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610d9c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 | import numpy as np
import onnx
from onnx import onnx_pb as onnx_proto
from ..quant_utils import (
TENSOR_NAME_QUANT_SUFFIX,
QuantizedValue,
QuantizedValueType,
attribute_to_kwarg,
find_by_name,
get_mul_node,
)
from .base_operator import QuantOperatorBase
from .qdq_base_operator import QDQOperatorBase
class ConvInteger(QuantOperatorBase):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def add_bias(self, nodes, scaled_output):
"""
Given a node, this function handles bias add by adding a "reshape" node on bias and an "add" node
parameter nodes: new nodes would be appended into nodes
parameter node: current node (Conv)
parameter scaled_output: output of quant conv without bias
parameter output: output of Conv
parameter bias_name: bias of Conv
return: the name of output
"""
node = self.node
model = self.quantizer.model
# Add tensors for the shape to be reshaped to
weight = find_by_name(node.input[1], model.initializer())
if weight is None:
raise ValueError(f"Expected {node.input[1]} to be an initializer")
# Add reshape for correct broadcase
output = node.output[0]
reshape_input_data = node.input[2] # bias of Conv
reshape_input_shape = output + "_bias_reshape_shape"
reshape_output = output + "_bias_reshape_output"
shape = np.ones((len(weight.dims)), dtype=np.int64)
shape[1] = -1
init_shape = onnx.helper.make_tensor(
reshape_input_shape, onnx_proto.TensorProto.INT64, [len(weight.dims)], shape
)
model.add_initializer(init_shape)
reshape_node = onnx.helper.make_node("Reshape", [reshape_input_data, reshape_input_shape], [reshape_output])
nodes.append(reshape_node)
# Add an Add operation for bias
add_node = onnx.helper.make_node("Add", [scaled_output, reshape_output], [output], output + "_bias_add")
nodes.append(add_node)
def quantize(self):
node = self.node
assert node.op_type == "Conv"
# Get Quantized from both activation(input[0]) and weight(input[1])
(
quantized_input_names,
zero_point_names,
scale_names,
nodes,
) = self.quantizer.quantize_activation(node, [0])
(
quantized_input_names_weight,
zero_point_names_weight,
scale_names_weight,
nodes_weight,
) = self.quantizer.quantize_weight(node, [1], reduce_range=self.quantizer.reduce_range)
quantized_input_names.extend(quantized_input_names_weight)
zero_point_names.extend(zero_point_names_weight)
scale_names.extend(scale_names_weight)
nodes.extend(nodes_weight)
conv_integer_output = node.output[0] + "_output_quantized"
conv_integer_name = node.name + "_quant" if node.name else ""
kwargs = {}
for attribute in node.attribute:
kwargs.update(attribute_to_kwarg(attribute))
conv_integer_node = onnx.helper.make_node(
"ConvInteger", quantized_input_names + zero_point_names, [conv_integer_output], conv_integer_name, **kwargs
)
nodes.append(conv_integer_node)
# Add cast operation to cast convInteger output to float.
onnx_type = self.quantizer.get_tensor_type(node.output[0], mandatory=True)
cast_op_output = conv_integer_output + "_cast_output"
cast_node = onnx.helper.make_node(
"Cast",
[conv_integer_output],
[cast_op_output],
conv_integer_output + "_cast",
to=onnx_type, # TODO: FLOAT ot FLOAT16
)
nodes.append(cast_node)
# Add mul operation to multiply scales of two inputs.
assert len(scale_names) == 2
if conv_integer_name:
scales_mul_op = conv_integer_name + "_scales_mul"
else:
scales_mul_op = scale_names[0] + "_" + scale_names[1] + "_mul"
scales_mul_node = find_by_name(scales_mul_op, self.quantizer.new_nodes)
if scales_mul_node is None:
scales_mul_node = get_mul_node(scale_names, scales_mul_op + ":0", scales_mul_op)
nodes.append(scales_mul_node)
scales_mul_op_output = scales_mul_node.output[0]
has_bias = len(node.input) == 3
scaled_output_name = node.output[0] if not has_bias else node.output[0] + "quant_scaled_output"
