Add --precision half cmd option

This commit is contained in:
huchenlei 2024-05-16 19:50:06 -04:00
parent ddb28b33a3
commit 2a8a60c2c5
6 changed files with 71 additions and 19 deletions

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@ -41,7 +41,7 @@ parser.add_argument("--lowvram", action='store_true', help="enable stable diffus
parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM")
parser.add_argument("--always-batch-cond-uncond", action='store_true', help="does not do anything")
parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.")
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast")
parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "half", "autocast"], default="autocast")
parser.add_argument("--upcast-sampling", action='store_true', help="upcast sampling. No effect with --no-half. Usually produces similar results to --no-half with better performance while using less memory.")
parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site")
parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None)

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@ -114,6 +114,9 @@ errors.run(enable_tf32, "Enabling TF32")
cpu: torch.device = torch.device("cpu")
fp8: bool = False
# Force fp16 for all models in inference. No casting during inference.
# This flag is controlled by "--precision half" command line arg.
force_fp16: bool = False
device: torch.device = None
device_interrogate: torch.device = None
device_gfpgan: torch.device = None
@ -127,6 +130,8 @@ unet_needs_upcast = False
def cond_cast_unet(input):
if force_fp16:
return input.to(torch.float16)
return input.to(dtype_unet) if unet_needs_upcast else input
@ -206,6 +211,11 @@ def autocast(disable=False):
if disable:
return contextlib.nullcontext()
if force_fp16:
# No casting during inference if force_fp16 is enabled.
# All tensor dtype conversion happens before inference.
return contextlib.nullcontext()
if fp8 and device==cpu:
return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
@ -269,3 +279,17 @@ def first_time_calculation():
x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
conv2d(x)
def force_model_fp16():
"""
ldm and sgm has modules.diffusionmodules.util.GroupNorm32.forward, which
force conversion of input to float32. If force_fp16 is enabled, we need to
prevent this casting.
"""
assert force_fp16
import sgm.modules.diffusionmodules.util as sgm_util
import ldm.modules.diffusionmodules.util as ldm_util
sgm_util.GroupNorm32 = torch.nn.GroupNorm
ldm_util.GroupNorm32 = torch.nn.GroupNorm
print("ldm/sgm GroupNorm32 replaced with normal torch.nn.GroupNorm due to `--precision half`.")

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@ -36,7 +36,7 @@ th = TorchHijackForUnet()
# Below are monkey patches to enable upcasting a float16 UNet for float32 sampling
def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
"""Always make sure inputs to unet are in correct dtype."""
if isinstance(cond, dict):
for y in cond.keys():
if isinstance(cond[y], list):
@ -45,7 +45,11 @@ def apply_model(orig_func, self, x_noisy, t, cond, **kwargs):
cond[y] = cond[y].to(devices.dtype_unet) if isinstance(cond[y], torch.Tensor) else cond[y]
with devices.autocast():
return orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs).float()
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
if devices.unet_needs_upcast:
return result.float()
else:
return result
class GELUHijack(torch.nn.GELU, torch.nn.Module):
@ -64,12 +68,11 @@ def hijack_ddpm_edit():
if not ddpm_edit_hijack:
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.decode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
ddpm_edit_hijack = CondFunc('modules.models.diffusion.ddpm_edit.LatentDiffusion.apply_model', apply_model)
unet_needs_upcast = lambda *args, **kwargs: devices.unet_needs_upcast
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model, unet_needs_upcast)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
@ -81,5 +84,17 @@ CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.decode_first_stage', first_s
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.encode_first_stage', first_stage_sub, first_stage_cond)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.get_first_stage_encoding', lambda orig_func, *args, **kwargs: orig_func(*args, **kwargs).float(), first_stage_cond)
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model, unet_needs_upcast)
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda orig_func, timesteps, *args, **kwargs: orig_func(timesteps, *args, **kwargs).to(torch.float32 if timesteps.dtype == torch.int64 else devices.dtype_unet), unet_needs_upcast)
CondFunc('ldm.models.diffusion.ddpm.LatentDiffusion.apply_model', apply_model)
CondFunc('sgm.modules.diffusionmodules.wrappers.OpenAIWrapper.forward', apply_model)
def timestep_embedding_cast_result(orig_func, timesteps, *args, **kwargs):
if devices.unet_needs_upcast and timesteps.dtype == torch.int64:
dtype = torch.float32
else:
dtype = devices.dtype_unet
return orig_func(timesteps, *args, **kwargs).to(dtype=dtype)
CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)
CondFunc('sgm.modules.diffusionmodules.openaimodel.timestep_embedding', timestep_embedding_cast_result)

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@ -1,7 +1,11 @@
import importlib
always_true_func = lambda *args, **kwargs: True
class CondFunc:
def __new__(cls, orig_func, sub_func, cond_func):
def __new__(cls, orig_func, sub_func, cond_func=always_true_func):
self = super(CondFunc, cls).__new__(cls)
if isinstance(orig_func, str):
func_path = orig_func.split('.')
@ -20,13 +24,13 @@ class CondFunc:
print(f"Warning: Failed to resolve {orig_func} for CondFunc hijack")
pass
self.__init__(orig_func, sub_func, cond_func)
return lambda *args, **kwargs: self(*args, **kwargs)
def __init__(self, orig_func, sub_func, cond_func):
self.__orig_func = orig_func
self.__sub_func = sub_func
self.__cond_func = cond_func
def __call__(self, *args, **kwargs):
if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
return self.__sub_func(self.__orig_func, *args, **kwargs)
else:
return self.__orig_func(*args, **kwargs)
return lambda *args, **kwargs: self(*args, **kwargs)
def __init__(self, orig_func, sub_func, cond_func):
self.__orig_func = orig_func
self.__sub_func = sub_func
self.__cond_func = cond_func
def __call__(self, *args, **kwargs):
if not self.__cond_func or self.__cond_func(self.__orig_func, *args, **kwargs):
return self.__sub_func(self.__orig_func, *args, **kwargs)
else:
return self.__orig_func(*args, **kwargs)

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@ -403,6 +403,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
model.float()
model.alphas_cumprod_original = model.alphas_cumprod
devices.dtype_unet = torch.float32
assert shared.cmd_opts.precision != "half", "Cannot use --precision half with --no-half"
timer.record("apply float()")
else:
vae = model.first_stage_model

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@ -31,6 +31,14 @@ def initialize():
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
if cmd_opts.precision == "half":
msg = "--no-half and --no-half-vae conflict with --precision half"
assert devices.dtype == torch.float16, msg
assert devices.dtype_vae == torch.float16, msg
assert devices.dtype_inference == torch.float16, msg
devices.force_fp16 = True
devices.force_model_fp16()
shared.device = devices.device
shared.weight_load_location = None if cmd_opts.lowram else "cpu"