GLM-130B/quantization/__init__.py

import torch

from .layers import QuantizedColumnParallelLinear
from .layers import QuantizedRowParallelLinear


def quantize(model, weight_bit_width):
    """Replace fp16 linear with quantized linear"""

    for layer in model.transformer.layers:
        layer.attention.query_key_value = QuantizedColumnParallelLinear(
            weight_bit_width=weight_bit_width,
            weight=layer.attention.query_key_value.weight.to(torch.cuda.current_device()),
            input_size=layer.attention.query_key_value.input_size,
            output_size=layer.attention.query_key_value.output_size,
            bias=True,
            gather_output=False,
            params_dtype=torch.half,
            name="query_key_value",
            skip_init=True,
            device=layer.attention.query_key_value.weight.device,
        )
        layer.attention.dense = QuantizedRowParallelLinear(
            weight_bit_width=weight_bit_width,
            weight=layer.attention.dense.weight.to(torch.cuda.current_device()),
            input_size=layer.attention.dense.input_size,
            output_size=layer.attention.dense.output_size,
            bias=True,
            input_is_parallel=True,
            params_dtype=torch.half,
            name="dense",
            skip_init=True,
            device=layer.attention.dense.weight.device,
        )
        layer.mlp.dense_h_to_4h = QuantizedColumnParallelLinear(
            weight_bit_width=weight_bit_width,
            weight=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()),
            input_size=layer.mlp.dense_h_to_4h.input_size,
            output_size=layer.mlp.dense_h_to_4h.output_size,
            bias=True,
            gather_output=False,
            params_dtype=torch.half,
            name="dense_h_to_4h",
            skip_init=True,
            device=layer.mlp.dense_h_to_4h.weight.device,
        )
        layer.mlp.dense_4h_to_h = QuantizedRowParallelLinear(
            weight_bit_width=weight_bit_width,
            weight=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()),
            input_size=layer.mlp.dense_4h_to_h.input_size,
            output_size=layer.mlp.dense_4h_to_h.output_size,
            bias=True,
            input_is_parallel=True,
            params_dtype=torch.half,
            name="dense_h_to_4h",
            skip_init=True,
            device=layer.mlp.dense_4h_to_h.weight.device,
        )

    return model