mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
cb856b08b2
Did some easy fixes from enabling TRY200. Most of these seem like oversights instead of intentional. The proper way to silence intentional errors is with `from None` to note that you thought about whether it should contain the cause and decided against it. Pull Request resolved: https://github.com/pytorch/pytorch/pull/111496 Approved by: https://github.com/malfet
74 lines
1.9 KiB
Python
74 lines
1.9 KiB
Python
import logging
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import warnings
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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 torchxla_trivial(gm, fake_tensor_inputs):
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return gm
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@register_experimental_backend
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def torchxla_trace_once(model, fake_tensor_inputs):
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warnings.warn(
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"This backend will be deprecated in 2.2, please use `openxla` backend instead"
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)
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return xla_backend_helper(model, fake_tensor_inputs)
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@register_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 # type: ignore[import]
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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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aot_torchxla_trivial = aot_autograd(
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fw_compiler=torchxla_trivial,
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)
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register_experimental_backend(
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name="aot_torchxla_trivial", compiler_fn=aot_torchxla_trivial
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)
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aot_torchxla_trace_once = aot_autograd(
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fw_compiler=torchxla_trace_once,
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)
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register_experimental_backend(
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name="aot_torchxla_trace_once", compiler_fn=aot_torchxla_trace_once
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)
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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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