mirror of https://github.com/vladmandic/automatic
350 lines
18 KiB
Python
350 lines
18 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, sd_models
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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.is_compiled = False
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self.model_hash_str = ""
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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.compiled_cache = {}
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self.partitioned_modules = {}
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deepcache_worker = None
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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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def ipex_optimize_model(model):
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import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
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model.eval()
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model.training = False
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model = ipex.optimize(model, dtype=devices.dtype, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
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return model
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if "Model" in shared.opts.ipex_optimize:
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if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
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sd_model.unet = ipex_optimize_model(sd_model.unet)
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if hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
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sd_model.transformer = ipex_optimize_model(sd_model.transformer)
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if hasattr(sd_model, 'decoder') and hasattr(sd_model.decoder, 'config'):
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sd_model.decoder = ipex_optimize_model(sd_model.decoder)
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if hasattr(sd_model, 'prior_prior') and hasattr(sd_model.prior_prior, 'config'):
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sd_model.prior_prior = ipex_optimize_model(sd_model.prior_prior)
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if "VAE" in shared.opts.ipex_optimize:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
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sd_model.vae = ipex_optimize_model(sd_model.vae)
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if hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
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sd_model.movq = ipex_optimize_model(sd_model.movq)
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if hasattr(sd_model, 'vqgan') and hasattr(sd_model.vqgan, 'decode'):
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sd_model.vqgan = ipex_optimize_model(sd_model.vqgan)
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if hasattr(sd_model, 'image_encoder') and hasattr(sd_model.image_encoder, 'config'):
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sd_model.image_encoder = ipex_optimize_model(sd_model.image_encoder)
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if "Text Encoder" in shared.opts.ipex_optimize:
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if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
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sd_model.text_encoder = ipex_optimize_model(sd_model.text_encoder)
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if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
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sd_model.text_encoder_2 = ipex_optimize_model(sd_model.text_encoder_2)
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if hasattr(sd_model, 'prior_text_encoder') and hasattr(sd_model.prior_text_encoder, 'config'):
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sd_model.prior_text_encoder = ipex_optimize_model(sd_model.prior_text_encoder)
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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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if sd_model.device.type == "meta":
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shared.log.warning("Compress Weights is not compatible with Sequential CPU offload")
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return sd_model
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def nncf_compress_model(model):
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return_device = model.device
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model = nncf.compress_weights(model.to(devices.device)).to(return_device)
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devices.torch_gc(force=True)
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return model
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import nncf
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shared.compiled_model_state = CompiledModelState()
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shared.compiled_model_state.is_compiled = True
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if "Model" in shared.opts.nncf_compress_weights:
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if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
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sd_model.unet = nncf_compress_model(sd_model.unet)
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if hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
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sd_model.transformer = nncf_compress_model(sd_model.transformer)
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if hasattr(sd_model, 'decoder') and hasattr(sd_model.decoder, 'config'):
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sd_model.decoder = nncf_compress_model(sd_model.decoder)
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if hasattr(sd_model, 'prior_prior') and hasattr(sd_model.prior_prior, 'config'):
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sd_model.prior_prior = nncf_compress_model(sd_model.prior_prior)
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if "VAE" in shared.opts.nncf_compress_weights:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
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sd_model.vae = nncf_compress_model(sd_model.vae)
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if hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
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sd_model.movq = nncf_compress_model(sd_model.movq)
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if hasattr(sd_model, 'vqgan') and hasattr(sd_model.vqgan, 'decode'):
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sd_model.vqgan = nncf_compress_model(sd_model.vqgan)
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if hasattr(sd_model, 'image_encoder') and hasattr(sd_model.image_encoder, 'config'):
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sd_model.image_encoder = nncf_compress_model(sd_model.image_encoder)
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if "Text Encoder" in shared.opts.nncf_compress_weights:
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if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
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sd_model.text_encoder = nncf_compress_model(sd_model.text_encoder)
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if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
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sd_model.text_encoder_2 = nncf_compress_model(sd_model.text_encoder_2)
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if hasattr(sd_model, 'prior_text_encoder') and hasattr(sd_model.prior_text_encoder, 'config'):
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sd_model.prior_text_encoder = nncf_compress_model(sd_model.prior_text_encoder)
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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(sd_model):
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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 not None:
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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 = CompiledModelState()
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shared.compiled_model_state.is_compiled = True
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shared.compiled_model_state.first_pass = True if not shared.opts.cuda_compile_precompile else False
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shared.compiled_model_state.first_pass_vae = True if not shared.opts.cuda_compile_precompile else False
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shared.compiled_model_state.first_pass_refiner = True if not shared.opts.cuda_compile_precompile else False
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sd_model.has_accelerate = True
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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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return sd_model
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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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t0 = time.time()
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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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def torch_compile_model(model):
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return torch.compile(model, 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_backend == "openvino_fx":
