def fake_quantize_per_tensor_affine(g,
inputs,
scale,
zero_point,
quant_min=-128,
quant_max=127):
if (quant_min, quant_max) not in [(0, 255), (-128, 127)]:
raise RuntimeError(
"For (quant_min, quant_max), ONNX allows only (0, 255) and (-128, 127). "
"Got ({}, {})".format(quant_min, quant_max))
scale = sym_help._maybe_get_scalar(scale)
if scale is None:
sym_help._onnx_opset_unsupported_detailed(
"fake_quantize_per_tensor_affine", 10, 13,
"Non-constant scale not supported")
scale = scale.float().data # Avoid exporter generating double type
if quant_min == 0:
zero_point = g.op("Cast",
zero_point,
to_i=torch.onnx.TensorProtoDataType.UINT8)
else:
zero_point = g.op("Cast",
zero_point,
to_i=torch.onnx.TensorProtoDataType.INT8)
return g.op("DequantizeLinear",
g.op("QuantizeLinear", inputs, scale, zero_point), scale,
zero_point)
def batch_norm(g, input, weight, bias, running_mean, running_var, training,
momentum, eps, cudnn_enabled):
if torch.is_autocast_enabled() and \
not args_have_same_dtype([input, weight, bias, running_mean, running_var]) and \
sym_help._export_onnx_opset_version < 15:
return sym_help._onnx_opset_unsupported_detailed(
"BatchNormalization", 14, 15,
"All input tensors must have the same `dtype`."
" Turn off Autocast or export using opset version 15.")
sym_help.check_training_mode(training, "batch_norm")
weight, bias, running_mean, running_var = sym_help._batchnorm_helper(
g, input, weight, bias, running_mean, running_var)
out = g.op("BatchNormalization",
input,
weight,
bias,
running_mean,
running_var,
epsilon_f=eps,
momentum_f=1 - momentum,
training_mode_i=0 if not training else 1,
outputs=1 if not training else 3)
if not training:
return out
else:
res, new_running_mean, new_running_var = out
new_running_mean.setType(running_mean.type())
new_running_var.setType(running_var.type())
return res
def fake_quantize_per_tensor_affine(g,
inputs,
scale,
zero_point,
quant_min=-128,
quant_max=127):
# NOTE: (0, 127) is a special case. PyTorch restricts activations to be in the range (0, 127).
# https://github.com/pytorch/pytorch/blob/b34b192d6b97325c9f78e5995c48c8498ede34bd/torch/ao/quantization/observer.py#L1422
if (quant_min, quant_max) == (0, 127):
symbolic_helper._onnx_opset_unsupported_detailed(
"fake_quantize_per_tensor_affine",
10,
13,
"Quantize range (0, 127) not supported, requires opset 13 Clip",
inputs,
)
if (quant_min, quant_max) not in [(0, 255), (-128, 127)]:
raise errors.SymbolicValueError(
f"For (quant_min, quant_max), ONNX allows only (0, 255) and (-128, 127). "
f"Got ({quant_min}, {quant_max})",
inputs,
)
scale = symbolic_helper._maybe_get_scalar(scale)
if scale is None:
symbolic_helper._onnx_opset_unsupported_detailed(
"fake_quantize_per_tensor_affine",
10,
13,
"Non-constant scale not supported",
inputs,
)
scale = scale.float().data # Avoid exporter generating double type
if quant_min == 0:
zero_point = g.op("Cast",
zero_point,
to_i=_C_onnx.TensorProtoDataType.UINT8)
else:
zero_point = g.op("Cast",
zero_point,
to_i=_C_onnx.TensorProtoDataType.INT8)
return g.op(
"DequantizeLinear",
g.op("QuantizeLinear", inputs, scale, zero_point),
scale,
zero_point,
)
