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import logging
import onnx
from ..quant_utils import (
FLOAT8_TYPES,
TENSOR_NAME_QUANT_SUFFIX,
QuantizedValue,
QuantizedValueType,
find_by_name,
get_mul_node,
)
from .base_operator import QuantOperatorBase
from .qdq_base_operator import QDQOperatorBase
class QOpMatMul(QuantOperatorBase):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def should_quantize(self):
if not self.quantizer.should_quantize_node(self.node):
logging.debug(f"Ignore MatMul {self.node.name}]")
return False
if (not self.quantizer.is_float_tensor(self.node.input[1])) and (
not self.quantizer.is_float_tensor(self.node.input[0])
):
logging.info(f"Ignore MatMul due to non float inputs {self.node.name}]")
return False
# do not quantize non-constant B matrices for matmul
if self.quantizer.q_matmul_const_b_only:
if not self.quantizer.find_initializer_in_path(self.node.input[1]):
logging.info(f"Ignore MatMul due to non constant B: {self.quantizer.graph_scope}[{self.node.name}]")
return False
return True
"""
Used when quantize mode is QuantizationMode.IntegerOps.
"""
class MatMulInteger(QOpMatMul):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def quantize(self):
node = self.node
assert node.op_type == "MatMul"
# 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=True, op_level_per_channel=True)
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)
matmul_integer_output = node.output[0] + "_output_quantized"
matmul_integer_name = node.name + "_quant" if node.name else ""
matmul_integer_node = onnx.helper.make_node(
"MatMulInteger",
quantized_input_names + zero_point_names,
[matmul_integer_output],
matmul_integer_name,
)
nodes.append(matmul_integer_node)
# Add cast operation to cast matmulInteger output to float.
cast_op_output = matmul_integer_output + "_cast_output"
otype = self.quantizer.get_tensor_type(node.output[0], mandatory=True)
cast_node = onnx.helper.make_node(
"Cast",
[matmul_integer_output],
[cast_op_output],
matmul_integer_output + "_cast",
to=otype,
)
nodes.append(cast_node)
# Add mul operation to multiply scales of two inputs.
assert len(scale_names) == 2
scales_mul_op = (
matmul_integer_name + "_scales_mul"
if matmul_integer_name
else 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]
# Add mul operation to multiply mul_scales_op result with output of MatMulInteger
# and make the output of this node the same as output of original matmul node.
output_scale_mul_op = ""
if matmul_integer_name:
output_scale_mul_op = matmul_integer_name + "_output_scale_mul"
nodes.append(
get_mul_node(
[cast_op_output, scales_mul_op_output],
node.output[0],
output_scale_mul_op,
)
)
self.quantizer.new_nodes += nodes
"""
Used when quantize mode is QuantizationMode.QLinearOps
"""
class QLinearMatMul(QOpMatMul):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def quantize(self):
node = self.node
assert node.op_type == "MatMul"
# 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=True, op_level_per_channel=True)
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)
(
data_found,
output_scale_name,
output_zp_name,
_,
_,
) = self.quantizer._get_quantization_params(node.output[0])
if not data_found or quantized_input_names is None:
return super().quantize()
qlinear_matmul_output = node.output[0] + TENSOR_NAME_QUANT_SUFFIX
qlinear_matmul_name = node.name + "_quant" if node.name else ""
qlinear_matmul_inputs = []
# Input 0
qlinear_matmul_inputs.append(quantized_input_names[0])
qlinear_matmul_inputs.append(scale_names[0])
qlinear_matmul_inputs.append(zero_point_names[0])
# Input 1
qlinear_matmul_inputs.append(quantized_input_names[1])
qlinear_matmul_inputs.append(scale_names[1])
qlinear_matmul_inputs.append(zero_point_names[1])
# Output quantization parameter
qlinear_matmul_inputs.append(output_scale_name)
qlinear_matmul_inputs.append(output_zp_name)
domain = "com.microsoft" if self.quantizer.weight_qType in FLOAT8_TYPES else ""
qlinear_matmul_node = onnx.helper.make_node(
"QLinearMatMul",
qlinear_matmul_inputs,
[qlinear_matmul_output],
qlinear_matmul_name,
domain=domain,
)
nodes.append(qlinear_matmul_node)
# Create an entry for this quantized value
q_output = QuantizedValue(
node.output[0],
qlinear_matmul_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 QDQMatMul(QDQOperatorBase):
def __init__(self, onnx_quantizer, onnx_node):
super().__init__(onnx_quantizer, onnx_node)
def quantize(self):
node = self.node
assert node.op_type == "MatMul"
if self.disable_qdq_for_node_output:
nodes_to_iterate = node.input
else:
nodes_to_iterate = itertools.chain(node.input, node.output)
for tensor_name in nodes_to_iterate:
if find_by_name(tensor_name, self.quantizer.model.initializer()):
is_per_channel, channel_axis = self.quantizer.is_tensor_per_channel(
tensor_name, default_axis=1, op_type=node.op_type
)
if is_per_channel:
self.quantizer.quantize_weight_tensor_per_channel(tensor_name, channel_axis)
else:
self.quantizer.quantize_weight_tensor(tensor_name)
else:
self.quantizer.quantize_activation_tensor(tensor_name)
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