bitsandbytes: upgrade and enable CUDA Graphs for 4bit by default

docs/source/basic_tutorials/preparing_model.md

@@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
 
 ## Quantization
 
-TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [Marlin](https://github.com/IST-DASLab/marlin), [EETQ](https://github.com/NetEase-FuXi/EETQ), [EXL2](https://github.com/turboderp/exllamav2), and [fp8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq`, `awq`, `marlin`, `exl2`, `eetq` or `fp8` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq). Similarly, when using AWQ quantization, you need to point to one of [these models](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
+TGI supports [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes#bitsandbytes), [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [Marlin](https://github.com/IST-DASLab/marlin), [EETQ](https://github.com/NetEase-FuXi/EETQ), [EXL2](https://github.com/turboderp/exllamav2), and [fp8](https://developer.nvidia.com/blog/nvidia-arm-and-intel-publish-fp8-specification-for-standardization-as-an-interchange-format-for-ai/) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq`, `awq`, `marlin`, `exl2`, `eetq` or `fp8` depending on the quantization technique you wish to use. When using GPTQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq). Similarly, when using AWQ quantization, you need to point to one of [these models](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to [quantization guide](./../conceptual/quantization)
 
 
 ## RoPE Scaling

docs/source/index.md

@@ -12,7 +12,7 @@ Text Generation Inference implements many optimizations and features, such as:
 - Token streaming using Server-Sent Events (SSE)
 - Continuous batching of incoming requests for increased total throughput
 - Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
-- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
+- Quantization with [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
 - [Safetensors](https://github.com/huggingface/safetensors) weight loading
 - Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
 - Logits warper (temperature scaling, top-p, top-k, repetition penalty)

launcher/src/main.rs

@@ -2075,15 +2075,8 @@ fn main() -> Result<(), LauncherError> {
     let cuda_graphs = match (&args.cuda_graphs, &quantize) {
         (Some(cuda_graphs), _) => cuda_graphs.iter().cloned().filter(|&c| c > 0).collect(),
         #[allow(deprecated)]
-        (
-            None,
-            Some(
-                Quantization::Bitsandbytes
-                | Quantization::BitsandbytesNf4
-                | Quantization::BitsandbytesFp4,
-            ),
-        ) => {
-            tracing::warn!("Bitsandbytes doesn't work with cuda graphs, deactivating them");
+        (None, Some(Quantization::Bitsandbytes)) => {
+            tracing::warn!("Bitsandbytes 8bit doesn't work with cuda graphs, deactivating them");
             vec![]
         }
         (None, Some(Quantization::Exl2)) => {

server/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
 
 [[package]]
 name = "accelerate"
@@ -290,22 +290,23 @@ tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"]
 
 [[package]]
 name = "bitsandbytes"
-version = "0.43.3"
+version = "0.45.0"
 description = "k-bit optimizers and matrix multiplication routines."
 optional = true
 python-versions = "*"
 files = [
-    {file = "bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:cc99507c352be0715098b2c7577b690dd158972dc4ea10c7495bac104c7c79f0"},
-    {file = "bitsandbytes-0.43.3-py3-none-win_amd64.whl", hash = "sha256:257f6552f2144748a84e6c44e1f7a98f3da888f675ed74e18fd7f7eb13c6cafa"},
+    {file = "bitsandbytes-0.45.0-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:0f0323de1ff1fdf8383e79bdad1283516a4c05a6fd2b44a363bf4e059422305b"},
+    {file = "bitsandbytes-0.45.0-py3-none-win_amd64.whl", hash = "sha256:ebbf96e0ecb466716a65ecdeaef3fa1983575447b9ab66b74e5211892507c6ff"},
 ]
 
 [package.dependencies]
 numpy = "*"
 torch = "*"
+typing_extensions = ">=4.8.0"
 
 [package.extras]
 benchmark = ["matplotlib", "pandas"]
-test = ["scipy"]
+test = ["lion_pytorch", "scipy"]
 
 [[package]]
 name = "certifi"
@@ -4097,4 +4098,4 @@ torch = ["torch"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "c7fdcff2b752cd3beb3995c1ecd15f0f4d9b4e117048b06ab991c6d0e0c86ff3"
+content-hash = "767757fffcf7bec05a8a60dcfe2a3c7d258f26efac3004f3d24c8d543b462413"

server/pyproject.toml

@@ -16,7 +16,7 @@ grpcio-reflection = "^1.51.1"
 grpc-interceptor = "^0.15.4"
 typer = "^0.12.5"
 accelerate = {version = "^1.1.0", optional = true}
-bitsandbytes = { version = "^0.43.0", optional = true }
+bitsandbytes = { version = "^0.45.0", optional = true }
 safetensors = "^0.4.5"
 loguru = "^0.7.2"
 opentelemetry-api = "^1.27.0"

server/text_generation_server/layers/bnb.py

@@ -20,23 +20,16 @@ class Linear8bitLt(torch.nn.Module):
         weight,
         bias,
         has_fp16_weights=True,
-        memory_efficient_backward=False,
         threshold=0.0,
         index=None,
     ):
         super().__init__()
-        assert (
-            not memory_efficient_backward
-        ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
         self.state = bnb.MatmulLtState()
         self.index = index
 
         # Necessary for stacked layers
         self.state.threshold = threshold
         self.state.has_fp16_weights = has_fp16_weights
-        self.state.memory_efficient_backward = memory_efficient_backward
-        if threshold > 0.0 and not has_fp16_weights:
-            self.state.use_pool = True
 
         self.weight = Int8Params(
             weight.data,
@@ -63,12 +56,9 @@ class Linear8bitLt(torch.nn.Module):
 
         out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
 
-        if not self.state.has_fp16_weights:
-            if self.state.CB is not None and self.state.CxB is not None:
-                # we converted 8-bit row major to turing/ampere format in the first inference pass
-                # we no longer need the row-major weight
-                del self.state.CB
-                self.weight.data = self.state.CxB
+        if not self.state.has_fp16_weights and self.state.CB is not None:
+            self.weight.data = self.state.CB
+
         return out
 
 
@@ -106,19 +96,12 @@ class Linear4bit(torch.nn.Module):
         if self.bias is not None and self.bias.dtype != x.dtype:
             self.bias.data = self.bias.data.to(x.dtype)
 
-        if getattr(self.weight, "quant_state", None) is None:
-            print(
-                "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
-            )
         inp_dtype = x.dtype
         if self.compute_dtype is not None:
             x = x.to(self.compute_dtype)
 
         bias = None if self.bias is None else self.bias.to(self.compute_dtype)
-        out = bnb.matmul_4bit(
+
+        return bnb.matmul_4bit(
             x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
-        )
-
-        out = out.to(inp_dtype)
-
-        return out
+        ).to(inp_dtype)