Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

Model compile support for IPEX

Browse Source
This commit is contained in:
Disty0
2023-08-03 17:25:15 +03:00
parent a293e3cdcb
commit 4535a99fff
3 changed files with 32 additions and 23 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sd_hijack.py
+21 -17
View File
@@ -174,27 +174,31 @@ class StableDiffusionModelHijack:
if m.cond_stage_key == "edit":
sd_hijack_unet.hijack_ddpm_edit()
if opts.ipex_optimize and shared.backend == shared.Backend.ORIGINAL:
try:
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
m.model.training = False
m.model = ipex.optimize(m.model, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
shared.log.info("Applied IPEX Optimize.")
except Exception as err:
shared.log.warning(f"IPEX Optimize not supported: {err}")
if opts.cuda_compile and opts.cuda_compile_mode != 'none' and shared.backend == shared.Backend.ORIGINAL:
try:
import logging
shared.log.info(f"Compiling pipeline={m.model.__class__.__name__} mode={opts.cuda_compile_mode}")
if opts.cuda_compile_mode == 'ipex':
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
m.model.training = False
m.model = ipex.optimize(m.model, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
else:
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
log_level = logging.WARNING if opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
if hasattr(torch, '_logging'):
torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
torch._dynamo.config.verbose = opts.cuda_compile_verbose # pylint: disable=protected-access
torch._dynamo.config.suppress_errors = opts.cuda_compile_errors # pylint: disable=protected-access
torch.backends.cudnn.benchmark = True
if opts.cuda_compile_mode == 'hidet':
import hidet
hidet.torch.dynamo_config.use_tensor_core(True)
hidet.torch.dynamo_config.search_space(2)
m.model = torch.compile(m.model, mode="default", backend=opts.cuda_compile_mode, fullgraph=opts.cuda_compile_fullgraph, dynamic=False)
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
log_level = logging.WARNING if opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
if hasattr(torch, '_logging'):
torch._logging.set_logs(dynamo=log_level, aot=log_level, inductor=log_level) # pylint: disable=protected-access
torch._dynamo.config.verbose = opts.cuda_compile_verbose # pylint: disable=protected-access
torch._dynamo.config.suppress_errors = opts.cuda_compile_errors # pylint: disable=protected-access
torch.backends.cudnn.benchmark = True
if opts.cuda_compile_mode == 'hidet':
import hidet
hidet.torch.dynamo_config.use_tensor_core(True)
hidet.torch.dynamo_config.search_space(2)
m.model = torch.compile(m.model, mode="default", backend=opts.cuda_compile_mode, fullgraph=opts.cuda_compile_fullgraph, dynamic=False)
shared.log.info("Model complilation done.")
except Exception as err:
shared.log.warning(f"Model compile not supported: {err}")
modules/sd_models.py
+8 -4
View File
@@ -708,7 +708,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
sd_model.unet.to(memory_format=torch.channels_last)
base_sent_to_cpu=False
if shared.opts.cuda_compile and torch.cuda.is_available():
if (shared.opts.cuda_compile or shared.opts.ipex_optimize) and torch.cuda.is_available():
if op == 'refiner' and not sd_model.has_accelerate:
gpu_vram = memory_stats().get('gpu', {})
free_vram = gpu_vram.get('total', 0) - gpu_vram.get('used', 0)
@@ -731,11 +731,15 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
elif not sd_model.has_accelerate:
sd_model.to(devices.device)
try:
shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_mode}")
if shared.opts.cuda_compile_mode == 'ipex':
if shared.opts.ipex_optimize:
sd_model.unet.training = False
sd_model.unet = torch.xpu.optimize(sd_model.unet, dtype=devices.dtype_unet, inplace=True, weights_prepack=False) # pylint: disable=attribute-defined-outside-init
else:
shared.log.info("Applied IPEX Optimize.")
except Exception as err:
shared.log.warning(f"IPEX Optimize not supported: {err}")
try:
if shared.opts.cuda_compile:
shared.log.info(f"Compiling pipeline={sd_model.__class__.__name__} shape={8 * sd_model.unet.config.sample_size} mode={shared.opts.cuda_compile_mode}")
import torch._dynamo # pylint: disable=unused-import,redefined-outer-name
log_level = logging.WARNING if shared.opts.cuda_compile_verbose else logging.CRITICAL # pylint: disable=protected-access
if hasattr(torch, '_logging'):
modules/shared.py
+3 -2
View File
@@ -384,12 +384,13 @@ options_templates.update(options_section(('cuda', "Compute Settings"), {
"cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
"cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
"cuda_allow_tf16_reduced": OptionInfo(True, "Allow TF16 reduced precision math ops"),
"cuda_compile": OptionInfo(True if devices.backend == "ipex" else False, "Enable model compile (experimental)"),
"cuda_compile_mode": OptionInfo("ipex" if devices.backend == "ipex" else "none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'inductor', 'reduce-overhead', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex']}),
"cuda_compile": OptionInfo(False, "Enable model compile (experimental)"),
"cuda_compile_mode": OptionInfo("none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'default', 'inductor', 'reduce-overhead', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'max-autotune', 'ipex']}),
"cuda_compile_fullgraph": OptionInfo(False, "Model compile fullgraph"),
"cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
"cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
"disable_gc": OptionInfo(True, "Disable Torch memory garbage collection"),
"ipex_optimize": OptionInfo(True if devices.backend == "ipex" else False, "Enable IPEX Optimize for Intel GPUs"),
"directml_memory_provider": OptionInfo(default_memory_provider, '[DirectML] Memory stats provider', gr.Dropdown, lambda: {"choices": memory_providers}),
}))