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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import logging
import numpy as np
import onnx
import onnx.numpy_helper
from onnx import onnx_pb as onnx_proto
from .base_quantizer import BaseQuantizer, QuantizationParams
from .calibrate import TensorData
from .onnx_model import ONNXModel
from .quant_utils import (
TENSOR_NAME_QUANT_SUFFIX,
QuantizationMode,
QuantizedValue,
QuantizedValueType,
__producer__,
__version__,
add_infer_metadata,
attribute_to_kwarg,
compute_scale_zp,
compute_scale_zp_float8,
find_by_name,
get_qmin_qmax_for_qType,
get_qrange_for_qType,
ms_domain,
quantize_onnx_initializer,
save_and_reload_model_with_shape_infer,
snap_zero_point_to_uint8,
tensor_proto_to_array,
)
from .registry import CreateOpQuantizer
class ONNXQuantizer(BaseQuantizer):
def __init__(
self,
model,
per_channel,
reduce_range,
mode,
static,
weight_qType,
activation_qType,
tensors_range,
nodes_to_quantize,
nodes_to_exclude,
op_types_to_quantize,
extra_options=None,
):
BaseQuantizer.__init__(
self,
model,
per_channel,
reduce_range,
weight_qType,
activation_qType,
tensors_range,
nodes_to_quantize,
nodes_to_exclude,
op_types_to_quantize,
extra_options,
)
if not static:
self.model.replace_gemm_with_matmul()
# We need to update value_infos.
model = save_and_reload_model_with_shape_infer(self.model.model)
self.value_infos = {vi.name: vi for vi in model.graph.value_info}
self.value_infos.update({ot.name: ot for ot in model.graph.output})
self.value_infos.update({it.name: it for it in model.graph.input})
self.model = ONNXModel(model)
self.mode = mode # QuantizationMode.Value
self.static = static # use static quantization for inputs.
self.fuse_dynamic_quant = self.opset_version > 10
self.q_matmul_const_b_only = "MatMulConstBOnly" in self.extra_options and self.extra_options["MatMulConstBOnly"]
self.new_nodes = []
self.graph_scope = "/" # for human readable debug information
self.tensor_names = {} # in case the shape inference not totally working
self.tensor_names.update({ot.name: 1 for ot in model.graph.output})
self.tensor_names.update({it.name: 1 for it in model.graph.input})
for node in self.model.model.graph.node:
self.tensor_names.update(dict.fromkeys(node.output, 1))
if self.mode not in QuantizationMode:
raise ValueError(f"unsupported quantization mode {self.mode}")
self.quantization_params = self.calculate_quantization_params()
# QuantizeRange tensor name and zero tensor name for scale and zero point calculation.
# Used when static is False
self.fixed_qrange_uint8_name = "fixed_quantization_range_uint8"
self.fixed_qrange_int8_name = "fixed_quantization_range_int8"
# For uint8 data-type, to compute zero point, we subtract rmin from 0 (represented by fixed_zero_name tensor)
self.fixed_zero_name = "fixed_zero"
# For int8 data-type, zero point is always zero (respresented by fixed_zero_point_name tensor)
self.fixed_zero_zp_name = "fixed_zero_zp"
# Map of all original value names to quantized value names
self.quantized_value_map = {}
# some output from nodes will be quantized, yet itself should be treat as existing so
# no dequantized will be applied when needed later
self.generated_value_names = self.model.get_non_initializer_inputs()
# routines for subgraph support
def quantize_subgraph(self, subgraph, graph_key):
"""
generate submodel for the subgraph, so that we re-utilize current quantization implementation.
quantize the submodel
update subgraph and set it back to node
"""
warped_model = onnx.helper.make_model(
subgraph,
producer_name="onnx-quantizer",
opset_imports=self.model.model.opset_import,
)
add_infer_metadata(warped_model)
sub_quantizer = ONNXQuantizer(
warped_model,
self.per_channel,
self.reduce_range,
self.mode,
self.static,
self.weight_qType,
self.activation_qType,
self.tensors_range,
self.nodes_to_quantize,
self.nodes_to_exclude,
self.op_types_to_quantize,
self.extra_options,
)
sub_quantizer.parent = self
sub_quantizer.graph_scope = f"{self.graph_scope}{graph_key}/"
sub_quantizer.quantize_model()
return sub_quantizer.model.model.graph
def quantize_node_with_sub_graph(self, node):
"""
Check subgraph, if any, quantize it and replace it.
return new_nodes added for quantizing subgraph
"""
graph_attrs = [
attr
for attr in node.attribute
if attr.type == onnx.AttributeProto.GRAPH or attr.type == onnx.AttributeProto.GRAPHS
]
if len(graph_attrs) == 0:
return node
node_name = node.name if node.name else f"{node.op_type}_node_count_{len(self.new_nodes)}"
kwargs = {}
for attr in node.attribute:
if attr.type == onnx.AttributeProto.GRAPH:
kv = {attr.name: self.quantize_subgraph(attr.g, f"{node_name}:{attr.name}")}
elif attr.type == onnx.AttributeProto.GRAPHS:
value = []
for subgraph in attr.graphs:
value.extend(
[
self.quantize_subgraph(
subgraph,
f"{node_name}:{attr.name}:{len(value)}",
)
]
)
kv = {attr.name: value}
else:
kv = attribute_to_kwarg(attr)
kwargs.update(kv)
return onnx.helper.make_node(node.op_type, node.input, node.output, name=node.name, **kwargs)
def has_QDQ_nodes(self): # noqa: N802
"""
Detect if model already has QuantizeLinear or DequantizeLinear.
