scale.py

import torch
import torch.nn as nn

from typing import Tuple
from awq.modules.act import ScaledActivation
from transformers.activations import NewGELUActivation
from awq.utils.module import get_op_by_name, set_op_by_name
from transformers.models.bloom.modeling_bloom import BloomGelu
from transformers.models.llama.modeling_llama import LlamaRMSNorm

allowed_norms = [nn.LayerNorm, LlamaRMSNorm]
allowed_act_fns = [nn.GELU, BloomGelu, NewGELUActivation]

@torch.no_grad()
def apply_clip(module, clip_list: Tuple[str, torch.Tensor]):
    for name, max_val in clip_list:
        layer: nn.Linear = get_op_by_name(module, name)
        layer.cuda()
        max_val = max_val.to(layer.weight.device)
        org_shape = layer.weight.shape
        layer.weight.data = layer.weight.data.reshape(*max_val.shape[:2], -1)
        layer.weight.data = torch.clamp(layer.weight.data, -max_val, max_val)
        layer.weight.data = layer.weight.data.reshape(org_shape)
        layer.cpu()


def apply_scale(module, scales_list, input_feat_dict=None):
    for prev_op_name, layer_names, scales in scales_list:
        prev_op = get_op_by_name(module, prev_op_name)
        layers = [get_op_by_name(module, name) for name in layer_names]

        prev_op.cuda()
        for layer in layers:
            layer.cuda()
        scales.cuda()
        
        if isinstance(prev_op, nn.Linear):
            assert len(layers) == 1
            scale_fc_fc(prev_op, layers[0], scales)

        elif any(isinstance(prev_op,t) for t in allowed_norms) \
             or 'rmsnorm' in str(prev_op.__class__).lower():
            scale_ln_fcs(prev_op, layers, scales)

        elif any(isinstance(prev_op,t) for t in allowed_act_fns):
            new_module = ScaledActivation(prev_op, scales)
            set_op_by_name(module, prev_op_name, new_module)
            scale_gelu_fc(prev_op, layers[0], scales)
            
        else:
            raise NotImplementedError(
                f"prev_op {type(prev_op)} not supported yet!")
            
        # apply the scaling to input feat if given; prepare it for clipping
        if input_feat_dict is not None:  
            for layer_name in layer_names:
                inp = input_feat_dict[layer_name]
                inp.div_(scales.view(1, -1).to(inp.device))

        prev_op.cpu()
        for layer in layers:
            layer.cpu()
        scales.cpu()

@torch.no_grad()
def scale_ln_fcs(ln: nn.Linear, fcs: list[nn.Linear], scales: torch.Tensor):
    if not isinstance(fcs, list):
        fcs = [fcs]
    
    scales = scales.to(ln.weight.device)

    ln.weight.div_(scales)
    if hasattr(ln, 'bias') and ln.bias is not None:
        ln.bias.div_(scales)

    for fc in fcs:
        fc.weight.mul_(scales.view(1, -1))

    for p in ln.parameters():
        assert torch.isnan(p).sum() == 0
    for fc in fcs:
        for p in fc.parameters():
            assert torch.isnan(p).sum() == 0

@torch.no_grad()
def scale_fc_fc(fc1: nn.Linear, fc2: nn.Linear, scales: torch.Tensor):
    assert isinstance(fc1, nn.Linear)
    assert isinstance(fc2, nn.Linear)
    
    scales = scales.to(fc1.weight.device)

    fc1.weight[-scales.size(0):].div_(scales.view(-1, 1))
    if fc1.bias is not None:
        fc1.bias.div_(scales.view(-1))

    fc2.weight.mul_(scales.view(1, -1))

    for p in fc1.parameters():
        assert torch.isnan(p).sum() == 0
    for p in fc2.parameters():
        assert torch.isnan(p).sum() == 0


@torch.no_grad()
def scale_gelu_fc(gelu: allowed_act_fns, fc: nn.Linear, scales: torch.Tensor):
    assert any(isinstance(gelu,t) for t in allowed_act_fns)
    assert isinstance(fc, nn.Linear)

    fc.weight.mul_(scales.view(1, -1).to(fc.weight.device))

    for p in fc.parameters():
        assert torch.isnan(p).sum() == 0