""" Copyright (C) 2023, Advanced Micro Devices, Inc. All rights reserved. SPDX-License-Identifier: MIT """ import argparse import copy from datetime import datetime import json import os import time from brevitas.core.zero_point import ParameterFromStatsFromParameterZeroPoint from brevitas.quant.experimental.float_quant_fnuz import Fp8e4m3FNUZActPerTensorFloat from brevitas.quant.scaled_int import Int8ActPerTensorFloat from brevitas.quant.shifted_scaled_int import ShiftedUint8WeightPerChannelFloat from brevitas_examples.common.generative.nn import LoRACompatibleQuantConv2d, LoRACompatibleQuantLinear from diffusers import DiffusionPipeline from diffusers.models.attention_processor import Attention from diffusers.models.attention_processor import AttnProcessor import pandas as pd import torch from torch import nn from tqdm import tqdm import brevitas.nn as qnn from brevitas.graph.base import ModuleToModuleByClass from brevitas.graph.calibrate import bias_correction_mode from brevitas.graph.calibrate import calibration_mode from brevitas.graph.equalize import activation_equalization_mode from brevitas.graph.quantize import layerwise_quantize from brevitas.inject.enum import StatsOp from brevitas.nn.equalized_layer import EqualizedModule from brevitas.utils.torch_utils import KwargsForwardHook import brevitas.config as config from brevitas_examples.common.parse_utils import add_bool_arg from brevitas_examples.stable_diffusion.sd_quant.export import export_quant_params from brevitas_examples.stable_diffusion.sd_quant.nn import QuantAttention from brevitas_examples.stable_diffusion.mlperf_evaluation.accuracy import compute_mlperf_fid TEST_SEED = 123456 torch.manual_seed(TEST_SEED) class WeightQuant(ShiftedUint8WeightPerChannelFloat): narrow_range = False scaling_min_val = 1e-4 quantize_zero_point = True scaling_impl_type = 'parameter_from_stats' zero_point_impl = ParameterFromStatsFromParameterZeroPoint class InputQuant(Int8ActPerTensorFloat): scaling_stats_op = StatsOp.MAX class OutputQuant(Fp8e4m3FNUZActPerTensorFloat): scaling_stats_op = StatsOp.MAX NEGATIVE_PROMPTS = ["normal quality, low quality, worst quality, low res, blurry, nsfw, nude"] def load_calib_prompts(calib_data_path, sep="\t"): df = pd.read_csv(calib_data_path, sep=sep) lst = df["caption"].tolist() return lst def run_val_inference( pipe, prompts, guidance_scale, total_steps, test_latents=None): with torch.no_grad(): for prompt in tqdm(prompts): # We don't want to generate any image, so we return only the latent encoding pre VAE pipe( prompt, negative_prompt=NEGATIVE_PROMPTS[0], latents=test_latents, output_type='latent', guidance_scale=guidance_scale, num_inference_steps=total_steps) def main(args): dtype = getattr(torch, args.dtype) calibration_prompts = load_calib_prompts(args.calibration_prompt_path) assert args.calibration_prompts <= len(calibration_prompts) , f"--calibration-prompts must be <= {len(calibration_prompts)}" calibration_prompts = calibration_prompts[:args.calibration_prompts] latents = torch.load(args.path_to_latents).to(torch.float16) # Create output dir. Move to tmp if None ts = datetime.fromtimestamp(time.time()) str_ts = ts.strftime("%Y%m%d_%H%M%S") output_dir = os.path.join(args.output_path, f'{str_ts}') os.mkdir(output_dir) print(f"Saving results in {output_dir}") # Dump args to json with open(os.path.join(output_dir, 'args.json'), 'w') as fp: json.dump(vars(args), fp) # Load model from float checkpoint print(f"Loading model from {args.model}...") variant = 'fp16' if dtype == torch.float16 else None pipe = DiffusionPipeline.from_pretrained(args.model, torch_dtype=dtype, variant=variant, use_safetensors=True) pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) pipe.vae.config.force_upcast=True print(f"Model loaded from {args.model}.") # Move model to target device print(f"Moving model to {args.device}...") pipe = pipe.to(args.device) # Enable attention slicing if args.attention_slicing: pipe.enable_attention_slicing() # Extract list of layers to avoid blacklist = [] for name, _ in pipe.unet.named_modules(): if 'time_emb' in name: blacklist.append(name.split('.')[-1]) print(f"Blacklisted layers: {blacklist}") # Make sure there all LoRA layers are fused first, otherwise raise an error for m in pipe.unet.modules(): if hasattr(m, 'lora_layer') and m.lora_layer is not None: raise RuntimeError("LoRA layers should be fused in before calling into quantization.") pipe.set_progress_bar_config(disable=True) if args.load_checkpoint is not None: with load_quant_model_mode(pipe.unet): pipe = pipe.to('cpu') print(f"Loading checkpoint: {args.load_checkpoint}... ", end="") pipe.unet.load_state_dict(torch.load(args.load_checkpoint, map_location='cpu')) print(f"Checkpoint loaded!") pipe = pipe.to(args.device) if args.load_checkpoint is not None: # Don't run full activation equalization if we're loading a quantized checkpoint num_ae_prompts = 2 else: num_ae_prompts = len(calibration_prompts) with activation_equalization_mode( pipe.unet, alpha=args.act_eq_alpha, layerwise=True, blacklist_layers=blacklist if args.exclude_blacklist_act_eq else None, add_mul_node=True): # Workaround to expose `in_features` attribute from the Hook Wrapper for m in pipe.unet.modules(): if isinstance(m, KwargsForwardHook) and