"""
return any(
node.op_type == "QuantizeLinear" or node.op_type == "DequantizeLinear" for node in self.model.nodes()
)
def find_initializer_in_path(self, initializer_name):
if find_by_name(initializer_name, self.model.initializer()) is not None:
return True
if self.parent is not None:
return self.parent.find_initializer_in_path(initializer_name)
return False
def add_new_nodes(self, nodes):
self.new_nodes.extend(nodes)
for node in nodes:
for output_name in node.output:
self.generated_value_names.add(output_name)
def quantize_model(self):
if self.has_QDQ_nodes():
logging.warning(
"Please check if the model is already quantized. "
"Note you don't need to quantize a QAT model. OnnxRuntime support to run QAT model directly."
)
for node in self.model.nodes():
# quantize subgraphes if have
if self.enable_subgraph_quantization:
node = self.quantize_node_with_sub_graph(node) # noqa: PLW2901
number_of_existing_new_nodes = len(self.new_nodes)
op_quantizer = CreateOpQuantizer(self, node)
op_quantizer.quantize()
for i in range(number_of_existing_new_nodes, len(self.new_nodes)):
for output_name in self.new_nodes[i].output:
self.generated_value_names.add(output_name)
self._dequantize_outputs()
# extend is used to append to the list for a protobuf fields
# https://developers.google.com/protocol-buffers/docs/reference/python-generated?csw=1#fields
self.model.graph().ClearField("node")
self.model.graph().node.extend(self.new_nodes)
# Remove ununsed initializers from graph, starting from the top level graph.
if self.parent is None:
_, initializers_not_found = self.model.clean_initializers()
if len(initializers_not_found) > 0:
raise RuntimeError("Invalid model with unknown initializers/tensors." + str(initializers_not_found))
self.model.model.producer_name = __producer__
self.model.model.producer_version = __version__
# Add ms domain if needed
ms_opset = [opset for opset in self.model.model.opset_import if opset.domain == ms_domain]
if not ms_opset:
ms_nodes = [node for node in self.new_nodes if node.domain == "com.microsoft"]
if ms_nodes:
opset = self.model.model.opset_import.add()
opset.version = 1
opset.domain = ms_domain
return self.model.model
def _get_default_tensor_type(self, tensor_name):
if "DefaultTensorType" in self.extra_options:
logging.info(
"get_tensor_type returns DefaultTensorType for tensor name %r, use %d",
tensor_name,
self.extra_options["DefaultTensorType"],
)
return self.extra_options["DefaultTensorType"]
raise RuntimeError(
f"Unable to find data type for weight_name={tensor_name!r}. "
f"shape_inference failed to return a type probably this node is "
f"from a different domain or using an input produced by such an operator. "
f"This may happen if you quantize a model already quantized. "
f"You may use extra_options `DefaultTensorType` to indicate "
f"the default weight type, usually `onnx.TensorProto.FLOAT`."
)
def get_tensor_type(self, tensor_name, mandatory=False):
weight = find_by_name(tensor_name, self.model.initializer())
if weight is not None:
return weight.data_type
if tensor_name in self.value_infos:
vi = self.value_infos[tensor_name]
if vi.type.HasField("tensor_type"):
if mandatory and vi.type.tensor_type.elem_type == 0:
return self._get_default_tensor_type(tensor_name)
return vi.type.tensor_type.elem_type
if (not self.enable_subgraph_quantization) or (self.parent is None):
if mandatory:
return self._get_default_tensor_type(tensor_name)
return None
otype = self.parent.is_valid_quantize_weight(tensor_name)
if otype is not None:
return otype
if self.enable_subgraph_quantization and self.parent:
res = self.parent.get_tensor_type(tensor_name)
if res is not None:
return res
if mandatory:
return self._get_default_tensor_type(tensor_name)
return None
def is_float_tensor(self, tensor_name):
if self.is_input_a_initializer(tensor_name):
return self.is_valid_quantize_weight(tensor_name)
if tensor_name in self.value_infos:
vi = self.value_infos[tensor_name]
if vi.type.HasField("tensor_type") and vi.type.tensor_type.elem_type in (
onnx_proto.TensorProto.FLOAT,
onnx_proto.TensorProto.FLOAT16,
):
return True
logging.warning(
f"Inference failed or unsupported type to quantize for tensor {tensor_name!r}, type is {vi.type}."
)
return False
if self.enable_subgraph_quantization and self.parent:
return self.parent.is_float_tensor(tensor_name)
logging.warning(
f"Failed to infer data type of tensor: {tensor_name!r}. Please add data type info for this tensor "
f"if your model has customized operators."
