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add weight_scale_inv impl

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yoinked 2025-11-26 17:17:39 -08:00 committed by GitHub
parent eaf68c9b5b
commit 701e4999dc
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1 changed files with 26 additions and 2 deletions
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comfy/ops.py
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@ -464,6 +464,22 @@ class fp8_ops(manual_cast):
uncast_bias_weight(self, weight, bias, offload_stream)
return x
def scale_hadamard(larger, smaller, k_value=None, divide=False):
if smaller.shape == torch.Size([]):
# unset
return larger
h, w = smaller.shape
if k_value is None:
#calculate from larger compared to smaller
k_value = larger.shape[-1] // smaller.shape[-1]
expected_shape = (h * k_value, w * k_value)
assert larger.shape == expected_shape, "weight_scale_inv mismatch, skipping"
if divide:
smaller = 1.0 / smaller
result = larger.view(h, k_value, w, k_value) * smaller.view(h, 1, w, 1)
return result.reshape(h * k_value, w * k_value)
def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
class scaled_fp8_op(manual_cast):
@ -476,6 +492,9 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
def reset_parameters(self):
if not hasattr(self, 'scale_weight'):
self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
if not hasattr(self, 'weight_scale_inv'):
self.weight_scale_inv = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
if not scale_input:
self.scale_input = None
@ -502,12 +521,17 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
def convert_weight(self, weight, inplace=False, **kwargs):
if inplace:
weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
if self.weight_scale_inv.shape == torch.Size([]):
return weight
weight = scale_hadamard(weight, self.weight_scale_inv.to(device=weight.device, dtype=weight.dtype), k_value=None, divide=False)
return weight
else:
return weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32)
return scale_hadamard(weight.to(dtype=torch.float32) * self.scale_weight.to(device=weight.device, dtype=torch.float32),
self.weight_scale_inv.to(device=weight.device, dtype=torch.float32), k_value=None, divide=False)
def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
weight = comfy.float.stochastic_rounding(scale_hadamard(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype),
self.weight_scale_inv.to(device=weight.device, dtype=weight.dtype), k_value=None), self.weight.dtype, seed=seed)
if return_weight:
return weight
if inplace_update: