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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 argparse
import logging
import os
import numpy as np
import numpy.typing as npt
import onnx
from onnx.onnx_pb import GraphProto, ModelProto, NodeProto, TensorProto
from onnxruntime.capi._pybind_state import quantize_matmul_bnb4
from .onnx_model import ONNXModel
from .quant_utils import attribute_to_kwarg
logger = logging.getLogger(__name__)
class MatMulBnb4Quantizer:
"""Perform 4b quantization of constant MatMul weights using FP4 or NF4 data type"""
##################
# quantization types, must be consistent with native code type
# Bnb_DataType_t defined in blockwise_quant_block_bnb4.h
# 4b floating point with bias of 3
FP4 = 0
# 4b NormalFloat
NF4 = 1
def __init__(self, model: ModelProto, quant_type: int, block_size: int, nodes_to_exclude=None):
nodes_to_exclude = nodes_to_exclude or []
assert quant_type in [MatMulBnb4Quantizer.FP4, MatMulBnb4Quantizer.NF4]
self.model = ONNXModel(model)
self.quant_type = quant_type
self.block_size = block_size
self.nodes_to_exclude = set(nodes_to_exclude)
@staticmethod
def __get_initializer(name, graph_path: list[GraphProto]) -> tuple[TensorProto, GraphProto]:
for gid in range(len(graph_path) - 1, -1, -1):
graph = graph_path[gid]
for tensor in graph.initializer:
if tensor.name == name:
return tensor, graph
return None, None
def bnb4_block_quant(self, fpweight: npt.ArrayLike) -> np.ndarray:
"""4b quantize fp32/fp16 weight"""
if len(fpweight.shape) != 2:
raise ValueError("Current bnb4 block quantization only supports 2D tensors!")
# need to copy since the transposed weight still has the original memory layout
# Linear4bit quantizes its weight data which is the transposed weight
fpweight_t = fpweight.transpose().copy()
rows, cols = fpweight.shape
numel = rows * cols
block_size = self.block_size
num_blocks = (numel + block_size - 1) // block_size
quantized_numel = (numel + 1) // 2
packed = np.zeros(quantized_numel, dtype="uint8")
absmax = np.zeros(num_blocks, dtype=fpweight.dtype)
# block wise quantization, fpweight_t is flattened and divided into blocks
quantize_matmul_bnb4(packed, fpweight_t, absmax, block_size, self.quant_type, cols, rows)
return (packed, absmax)
def _bnb4_matmul_node_weight(self, node: NodeProto, graph_stack: list[GraphProto]) -> NodeProto:
"""If the node is MatMul with fp32 const weight, quantize the weight with int4, and return the new node"""
if node.op_type != "MatMul":
return node # only care about MatMul for now
logger.debug(f"start to quantize {node.name} ...")
if node.name in self.nodes_to_exclude:
logger.debug(f"exclude to quantize {node.name} as specified by nodes_to_exclude...")
return node
inputB = node.input[1] # noqa: N806
B, Bs_graph = MatMulBnb4Quantizer.__get_initializer(inputB, graph_stack) # noqa: N806
if B is None:
logger.debug("MatMul doesn't have const weight. Skip to quantize")
return node # only care about constant weight
B_array = onnx.numpy_helper.to_array(B) # noqa: N806
if len(B_array.shape) != 2:
logger.debug("MatMul weight is not 2D. Skip to quantize")
return node # can only process 2-D matrix
packed, absmax = self.bnb4_block_quant(B_array)
B_quant = onnx.numpy_helper.from_array(packed) # noqa: N806
B_quant.name = B.name + "_Bnb4"
for input in Bs_graph.input:
if input.name == inputB:
Bs_graph.input.remove(input)
break
absmax_tensor = onnx.numpy_helper.from_array(absmax)
absmax_tensor.name = B.name + "_absmax"
Bs_graph.initializer.extend([B_quant, absmax_tensor])
kwargs = {}
rows, cols = B_array.shape
kwargs["K"] = rows
kwargs["N"] = cols
kwargs["block_size"] = self.block_size
kwargs["quant_type"] = self.quant_type
matmul_bnb4_node = onnx.helper.make_node(
"MatMulBnb4",
inputs=[node.input[0], B_quant.name, absmax_tensor.name],
outputs=[node.output[0]],
name=node.name + "_Bnb4" if node.name else "",
domain="com.microsoft",
**kwargs,
)
logger.debug(f"complete quantization of {node.name} ...")