)
return False
def _get_dynamic_input_quantization_params(self, input_name, nodes_list, qType, initial_type):
"""
Create nodes for dynamic quantization of input and add them to nodes_list.
parameter input_name: Name of the input.
parameter nodes_list: new nodes are appended to this list.
parameter qType: type to quantize to.
parameter initial_type: type to quantize from
return: scale_name, zero_point_name, scale_shape, zero_point_shape.
"""
if qType == onnx_proto.TensorProto.INT8:
return self._get_dynamic_input_quantization_params_int8(input_name, nodes_list, initial_type)
if qType == onnx_proto.TensorProto.UINT8:
return self._get_dynamic_input_quantization_params_uint8(input_name, nodes_list, initial_type)
raise ValueError(f"Unexpected value for qType={qType}.")
def _get_dynamic_input_quantization_params_int8(self, input_name, nodes_list, initial_type):
"""
Create nodes for dynamic quantization of input to int8 and add them to nodes_list
parameter input_name: Name of the input.
parameter nodes_list: new nodes are appended to this list.
parameter initial_type: initial weight type (FLOAT or FLOAT16)
return: scale_name, zero_point_name, scale_shape, zero_point_shape.
"""
qType = onnx_proto.TensorProto.INT8 # noqa: N806
# Reduce min and Reduce max
input_scale_name = input_name + "_scale"
reduce_min_name = input_name + "_ReduceMin"
reduce_min_node = onnx.helper.make_node(
"ReduceMin",
[input_name],
[reduce_min_name + ":0"],
reduce_min_name,
keepdims=0,
)
nodes_list.append(reduce_min_node)
reduce_max_name = input_name + "_ReduceMax"
reduce_max_node = onnx.helper.make_node(
"ReduceMax",
[input_name],
[reduce_max_name + ":0"],
reduce_max_name,
keepdims=0,
)
nodes_list.append(reduce_max_node)
# Compute scale
# Find abs(rmin)
reduce_min_abs_name = reduce_min_name + "_Abs"
reduce_min_abs_node = onnx.helper.make_node(
"Abs",
[reduce_min_node.output[0]],
[reduce_min_abs_name + ":0"],
reduce_min_abs_name,
)
nodes_list.append(reduce_min_abs_node)
# Find abs(rmax)
reduce_max_abs_name = reduce_max_name + "_Abs"
reduce_max_abs_node = onnx.helper.make_node(
"Abs",
[reduce_max_node.output[0]],
[reduce_max_abs_name + ":0"],
reduce_max_abs_name,
)
nodes_list.append(reduce_max_abs_node)
# Compute max of abs(rmin) and abs(rmax)
abs_max_name = input_name + "_Abs_Max"
abs_max_node = onnx.helper.make_node(
"Max",
[reduce_min_abs_node.output[0], reduce_max_abs_node.output[0]],
[abs_max_name + ":0"],
abs_max_name,
)
nodes_list.append(abs_max_node)
# and divide by (quantize_range/2.0) which will be equal to max(...)*2.0/quantize_range
initializer_div = onnx.helper.make_tensor(
self.fixed_qrange_int8_name,
initial_type,
[],
[get_qrange_for_qType(qType) / 2.0],
)
self.model.add_initializer(initializer_div)
scale_div_name = input_name + "scale_Div"
scale_div_node = onnx.helper.make_node(
"Div",
[abs_max_node.output[0], self.fixed_qrange_int8_name],
[input_scale_name],
scale_div_name,
)
nodes_list.append(scale_div_node)
# Zero point
initializer_zp = onnx.helper.make_tensor(self.fixed_zero_zp_name, qType, [], [0])
self.model.add_initializer(initializer_zp)
return input_scale_name, self.fixed_zero_zp_name, [], []
def _get_dynamic_input_quantization_params_uint8(self, input_name, nodes_list, initial_type):
"""
Create nodes for dynamic quantization of input to uint8 and add them to nodes_list
parameter input_name: Name of the input.
parameter nodes_list: new nodes are appended to this list.
parameter initial_type: initial weight type (FLAOT or FLOAT16)
return: scale_name, zero_point_name, scale_shape, zero_point_shape.
"""
qType = onnx_proto.TensorProto.UINT8 # noqa: N806
# Reduce min and Reduce max
input_scale_name = input_name + "_scale"
input_zp_name = input_name + "_zero_point"
reduce_min_name = input_name + "_ReduceMin"
reduce_min_node = onnx.helper.make_node(
"ReduceMin",
[input_name],
[reduce_min_name + ":0"],
reduce_min_name,
keepdims=0,
)
nodes_list.append(reduce_min_node)
reduce_max_name = input_name + "_ReduceMax"
reduce_max_node = onnx.helper.make_node(
"ReduceMax",
[input_name],
[reduce_max_name + ":0"],
reduce_max_name,
keepdims=0,
)
nodes_list.append(reduce_max_node)
