62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
from torch import nn
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from sglang.srt.utils import is_cuda, is_hip
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_is_cuda = is_cuda()
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_is_hip = is_hip()
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class CustomOp(nn.Module):
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def __init__(self):
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super().__init__()
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self._forward_method = self.dispatch_forward()
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def enter_torch_compile(self, num_tokens: int):
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# NOTE: Temporarily workaround MoE
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if "FusedMoE" in self.__class__.__name__:
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if num_tokens == 1:
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from sglang.srt.layers.moe.fused_moe_native import (
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fused_moe_forward_native,
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)
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# The performance of torch.compile on this layer is not always good when bs > 1,
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# so we decide to only use torch.compile when bs =1
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self._forward_method = fused_moe_forward_native
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else:
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self._forward_method = self.forward_native
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self.is_torch_compile = True
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def leave_torch_compile(self):
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self._forward_method = self.forward_cuda
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self.is_torch_compile = False
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# Please do not override this method, because `self._forward_method` can change when in torch compile mode
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def forward(self, *args, **kwargs):
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return self._forward_method(*args, **kwargs)
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def forward_native(self, *args, **kwargs):
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raise NotImplementedError
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def forward_cuda(self, *args, **kwargs):
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raise NotImplementedError
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def forward_hip(self, *args, **kwargs):
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return self.forward_cuda(*args, **kwargs)
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def forward_xpu(self, *args, **kwargs):
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return self.forward_native(*args, **kwargs)
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def forward_hpu(self, *args, **kwargs):
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return self.forward_native(*args, **kwargs)
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def forward_cpu(self, *args, **kwargs):
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return self.forward_native(*args, **kwargs)
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def dispatch_forward(self):
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if _is_cuda:
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return self.forward_cuda
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elif _is_hip:
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return self.forward_hip
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else:
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return self.forward_native
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