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Avoid resizing in MinMaxObserver #43789

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Avoid resizing in MinMaxObserver #43789

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29 changes: 13 additions & 16 deletions torch/quantization/observer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -231,6 +231,13 @@ def _calculate_qparams(self, min_val, max_val):
return torch.tensor([1.0]), torch.tensor([0])

if min_val.dim() == 0 or max_val.dim() == 0:
if min_val == float('inf') and max_val == float('-inf'):
warnings.warn(
"must run observer before calling calculate_qparams.\
Returning default scale and zero point "
)
return torch.tensor([1.0]), torch.tensor([0])

assert min_val <= max_val, "min {} should be less than max {}".format(
min_val, max_val
)
Expand Down Expand Up @@ -364,8 +371,8 @@ def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
reduce_range=reduce_range,
quant_min=quant_min,
quant_max=quant_max)
self.register_buffer('min_val', torch.tensor([]))
self.register_buffer('max_val', torch.tensor([]))
self.register_buffer('min_val', torch.tensor(float('inf')))
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@raghuramank100 raghuramank100 Aug 28, 2020

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Can we serialize tensors with inf/-inf correctly?

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@qizzzh qizzzh Aug 28, 2020

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Seems ok

>>> m
EmbeddingBag(1, 2, mode=mean)
>>> m.register_buffer("v", torch.tensor([float('inf')]))
>>> m.v
tensor([inf])
>>> ms = torch.jit.script(m)
>>> ms
RecursiveScriptModule(original_name=EmbeddingBag)
>>> ms.v
tensor([inf])
>>> torch.jit.save(ms, "/tmp/test.pt")
>>> ms_2 = torch.jit.load("/tmp/test.pt")
>>> ms_2.v
tensor([inf])

self.register_buffer('max_val', torch.tensor(float('-inf')))
if self.qscheme == torch.per_tensor_symmetric and \
self.reduce_range and \
self.dtype == torch.quint8:
Expand All @@ -376,16 +383,8 @@ def forward(self, x_orig):
r"""Records the running minimum and maximum of ``x``."""
x = x_orig.detach() # avoid keeping autograd tape
x = x.to(self.min_val.dtype)
min_val = self.min_val
max_val = self.max_val
if min_val.numel() == 0 or max_val.numel() == 0:
min_val = torch.min(x)
max_val = torch.max(x)
else:
min_val = torch.min(torch.min(x), min_val)
max_val = torch.max(torch.max(x), max_val)
self.min_val.resize_(min_val.shape)
self.max_val.resize_(max_val.shape)
min_val = torch.min(torch.min(x), self.min_val)
max_val = torch.max(torch.max(x), self.max_val)
self.min_val.copy_(min_val)
self.max_val.copy_(max_val)
return x_orig
Expand Down Expand Up @@ -477,14 +476,12 @@ def forward(self, x_orig):
x = x.to(self.min_val.dtype)
min_val = self.min_val
max_val = self.max_val
if min_val.numel() == 0 or max_val.numel() == 0:
if min_val == float('inf') and max_val == float('-inf'):
min_val = torch.min(x)
max_val = torch.max(x)
else:
min_val = min_val + self.averaging_constant * (torch.min(x) - min_val)
max_val = max_val + self.averaging_constant * (torch.max(x) - max_val)
self.min_val.resize_(min_val.shape)
self.max_val.resize_(max_val.shape)
self.min_val.copy_(min_val)
self.max_val.copy_(max_val)
return x_orig
Expand Down Expand Up @@ -514,7 +511,7 @@ class MinMaxDynamicQuantObserver(MinMaxObserver):
def calculate_qparams(self):
r"""Calculates the quantization parameters."""

if self.max_val.numel() == 0 or self.min_val.numel() == 0:
if self.max_val == float('-inf') and self.min_val == float('inf'):
return torch.tensor([1.0]), torch.tensor([0])

assert self.min_val <= self.max_val, "min {} should be less than max {}".format(
Expand Down