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Add `strict=True/False` to zip calls in test utils. `strict=True` is passed when possible. Pull Request resolved: https://github.com/pytorch/pytorch/pull/165531 Approved by: https://github.com/Skylion007
114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
# mypy: ignore-errors
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import contextlib
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import functools
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import inspect
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import torch
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def bf32_is_not_fp32():
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if not torch.backends.mkldnn.is_available():
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return False
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if not torch.ops.mkldnn._is_mkldnn_bf16_supported():
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return False
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return True
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def tf32_is_not_fp32():
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if not torch.backends.mkldnn.is_available():
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return False
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if not torch._C._cpu._is_amx_fp16_supported():
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return False
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return True
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@contextlib.contextmanager
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def reduced_f32_off():
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old_matmul_precision = torch.backends.mkldnn.matmul.fp32_precision
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old_conv_precision = torch.backends.mkldnn.conv.fp32_precision
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try:
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torch.backends.mkldnn.matmul.fp32_precision = "ieee"
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torch.backends.mkldnn.conv.fp32_precision = "ieee"
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yield
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finally:
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torch.backends.mkldnn.matmul.fp32_precision = old_matmul_precision
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torch.backends.mkldnn.conv.fp32_precision = old_conv_precision
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@contextlib.contextmanager
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def bf32_on(self, bf32_precision=1e-2):
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old_matmul_precision = torch.backends.mkldnn.matmul.fp32_precision
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old_conv_precision = torch.backends.mkldnn.conv.fp32_precision
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old_precision = self.precision
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try:
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torch.backends.mkldnn.matmul.fp32_precision = "bf16"
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torch.backends.mkldnn.conv.fp32_precision = "bf16"
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self.precision = bf32_precision
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yield
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finally:
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torch.backends.mkldnn.matmul.fp32_precision = old_matmul_precision
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torch.backends.mkldnn.conv.fp32_precision = old_conv_precision
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self.precision = old_precision
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@contextlib.contextmanager
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def tf32_on(self, tf32_precision=1e-5):
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old_matmul_precision = torch.backends.mkldnn.matmul.fp32_precision
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old_conv_precision = torch.backends.mkldnn.conv.fp32_precision
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old_precision = self.precision
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try:
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torch.backends.mkldnn.matmul.fp32_precision = "tf32"
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torch.backends.mkldnn.conv.fp32_precision = "tf32"
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self.precision = tf32_precision
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yield
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finally:
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torch.backends.mkldnn.matmul.fp32_precision = old_matmul_precision
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torch.backends.mkldnn.conv.fp32_precision = old_conv_precision
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self.precision = old_precision
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# This is a wrapper that wraps a test to run this test three times, one with
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# reduced_f32 OFF, the others with reduced_f32 ON (including bf32 ON and tf32
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# ON). When running with reduced_f32 ON, it will use reduced precision (bf16/
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# tf32) as specified by the argument.
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def reduced_f32_on_and_off(bf32_precision=1e-2, tf32_precision=1e-5):
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def with_reduced_f32_disabled(self, function_call):
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with reduced_f32_off():
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function_call()
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def with_bf32_enabled(self, function_call):
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with bf32_on(self, bf32_precision):
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function_call()
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def with_tf32_enabled(self, function_call):
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with tf32_on(self, tf32_precision):
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function_call()
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def wrapper(f):
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params = inspect.signature(f).parameters
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arg_names = tuple(params.keys())
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@functools.wraps(f)
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def wrapped(*args, **kwargs):
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kwargs.update(zip(arg_names, args, strict=False))
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cond = True
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if "device" in kwargs:
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cond = cond and (torch.device(kwargs["device"]).type == "cpu")
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if "dtype" in kwargs:
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cond = cond and (kwargs["dtype"] == torch.float)
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bf32_cond = cond and bf32_is_not_fp32()
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tf32_cond = cond and tf32_is_not_fp32()
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if bf32_cond or tf32_cond:
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with_reduced_f32_disabled(kwargs["self"], lambda: f(**kwargs))
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if bf32_cond:
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with_bf32_enabled(kwargs["self"], lambda: f(**kwargs))
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if tf32_cond:
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with_tf32_enabled(kwargs["self"], lambda: f(**kwargs))
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else:
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f(**kwargs)
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return wrapped
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return wrapper
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