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Merge pull request #3819 from vladmandic/dev

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Dev
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Disty0
2025-03-14 20:05:04 +03:00
committed by GitHub
parent 8513cd85f2 fdf4999f40
commit 6633e8e5e7
8 changed files with 56 additions and 28 deletions
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CHANGELOG.md
+5 -1
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@@ -1,16 +1,20 @@
# Change Log for SD.Next
## Update for 2025-03-12
## Update for 2025-03-14
- fix installer not starting when older version of rich is installed
- fix circular imports when debug flags are enabled
- fix cuda errors with directml
- fix memory stats not displaying the ram usage
- fix runpod memory limit reporting
- fix remote vae not being stored in metadata, thanks @iDeNoh
- add --upgrade to torch_command when using --use-nightly for ipex and rocm
- **ipex**
- add xpu to profiler
- fix untyped_storage, torch.eye and torch.cuda.device ops
- fix torch 2.7 compatibility
- fix performance with balanced offload
- fix triton and torch.compile
## Update for 2025-02-28
installer.py
+4 -4
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@@ -667,11 +667,11 @@ def install_rocm_zluda():
if args.use_nightly:
if rocm.version is None or float(rocm.version) >= 6.3: # assume the latest if version check fails
torch_command = os.environ.get('TORCH_COMMAND', '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.3')
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.3')
elif rocm.version == "6.2": # use rocm 6.2.4 instead of 6.2 as torch+rocm6.2 doesn't exists
torch_command = os.environ.get('TORCH_COMMAND', '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4')
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4')
else: # oldest rocm version on nightly is 6.1
torch_command = os.environ.get('TORCH_COMMAND', '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.1')
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/rocm6.1')
else:
if rocm.version is None or float(rocm.version) >= 6.2: # assume the latest if version check fails
# use rocm 6.2.4 instead of 6.2 as torch==2.6.0+rocm6.2 doesn't exists
@@ -735,7 +735,7 @@ def install_ipex(torch_command):
# os.environ.setdefault('IGC_EnableDPEmulation', '1') # FP64 Emulation
if args.use_nightly:
torch_command = os.environ.get('TORCH_COMMAND', '--pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/xpu')
torch_command = os.environ.get('TORCH_COMMAND', '--upgrade --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/xpu')
else:
torch_command = os.environ.get('TORCH_COMMAND', 'torch==2.6.0+xpu torchvision==0.21.0+xpu --index-url https://download.pytorch.org/whl/xpu')
modules/call_queue.py
+5 -3
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@@ -83,12 +83,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False, name=None):
vram = {k: v//1048576 for k, v in mem_mon_read.items()}
peak = max(vram['active_peak'], vram['reserved_peak'], vram['used'])
used = round(100.0 * peak / vram['total']) if vram['total'] > 0 else 0
if used > 0:
gpu += f"| GPU {peak} MB {used}%"
if peak > 0:
gpu += f"| GPU {peak} MB"
gpu += f" {used}%" if used > 0 else ''
gpu += f" | retries {retries} oom {ooms}" if retries > 0 or ooms > 0 else ''
ram = shared.ram_stats()
if ram['used'] > 0:
cpu += f"| RAM {ram['used']} GB {round(100.0 * ram['used'] / ram['total'])}%"
cpu += f"| RAM {ram['used']} GB"
cpu += f" {round(100.0 * ram['used'] / ram['total'])}%" if ram['total'] > 0 else ''
if isinstance(res, list):
res[-1] += f"<div class='performance'><p>Time: {elapsed_text} | {summary} {gpu} {cpu}</p></div>"
return tuple(res)
modules/intel/ipex/__init__.py
+9 -3
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@@ -18,7 +18,10 @@ def ipex_init(): # pylint: disable=too-many-statements
if hasattr(torch, "cuda") and hasattr(torch.cuda, "is_xpu_hijacked") and torch.cuda.is_xpu_hijacked:
return True, "Skipping IPEX hijack"
else:
try: # force xpu device on torch compile and triton
try:
# force xpu device on torch compile and triton
# import inductor utils to get around lazy import
from torch._inductor import utils as torch_inductor_utils # pylint: disable=import-error, unused-import
torch._inductor.utils.GPU_TYPES = ["xpu"]
torch._inductor.utils.get_gpu_type = lambda *args, **kwargs: "xpu"
from triton import backends as triton_backends # pylint: disable=import-error
@@ -187,11 +190,13 @@ def ipex_init(): # pylint: disable=too-many-statements
ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_subslice_count
ipex._C._DeviceProperties.major = 12
ipex._C._DeviceProperties.minor = 1
ipex._C._DeviceProperties.L2_cache_size = 16*1024*1024 # A770 and A750
else:
torch._C._cuda_getCurrentRawStream = torch._C._xpu_getCurrentRawStream
torch._C._XpuDeviceProperties.multi_processor_count = torch._C._XpuDeviceProperties.gpu_subslice_count
torch._C._XpuDeviceProperties.major = 12
torch._C._XpuDeviceProperties.minor = 1
torch._C._XpuDeviceProperties.L2_cache_size = 16*1024*1024 # A770 and A750
# Fix functions with ipex:
# torch.xpu.mem_get_info always returns the total memory as free memory
@@ -200,14 +205,15 @@ def ipex_init(): # pylint: disable=too-many-statements