# Add tensors for quantize range and zero value.
initializer_qrange = onnx.helper.make_tensor(
self.fixed_qrange_uint8_name,
initial_type,
[],
[get_qrange_for_qType(qType)],
)
self.model.add_initializer(initializer_qrange)
initializer_qvalue = onnx.helper.make_tensor(self.fixed_zero_name, initial_type, [], [0.0])
self.model.add_initializer(initializer_qvalue)
# Compute Scale
# Subtract rmax and rmin
scale_sub_name = input_name + "_scale_Sub"
scale_sub_node = onnx.helper.make_node(
"Sub",
[reduce_max_node.output[0], reduce_min_node.output[0]],
[scale_sub_name + ":0"],
scale_sub_name,
)
nodes_list.append(scale_sub_node)
# and divide by quantize range
scale_div_name = input_name + "_scale_Div"
scale_div_node = onnx.helper.make_node(
"Div",
[scale_sub_node.output[0], self.fixed_qrange_uint8_name],
[input_scale_name],
scale_div_name,
)
nodes_list.append(scale_div_node)
# Compute zero point
# Subtract zero and rmin
zp_sub_name = input_name + "_zero_point_Sub"
zp_sub_node = onnx.helper.make_node(
"Sub",
[self.fixed_zero_name, reduce_min_node.output[0]],
[zp_sub_name + ":0"],
zp_sub_name,
)
nodes_list.append(zp_sub_node)
# Divide by scale
zp_div_name = input_name + "_zero_point_Div"
zp_div_node = onnx.helper.make_node(
"Div",
[zp_sub_node.output[0], input_scale_name],
[zp_div_name + ":0"],
zp_div_name,
)
nodes_list.append(zp_div_node)
# Compute floor
zp_floor_name = input_name + "_zero_point_Floor"
zp_floor_node = onnx.helper.make_node("Floor", zp_div_node.output, [zp_floor_name + ":0"], zp_floor_name)
nodes_list.append(zp_floor_node)
# Cast to integer
zp_cast_name = input_name + "_zero_point_Cast"
zp_cast_node = onnx.helper.make_node("Cast", zp_floor_node.output, [input_zp_name], zp_cast_name, to=qType)
nodes_list.append(zp_cast_node)
return input_scale_name, input_zp_name, [], []
def _get_quantization_params(self, param_name, use_scale=None, use_zeropoint=None):
"""
Create initializers and inputs in the graph for zero point and scale of output.
Zero point and scale values are obtained from self.quantization_params if specified.
parameter param_name: Name of the quantization parameter.
return: result, scale_name, zero_point_name, scale_shape, zero_point_shape.
"""
zero_point_type = self.activation_qType
if use_scale is None or use_zeropoint is None:
if self.quantization_params is None or param_name not in self.quantization_params:
logging.info(f'Quantization parameters for tensor:"{param_name}" not specified')
return False, "", "", "", ""
params = self.quantization_params[param_name]
if not isinstance(params, QuantizationParams):
raise TypeError(f"Unexpected type {type(params)} for {param_name!r}.")
if params is None or len(params) != 3:
raise ValueError(
"Quantization parameters should contain zero point, scale, quant type. "
f"Specified values for output {param_name}: {params}"
)
zero_point_values = np.array([params["zero_point"]])
if not hasattr(params["scale"], "dtype") or params["scale"].dtype not in (np.float32, np.float16):
raise ValueError(f"Unexpected type {type(params['scale'])} and param_name={param_name!r}")
scale_values = np.array([params["scale"]])
assert scale_values.dtype != np.float64
zero_point_type = params["quant_type"]
else:
zero_point_values = np.array([use_zeropoint])
scale_values = np.array([use_scale])
params = self.quantization_params[param_name]
if "scale" in params:
dtype = params["scale"].dtype
scale_values = scale_values.astype(dtype)
assert scale_values.dtype != np.float64
zero_point_shape = []
zero_point_name = param_name + "_zero_point"
scale_shape = []
scale_name = param_name + "_scale"
# Add initializers
init_zp = onnx.helper.make_tensor(
zero_point_name, zero_point_type, zero_point_shape, zero_point_values.ravel().tolist()
)
self.model.add_initializer(init_zp)
if scale_values.dtype == np.float32:
scale_type = onnx_proto.TensorProto.FLOAT
elif scale_values.dtype == np.float16:
scale_type = onnx_proto.TensorProto.FLOAT16
else:
raise ValueError(f"Unexpected dtype={scale_values.dtype} for param_name={param_name!r}")
init_scale = onnx.helper.make_tensor(scale_name, scale_type, scale_shape, scale_values.reshape((-1,)).tolist())
self.model.add_initializer(init_scale)
return True, scale_name, zero_point_name, scale_shape, zero_point_shape
def _get_quantize_input_nodes(
self, node, input_index, qType, given_scale_name=None, given_zp_name=None, initial_type=None
):
"""
Given an input for a node (which is not a initializer), this function
- add nodes to compute zero point and scale for this input if they don't exist.
- add new QuantizeLinear node to quantize the input.
:param node: node being quantized in NodeProto format.
:param input_index: index of input in node.input.
:param qType: type to quantize to.
:param given_scale_name: if those inputs need to be quanitzed using this scale tensor.