hasattr(m.module, 'in_features'): m.in_features = m.module.in_features total_steps = args.calibration_steps run_val_inference( pipe, calibration_prompts[:num_ae_prompts], total_steps=total_steps, test_latents=latents, guidance_scale=args.guidance_scale) # Workaround to expose `in_features` attribute from the EqualizedModule Wrapper for m in pipe.unet.modules(): if isinstance(m, EqualizedModule) and hasattr(m.layer, 'in_features'): m.in_features = m.layer.in_features quant_layer_kwargs = { 'input_quant': InputQuant, 'weight_quant': WeightQuant, 'dtype': dtype, 'device': args.device, 'input_dtype': dtype, 'input_device': args.device} quant_linear_kwargs = copy.deepcopy(quant_layer_kwargs) if args.quantize_sdp: output_quant = OutputQuant rewriter = ModuleToModuleByClass( Attention, QuantAttention, softmax_output_quant=output_quant, query_dim=lambda module: module.to_q.in_features, dim_head=lambda module: int(1 / (module.scale ** 2)), processor=AttnProcessor(), is_equalized=True) config.IGNORE_MISSING_KEYS = True pipe.unet = rewriter.apply(pipe.unet) config.IGNORE_MISSING_KEYS = False pipe.unet = pipe.unet.to(args.device) pipe.unet = pipe.unet.to(dtype) # quant_kwargs = layer_map[torch.nn.Linear][1] what_to_quantize = ['to_q', 'to_k', 'to_v'] quant_linear_kwargs['output_quant'] = lambda module, name: output_quant if any(ending in name for ending in what_to_quantize) else None quant_linear_kwargs['output_dtype'] = dtype quant_linear_kwargs['output_device'] = args.device layer_map = { nn.Linear: (qnn.QuantLinear, quant_linear_kwargs), nn.Conv2d: (qnn.QuantConv2d, quant_layer_kwargs), 'diffusers.models.lora.LoRACompatibleLinear': (LoRACompatibleQuantLinear, quant_linear_kwargs), 'diffusers.models.lora.LoRACompatibleConv': (LoRACompatibleQuantConv2d, quant_layer_kwargs)} pipe.unet = layerwise_quantize( model=pipe.unet, compute_layer_map=layer_map, name_blacklist=blacklist) print("Model quantization applied.") pipe.set_progress_bar_config(disable=True) if args.load_checkpoint is None: print("Applying activation calibration") with torch.no_grad(), calibration_mode(pipe.unet): run_val_inference( pipe, calibration_prompts, total_steps=args.calibration_steps, test_latents=latents, guidance_scale=args.guidance_scale) print("Applying bias correction") with torch.no_grad(), bias_correction_mode(pipe.unet): run_val_inference( pipe, calibration_prompts, total_steps=args.calibration_steps, test_latents=latents, guidance_scale=args.guidance_scale) if args.checkpoint_name is not None: torch.save(pipe.unet.state_dict(), os.path.join(output_dir, args.checkpoint_name)) # Perform inference if args.validation_prompts > 0: print(f"Computing validation accuracy") compute_mlperf_fid(args.model, args.path_to_coco, pipe, args.validation_prompts, output_dir) if args.export_target: pipe.unet.to('cpu').to(dtype) export_quant_params(pipe, output_dir) if __name__ == "__main__": parser = argparse.ArgumentParser(description='Stable Diffusion quantization') parser.add_argument( '-m', '--model', type=str, default=None, help='Path or name of the model.') parser.add_argument( '-d', '--device', type=str, default='cuda:0', help='Target device for quantized model.') parser.add_argument( '--calibration-prompt-path', type=str, default=None, help='Path to calibration prompt') parser.add_argument( '--calibration-prompts', type=int, default=500, help='Number of prompts to use for calibration. Default: %(default)s') parser.add_argument( '--validation-prompts', type=int, default=0, help='Number of prompt to use for validation. Default: %(default)s') parser.add_argument( '--path-to-coco', type=str, default=None, help= 'Path to MLPerf compliant Coco dataset. Required when the --validation-prompts > 0 flag is set. Default: None' ) parser.add_argument( '--checkpoint-name', type=str, default=None, help= 'Name to use to store the checkpoint in the output dir. If not provided, no checkpoint is saved.' ) parser.add_argument( '--load-checkpoint', type=str, default=None, help='Path to checkpoint to load. If provided, PTQ techniques are skipped.') parser.add_argument( '--path-to-latents', type=str, required=True, help= 'Path to pre-defined latents.') parser.add_argument('--guidance-scale', type=float, default=8., help='Guidance scale.') parser.add_argument( '--calibration-steps', type=float, default=8, help='Steps used during calibration') add_bool_arg( parser, 'output-path', str_true=True, default='.', help='Path where to generate output folder.') parser.add_argument( '--dtype', default='float16', choices=['float32', 'float16', 'bfloat16'], help='Model Dtype, choices are float32, float16, bfloat16. Default: float16') add_bool_arg( parser, 'attention-slicing', default=False, help='Enable attention slicing. Default: Disabled') add_bool_arg( parser, 'export-target', default=True, help='Export flow.') parser.add_argument( '--act-eq-alpha', type=float, default=0.9, help='Alpha for activation equalization. Default: 0.9') add_bool_arg(parser, 'quantize-sdp', default=False, help='Quantize SDP. Default: Disabled') add_bool_arg( parser, 'exclude-blacklist-act-eq', default=False, help='Exclude unquantized layers from activation equalization. Default: Disabled') args = parser.parse_args() print("Args: " + str(vars(args))) main(args)