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Running
on
Zero
Running
on
Zero
Update comfy/float.py
Browse files- comfy/float.py +2 -2
comfy/float.py
CHANGED
@@ -55,7 +55,7 @@ def stochastic_rounding(value, dtype, seed=0):
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if dtype == torch.bfloat16:
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return value.to(dtype=torch.bfloat16)
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if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
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-
generator = torch.Generator(device='cuda' if torch.cuda.is_available() else 'cpu')
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torch.manual_seed(seed)
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if(torch.cuda.is_available()):
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torch.cuda.manual_seed(seed)
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@@ -64,7 +64,7 @@ def stochastic_rounding(value, dtype, seed=0):
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slice_size = max(1, round(value.shape[0] / num_slices))
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with torch.no_grad():
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for i in range(0, value.shape[0], slice_size):
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output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype
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return output
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return value.to(dtype=dtype)
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if dtype == torch.bfloat16:
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return value.to(dtype=torch.bfloat16)
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if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
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+
#generator = torch.Generator(device='cuda' if torch.cuda.is_available() else 'cpu')
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torch.manual_seed(seed)
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if(torch.cuda.is_available()):
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torch.cuda.manual_seed(seed)
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slice_size = max(1, round(value.shape[0] / num_slices))
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with torch.no_grad():
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for i in range(0, value.shape[0], slice_size):
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+
output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype))
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return output
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return value.to(dtype=dtype)
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