import mixdq_extension._C import inspect from typing import Any, Callable, Dict, List, Optional, Union, Tuple from collections import namedtuple import sys import os import torch from typing import Optional import torch.nn.functional as F import math import torch.nn as nn import torch from torch.ao.quantization import QConfig from transformers import ( CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection, ) from diffusers import StableDiffusionXLPipeline from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import ( FromSingleFileMixin, IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, ) from diffusers.models import AutoencoderKL, UNet2DConditionModel from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.models.attention_processor import ( AttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor, ) from diffusers.utils import ( USE_PEFT_BACKEND, deprecate, is_invisible_watermark_available, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.torch_utils import randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffusion_xl.pipeline_output import StableDiffusionXLPipelineOutput import torch import torch.nn as nn from torch.ao.quantization import QConfig, MinMaxObserver, PlaceholderObserver, QuantStub, DeQuantStub import copy import itertools import warnings import torch import torch.nn as nn import torch.ao.nn.quantized as nnq from torch.ao.nn.intrinsic import _FusedModule from torch.ao.quantization.quantization_mappings import ( get_default_dynamic_quant_module_mappings, get_default_static_quant_module_mappings, get_default_static_quant_reference_module_mappings, get_default_qat_module_mappings, get_default_qconfig_propagation_list, no_observer_set, _has_special_act_post_process, _get_special_act_post_process, ) from torch.ao.quantization.utils import get_qparam_dict, has_no_children_ignoring_parametrizations from torch.ao.quantization.stubs import DeQuantStub, QuantWrapper from torch.ao.quantization.qconfig import ( _add_module_to_qconfig_obs_ctr, default_dynamic_qconfig, float16_dynamic_qconfig, float_qparams_weight_only_qconfig, float_qparams_weight_only_qconfig_4bit, _activation_is_memoryless) from torch.nn.utils.parametrize import type_before_parametrizations from torch.ao.quantization.observer import _is_activation_post_process # TODO remove this once BC is no longer required to avoid a SEV from torch.ao.quantization.observer import ( # noqa: F401 _is_activation_post_process as is_activation_post_process ) if is_invisible_watermark_available(): from diffusers.pipelines.stable_diffusion_xl.watermark import StableDiffusionXLWatermarker # if is_torch_xla_available(): # import torch_xla.core.xla_model as xm # XLA_AVAILABLE = True # else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import StableDiffusionXLPipeline >>> pipe = StableDiffusionXLPipeline.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] ``` """ ###################################################################################################### # quant ops qlinear = mixdq_extension._C.qlinear_w8_a8_ohalf quantize_per_tensor = mixdq_extension._C.quantize_per_tensor_to_int8 def qconv2d( input_int, weight_int, weight_scale, input_scale, input_zp, bias=None, stride=1, padding=0, ): dilation = 1 if padding > 0: return mixdq_extension._C.qconv2d_with_padding_w8_a8_ohalf( input_int, weight_int, weight_scale, input_scale, input_zp, bias, stride, padding, dilation ) if padding == 0: return mixdq_extension._C.qconv2d_w8_a8_ohalf( input_int, weight_int, weight_scale, input_scale, input_zp, bias, stride, padding, dilation ) else: raise ValueError(f"Padding should be integers >= 0, got {padding}") # quant ops ###################################################################################################### ###################################################################################################### # the code below is for converting the NN model __all__ = [ "get_default_custom_config_dict", "propagate_qconfig_", "add_quant_dequant", "prepare", "quantize", "quantize_dynamic", "prepare_qat", "quantize_qat", "convert", "swap_module", 'QuantizedLinear', 'QuantizedConv2d', ] _DEFAULT_CUSTOM_CONFIG_DICT = { 'float_to_observed_custom_module_class': { nn.LSTM: nn.quantizable.LSTM, nn.MultiheadAttention: nn.quantizable.MultiheadAttention, }, 'observed_to_quantized_custom_module_class': { nn.quantizable.LSTM: nn.quantized.LSTM, nn.quantizable.MultiheadAttention: nn.quantized.MultiheadAttention, } } _SPLIT = [1280, 1280, 1280, 1280, 640, 640, 640, 320, 320] # For SDXL-Turbo # global num _NUM = 0 def get_default_custom_config_dict(): r"""Defines the default custom config dict. """ return _DEFAULT_CUSTOM_CONFIG_DICT def _propagate_qconfig_helper(module, qconfig_dict, qconfig_parent=None, prefix='', prepare_custom_config_dict=None): r"""This is a helper function for `propagate_qconfig_` Args: module: input module qconfig_dict: dictionary that maps from name of submodule to quantization configuration qconfig_parent: quantization config of parent module, we will fallback to this config when there is no specified config for current module prefix: corresponding prefix of the current module, used as key in qconfig_dict prepare_custom_config_dict: dictionary for custom handling of modules see docs for :func:`~torch.ao.quantization.prepare_fx` Return: None, module is modified inplace with qconfig attached """ module_qconfig = qconfig_dict.get( type_before_parametrizations(module), qconfig_parent) module_qconfig = qconfig_dict.get(prefix, module_qconfig) module_qconfig = getattr(module, 'qconfig', module_qconfig) torch.ao.quantization.qconfig._assert_valid_qconfig(module_qconfig, module) qconfig_with_device_check = _add_module_to_qconfig_obs_ctr( module_qconfig, module) module.qconfig = qconfig_with_device_check for name, child in module.named_children(): module_prefix = prefix + '.' + name if prefix else name # do no not propagate qconfig to child if child is non traceable if prepare_custom_config_dict is None or not ( name in prepare_custom_config_dict.get( "non_traceable_module_name", []) or type(child) in prepare_custom_config_dict.get("non_traceable_module_class", []) ): _propagate_qconfig_helper( child, qconfig_dict, qconfig_with_device_check, module_prefix ) def propagate_qconfig_(module, qconfig_dict=None, prepare_custom_config_dict=None): r"""Propagate qconfig through the module hierarchy and assign `qconfig` attribute on each leaf module Args: module: input module qconfig_dict: dictionary that maps from name or type of submodule to quantization configuration, qconfig applies to all submodules of a given module unless qconfig for the submodules are specified (when the submodule already has qconfig attribute) prepare_custom_config_dict: dictionary for custom handling of modules see docs for :func:`~torch.ao.quantization.prepare_fx` Return: None, module is modified inplace with qconfig attached """ if qconfig_dict is None: qconfig_dict = {} if prepare_custom_config_dict is None: prepare_custom_config_dict = {} _propagate_qconfig_helper( module, qconfig_dict, prepare_custom_config_dict=prepare_custom_config_dict) def _observer_forward_hook(self, input, output): r"""Forward hook that calls observer on the output """ return self.activation_post_process(output) def _observer_forward_pre_hook(self, input): r"""Forward pre hook that calls observer on the output """ return self.activation_post_process(input[0]) def _register_activation_post_process_hook(module, pre_hook=False): assert hasattr(module, 'activation_post_process'), \ 'Expect activation_post_process attribute already attached to the module' if pre_hook: handle = module.register_forward_pre_hook( _observer_forward_pre_hook, prepend=True ) else: handle = module.register_forward_hook( _observer_forward_hook, prepend=True ) def _add_observer_(module, qconfig_propagation_list=None, non_leaf_module_list=None, device=None, custom_module_class_mapping=None): r"""Add observer for the leaf child of the module. This function insert observer module to all leaf child module that has a valid qconfig attribute. Args: module: input module with qconfig attributes for all the leaf modules that we want to quantize qconfig_propagation_list: a list of quantizable modules that will have observers added to them if they are leaf nodes device: parent device, if any non_leaf_module_list: list of non-leaf modules we want to add observer Return: None, module is modified inplace with added observer modules and forward_hooks """ if qconfig_propagation_list is None: qconfig_propagation_list = get_default_qconfig_propagation_list() if custom_module_class_mapping is None: custom_module_class_mapping = {} # respect device affinity when adding observers if device is None: devices = _get_unique_devices_(module) assert len(devices) <= 1, ( f"_add_observer_ only works with cpu or single-device CUDA modules, but got devices {devices}" ) device = next(iter(devices)) if len(devices) > 0 else None def get_activation_post_process(qconfig, device, special_act_post_process=None): activation = qconfig.activation( ) if special_act_post_process is None else special_act_post_process() if device is not None: activation.to(device) return activation def needs_observation(m): return hasattr(m, 'qconfig') and m.qconfig is not None def insert_activation_post_process(m, special_act_post_process=None): """ Adds an activation post process module and register a pre or post hook that calls the module """ # We don't insert observer/fake_quantize for DeQuantStub if needs_observation(m) and not isinstance(m, DeQuantStub): # observer and hook will be gone after we swap the module m.add_module('activation_post_process', get_activation_post_process( m.qconfig, device, special_act_post_process)) # Register observer as the first entry in the hook list # All post forward hooks are preserved and will be executed after the observer before convert _register_activation_post_process_hook( m, pre_hook=_activation_is_memoryless(m.qconfig)) for