# Copyright (c) Microsoft Corporation.

# SPDX-License-Identifier: Apache-2.0

# DeepSpeed Team

import torch

from ..module_inject.replace_policy import HFBertLayerPolicy, replace_policies

from deepspeed.accelerator import get_accelerator

class WeightQuantization(object):

def __init__(self, mlp_extra_grouping=True, mp_size=1):

self.dense_scales = []

self.qkv_scales = []

self.mlp4hh_scales = []

self.mlph4h_scales = []

self.mlp_extra_grouping = mlp_extra_grouping

self.mp_size = mp_size

def quantize_data(self, data, quantize_bits, groups, key=None):

data_groups = torch.split(data.float().view(-1), data.numel() // groups)

max_d = [max(g.max(), g.min().abs()) for g in data_groups]

data_scale = [float(1 << quantize_bits) / (2 * mx + 1e-5) for mx in max_d]

data_int = [(g * s) for g, s in zip(data_groups, data_scale)]

data_int = [

di.round().clamp(-(1 << (quantize_bits - 1)), (((1 << (quantize_bits - 1)) - 1))) for di in data_int

]

data_int = torch.cat(data_int).reshape(data.shape)

data_int = data_int.to(torch.int8)

data_scale = torch.cat([s.unsqueeze(0).unsqueeze(0) for s in data_scale])

return data_int, data_scale

def is_mlp(self, data, merge_count=1):

return ((self.mp_size *data.shape[0] * merge_count) / data.shape[1] == 4 or \

(self.mp_size *data.shape[1] * merge_count) / data.shape[0] == 4)

def is_qkv(self, data):

return ((self.mp_size * data.shape[0]) / data.shape[1] == 3 or \

(self.mp_size * data.shape[1]) / data.shape[0] == 3)

def Quantize(self, value_list, quantize_bits, groups, key, merge_dim=0):

if self.mlp_extra_grouping and self.is_mlp(value_list[0], merge_count=len(value_list)):

groups *= 2

q_scale = []

index = 0

for data in value_list:

data_int, data_scale = self.quantize_data(data, quantize_bits, groups, key)

q_scale.append(data_scale)

value_list[index] = data_int

index += 1

q_scale = (1 /

torch.cat(q_scale, dim=merge_dim).to(get_accelerator().current_device_name()).view(-1).unsqueeze(0))

if "mlp.dense_4h_to_h.weight" in key:

self.mlp4hh_scales.append(q_scale)

elif "mlp.dense_h_to_4h.weight" in key:

self.mlph4h_scales.append(q_scale)

elif "attention.query_key_value.weight" in key:

self.qkv_scales.append(q_scale)

else:

self.dense_scales.append(q_scale)

return value_list

def merge_layer_scales(self, layer_scales):

max_dim = max([s.shape[-1] for s in layer_scales])

layer_scales = [

torch.cat((s, torch.zeros((1, max_dim - s.shape[-1]), device=get_accelerator().current_device_name())),

dim=-1) if s.shape[-1] < max_dim else s for s in layer_scales

]

return torch.cat(layer_scales).unsqueeze(0)

def merge_scales(self):

all_scales = []

for dense_scale, qkv_scale, m4hh_scale, mh4h_scale in \

zip(self.dense_scales, self.qkv_scales, self.mlp4hh_scales, self.mlph4h_scales):

all_scales.append(self.merge_layer_scales([qkv_scale, dense_scale, mh4h_scale, m4hh_scale]))

return torch.cat(all_scales)

def merge_scales_split(self, split_count):

all_scales = [[] for _ in range(split_count)]

for dense_scale, qkv_scale, m4hh_scale, mh4h_scale in \

zip(self.dense_scales, self.qkv_scales, self.mlp4hh_scales, self.mlph4h_scales):

dense_scale = torch.split(dense_scale, dense_scale.numel() // split_count)

qkv_scale = torch.split(qkv_scale, qkv_scale.numel() // split_count)

m4hh_scale = torch.split(m4hh_scale, m4hh_scale.numel() // split_count)

mh4h_scale = torch.split(mh4h_scale, mh4h_scale.numel() // split_count)

for s in range(split_count):

all_scales[s].append(

torch.cat([

torch.cat((qkv_scale[s], torch.zeros_like(qkv_scale[s])), dim=1),

torch.cat((dense_scale[s], torch.zeros_like(dense_scale[s])), dim=1), mh4h_scale[s],

m4hh_scale[s]

]).unsqueeze(0))

for scales_a in all_scales:

torch.cat(scales_a)

return all_scales

def sd_quantize_megatron(self, sd, quantize_bits, groups):

keys = sd.keys()

for key in keys:

value_list = [sd[key]]

if "attention.dense.weight" in key or "mlp.dense_4h_to_h.weight" in key or \

"mlp.dense_h_to_4h.weight" in key or "attention.query_key_value.weight" in key:

value_list = self.Quantize(value_list, quantize_bits, groups, key=key)

sd[key] = value_list[0]

all_scales = self.merge_scales()

return sd, all_scales

def model_quantize(self, model, quantize_policy, quantize_bits, groups):

all_scales = []

def quantize_fn(layer, policy_cls):

policy = policy_cls(layer)

_, qkvw, _, dense_w, _, _ = policy.attention()

_, _h4h_w, _, _4hh_w, _ = policy.mlp()

keys = [qkvw, dense_w, _h4h_w, _4hh_w]

layer_scales = []

for key in range(len(keys)):

if self.mlp_extra_grouping and self.is_mlp(keys[key]):

data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups * 2)

elif policy_cls is HFBertLayerPolicy and self.is_qkv(keys[key]):

data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups * 3)

else:

data_quantized, data_scale = self.quantize_data(keys[key], quantize_bits, groups)

keys[key].copy_(data_quantized)

layer_scales.append((1 / data_scale.to(get_accelerator().current_device_name()).view(-1).unsqueeze(0)))

all_scales.append(self.merge_layer_scales(layer_scales))

return layer

def _quantize_module(model, policies):

for name, child in model.named_children():

if child.__class__ in policies:

quantize_fn, replace_policy = policies[child.__class__]

setattr(model, name, quantize_fn(child, replace_policy))

else:

_quantize_module(child, policies)

return model

policy = {}

if quantize_policy is not None:

for layer_name, replace_policy in quantize_policy.items():

policy.update({layer_name: (quantize_fn, replace_policy)})

else:

for plcy in replace_policies:

policy.update({plcy._orig_layer_class: (quantize_fn, plcy)})

quantized_module = _quantize_module(model, policy)

return quantized_module, torch.cat(all_scales)