# Add mul operation to multiply mul_scales_op result with output of ConvInteger
# and make the output of this node the same as output of original conv node.
output_scale_mul_op = conv_integer_name + "_output_scale_mul" if conv_integer_name else ""
nodes.append(
get_mul_node(
[cast_op_output, scales_mul_op_output],
scaled_output_name,
output_scale_mul_op,
)
)
if has_bias:
self.add_bias(nodes, scaled_output_name)
self.quantizer.new_nodes += nodes
class QLinearConv(QuantOperatorBase):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def quantize(self):
node = self.node
assert node.op_type == "Conv"
(
data_found,
output_scale_name,
output_zp_name,
_,
_,
) = self.quantizer._get_quantization_params(node.output[0])
if self.quantizer.is_input_a_initializer(node.input[1]) and self.quantizer.is_per_channel():
(
quantized_input_names,
zero_point_names,
scale_names,
nodes,
) = self.quantizer.quantize_activation(node, [0])
quant_weight_tuple = self.quantizer.quantize_weight_per_channel(
node.input[1],
onnx_proto.TensorProto.INT8,
0, # self.quantizer.weight_qType?
)
quantized_input_names.append(quant_weight_tuple[0])
zero_point_names.append(quant_weight_tuple[1])
scale_names.append(quant_weight_tuple[2])
else:
(
quantized_input_names,
zero_point_names,
scale_names,
nodes,
) = self.quantizer.quantize_activation(node, [0])
(
quantized_input_names_weight,
zero_point_names_weight,
scale_names_weight,
nodes_weight,
) = self.quantizer.quantize_weight(node, [1], reduce_range=self.quantizer.reduce_range)
quantized_input_names.extend(quantized_input_names_weight)
zero_point_names.extend(zero_point_names_weight)
scale_names.extend(scale_names_weight)
nodes.extend(nodes_weight)
if not data_found or quantized_input_names is None:
return super().quantize()
quantized_bias_name = ""
bias_present = False
if len(node.input) == 3:
if self.quantizer.weight_qType == onnx_proto.TensorProto.FLOAT8E4M3FN:
raise RuntimeError("Quantization to FLOAT8E4M3FN for operator Conv is not supported.")
quantized_bias_name = self.quantizer.quantize_bias_static(node.input[2], node.input[0], node.input[1])
bias_present = True
qlinear_conv_output = node.output[0] + TENSOR_NAME_QUANT_SUFFIX
qlinear_conv_name = node.name + "_quant" if node.name else ""
kwargs = {}
for attribute in node.attribute:
kwargs.update(attribute_to_kwarg(attribute))
qlinear_conv_inputs = []
# Input 0
qlinear_conv_inputs.append(quantized_input_names[0])
qlinear_conv_inputs.append(scale_names[0])
qlinear_conv_inputs.append(zero_point_names[0])
# Input 1
qlinear_conv_inputs.append(quantized_input_names[1])
qlinear_conv_inputs.append(scale_names[1])
qlinear_conv_inputs.append(zero_point_names[1])
# Output
qlinear_conv_inputs.append(output_scale_name)
qlinear_conv_inputs.append(output_zp_name)
if bias_present:
qlinear_conv_inputs.append(quantized_bias_name)
qlinear_conv_node = onnx.helper.make_node(
"QLinearConv", qlinear_conv_inputs, [qlinear_conv_output], qlinear_conv_name, **kwargs
)
nodes.append(qlinear_conv_node)
# Create an entry for this quantized value
q_output = QuantizedValue(
node.output[0],
qlinear_conv_output,
output_scale_name,
output_zp_name,
QuantizedValueType.Input,
)
self.quantizer.quantized_value_map[node.output[0]] = q_output
self.quantizer.new_nodes += nodes
class QDQConv(QDQOperatorBase):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def quantize(self):
node = self.node
assert node.op_type == "Conv" or node.op_type == "ConvTranspose"
self.quantizer.quantize_activation_tensor(node.input[0])
if not self.disable_qdq_for_node_output:
self.quantizer.quantize_activation_tensor(node.output[0])
is_weight_per_channel, weight_axis = self.quantizer.is_tensor_per_channel(
node.input[1], default_axis=0 if node.op_type == "Conv" else 1
)
if is_weight_per_channel:
self.quantizer.quantize_weight_tensor_per_channel(node.input[1], weight_axis)
else:
self.quantizer.quantize_weight_tensor(node.input[1])
if len(node.input) == 3:
self.quantizer.quantize_bias_tensor(node.name, node.input[2], node.input[0], node.input[1])
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