fix: move GPTQWeight into file to avoid circular import

This commit is contained in:
drbh 2024-08-08 19:52:23 +00:00
parent 700e64c5b9
commit e99dd84b9a
2 changed files with 72 additions and 67 deletions

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@ -1,76 +1,12 @@
import os import os
from dataclasses import dataclass from typing import List, Union
from typing import List, Optional, Union
import torch import torch
from loguru import logger from loguru import logger
from text_generation_server.utils.import_utils import SYSTEM from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.log import log_once from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import Weight, Weights, WeightsLoader from text_generation_server.utils.weights import Weights, WeightsLoader
from text_generation_server.layers.gptq.gptq_types import GPTQWeight
@dataclass
class GPTQWeight(Weight):
qweight: torch.Tensor
qzeros: torch.Tensor
scales: torch.Tensor
g_idx: Optional[torch.Tensor]
bits: int
groupsize: int
use_awq_kernel: bool
use_exllama: bool
def __post_init__(self):
if self.scales.dtype == torch.float:
self.scales = self.scales.half()
@property
def device(self) -> torch.device:
return self.qweight.device
def get_linear(self, bias: torch.Tensor):
if self.use_awq_kernel:
if SYSTEM == "rocm":
raise NotImplementedError(
"AWQ GEMM kernel can't be used on ROCm systems, please use `--quantize gptq` instead "
"to use Exllama/GPTQ kernels for AWQ inference."
)
try:
from text_generation_server.layers.awq.quantize.qmodule import WQLinear
return WQLinear(
w_bit=self.bits,
group_size=self.groupsize,
qweight=self.qweight,
qzeros=self.qzeros,
scales=self.scales,
bias=bias,
)
except ImportError:
raise NotImplementedError(
"You do not seem to have awq installed, either install it (cd server && make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly"
)
elif self.use_exllama:
try:
from text_generation_server.layers.gptq import ExllamaQuantLinear
except ImportError:
raise NotImplementedError(
"Exllama gptq kernels are not installed. Install them `cd server/exllama_kernels && python setup.py install && cd ../exllamav2_kernels && python setup.py install`"
)
return ExllamaQuantLinear(self, bias)
else:
from text_generation_server.layers.gptq.quant_linear import QuantLinear
return QuantLinear(
self.qweight,
self.qzeros,
self.scales,
self.g_idx,
bias,
self.bits,
self.groupsize,
)
class GPTQWeightsLoader(WeightsLoader): class GPTQWeightsLoader(WeightsLoader):

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@ -0,0 +1,69 @@
from dataclasses import dataclass
from typing import Optional
import torch
from text_generation_server.utils.weights import Weight
from text_generation_server.utils.import_utils import SYSTEM
@dataclass
class GPTQWeight(Weight):
qweight: torch.Tensor
qzeros: torch.Tensor
scales: torch.Tensor
g_idx: Optional[torch.Tensor]
bits: int
groupsize: int
use_awq_kernel: bool
use_exllama: bool
def __post_init__(self):
if self.scales.dtype == torch.float:
self.scales = self.scales.half()
@property
def device(self) -> torch.device:
return self.qweight.device
def get_linear(self, bias: torch.Tensor):
if self.use_awq_kernel:
if SYSTEM == "rocm":
raise NotImplementedError(
"AWQ GEMM kernel can't be used on ROCm systems, please use `--quantize gptq` instead "
"to use Exllama/GPTQ kernels for AWQ inference."
)
try:
from text_generation_server.layers.awq.quantize.qmodule import WQLinear
return WQLinear(
w_bit=self.bits,
group_size=self.groupsize,
qweight=self.qweight,
qzeros=self.qzeros,
scales=self.scales,
bias=bias,
)
except ImportError:
raise NotImplementedError(
"You do not seem to have awq installed, either install it (cd server && make install-awq), or try using GPTQ `---quantize gptq` a conversion AWQ->GPTQ will happen on the fly"
)
elif self.use_exllama:
try:
from text_generation_server.layers.gptq import ExllamaQuantLinear
except ImportError:
raise NotImplementedError(
"Exllama gptq kernels are not installed. Install them `cd server/exllama_kernels && python setup.py install && cd ../exllamav2_kernels && python setup.py install`"
)
return ExllamaQuantLinear(self, bias)
else:
from text_generation_server.layers.gptq.quant_linear import QuantLinear
return QuantLinear(
self.qweight,
self.qzeros,
self.scales,
self.g_idx,
bias,
self.bits,
self.groupsize,
)