name, child in module.named_children(): # TODO remove Dropout special after codebase stable if type_before_parametrizations(child) in [nn.Dropout]: continue elif issubclass(type_before_parametrizations(child), (nnq.FloatFunctional, nnq.QFunctional)): if needs_observation(child): assert hasattr(child, "activation_post_process"), ( f"functional class {type_before_parametrizations(child)} has no pre-defined `activation_post_process`" ) child.activation_post_process = get_activation_post_process( child.qconfig, device) elif isinstance(child, _FusedModule): # activation_post_process are now added directly to nn.Sequential/_FusedModule if needs_observation(child): insert_activation_post_process(child) elif non_leaf_module_list is not None and type_before_parametrizations(child) in non_leaf_module_list: if needs_observation(child): insert_activation_post_process(child) elif _has_special_act_post_process(child): special_act_post_process = _get_special_act_post_process(child) insert_activation_post_process(child, special_act_post_process) elif needs_observation(child) and type_before_parametrizations(child) in custom_module_class_mapping: observed_child = custom_module_class_mapping[type_before_parametrizations( child)].from_float(child) setattr(module, name, observed_child) # TODO: These are the modules that cannot be observed # Once there are more, we should move them to a separate list if custom_module_class_mapping[type_before_parametrizations(child)] not in no_observer_set(): insert_activation_post_process(observed_child) else: _add_observer_(child, qconfig_propagation_list, non_leaf_module_list, device, custom_module_class_mapping) # Insert observers only for leaf nodes, note that this observer is for # the output of the module, for input QuantStub will observe them if has_no_children_ignoring_parametrizations(module) and not isinstance(module, torch.nn.Sequential) \ and type_before_parametrizations(module) in qconfig_propagation_list: insert_activation_post_process(module) def _get_unique_devices_(module): return {p.device for p in module.parameters()} | \ {p.device for p in module.buffers()} def add_quant_dequant(module): r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig Note that this function will modify the children of module inplace and it can return a new module which wraps the input module as well. Args: module: input module with qconfig attributes for all the leaf modules that we want to quantize Return: Either the inplace modified module with submodules wrapped in `QuantWrapper` based on qconfig or a new `QuantWrapper` module which wraps the input module, the latter case only happens when the input module is a leaf module and we want to quantize it. """ if has_no_children_ignoring_parametrizations(module) and hasattr(module, 'qconfig') and module.qconfig: return QuantWrapper(module) for name, child in module.named_children(): module._modules[name] = add_quant_dequant(child) return module def prepare(model, inplace=False, allow_list=None, observer_non_leaf_module_list=None, prepare_custom_config_dict=None): r"""Prepares a copy of the model for quantization calibration or quantization-aware training. Quantization configuration should be assigned preemptively to individual submodules in `.qconfig` attribute. The model will be attached with observer or fake quant modules, and qconfig will be propagated. Args: `model`: input model to be modified in-place `inplace`: carry out model transformations in-place, the original module is mutated `allow_list`: list of quantizable modules `observer_non_leaf_module_list`: list of non-leaf modules we want to add observer `prepare_custom_config_dict`: customization configuration dictionary for prepare function .. code-block:: python # Example of prepare_custom_config_dict: prepare_custom_config_dict = { # user will manually define the corresponding observed # module class which has a from_float class method that converts # float custom module to observed custom module "float_to_observed_custom_module_class": { CustomModule: ObservedCustomModule } } """ torch._C._log_api_usage_once("quantization_api.quantize.prepare") if prepare_custom_config_dict is None: prepare_custom_config_dict = get_default_custom_config_dict() custom_module_class_mapping = prepare_custom_config_dict.get( "float_to_observed_custom_module_class", {}) if not inplace: model = copy.deepcopy(model) # TODO: remove allow_list qconfig_propagation_list = allow_list if allow_list is None: qconfig_propagation_list = get_default_qconfig_propagation_list() propagate_qconfig_(model, qconfig_dict=None) # sanity check common API misusage if not any(hasattr(m, 'qconfig') and m.qconfig for m in model.modules()): warnings.warn("None of the submodule got qconfig applied. Make sure you " "passed correct configuration through `qconfig_dict` or " "by assigning the `.qconfig` attribute directly on submodules") _add_observer_( model, qconfig_propagation_list, observer_non_leaf_module_list, custom_module_class_mapping=custom_module_class_mapping) return model def _remove_activation_post_process(module): # TODO: maybe we should change activation_post_process to _activation_post_process # to prevent it from being used by user if hasattr(module, 'activation_post_process') and \ _is_activation_post_process(module.activation_post_process): delattr(module, 'activation_post_process') # remove activation_post_process pre and post hooks def remove_hooks(pre_hook=False): hook_map = module._forward_pre_hooks if pre_hook else module._forward_hooks observer_hook = _observer_forward_pre_hook if pre_hook else _observer_forward_hook handle_ids_to_remove = set() for handle_id, hook_fn in hook_map.items(): if hook_fn is observer_hook: handle_ids_to_remove.add(handle_id) for handle_id in handle_ids_to_remove: hook_map.pop(handle_id) remove_hooks(pre_hook=True) remove_hooks(pre_hook=False) # TODO: rename to something more general def _remove_qconfig(module): r"""Clean up the qconfig left in the module so that new qconfig can be propagated. Args: module: module to be cleaned up """ for child in module.children(): _remove_qconfig(child) if hasattr(module, "qconfig"): del module.qconfig _remove_activation_post_process(module) def quantize(model, run_fn, run_args, mapping=None, inplace=False): r"""Quantize the input float model with post training static quantization. First it will prepare the model for calibration, then it calls `run_fn` which will run the calibration step, after that we will convert the model to a quantized model. Args: model: input float model run_fn: a calibration function for calibrating the prepared model run_args: positional arguments for `run_fn` inplace: carry out model transformations in-place, the original module is mutated mapping: correspondence between original module types and quantized counterparts Return: Quantized model. """ torch._C._log_api_usage_once("quantization_api.quantize.quantize") if mapping is None: mapping = get_default_static_quant_module_mappings() if not inplace: model = copy.deepcopy(model) model.eval() prepare(model, inplace=True) run_fn(model, *run_args) convert(model, mapping, inplace=True) return model def quantize_dynamic(model, qconfig_spec=None, dtype=torch.qint8, mapping=None, inplace=False): r"""Converts a float model to dynamic (i.e. weights-only) quantized model. Replaces specified modules with dynamic weight-only quantized versions and output the quantized model. For simplest usage provide `dtype` argument that can be float16 or qint8. Weight-only quantization by default is performed for layers with large weights size - i.e. Linear and RNN variants. Fine grained control is possible with `qconfig` and `mapping` that act similarly to `quantize()`. If `qconfig` is provided, the `dtype` argument is ignored. Args: model: input model qconfig_spec: Either: - A dictionary that maps from name or type of submodule to quantization configuration, qconfig applies to all submodules of a given module unless qconfig for the submodules are specified (when the submodule already has qconfig attribute). Entries in the dictionary need to be QConfig instances. - A set of types and/or submodule names to apply dynamic quantization to, in which case the `dtype` argument is used to specify the bit-width inplace: carry out model transformations in-place, the original module is mutated mapping: maps type of a submodule to a type of corresponding dynamically quantized version with which the submodule needs to be replaced """ torch._C._log_api_usage_once("quantization_api.quantize.quantize_dynamic") if qconfig_spec is None: if dtype == torch.qint8: qconfig_spec = { nn.Linear: default_dynamic_qconfig, nn.LSTM: default_dynamic_qconfig, nn.GRU: default_dynamic_qconfig, nn.LSTMCell: default_dynamic_qconfig, nn.RNNCell: default_dynamic_qconfig, nn.GRUCell: default_dynamic_qconfig, } elif dtype == torch.float16: qconfig_spec = { nn.Linear: float16_dynamic_qconfig, nn.LSTM: float16_dynamic_qconfig, nn.GRU: float16_dynamic_qconfig, nn.LSTMCell: float16_dynamic_qconfig, nn.RNNCell: float16_dynamic_qconfig, nn.GRUCell: float16_dynamic_qconfig, } elif dtype == torch.quint8: qconfig_spec = { nn.EmbeddingBag: float_qparams_weight_only_qconfig, nn.Embedding: float_qparams_weight_only_qconfig, } elif dtype == torch.quint4x2: qconfig_spec = { nn.EmbeddingBag: float_qparams_weight_only_qconfig_4bit, } else: raise ValueError( f"Don't know how to quantize with default settings for {dtype}. Provide full qconfig please") elif isinstance(qconfig_spec, set): if dtype is torch.qint8: default_qconfig = default_dynamic_qconfig elif dtype is torch.float16: default_qconfig = float16_dynamic_qconfig elif dtype is torch.quint8: default_qconfig = float_qparams_weight_only_qconfig elif dtype is torch.quint4x2: default_qconfig = float_qparams_weight_only_qconfig_4bit else: raise RuntimeError( 'Unknown dtype specified for quantize_dynamic: ', str(dtype)) qconfig_spec = dict( zip(qconfig_spec, itertools.repeat(default_qconfig))) if mapping is None: mapping = get_default_dynamic_quant_module_mappings() if not inplace: model = copy.deepcopy(model) model.eval() propagate_qconfig_(model, qconfig_spec) convert(model, mapping, inplace=True) return model def prepare_qat(model, mapping=None, inplace=False): r""" Prepares a copy of the model for quantization calibration or quantization-aware training and converts it to quantized version. Quantization configuration should be assigned preemptively to individual submodules in `.qconfig` attribute. Args: model: input model to be modified in-place mapping: dictionary that maps float modules to quantized modules to be replaced. inplace: carry out model transformations in-place, the original module is mutated """ torch._C._log_api_usage_once("quantization_api.quantize.prepare_qat") assert model.training, "prepare_qat only works on models in training mode" if mapping is None: mapping = get_default_qat_module_mappings() if not inplace: model = copy.deepcopy(model) propagate_qconfig_(model, qconfig_dict=None) convert(model, mapping=mapping, inplace=True, remove_qconfig=False) prepare(model, observer_non_leaf_module_list=set( mapping.values()), inplace=True) return model def quantize_qat(model, run_fn, run_args, inplace=False): r"""Do quantization aware training and output a quantized model Args: model: input model run_fn: a function for evaluating the prepared model, can be a function that simply runs the prepared model or a training loop run_args: positional arguments for `run_fn` Return: Quantized model. """ torch._C._log_api_usage_once("quantization_api.quantize.quantize_qat") if not inplace: model = copy.deepcopy(model) model.train() prepare_qat(model, inplace=True) run_fn(model, *run_args) convert(model, inplace=True) return model def convert( module, mapping=None, inplace=False, remove_qconfig=True, is_reference=False, convert_custom_config_dict=None, ckpt=None): r"""Converts submodules in input module to a different module according to `mapping` by calling `from_float` method on the target module class. And remove qconfig at the end if remove_qconfig is set to True. Args: `module`: prepared and calibrated module `mapping`: a dictionary that maps from source module type to target module type, can be overwritten to allow swapping user defined Modules `inplace`: carry out model transformations in-place, the original module is mutated `convert_custom_config_dict`: custom configuration dictionary for convert function .. code-block:: python # Example of convert_custom_config_dict: convert_custom_config_dict = { # user will manually define the corresponding quantized # module class which has a from_observed class method that converts # observed custom module to quantized custom module "observed_to_quantized_custom_module_class": { ObservedCustomModule: QuantizedCustomModule } } """ torch._C._log_api_usage_once("quantization_api.quantize.convert") if not inplace: module = copy.deepcopy(module) _convert( module, mapping, inplace=True, is_reference=is_reference, convert_custom_config_dict=convert_custom_config_dict, ckpt=ckpt) if remove_qconfig: _remove_qconfig(module) return module def _convert( module, mapping=None, inplace=False, is_reference=False, convert_custom_config_dict=None, ckpt=None): r"""Converts submodules in input module to a different module according to `mapping` by calling `from_float` method on the target module class Args: module: input module mapping: a dictionary that maps from source module type to target module type, can be overwritten to allow swapping user defined Modules inplace: carry out model transformations in-place, the original module is mutated is_reference: a flag to enable quantized reference module """ if mapping is None: mapping = get_default_static_quant_reference_module_mappings() if is_reference \ else get_default_static_quant_module_mappings() if convert_custom_config_dict is None: convert_custom_config_dict = get_default_custom_config_dict() custom_module_class_mapping = convert_custom_config_dict.get( "observed_to_quantized_custom_module_class", {}) if not inplace: module = copy.deepcopy(module) reassign = {} for name, mod in module.named_children(): # both fused modules and observed custom modules are # swapped as one unit if not isinstance(mod, _FusedModule) and \ type_before_parametrizations(mod) not in custom_module_class_mapping: _convert(mod, mapping, True, # inplace is_reference, convert_custom_config_dict, ckpt=ckpt) reassign[name] = swap_module( mod, mapping, custom_module_class_mapping, ckpt=ckpt) for key, value in reassign.items(): module._modules[key] = value return module def swap_module(mod, mapping, custom_module_class_mapping, ckpt=None): global _NUM r"""Swaps the module if it has a quantized counterpart and it has an `observer` attached. Args: mod: input module mapping: a dictionary that maps from nn module to nnq module Return: The corresponding quantized module of `mod` """ new_mod = mod if hasattr(mod, 'qconfig') and mod.qconfig is not None: swapped = False if type_before_parametrizations(mod) in custom_module_class_mapping: new_mod = custom_module_class_mapping[type_before_parametrizations( mod)].from_observed(mod) swapped = True elif type_before_parametrizations(mod) in mapping: qmod = mapping[type_before_parametrizations(mod)] if hasattr(qmod, '_IS_REFERENCE') and qmod._IS_REFERENCE: assert mod.qconfig is not None weight_post_process = mod.qconfig.weight() weight_post_process(mod.weight) weight_qparams = get_qparam_dict(weight_post_process) if 'up_blocks' in mod.module_name and 'conv_shortcut' in mod.module_name: # _NUM = _NUM + 1 _split = _SPLIT[_NUM] _NUM = _NUM + 1 # num = num + 1 else: _split = 0 new_mod = qmod.from_float(mod, weight_qparams, split=_split) else: if 'up_blocks' in mod.module_name and 'conv_shortcut' in mod.module_name: # _NUM = _NUM + 1 _split = _SPLIT[_NUM] _NUM = _NUM + 1 # num = num + 1 print(f"split at {_split}") else: _split = 0 new_mod = qmod.from_float(mod, split=_split, ckpt=ckpt) swapped = True if swapped: # Preserve module's pre forward hooks. They'll be called on quantized input for pre_hook_fn in mod._forward_pre_hooks.values(): new_mod.register_forward_pre_hook(pre_hook_fn) # Preserve module's post forward hooks except _observer_forward_hook # After convert they'll work with quantized output for hook_fn in mod._forward_hooks.values(): if hook_fn is not _observer_forward_hook: new_mod.register_forward_hook(hook_fn) # respect device affinity when swapping modules devices = _get_unique_devices_(mod) assert len(devices) <= 1, ( f"swap_module only works with cpu or single-device CUDA modules, but got devices {devices}" ) device = next(iter(devices)) if len(devices) > 0 else None if device: new_mod.to(device) return new_mod def _get_observer_dict(mod, target_dict, prefix=""): r"""Traverse the modules and save all observers into dict. This is mainly used for quantization accuracy debug Args: mod: the top module we want to save all observers prefix: the prefix for the current module target_dict: the dictionary used to save all the observers """ def get_prefix(prefix): return prefix if prefix == "" else prefix + '.' if hasattr(mod, 'activation_post_process'): target_dict[get_prefix( prefix) + 'activation_post_process'] = mod.activation_post_process for name, child in mod.named_children(): module_prefix = get_prefix(prefix) + name if prefix else name _get_observer_dict(child, target_dict, module_prefix) def filter_mod_name_prefix(mod_name): if 'model.' in mod_name: pos = mod_name.index('model.') mod_name = mod_name[pos + 6:] return mod_name def register_qconfig_from_input_files( unet, # args, w_bit=8, a_bit=None, bos=True, bos_dict=None ): import yaml bw_to_dtype = { 8: torch.qint8, 4: torch.quint4x2, 2: torch.quint4x2, # !!!TODO: 2 is not supported, treat as 4 } # load weight bits # with open(w_config, 'r') as input_file: if w_bit==8: mod_name_to_weight_width = w8_uniform_config else: raise RuntimeError("we only support int8 quantization") # filter 'model.' from all names mod_name_to_weight_width_copy = {} for mod_name, bit_width in mod_name_to_weight_width.items(): new_name = filter_mod_name_prefix(mod_name) mod_name_to_weight_width_copy[new_name] = bit_width mod_name_to_weight_width = mod_name_to_weight_width_copy # add qconfig to all modules whose name are in the yaml mod_name_to_weight_width_copy = mod_name_to_weight_width for name, mod in unet.named_modules(): if name in mod_name_to_weight_width: assert not hasattr(mod, 'qconfig') # get the corresponding bit-width of the layer w_bitwidth = mod_name_to_weight_width[name] w_dtype = bw_to_dtype[w_bitwidth] act_preprocess = PlaceholderObserver.with_args( dtype=torch.float16) # get the statistic info in the tensor weight_process = PlaceholderObserver.with_args(dtype=w_dtype) mod.qconfig = \ QConfig(activation=act_preprocess, weight=weight_process) # init some parameters for each unquantized module mod.module_name = name # set module name for each module # record the bit_width of the weight mod.w_bit = mod_name_to_weight_width[name] if 'attn2' in name: if 'to_k' in name or 'to_v' in name: mod.bos = bos # set bos for corss attn layers mod.bos_pre_computed = bos_dict[name] del mod_name_to_weight_width_copy[name] # check if there is any module not in the unet if len(mod_name_to_weight_width_copy): for name in mod_name_to_weight_width_copy.keys(): print(f"{name} not found in UNet!") raise RuntimeError("Not all keys in weight yaml map to a module in " "UNet.") # load activation bits if a_bit is None: return # with open(a_config, 'r') as input_file: if a_bit == 8: mod_name_to_act_width = a8_mixed_precision_config else: raise RuntimeError("we only support int8 quantization now") # filter 'model.' from all names mod_name_to_act_width_copy = {} for mod_name, bit_width in mod_name_to_act_width.items(): new_name = filter_mod_name_prefix(mod_name) mod_name_to_act_width_copy[new_name] = bit_width mod_name_to_act_width = mod_name_to_act_width_copy # add qconfig to all modules whose name are in the yaml mod_name_to_act_width_copy = mod_name_to_act_width for name, mod in unet.named_modules(): if name in mod_name_to_act_width: a_bitwidth = mod_name_to_act_width[name] a_dtype = bw_to_dtype[a_bitwidth] act_preprocess = PlaceholderObserver.with_args(dtype=a_dtype) if hasattr(mod, 'qconfig') and mod.qconfig: assert isinstance(mod.qconfig, QConfig) mod.qconfig = QConfig(weight=mod.qconfig.weight, activation=act_preprocess) else: weight_process = PlaceholderObserver.with_args( dtype=torch.float16) mod.qconfig = QConfig(activation=act_preprocess, weight=weight_process) # init some parameters for each unquantized module # record the bit_width of the act mod.a_bit = mod_name_to_act_width[name] del mod_name_to_act_width_copy[name] # check if there is any module not in the unet if len(mod_name_to_act_width_copy): for name in mod_name_to_act_width_copy.keys(): print(f"{name} not found in UNet!") raise RuntimeError("Not all keys in act yaml map to a module in " "UNet.") def convert_to_quantized(unet, ckpt): # from quantize import convert convert(unet, mapping={nn.Linear: QuantizedLinear, nn.Conv2d: QuantizedConv2d, # QuantStub: Quantizer, # DeQuantStub: DeQuantizer }, inplace=True, ckpt=ckpt) # print("unet after quantization") # print(unet) # the code above is for converting the NN model ###################################################################################################### ###################################################################################################### # mixdq utils def quantize_per_tensor_uint4( input: torch.Tensor, scale, zero_point, ): # reshape the quant parameters for quantizing scale = scale.view(-1, *([1] * (len(input.shape) - 1))) zero_point = zero_point.view(-1, *([1] * (len(input.shape) - 1))) # scale = scale.reshape() scale_inv = 1.0 / scale int_repr = torch.clamp(torch.round(input * scale_inv) + zero_point, 0, 15).to(torch.uint8) if len(input.shape) >= 4: assert input.shape[1] % 2 == 0 return (int_repr[:, ::2, ...] << 4 | int_repr[:, 1::2, ...]) assert input.shape[-1] % 2 == 0 return (int_repr[..., ::2] << 4 | int_repr[..., 1::2]) def unpack_uint4(input): shape = input.shape if len(shape) >= 4: packed_dim = 2 new_shape = (input.shape[0], input.shape[1]*2, *input.shape[2:]) else: packed_dim = -1 new_shape = (*input.shape[:-1], input.shape[-1]*2) first_elements = (input >> 4).to(torch.uint8) second_elements = (input & 0b1111).to(torch.uint8) return torch.stack([first_elements, second_elements], dim=packed_dim).view(new_shape) def dequantize_per_tensor_uint4( input, scale, zero_point, ): # reshape the quant parameters for dequantizing scale = scale.view(-1, *([1] * (len(input.shape) - 1))) zero_point = zero_point.view(-1, *([1] * (len(input.shape) - 1))) input = unpack_uint4(input) return (input.view(torch.uint8).to(torch.float32) - zero_point) * scale dtype_to_bw = { torch.quint8: 8, torch.quint4x2: 4, torch.quint2x4: 2, torch.float16: 16, } class QParam(namedtuple("QParam", ["qscheme", "dtype", "scales", "zero_points", "axis"], defaults=[torch.per_tensor_affine, torch.quint8, 1.0, 0.0, 0])): @property def zp_float(self): return self.scales * self.zero_points pass def create_qparams_from_dtype( dtype, device, is_channel_wise=False, num_kernels=None, ckpt=None, module_name=None, bit_width=0, quant_type=None, split=0, ): if dtype == torch.float16: return None elif dtype in [torch.qint8, torch.quint8, torch.quint4x2]: if quant_type == 'weight': scales, zero_points, scales_0, zero_points_0 = get_quant_para(ckpt, bit_width, module_name, quant_type='weight', split=split, device=device) elif quant_type == 'act': scales, zero_points, scales_0, zero_points_0 = get_quant_para(ckpt, bit_width, module_name, quant_type='act', split=split, device=device) else: raise ValueError(f"Unsupported quantize dtype {dtype}") if is_channel_wise: assert num_kernels is not None qparam = QParam(qscheme=torch.per_channel_affine, scales=scales, zero_points=zero_points, dtype=dtype, axis=0) if split > 0: qparam_0 = QParam(qscheme=torch.per_channel_affine, scales=scales_0, zero_points=zero_points_0, dtype=dtype, axis=0) else: qparam_0 = None else: qparam = QParam(qscheme=torch.per_tensor_affine, scales=scales, zero_points=zero_points, dtype=dtype) if split > 0: qparam_0 = QParam(qscheme=torch.per_tensor_affine, scales=scales_0, zero_points=zero_points_0, dtype=dtype) else: qparam_0 = None return qparam, qparam_0 def quantize_from_qparams(x: torch.Tensor, qparams: QParam): if qparams.dtype == torch.quint4x2: # TODO: support both per-channel and per-tensor # assert qparams.qscheme == torch.per_tensor_affine # print(x.shape) return quantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device)) if qparams.qscheme in [torch.per_tensor_affine]: scales = qparams.scales scales = scales.clone().detach().to(x.device) \ if isinstance(scales, torch.Tensor) \ else torch.tensor(scales, dtype=torch.float16, device=x.device) zps = qparams.zero_points zps = zps.clone().detach().to(x.device) \ if isinstance(zps, torch.Tensor) \ else torch.tensor(zps, dtype=torch.float16, device=x.device) # Quantize only works on Float Tensor not Half. TODO: custom kernels x = x.to(torch.float32) x_quant = torch.quantize_per_tensor(x, scales, zps, qparams.dtype) elif qparams.qscheme in [torch.per_channel_affine]: scales = qparams.scales assert isinstance(scales, torch.Tensor) scales = scales.clone().detach().to(x.device) zps = qparams.zero_points assert isinstance(zps, torch.Tensor) zps = zps.clone().detach().to(x.device) assert qparams.axis < len(x.shape) # Quantize only works on Float Tensor not Half TODO: custom kernels x = x.to(torch.float32) # print(scales.shape) # if scales.shape == torch.Size([]): # # torch.quantize_per_channel need the shape of scales and zps to be torch.size([N]) # scales = scales.reshape(1) # zps = zps.reshape(1) x_quant = torch.quantize_per_channel(x, scales, zps, axis=qparams.axis, dtype=qparams.dtype) else: raise ValueError(f"Unknown qscheme {qparams.qscheme}") return x_quant def dequantize_to_float16_linear(x: torch.Tensor, qparams: QParam): if x.dtype == torch.float16: return x if x.dtype in [torch.quint8, torch.qint8]: return x.dequantize().to(torch.float32) elif x.dtype in [torch.int8]: scale = (qparams.scales.view(-1, * ([1] * (len(x.shape) - 1)))).cuda().float() zero_points = (qparams.zero_points.view(-1, * ([1] * (len(x.shape) - 1)))).cuda().float() x = scale*(x - zero_points) return x assert x.dtype == torch.uint8 # the current way to support uint4 return dequantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device)).to(torch.float16) def dequantize_to_float16(x: torch.Tensor, qparams: QParam): if x.dtype == torch.float16: return x if x.dtype in [torch.quint8, torch.qint8]: return x.dequantize().to(torch.float16) elif x.dtype in [torch.int8]: scale = (qparams.scales.view(-1, *([1] * (len(x.shape) - 1)))).cuda() zero_points = (qparams.zero_points.view(-1, * ([1] * (len(x.shape) - 1)))).cuda() x = scale*(x - zero_points) return x assert x.dtype == torch.uint8 # the current way to support uint4 return dequantize_per_tensor_uint4(x, qparams.scales.to(x.device), qparams.zero_points.to(x.device)).to(torch.float16) def linear_on_quantized_data( w_tensor: torch.Tensor = None, w_tensor_org: torch.Tensor = None, w_qparams: QParam = None, key_first_token: torch.Tensor = None, a_tensor: torch.Tensor = None, a_qparams: QParam = None, bias: Optional[torch.Tensor] = None, bos: bool = False, module_name=None, bos_pre_computed=None, # k_tensor_text = None, # v_tensor_text = None ) -> torch.Tensor: if not bos: # functional simulation for now (TODO: kernel support) if a_qparams is not None: out = gemm_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias) return out # , _ else: # out, _ = gemm_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias) # a_tensor_org = a_tensor # w_tensor_org = w_tensor # bias_org = bias # a_tensor = dequantize_to_float16_linear(a_tensor, a_qparams) if a_qparams is not None else a_tensor.float() # w_tensor = dequantize_to_float16_linear(w_tensor, w_qparams) # bias = bias.float() if bias is not None else bias # output = F.linear(a_tensor, w_tensor, bias).half() # torch.testing.assert_close(output, _) # return output # F.linear(a_tensor, w_tensor, bias).half() a_tensor = dequantize_to_float16( a_tensor, a_qparams) if a_qparams is not None else a_tensor w_tensor = dequantize_to_float16(w_tensor, w_qparams) return F.linear(a_tensor, w_tensor, bias) else: print("apply bos!") # TODO: pre-compute the first token or not # compute the first token and the the others seperately # out_0 = F.linear(key_first_token.unsqueeze(1), w_tensor_org, bias) # TODO:Note that batch_size of the bos_pre_computed is 1, if bs!