def input_symbolic_execution(self, inp: Tensor):
input_quant_symbolic_kwargs = self.symbolic_kwargs['input_quant_symbolic_kwargs']
input_dequant_symbolic_kwargs = self.symbolic_kwargs['input_dequant_symbolic_kwargs']
input_redequant_symbolic_kwargs = self.symbolic_kwargs['input_redequant_symbolic_kwargs']
if input_dequant_symbolic_kwargs is not None:
inp = DequantizeLinearFn.apply(inp, *input_dequant_symbolic_kwargs.values())
if input_quant_symbolic_kwargs is not None:
inp = QuantizeLinearFn.apply(inp, *input_quant_symbolic_kwargs.values())
inp = DequantizeLinearFn.apply(inp, *input_redequant_symbolic_kwargs.values())
return inp
def output_symbolic_execution(self, out: Tensor):
input_dequant_symbolic_kwargs = self.symbolic_kwargs['input_dequant_symbolic_kwargs']
output_quant_symbolic_kwargs = self.symbolic_kwargs['output_quant_symbolic_kwargs']
output_dequant_symbolic_kwargs = self.symbolic_kwargs['output_dequant_symbolic_kwargs']
if input_dequant_symbolic_kwargs:
out = DequantizeLinearFn.apply(out, *input_dequant_symbolic_kwargs.values())
out = QuantizeLinearFn.apply(out, *output_quant_symbolic_kwargs.values())
if output_dequant_symbolic_kwargs is not None:
out = DequantizeLinearFn.apply(out, *output_dequant_symbolic_kwargs.values())
return out
def output_symbolic_execution(self, out: Tensor):
output_dequant_symbolic_kwargs = self.symbolic_kwargs['output_dequant_symbolic_kwargs']
output_quant_symbolic_kwargs = self.symbolic_kwargs['output_quant_symbolic_kwargs']
bias = self.symbolic_kwargs['bias']
if output_dequant_symbolic_kwargs is not None:
out = DequantizeLinearFn.apply(out, *output_dequant_symbolic_kwargs.values())
if bias is not None:
out = out.add(bias)
if output_quant_symbolic_kwargs is not None:
out = QuantizeLinearFn.apply(out, *output_quant_symbolic_kwargs.values())
return out