Mojo package

quantization

APIs to quantize graph tensors.

This package includes a generic quantization encoding interface and some quantization encodings that conform to it, such as bfloat16 and Q4_0 encodings.

The main interface for defining a new quantized type is QuantizationEncoding.quantize(). This takes a full-precision tensor represented as float32 and quantizes it according to the encoding. The resulting quantized tensor is represented as a bytes tensor. For that reason, the QuantizationEncoding must know how to translate between the tensor shape and its corresponding quantized buffer shape.

For example, this code quantizes a tensor with the Q4_0 encoding:

from max.tensor import Tensor
from max.graph.quantization import Q4_0Encoding

var tensor: Tensor[DType.float32]
# Initialize `tensor`.

# Quantize using the `Q4_0` quantization encoding.
var quantized: Tensor[DType.uint8] = Q4_0Encoding.quantize(tensor)

# Now `quantized` is packed according to the `Q4_0` encoding and can be
# used to create graph constants and serialized to disk.
from max.tensor import Tensor
from max.graph.quantization import Q4_0Encoding

var tensor: Tensor[DType.float32]
# Initialize `tensor`.

# Quantize using the `Q4_0` quantization encoding.
var quantized: Tensor[DType.uint8] = Q4_0Encoding.quantize(tensor)

# Now `quantized` is packed according to the `Q4_0` encoding and can be
# used to create graph constants and serialized to disk.

Specific ops in the MAX Graph API that use quantization can be found in the ops.quantized_ops module. You can also add a quantized node in your graph with Graph.quantize().

To save the quantized tensors to disk, use graph.checkpoint.save().

Modules

encodings: Implementations of quantization encodings.
quantization_encoding: Defines the interface for quantized types.

Modules​

Modules