205 lines
6.4 KiB
Python
205 lines
6.4 KiB
Python
"""
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Based on:
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Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
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Punica: Multi-Tenant LoRA Serving.
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https://arxiv.org/abs/2310.18547
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"""
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import torch
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import triton
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import triton.language as tl
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from vllm.triton_utils import libentry
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@libentry()
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@triton.jit
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def _sgmv_expand_kernel(
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input_ptr,
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lora_ptr,
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out_ptr,
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N,
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K,
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b_seq_start_loc,
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seq_lens,
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lora_indices,
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xm_stride,
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xk_stride, # 1
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l0_stride, # hidden_size*max_rank
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lora_k_stride,
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lora_n_stride,
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cm_stride,
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cn_stride,
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BLOCK_M: tl.constexpr,
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BLOCK_N: tl.constexpr,
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BLOCK_K: tl.constexpr,
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EVEN_K: tl.constexpr,
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ADD_INPUTS: tl.constexpr,
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CAST_TYPE: tl.constexpr,
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):
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"""
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The sgmv's expand triton kernel is based on GroupGEMM.
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"""
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pid = tl.program_id(axis=0)
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cur_batch = tl.program_id(axis=1)
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cta_n_num = tl.cdiv(N, BLOCK_N)
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pid_m = pid // cta_n_num
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pid_n = pid % cta_n_num
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M = tl.load(seq_lens + cur_batch)
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if pid_m * BLOCK_M > M:
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return
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lora_index = tl.load(lora_indices + cur_batch)
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if lora_index == -1:
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return
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cur_seq_start = tl.load(b_seq_start_loc + cur_batch)
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offset_m = tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
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offset_n = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
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offset_k = tl.arange(0, BLOCK_K)
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ram = tl.max_contiguous(tl.multiple_of(offset_m % M, BLOCK_M), BLOCK_M)
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rbn = tl.max_contiguous(tl.multiple_of(offset_n % N, BLOCK_N), BLOCK_N)
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a_ptr = (input_ptr + cur_seq_start * xm_stride + ram[:, None] * xm_stride +
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offset_k[None, :] * xk_stride, )
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b_ptr = (lora_ptr + l0_stride * lora_index +
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offset_k[:, None] * lora_n_stride + rbn[None, :] * lora_k_stride)
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accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
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for k in range(tl.cdiv(K, BLOCK_K)):
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if EVEN_K:
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tiled_a = tl.load(a_ptr)
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tiled_b = tl.load(b_ptr)
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else:
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tiled_a = tl.load(a_ptr,
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mask=offset_k[None, :] < K - k * BLOCK_K,
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other=0)
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tiled_b = tl.load(b_ptr,
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mask=offset_k[:, None] < K - k * BLOCK_K,
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other=0)
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if CAST_TYPE:
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tiled_a = tiled_a.to(lora_ptr.dtype.element_ty)
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accumulator += tl.dot(
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tiled_a,
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tiled_b,
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)
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a_ptr += BLOCK_K * xk_stride
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b_ptr += BLOCK_K * lora_n_stride
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tiled_c = accumulator.to(lora_ptr.dtype.element_ty)
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offset_cm = cur_seq_start + tl.arange(0, BLOCK_M) + pid_m * BLOCK_M
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offset_cn = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N
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c_ptr = (out_ptr + offset_cm[:, None] * cm_stride +
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offset_cn[None, :] * cn_stride)
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M = tl.load(seq_lens + cur_batch)
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c_mask = (offset_cm[:, None] <
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(cur_seq_start + M)) & (offset_cn[None, :] < N)
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if ADD_INPUTS:
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tiled_out = tl.load(c_ptr, mask=c_mask)
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tiled_c += tiled_out
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tl.store(c_ptr, tiled_c, mask=c_mask)
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@torch.inference_mode()
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def _sgmv_expand(
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inputs: torch.Tensor,
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lora_b_weights: torch.Tensor,
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output_tensor: torch.Tensor,
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b_seq_start_loc: torch.Tensor,
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seq_len_tensor: torch.Tensor,
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lora_indices_tensor: torch.Tensor,
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batches: int,
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max_seq_length: int,
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token_nums: int,
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add_inputs: bool = False,
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) -> None:
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"""
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Args:
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inputs (torch.Tensor): input tensor
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lora_b_weights (torch.Tensor): lora'a weight
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output_tensor (torch.Tensor): output tensor
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b_seq_start_loc (torch.Tensor): (batch_size,). The cumulative
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sequence lengths of the sequences in the batch, used to index
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into sequence. E.g., if the sequence length is [4, 6], it is
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[0, 4, 10].
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seq_len_tensor (torch.Tensor): (batch_size,). Record the sequence
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length of the sequences in the batch.
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lora_indices_tensor (torch.Tensor): (batch_size,). The LoRA index
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corresponding to each batch. An index of -1 means no lora should be
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applied.
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batches (int): batch size
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max_seq_length (int): The max sequence lengths of the sequences in the
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batch.
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token_nums (int): The token numbers in the batch. Used to verify if the
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token numbers in the inputs matches the one in the metadata.
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add_inputs (bool, optional): Defaults to False, adds the final lora
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results to the output.
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"""
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assert inputs.dtype in [torch.float16, torch.bfloat16, torch.float32]
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assert lora_b_weights.dtype in [
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torch.float16,
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torch.bfloat16,
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]
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assert inputs.size(0) == token_nums
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assert inputs.size(1) == lora_b_weights.size(-1)
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assert b_seq_start_loc.size(0) == batches
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assert lora_indices_tensor.size(0) == batches
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assert inputs.is_contiguous()
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assert output_tensor.is_contiguous()
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if lora_b_weights.ndim == 4: # shape:(lora_num,1,size,rank)
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assert lora_b_weights.size(1) == 1
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lora_b_weights = lora_b_weights.squeeze(dim=1)
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else:
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assert lora_b_weights.ndim == 3 # shape:(lora_num,size,rank)
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assert lora_b_weights.is_contiguous()
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# TODO tuning this config
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N, K = lora_b_weights.shape[-2:] # K= rank,N=hidden_size
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BLOCK_M = 32
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BLOCK_N = 32
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BLOCK_K = 16
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EVEN_K = K % BLOCK_K == 0
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ADD_INPUTS = add_inputs
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CAST_TYPE = False
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if inputs.dtype == torch.float32 and lora_b_weights.dtype in [
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torch.float16,
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torch.bfloat16,
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]:
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CAST_TYPE = True
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grid = (
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triton.cdiv(max_seq_length, BLOCK_M) * triton.cdiv(N, BLOCK_N),
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batches,
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)
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_sgmv_expand_kernel[grid](
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inputs,
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lora_b_weights,
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output_tensor,
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N,
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K,
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b_seq_start_loc,
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seq_len_tensor,
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lora_indices_tensor,
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inputs.stride(0),
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inputs.stride(1),
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lora_b_weights.stride(0),
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lora_b_weights.stride(1),
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lora_b_weights.stride(2),
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output_tensor.stride(0),
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output_tensor.stride(1),
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BLOCK_M,
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BLOCK_N,
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BLOCK_K,
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EVEN_K,
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ADD_INPUTS,
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CAST_TYPE,
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)
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return
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try:
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sgmv_expand = torch.library.custom_op("lora::sgmv_expand",
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_sgmv_expand,
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mutates_args=["output_tensor"])
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except AttributeError:
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sgmv_expand = _sgmv_expand
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