mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Tidy up GIG -> GB and remove unused GIG constant.
This commit is contained in:
parent
c578b8df1e
commit
6ba9b1b6b0
@ -25,7 +25,7 @@ from invokeai.backend.util.devices import TorchDevice
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
|
||||
# Size of a GB in bytes.
|
||||
GIG = 2**30
|
||||
GB = 2**30
|
||||
|
||||
# Size of a MB in bytes.
|
||||
MB = 2**20
|
||||
@ -196,7 +196,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
# more stats
|
||||
if self.stats:
|
||||
stats_name = stats_name or key
|
||||
self.stats.cache_size = int(self._max_cache_size * GIG)
|
||||
self.stats.cache_size = int(self._max_cache_size * GB)
|
||||
self.stats.high_watermark = max(self.stats.high_watermark, self.cache_size())
|
||||
self.stats.in_cache = len(self._cached_models)
|
||||
self.stats.loaded_model_sizes[stats_name] = max(
|
||||
@ -228,9 +228,9 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
|
||||
:param size_required: The amount of space to clear in the execution_device cache, in bytes.
|
||||
"""
|
||||
reserved = self._max_vram_cache_size * GIG
|
||||
reserved = self._max_vram_cache_size * GB
|
||||
vram_in_use = torch.cuda.memory_allocated() + size_required
|
||||
self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM needed for models; max allowed={(reserved/GIG):.2f}GB")
|
||||
self.logger.debug(f"{(vram_in_use/GB):.2f}GB VRAM needed for models; max allowed={(reserved/GB):.2f}GB")
|
||||
for _, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
|
||||
if vram_in_use <= reserved:
|
||||
break
|
||||
@ -241,7 +241,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
cache_entry.loaded = False
|
||||
vram_in_use = torch.cuda.memory_allocated() + size_required
|
||||
self.logger.debug(
|
||||
f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GIG):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GIG):.2f}GB"
|
||||
f"Removing {cache_entry.key} from VRAM to free {(cache_entry.size/GB):.2f}GB; vram free = {(torch.cuda.memory_allocated()/GB):.2f}GB"
|
||||
)
|
||||
|
||||
TorchDevice.empty_cache()
|
||||
@ -299,7 +299,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
self.logger.debug(
|
||||
f"Moved model '{cache_entry.key}' from {source_device} to"
|
||||
f" {target_device} in {(end_model_to_time-start_model_to_time):.2f}s."
|
||||
f"Estimated model size: {(cache_entry.size/GIG):.3f} GB."
|
||||
f"Estimated model size: {(cache_entry.size/GB):.3f} GB."
|
||||
f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
|
||||
)
|
||||
|
||||
@ -322,14 +322,14 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
f"Moving model '{cache_entry.key}' from {source_device} to"
|
||||
f" {target_device} caused an unexpected change in VRAM usage. The model's"
|
||||
" estimated size may be incorrect. Estimated model size:"
|
||||
f" {(cache_entry.size/GIG):.3f} GB.\n"
|
||||
f" {(cache_entry.size/GB):.3f} GB.\n"
|
||||
f"{get_pretty_snapshot_diff(snapshot_before, snapshot_after)}"
|
||||
)
|
||||
|
||||
def print_cuda_stats(self) -> None:
|
||||
"""Log CUDA diagnostics."""
|
||||
vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
|
||||
ram = "%4.2fG" % (self.cache_size() / GIG)
|
||||
vram = "%4.2fG" % (torch.cuda.memory_allocated() / GB)
|
||||
ram = "%4.2fG" % (self.cache_size() / GB)
|
||||
|
||||
in_ram_models = 0
|
||||
in_vram_models = 0
|
||||
@ -356,13 +356,13 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
garbage-collected.
|
||||
"""
|
||||
bytes_needed = size
|
||||
maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes
|
||||
maximum_size = self.max_cache_size * GB # stored in GB, convert to bytes
|
||||
current_size = self.cache_size()
|
||||
|
||||
if current_size + bytes_needed > maximum_size:
|
||||
self.logger.debug(
|
||||
f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional"
|
||||
f" {(bytes_needed/GIG):.2f} GB"
|
||||
f"Max cache size exceeded: {(current_size/GB):.2f}/{self.max_cache_size:.2f} GB, need an additional"
|
||||
f" {(bytes_needed/GB):.2f} GB"
|
||||
)
|
||||
|
||||
self.logger.debug(f"Before making_room: cached_models={len(self._cached_models)}")
|
||||
@ -379,7 +379,7 @@ class ModelCache(ModelCacheBase[AnyModel]):
|
||||
|
||||
if not cache_entry.locked:
|
||||
self.logger.debug(
|
||||
f"Removing {model_key} from RAM cache to free at least {(size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
|
||||
f"Removing {model_key} from RAM cache to free at least {(size/GB):.2f} GB (-{(cache_entry.size/GB):.2f} GB)"
|
||||
)
|
||||
current_size -= cache_entry.size
|
||||
models_cleared += 1
|
||||
|
@ -3,10 +3,9 @@ Initialization file for invokeai.backend.util
|
||||
"""
|
||||
|
||||
from invokeai.backend.util.logging import InvokeAILogger
|
||||
from invokeai.backend.util.util import GIG, Chdir, directory_size
|
||||
from invokeai.backend.util.util import Chdir, directory_size
|
||||
|
||||
__all__ = [
|
||||
"GIG",
|
||||
"directory_size",
|
||||
"Chdir",
|
||||
"InvokeAILogger",
|
||||
|
@ -7,9 +7,6 @@ from pathlib import Path
|
||||
|
||||
from PIL import Image
|
||||
|
||||
# actual size of a gig
|
||||
GIG = 1073741824
|
||||
|
||||
|
||||
def slugify(value: str, allow_unicode: bool = False) -> str:
|
||||
"""
|
||||
|
Loading…
Reference in New Issue
Block a user