@@ -124,9 +124,9 @@ def cuda(self):
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def guard_dtype_size (tensor_arg , arg_name , dtype = None , size = None ):
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if dtype is not None and tensor_arg .dtype != dtype :
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- raise ValueError ("Expected Tensor argument {arg_name} to have dtype {dtype}, but got {tensor_arg.dtype} instead." )
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+ raise ValueError (f "Expected Tensor argument { arg_name } to have dtype { dtype } , but got { tensor_arg .dtype } instead." )
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if size is not None and tensor_arg .size () != size :
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- raise ValueError ("Expected Tensor argument {arg_name} to have size {size}, but got {tensor_arg.size()} instead." )
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+ raise ValueError (f "Expected Tensor argument { arg_name } to have size { size } , but got { tensor_arg .size ()} instead." )
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# taken from
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# https://github.com/mit-han-lab/smoothquant/blob/2f87951dacfb9238d8d657f52ae83a82a3c9ba0c/smoothquant/fake_quant.py#L26
@@ -464,13 +464,13 @@ def recommended_inductor_config_setter():
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coordinate_descent_tuning = True
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coordinate_descent_check_all_directions = True
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force_fuse_int_mm_with_mul = True
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- fx_graph_cache = True
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+ fx_graph_cache = True
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triton.unique_kernel_names = True
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torch.set_float32_matmul_precision("high")
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"""
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torch ._inductor .config .coordinate_descent_tuning = True
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torch ._inductor .config .coordinate_descent_check_all_directions = True
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- torch ._inductor .config .force_fuse_int_mm_with_mul = True
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- torch ._inductor .config .fx_graph_cache = True
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+ torch ._inductor .config .force_fuse_int_mm_with_mul = True
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+ torch ._inductor .config .fx_graph_cache = True
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torch ._inductor .config .triton .unique_kernel_names = True
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torch .set_float32_matmul_precision ("high" )
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