masahi commented on a change in pull request #6782:
URL: https://github.com/apache/incubator-tvm/pull/6782#discussion_r513771153
##########
File path: python/tvm/relay/frontend/qnn_torch.py
##########
@@ -826,6 +826,74 @@ def _impl(inputs, _):
return _impl
+def _linear_dynamic():
+ def _calculate_qparam(inp):
+ # reference ATen/native/quantized/cpu/qlinear_dynamic.cpp
+ # ChooseQuantizationParams function
+ mn = _op.min(inp)
+ mx = _op.max(inp)
+
+ # Ensure that the interval contains 0
+ mn = _op.minimum(mn, _op.const(0.0, dtype="float32"))
+ mx = _op.maximum(mx, _op.const(0.0, dtype="float32"))
+
+ qmax = 255
+
+ # reduce_range became True in v1.6
+ if is_version_greater_than("1.5.1"):
+ qmax = 127
Review comment:
This comes from
https://github.com/pytorch/pytorch/blob/d642992877139671466d2a96663abede9e39ad55/aten/src/ATen/native/quantized/cpu/quant_utils.h#L64-L66
Here, they intentionally reduce the possible range of quantized values by
half, i.e. [qmin, qmax] to [qmin/2, qmax/2]. Since PyTorch only uses uint8,
this is fine.
It's not clear to me why they do this, but the following PR has some
explanation: "reduce_range option restricts the activation tensor to 7 bits
instead of 8.This is necessary to enable per channel quant for RNNs and LSTMs"
https://github.com/pytorch/pytorch/pull/39041
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
[email protected]
