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Fix small performance regression with fp8 fast and scaled fp8. (#10537)

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comfyanonymous
2025-10-29 16:29:01 -07:00
committed by GitHub
parent 25de7b1bfa
commit 906c089957
2 changed files with 8 additions and 3 deletions
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6
comfy/ops.py
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@@ -421,14 +421,18 @@ def fp8_linear(self, input):
if scale_input is None:
scale_input = torch.ones((), device=input.device, dtype=torch.float32)
input = torch.clamp(input, min=-448, max=448, out=input)
input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
layout_params_weight = {'scale': scale_input, 'orig_dtype': input_dtype}
quantized_input = QuantizedTensor(input.reshape(-1, input_shape[2]).to(dtype).contiguous(), TensorCoreFP8Layout, layout_params_weight)
else:
scale_input = scale_input.to(input.device)
quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
# Wrap weight in QuantizedTensor - this enables unified dispatch
# Call F.linear - __torch_dispatch__ routes to fp8_linear handler in quant_ops.py!
layout_params_weight = {'scale': scale_weight, 'orig_dtype': input_dtype}
quantized_weight = QuantizedTensor(w, TensorCoreFP8Layout, layout_params_weight)
quantized_input = QuantizedTensor.from_float(input.reshape(-1, input_shape[2]), TensorCoreFP8Layout, scale=scale_input, dtype=dtype)
o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)
uncast_bias_weight(self, w, bias, offload_stream)