mirror of https://github.com/vladmandic/automatic
208 lines
10 KiB
Python
208 lines
10 KiB
Python
import time
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import logging
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import torch
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from modules import shared, devices
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from installer import setup_logging
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#Used by OpenVINO, can be used with TensorRT or Olive
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class CompiledModelState:
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def __init__(self):
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self.first_pass = True
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self.first_pass_refiner = True
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self.first_pass_vae = True
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self.height = 512
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self.width = 512
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self.batch_size = 1
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self.partition_id = 0
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self.cn_model = []
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self.lora_model = []
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self.lora_compile = False
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self.compiled_cache = {}
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self.partitioned_modules = {}
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def ipex_optimize(sd_model):
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try:
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t0 = time.time()
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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if hasattr(sd_model, 'unet'):
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sd_model.unet.training = False
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sd_model.unet = ipex.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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else:
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shared.log.warning('IPEX Optimize enabled but model has no Unet')
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if hasattr(sd_model, 'vae'):
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sd_model.vae.training = False
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sd_model.vae = ipex.optimize(sd_model.vae, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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if hasattr(sd_model, 'movq'):
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sd_model.movq.training = False
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sd_model.movq = ipex.optimize(sd_model.movq, dtype=devices.dtype_vae, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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t1 = time.time()
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shared.log.info(f"IPEX Optimize: time={t1-t0:.2f}")
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return sd_model
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except Exception as e:
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shared.log.warning(f"IPEX Optimize: error: {e}")
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def nncf_compress_weights(sd_model):
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try:
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t0 = time.time()
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import nncf
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if hasattr(sd_model, 'unet'):
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sd_model.unet = nncf.compress_weights(sd_model.unet)
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else:
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shared.log.warning('Compress Weights enabled but model has no Unet')
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if shared.opts.nncf_compress_vae_weights:
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if hasattr(sd_model, 'vae'):
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sd_model.vae = nncf.compress_weights(sd_model.vae)
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if hasattr(sd_model, 'movq'):
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sd_model.movq = nncf.compress_weights(sd_model.movq)
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t1 = time.time()
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shared.log.info(f"Compress Weights: time={t1-t0:.2f}")
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return sd_model
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except Exception as e:
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shared.log.warning(f"Compress Weights: error: {e}")
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def optimize_openvino():
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try:
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from modules.intel.openvino import openvino_fx # pylint: disable=unused-import
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torch._dynamo.eval_frame.check_if_dynamo_supported = lambda: True # pylint: disable=protected-access
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if shared.compiled_model_state is None:
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shared.compiled_model_state = CompiledModelState()
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else:
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if not shared.compiled_model_state.lora_compile:
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shared.compiled_model_state.lora_compile = False
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shared.compiled_model_state.lora_model = []
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shared.compiled_model_state.compiled_cache.clear()
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shared.compiled_model_state.partitioned_modules.clear()
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shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
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except Exception as e:
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shared.log.warning(f"Model compile: task=OpenVINO: {e}")
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def compile_stablefast(sd_model):
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try:
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import sfast.compilers.stable_diffusion_pipeline_compiler as sf
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except Exception as e:
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shared.log.warning(f'Model compile using stable-fast: {e}')
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return sd_model
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config = sf.CompilationConfig.Default()
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try:
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import xformers # pylint: disable=unused-import
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config.enable_xformers = True
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except Exception:
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pass
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try:
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import triton # pylint: disable=unused-import
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config.enable_triton = True
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except Exception:
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pass
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import warnings
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warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
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config.enable_cuda_graph = shared.opts.cuda_compile_fullgraph
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config.enable_jit_freeze = shared.opts.diffusers_eval
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config.memory_format = torch.channels_last if shared.opts.opt_channelslast else torch.contiguous_format
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# config.trace_scheduler = False
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# config.enable_cnn_optimization
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# config.prefer_lowp_gemm
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try:
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t0 = time.time()
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sd_model = sf.compile(sd_model, config)
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setup_logging() # compile messes with logging so reset is needed
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if shared.opts.cuda_compile_precompile:
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sd_model("dummy prompt")
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t1 = time.time()
