Fp8 Support (#1726)

# 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

 -->

---------

Co-authored-by: Dong Shin <d0104.shin@gmail.com>

Author: Narsil

docs/source/basic_tutorials/launcher.md

@@ -66,6 +66,7 @@ Options:
           - bitsandbytes:     Bitsandbytes 8bit. Can be applied on any model, will cut the memory requirement in half, but it is known that the model will be much slower to run than the native f16
           - bitsandbytes-nf4: Bitsandbytes 4bit. Can be applied on any model, will cut the memory requirement by 4x, but it is known that the model will be much slower to run than the native f16
           - bitsandbytes-fp4: Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better perplexity performance for you model
+          - fp8:              [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above This dtype has native ops should be the fastest if available. This is currently not the fastest because of local unpacking + padding to satisfy matrix multiplication limitations
 
 ```
 ## SPECULATE

launcher/src/main.rs

@@ -47,6 +47,11 @@ enum Quantization {
     /// Bitsandbytes 4bit. nf4 should be preferred in most cases but maybe this one has better
     /// perplexity performance for you model
     BitsandbytesFP4,
+    /// [FP8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) (e4m3) works on H100 and above
+    /// This dtype has native ops should be the fastest if available.
+    /// This is currently not the fastest because of local unpacking + padding to satisfy matrix
+    /// multiplication limitations.
+    Fp8,
 }
 
 impl std::fmt::Display for Quantization {
@@ -73,6 +78,9 @@ impl std::fmt::Display for Quantization {
             Quantization::Eetq => {
                 write!(f, "eetq")
             }
+            Quantization::Fp8 => {
+                write!(f, "fp8")
+            }
         }
     }
 }

server/text_generation_server/cli.py

@@ -19,6 +19,7 @@ class Quantization(str, Enum):
     gptq = "gptq"
     awq = "awq"
     eetq = "eetq"
+    fp8 = "fp8"
 
 
 class Dtype(str, Enum):

server/text_generation_server/utils/layers.py

@@ -182,6 +182,48 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
         return output
 
 
+def fp8_quantize(weight, qdtype=torch.float8_e4m3fn):
+    device = weight.device
+    # weight, scale = quant_weights(weight, torch.int8, False)
+    finfo = torch.finfo(qdtype)
+    # Calculate the scale as dtype max divided by absmax
+    scale = finfo.max / weight.abs().max().clamp(min=1e-12)
+    # scale and clamp the tensor to bring it to
+    # the representative range of float8 data type
+    # (as default cast is unsaturated)
+    qweight = (weight * scale).clamp(min=finfo.min, max=finfo.max)
+    # Return both float8 data and the inverse scale (as float),
+    # as both required as inputs to torch._scaled_mm
+    qweight = qweight.to(qdtype)
+    scale = scale.float().reciprocal()
+    return qweight, scale
+
+
+class Fp8Linear(nn.Module):
+    def __init__(
+        self,
+        weight,
+        bias,
+    ) -> None:
+        super().__init__()
+        self.dtype = weight.dtype
+        self.qweight, self.scale = fp8_quantize(weight)
+
+        self.bias = bias if bias is not None else None
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        qinput, scale = fp8_quantize(input)
+        output, _ = torch._scaled_mm(
+            qinput,
+            self.qweight.t(),
+            out_dtype=self.dtype,
+            scale_a=scale,
+            scale_b=self.scale,
+            bias=self.bias,
+        )
+        return output
+
+
 class Linear8bitLt(nn.Module):
     def __init__(
         self,
@@ -293,6 +335,8 @@ def get_linear(weight, bias, quantize):
             raise ImportError(
                 "Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
             )
+    elif quantize == "fp8":
+        linear = Fp8Linear(weight, bias)
     elif quantize == "bitsandbytes":
         warn_deprecate_bnb()
         linear = Linear8bitLt(