Baichuan2-13B-Chat / quantizer.py

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	import bitsandbytes as bnb
	from accelerate import init_empty_weights
	from bitsandbytes.nn.modules import Params4bit, Int8Params
	import torch

	def Params4bitCuda(self, device):
	self.data = self.data.cuda(device)
	self.quant_state[0] = self.quant_state[0].cuda(device)
	self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
	self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
	self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)

	self.quant_state[6] = self.quant_state[6].cuda(device)
	return self

	class Linear4bitOnline(torch.nn.Module):
	def __init__(self, weight, bias, quant_type):
	super().__init__()
	self.weight = Params4bit(
	weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
	)
	self.compute_dtype = None
	#self.weight.cuda(weight.device)
	self.bias = bias

	def forward(self, x: torch.Tensor):
	# weights are cast automatically as Int8Params, but the bias has to be cast manually
	if self.bias is not None and self.bias.dtype != x.dtype:
	self.bias.data = self.bias.data.to(x.dtype)

	if getattr(self.weight, "quant_state", None) is None:
	print(
	"FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
	)
	inp_dtype = x.dtype
	if self.compute_dtype is not None:
	x = x.to(self.compute_dtype)

	bias = None if self.bias is None else self.bias.to(self.compute_dtype)
	out = bnb.matmul_4bit(
	x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
	)

	out = out.to(inp_dtype)

	return out

	class Linear8bitLtOnline(torch.nn.Module):
	def __init__(
	self,
	weight,
	bias,
	has_fp16_weights=True,
	memory_efficient_backward=False,
	threshold=0.0,
	index=None,
	):
	super().__init__()
	assert (
	not memory_efficient_backward
	), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
	self.state = bnb.MatmulLtState()
	self.index = index

	# Necessary for stacked layers
	self.state.threshold = threshold
	self.state.has_fp16_weights = has_fp16_weights
	self.state.memory_efficient_backward = memory_efficient_backward
	if threshold > 0.0 and not has_fp16_weights:
	self.state.use_pool = True

	self.weight = Int8Params(
	weight.data,
	has_fp16_weights=has_fp16_weights,
	requires_grad=has_fp16_weights,
	)
	self.bias = bias

	def init_8bit_state(self):
	self.state.CB = self.weight.CB
	self.state.SCB = self.weight.SCB
	self.weight.CB = None
	self.weight.SCB = None

	def forward(self, x: torch.Tensor):
	self.state.is_training = self.training
	if self.weight.CB is not None:
	self.init_8bit_state()

	# weights are cast automatically as Int8Params, but the bias has to be cast manually
	if self.bias is not None and self.bias.dtype != x.dtype:
	self.bias.data = self.bias.data.to(x.dtype)

	out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)

	if not self.state.has_fp16_weights:
	if self.state.CB is not None and self.state.CxB is not None:
	# we converted 8-bit row major to turing/ampere format in the first inference pass
	# we no longer need the row-major weight
	del self.state.CB
	self.weight.data = self.state.CxB
	return out

	def quantize_offline(model, bits: int):
	assert (bits == 4), f'bits: {bits} is not supported'

	for i, layer in enumerate(model.model.layers):
	layer.self_attn.W_pack = bnb.nn.Linear4bit(
	layer.self_attn.W_pack.weight.shape[1],
	layer.self_attn.W_pack.weight.shape[0],
	False,
	torch.float16,
	compress_statistics=True,
	quant_type="nf4",
	)
	layer.self_attn.o_proj = bnb.nn.Linear4bit(
	layer.self_attn.o_proj.weight.shape[1],
	layer.self_attn.o_proj.weight.shape[0],
	False,
	torch.float16,
	compress_statistics=True,
	quant_type="nf4",
	)

	layer.mlp.gate_proj = bnb.nn.Linear4bit(
	layer.mlp.gate_proj.weight.shape[1],
	layer.mlp.gate_proj.weight.shape[0],
	False,
	torch.float16,
	compress_statistics=True,
	quant_type="nf4",
	)
	layer.mlp.down_proj = bnb.nn.Linear4bit(
	layer.mlp.down_proj.weight.shape[1],
	layer.mlp.down_proj.weight.shape[0],
	False,
	torch.float16,
	compress_statistics=True,
	quant_type="nf4",
	)
	layer.mlp.up_proj = bnb.nn.Linear4bit(
	layer.mlp.up_proj.weight.shape[1],
	layer.mlp.up_proj.weight.shape[0],
	False,
	torch.float16,
	compress_statistics=True,
	quant_type="nf4",
	)
	return model

	def quantize_online(model, bits: int):
	def quant(weight, bias=None):
	if bits == 8:
	linear = Linear8bitLtOnline(
	weight,
	bias,
	has_fp16_weights=False,
	threshold=6.0,
	)
	if bias is not None:
	linear.bias = torch.nn.Parameter(bias)
	elif bits == 4:
	linear = Linear4bitOnline(
	weight,
	bias,
	quant_type="nf4", #fp4/nf4
	)
	else:
	raise ValueError("quantize only support 4/8 bit")
	return linear

	for i, layer in enumerate(model.model.layers):
	layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
	layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
	layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
	layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
	layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
	return model

	def init_model_weight_int4(config, model, state_dict):
	#replace Params4bit.cuda with Params4bitCuda
	Params4bit.cuda = Params4bitCuda

	for i in range(config.num_hidden_layers):
	weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
	weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
	model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)

	weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
	weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
	model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)

	weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
	weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
	model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)

	weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
	weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
	model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)

	weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
	weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
	model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)

	model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
	model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']

	model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
	model.model.norm.weight = state_dict['model.norm.weight']
	model.lm_head.weight = state_dict['lm_head.weight']
	return model