:param given_zp_name: if those inputs to be quantized using this zeropoint tensor.
:param initial_type: type of the weight to quantize
:return: List of newly created nodes in NodeProto format.
"""
input_name = node.input[input_index]
assert input_name != "", "Cannot access undefined variable in graph."
output_name = input_name + TENSOR_NAME_QUANT_SUFFIX
ql_node_name = input_name + "_QuantizeLinear"
if (given_scale_name is not None) and (given_zp_name is not None):
data_found, scale_name, zp_name = (True, given_scale_name, given_zp_name)
else:
data_found, scale_name, zp_name, _, _ = self._get_quantization_params(input_name)
nodes = []
if data_found:
qlinear_node = onnx.helper.make_node(
"QuantizeLinear",
[input_name, scale_name, zp_name],
[output_name],
ql_node_name,
)
else:
if self.static:
return None
# dynamic mode
# Scale and Zero Points not available for this input. Add nodes to dynamically compute it
if self.fuse_dynamic_quant and qType == onnx_proto.TensorProto.UINT8:
scale_name = input_name + "_scale"
zp_name = input_name + "_zero_point"
qlinear_node = onnx.helper.make_node(
"DynamicQuantizeLinear",
[input_name],
[output_name, scale_name, zp_name],
ql_node_name,
)
else:
assert initial_type is not None, (
f"Cannot quantize input without knowing the initial type, "
f"input_name={input_name!r}, input_index={input_index}, qType={qType}, node={node}"
)
(
scale_name,
zp_name,
scale_shape,
zp_shape,
) = self._get_dynamic_input_quantization_params(input_name, nodes, qType, initial_type=initial_type)
qlinear_node = onnx.helper.make_node(
"QuantizeLinear",
[input_name, scale_name, zp_name],
[output_name],
ql_node_name,
)
self.quantized_value_map[input_name] = QuantizedValue(input_name, output_name, scale_name, zp_name, qType)
return [*nodes, qlinear_node]
def find_quantized_value(self, input_name):
if input_name in self.quantized_value_map:
return self.quantized_value_map[input_name]
if self.parent is not None:
return self.parent.find_quantized_value(input_name)
return None
def adjust_single_weight_scale_if_needed(
self,
bias_val,
input_scale,
weight_scale,
weight_scale_dtype,
weight_name,
bias_name,
qrange,
multiplicative_epsilon,
idx=None,
):
"""Adjust a single weight scale to ensure the int32 bias does not overflow."""
absmax = np.abs(bias_val)
bias_smallest_valid_scale = multiplicative_epsilon * (2.0 * absmax) / qrange
input_scale_fp64 = np.array(input_scale.item(), dtype=np.float64)
weight_scale_fp64 = np.array(weight_scale.item(), dtype=np.float64)
bias_candidate_scale = input_scale_fp64 * weight_scale_fp64
if (bias_candidate_scale < bias_smallest_valid_scale) and (bias_candidate_scale > 0.0):
ratio = bias_smallest_valid_scale / bias_candidate_scale
new_scale = weight_scale_fp64 * ratio
if idx is None:
logging.info(
f"Increasing scale for weight `{weight_name}` by the ratio {ratio} to "
f"ensure bias `{bias_name}` has a valid scale."
)
return True, np.array(new_scale, dtype=weight_scale_dtype)
else:
logging.info(
f"Increased scale[{idx}] for weight `{weight_name}` by ratio {ratio} "
f"to ensure bias `{bias_name}` has a valid scale."
)
return True, new_scale.astype(weight_scale_dtype)
return False, weight_scale
def _adjust_weight_scale_for_int32_bias(
self,
input_scale: np.ndarray,
weight_scale: np.ndarray,
weight_name: str,
bias_tp: onnx.TensorProto,
is_per_channel: bool,
) -> tuple[bool, np.ndarray | None]:
"""Checks if the bias scale is too small and increases the weight scale if needed."""
if not weight_scale.size:
return False, None
bias_float_data = tensor_proto_to_array(bias_tp)
int32_info = np.iinfo(np.int32)
multiplicative_epsilon = 1.0001
qrange = np.array(int32_info.max, dtype=np.float64) - np.array(int32_info.min + 1, dtype=np.float64)
weight_scale_dtype = weight_scale.dtype
updated = False
if not is_per_channel:
rmin = np.minimum(bias_float_data.min(), np.array(0, dtype=np.float64))
rmax = np.maximum(bias_float_data.max(), np.array(0, dtype=np.float64))
absmax = np.maximum(np.abs(rmin), np.abs(rmax))
changed, new_scale = self.adjust_single_weight_scale_if_needed(
absmax,
input_scale,
weight_scale,
weight_scale_dtype,
weight_name,
bias_tp.name,
qrange,
multiplicative_epsilon,
)
if changed:
weight_scale = new_scale
updated = True
elif weight_scale.shape and len(weight_scale.shape) == 1:
for i in range(weight_scale.shape[0]):
changed, new_scale = self.adjust_single_weight_scale_if_needed(
bias_float_data[i],
input_scale,
weight_scale[i],
weight_scale_dtype,
weight_name,
bias_tp.name,
qrange,
multiplicative_epsilon,
idx=i,
)
if changed:
weight_scale[i] = new_scale
updated = True
return updated, weight_scale
def _requantize_weight(self, weight_name: str, new_scale: np.ndarray) -> None:
"""Re-quantizes the given weight initializer using the provided scale."""
