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_utils.py
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_utils.py
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from logging import getLogger
from typing import Union, Optional
import accelerate
import torch
import torch.nn as nn
from transformers import AutoConfig
import transformers
from ._const import SUPPORTED_MODELS, CPU, CUDA_0, EXLLAMA_DEFAULT_MAX_INPUT_LENGTH
from ..utils.import_utils import dynamically_import_QuantLinear
logger = getLogger(__name__)
def get_device(obj: Union[torch.Tensor, nn.Module]):
if isinstance(obj, torch.Tensor):
return obj.device
return next(obj.parameters()).device
def move_to_device(obj: Union[torch.Tensor, nn.Module], device: torch.device):
if get_device(obj) != device:
obj = obj.to(device)
return obj
def find_layers(module, layers=None, name=''):
if not layers:
layers = [transformers.pytorch_utils.Conv1D, nn.Conv2d, nn.Linear]
for layer in layers:
if isinstance(module,layer):
return {name: module}
res = {}
for name1, child in module.named_children():
res.update(find_layers(child, layers=layers, name=name + '.' + name1 if name != '' else name1))
return res
def get_module_by_name_prefix(model, module_name: str):
for name, module in model.named_modules():
if name.startswith(module_name):
return module
def get_module_by_name_suffix(model, module_name: str):
for name, module in model.named_modules():
if name.endswith(module_name):
return module
def make_quant(
module,
names,
bits,
group_size,
name='',
use_triton: bool = False,
disable_exllama: bool = True,
disable_exllamav2: bool = False,
use_qigen: bool = False,
use_cuda_fp16: bool = True,
desc_act: bool = False,
trainable: bool = False
):
QuantLinear = dynamically_import_QuantLinear(use_triton=use_triton, desc_act=desc_act, group_size=group_size, bits=bits, disable_exllama=disable_exllama, disable_exllamav2=disable_exllamav2, use_qigen=use_qigen)
if isinstance(module, QuantLinear):
return
for attr in dir(module):
tmp = getattr(module, attr)
name1 = name + '.' + attr if name != '' else attr
if name1 in names:
ori_layer_device = get_device(getattr(module, attr))
delattr(module, attr)
if isinstance(tmp,nn.Linear):
in_features = tmp.in_features
out_features = tmp.out_features
elif isinstance(tmp,nn.Conv2d):
in_features = tmp.in_channels
out_features = tmp.out_channels
elif isinstance(tmp,transformers.pytorch_utils.Conv1D):
in_features = tmp.weight.shape[0]
out_features = tmp.weight.shape[1]
if (not(desc_act) or group_size == -1) and not use_triton and not use_qigen:
new_layer = QuantLinear(
bits, group_size, in_features, out_features, True, use_cuda_fp16=use_cuda_fp16, trainable=trainable
)
else:
new_layer = QuantLinear(bits, group_size, in_features, out_features, True, trainable=trainable)
new_layer.device = ori_layer_device
setattr(module, attr, new_layer.to(ori_layer_device))
for name1, child in module.named_children():
make_quant(
child,
names,
bits,
group_size,
name + '.' + name1 if name != '' else name1,
use_triton=use_triton,
use_cuda_fp16=use_cuda_fp16,
desc_act=desc_act,
trainable=trainable,
disable_exllama=disable_exllama,
disable_exllamav2=disable_exllamav2,
use_qigen=use_qigen
)
def preprocess_checkpoint_qigen(
module,
names,
bits,
group_size,
checkpoint,
name='',
):
try:
import cQIGen as qinfer
except ImportError:
logger.error('cQIGen not installed.')