=1, out_0 should be repeated out_0 = bos_pre_computed.cuda() # a_tensor = dequantize_to_float16_linear(a_tensor, a_qparams) # w_tensor = dequantize_to_float16_linear(w_tensor, w_qparams) # bias = bias.float() if bias is not None else bias # out_1 = F.linear(a_tensor, w_tensor, bias).half() out_1 = gemm_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias) out_0 = out_0.expand(out_1.shape[0], -1, -1) # , torch.cat([out_0, _],dim=1) return torch.cat([out_0, out_1], dim=1) def conv2d_on_quantized_data( w_tensor: torch.Tensor = None, w_tensor_0: torch.Tensor = None, w_qparams: QParam = None, w_qparams_0: QParam = None, a_tensor: torch.Tensor = None, a_tensor_0: torch.Tensor = None, a_qparams: QParam = None, a_qparams_0: QParam = None, bias: Optional[torch.Tensor] = None, stride=1, padding=0, dilation=1, groups=1, split=0 ) -> torch.Tensor: # functional simulation for now (TODO: kernel support) if split == 0: if a_qparams is not None: out = conv_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias, stride, padding, dilation, groups) return out else: a_tensor = dequantize_to_float16( a_tensor, a_qparams) if a_qparams is not None else a_tensor w_tensor = dequantize_to_float16(w_tensor, w_qparams) return F.conv2d(a_tensor, w_tensor, bias, stride, padding, dilation, groups) elif split > 0: if a_qparams is not None: # weight = dequantize_to_float16(w_tensor, w_qparams) # weight_0 = dequantize_to_float16(w_tensor_0, w_qparams_0) # input = dequantize_to_float16(a_tensor, a_qparams) # input_0 = dequantize_to_float16(a_tensor_0, a_qparams_0) # a_tensor = torch.cat([input, input_0], dim=1) if a_qparams_0 is not None else a_tensor # out = F.conv2d(input, weight, None, stride, padding, dilation, groups) # out_0 = F.conv2d(input_0, weight_0, None, stride, padding, dilation, groups) out = conv_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, None, stride, padding, dilation, groups) out_0 = conv_cutlass(w_qparams_0, a_qparams_0, w_tensor_0, a_tensor_0, None, stride, padding, dilation, groups) shape = bias.size() bias = bias.reshape(1, shape[0], 1, 1) out = out + out_0 + bias else: weight = dequantize_to_float16(w_tensor, w_qparams) weight_0 = dequantize_to_float16(w_tensor_0, w_qparams_0) a_tensor = a_tensor w_tensor = torch.cat([weight, weight_0], dim=1) out = F.conv2d(a_tensor, w_tensor, bias, stride, padding, dilation, groups) # w_tensor = torch.cat([weight, weight_0], dim=1) return out def gemm_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias): s_w = w_qparams.scales.cuda().float() s_a = a_qparams.scales.cuda().float() z_a = a_qparams.zero_points.cuda().float() zps_a = a_qparams.zp_float.cuda().float() a_int = a_tensor # if w_tensor.dtype is torch.qint8 else w_tensor.transpose(0,1) w_int = w_tensor.int_repr() output_ref = qlinear( a_int, w_int, s_w, s_a, z_a, bias ) # original_size = a_int.size() # if len(original_size)>2: # # reshape # a_int = a_int.view(-1, original_size[-1]) # # reshape the matrix # _, s_w = torch.broadcast_tensors(w_int, s_w) # _, s_a = torch.broadcast_tensors(a_int, s_a) # _, zps_a = torch.broadcast_tensors(a_int, zps_a) # # output = gemm_int8_tensorcore_test.run(a_tensor, w_tensor) the shape of the tensor should be [xx, in_features] # out_int = a_int.to(torch.float32)@w_int.to(torch.float32) # inf_check = torch.isinf(out_int) # has_inf = torch.any(inf_check) # assert not has_inf, "there are inf in the tensor!" # output = (s_a@s_w)/s_w.shape[0]*out_int # inf_check = torch.isinf(output) # has_inf = torch.any(inf_check) # assert not has_inf, "there are inf in the tensor!" # output = output - zps_a@(s_w*w_int) # a_int = (a_float+zps_a)/s a_int:[-128,127] # if bias is not None: # output = output+bias # if len(original_size)>2: # output = output.view(*original_size[:-1], w_int.size(1)) # output = output.to(torch.float16) # inf_check = torch.isinf(output) # has_inf = torch.any(inf_check) # assert not has_inf, "there are inf in the tensor!" print("run gemm on tensor core") # torch.testing.assert_close(output, output_ref) return output_ref def conv_cutlass(w_qparams, a_qparams, w_tensor, a_tensor, bias, stride, padding, dilation, groups): print("run qconv2d!") s_w = w_qparams.scales.cuda().to(torch.float32) s_a = a_qparams.scales.cuda().to(torch.float32) z_a = a_qparams.zero_points.cuda().to(torch.float32) zps_a = a_qparams.zp_float.cuda().to(torch.float32) a_int = a_tensor w_int = w_tensor.int_repr() a_int = a_int.to(memory_format=torch.channels_last) w_int = w_int.to(memory_format=torch.channels_last) if len(set(padding)) == 1: padding = padding[0] else: raise RuntimeError("the padding has different elements") if len(set(stride)) == 1: stride = stride[0] else: raise RuntimeError("the stride has different elements") output = qconv2d( a_int, w_int, s_w, s_a, z_a, bias, stride, padding,) return output def get_quant_para(ckpt, n_bit, module_name, quant_type, split=0, device=None): if split == 0: bit_idx = int(math.log2(n_bit)-1) if quant_type == 'weight': module_name = module_name + '.weight_quantizer' assert module_name in ckpt.keys() scales = ckpt[module_name]['delta_list'][bit_idx] # sym quantization, zp=0 zero_point = ckpt[module_name]['zero_point_list'][bit_idx] # print(zero_point) elif quant_type == 'act': module_name = module_name + '.act_quantizer' assert module_name in ckpt.keys() scales = ckpt[module_name]['delta_list'][bit_idx] # change the data type from uint8 to int8 zero_point = ckpt[module_name]['zero_point_list'][bit_idx] - 128 return scales.to(device), zero_point.to(device), None, None elif split > 0: bit_idx = int(math.log2(n_bit)-1) if quant_type == 'weight': module_name = module_name + '.weight_quantizer' assert module_name in ckpt.keys() scales = ckpt[module_name]['delta_list'][bit_idx] zero_point = ckpt[module_name]['zero_point_list'][bit_idx] module_name = module_name + '_0' assert module_name in ckpt.keys() scales_0 = ckpt[module_name]['delta_list'][bit_idx] zero_point_0 = ckpt[module_name]['zero_point_list'][bit_idx] # print(zero_point, zero_point_0) elif quant_type == 'act': module_name = module_name + '.act_quantizer' assert module_name in ckpt.keys() scales = ckpt[module_name]['delta_list'][bit_idx] zero_point = ckpt[module_name]['zero_point_list'][bit_idx]-128 module_name = module_name + '_0' assert module_name in ckpt.keys() scales_0 = ckpt[module_name]['delta_list'][bit_idx] zero_point_0 = ckpt[module_name]['zero_point_list'][bit_idx]-128 return scales.to(device), zero_point.to(device), scales_0.to(device), zero_point_0.to(device) # mixdq utils ###################################################################################################### ###################################################################################################### # mixdq quantized module # from .utils import (quantize_from_qparams, # dtype_to_bw, linear_on_quantized_data, # create_qparams_from_dtype, get_quant_para) # from mixdq_extension.op.quant import quantize_per_tensor # from .utils import QParam, gemm_cutlass # all = [ # 'QuantizedLinear', # 'QuantizedConv2d' # ] class QuantizedLinear(nn.Module): def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None, w_qparams=None, a_qparams=None, module_name=None) -> None: super().__init__() self.module_name = module_name # print(module_name) self.in_features = in_features self.out_features = out_features self.device = device self.w_qparams = w_qparams self.a_qparams = a_qparams if self.w_qparams is not None: self.register_buffer("weight_scales", self.w_qparams.scales) self.register_buffer("weight_zero_points", self.w_qparams.zero_points) if self.a_qparams is not None: self.register_buffer("act_scales", self.a_qparams.scales) self.register_buffer("act_zero_points", self.a_qparams.zero_points) @classmethod def from_float(cls, float_mod, split=0, ckpt=None): assert hasattr(float_mod, 'qconfig') and isinstance(float_mod.qconfig, QConfig) weight_process = float_mod.qconfig.weight() w_dtype = weight_process.dtype num_kernels = float_mod.weight.shape[0] device = float_mod.weight.device w_qparams, w_qparams_0 = create_qparams_from_dtype(dtype=w_dtype, device=device, is_channel_wise=True, num_kernels=num_kernels, ckpt=ckpt, module_name=float_mod.module_name, quant_type='weight', bit_width=float_mod.w_bit, split=split) act_process = float_mod.qconfig.activation() act_dtype = act_process.dtype if hasattr(float_mod, 'a_bit'): a_qparams, a_qparams_0 = create_qparams_from_dtype(dtype=act_dtype, device=device, is_channel_wise=False, num_kernels=num_kernels, ckpt=ckpt, module_name=float_mod.module_name, quant_type='act', bit_width=float_mod.a_bit, split=split) else: a_qparams = None a_qparams_0 = None new_mod = cls(float_mod.in_features, float_mod.out_features, float_mod.bias is not None, device=float_mod.weight.device, w_qparams=w_qparams, a_qparams=a_qparams, module_name=float_mod.module_name, ) weight = float_mod.weight.detach() if 'attn2' in float_mod.module_name: if 'to_k' in float_mod.module_name or 'to_v' in float_mod.module_name: new_mod.bos = float_mod.bos new_mod.bos_pre_computed = float_mod.bos_pre_computed # the input of the org_weight is key_first_token # new_mod.register_buffer("org_weight", weight) if w_qparams is not None: weight = quantize_from_qparams(weight, w_qparams) new_mod.register_buffer("weight", weight) if float_mod.bias is not None: bias = float_mod.bias.detach() new_mod.register_buffer("bias", bias) else: new_mod.bias = None return new_mod def _get_name(self): w_width = 16 if self.w_qparams is None else \ dtype_to_bw[self.w_qparams.dtype] a_width = 16 if self.a_qparams is None else \ dtype_to_bw[self.a_qparams.dtype] return f"QuantizedLinear(W({w_width})A({a_width}))" def forward(self, x: torch.Tensor) -> torch.Tensor: if not hasattr(self, 'bos'): if self.a_qparams is not None and x.dtype == torch.float16: # x = quantize_from_qparams(x, self.a_qparams) x = quantize_per_tensor(x, self.a_qparams.scales.cuda().float( ), self.a_qparams.zero_points.cuda().float()) if x.dtype is not torch.int8 else x return linear_on_quantized_data(w_tensor=self.weight, w_qparams=self.w_qparams, a_tensor=x, a_qparams=self.a_qparams, bias=self.bias) else: if self.a_qparams is not None and x.dtype == torch.float16 and self.bos: # use bos and quantize the activation # x_0 = quantize_from_qparams(x[:,1:,:], self.a_qparams) x_0 = quantize_per_tensor(x[:, 1:, :], self.a_qparams.scales.cuda().float( ), self.a_qparams.zero_points.cuda().float()) if x.dtype is not torch.int8 else x[:, 1:, :] # shape = x.shape # key_first_token = x[:,0,:].reshape(shape[0], 1, shape[2]) # key_first_token = x[:,0,:] result = linear_on_quantized_data(w_tensor=self.weight, w_qparams=self.w_qparams, a_tensor=x_0, a_qparams=self.a_qparams, bias=self.bias, bos=True, module_name=self.module_name, bos_pre_computed=self.bos_pre_computed) # self.out_0 = out_0 # save bos return result else: if self.a_qparams is not None and x.dtype == torch.float16: # x = quantize_from_qparams(x, self.a_qparams) x = quantize_per_tensor(x, self.a_qparams.scales.cuda().float( ), self.a_qparams.zero_points.cuda().float()) if x.dtype is not torch.int8 else x return linear_on_quantized_data(w_tensor=self.weight, w_qparams=self.w_qparams, a_tensor=x, a_qparams=self.a_qparams, bias=self.bias) class QuantizedConv2d(nn.Module): def __init__(self, in_channels: int, out_channels: int, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, device=None, w_qparams=None, w_qparams_0=None, a_qparams=None, a_qparams_0=None, module_name=None, split=0) -> None: super().