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sd_model = optimize_openvino(sd_model)
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elif shared.opts.cuda_compile_backend == "olive-ai":
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if shared.compiled_model_state is None:
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shared.compiled_model_state = CompiledModelState()
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return sd_model
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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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if "Model" in shared.opts.cuda_compile:
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if hasattr(sd_model, 'unet') and hasattr(sd_model.unet, 'config'):
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sd_model.unet = torch_compile_model(sd_model.unet)
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if hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'config'):
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sd_model.transformer = torch_compile_model(sd_model.transformer)
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if hasattr(sd_model, 'decoder') and hasattr(sd_model.decoder, 'config'):
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sd_model.decoder = torch_compile_model(sd_model.decoder)
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if hasattr(sd_model, 'prior_prior') and hasattr(sd_model.prior_prior, 'config'):
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sd_model.prior_prior = torch_compile_model(sd_model.prior_prior)
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if "VAE" in shared.opts.cuda_compile:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'decode'):
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sd_model.vae = torch_compile_model(sd_model.vae)
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if hasattr(sd_model, 'movq') and hasattr(sd_model.movq, 'decode'):
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sd_model.movq = torch_compile_model(sd_model.movq)
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if hasattr(sd_model, 'vqgan') and hasattr(sd_model.vqgan, 'decode'):
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sd_model.vqgan = torch_compile_model(sd_model.vqgan)
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if hasattr(sd_model, 'image_encoder') and hasattr(sd_model.image_encoder, 'config'):
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sd_model.image_encoder = torch_compile_model(sd_model.image_encoder)
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if "Text Encoder" in shared.opts.cuda_compile:
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if hasattr(sd_model, 'text_encoder') and hasattr(sd_model.text_encoder, 'config'):
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sd_model.text_encoder = torch_compile_model(sd_model.text_encoder)
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if hasattr(sd_model, 'text_encoder_2') and hasattr(sd_model.text_encoder_2, 'config'):
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sd_model.text_encoder_2 = torch_compile_model(sd_model.text_encoder_2)
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if hasattr(sd_model, 'prior_text_encoder') and hasattr(sd_model.prior_text_encoder, 'config'):
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sd_model.prior_text_encoder = torch_compile_model(sd_model.prior_text_encoder)
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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 check_deepcache(enable: bool):
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if deepcache_worker is not None:
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if enable:
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deepcache_worker.enable()
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else:
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deepcache_worker.disable()
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def compile_deepcache(sd_model):
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global deepcache_worker # pylint: disable=global-statement
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try:
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from DeepCache import DeepCacheSDHelper
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except Exception as e:
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shared.log.warning(f'Model compile using deep-cache: {e}')
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return sd_model
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t0 = time.time()
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check_deepcache(False)
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deepcache_worker = DeepCacheSDHelper(pipe=sd_model)
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deepcache_worker.set_params(cache_interval=shared.opts.deep_cache_interval, cache_branch_id=0)
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t1 = time.time()
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shared.log.info(f"Model compile: task=DeepCache config={deepcache_worker.params} time={t1-t0:.2f}")
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# config={'cache_interval': 3, 'cache_layer_id': 0, 'cache_block_id': 0, 'skip_mode': 'uniform'} time=0.00
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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:
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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} compile={shared.opts.cuda_compile}")
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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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elif shared.opts.cuda_compile_backend == 'deep-cache':
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sd_model = compile_deepcache(sd_model)
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else:
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check_deepcache(False)
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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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def openvino_recompile_model(p, hires=False, refiner=False): # recompile if a parameter changes
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none':
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if shared.opts.cuda_compile_backend == "openvino_fx":
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compile_height = p.height if not hires and hasattr(p, 'height') else p.hr_upscale_to_y
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compile_width = p.width if not hires and hasattr(p, 'width') else p.hr_upscale_to_x
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if (shared.compiled_model_state is None or
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(not shared.compiled_model_state.first_pass
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and (shared.compiled_model_state.height != compile_height
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or shared.compiled_model_state.width != compile_width
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or shared.compiled_model_state.batch_size != p.batch_size))):
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if refiner:
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shared.log.info("OpenVINO: Recompiling refiner")
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sd_models.unload_model_weights(op='refiner')
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sd_models.reload_model_weights(op='refiner')
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else:
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shared.log.info("OpenVINO: Recompiling base model")
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sd_models.unload_model_weights(op='model')
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sd_models.reload_model_weights(op='model')
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shared.compiled_model_state.height = compile_height
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shared.compiled_model_state.width = compile_width
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shared.compiled_model_state.batch_size = p.batch_size
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def openvino_post_compile(op="base"): # delete unet after OpenVINO compile
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if shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx":
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if shared.compiled_model_state.first_pass and op == "base":
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shared.compiled_model_state.first_pass = False
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if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_model, "unet"):
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shared.sd_model.unet.apply(sd_models.convert_to_faketensors)
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devices.torch_gc(force=True)
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if shared.compiled_model_state.first_pass_refiner and op == "refiner":
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shared.compiled_model_state.first_pass_refiner = False
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if not shared.opts.openvino_disable_memory_cleanup and hasattr(shared.sd_refiner, "unet"):
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shared.sd_refiner.unet.apply(sd_models.convert_to_faketensors)
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devices.torch_gc(force=True)
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