hf_text-generation-inference/server/text_generation_server/layers/linear.py

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Refactor layers. (#1866) # What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
2024-05-13 04:44:30 -06:00
import torch
from torch.nn import functional as F
from text_generation_server.utils.import_utils import SYSTEM
class FastLinear(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, input: torch.Tensor) -> torch.Tensor:
return F.linear(input, self.weight, self.bias)
def get_linear(weight, bias, quantize):
if quantize is None:
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 == "gptq":
try:
qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama = weight
except Exception:
raise NotImplementedError(
f"The passed weight is not `gptq` compatible, loader needs to be updated."
)
if 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(
qweight, qzeros, scales, g_idx, bias, bits, groupsize
)
else:
from text_generation_server.layers.gptq.quant_linear import QuantLinear
linear = QuantLinear(
qweight,
qzeros,
scales,
g_idx,
bias,
bits,
groupsize,
)
elif quantize == "awq":
try:
qweight, qzeros, scales, _, bits, groupsize, _ = weight
except Exception:
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=bits,
group_size=groupsize,
qweight=qweight,
qzeros=qzeros,
scales=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"
)
else:
raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
return linear