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Add locks to model_management to prevent multiple copies of the models from being loaded at the same time

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doctorpangloss 2024-02-07 15:18:13 -08:00
parent 8e9052c843
commit d9b4607c36
3 changed files with 142 additions and 117 deletions
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11
comfy/client/embedded_comfy_client.py
View File

@ -47,9 +47,10 @@ class EmbeddedComfyClient:
```
"""
def __init__(self, configuration: Optional[Configuration] = None, loop: Optional[AbstractEventLoop] = None):
def __init__(self, configuration: Optional[Configuration] = None, loop: Optional[AbstractEventLoop] = None,
max_workers: int = 1):
self._server_stub = ServerStub()
self._executor = ThreadPoolExecutor(max_workers=1)
self._executor = ThreadPoolExecutor(max_workers=max_workers)
self._loop = loop or asyncio.get_event_loop()
self._configuration = configuration
# we don't want to import the executor yet
@ -71,6 +72,10 @@ class EmbeddedComfyClient:
except:
pass
# wait until the queue is done
while self._executor._work_queue.qsize() > 0:
await asyncio.sleep(0.1)
await self._loop.run_in_executor(self._executor, cleanup)
self._executor.shutdown(wait=True)
@ -105,6 +110,6 @@ class EmbeddedComfyClient:
if self._prompt_executor.success:
return self._prompt_executor.outputs_ui
else:
raise RuntimeError("\n".join(self._prompt_executor.status_messages))
raise RuntimeError("\n".join(event for (event, data) in self._prompt_executor.status_messages))
return await self._loop.run_in_executor(self._executor, execute_prompt)

239
comfy/model_management.py
View File

@ -2,10 +2,13 @@ import psutil
from enum import Enum
from .cli_args import args
from . import utils
from threading import RLock
import torch
import sys
model_management_lock = RLock()
class VRAMState(Enum):
DISABLED = 0 #No vram present: no need to move models to vram
NO_VRAM = 1 #Very low vram: enable all the options to save vram
@ -343,113 +346,118 @@ def minimum_inference_memory():
return (1024 * 1024 * 1024)
def unload_model_clones(model):
to_unload = []
for i in range(len(current_loaded_models)):
if model.is_clone(current_loaded_models[i].model):
to_unload = [i] + to_unload
with model_management_lock:
to_unload = []
for i in range(len(current_loaded_models)):
if model.is_clone(current_loaded_models[i].model):
to_unload = [i] + to_unload
for i in to_unload:
print("unload clone", i)
current_loaded_models.pop(i).model_unload()
for i in to_unload:
print("unload clone", i)
current_loaded_models.pop(i).model_unload()
def free_memory(memory_required, device, keep_loaded=[]):
unloaded_model = False
for i in range(len(current_loaded_models) -1, -1, -1):
if not DISABLE_SMART_MEMORY:
if get_free_memory(device) > memory_required:
break
shift_model = current_loaded_models[i]
if shift_model.device == device:
if shift_model not in keep_loaded:
m = current_loaded_models.pop(i)
m.model_unload()
del m
unloaded_model = True
with model_management_lock:
unloaded_model = False
for i in range(len(current_loaded_models) -1, -1, -1):
if not DISABLE_SMART_MEMORY:
if get_free_memory(device) > memory_required:
break
shift_model = current_loaded_models[i]
if shift_model.device == device:
if shift_model not in keep_loaded:
m = current_loaded_models.pop(i)
m.model_unload()
del m
unloaded_model = True
if unloaded_model:
soft_empty_cache()
else:
if vram_state != VRAMState.HIGH_VRAM:
mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
if mem_free_torch > mem_free_total * 0.25:
soft_empty_cache()
if unloaded_model:
soft_empty_cache()
else:
if vram_state != VRAMState.HIGH_VRAM:
mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
if mem_free_torch > mem_free_total * 0.25:
soft_empty_cache()
def load_models_gpu(models, memory_required=0):
global vram_state
inference_memory = minimum_inference_memory()
extra_mem = max(inference_memory, memory_required)
with model_management_lock:
inference_memory = minimum_inference_memory()
extra_mem = max(inference_memory, memory_required)
models_to_load = []
models_already_loaded = []
for x in models:
loaded_model = LoadedModel(x)
models_to_load = []
models_already_loaded = []
for x in models:
loaded_model = LoadedModel(x)
if loaded_model in current_loaded_models:
index = current_loaded_models.index(loaded_model)
current_loaded_models.insert(0, current_loaded_models.pop(index))
models_already_loaded.append(loaded_model)
else:
if hasattr(x, "model"):
print(f"Requested to load {x.model.__class__.__name__}")
models_to_load.append(loaded_model)
if len(models_to_load) == 0:
devs = set(map(lambda a: a.device, models_already_loaded))
for d in devs:
if d != torch.device("cpu"):
free_memory(extra_mem, d, models_already_loaded)
return
print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
total_memory_required = {}
for loaded_model in models_to_load:
unload_model_clones(loaded_model.model)
total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
for device in total_memory_required:
if device != torch.device("cpu"):
free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
for loaded_model in models_to_load:
model = loaded_model.model
torch_dev = model.load_device
if is_device_cpu(torch_dev):
vram_set_state = VRAMState.DISABLED
else:
vram_set_state = vram_state
lowvram_model_memory = 0
if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
