Joshna-MedisettyandCursor
24c49cf392
Signed-off-by: Joshna Medisetty <joshna.medisetty@intel.com> Signed-off-by: Joshna-Medisetty <joshna.medisetty@intel.com> Co-authored-by: Cursor <cursoragent@cursor.com>
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
from collections.abc import Callable
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from typing import Any
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import torch.nn as nn
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from vllm_omni.platforms import current_omni_platform
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class CustomOp(nn.Module):
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"""
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Base class for custom ops.
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Dispatches the forward method to the appropriate backend.
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"""
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def __init__(self) -> None:
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super().__init__()
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self._forward_method = self.dispatch_forward()
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def dispatch_forward(self) -> Callable:
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if current_omni_platform.is_rocm():
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return self.forward_hip
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elif current_omni_platform.is_cuda():
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return self.forward_cuda
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elif current_omni_platform.is_npu():
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return self.forward_npu
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elif current_omni_platform.is_xpu():
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return self.forward_xpu
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elif current_omni_platform.is_musa():
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return self.forward_musa
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else:
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return self.forward_native
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def forward(self, *args, **kwargs) -> Any:
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return self._forward_method(*args, **kwargs)
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def forward_native(self, *args, **kwargs):
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"""PyTorch-native implementation of the forward method.
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This method is optional. If implemented, it can be used with compilers
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such as torch.compile or PyTorch XLA. Also, it can be used for testing
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purposes.
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"""
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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_npu(self, *args, **kwargs):
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raise NotImplementedError
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def forward_xpu(self, *args, **kwargs):
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# By default, we assume that XPU ops are compatible with the
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# PyTorch-native implementation.
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return self.forward_native(*args, **kwargs)
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def forward_hip(self, *args, **kwargs):
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# By default, we assume that HIP ops are compatible with CUDA ops.
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return self.forward_cuda(*args, **kwargs)
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def forward_musa(self, *args, **kwargs):
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# By default, we assume that MUSA ops are compatible with CUDA ops.
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return self.forward_cuda(*args, **kwargs)
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