return matmul_bnb4_node
def _process_subgraph(self, graph_stack: list[GraphProto]):
new_nodes = []
graph = graph_stack[-1]
for node in graph.node:
graph_attrs = [
attr
for attr in node.attribute
if attr.type == onnx.AttributeProto.GRAPH or attr.type == onnx.AttributeProto.GRAPHS
]
if graph_attrs:
kwargs = {}
for attr in node.attribute:
if attr.type == onnx.AttributeProto.GRAPH:
# recursive call to take care of sub-graph
graph_stack.append(attr.g)
kv = {attr.name: self._process_subgraph(graph_stack)}
elif attr.type == onnx.AttributeProto.GRAPHS:
value = []
for subgraph in attr.graphs:
# recursive call to take care of sub-graph
graph_stack.append(subgraph)
value.extend([self._process_subgraph(graph_stack)])
kv = {attr.name: value}
else:
kv = attribute_to_kwarg(attr)
kwargs.update(kv)
node = onnx.helper.make_node( # noqa: PLW2901
node.op_type, node.input, node.output, name=node.name, **kwargs
)
new_nodes.append(self._bnb4_matmul_node_weight(node, graph_stack))
graph.ClearField("node")
graph.node.extend(new_nodes)
graph_stack.pop()
return graph
def process(self):
# use a stack to keep track of sub-graphs
graph_stack = [self.model.graph()]
opset_import = self.model.opset_import()
has_ms_domain = False
for opset in opset_import:
if opset.domain == "com.microsoft":
has_ms_domain = True
if not has_ms_domain:
opset_import.extend([onnx.helper.make_opsetid("com.microsoft", 1)])
self._process_subgraph(graph_stack)
self.model.clean_initializers()
def parse_args():
parser = argparse.ArgumentParser(
description="""Blockwise FP4/NF4 quantization for MatMul 2D weight matrices.
A weight matrix is partitioned into blocks, where each block is a contiguous
subset inside the flattened transposed weight matrix. Each block is quantized
into a set of 4b integers with an absolute value scaling factor.
"""
)
parser.add_argument("--input_model", required=True, help="Path to the input model file")
parser.add_argument("--output_model", required=True, help="Path to the output model file")
parser.add_argument(
"--quant_type",
required=False,
default=1,
choices=[MatMulBnb4Quantizer.FP4, MatMulBnb4Quantizer.NF4],
help="Quantization data type. 0: FP4, 1: NF4",
)
parser.add_argument(
"--block_size",
required=False,
default=64,
help="Block size for blockwise quantization. Note: bnb.nn.Linear4bit only uses block_size=64",
)
parser.add_argument("-v", "--verbose", required=False, action="store_true")
parser.set_defaults(verbose=False)
parser.add_argument(
"--nodes_to_exclude",
nargs="+",
type=str,
required=False,
default=[],
help="Specify the nodes to be excluded from quantization with node names",
)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
if args.verbose:
logger.setLevel(logging.DEBUG)
input_model_path = args.input_model
output_model_path = args.output_model
if os.path.exists(output_model_path):
logger.error(f"file {output_model_path} already exists")
raise Exception(f"file {output_model_path} already exists")
model = onnx.load(input_model_path)
quant = MatMulBnb4Quantizer(model, args.quant_type, args.block_size, nodes_to_exclude=args.nodes_to_exclude)
quant.process()
quant.model.save_model_to_file(output_model_path, True)
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