if weight_name not in self.quantized_value_map:
return
qv = self.quantized_value_map[weight_name]
weight_tp = find_by_name(weight_name, self.model.initializer())
scale_init = find_by_name(qv.scale_name, self.model.initializer())
zp_init = find_by_name(qv.zp_name, self.model.initializer())
q_weight_init = find_by_name(qv.q_name, self.model.initializer())
if weight_tp is None or scale_init is None or zp_init is None or q_weight_init is None:
return
self.model.remove_initializer(scale_init)
self.model.remove_initializer(q_weight_init)
weight_zero_point = onnx.numpy_helper.to_array(zp_init)
axis = qv.axis
# Add new scale initializer
scale_np = np.asarray(new_scale, dtype=onnx.helper.tensor_dtype_to_np_dtype(weight_tp.data_type))
new_scale_init = onnx.numpy_helper.from_array(scale_np.reshape(scale_init.dims), qv.scale_name)
self.model.add_initializer(new_scale_init)
# Add new quantized weight initializer
new_q_weight = quantize_onnx_initializer(
weight_tp,
self.weight_qType,
weight_zero_point,
scale_np,
axis,
quant_weight_name=qv.q_name,
)
self.model.add_initializer(new_q_weight)
def quantize_bias_static(self, bias_name, input_name, weight_name, beta=1.0):
"""
Quantized the bias. Zero Point == 0 and Scale == Input_Scale * Weight_Scale
"""
# Handle case where bias already in quantization map
if bias_name in self.quantized_value_map:
return self.quantized_value_map[bias_name].q_name
# get scale for weight
weight_scale_name = self.quantized_value_map[weight_name].scale_name
weight_initializer = find_by_name(weight_scale_name, self.model.initializer())
weight_scale = tensor_proto_to_array(weight_initializer)
# get scale for input
if input_name in self.quantized_value_map:
input_scale_name = self.quantized_value_map[input_name].scale_name
elif input_name in self.quantization_params:
_, input_scale_name, _, _, _ = self._get_quantization_params(input_name)
else:
raise ValueError(f"Expected {input_name} to be in quantized value map for static quantization")
inputscale_initializer = find_by_name(input_scale_name, self.model.initializer())
input_scale = tensor_proto_to_array(inputscale_initializer)
# Adjust weight scale if quantizing to int32 may overflow due to a small scale
weight_zp_name = self.quantized_value_map[weight_name].zp_name
weight_zp_init = find_by_name(weight_zp_name, self.model.initializer())
weight_zero_point = onnx.numpy_helper.to_array(weight_zp_init) if weight_zp_init is not None else None
is_per_channel = self.per_channel
if (
weight_zero_point is not None
and weight_zero_point.size
and not weight_zero_point.any()
and self.weight_qType in (onnx_proto.TensorProto.INT8,)
):
bias_initializer = find_by_name(bias_name, self.model.initializer())
did_update, new_weight_scale = self._adjust_weight_scale_for_int32_bias(
input_scale,
weight_scale,
weight_name,
bias_initializer,
is_per_channel,
)
if did_update:
self._requantize_weight(weight_name, new_weight_scale)
weight_scale = new_weight_scale
(
quantized_bias_name,
quantized_bias_scale_name,
quantized_bias_zp_name,
bias_scale_data,
node_type,
node_qtype,
) = self.quantize_bias_static_impl(bias_name, input_scale, weight_scale, beta)
assert bias_name not in self.quantized_value_map
quantized_value = QuantizedValue(
bias_name,
quantized_bias_name,
quantized_bias_scale_name,
quantized_bias_zp_name,
QuantizedValueType.Initializer,
0 if bias_scale_data.size > 1 else None,
node_type=node_type,
node_qtype=node_qtype,
)
self.quantized_value_map[bias_name] = quantized_value
return quantized_bias_name
def contains_tensor(self, tensor_name):
"""
only check for value info and newly generated tensor names, initializers are checked separately
"""
return (
(tensor_name in self.value_infos)
or (tensor_name in self.tensor_names)
or (tensor_name in self.generated_value_names)
)
def quantize_activation(self, node, indices, from_subgraph=False):
return self.__quantize_inputs(
node=node,
indices=indices,
initializer_use_weight_qType=False,
reduce_range=False,
op_level_per_channel=False,
axis=-1,
from_subgraph=from_subgraph,
)
# In some circumstances a weight is not an initializer, for example of MatMul, if both A and B are not
# initializer, B can still be considered as Weight
def quantize_weight(
self,
node,
indices,
reduce_range=False,
op_level_per_channel=False,
axis=-1,
from_subgraph=False,
):
return self.__quantize_inputs(
node=node,
indices=indices,
initializer_use_weight_qType=True,
reduce_range=reduce_range,
op_level_per_channel=op_level_per_channel,
axis=axis,
from_subgraph=from_subgraph,
)
def __quantize_inputs(
self,
node,
indices,
initializer_use_weight_qType=True,
reduce_range=False,
op_level_per_channel=False,
axis=-1,
from_subgraph=False,
):
"""
Given a node, this function quantizes the inputs as follows:
- If input is an initializer, quantize the initializer data, replace old initializer
with new initializer
- Else, add QuantizeLinear nodes to perform quantization
parameter node: node being quantized in NodeProto format.