__init__() self.module_name = module_name self.split = split # for shortcut layer self.in_channels = in_channels self.out_channels = out_channels self.device = device self.kernel_size = kernel_size self.stride = stride self.padding = padding self.dilation = dilation self.groups = groups # if split == 0, w_qparams_0 and a_params_0 are None self.w_qparams = w_qparams self.w_qparams_0 = w_qparams_0 self.a_qparams = a_qparams self.a_qparams_0 = a_qparams_0 if self.w_qparams is not None: self.register_buffer("weight_scales", self.w_qparams.scales) self.register_buffer("weight_zero_points", self.w_qparams.zero_points) if self.w_qparams_0 is not None: self.register_buffer("weight_scales_0", self.w_qparams.scales) self.register_buffer("weight_zero_points_0", self.w_qparams.zero_points) if self.a_qparams is not None: self.register_buffer("act_scales", self.a_qparams.scales) self.register_buffer("act_zero_points", self.a_qparams.zero_points) if self.a_qparams_0 is not None: self.register_buffer("act_scales_0", self.a_qparams.scales) self.register_buffer("act_zero_points_0", self.a_qparams.zero_points) @classmethod def from_float(cls, float_mod, split=0, ckpt=None): assert hasattr(float_mod, 'qconfig') and isinstance(float_mod.qconfig, QConfig) weight_process = float_mod.qconfig.weight() w_dtype = weight_process.dtype num_kernels = float_mod.weight.shape[0] device = float_mod.weight.device # init the w & a quant parameters # split = 0 # if split == 0: # init the quant parameters w_qparams, w_qparams_0 = create_qparams_from_dtype(dtype=w_dtype, device=device, is_channel_wise=True, num_kernels=num_kernels, ckpt=ckpt, module_name=float_mod.module_name, quant_type='weight', bit_width=float_mod.w_bit, split=split) act_process = float_mod.qconfig.activation() act_dtype = act_process.dtype # if split == 0: if hasattr(float_mod, 'a_bit'): # if we want to quantized the act a_qparams, a_qparams_0 = create_qparams_from_dtype(dtype=act_dtype, device=device, is_channel_wise=False, num_kernels=num_kernels, ckpt=ckpt, module_name=float_mod.module_name, quant_type='act', bit_width=float_mod.a_bit, split=split) else: a_qparams = None a_qparams_0 = None new_mod = cls(float_mod.in_channels, float_mod.out_channels, float_mod.kernel_size, float_mod.stride, float_mod.padding, float_mod.dilation, float_mod.groups, float_mod.bias is not None, device=float_mod.weight.device, w_qparams=w_qparams, w_qparams_0=w_qparams_0, a_qparams=a_qparams, a_qparams_0=a_qparams_0, module_name=float_mod.module_name, split=split ) weight = float_mod.weight.detach() if split == 0: if w_qparams is not None: weight = quantize_from_qparams(weight, w_qparams) new_mod.register_buffer("weight", weight) if float_mod.bias is not None: bias = float_mod.bias.detach() new_mod.register_buffer("bias", bias) else: new_mod.bias = None # for the weight of the shortcut elif split > 0: if w_qparams is not None: weight_0 = quantize_from_qparams( weight[:, :split, ...], w_qparams) weight_1 = quantize_from_qparams( weight[:, split:, ...], w_qparams_0) new_mod.register_buffer("weight", weight_0) new_mod.register_buffer("weight_0", weight_1) if float_mod.bias is not None: bias = float_mod.bias.detach() new_mod.register_buffer("bias", bias) else: new_mod.bias = None return new_mod def _get_name(self): w_width = 16 if self.w_qparams is None else \ dtype_to_bw[self.w_qparams.dtype] a_width = 16 if self.a_qparams is None else \ dtype_to_bw[self.a_qparams.dtype] return f"QuantizedConv2d(W({w_width})A({a_width}))" def forward(self, x: torch.Tensor) -> torch.Tensor: if self.a_qparams is not None and x.dtype == torch.float16: if self.split == 0: # x_0 = quantize_from_qparams(x, self.a_qparams) x_0 = quantize_per_tensor(x, self.a_qparams.scales.cuda( ).float(), self.a_qparams.zero_points.cuda().float()) return conv2d_on_quantized_data(w_tensor=self.weight, w_qparams=self.w_qparams, a_tensor=x_0, a_qparams=self.a_qparams, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, split=self.split) elif self.split > 0: # x_0 = quantize_from_qparams(x[:, :self.split, :, :], self.a_qparams) # x_1 = quantize_from_qparams(x[:, self.split:, :, :], self.a_qparams_0) x_0 = quantize_per_tensor(x[:, :self.split, :, :], self.a_qparams.scales.cuda( ).float(), self.a_qparams.zero_points.cuda().float()) x_1 = quantize_per_tensor(x[:, self.split:, :, :], self.a_qparams_0.scales.cuda( ).float(), self.a_qparams_0.zero_points.cuda().float()) return conv2d_on_quantized_data(w_tensor=self.weight, w_tensor_0=self.weight_0, w_qparams=self.w_qparams, w_qparams_0=self.w_qparams_0, a_tensor=x_0, a_tensor_0=x_1, a_qparams=self.a_qparams, a_qparams_0=self.a_qparams_0, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, split=self.split) else: if self.split == 0: return conv2d_on_quantized_data(w_tensor=self.weight, w_qparams=self.w_qparams, a_tensor=x, a_qparams=self.a_qparams, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, split=self.split) elif self.split > 0: return conv2d_on_quantized_data(w_tensor=self.weight, w_tensor_0=self.weight_0, w_qparams=self.w_qparams, w_qparams_0=self.w_qparams_0, a_tensor=x, a_tensor_0=None, a_qparams=self.a_qparams, a_qparams_0=self.a_qparams_0, bias=self.bias, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups, split=self.split) # mixdq quantized module ###################################################################################################### def make_memory_friendly(bytes): MBs = bytes / (1024*1024) B = bytes % 1024 bytes = bytes // 1024 kB = bytes % 1024 bytes = bytes // 1024 MB = bytes % 1024 GB = bytes // 1024 return f"{GB} G {MB} M {B} {kB} K {B} Bytes ({MBs} MBs)" # class MixDQ_SDXLTurbo_Pipeline_W8A8(StableDiffusionXLPipeline): # def __init__( # self, # vae, # text_encoder, # text_encoder_2, # tokenizer, # tokenizer_2, # unet, # scheduler, # image_encoder=None, # feature_extractor=None, # force_zeros_for_empty_prompt=True, # add_watermarker=None # ): # super().__init__( # vae=vae, # text_encoder=text_encoder, # text_encoder_2=text_encoder_2, # tokenizer=tokenizer, # tokenizer_2=tokenizer_2, # unet=unet, # scheduler=scheduler, # image_encoder=image_encoder, # feature_extractor=feature_extractor, # force_zeros_for_empty_prompt=force_zeros_for_empty_prompt, # add_watermarker=add_watermarker, # ) # def quantize_unet( # self, # w_bit = None, # a_bit = None, # bos=True, # # bos_dict_path="", # ): # r""" # This function helps quantize the UNet in the SDXL Pipeline # Now we only support quantization with the setting W8A8 # Args: # w_config_path: (`str`): # the path for mixed precision config of weight # a_config_path: (`str`): # the path for mixed precision config of activation # ckpt_path: (`str`): # the path for the checkpoint of quant para # bos: (`bool`): # if to use bos technique # bos_dict_path: (`str`): # the path for mixed precision config of weight # """ # # load the quant para and the pre-computed bos tensor # from huggingface_hub import hf_hub_download # path = hf_hub_download( # repo_id="Stein-Fun/mixdq_test", # filename="bos_pre_computed.pt", # revision="version_0", # ) # bos_dict = torch.load(path, map_location='cpu') # path = hf_hub_download( # repo_id="Stein-Fun/mixdq_test", # filename="quant_para_wsym_fp16.pt", # revision="version_0", # ) # ckpt = torch.load(path, map_location='cpu') # register_qconfig_from_input_files( # self.unet, # # args, # w_bit = w_bit, # a_bit = a_bit, # bos=bos, # bos_dict=bos_dict # ) # convert_to_quantized(self.unet, ckpt) # def run_for_test( # self, # device, # prompt: str = "A black and white photo of an older man skiing.", # batch_size: int = 1, # output_type: str = "latent", # run_pipeline: bool = False, # path: str = "result.png" # ): # r""" # run for test the memory reduction after quantization on GPUs # Args: # device: (`torch.device`): # torch device, 'CUDA' is supported only # prompt: (`str` or `List[str]`, *optional*): # prompt to be encoded # batch_size: (`int`): # the batch size of inputs # output_type (`str`, *optional*, defaults to `"pil"`): # The output format of the generate image. Choose between # [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. # batch_size: (`int`): # the batch size of inputs # run_pipeline: (`bool`): # if to run the whole pipeline or just run the UNet # path: (`str`): # the path to save the output image # """ # if run_pipeline: # self.to(device) # else: # self.unet.to(device) # model_memory = torch.cuda.memory_allocated() # print("Static (weights) memory usage:", # make_memory_friendly(model_memory)) # # start = time.time() # if run_pipeline: # # test the time cost for the pipeline # latents = self(prompt=[prompt]*batch_size, # guidance_scale=0.0, # num_inference_steps=2, # output_type=output_type).images[0] # else: # sample_shape = ( # batch_size * 1, # self.unet.config.in_channels, # self.unet.config.sample_size, # self.unet.config.sample_size, # ) # encoder_embedding_shape = ( # batch_size * 1, # 77, # just an example, # 2048, # ) # # device=torch.device('cuda') # example_sample = torch.rand(*sample_shape, device=device, # dtype=torch.float16) # example_embedding = torch.rand(*encoder_embedding_shape, # device=device, dtype=torch.float16) # timestep = torch.tensor(999., device=device) # text_embeds = torch.rand(batch_size, 1280, device=device, # dtype=torch.float16) # time_ids = torch.tensor([[512., 512., 0., 0., 512., 512.]], dtype=torch.float16, # device=device) # time_ids = torch.concat([time_ids] * batch_size) # with torch.no_grad(): # # start = time.time() # latents = self.unet(sample=example_sample, # timestep=timestep, # encoder_hidden_states=example_embedding, # added_cond_kwargs={ # 'time_ids': time_ids, # 'text_embeds': text_embeds # }, # return_dict=False)[0] # # end = time.time() # peak_memory = torch.cuda.max_memory_allocated() # print("Dynamic (acts) memory usage:", # make_memory_friendly(peak_memory - model_memory)) # print("Peak (total) memory usage:", make_memory_friendly(peak_memory)) # if output_type == "pil": # image = latents # image.save(path) # return latents def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): """ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg return noise_cfg def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, **kwargs, ): """ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps class MixDQ_SDXLTurbo_Pipeline_W8A8( DiffusionPipeline, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin, IPAdapterMixin, ): r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) In addition the pipeline inherits the following loading methods: - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] as well as the following saving methods: - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder ([`CLIPTextModel`]): Frozen text-encoder. Stable Diffusion XL uses the text portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. text_encoder_2 ([` CLIPTextModelWithProjection`]): Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), specifically the [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) variant. tokenizer (`CLIPTokenizer`): Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). tokenizer_2 (`CLIPTokenizer`): Second Tokenizer of class [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of `stabilityai/stable-diffusion-xl-base-1-0`. add_watermarker (`bool`, *optional*): Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to watermark output images. If not defined, it will default to True if the package is installed, otherwise no watermarker will be used. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" _optional_components = [ "tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2", "image_encoder", "feature_extractor", ] _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", "add_text_embeds", "add_time_ids", "negative_pooled_prompt_embeds", "negative_add_time_ids", ] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, text_encoder_2: CLIPTextModelWithProjection, tokenizer: CLIPTokenizer, tokenizer_2: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: KarrasDiffusionSchedulers, image_encoder: CLIPVisionModelWithProjection = None, feature_extractor: CLIPImageProcessor = None, force_zeros_for_empty_prompt: bool = True, add_watermarker: Optional[bool] = None, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, text_encoder_2=text_encoder_2, tokenizer=tokenizer, tokenizer_2=tokenizer_2, unet=unet, scheduler=scheduler, image_encoder=image_encoder, feature_extractor=feature_extractor, ) self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.default_sample_size = self.unet.config.sample_size add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() if add_watermarker: self.watermark = StableDiffusionXLWatermarker() else: self.watermark = None # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() def encode_prompt( self, prompt: str, prompt_2: Optional[str] = None, device: Optional[torch.device] = None, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: Optional[str] = None, negative_prompt_2: Optional[str] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. """ device = device or self._execution_device # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if self.text_encoder is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) else: scale_lora_layers(self.text_encoder_2, lora_scale) prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] # Define tokenizers and text encoders tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] text_encoders = ( [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] ) if prompt_embeds is None: prompt_2 = prompt_2 or prompt prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 # textual inversion: procecss multi-vector tokens if necessary prompt_embeds_list = [] prompts = [prompt, prompt_2] for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, tokenizer) text_inputs = tokenizer( prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {tokenizer.model_max_length} tokens: {removed_text}" ) prompt_embeds = text_encoder(text_input_ids.to(device), output_hidden_states=True) # We are only ALWAYS interested in the pooled output of the final text encoder pooled_prompt_embeds = prompt_embeds[0] if clip_skip is None: prompt_embeds = prompt_embeds.hidden_states[-2] else: # "2" because SDXL always indexes from the penultimate layer. prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + 2)] prompt_embeds_list.append(prompt_embeds) prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) # get unconditional embeddings for classifier free guidance zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: negative_prompt_embeds = torch.zeros_like(prompt_embeds) negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds) elif do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt_2 = negative_prompt_2 or negative_prompt # normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt negative_prompt_2 = ( batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 ) uncond_tokens: List[str] if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = [negative_prompt, negative_prompt_2] negative_prompt_embeds_list = [] for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): if isinstance(self, TextualInversionLoaderMixin): negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) max_length = prompt_embeds.shape[1] uncond_input = tokenizer( negative_prompt, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device), output_hidden_states=True, ) # We are only ALWAYS interested in the pooled output of the final text encoder negative_pooled_prompt_embeds = negative_prompt_embeds[0] negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] negative_prompt_embeds_list.append(negative_prompt_embeds) negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) if self.text_encoder_2 is not None: prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] if self.text_encoder_2 is not None: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) else: negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.unet.dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) pooled_prompt_embeds = pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if do_classifier_free_guidance: negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( bs_embed * num_images_per_prompt, -1 ) if self.text_encoder is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) if self.text_encoder_2 is not None: if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder_2, lora_scale) return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image def encode_image(self, image, device, num_images_per_prompt): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) image_embeds = self.image_encoder(image).image_embeds image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_embeds = torch.zeros_like(image_embeds) return image_embeds, uncond_image_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, prompt_2, height, width, callback_steps, negative_prompt=None, negative_prompt_2=None, prompt_embeds=None, negative_prompt_embeds=None, pooled_prompt_embeds=None, negative_pooled_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt_2 is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") elif prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)): raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) elif negative_prompt_2 is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if prompt_embeds is not None and pooled_prompt_embeds is None: raise ValueError( "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." ) if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: raise ValueError( "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def _get_add_time_ids( self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None ): add_time_ids = list(original_size + crops_coords_top_left + target_size) passed_add_embed_dim = ( self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim ) expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features if expected_add_embed_dim != passed_add_embed_dim: raise ValueError( f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." ) add_time_ids = torch.tensor([add_time_ids], dtype=dtype) return add_time_ids # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae def upcast_vae(self): dtype = self.vae.dtype self.vae.to(dtype=torch.float32) use_torch_2_0_or_xformers = isinstance( self.vae.decoder.mid_block.attentions[0].processor, ( AttnProcessor2_0, XFormersAttnProcessor, LoRAXFormersAttnProcessor, LoRAAttnProcessor2_0, ), ) # if xformers or torch_2_0 is used attention block does not need # to be in float32 which can save lots of memory if use_torch_2_0_or_xformers: self.vae.post_quant_conv.to(dtype) self.vae.decoder.conv_in.to(dtype) self.vae.decoder.mid_block.to(dtype) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stages where they are being applied. Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. Args: s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. """ if not hasattr(self, "unet"): raise ValueError("The pipeline must have `unet` for using FreeU.") self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu def disable_freeu(self): """Disables the FreeU mechanism if enabled.""" self.unet.disable_freeu() # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): """ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: timesteps (`torch.Tensor`): generate embedding vectors at these timesteps embedding_dim (`int`, *optional*, defaults to 512): dimension of the embeddings to generate dtype: data type of the generated embeddings Returns: `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb def quantize_unet( self, w_bit = None, a_bit = None, bos=True, # bos_dict_path="", ): r""" This function helps quantize the UNet in the SDXL Pipeline Now we only support quantization with the setting W8A8 Args: w_config_path: (`str`): the path for mixed precision config of weight a_config_path: (`str`): the path for mixed precision config of activation ckpt_path: (`str`): the path for the checkpoint of quant para bos: (`bool`): if to use bos technique bos_dict_path: (`str`): the path for mixed precision config of weight """ # load the quant para and the pre-computed bos tensor from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="Stein-Fun/mixdq_test", filename="bos_pre_computed.pt", revision="version_0", ) bos_dict = torch.load(path, map_location='cpu') path = hf_hub_download( repo_id="Stein-Fun/mixdq_test", filename="quant_para_wsym_fp16.pt", revision="version_0", ) ckpt = torch.load(path, map_location='cpu') register_qconfig_from_input_files( self.unet, # args, w_bit = w_bit, a_bit = a_bit, bos=bos, bos_dict=bos_dict ) convert_to_quantized(self.unet, ckpt) def run_for_test( self, device, prompt: str = "A black and white photo of an older man skiing.", batch_size: int = 1, output_type: str = "latent", run_pipeline: bool = False, path: str = "result.png" ): r""" run for test the memory reduction after quantization on GPUs Args: device: (`torch.device`): torch device, 'CUDA' is supported only prompt: (`str` or `List[str]`, *optional*): prompt to be encoded batch_size: (`int`): the batch size of inputs output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. batch_size: (`int`): the batch size of inputs run_pipeline: (`bool`): if to run the whole pipeline or just run the UNet path: (`str`): the path to save the output image """ if run_pipeline: self.to(device) else: self.unet.to(device) model_memory = torch.cuda.memory_allocated() print("Static (weights) memory usage:", make_memory_friendly(model_memory)) # start = time.time() if run_pipeline: # test the time cost for the pipeline latents = self(prompt=[prompt]*batch_size, guidance_scale=0.0, num_inference_steps=2, output_type=output_type).images[0] else: sample_shape = ( batch_size * 1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size, ) encoder_embedding_shape = ( batch_size * 1, 77, # just an example, 2048, ) # device=torch.device('cuda') example_sample = torch.rand(*sample_shape, device=device, dtype=torch.float16) example_embedding = torch.rand(*encoder_embedding_shape, device=device, dtype=torch.float16) timestep = torch.tensor(999., device=device) text_embeds = torch.rand(batch_size, 1280, device=device, dtype=torch.float16) time_ids = torch.tensor([[512., 512., 0., 0., 512., 512.]], dtype=torch.float16, device=device) time_ids = torch.concat([time_ids] * batch_size) with torch.no_grad(): # start = time.time() latents = self.unet(sample=example_sample, timestep=timestep, encoder_hidden_states=example_embedding, added_cond_kwargs={ 'time_ids': time_ids, 'text_embeds': text_embeds }, return_dict=False)[0] # end = time.time() peak_memory = torch.cuda.max_memory_allocated() print("Dynamic (acts) memory usage:", make_memory_friendly(peak_memory - model_memory)) print("Peak (total) memory usage:", make_memory_friendly(peak_memory)) if output_type == "pil": image = latents image.save(path) return latents @property def guidance_scale(self): return self._guidance_scale @property def guidance_rescale(self): return self._guidance_rescale @property def clip_skip(self): return self._clip_skip # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property def cross_attention_kwargs(self): return self._cross_attention_kwargs @property def denoising_end(self): return self._denoising_end @property def num_timesteps(self): return self._num_timesteps @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, prompt_2: Optional[Union[str, List[str]]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, timesteps: List[int] = None, denoising_end: Optional[float] = None, guidance_scale: float = 5.0, negative_prompt: Optional[Union[str, List[str]]] = None, negative_prompt_2: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, original_size: Optional[Tuple[int, int]] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), target_size: Optional[Tuple[int, int]] = None, negative_original_size: Optional[Tuple[int, int]] = None, negative_crops_coords_top_left: Tuple[int, int] = (0, 0), negative_target_size: Optional[Tuple[int, int]] = None, clip_skip: Optional[int] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is used in both text-encoders height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. Anything below 512 pixels won't work well for [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and checkpoints that are not specifically fine-tuned on low resolutions. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. denoising_end (`float`, *optional*): When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be completed before it is intentionally prematurely terminated. As a result, the returned sample will still retain a substantial amount of noise as determined by the discrete timesteps selected by the scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) guidance_scale (`float`, *optional*, defaults to 5.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). negative_prompt_2 (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to [`schedulers.DDIMScheduler`], will be ignored for others. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled text embeddings will be generated from `prompt` input argument. negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when using zero terminal SNR. original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): For most cases, `target_size` should be set to the desired height and width of the generated image. If not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a specific image resolution. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): To negatively condition the generation process based on a target image resolution. It should be as same as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. Examples: Returns: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", ) # 0. Default height and width to unet height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor original_size = original_size or (height, width) target_size = target_size or (height, width) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, prompt_2, height, width, callback_steps, negative_prompt, negative_prompt_2, prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._denoising_end = denoising_end # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # 3. Encode input prompt lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) ( prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds, ) = self.encode_prompt( prompt=prompt, prompt_2=prompt_2, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, pooled_prompt_embeds=pooled_prompt_embeds, negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, lora_scale=lora_scale, clip_skip=self.clip_skip, ) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Prepare added time ids & embeddings add_text_embeds = pooled_prompt_embeds if self.text_encoder_2 is None: text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) else: text_encoder_projection_dim = self.text_encoder_2.config.projection_dim add_time_ids = self._get_add_time_ids( original_size, crops_coords_top_left, target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) if negative_original_size is not None and negative_target_size is not None: negative_add_time_ids = self._get_add_time_ids( negative_original_size, negative_crops_coords_top_left, negative_target_size, dtype=prompt_embeds.dtype, text_encoder_projection_dim=text_encoder_projection_dim, ) else: negative_add_time_ids = add_time_ids if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0) add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0) prompt_embeds = prompt_embeds.to(device) add_text_embeds = add_text_embeds.to(device) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) if ip_adapter_image is not None: image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt) if self.do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) image_embeds = image_embeds.to(device) # 8. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) # 8.1 Apply denoising_end if ( self.denoising_end is not None and isinstance(self.denoising_end, float) and self.denoising_end > 0 and self.denoising_end < 1 ): discrete_timestep_cutoff = int( round( self.scheduler.config.num_train_timesteps - (self.denoising_end * self.scheduler.config.num_train_timesteps) ) ) num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) timesteps = timesteps[:num_inference_steps] # 9. Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} if ip_adapter_image is not None: added_cond_kwargs["image_embeds"] = image_embeds noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, return_dict=False, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) negative_pooled_prompt_embeds = callback_outputs.pop( "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds ) add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) # if XLA_AVAILABLE: # xm.mark_step() if not output_type == "latent": # make sure the VAE is in float32 mode, as it overflows in float16 needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast if needs_upcasting: self.upcast_vae() latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] # cast back to fp16 if needed if needs_upcasting: self.vae.to(dtype=torch.float16) else: image = latents if not output_type == "latent": # apply watermark if available if self.watermark is not None: image = self.watermark.apply_watermark(image) image = self.image_processor.postprocess(image, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image,) return StableDiffusionXLPipelineOutput(images=image) ###################################################################################################### # mixed precision config a8_mixed_precision_config = \ { 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