mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-24 15:30:11 +01:00
122 lines
4.1 KiB
Python
122 lines
4.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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"""
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Classic uniform quantization over n bits.
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"""
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from typing import Tuple
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import torch
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from .base import BaseQuantizer
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from .utils import simple_repr
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def uniform_quantize(p: torch.Tensor, bits: torch.Tensor = torch.tensor(8.)):
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"""
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Quantize the given weights over `bits` bits.
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Returns:
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- quantized levels
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- (min, max) range.
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"""
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assert (bits >= 1).all() and (bits <= 15).all()
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num_levels = (2 ** bits.float()).long()
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mn = p.min().item()
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mx = p.max().item()
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p = (p - mn) / (mx - mn) # put p in [0, 1]
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unit = 1 / (num_levels - 1) # quantization unit
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levels = (p / unit).round()
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if (bits <= 8).all():
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levels = levels.byte()
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else:
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levels = levels.short()
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return levels, (mn, mx)
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def uniform_unquantize(levels: torch.Tensor, scales: Tuple[float, float],
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bits: torch.Tensor = torch.tensor(8.)):
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"""
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Unquantize the weights from the levels and scale. Return a float32 tensor.
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"""
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mn, mx = scales
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num_levels = 2 ** bits.float()
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unit = 1 / (num_levels - 1)
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levels = levels.float()
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p = levels * unit # in [0, 1]
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return p * (mx - mn) + mn
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class UniformQuantizer(BaseQuantizer):
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def __init__(self, model: torch.nn.Module, bits: float = 8., min_size: float = 0.01,
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float16: bool = False, qat: bool = False, exclude=[], detect_bound=True):
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"""
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Args:
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model (torch.nn.Module): model to quantize
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bits (float): number of bits to quantize over.
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min_size (float): minimum size in MB of a parameter to be quantized.
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float16 (bool): if a layer is smaller than min_size, should we still do float16?
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qat (bool): perform quantized aware training.
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exclude (list[str]): list of patterns used to match parameters to exclude.
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For instance `['bias']` to exclude all bias terms.
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detect_bound (bool): if True, will detect bound parameters and reuse
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the same quantized tensor for both.
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"""
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self.bits = float(bits)
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self.qat = qat
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super().__init__(model, min_size, float16, exclude, detect_bound)
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def __repr__(self):
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return simple_repr(self, )
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def _pre_forward_train(self):
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if self.qat:
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for qparam in self._qparams:
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if qparam.other is not None:
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new_param = qparam.other.module._parameters[qparam.other.name]
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else:
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quantized = self._quantize_param(qparam)
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qvalue = self._unquantize_param(qparam, quantized)
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new_param = qparam.param + (qvalue - qparam.param).detach()
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qparam.module._parameters[qparam.name] = new_param
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return True
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return False
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def _post_forward_train(self):
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if self.qat:
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for qparam in self._qparams:
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qparam.module._parameters[qparam.name] = qparam.param
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return True
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return False
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def _quantize_param(self, qparam):
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levels, scales = uniform_quantize(qparam.param.data, torch.tensor(self.bits))
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return (levels, scales)
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def _unquantize_param(self, qparam, quantized):
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levels, scales = quantized
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return uniform_unquantize(levels, scales, torch.tensor(self.bits))
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def model_size(self):
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"""
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Non differentiable model size in MB.
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"""
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total = super().model_size()
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subtotal = 0
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for qparam in self._qparams:
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if qparam.other is None: # if parameter is bound, count only one copy.
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subtotal += self.bits * qparam.param.numel() + 64 # 2 float for the overall scales
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subtotal /= 2**20 * 8 # bits to MegaBytes
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return total + subtotal
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def true_model_size(self):
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"""
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Return the true quantized model size, in MB, without extra
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compression.
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"""
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return self.model_size().item()
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