furiosa.quantizer.frontend.onnx.quantizer package
Submodules
furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper module
- class furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.FuseClipper(*args, **kwds)
Bases:
furiosa.quantizer.interfaces.transformer.Transformer- transform(model: onnx.onnx_ml_pb2.ModelProto) onnx.onnx_ml_pb2.ModelProto
- class furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_1(model)
Bases:
furiosa.quantizer.frontend.onnx.transformer.ONNXTransformer- transform
prev –> Conv –> Relu –> next
- to
prev –> Conv –> next
- make_new_node(matched_nodes)
- pattern_matching(base_node)
- pattern_to_match = ['Conv', 'Relu']
- class furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_2(model)
Bases:
furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_1- transform
prev –> Conv –> Clip –> next
- to
prev –> Conv –> next
- pattern_to_match = ['Conv', 'Clip']
- class furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_3(model)
Bases:
furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_1- transform
prev –> Add –> Relu –> next
- to
prev –> Add –> next
- make_new_node(matched_nodes)
- pattern_to_match = ['Add', 'Relu']
- class furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_4(model)
Bases:
furiosa.quantizer.frontend.onnx.quantizer.fuse_clipper.Pattern_3- transform
prev –> Add –> Clip –> next
- to
prev –> Add –> next
- pattern_to_match = ['Add', 'Clip']
furiosa.quantizer.frontend.onnx.quantizer.quantizer module
- class furiosa.quantizer.frontend.onnx.quantizer.quantizer.DFGImportable(model, raw_data)
Bases:
object- remove_quantizelinear_operator_with_initializer()
- transform()
- transform_to_integer_arithmetic_operator()
- class furiosa.quantizer.frontend.onnx.quantizer.quantizer.FuriosaONNXQuantizer(model: onnx.onnx_ml_pb2.ModelProto, per_channel: bool, static: bool, mode: furiosa.quantizer.frontend.onnx.quantizer.utils.QuantizationMode, dynamic_ranges: Dict[str, Tuple[float, float]], raw_data=True)
Bases:
object- build_quantized_model()
- check_model()
- make_quant_dequant_node(node_input)
- pre_optimize()
- quantize() onnx.onnx_ml_pb2.ModelProto
- quantize_model()
- class furiosa.quantizer.frontend.onnx.quantizer.quantizer.ONNXRuntimeExecutable(model, raw_data)
Bases:
furiosa.quantizer.frontend.onnx.quantizer.quantizer.DFGImportable- transform()
furiosa.quantizer.frontend.onnx.quantizer.utils module
- class furiosa.quantizer.frontend.onnx.quantizer.utils.QuantizationMode
Bases:
object- dfg = 0
- fake = 1
- furiosa.quantizer.frontend.onnx.quantizer.utils.activation_scale_zeropoint(rmin, rmax, activation_qtype)
- furiosa.quantizer.frontend.onnx.quantizer.utils.append_suffix(name: str, suffix: List[str]) List[str]
Helper function to append suffixes to the given name.
- furiosa.quantizer.frontend.onnx.quantizer.utils.asymmetric_scale_zeropoint(rmin, rmax, activation_qtype)
source: onnxruntime quantization tools
- furiosa.quantizer.frontend.onnx.quantizer.utils.calculate_activation_quant_params(dynamic_ranges: Dict, node_list: List[onnx.onnx_ml_pb2.NodeProto], activation_qtype: onnx.onnx_ml_pb2.TensorProto, value_info: Dict) Dict
- furiosa.quantizer.frontend.onnx.quantizer.utils.calculate_weight_quant_params(data: numpy.array, weight_qtype: onnx.onnx_ml_pb2.TensorProto, name: str) Tuple[int, float]
- Parameters
data – data to quantize
weight_qtype – quantization data type of weight
name – name of tensor to quantize
- Returns
quantized weights, zero point, scale
- To pack weights, we compute a linear transformation
when data type == uint8 mode, from [rmin, rmax] -> [0, 2^{b-1}] and
- when data type == int8, from [-m , m] -> [-(2^{b-1}-1), 2^{b-1}-1] where
m = max(abs(rmin), abs(rmax))
and add necessary intermediate nodes to trasnform quantized weight to full weight using the equation r = S(q-z), where
r: real original value q: quantized value S: scale z: zero point
source: onnxruntime quantization tools
- furiosa.quantizer.frontend.onnx.quantizer.utils.get_input_tensors(model: onnx.onnx_ml_pb2.ModelProto) List[Tuple[str, List[int], str]]
- furiosa.quantizer.frontend.onnx.quantizer.utils.get_qrange(qtype)
source: onnxruntime quantization tools
- furiosa.quantizer.frontend.onnx.quantizer.utils.get_vi_dtype(vi)
This function returns value_info’s data type
- Parameters
vi – graph.value_info
- Returns
graph.value_info.type.tensor_type.elem_type
- furiosa.quantizer.frontend.onnx.quantizer.utils.is_float_tensor(vi)