parameter indices: input indices to quantize.
return: (List of quantized input names,
List of zero point names used for input quantization,
List of scale names used for input quantization,
List of new QuantizeLinear nodes created)
"""
scale_names = []
zero_point_names = []
quantized_input_names = []
nodes = []
for input_index in indices:
node_input = node.input[input_index]
# Find if this input is already quantized
if node_input in self.quantized_value_map:
quantized_value = self.quantized_value_map[node_input]
scale_names.append(quantized_value.scale_name)
zero_point_names.append(quantized_value.zp_name)
quantized_input_names.append(quantized_value.q_name)
continue
# adding this for case embed_layernorm.py has optional segment_embedding
if not node_input:
quantized_input_names.append("")
scale_names.append("")
zero_point_names.append("")
continue
# Quantize the input
initializer = find_by_name(node_input, self.model.initializer())
if initializer is not None:
if self.per_channel and op_level_per_channel:
(
q_weight_name,
zp_name,
scale_name,
) = self.quantize_weight_per_channel(
initializer.name,
self.weight_qType if initializer_use_weight_qType else self.activation_qType,
axis,
reduce_range,
)
else:
q_weight_name, zp_name, scale_name = self.quantize_initializer(
initializer,
self.weight_qType if initializer_use_weight_qType else self.activation_qType,
reduce_range,
)
quantized_input_names.append(q_weight_name)
zero_point_names.append(zp_name)
scale_names.append(scale_name)
elif self.contains_tensor(node_input):
# Add QuantizeLinear node.
qlinear_node = self.model.find_node_by_name(
node_input + "_QuantizeLinear", self.new_nodes, self.model.graph()
)
if qlinear_node is None:
input_name = node.input[input_index]
if input_name in self.value_infos:
value_info = self.value_infos[input_name]
assert value_info.HasField("type"), f"value_info={value_info} has no type."
assert value_info.type.HasField("tensor_type"), f"value_info={value_info} is not a tensor."
initial_type = value_info.type.tensor_type.elem_type
else:
# Shape inference failed. Fallback to self.tensor_names.
assert input_name in self.tensor_names, (
f"shape inference failed for {input_name!r} and "
f"attribute 'tensor_names' does not have any value for "
f"this tensor."
)
initial_type = self.tensor_names[input_name]
quantize_input_nodes = self._get_quantize_input_nodes(
node, input_index, self.activation_qType, initial_type=initial_type
)
if quantize_input_nodes is None:
return (None, None, None, None)
if from_subgraph:
self.add_new_nodes(quantize_input_nodes)
else:
nodes.extend(quantize_input_nodes)
qlinear_node = quantize_input_nodes[-1]
if qlinear_node.op_type == "QuantizeLinear":
quantized_input_names.extend(qlinear_node.output)
scale_names.append(qlinear_node.input[1])
zero_point_names.append(qlinear_node.input[2])
else:
quantized_input_names.append(qlinear_node.output[0])
scale_names.append(qlinear_node.output[1])
zero_point_names.append(qlinear_node.output[2])
elif self.parent is not None:
(
parent_quantized_input_names,
parent_zero_point_names,
parent_scale_names,
_,
) = self.parent.__quantize_inputs(
node,
[input_index],
initializer_use_weight_qType=initializer_use_weight_qType,
reduce_range=reduce_range,
op_level_per_channel=op_level_per_channel,
axis=axis,
from_subgraph=True,
)
quantized_input_names.append(parent_quantized_input_names[0])
scale_names.append(parent_scale_names[0])
zero_point_names.append(parent_zero_point_names[0])
# node should not be add this child level here
else:
raise ValueError(f"Invalid tensor name to quantize: {node_input} @graph scope{self.graph_scope}")
return quantized_input_names, zero_point_names, scale_names, nodes
def quantize_initializer(self, weight, qType, reduce_range=False, keep_float_weight=False):
"""
:param weight: TensorProto initializer
:param qType: type to quantize to
:param keep_float_weight: Whether to quantize the weight. In some cases, we only want to qunatize scale and zero point.
If keep_float_weight is False, quantize the weight, or don't quantize the weight.
