# 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. """Base class for all quantizers.""" from contextlib import contextmanager from dataclasses import dataclass from concurrent import futures from fnmatch import fnmatch from functools import partial import io import math from multiprocessing import cpu_count import pickle import typing as tp import zlib import torch from . import bitpack from . import torch_pack as torch_pack_mod class BaseQuantizer: @dataclass class _QuantizedParam: name: str param: torch.nn.Parameter module: torch.nn.Module # If a Parameter is used multiple times, `other` can be used # to share state between the different Quantizers other: tp.Optional[tp.Any] def __init__(self, model: torch.nn.Module, min_size: float = 0.01, float16: bool = False, exclude: tp.Optional[tp.List[str]] = [], detect_bound: bool = True): self.model = model self.min_size = min_size self.float16 = float16 self.exclude = exclude self.detect_bound = detect_bound self._quantized = False self._need_unquantize = None self._pre_handle = self.model.register_forward_pre_hook(self._forward_pre_hook) self._post_handle = self.model.register_forward_hook(self._forward_hook) self._qparams = [] self._float16 = [] self._others = [] self._rnns = [] self._saved = [] self._find_params() def _find_params(self): min_params = self.min_size * 2**20 // 4 previous = {} for module_name, module in self.model.named_modules(): if isinstance(module, torch.nn.RNNBase): self._rnns.append(module) for name, param in list(module.named_parameters(recurse=False)): full_name = f"{module_name}.{name}" matched = False for pattern in self.exclude: if fnmatch(full_name, pattern) or fnmatch(name, pattern): matched = True break if param.numel() <= min_params or matched: if id(param) in previous: continue if self.detect_bound: previous[id(param)] = None if self.float16: self._float16.append(param) else: self._others.append(param) else: qparam = self._register_param(name, param, module, previous.get(id(param))) if self.detect_bound: previous[id(param)] = qparam self._qparams.append(qparam) def _register_param(self, name, param, module, other): return self.__class__._QuantizedParam(name, param, module, other) def _forward_pre_hook(self, module, input): if self.model.training: self._quantized_state = None self.unquantize() if self._pre_forward_train(): self._fix_rnns() else: assert self._need_unquantize is None self._need_unquantize = self.quantize() def _forward_hook(self, module, input, output): if self.model.training: if self._post_forward_train(): self._fix_rnns(flatten=False) # Hacky, next forward will flatten else: if self._need_unquantize: self._need_unquantize = None self.unquantize() def quantize(self): """ Immediately apply quantization to the model parameters. Model parameters are saved to later allow restoring the unquantized state. Note that you shouldn't need to call this for model evaluation, as long as you properly call `model.train()` and `model.eval()`, but this can be useful for weight inspection. """ if self._quantized: return False self._saved = [qp.param.data.to('cpu', copy=True) for qp in self._qparams if qp.other is None] self.restore_quantized_state(self.get_quantized_state(packed=False)) self._quantized = True self._fix_rnns() return True @contextmanager def enter_quantize(self): """Context manager for entering quantized state.""" self.quantize() try: yield finally: self.unquantize() def unquantize(self): """ Revert a previous call to `quantize()`. """ if not self._quantized: return if not self._saved: raise RuntimeError("Nothing to restore. This shouldn't happen") for qparam in self._qparams: if qparam.other is None: qparam.param.data[:] = self._saved.pop(0) assert len(self._saved) == 0 self._quantized = False self._fix_rnns() def _pre_forward_train(self) -> bool: """ Called once before each forward for continuous quantization. Should return True if parameters were changed. """ return False def _post_forward_train(self) -> bool: """ Called once after each forward (to restore state for instance). Should return True if parameters were changed. """ return False def _fix_rnns(self, flatten=True): """ To be called after quantization happened to fix RNNs. """ for rnn in self._rnns: rnn._flat_weights = [ (lambda wn: getattr(rnn, wn) if hasattr(rnn, wn) else None)(wn) for wn in rnn._flat_weights_names] if flatten: rnn.flatten_parameters() def _bit_pack_param(self, qparam: _QuantizedParam, quantized: tp.Any, pack_fn: tp.Any) -> tp.Any: """Further bitpack the quantized representation. This is used to return the quantized state. Should be overriden. """ return