ultimatevocalremovergui/diffq/base.py

# 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))