quantizers 1.0.0.post1

No project description provided

Quantizers

Hardware-oriented numerical quantizers for deep learning models, implemented in Keras v3 and NumPy. Provides bit-accurate precision matching with Vivado/Vitis HLS implementations.

Features

• Bit-accurate to the HLS implementation up to 32/64-bit floating point precision
• Support for fixed-point and minifloat number formats
• Differentiable Keras v3 implementations with gradients on inputs
  • With surrogate gradients for bit-width optimization as described in Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
• Supports stochastic rounding for training

Supported Quantizers

Fixed-Point Quantizer

Parameters:

• k (keep_negative): Enable negative numbers
• i (integer_bits): Number of bits before decimal point (excludes sign bit)
• f (fractional_bits): Number of bits after decimal point
• For C++: W = k + i + f, I = k + i, S = k

Supported modes:

• Rounding: TRN, RND, RND_CONV, TRN_ZERO, RND_ZERO, RND_MIN_INF, RND_INF
  • S_RND and S_RND_CONV for stochastic rounding; Not available in NumPy implementation as it is for training only
• Overflow: WRAP, SAT, SAT_SYM, WRAP_SM

Limitations:

• WRAP_SM only works with RND or RND_CONV rounding
• WRAP* modes don't provide surrogate gradients for integer bits
• Saturation bit forced to zero for WRAP and WRAP_SM

Minifloat Quantizer

Parameters:

• m (mantissa_bits): Mantissa width
• e (exponent_bits): Exponent width
• e0 (exponent_zero): Exponent bias (default: 0)
• Range: [-2^(e-1) + e0, 2^(e-1) - 1 + e0]

Features:

• Supports subnormal numbers
• Uses RND_CONV rounding and SAT overflow
• HLS-synthesizable implementation in test/cpp_source/ap_types/ap_float.h

Simplified Quantizers

• Binary: Maps to {-1,1} with 0 to -1. (preliminary implementation)
• Ternary: Shorthand for fixed-point fixed<2, 1, RND_CONV, SAT_SYM>

Installation

requires python>=3.10

pip install quantizers

keras>=3.0 and at least one compatible backend (pytorch, jax, or tensorflow) is required for training.

Usage

Stateless Quantizers

from quantizers import (
  float_quantize(_np), # add _np for NumPy implementation
  get_fixed_quantizer(_np),
  binary_quantize(_np),
  ternary_quantize(_np),
)

# Fixed-point quantizer
fixed_quantizer = get_fixed_quantizer(round_mode, overflow_mode)
fixedp_qtensor = fixed_quantizer(
    x,
    integer_bits,
    fractional_bits,
    keep_negative,
    training, # For stochastic rounding, and WRAP does not happen during training
    seed, # For stochastic rounding only
)

# Minifloat quantizer
floatp_qtensor = float_quantize(x, mantissa_bits, exponent_bits, exponent_zero)

# Simplified quantizers
binary_qtensor = binary_quantize(x)
ternary_qtensor = ternary_quantize(x)

Stateful Quantizers

# Can be used for, but not intended for training
fixed_q = FixedQ(
    width,
    integer_bits, # including the sign bit)
    keep_negative,
    fixed_round_mode, # No stochastic rounding
    fixed_overflow_mode
)
quantized = fixed_q(x)

mfloat_q = MinifloatQ(mantissa_bits, exponent_bits, exponent_zero)
quantized = mfloat_q(x)