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Cleanup

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This commit is contained in:
somebody
2023-07-24 19:45:33 -05:00
parent ad4528b5a6
commit c80de5120c
1 changed files with 22 additions and 47 deletions
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69
modeling/inference_models/gptq_hf_torch/class.py
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@@ -100,8 +100,6 @@ def load_quant_offload_device_map(
from transformers.models.gpt_bigcode.modeling_gpt_bigcode import GPTBigCodeModel
model = load_quant_func(model, checkpoint, wbits, groupsize, force_bias=force_bias)
print(device_map)
m, layers, remaining = find_layers(model)
type(m).non_offload_forward = type(m).forward
@@ -120,7 +118,6 @@ def load_quant_offload_device_map(
raise RuntimeError(f"Model type {type(m)} not supported by CPU offloader")
layers_done = len([1 for v in device_map.values() if v != "cpu"])
print("LDone", layers_done)
m.cpu_device = torch.device("cpu")
m.fast_offload = layers_done > len(layers) // 2
@@ -134,10 +131,6 @@ def load_quant_offload_device_map(
if "layers" not in dir(m):
m.layers = layers
print(len(layers))
print(len(device_map))
print(m.primary_gpu)
for i in range(len(layers)):
dev = None
for key, device in device_map.items():
@@ -184,10 +177,6 @@ class model_backend(HFTorchInferenceModel):
except (ValueError, AttributeError):
self.gpu_layers_list = [utils.num_layers(self.model_config)]
tf_kwargs = {
"low_cpu_mem_usage": True,
}
# If we're using torch_lazy_loader, we need to get breakmodel config
# early so that it knows where to load the individual model tensors
logger.debug("lazy_load: {} hascuda: {} breakmodel: {} nobreakmode: {}".format(self.lazy_load, utils.koboldai_vars.hascuda, self.breakmodel, self.nobreakmodel))
@@ -200,9 +189,6 @@ class model_backend(HFTorchInferenceModel):
self.breakmodel_device_config(self.model_config)
if self.lazy_load:
# torch_lazy_loader.py and low_cpu_mem_usage can't be used at the same time
tf_kwargs.pop("low_cpu_mem_usage", None)
# If we're using lazy loader, we need to figure out what the model's hidden layers are called
with lazy_loader.use_lazy_load(dematerialized_modules=True):
try:
@@ -218,7 +204,7 @@ class model_backend(HFTorchInferenceModel):
if self.get_local_model_path():
# Model is stored locally, load it.
self.model = self._get_model(self.get_local_model_path(), tf_kwargs)
self.model = self._get_model(self.get_local_model_path())
self.tokenizer = self._get_tokenizer(self.get_local_model_path())
else:
raise NotImplementedError("GPTQ Model downloading not implemented")
@@ -238,17 +224,9 @@ class model_backend(HFTorchInferenceModel):
self.model.kai_model = self
utils.koboldai_vars.modeldim = self.get_hidden_size()
def _patch_quant(self, device_map) -> None:
# QuantLinear loads on the CPU by default, using a lot of RAM! If we
# load it to the same device that the weights are gonna be on, it
# mysteriously uses no additional VRAM
from gptq import quant_v3
from gptq import quant_v2
from gptq import quant_v1
def make_quant(module, names, bits, groupsize, name='', force_bias=False):
if isinstance(module, quant_v3.QuantLinear):
def _patch_quant(self, device_map, quant_module) -> None:
def make_quant(module, names, bits, groupsize, name='', force_bias=False, **kwargs):
if isinstance(module, quant_module.QuantLinear):
return
for attr in dir(module):
@@ -264,19 +242,17 @@ class model_backend(HFTorchInferenceModel):
break
if device is None:
print(name1)
print(device_map)
raise ValueError
raise ValueError(f"No device for {name1}")
print("[ql]", name1, device)
delattr(module, attr)
ql = quant_v3.QuantLinear(
ql = quant_module.QuantLinear(
bits,
groupsize,
tmp.in_features,
tmp.out_features,
force_bias or tmp.bias is not None
force_bias or tmp.bias is not None,
**kwargs,
)
ql = ql.to(device)
@@ -285,19 +261,21 @@ class model_backend(HFTorchInferenceModel):
for name1, child in module.named_children():
make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1, force_bias=force_bias)
quant_v3.make_quant = make_quant
# def _ql_init_(self, *args, **kwargs):
# ret = type(self)._unpatched_init(self, *args, **kwargs)
# self.to("cuda:0")
# return ret
# for quant_module in [quant_v3, quant_v2, quant_v1]:
# quant_module.QuantLinear._unpatched_init = quant_module.QuantLinear.__init__
# quant_module.QuantLinear.__init__ = _ql_init_
quant_module.make_quant = make_quant
def _get_model(self, location: str, tf_kwargs: Dict):
def _patch_quants(self, device_map) -> None:
# Load QuantLinears on the device corresponding to the device map
from gptq import quant_v3
from gptq import quant_v2
from gptq import quant_v1
for quant_module in [quant_v3, quant_v2, quant_v1]:
self._patch_quant(device_map, quant_module)
def _get_model(self, location: str):
import gptq
from gptq.gptj import load_quant as gptj_load_quant
from gptq.gptneox import load_quant as gptneox_load_quant
@@ -339,7 +317,7 @@ class model_backend(HFTorchInferenceModel):
metamodel
)
self._patch_quant(device_map)
self._patch_quants(device_map)
with lazy_loader.use_lazy_load(
enable=self.lazy_load,
@@ -350,9 +328,6 @@ class model_backend(HFTorchInferenceModel):
elif model_type == "gpt_neox":
model = load_quant_offload_device_map(gptneox_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
elif model_type == "llama":
print("YE LAMA")
# model = llama_load_quant(location, gptq_file, gptq_bits, gptq_groupsize, force_bias=v2_bias)
model = load_quant_offload_device_map(llama_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)
elif model_type == "opt":
model = load_quant_offload_device_map(opt_load_quant, location, gptq_file, gptq_bits, gptq_groupsize, device_map, force_bias=v2_bias)