aphrodite-engine/aphrodite/kv_quant/calibration.py

# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Union

import torch
import transformers
from pkg_resources import parse_version
from torch import nn
from transformers import PreTrainedTokenizer

from aphrodite.kv_quant.observer import ActivationObserver, KVCacheObserver
from aphrodite.kv_quant.utils import (bimap_name_mod, collect_target_modules,
                                      concat_decoder_layer_outputs,
                                      split_decoder_layer_inputs)


class CalibrationContext():
    """Calibration context manager for model quantization.
    Parameters:
      - model: The target model to be calibrated and quantized
      - tokenizer: The tokenizer used in the model training
      - layer_type: Layer type to be targeted for calibration
      - norm_type: Normalization type used for calibration
      - device: Device on which model is to be calibrated ('cpu' or 'cuda')
    """

    inp_obs_group = 'inputs'
    out_obs_group = 'outputs'
    key_obs_group = 'keys'
    value_obs_group = 'values'

    def __init__(self,
                 model: nn.Module,
                 tokenizer: PreTrainedTokenizer,
                 layer_type: Union[str, type],
                 norm_type: Union[str, type],
                 device: str = 'cuda') -> None:
        """Initiate calibration context.
        Args:
            model (nn.Module): Model to be calibrated.
            tokenizer (PreTrainedTokenizer): Tokenizer of the given model.
            layer_type (Union[str, type]): Type of the layers to be observed.
            norm_type (Union[str, type]): Norm type used in the model.
            device (str, optional): Device where the model should run.
                Defaults to 'cuda'.
        """

        self.layer_type = layer_type
        self.norm_type = norm_type

        num_kv_heads, num_attn_heads = self._guess_num_heads(model)
        self.num_kv_heads = num_kv_heads
        self.head_dim = model.config.hidden_size // num_attn_heads
        self.model = model
        del self.model.lm_head

        self.tokenizer = tokenizer

        # Collect modules to observe
        self.name2layer = collect_target_modules(self.model, layer_type)
        self.name2fc = {}
        for l_name, layer in self.name2layer.items():
            name2fc = collect_target_modules(layer, nn.Linear, prefix=l_name)
            self.name2fc.update(name2fc)
        self.name2norm = collect_target_modules(self.model, norm_type)

        maps = bimap_name_mod([self.name2layer, self.name2fc, self.name2norm])
        self.name2mod, self.mod2name = maps

        # Initialize observers
        self._init_input_observers(self.name2fc)
        self._init_output_observers(self.name2norm)
        self._init_output_observers(self.name2fc)
        self._init_kv_observers(self.name2layer)

        self.device = device

    def _guess_num_heads(self, model):

        if hasattr(model.config, 'num_key_value_heads'):
            num_kv_heads = model.config.num_key_value_heads
        else:
            num_kv_heads = model.config.num_attention_heads

        num_attn_heads = model.config.num_attention_heads

        return num_kv_heads, num_attn_heads

    def _init_input_observers(self, name2mod):
        """Initialize input observers for given modules."""
        for name, mod in name2mod.items():
            obs = ActivationObserver(mod.weight.size(-1))
            obs.global_available(name, group=self.inp_obs_group)

    def _init_output_observers(self, name2mod):
        """Initialize output observers for given modules."""
        for name, mod in name2mod.items():
            obs = ActivationObserver(mod.weight.size(0))
            obs.global_available(name, group=self.out_obs_group)

    def _init_kv_observers(self, name2mod):
        """Initialize KV observers for given modules."""
        for name in name2mod:
            k_obs = KVCacheObserver(self.num_kv_heads, self.head_dim)
            v_obs = KVCacheObserver(self.num_kv_heads, self.head_dim)
            k_obs.global_available(name, group=self.key_obs_group)
            v_obs.global_available(name, group=self.value_obs_group)

    def _insert_input_observers(self):
        """Insert input observers into the target modules.
        This function registers a forward pre-hook on each target module to
        observe the inputs.
        """

        def _input_hook(mod: nn.Module, inp: torch.Tensor):
            m_name = self.mod2name[mod]
            obs = ActivationObserver.find(m_name, group=self.inp_obs_group)
            obs.observe(inp[0])

        group = ActivationObserver.find_group(self.inp_obs_group)
        for name in group:
            mod = self.name2mod[name]
            hook_fn = mod.register_forward_pre_hook(_input_hook)
            self._hooks.append(hook_fn)

    def _insert_output_observers(self):
        """Insert output observers into the target modules.
        This function registers a forward hook on each target module to observe
        the outputs.
        """

        def _output_hook(mod: nn.Module, inp: torch.Tensor, out: torch.Tensor):
            m_name = self.mod2name[mod]
            obs = ActivationObserver.find(m_name, group=self.out_obs_group)
            obs.observe(out)

        group = ActivationObserver.find_group(self.out_obs_group)
        for name in group:
            mod = self.name2mod[name]
            hook_fn = mod.register_forward_hook(_output_hook)
            self._hooks.append(hook_fn)

    def _wrap_decoder_layers(self):
        """Method to wrap the decoder layers' forward functions for observing
        their key/value cache during batched forward passes."""

        def _forward(mod, *args, **kwargs):

            mod.to(self.device)
            batch_args, batch_kwargs = split_decoder_layer_inputs(
                *args, **kwargs)
            batch_outputs = []
            samples = len(batch_args)

            m_name = self.mod2name[mod]
            k_obs = KVCacheObserver.find(m_name, group=self.key_obs_group)
            v_obs = KVCacheObserver.find(m_name, group=self.value_obs_group)

            for i in range(len(batch_args)):

                if k_obs and v_obs:
                    batch_kwargs[i]['use_cache'] = True
                    version = parse_version(transformers.__version__)
                    use_new_cache = type(mod).__name__ == 'LlamaDecoderLayer'
                    if version > parse_version('4.36.0') and use_new_cache:
                        from transformers.cache_utils import DynamicCache
                        batch_kwargs[i]['past_key_value'] = DynamicCache()