torch._utils._get_available_device_type = lambda: "xpu"
torch.has_cuda = True
torch.cuda.has_half = True
torch.cuda.is_bf16_supported = lambda *args, **kwargs: True
torch.cuda.is_bf16_supported = getattr(torch.xpu, "is_bf16_supported", lambda *args, **kwargs: True)
torch.cuda.is_fp16_supported = lambda *args, **kwargs: True
torch.backends.cuda.is_built = lambda *args, **kwargs: True
torch.version.cuda = "12.1"
torch.cuda.get_arch_list = lambda: ["ats-m150", "pvc"]
torch.cuda.get_arch_list = getattr(torch.xpu, "get_arch_list", lambda: ["pvc", "dg2", "ats-m150"])
torch.cuda.get_device_capability = lambda *args, **kwargs: (12,1)
torch.cuda.get_device_properties.major = 12
torch.cuda.get_device_properties.minor = 1
torch.cuda.get_device_properties.L2_cache_size = 16*1024*1024 # A770 and A750
torch.cuda.ipc_collect = lambda *args, **kwargs: None
torch.cuda.utilization = lambda *args, **kwargs: 0
modules/intel/ipex/hijacks.py
+14 -9
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@@ -332,14 +332,6 @@ def torch_load(f, map_location=None, *args, **kwargs):
else:
return original_torch_load(f, *args, map_location=map_location, **kwargs)
original_torch_Generator = torch.Generator
@wraps(torch.Generator)
def torch_Generator(device=None):
if check_cuda(device):
return original_torch_Generator(return_xpu(device))
else:
return original_torch_Generator(device)
@wraps(torch.cuda.synchronize)
def torch_cuda_synchronize(device=None):
if check_cuda(device):
@@ -355,6 +347,17 @@ def torch_cuda_device(device):
return torch.xpu.device(device)
# torch.Generator has to be a class for isinstance checks
original_torch_Generator = torch.Generator
class torch_Generator(original_torch_Generator):
def __new__(self, device=None):
# can't hijack __init__ because of C override so use return super().__new__
if check_cuda(device):
return super().__new__(self, return_xpu(device))
else:
return super().__new__(self, device)
# Hijack Functions:
def ipex_hijacks():
global device_supports_fp64, can_allocate_plus_4gb
@@ -374,10 +377,12 @@ def ipex_hijacks():
torch.linspace = torch_linspace
torch.eye = torch_eye
torch.load = torch_load
torch.Generator = torch_Generator
torch.cuda.synchronize = torch_cuda_synchronize
torch.cuda.device = torch_cuda_device
torch.Generator = torch_Generator
torch._C.Generator = torch_Generator
torch.backends.cuda.sdp_kernel = return_null_context
torch.nn.DataParallel = DummyDataParallel
torch.UntypedStorage.is_cuda = is_cuda
modules/memstats.py
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@@ -24,6 +24,8 @@ def get_docker_limit():
docker_limit = float(f.read())
except Exception:
docker_limit = sys.float_info.max
if docker_limit == 0:
docker_limit = sys.float_info.max
return docker_limit
@@ -31,7 +33,9 @@ def get_runpod_limit():
global runpod_limit # pylint: disable=global-statement
if runpod_limit is not None:
return runpod_limit
runpod_limit = float(os.environ.get('RUNPOD_MEM_GB', sys.float_info.max))
runpod_limit = float(os.environ.get('RUNPOD_MEM_GB', 0)) * 1024 * 1024 * 1024
if runpod_limit == 0:
runpod_limit = sys.float_info.max
return runpod_limit
modules/processing_info.py
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@@ -74,6 +74,8 @@ def create_infotext(p: StableDiffusionProcessing, all_prompts=None, all_seeds=No
args["VAE"] = (None if not shared.opts.add_model_name_to_info or sd_vae.loaded_vae_file is None else os.path.splitext(os.path.basename(sd_vae.loaded_vae_file))[0])
elif p.vae_type == 'Tiny':
args["VAE"] = 'TAESD'
elif p.vae_type == 'Remote':
args["VAE"] = 'Remote'
if shared.opts.add_model_name_to_info and getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None:
args["Model"] = shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')
if shared.opts.add_model_hash_to_info and getattr(shared.sd_model, 'sd_model_hash', None) is not None:
modules/ui_control.py
+12 -7
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@@ -33,15 +33,20 @@ def return_stats(t: float = None):
gpu = ''
cpu = ''
if not shared.mem_mon.disabled:
vram = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.read().items()}
mem_mon_read = shared.mem_mon.read()
ooms = mem_mon_read.pop("oom")
retries = mem_mon_read.pop("retries")
vram = {k: v//1048576 for k, v in mem_mon_read.items()}
peak = max(vram['active_peak'], vram['reserved_peak'], vram['used'])
used = round(100.0 * peak / vram['total']) if vram['total'] > 0 else 0
if used > 0:
gpu += f"| GPU {peak} MB {used}%"
gpu += f" | retries {vram['retries']} oom {vram['oom']}" if vram.get('retries', 0) > 0 or vram.get('oom', 0) > 0 else ''
ram = shared.ram_stats()
if ram['used'] > 0:
cpu += f"| RAM {ram['used']} GB {round(100.0 * ram['used'] / ram['total'])}%"
if peak > 0:
gpu += f"| GPU {peak} MB"
gpu += f" {used}%" if used > 0 else ''
gpu += f" | retries {retries} oom {ooms}" if retries > 0 or ooms > 0 else ''
ram = shared.ram_stats()
if ram['used'] > 0:
cpu += f"| RAM {ram['used']} GB"
cpu += f" {round(100.0 * ram['used'] / ram['total'])}%" if ram['total'] > 0 else ''
return f"<div class='performance'><p>{elapsed_text} {summary} {gpu} {cpu}</p></div>"