model_size = loaded_model.model_memory_required(torch_dev)
current_free_mem = get_free_memory(torch_dev)
lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
vram_set_state = VRAMState.LOW_VRAM
if loaded_model in current_loaded_models:
index = current_loaded_models.index(loaded_model)
current_loaded_models.insert(0, current_loaded_models.pop(index))
models_already_loaded.append(loaded_model)
else:
lowvram_model_memory = 0
if hasattr(x, "model"):
print(f"Requested to load {x.model.__class__.__name__}")
models_to_load.append(loaded_model)
if vram_set_state == VRAMState.NO_VRAM:
lowvram_model_memory = 64 * 1024 * 1024
if len(models_to_load) == 0:
devs = set(map(lambda a: a.device, models_already_loaded))
for d in devs:
if d != torch.device("cpu"):
free_memory(extra_mem, d, models_already_loaded)
return
cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
current_loaded_models.insert(0, loaded_model)
return
print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
total_memory_required = {}
for loaded_model in models_to_load:
unload_model_clones(loaded_model.model)
total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
for device in total_memory_required:
if device != torch.device("cpu"):
free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
for loaded_model in models_to_load:
model = loaded_model.model
torch_dev = model.load_device
if is_device_cpu(torch_dev):
vram_set_state = VRAMState.DISABLED
else:
vram_set_state = vram_state
lowvram_model_memory = 0
if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
model_size = loaded_model.model_memory_required(torch_dev)
current_free_mem = get_free_memory(torch_dev)
lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
vram_set_state = VRAMState.LOW_VRAM
else:
lowvram_model_memory = 0
if vram_set_state == VRAMState.NO_VRAM:
lowvram_model_memory = 64 * 1024 * 1024
cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
current_loaded_models.insert(0, loaded_model)
return
def load_model_gpu(model):
return load_models_gpu([model])
with model_management_lock:
return load_models_gpu([model])
def cleanup_models():
to_delete = []
for i in range(len(current_loaded_models)):
if sys.getrefcount(current_loaded_models[i].model) <= 2:
to_delete = [i] + to_delete
with model_management_lock:
to_delete = []
for i in range(len(current_loaded_models)):
if sys.getrefcount(current_loaded_models[i].model) <= 2:
to_delete = [i] + to_delete
for i in to_delete:
x = current_loaded_models.pop(i)
x.model_unload()
del x
for i in to_delete:
x = current_loaded_models.pop(i)
x.model_unload()
del x
def dtype_size(dtype):
dtype_size = 4
@ -593,26 +601,27 @@ def device_supports_non_blocking(device):
return True
def cast_to_device(tensor, device, dtype, copy=False):
device_supports_cast = False
if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
device_supports_cast = True
elif tensor.dtype == torch.bfloat16:
if hasattr(device, 'type') and device.type.startswith("cuda"):
device_supports_cast = True
elif is_intel_xpu():
with model_management_lock:
device_supports_cast = False
if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
device_supports_cast = True
elif tensor.dtype == torch.bfloat16:
if hasattr(device, 'type') and device.type.startswith("cuda"):
device_supports_cast = True
elif is_intel_xpu():
device_supports_cast = True
non_blocking = device_supports_non_blocking(device)
non_blocking = device_supports_non_blocking(device)
if device_supports_cast:
if copy:
if tensor.device == device:
return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
if device_supports_cast:
if copy:
if tensor.device == device:
return tensor.to(dtype, copy=copy, non_blocking=non_blocking)
return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
else:
return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
else:
return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking)
else:
return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)
return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking)
def xformers_enabled():
global directml_enabled
@ -759,18 +768,20 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
return True
def soft_empty_cache(force=False):
global cpu_state
if cpu_state == CPUState.MPS:
torch.mps.empty_cache()
elif is_intel_xpu():
torch.xpu.empty_cache()
elif torch.cuda.is_available():
if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
with model_management_lock:
global cpu_state
if cpu_state == CPUState.MPS:
torch.mps.empty_cache()
elif is_intel_xpu():
torch.xpu.empty_cache()
elif torch.cuda.is_available():
if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def unload_all_models():
free_memory(1e30, get_torch_device())
with model_management_lock:
free_memory(1e30, get_torch_device())
def resolve_lowvram_weight(weight, model, key): #TODO: remove

9
tests/distributed/test_embedded_client.py
View File

@ -1,3 +1,5 @@
import asyncio
import pytest
import torch
@ -31,3 +33,10 @@ async def test_embedded_comfy():
prompt = sdxl_workflow_with_refiner("test")
outputs = await client.queue_prompt(prompt)
assert outputs["13"]["images"][0]["abs_path"] is not None
@pytest.mark.asyncio
async def test_multithreaded_comfy():
async with EmbeddedComfyClient(max_workers=2) as client:
prompt = sdxl_workflow_with_refiner("test")
outputs_iter = await asyncio.gather(*[client.queue_prompt(prompt) for _ in range(4)])
assert all(outputs["13"]["images"][0]["abs_path"] is not None for outputs in outputs_iter)