Tmp tp transformers (#2942)

* Upgrade the version number.

* Remove modifications in Lock.

* Tmp branch to test transformers backend with 2.5.1 and TP>1

* Fixing the transformers backend.

inference_mode forces the use of `aten.matmul` instead of `aten.mm` the
former doesn't have sharding support crashing the transformers TP
support.

`lm_head.forward` also crashes because it skips the hook that
cast/decast the DTensor.

Torch 2.5.1 is required for sharding support.

* Put back the attention impl.

* Revert the flashinfer (this will fails).

* Building AOT.

* Using 2.5 kernels.

* Remove the archlist, it's defined in the docker anyway.

Dockerfile

@@ -47,7 +47,7 @@ RUN cargo build --profile release-opt --frozen
 FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS pytorch-install
 
 # NOTE: When updating PyTorch version, beware to remove `pip install nvidia-nccl-cu12==2.22.3` below in the Dockerfile. Context: https://github.com/huggingface/text-generation-inference/pull/2099
-ARG PYTORCH_VERSION=2.4.0
+ARG PYTORCH_VERSION=2.5.1
 
 ARG PYTHON_VERSION=3.11
 # Keep in sync with `server/pyproject.toml
@@ -235,8 +235,8 @@ RUN cd server && \
     make gen-server && \
     python -c "from text_generation_server.pb import generate_pb2" && \
     pip install -U pip uv && \
-    uv pip install -e ".[attention, bnb, accelerate, compressed-tensors, marlin, moe, quantize, peft, outlines]" --no-cache-dir && \
-    uv pip install nvidia-nccl-cu12==2.22.3
+    uv pip install -e ".[attention, bnb, accelerate, compressed-tensors, marlin, moe, quantize, peft, outlines]" --no-cache-dir # && \
+    # uv pip install nvidia-nccl-cu12==2.22.3
 
 ENV LD_PRELOAD=/opt/conda/lib/python3.11/site-packages/nvidia/nccl/lib/libnccl.so.2
 # Required to find libpython within the rust binaries

README.md

@@ -84,7 +84,7 @@ model=HuggingFaceH4/zephyr-7b-beta
 volume=$PWD/data
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
+    ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model
 ```
 
 And then you can make requests like
@@ -121,7 +121,7 @@ curl localhost:8080/v1/chat/completions \
 
 **Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
 
-**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/installation_amd#using-tgi-with-amd-gpus). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.0-rocm --model-id $model` instead of the command above.
+**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/installation_amd#using-tgi-with-amd-gpus). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2-rocm --model-id $model` instead of the command above.
 
 To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
 ```
@@ -152,7 +152,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 token=<your cli READ token>
 
 docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.0 --model-id $model
+    ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model
 ```
 
 ### A note on Shared Memory (shm)

docs/source/basic_tutorials/gated_model_access.md

@@ -19,6 +19,6 @@ docker run --gpus all \
     --shm-size 1g \
     -e HF_TOKEN=$token \
     -p 8080:80 \
-    -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```

docs/source/conceptual/quantization.md

@@ -19,15 +19,15 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models.
 In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 --model-id $model --quantize bitsandbytes
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize bitsandbytes
 ```
 
 4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load.
 
 In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 --model-id $model --quantize bitsandbytes-nf4
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize bitsandbytes-nf4
 ```
 
 You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).
@@ -48,7 +48,7 @@ $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$
 TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇
 
 ```bash
-docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.1 --model-id $model --quantize gptq
+docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize gptq
 ```
 
 Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI.

docs/source/installation_amd.md

@@ -11,7 +11,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
     --device=/dev/kfd --device=/dev/dri --group-add video \
     --ipc=host --shm-size 256g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-rocm \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-rocm \
     --model-id $model
 ```
 

docs/source/installation_intel.md

@@ -12,7 +12,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-intel-xpu \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-intel-xpu \
     --model-id $model --cuda-graphs 0
 ```
 
@@ -29,7 +29,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --rm --privileged --cap-add=sys_nice \
     --device=/dev/dri \
     --ipc=host --shm-size 1g --net host -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1-intel-cpu \
+    ghcr.io/huggingface/text-generation-inference:3.0.2-intel-cpu \
     --model-id $model --cuda-graphs 0
 ```
 

docs/source/installation_nvidia.md

@@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 64g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```
 

docs/source/quicktour.md

@@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
-    ghcr.io/huggingface/text-generation-inference:3.0.1 \
+    ghcr.io/huggingface/text-generation-inference:3.0.2 \
     --model-id $model
 ```
 
@@ -96,7 +96,7 @@ curl 127.0.0.1:8080/generate \
 To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
 