                        ori_idx = mod.self_attn.layer_idx
                        mod.self_attn.layer_idx = 0

                        out = self._ori_forwards[mod](*batch_args[i],
                                                      **batch_kwargs[i])
                        mod.self_attn.layer_idx = ori_idx

                        out = list(out)
                        cache = out.pop(-1)

                        key = cache.key_cache.pop(-1)
                        value = cache.value_cache.pop(-1)

                        k_obs.observe(key)
                        v_obs.observe(value)
                    else:
                        out = self._ori_forwards[mod](*batch_args[i],
                                                      **batch_kwargs[i])
                        out = list(out)
                        key, value = out.pop(-1)
                        k_obs.observe(key)
                        v_obs.observe(value)

                    del key, value
                    torch.cuda.empty_cache()
                    batch_outputs.append(tuple(out))
                else:
                    batch_outputs.append(self._ori_forwards[mod](
                        *batch_args[i], **batch_kwargs[i]))

            outputs = concat_decoder_layer_outputs(batch_outputs)

            del batch_outputs, batch_args, batch_kwargs, args
            mod.to('cpu')
            torch.cuda.empty_cache()
            max_memory = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024
            print(f'{m_name}, samples: {samples}, '
                  f'max gpu memory: {max_memory:.2f} GB')
            return outputs

        for layer in self.name2layer.values():
            self._ori_forwards[layer] = layer.forward
            layer.forward = partial(_forward, layer)

    def collect_inputs_stats(self):
        """Collect statistics (min, max, absmax values) of the observed inputs.
        Returns a dictionary with these collected stats.
        """
        inputs_stats = {
            'max': {},
            'min': {},
            'mean': {},
            'absmax': {},
            'absmean': {}
        }
        obs_group = ActivationObserver.find_group(self.inp_obs_group)
        for name, obs in obs_group.items():
            inputs_stats['max'][name] = obs.max_val
            inputs_stats['min'][name] = obs.min_val
            inputs_stats['mean'][name] = obs.mean_val
            inputs_stats['absmax'][name] = obs.absmax_val
            inputs_stats['absmean'][name] = obs.absmean_val
        return inputs_stats

    def collect_outputs_stats(self):
        """Collect statistics (min, max, absmax values) of the observed
        outputs.
        Returns a dictionary with these collected stats.
        """
        outputs_stats = {
            'max': {},
            'min': {},
            'mean': {},
            'absmax': {},
            'absmean': {}
        }
        obs_group = ActivationObserver.find_group(self.out_obs_group)
        for name, obs in obs_group.items():
            outputs_stats['max'][name] = obs.max_val
            outputs_stats['min'][name] = obs.min_val
            outputs_stats['mean'][name] = obs.mean_val
            outputs_stats['absmax'][name] = obs.absmax_val
            outputs_stats['absmean'][name] = obs.absmean_val
        return outputs_stats

    def collect_kv_stats(self):
        """Collect statistics (min, max, absmax values) of the observed keys
        and values.
        Returns a tuple of two dictionaries with these collected stats.
        """
        key_stats = {'max': {}, 'min': {}, 'absmax': {}}
        obs_group = KVCacheObserver.find_group(self.key_obs_group)
        for name, obs in obs_group.items():
            key_stats['max'][name] = obs.max_val
            key_stats['min'][name] = obs.min_val
            key_stats['absmax'][name] = obs.absmax_val

        value_stats = {'max': {}, 'min': {}, 'absmax': {}}
        obs_group = KVCacheObserver.find_group(self.value_obs_group)
        for name, obs in obs_group.items():
            value_stats['max'][name] = obs.max_val
            value_stats['min'][name] = obs.min_val
            value_stats['absmax'][name] = obs.absmax_val
        return key_stats, value_stats

    def export(self, out_dir):
        """Export the calibration statistics (inputs, outputs, keys and values)
        to specified directory.
        Args:
            out_dir (Union[str, Path]): The directory path where the stats
                will be saved.
        """

        inp_stats = self.collect_inputs_stats()
        torch.save(inp_stats, out_dir / 'inputs_stats.pth')

        out_stats = self.collect_outputs_stats()
        torch.save(out_stats, out_dir / 'outputs_stats.pth')

        key_stats, value_stats = self.collect_kv_stats()
        torch.save(key_stats, out_dir / 'key_stats.pth')
        torch.save(value_stats, out_dir / 'value_stats.pth')

    def calibrate(self, data):
        """Forward pass through the model in inference mode with given data."""

        if type(self.model).__name__ == 'QWenLMHeadModel':
            model = self.model.transformer
        else:
            model = self.model.model
        with torch.inference_mode():
            _ = model(data.to(self.device))

    def __enter__(self):
        """Prepares the Calibration object for a 'with' statement by
        registering hooks and wrapping layer forward methods."""

        self._hooks = list()

        self._ori_forwards = {}
        for layer in self.name2layer.values():
            self._ori_forwards[layer] = layer.forward

        self._insert_input_observers()
        self._insert_output_observers()
        self._wrap_decoder_layers()

    def __exit__(self, exc_type, exc_value, traceback):
        """Clean up after a 'with' statement by removing registered hooks,
        restoring original forward methods, and if no exception occurred,
        collecting all gathered statistics and saving them."""
        for h in self._hooks:
            h.remove()

        for layer in self.name2layer.values():
            layer.forward = self._ori_forwards[layer]