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