K)
+    EVEN_K = K % BLOCK_K == 0
+    ADD_INPUTS = add_inputs
+    CAST_TYPE = False
+    if inputs.dtype == torch.float32 and lora_b_weights.dtype in [
+            torch.float16,
+            torch.bfloat16,
+    ]:
+        CAST_TYPE = True
+
+    batches = lora_indices_tensor.size(0)
+
+    config = get_lora_op_configs("expand", batches, N)
+
+    grid = lambda META: (
+        META["SPLIT_N"],
+        batches,
+    )
+    _bgmv_expand_slice_kernel[grid](
+        inputs,
+        lora_b_weights,
+        output_tensor,
+        N,
+        K,
+        lora_indices_tensor,
+        inputs.stride(0),
+        inputs.stride(1),
+        lora_b_weights.stride(0),
+        lora_b_weights.stride(1),
+        lora_b_weights.stride(2),
+        output_tensor.stride(0),
+        output_tensor.stride(1),
+        slice_offset,
+        BLOCK_K=BLOCK_K,
+        EVEN_K=EVEN_K,
+        ADD_INPUTS=ADD_INPUTS,
+        CAST_TYPE=CAST_TYPE,
+        **config,
+    )
+    return
+
+
+try:
+    bgmv_expand_slice = torch.library.custom_op("lora::bgmv_expand_slice",
+                                                _bgmv_expand_slice,
+                                                mutates_args=["output_tensor"])
+except AttributeError:
+    bgmv_expand_slice = _bgmv_expand_slice