raise
QuantLinear = dynamically_import_QuantLinear(use_triton=False, desc_act=False, group_size=group_size, bits=bits, disable_exllama=False, use_qigen=True)
if isinstance(module, QuantLinear):
in_features = module.infeatures
out_features = module.outfeatures
zeros = checkpoint[name + '.qzeros']
scales = checkpoint[name + '.scales'].float()
if zeros.dtype != torch.float32:
new_zeros = torch.zeros_like(scales).float().contiguous()
if bits == 4:
qinfer.unpack_zeros4(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
elif bits == 2:
qinfer.unpack_zeros2(zeros, new_zeros, new_zeros.shape[0], new_zeros.shape[1])
elif bits == 3:
logger.info("Unpacking zeros for 3 bits")
new_scales = scales.contiguous()
else:
if scales.shape[1] != out_features:
new_scales = scales.transpose(0,1).contiguous()
else:
new_scales = scales.contiguous()
if zeros.shape[1] != out_features:
new_zeros = zeros.transpose(0,1).contiguous()
else:
new_zeros = zeros.contiguous()
checkpoint[name + '.zeros'],checkpoint[name + '.scales'] = new_zeros, new_scales
del checkpoint[name + '.qzeros']
del checkpoint[name + '.g_idx']
if name + '.bias' in checkpoint:
checkpoint[name + '.bias'] = checkpoint[name + '.bias'].float()
else:
checkpoint[name + '.bias'] = torch.zeros(out_features)
checkpoint_qweight = checkpoint[name + '.qweight'].int().contiguous()
if bits == 4:
qweight = torch.zeros(int(in_features // 8 * out_features)).int().contiguous()
qinfer.pack4(checkpoint_qweight, qweight, in_features // 8, out_features, module.mb, module.tb, module.cutoff)# * (module.tt//tb))
elif bits == 3:
qweight = torch.zeros(int(in_features // 32 * 3 * out_features)).int().contiguous()
qinfer.pack3(checkpoint_qweight, qweight, in_features // 32 * 3, out_features, module.mb // 32 * 3, module.tb, module.cutoff)
elif bits == 2:
qweight = torch.zeros(int(in_features // 16 * out_features)).int().contiguous()
qinfer.pack2(checkpoint_qweight, qweight, in_features // 16, out_features, module.mb, module.tb, module.cutoff)# * (module.tt//tb))
checkpoint[name + '.qweight'] = qweight
return
for name1, child in module.named_children():
preprocess_checkpoint_qigen(
child,
names,
bits,
group_size,
checkpoint,
name + '.' + name1 if name != '' else name1,
)
def pack_model(
model,
quantizers,
bits,
group_size,
use_triton=False,
use_cuda_fp16=True,
desc_act=False,
warmup_triton: bool = False,
force_layer_back_to_cpu: bool = False
):
QuantLinear = dynamically_import_QuantLinear(use_triton=use_triton, desc_act=desc_act, group_size=group_size, bits=bits, disable_exllama=False, disable_exllamav2=True)
if force_layer_back_to_cpu:
model.to(CPU)
logger.info('Packing model...')
layers = find_layers(model)
layers = {n: layers[n] for n in quantizers}
make_quant(model, quantizers, bits, group_size, use_triton=use_triton, use_cuda_fp16=use_cuda_fp16, desc_act=desc_act, disable_exllama=False, disable_exllamav2=True)
qlayers = find_layers(model, [QuantLinear])
for name in qlayers:
logger.info(name)
quantizers[name], scale, zero, g_idx = quantizers[name]
# so far can only pack layer on CPU
layer_device = qlayers[name].device
qlayers[name].to(CPU)
layers[name], scale, zero, g_idx = layers[name].to(CPU), scale.to(CPU), zero.to(CPU), g_idx.to(CPU)
qlayers[name].pack(layers[name], scale, zero, g_idx)
qlayers[name].to(layer_device)
logger.info('Model packed.')
if use_triton and warmup_triton:
logger.warning(
"using autotune_warmup will move model to GPU, make sure you have enough VRAM to load the whole model."
)
QuantLinear.warmup(model.to(CUDA_0), seqlen=model.seqlen)
def check_and_get_model_type(model_dir, trust_remote_code=False):
config = AutoConfig.from_pretrained(model_dir, trust_remote_code=trust_remote_code)
if config.model_type not in SUPPORTED_MODELS:
raise TypeError(f"{config.model_type} isn't supported yet.")
model_type = config.model_type
return model_type
def simple_dispatch_model(model, device_map):
from accelerate.hooks import add_hook_to_module, AlignDevicesHook
if "" in device_map:
d = device_map[""]
model = model.to(torch.device(d))
model.hf_device_map = device_map
return model
tied_params = accelerate.utils.modeling.find_tied_parameters(model)
if set(device_map.values()) == {"cpu"} or set(device_map.values()) == {"cpu", "disk"}:
main_device = "cpu"
else:
main_device = [d for d in device_map.values() if d not in ["cpu", "disk"]][0]
cpu_offload_group = [(n, d) for n, d in device_map.items() if d == "cpu"]
prev_hook = None
for idx, (n, d) in enumerate(cpu_offload_group):
m = get_module_by_name_suffix(model, n)
_, prev_hook = accelerate.cpu_offload_with_hook(m, execution_device=main_device, prev_module_hook=prev_hook)
# set first cpu offload module's prev_module_hook to the last cpu offload module's hook
if len(cpu_offload_group) > 1:
get_module_by_name_suffix(model, cpu_offload_group[0][0])._hf_hook.prev_module_hook = prev_hook
for n, d in device_map.items():
m = get_module_by_name_suffix(model, n)
if d != "cpu":
d = torch.device(d)
hook = AlignDevicesHook(d, io_same_device=True, place_submodules=True)
add_hook_to_module(m, hook)
accelerate.utils.modeling.retie_parameters(model, tied_params)
model.hf_device_map = device_map
return model
def autogptq_post_init(model, use_act_order: bool, max_input_length: Optional[int] = None):
"""
The max_input_length argument is specific to the exllama backend, that requires to initialize a buffer temp_state.