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shared.log.info(f"Model compile: task=Stable-fast config={config.__dict__} time={t1-t0:.2f}")
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except Exception as e:
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shared.log.info(f"Model compile: task=Stable-fast error: {e}")
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return sd_model
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def compile_torch(sd_model):
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try:
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import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
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torch._dynamo.reset() # pylint: disable=protected-access
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shared.log.debug(f"Model compile available backends: {torch._dynamo.list_backends()}") # pylint: disable=protected-access
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if shared.opts.cuda_compile_backend == "openvino_fx":
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optimize_openvino()
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log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
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if hasattr(torch, '_logging'):
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torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
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torch._dynamo.config.verbose = shared.opts.cuda_compile_verbose # pylint: disable=protected-access
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torch._dynamo.config.suppress_errors = shared.opts.cuda_compile_errors # pylint: disable=protected-access
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try:
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torch._inductor.config.conv_1x1_as_mm = True # pylint: disable=protected-access
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torch._inductor.config.coordinate_descent_tuning = True # pylint: disable=protected-access
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torch._inductor.config.epilogue_fusion = False # pylint: disable=protected-access
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torch._inductor.config.coordinate_descent_check_all_directions = True # pylint: disable=protected-access
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torch._inductor.config.use_mixed_mm = True # pylint: disable=protected-access
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# torch._inductor.config.force_fuse_int_mm_with_mul = True # pylint: disable=protected-access
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except Exception as e:
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shared.log.error(f"Torch inductor config error: {e}")
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t0 = time.time()
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if shared.opts.cuda_compile:
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if shared.opts.cuda_compile and (not hasattr(sd_model, 'unet') or not hasattr(sd_model.unet, 'config')):
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shared.log.warning('Model compile enabled but model has no Unet')
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else:
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sd_model.unet = torch.compile(sd_model.unet, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
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if shared.opts.cuda_compile_vae:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
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sd_model.vae.decode = torch.compile(sd_model.vae.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
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elif hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
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sd_model.movq.decode = torch.compile(sd_model.movq.decode, mode=shared.opts.cuda_compile_mode, backend=shared.opts.cuda_compile_backend, fullgraph=shared.opts.cuda_compile_fullgraph)
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else:
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shared.log.warning('Model compile enabled but model has no VAE')
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setup_logging() # compile messes with logging so reset is needed
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if shared.opts.cuda_compile_precompile:
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sd_model("dummy prompt")
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t1 = time.time()
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shared.log.info(f"Model compile: time={t1-t0:.2f}")
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except Exception as e:
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shared.log.warning(f"Model compile error: {e}")
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return sd_model
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def compile_diffusers(sd_model):
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if shared.opts.ipex_optimize:
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sd_model = ipex_optimize(sd_model)
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if shared.opts.nncf_compress_weights and not (shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):
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sd_model = nncf_compress_weights(sd_model)
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if not (shared.opts.cuda_compile or shared.opts.cuda_compile_vae or shared.opts.cuda_compile_upscaler):
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return sd_model
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if shared.opts.cuda_compile_backend == 'none':
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shared.log.warning('Model compile enabled but no backend specified')
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return sd_model
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shared.log.info(f"Model compile: pipeline={sd_model.__class__.__name__} mode={shared.opts.cuda_compile_mode} backend={shared.opts.cuda_compile_backend} fullgraph={shared.opts.cuda_compile_fullgraph} unet={shared.opts.cuda_compile} vae={shared.opts.cuda_compile_vae} upscaler={shared.opts.cuda_compile_upscaler}")
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if shared.opts.cuda_compile_backend == 'stable-fast':
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sd_model = compile_stablefast(sd_model)
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else:
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sd_model = compile_torch(sd_model)
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return sd_model
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def dynamic_quantization(sd_model):
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try:
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from torchao.quantization import quant_api
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except Exception as e:
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shared.log.error(f"Model dynamic quantization not supported: {e}")
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return sd_model
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def dynamic_quant_filter_fn(mod, *args): # pylint: disable=unused-argument
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return (isinstance(mod, torch.nn.Linear) and mod.in_features > 16 and (mod.in_features, mod.out_features)
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not in [(1280, 640), (1920, 1280), (1920, 640), (2048, 1280), (2048, 2560), (2560, 1280), (256, 128), (2816, 1280), (320, 640), (512, 1536), (512, 256), (512, 512), (640, 1280), (640, 1920), (640, 320), (640, 5120), (640, 640), (960, 320), (960, 640)])
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def conv_filter_fn(mod, *args): # pylint: disable=unused-argument
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return (isinstance(mod, torch.nn.Conv2d) and mod.kernel_size == (1, 1) and 128 in [mod.in_channels, mod.out_channels])
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shared.log.info(f"Model dynamic quantization: pipeline={sd_model.__class__.__name__}")
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try:
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quant_api.swap_conv2d_1x1_to_linear(sd_model.unet, conv_filter_fn)
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quant_api.swap_conv2d_1x1_to_linear(sd_model.vae, conv_filter_fn)
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quant_api.apply_dynamic_quant(sd_model.unet, dynamic_quant_filter_fn)
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quant_api.apply_dynamic_quant(sd_model.vae, dynamic_quant_filter_fn)
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except Exception as e:
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shared.log.error(f"Model dynamic quantization error: {e}")
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return sd_model
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