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e38d60bc07
`torchxla_trace_once ` and `aot_torchxla_trivial ` should be removed. In our internal(hopefully dashboard can be open source soon) torchbench daily runs, `openxla` backend has much higher passing rate and similar perfomrance as the `openxla_eval`(non-aot-auto-grad backend). We still use `openxla_eval` in llama2 example but I think we should move user to `openxla` backend going forward. Pull Request resolved: https://github.com/pytorch/pytorch/pull/122128 Approved by: https://github.com/alanwaketan, https://github.com/jansel
47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
# mypy: ignore-errors
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import logging
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from functorch.compile import make_boxed_func
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from ..backends.common import aot_autograd
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from .registry import register_backend, register_experimental_backend
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log = logging.getLogger(__name__)
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@register_experimental_backend
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def openxla_eval(model, fake_tensor_inputs):
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return xla_backend_helper(model, fake_tensor_inputs, boxed=False)
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def openxla_eval_boxed(model, fake_tensor_inputs):
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return xla_backend_helper(model, fake_tensor_inputs, boxed=True)
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def xla_backend_helper(model, fake_tensor_inputs, boxed=False):
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try:
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import torch_xla.core.dynamo_bridge as bridge
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except ImportError as e:
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raise ImportError(
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"Please follow the instruction in https://github.com/pytorch/xla#pytorchxla to install torch_xla"
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) from e
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compiled_graph = None
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def fwd(*args):
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nonlocal model
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nonlocal compiled_graph
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if compiled_graph is None:
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compiled_graph = bridge.extract_compiled_graph(model, args)
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del model
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return compiled_graph(*args)
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return make_boxed_func(fwd) if boxed else fwd
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openxla = aot_autograd(
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fw_compiler=openxla_eval_boxed,
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)
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register_backend(name="openxla", compiler_fn=openxla)
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