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https://github.com/comfyanonymous/ComfyUI.git
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ops: Implement prefetching API
Implement an API that allows instrumenting a model with a prefetch queue. Units of work are on the nn.Module level.
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parent
c350009236
commit
e279e1f26e
134
comfy/ops.py
134
comfy/ops.py
@ -22,7 +22,6 @@ import comfy.model_management
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from comfy.cli_args import args, PerformanceFeature
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import comfy.float
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import comfy.rmsnorm
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import contextlib
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def run_every_op():
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if torch.compiler.is_compiling():
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@ -71,6 +70,93 @@ def cast_to_input(weight, input, non_blocking=False, copy=True):
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return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
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def cast_prefetch_all(module, device):
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if not comfy.model_management.device_supports_non_blocking(device):
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#Adios! prefetching works against you if you can't get the CPU past it
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return None
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offload_stream = None
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for n, m in module.named_modules():
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if hasattr(m, "comfy_cast_weights"):
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if m.weight is not None and m.weight.device != device and not hasattr(m, "weight_prefetch"):
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if offload_stream is None:
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offload_stream = comfy.model_management.get_offload_stream(device)
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if offload_stream is None:
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return None
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m.weight_prefetch = comfy.model_management.cast_to(m.weight, None, device, non_blocking=True, copy=True, stream=offload_stream)
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if m.bias is not None and m.bias.device != device and not hasattr(m, "bias_prefetch"):
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if offload_stream is None:
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offload_stream = comfy.model_management.get_offload_stream(device)
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if offload_stream is None:
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return None
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m.bias_prefetch = comfy.model_management.cast_to(m.bias, None, device, non_blocking=True, copy = True, stream=offload_stream)
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return offload_stream
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def uncast_prefetch_all(module):
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for n, m in module.named_modules():
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if hasattr(m, "comfy_cast_weights"):
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if hasattr(m, "weight_prefetch"):
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delattr(m, "weight_prefetch")
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if hasattr(m, "bias_prefetch"):
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delattr(m, "bias_prefetch")
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def prefetch_queue_pop(queue, device, module):
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consumed = queue.pop(0)
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if consumed is not None:
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offload_stream, m = consumed
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#Sync the offload stream with compute so when it starts
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#freeing the prefetches the compute stream has finished
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if offload_stream is not None:
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offload_stream.wait_stream(comfy.model_management.current_stream(device))
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uncast_prefetch_all(m)
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active = queue[0]
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if active is not None:
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offload_stream, m = active
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assert m == module
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#wait for the prefetch to complete before using the data
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if offload_stream is not None:
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comfy.model_management.sync_stream(device, offload_stream)
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prefetch = queue[1]
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if prefetch is not None:
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offload_stream = comfy.ops.cast_prefetch_all(prefetch, device)
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queue[1] = (offload_stream, prefetch)
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def make_prefetch_queue(queue):
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return [None, None] + queue + [None, None]
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def move_bias_weight(s, device, offloadable=False):
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bias_has_function = len(s.bias_function) > 0
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weight_has_function = len(s.weight_function) > 0
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if offloadable and (
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s.weight.device != device or (s.bias is not None and s.bias.device != device) or
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bias_has_function or weight_has_function):
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offload_stream = comfy.model_management.get_offload_stream(device)
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else:
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offload_stream = None
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bias = None
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non_blocking = comfy.model_management.device_supports_non_blocking(device)
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weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream)
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if s.bias is not None:
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bias = comfy.model_management.cast_to(s.bias, None, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
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comfy.model_management.sync_stream(device, offload_stream)
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return weight, bias, offload_stream
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def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False):
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# NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
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# offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This
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@ -83,40 +169,30 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, of
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if device is None:
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device = input.device
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if offloadable and (device != s.weight.device or
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(s.bias is not None and device != s.bias.device)):
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offload_stream = comfy.model_management.get_offload_stream(device)
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else:
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offload_stream = None
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if offload_stream is not None:
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wf_context = offload_stream
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else:
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wf_context = contextlib.nullcontext()
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non_blocking = comfy.model_management.device_supports_non_blocking(device)
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weight_has_function = len(s.weight_function) > 0
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bias_has_function = len(s.bias_function) > 0
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weight_has_function = len(s.weight_function) > 0
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weight = comfy.model_management.cast_to(s.weight, None, device, non_blocking=non_blocking, copy=weight_has_function, stream=offload_stream)
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if hasattr(s, "weight_prefetch") or hasattr(s, "bias_prefetch"):
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weight = getattr(s, "weight_prefetch", None)
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bias = getattr(s, "bias_prefetch", None)
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offload_stream = None
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else:
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weight, bias, offload_stream = move_bias_weight(s, device, offloadable=offloadable)
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bias = None
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if s.bias is not None:
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bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=bias_has_function, stream=offload_stream)
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if weight_has_function:
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weight=weight.to(dtype=dtype)
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for f in s.weight_function:
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weight = f(weight)
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if bias_has_function:
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with wf_context:
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for f in s.bias_function:
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bias = f(bias)
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if s.bias is not None and bias_has_function:
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bias=bias.to(dtype=bias_dtype)
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for f in s.bias_function:
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bias = f(bias)
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if weight_has_function or weight.dtype != dtype:
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with wf_context:
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weight = weight.to(dtype=dtype)
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for f in s.weight_function:
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weight = f(weight)
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weight=weight.to(dtype=dtype)
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if bias is not None:
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bias=bias.to(dtype=bias_dtype)
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comfy.model_management.sync_stream(device, offload_stream)
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if offloadable:
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return weight, bias, offload_stream
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else:
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