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Add the functionality to dump MPS ops.
1. DUMP_MPS_OPS to use LoggingTensor to dump out the ATen ops. 2. Skip running the EXPECTTEST list, as some tests are still seg-faulting
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| import torch | ||
| from torch.testing._internal.common_utils import TestCase, run_tests | ||
| from torch.utils._pytree import tree_map | ||
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| from typing import Iterator, List | ||
| import logging | ||
| import contextlib | ||
| import itertools | ||
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| class LoggingTensor(torch.Tensor): | ||
| elem: torch.Tensor | ||
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| __slots__ = ['elem'] | ||
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| @staticmethod | ||
| def __new__(cls, elem, *args, **kwargs): | ||
| # The wrapping tensor (LoggingTensor) shouldn't hold any | ||
| # memory for the class in question, but it should still | ||
| # advertise the same device as before | ||
| r = torch.Tensor._make_wrapper_subclass( | ||
| cls, elem.size(), | ||
| # TODO: clone strides and storage aliasing | ||
| dtype=elem.dtype, layout=elem.layout, | ||
| device=elem.device, requires_grad=elem.requires_grad | ||
| ) | ||
| # ...the real tensor is held as an element on the tensor. | ||
| r.elem = elem | ||
| return r | ||
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| def __repr__(self): | ||
| return f"LoggingTensor({self.elem})" | ||
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| @classmethod | ||
| def __torch_dispatch__(cls, func, types, args=(), kwargs=None): | ||
| def unwrap(e): | ||
| return e.elem if isinstance(e, LoggingTensor) else e | ||
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| def wrap(e): | ||
| return LoggingTensor(e) if isinstance(e, torch.Tensor) else e | ||
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| rs = tree_map(wrap, func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs))) | ||
| logging.getLogger("LoggingTensor").info(f"{func.__module__}.{func.__name__}", args, kwargs, rs) | ||
| return rs | ||
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||
| # https://stackoverflow.com/questions/36408496/python-logging-handler-to-append-to-list | ||
| class LoggingTensorHandler(logging.Handler): | ||
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| def __init__(self, log_list) -> None: | ||
| logging.Handler.__init__(self) | ||
| self.log_list = log_list | ||
| self.next_shortid = 0 | ||
|
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||
| # WARNING: not deterministic over multiple threads, this matters for | ||
| # autograd | ||
| def _shortid(self, o: object) -> int: | ||
| if not hasattr(o, '_shortid'): | ||
| o._shortid = self.next_shortid | ||
| self.next_shortid += 1 | ||
| return o._shortid | ||
|
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| def _fmt(self, a: object) -> str: | ||
| if isinstance(a, LoggingTensor): | ||
| return f'${self._shortid(a)}' | ||
| elif isinstance(a, torch.nn.Parameter): | ||
| return f'Parameter(..., size={tuple(a.size())})' | ||
| elif isinstance(a, torch.Tensor): | ||
| return f'Tensor(..., size={tuple(a.size())})' | ||
| else: | ||
| return repr(a) | ||
|
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| def emit(self, record): | ||
| fmt_args = ", ".join(itertools.chain( | ||
| (self._fmt(a) for a in record.args[0]), | ||
| (f"{k}={self._fmt(v)}" for k, v in record.args[1].items()) | ||
| )) | ||
| fmt_rets = ", ".join(self._fmt(a) for a in record.args[2]) \ | ||
| if isinstance(record.args[2], (list, tuple)) else self._fmt(record.args[2]) | ||
| self.log_list.append(f'{fmt_rets} = {record.msg}({fmt_args})') | ||
|
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||
| @contextlib.contextmanager | ||
| def capture_logs(): | ||
| logger = logging.getLogger("LoggingTensor") | ||
| log_list = [] | ||
| handler = LoggingTensorHandler(log_list) | ||
| logger.addHandler(handler) | ||
| logger.setLevel(logging.INFO) | ||
| logger.propagate = False | ||
| try: | ||
| yield log_list | ||
| finally: | ||
| logger.removeHandler(handler) | ||
|
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| def tracefunc(frame, event, arg, indent=[0]): | ||
| if event == "call": | ||
| indent[0] += 2 | ||
| print("-" * indent[0] + "> call function", frame.f_code.co_name) | ||
| elif event == "return": | ||
| print("<" + "-" * indent[0], "exit function", frame.f_code.co_name) | ||
| indent[0] -= 2 | ||
| return tracefunc | ||
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||
| import sys | ||
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