from typing import Optional
import torch
from torch.nn import functional as F
from text_generation_server.layers.marlin import GPTQMarlinLinear
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM == "rocm":
try:
from vllm import _custom_C
except Exception as e:
raise ImportError(f"Could not load `vllm._custom_C`. Full error: {e}")
class FastLinear(torch.nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
self.weight = torch.nn.Parameter(weight, requires_grad=False)
if bias is not None:
self.bias = torch.nn.Parameter(bias, requires_grad=False)
else:
self.bias = None
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
if bias:
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(weight, bias)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.linear(input, self.weight, self.bias)
class FastLinearROCm(torch.nn.Module):
def __init__(
self,
weight,
bias,
) -> None:
super().__init__()
self.weight = torch.nn.Parameter(weight)
if bias is not None:
self.bias = torch.nn.Parameter(bias)
else:
self.bias = None
@classmethod
def load(cls, config, prefix: str, weights, bias: bool):
weight = weights.get_tensor(f"{prefix}.weight")
if bias:
bias = weights.get_tensor(f"{prefix}.bias")
else:
bias = None
return cls(weight, bias)
def forward(self, inp: torch.Tensor) -> torch.Tensor:
weight = self.weight
bias = self.bias
if SYSTEM == "rocm" and inp.numel() // inp.shape[-1] == 1:
batched = False
inp_shape = inp.shape
if inp.dim() == 3:
inp = inp.view(-1, inp_shape[-1])
batched = True
m, k = weight.shape[0], inp_shape[1]
out = torch.empty(
inp_shape[0], weight.shape[0], dtype=inp.dtype, device="cuda"
)
if (k == 8192 and (m == 1280 or m == 7168)) or (k == 3584 and m == 8192):
_custom_C.LLMM1(weight, inp, out, 8)
elif k <= 8192 and k % 8 == 0 and m % 4 == 0:
_custom_C.LLMM1(weight, inp, out, 4)
else:
out = F.linear(inp, weight)
if batched:
out.view(*inp_shape[:-1], out.shape[-1])
if bias is not None:
out = out + bias
return out
return F.linear(inp, self.weight, self.bias)
def get_linear(weight, bias, quantize):
if quantize is None:
if SYSTEM == "rocm":
linear = FastLinearROCm(weight, bias)
else:
linear = FastLinear(weight, bias)
elif quantize == "eetq":
try:
from text_generation_server.layers.eetq import EETQLinear
linear = EETQLinear(weight, bias)
except ImportError:
raise ImportError(
"Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
)
elif quantize == "fp8":
from text_generation_server.layers.fp8 import Fp8Linear
linear = Fp8Linear(weight, bias)
elif quantize == "bitsandbytes":
try:
from text_generation_server.layers.bnb import (
warn_deprecate_bnb,
Linear8bitLt,
)
except ImportError:
raise NotImplementedError(
f"Bitsandbytes is missing install it with `pip install bitsandbytes`."
)
warn_deprecate_bnb()
linear = Linear8bitLt(
weight,
bias,
has_fp16_weights=False,
threshold=6.0,
)
if bias is not None:
linear.bias = nn.Parameter(bias)
elif quantize == "bitsandbytes-fp4":
try:
from text_generation_server.layers.bnb import Linear4bit
except ImportError:
raise NotImplementedError(
f"Bitsandbytes is missing install it with `pip install bitsandbytes`."
)
linear = Linear4bit(
weight,
bias,
quant_type="fp4",
)
elif quantize == "bitsandbytes-nf4":
try:
from text_generation_server.layers.bnb import Linear4bit
except ImportError:
raise NotImplementedError(
f"Bitsandbytes is missing install it with `pip install bitsandbytes`."
)
linear = Linear4bit(
weight,
bias,
quant_type="nf4",
)
elif quantize == "exl2":
from text_generation_server.layers.exl2 import Exl2Weight
if not isinstance(weight, Exl2Weight):
raise NotImplementedError(
f"The passed weight is not `exl2` compatible, loader needs to be updated."
)
from text_generation_server.layers.gptq import ExllamaQuantLinear
linear = ExllamaQuantLinear(weight, bias)
elif quantize == "gptq":
from text_generation_server.layers.gptq import GPTQWeight
if not isinstance(weight, GPTQWeight):
raise NotImplementedError(
f"The passed weight is not `gptq` compatible, loader needs to be updated."
)
if weight.use_exllama:
try:
from text_generation_server.layers.gptq import (
ExllamaQuantLinear,
)
except ImportError:
raise NotImplementedError(
f"Exllama gptq kernels are not installed. Install them `cd server/exllama_kernels && python setup.py install && cd ../exllamav2_kernels && python setup.py install`"
)
linear = ExllamaQuantLinear(weight, bias)
else:
from text_generation_server.layers.gptq.quant_linear import QuantLinear
linear = QuantLinear(
weight.qweight,
weight.qzeros,
weight.scales,
weight.g_idx,
bias,
weight.bits,
weight.groupsize,
)
elif quantize == "awq":
from text_generation_server.layers.gptq import GPTQWeight
if not isinstance(weight, GPTQWeight):
raise NotImplementedError(
f"The passed weight is not `awq` compatible, loader needs to be updated."
)
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
linear = WQLinear(
w_bit=weight.bits,
group_size=weight.groupsize,
qweight=weight.qweight,
qzeros=weight.qzeros,
scales=weight.scales,
bias=bias is not None,
)
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 quantize == "marlin":
from text_generation_server.layers.marlin import (
GPTQMarlinWeight,
MarlinLinear,
MarlinWeight,
)
if isinstance(weight, GPTQMarlinWeight):
linear = GPTQMarlinLinear(
weight=weight,
bias=bias,
)
elif isinstance(weight, MarlinWeight):
linear = MarlinLinear(weight=weight, bias=bias)
else:
raise NotImplementedError(
f"The passed weight is not `marlin` compatible, loader needs to be updated."
)
else:
raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
return linear