:return: quantized weight name, zero point name, scale name
"""
# Find if this input is already quantized
if weight.name in self.quantized_value_map:
quantized_value = self.quantized_value_map[weight.name]
return (
quantized_value.q_name,
quantized_value.zp_name,
quantized_value.scale_name,
)
q_weight_name, zp_name, scale_name = self.quantize_initializer_impl(
weight, qType, reduce_range, keep_float_weight
)
# Log entry for this quantized weight
quantized_value = QuantizedValue(
weight.name,
q_weight_name,
scale_name,
zp_name,
QuantizedValueType.Initializer,
None,
)
self.quantized_value_map[weight.name] = quantized_value
return q_weight_name, zp_name, scale_name
def quantize_weight_per_channel(
self,
weight_name,
weight_qType,
channel_axis,
reduce_range=True,
keep_float_weight=False,
):
# Find if this input is already quantized
if weight_name in self.quantized_value_map:
quantized_value = self.quantized_value_map[weight_name]
return (
quantized_value.q_name,
quantized_value.zp_name,
quantized_value.scale_name,
)
q_weight_name, zp_name, scale_name = self.quantize_weight_per_channel_impl(
weight_name, weight_qType, channel_axis, reduce_range, keep_float_weight
)
quantized_value = QuantizedValue(
weight_name,
q_weight_name,
scale_name,
zp_name,
QuantizedValueType.Initializer,
channel_axis,
)
self.quantized_value_map[weight_name] = quantized_value
return q_weight_name, zp_name, scale_name
def _dequantize_value(self, value_name):
"""
Given a value (input/output) which is quantized, add a DequantizeLinear node to dequantize
it back to float32 or float16
parameter value_name: value to dequantize
parameter new_nodes_list: List of new nodes created before processing current node
return: None if there is already a DequantizeLinear node that dequantizes it
A DequantizeLinear node otherwise
"""
if (value_name in self.quantized_value_map) and (value_name not in self.generated_value_names):
quantized_value = self.quantized_value_map[value_name]
# Add DequantizeLinear Node for this input
scale_init = find_by_name(quantized_value.scale_name, self.model.initializer())
# In case we are working with subgraphs, the graph `producer_name` is set to `"onnx-quantizer"` in the `quantize_subgraph` method. In this case, the scale initializer may be on the top level graph, so the check below can not be done.
if self.model.model.producer_name != "onnx-quantizer" or (
self.model.model.producer_name == "onnx-quantizer" and scale_init is not None
):
# Per-tensor (axis=None) requires a scalar scale.
if quantized_value.axis is None:
assert scale_init is None or onnx.numpy_helper.to_array(scale_init).size == 1
dqlinear_name = value_name + "_DequantizeLinear"
dqlinear_node = self.model.find_node_by_name(dqlinear_name, self.new_nodes, self.model.graph())
if dqlinear_node is None:
dqlinear_inputs = [
quantized_value.q_name,
quantized_value.scale_name,
quantized_value.zp_name,
]
dequantize_node = onnx.helper.make_node(
"DequantizeLinear",
dqlinear_inputs,
[value_name],
dqlinear_name,
axis=quantized_value.axis,
)
return dequantize_node
else:
# DQ op is already present, assert it's output matches the input of current node
assert value_name == dqlinear_node.output[0]
return None
def _dequantize_outputs(self):
"""
Dequantize output if it is quantized
parameter new_nodes_list: List of new nodes created before processing current node
return: List of new nodes created
"""
for output in self.model.graph().output:
dequantize_node = self._dequantize_value(output.name)
if dequantize_node is not None:
self.new_nodes.append(dequantize_node)
def calculate_quantization_params(self):
if self.tensors_range is None:
return None
self.adjust_tensor_ranges()
quantization_params = {}
for tensor_name in self.tensors_range:
td = self.tensors_range[tensor_name]
if not isinstance(td, TensorData):
raise TypeError(f"Unexpected type {type(td)} for {tensor_name!r}.")
quant_overrides = self.tensor_quant_overrides.get_per_tensor_overrides(tensor_name, default_val={})
quant_type = self.activation_qType
if "quant_type" in quant_overrides:
quant_type = quant_overrides["quant_type"].tensor_type
if "scale" in quant_overrides and "zero_point" in quant_overrides:
zero, scale = quant_overrides["zero_point"], quant_overrides["scale"]
elif quant_type == onnx.TensorProto.FLOAT8E4M3FN:
zero, scale = compute_scale_zp_float8(quant_type, td.avg_std[1])
else:
rmin = quant_overrides.get("rmin", td.range_value[0])
rmax = quant_overrides.get("rmax", td.range_value[1])
symmetric = quant_overrides.get("symmetric", self.is_activation_symmetric)
reduce_range = quant_overrides.get("reduce_range", False)
qmin, qmax = get_qmin_qmax_for_qType(quant_type, reduce_range=reduce_range, symmetric=symmetric)
zero, scale = compute_scale_zp(rmin, rmax, qmin, qmax, symmetric, self.min_real_range)
if self.is_activation_restricted_asymmetric and quant_type == onnx.TensorProto.UINT8 and not symmetric:
# Forward effective qmin/qmax and min_real_range so reduce_range / MinimumRealRange are honored.
zero, scale = snap_zero_point_to_uint8(
rmin, rmax, qmin=qmin, qmax=qmax, min_real_range=self.min_real_range
)
quantization_params[tensor_name] = QuantizationParams(zero_point=zero, scale=scale, quant_type=quant_type)
return quantization_params
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