# ------------------------------------------------------------------------- # 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 from typing import List, Tuple 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_4bits from .onnx_model import ONNXModel from .quant_utils import attribute_to_kwarg logging.basicConfig(format="%(asctime)s %(name)s [%(levelname)s] - %(message)s", level=logging.INFO) logger = logging.getLogger(__name__) class MatMul4BitsQuantizer: """Perform 4b quantization of constant MatMul weights""" def __init__(self, model: ModelProto, block_size: int, is_symmetric: bool, nodes_to_exclude=None): if nodes_to_exclude is None: nodes_to_exclude = [] self.model = ONNXModel(model) self.block_size = block_size self.is_symmetric = is_symmetric 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 int4_block_quant(self, fp32weight: npt.ArrayLike) -> np.ndarray: """4b quantize fp32 weight to a blob""" if len(fp32weight.shape) != 2: raise ValueError("Current int4 block quantization only supports 2D tensors!") rows, cols = fp32weight.shape block_size = self.block_size blob_size = block_size // 2 k_blocks = (rows + block_size - 1) // block_size padded_rows = k_blocks * block_size pad_len = padded_rows - rows if pad_len > 0: fp32weight = np.pad(fp32weight, ((0, pad_len), (0, 0)), "constant") # block wise quantization, each block comes from a single column packed = np.zeros((cols, k_blocks, blob_size), dtype="uint8") scales = np.zeros((cols * k_blocks), dtype=fp32weight.dtype) zero_point = np.zeros((cols * k_blocks + 1) // 2, dtype="uint8") quantize_matmul_4bits(packed, fp32weight, scales, zero_point, block_size, cols, rows, self.is_symmetric) return (packed, scales, zero_point) def _q4_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.info(f"start to quantize {node.name} ...") if node.name in self.nodes_to_exclude: logger.info(f"exclude to quantize {node.name} as specified by nodes_to_exclude...") return node inputB = node.input[1] # noqa: N806 B, Bs_graph = MatMul4BitsQuantizer.__get_initializer(inputB, graph_stack) # noqa: N806 if B is None: logger.info("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.info("MatMul weight is not 2D. Skip to quantize") return node # can only process 2-D matrix packed, scales, zero_points = self.int4_block_quant(B_array) B_quant = onnx.numpy_helper.from_array(packed) # noqa: N806 B_quant.name = B.name + "_Q4" for input in Bs_graph.input: if input.name == inputB: Bs_graph.input.remove(input) break scales_tensor = onnx.numpy_helper.from_array(scales) scales_tensor.name = B.name + "_scales" Bs_graph.initializer.extend([B_quant, scales_tensor]) input_names = [node.input[0], B_quant.name, scales_tensor.name] if not self.is_symmetric: zp_tensor = onnx.numpy_helper.from_array(zero_points) zp_tensor.name = B.name + "_zero_points" Bs_graph.initializer.extend([zp_tensor]) input_names.append(zp_tensor.name) kwargs = {} rows, cols = B_array.shape kwargs["K"] = rows kwargs["N"] = cols kwargs["bits"] = 4 kwargs["block_size"] = self.block_size matmul_q4_node = onnx.helper.make_node( "MatMulNBits", inputs=input_names, outputs=[node.output[0]], name=node.name + "_Q4" if node.name else "", domain="com.microsoft", **kwargs, ) logger.info(f"complete quantization of {node.name} ...") return matmul_q4_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 len(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._q4_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 int4 quantization for MatMul 2D weight matrices. A weight matrix is partitioned into into blocks, where each block is a continguous subset inside each column. Each block is quantized into a set of 4b integers with a scaling factor and an optional offset. """ ) 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("--block_size", required=False, default=32) parser.add_argument( "--symmetric", required=False, default=True, help="Indicate whether to quantize the model symmetrically" ) 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 = MatMul4BitsQuantizer(model, args.block_size, args.symmetric, nodes_to_exclude=args.nodes_to_exclude) quant.process() quant.model.save_model_to_file(output_model_path, True)