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from contextlib import contextmanager |
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import torch |
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import torch._custom_ops |
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from torch._C import DispatchKey |
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from torch._higher_order_ops.strict_mode import strict_mode |
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from torch._higher_order_ops.utils import autograd_not_implemented |
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from torch._ops import HigherOrderOperator |
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from torch._subclasses.fake_tensor import FakeTensorMode |
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from torch.fx.experimental.proxy_tensor import ProxyTorchDispatchMode, track_tensor_tree |
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from torch.utils import _pytree as pytree |
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_export_tracepoint = HigherOrderOperator("_export_tracepoint") |
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@_export_tracepoint.py_impl(ProxyTorchDispatchMode) |
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def export_tracepoint_dispatch_mode(mode, *args, **kwargs): |
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if not mode.enable_tracing: |
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return _export_tracepoint(*args, **kwargs) |
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p_args, p_kwargs = pytree.tree_map(mode.tracer.unwrap_proxy, (args, kwargs)) |
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proxy = mode.tracer.create_proxy( |
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"call_function", _export_tracepoint, p_args, p_kwargs |
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) |
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return track_tensor_tree(args, proxy, constant=None, tracer=mode.tracer) |
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@_export_tracepoint.py_impl(FakeTensorMode) |
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def export_tracepoint_fake_tensor_mode(mode, *args, **kwargs): |
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with mode: |
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return args |
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@_export_tracepoint.py_functionalize_impl |
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def export_tracepoint_functional(ctx, *args, **kwargs): |
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unwrapped_args = ctx.unwrap_tensors(args) |
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unwrapped_kwargs = ctx.unwrap_tensors(kwargs) |
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with ctx.redispatch_to_next(): |
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out = _export_tracepoint(*unwrapped_args, **unwrapped_kwargs) |
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return ctx.wrap_tensors(out) |
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_export_tracepoint.py_impl(DispatchKey.Autograd)( |
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autograd_not_implemented(_export_tracepoint, deferred_error=True) |
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) |
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@_export_tracepoint.py_impl(DispatchKey.CPU) |
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def export_tracepoint_cpu(*args, **kwargs): |
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return args |
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def _wrap_submodule(mod, path, module_call_specs): |
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assert isinstance(mod, torch.nn.Module) |
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assert path != "" |
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submodule = mod |
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for name in path.split("."): |
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if not hasattr(submodule, name): |
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raise RuntimeError(f"Couldn't find submodule at path {path}") |
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submodule = getattr(submodule, name) |
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def update_module_call_signatures(path, in_spec, out_spec): |
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if path in module_call_specs: |
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assert module_call_specs[path]["in_spec"] == in_spec |
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assert module_call_specs[path]["out_spec"] == out_spec |
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module_call_specs[path] = {"in_spec": in_spec, "out_spec": out_spec} |
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def check_flattened(flat_args): |
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for a in flat_args: |
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if not (isinstance(a, (torch.Tensor, str, int, float, bool)) or a is None): |
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raise AssertionError( |
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f"Only Tensors or scalars are supported as pytree flattened inputs, got: {a}" |
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) |
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def pre_hook(module, args, kwargs): |
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flat_args, in_spec = pytree.tree_flatten((args, kwargs)) |
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check_flattened(flat_args) |
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flat_args = _export_tracepoint(*flat_args, kind="module_call_inputs", path=path) |
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args, kwargs = pytree.tree_unflatten(flat_args, in_spec) |
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return args, kwargs |
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def post_hook(module, args, kwargs, res): |
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_, in_spec = pytree.tree_flatten((args, kwargs)) |
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flat_res, out_spec = pytree.tree_flatten(res) |
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check_flattened(flat_res) |
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flat_res = _export_tracepoint(*flat_res, kind="module_call_outputs", path=path) |
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update_module_call_signatures(path, in_spec, out_spec) |
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return pytree.tree_unflatten(flat_res, out_spec) |
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pre_handle = submodule.register_forward_pre_hook(pre_hook, with_kwargs=True) |
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post_handle = submodule.register_forward_hook(post_hook, with_kwargs=True) |
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return pre_handle, post_handle |
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@contextmanager |
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def _wrap_submodules(f, preserve_signature, module_call_signatures): |
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handles = [] |
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try: |
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for path in preserve_signature: |
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handles.extend(_wrap_submodule(f, path, module_call_signatures)) |
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yield |
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finally: |
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for handle in handles: |
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handle.remove() |
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def _mark_strict_experimental(cls): |
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def call(self, *args): |
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return strict_mode(self, args) |
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cls.__call__ = call |
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return cls |
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