"""
device_to_buffers_size = {}
model_uses_exllama = False
for name, submodule in model.named_modules():
if hasattr(submodule, "QUANT_TYPE") and submodule.QUANT_TYPE == "exllama":
model_uses_exllama = True
device = submodule.qweight.device
if device not in device_to_buffers_size:
device_to_buffers_size[device] = {
"max_dq_buffer_size": 1,
"max_inner_outer_dim": 1
}
if not use_act_order:
submodule._use_act_order = False
else:
submodule._use_act_order = True
# Disable this heuristic for detecting act_order, but it could be used instead of the config.
"""
if submodule.g_idx is None:
submodule.act_order = False
elif submodule.g_idx is not None and ((submodule.g_idx == 0).all() or torch.equal(submodule.g_idx.cpu(), torch.tensor([i // submodule.group_size for i in range(submodule.g_idx.shape[0])], dtype=torch.int32))):
submodule.g_idx = None
submodule.act_order = False
else:
submodule.act_order = True
"""
device_to_buffers_size[device]["max_dq_buffer_size"] = max(device_to_buffers_size[device]["max_dq_buffer_size"], submodule.qweight.numel() * 8)
if use_act_order:
device_to_buffers_size[device]["max_inner_outer_dim"] = max(device_to_buffers_size[device]["max_inner_outer_dim"], submodule.infeatures, submodule.outfeatures)
if model_uses_exllama:
# To be honest this is quite ugly, not proud of this.
from exllama_kernels import prepare_buffers, set_tuning_params
device_to_buffers = {}
if use_act_order:
if max_input_length is None:
max_input_len = EXLLAMA_DEFAULT_MAX_INPUT_LENGTH
else:
max_input_len = max_input_length
else:
if max_input_length is not None:
logger.info("Using exllama backend without act-order, the parameter max_input_length was set although not needed, it will be ignored.")
max_input_len = 1
for device, buffers_size in device_to_buffers_size.items():
# The temp_state buffer is required to reorder X in the act-order case.
# The temp_dq buffer is required to dequantize weights when using cuBLAS, typically for the prefill.
device_to_buffers[device] = {
"temp_state": torch.zeros((max_input_len, buffers_size["max_inner_outer_dim"]), dtype=torch.float16, device=device),
"temp_dq": torch.zeros((1, buffers_size["max_dq_buffer_size"]), dtype=torch.float16, device=device),
"max_dq_buffer_size": buffers_size["max_dq_buffer_size"],
"max_inner_outer_dim": buffers_size["max_inner_outer_dim"],
}
# Buffers need to be persistent to avoid any bug.
model.device_to_buffers = device_to_buffers
for device, buffers in model.device_to_buffers.items():
prepare_buffers(device, buffers["temp_state"], buffers["temp_dq"])
# Using the default from exllama repo here.
matmul_recons_thd = 8
matmul_fused_remap = False
matmul_no_half2 = False
set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
# The buffers need to have been initialized first before calling make_q4.
for name, submodule in model.named_modules():
if hasattr(submodule, "QUANT_TYPE") and submodule.QUANT_TYPE == "exllama":
submodule.post_init()
## exllamav2
fixed_bytes = {}
model_uses_exllamav2 = False
for _, submodule in model.named_modules():
if hasattr(submodule, "QUANT_TYPE") and submodule.QUANT_TYPE == "exllamav2":
model_uses_exllamav2 = True
device = submodule.qweight.device
scratch_fixed = submodule.scratch_space_fixed()
fixed_bytes[device] = max(scratch_fixed, fixed_bytes.get(device,0))
if model_uses_exllamav2:
from ..nn_modules.qlinear.qlinear_exllamav2 import ExLlamaV2DeviceTensors
device_tensors = {}
for device, scratch_bytes in fixed_bytes.items():
device_tensors[device] = ExLlamaV2DeviceTensors(device.index, scratch_bytes)
# have persistent buffers, otherwise we will get OOM
model.device_tensors = device_tensors
for _, submodule in model.named_modules():
if hasattr(submodule, "QUANT_TYPE") and submodule.QUANT_TYPE == "exllamav2":
device = submodule.qweight.device
submodule.post_init(temp_dq = model.device_tensors[device])
torch.cuda.empty_cache()
return model
def make_sure_no_tensor_in_meta_device(model, use_triton, desc_act, group_size, bits: int):
QuantLinear = dynamically_import_QuantLinear(use_triton, desc_act, group_size, bits=bits)
for n, m in model.named_modules():
if isinstance(m, QuantLinear) and m.bias.device == torch.device("meta"):
m.register_buffer('bias', torch.zeros((m.outfeatures), dtype=torch.float16, device="cpu"))
__all__ = [
"get_device",
"move_to_device",
"find_layers",
"get_module_by_name_prefix",
"get_module_by_name_suffix",
"make_quant",
"preprocess_checkpoint_qigen",
"pack_model",
"autogptq_post_init",
"check_and_get_model_type",
"simple_dispatch_model",
"make_sure_no_tensor_in_meta_device"
]