65 lines
2.8 KiB
Python
65 lines
2.8 KiB
Python
import torch
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import torch.nn as nn
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from .utils import log
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#based on ComfyUI's and MinusZoneAI's fp8_linear optimization
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def fp8_linear_forward(cls, base_dtype, input):
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weight_dtype = cls.weight.dtype
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if weight_dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
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if len(input.shape) == 3:
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input_shape = input.shape
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scale_weight = getattr(cls, 'scale_weight', None)
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if scale_weight is None:
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scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
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else:
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scale_weight = scale_weight.to(input.device)
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scale_input = torch.ones((), device=input.device, dtype=torch.float32)
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input = torch.clamp(input, min=-448, max=448, out=input)
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inn = input.reshape(-1, input_shape[2]).to(torch.float8_e4m3fn).contiguous() #always e4m3fn because e5m2 * e5m2 is not supported
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bias = cls.bias.to(base_dtype) if cls.bias is not None else None
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o = torch._scaled_mm(inn, cls.weight.t(), out_dtype=base_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
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return o.reshape((-1, input_shape[1], cls.weight.shape[0]))
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else:
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return cls.original_forward(input.to(base_dtype))
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else:
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return cls.original_forward(input)
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@torch.compiler.disable()
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def apply_lora(weight, lora, step=None):
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for lora_diff, lora_strength in zip(lora[0], lora[1]):
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if isinstance(lora_strength, list):
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lora_strength = lora_strength[step]
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if lora_strength == 0.0:
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continue
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elif lora_strength == 0.0:
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continue
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patch_diff = torch.mm(
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lora_diff[0].flatten(start_dim=1).to(weight.device),
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lora_diff[1].flatten(start_dim=1).to(weight.device)
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).reshape(weight.shape)
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alpha = lora_diff[2] / lora_diff[1].shape[0] if lora_diff[2] is not None else 1.0
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scale = lora_strength * alpha
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weight = weight.add(patch_diff, alpha=scale)
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return weight
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def convert_fp8_linear(module, base_dtype, params_to_keep={}, scale_weight_keys=None):
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log.info("FP8 matmul enabled")
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for name, submodule in module.named_modules():
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if not any(keyword in name for keyword in params_to_keep):
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if isinstance(submodule, nn.Linear):
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if scale_weight_keys is not None:
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scale_key = f"{name}.scale_weight"
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if scale_key in scale_weight_keys:
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print("Setting scale_weight for", name)
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setattr(submodule, "scale_weight", scale_weight_keys[scale_key])
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original_forward = submodule.forward
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setattr(submodule, "original_forward", original_forward)
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setattr(submodule, "forward", lambda input, m=submodule: fp8_linear_forward(m, base_dtype, input))
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