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ec1e53b462
Summary: As suggested in https://github.com/pytorch/pytorch/pull/22891, we will add an overload for torch.fbgemm_linear_int8_weight (dynamic quantized version of linear function) that takes PackedLinearWeight as input and is pretty much the same in signature as regular aten::linear. The previous Diff D16381552 is reverted because `quantize_linear` expects the scale to be `float`, and the zero_point to be `int`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/23464 ghstack-source-id: 88257231 Differential Revision: D16527741 fbshipit-source-id: 66585f668c6e623c50514eb11633bb711d8767f2
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
r"""Importing this file includes common utility methods for checking quantized
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tensors and modules.
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"""
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from __future__ import absolute_import, division, print_function, unicode_literals
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import numpy as np
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import torch
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"""Computes the output shape given convolution parameters."""
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def _conv_output_shape(input_size, kernel_size, padding, stride, dilation,
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output_padding=0):
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return np.floor((input_size + 2 * padding - kernel_size - (kernel_size - 1)
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* (dilation - 1)) / stride) + 2 * output_padding + 1
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# Quantization references
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def _quantize(x, scale, zero_point, qmin=None, qmax=None, dtype=np.uint8):
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"""Quantizes a numpy array."""
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if qmin is None:
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qmin = np.iinfo(dtype).min
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if qmax is None:
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qmax = np.iinfo(dtype).max
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qx = np.round(x / scale + zero_point).astype(np.int64)
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qx = np.clip(qx, qmin, qmax)
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qx = qx.astype(dtype)
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return qx
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def _dequantize(qx, scale, zero_point):
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"""Dequantizes a numpy array."""
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x = (qx.astype(np.float) - zero_point) * scale
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return x
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def _requantize(x, multiplier, zero_point, qmin=0, qmax=255, qtype=np.uint8):
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"""Requantizes a numpy array, i.e., intermediate int32 or int16 values are
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converted back to given type"""
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qx = (x * multiplier).round() + zero_point
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qx = np.clip(qx, qmin, qmax).astype(qtype)
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return qx
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def _calculate_dynamic_qparams(X, dtype):
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"""Calculate the dynamic quantization parameters (scale, zero_point)
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according to the min and max element of the tensor"""
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if dtype == torch.qint8:
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qmin, qmax = -128, 127
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else: # dtype == torch.quint8
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qmin, qmax = 0, 255
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n_levels = 255.0
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min_val = torch.min(X).item()
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max_val = torch.max(X).item()
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if min_val == max_val:
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scale = 1.0
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zero_point = 0
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else:
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max_val = max(max_val, 0.0)
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min_val = min(min_val, 0.0)
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scale = (max_val - min_val) / n_levels
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scale = max(scale, torch.finfo(torch.float32).eps)
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zero_point = qmin - round(min_val / scale)
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zero_point = max(qmin, zero_point)
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zero_point = min(qmax, zero_point)
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return [float(scale), int(zero_point)]
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