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[perf][WIP]: using NZ optimization for quantized GMM #906

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[perf][WIP]: using NZ optimization for quantized GMM #906

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7 changes: 7 additions & 0 deletions vllm_ascend/quantization/w8a8_dynamic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -663,6 +663,13 @@ def process_weights_after_loading(self, layer):
1, 2).contiguous()
layer.w2_weight.data = layer.w2_weight.data.transpose(
1, 2).contiguous()
# This optimization relies on the modifications in torch_npu, otherwise accuracy
# problem will happen. But we can evaluate the inference speed by transforming
# weights to NZ (29)
layer.w13_weight.data = torch_npu.npu_format_cast(
layer.w13_weight.data, 29)
layer.w2_weight.data = torch_npu.npu_format_cast(
layer.w2_weight.data, 29)
layer.w13_weight_scale.data = layer.w13_weight_scale.data.view(
layer.w13_weight_scale.data.shape[0], -1)
layer.w13_weight_offset.data = layer.w13_weight_offset.data.view(
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