quantized def _bit_unpack_param(self, qparam: _QuantizedParam, packed: tp.Any, unpack_fn: tp.Any) -> tp.Any: """Unpack bitpacked representation. Should be overriden """ return packed def _quantize_param(self, qparam: _QuantizedParam) -> tp.Any: """ To be overriden. """ raise NotImplementedError() def _unquantize_param(self, qparam: _QuantizedParam, quantized: tp.Any) -> torch.Tensor: """ To be overriden. """ raise NotImplementedError() def get_quantized_state(self, packed=True, torch_pack=False): """ Return a quantized representation fo the weights. If `packed` is True, this will also perform bitpacking to ensure optimal store. If `torck_pack` is true, the bitpacking from `torch_pack` will be used. It is slower (except maybe on GPU), but is compatible with torchscript. You can restore a model from a quantized state either using `BaseQuantizer.restore_quantized_state` or `diffq.restore_quantized_state` if you do not have the original quantizer around anymore. """ float16_params = [] for p in self._float16: q = p.data.half() float16_params.append(q) if torch_pack: pack_fn = torch_pack_mod.pack else: pack_fn = bitpack.pack all_quantized = [] for qparam in self._qparams: if qparam.other is not None: continue quantized = self._quantize_param(qparam) if packed: quantized = self._bit_pack_param(qparam, quantized, pack_fn=pack_fn) all_quantized.append(quantized) state = { "quantized": all_quantized, "float16": float16_params, "others": [p.data.clone() for p in self._others], } kwargs = dict(self._init_kwargs) kwargs.pop("model") state["meta"] = { "init_kwargs": kwargs, "klass": self.__class__, "packed": packed, "torch_pack": torch_pack } return state def restore_quantized_state(self, state) -> None: """ Restore the state of the model from the quantized state. """ for p, q in zip(self._float16, state["float16"]): p.data[:] = q.to(p) for p, q in zip(self._others, state["others"]): p.data[:] = q meta = state.get("meta", {}) packed = meta.get("packed", False) torch_pack = meta.get("torch_pack", False) if torch_pack: unpack_fn = torch_pack_mod.unpack else: unpack_fn = bitpack.unpack remaining = list(state["quantized"]) for qparam in self._qparams: if qparam.other is not None: # Only unquantize first appearance of nn.Parameter. continue quantized = remaining.pop(0) if packed: quantized = self._bit_unpack_param(qparam, quantized, unpack_fn) qparam.param.data[:] = self._unquantize_param(qparam, quantized) assert not remaining self._fix_rnns() def detach(self) -> None: """ Detach from the model, removes hooks and anything else. """ self._pre_handle.remove() self._post_handle.remove() def model_size(self) -> torch.Tensor: """ Returns an estimate of the quantized model size. """ total = torch.tensor(0.) for p in self._float16: total += 16 * p.numel() for p in self._others: total += 32 * p.numel() return total / 2**20 / 8 # bits to MegaBytes def true_model_size(self) -> float: """ Return the true quantized model size, in MB, without extra compression. """ return self.model_size().item() def packed_model_size(self) -> float: """Return the packed model size, when stored with pickle. This should be mostly equivalent to `true_model_size` up to some slight overhead for storing metadata. """ state = self.get_quantized_state(packed=True) return len(pickle.dumps(state)) / 2 ** 20 def compressed_model_size(self, compress_level=-1, num_workers=8) -> float: """ Return the compressed quantized model size, in MB. Args: compress_level (int): compression level used with zlib, see `zlib.compress` for details. num_workers (int): will split the final big byte representation in that many chunks processed in parallels. """ out = io.BytesIO() torch.save(self.get_quantized_state(packed=False), out) ms = _parallel_compress_len(out.getvalue(), compress_level, num_workers) return ms / 2 ** 20 def restore_quantized_state(model: torch.nn.Module, state: dict): assert "meta" in state quantizer = state["meta"]["klass"](model, **state["meta"]["init_kwargs"]) quantizer.restore_quantized_state(state) quantizer.detach() def _compress_len(data, compress_level): return len(zlib.compress(data, level=compress_level)) def _parallel_compress_len(data, compress_level, num_workers): num_workers = min(cpu_count(), num_workers) chunk_size = int(math.ceil(len(data) / num_workers)) chunks = [data[offset:offset + chunk_size] for offset in range(0, len(data), chunk_size)] with futures.ThreadPoolExecutor(num_workers) as pool: # thread pool is okay here, zlib calls an external C lib and GIL is released # before the call. return sum(pool.map(partial(_compress_len, compress_level=compress_level), chunks))