 ```bash
-docker run ghcr.io/huggingface/text-generation-inference:3.0.1 --help
+docker run ghcr.io/huggingface/text-generation-inference:3.0.2 --help
 ```
 
 </Tip>

docs/source/reference/api_reference.md

@@ -163,7 +163,7 @@ hub = {
 
 # create Hugging Face Model Class
 huggingface_model = HuggingFaceModel(
- image_uri=get_huggingface_llm_image_uri("huggingface",version="3.0.1"),
+ image_uri=get_huggingface_llm_image_uri("huggingface",version="3.0.2"),
  env=hub,
  role=role,
 )

server/Makefile-flashinfer

@@ -1,5 +1,6 @@
 install-flashinfer:
 	# We need fsspec as an additional dependency, but
 	# `pip install flashinfer` cannot resolve it.
-	pip install fsspec
-	pip install flashinfer==0.2.0.post1 -i https://flashinfer.ai/whl/cu124/torch2.4
+	pip install fsspec sympy==1.13.1 numpy
+	pip install -U setuptools
+	FLASHINFER_ENABLE_AOT=1 pip install git+https://github.com/flashinfer-ai/[email protected]#egg=flashinfer  --no-build-isolation

server/pyproject.toml

@@ -69,22 +69,22 @@ gen = [
 
 [tool.uv.sources]
 attention-kernels = [
-  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
-  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
-  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
-  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.4-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
+  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.5-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
+  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.5-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
+  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.5-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
+  { url = "https://github.com/danieldk/attention-kernels/releases/download/v0.1.1/attention_kernels-0.1.1+cu123torch2.5-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
 ]
 marlin-kernels = [
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
-  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.4-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
+  { url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.7/marlin_kernels-0.3.7+cu123torch2.5-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
 ]
 moe-kernels = [
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
-  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.5-cp39-cp39-linux_x86_64.whl", marker = "python_version == '3.9'" },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.5-cp310-cp310-linux_x86_64.whl", marker = "python_version == '3.10'" },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.5-cp311-cp311-linux_x86_64.whl", marker = "python_version == '3.11'" },
+  { url = "https://github.com/danieldk/moe-kernels/releases/download/v0.7.0/moe_kernels-0.7.0+cu123torch2.5-cp312-cp312-linux_x86_64.whl", marker = "python_version == '3.12'" },
 ]
 
 [tool.pytest.ini_options]

server/text_generation_server/models/__init__.py

@@ -931,10 +931,10 @@ def get_model(
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
-        elif sharded:
-            raise NotImplementedError(
-                FLASH_ATT_ERROR_MESSAGE.format(f"Sharded {model_type}")
-            )
+        # elif sharded:
+        #     raise NotImplementedError(
+        #         FLASH_ATT_ERROR_MESSAGE.format(f"Sharded {model_type}")
+        #     )
         else:
             return transformers_causal_lm_class.fallback(
                 model_id,

server/text_generation_server/models/flash_causal_lm.py

@@ -1571,7 +1571,7 @@ def warmup(
             real_free_memory = get_free_memory(self.device, MEMORY_FRACTION)
             log_master(
                 logger.debug,
-                f"Free memory {free_memory/1e9:.2f}GB , (real: {real_free_memory/1e9:.2f}GB",
+                f"Free memory {free_memory / 1e9:.2f}GB , (real: {real_free_memory / 1e9:.2f}GB",
             )
 
             _, _batch, _ = self.generate_token(batch)

server/text_generation_server/models/transformers_flash_causal_lm.py

@@ -3,7 +3,7 @@
 
 import torch
 from opentelemetry import trace
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers.modeling_utils
 
 from text_generation_server.models.flash_causal_lm import FlashCausalLM
@@ -36,9 +36,7 @@ def tgi_flash_attention_forward(
     softcap: Optional[float] = None,
     **kwargs,  # This is needed to "absorb" other args passed by Transformers modeling
 ):
-
     kv_cache = kv_cache[module.layer_idx]
-
     query_states = query_states.transpose(1, 2).squeeze(dim=0)
     key_states = key_states.transpose(1, 2).squeeze(dim=0)
     value_states = value_states.transpose(1, 2).squeeze(dim=0)
@@ -95,7 +93,6 @@ def __init__(
         default_dtype=torch.float16,
         trust_remote_code: bool = False,
         tokenizer_class=AutoTokenizer,
-        config_class=AutoConfig,
         kv_cache_dtype: Optional[torch.dtype] = None,
     ):
         self.quantize = quantize
@@ -105,17 +102,17 @@ def __init__(
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
         if torch.cuda.is_available():
-            device = torch.device("cuda:0")
-            dtype = torch.float16 if dtype is None else dtype
+            device = torch.device(f"cuda:{rank}")
+            dtype = default_dtype if dtype is None else dtype
         elif hasattr(torch, "xpu") and torch.xpu.is_available():
             device = torch.device("xpu")
-            dtype = torch.float16 if dtype is None else dtype
+            dtype = default_dtype if dtype is None else dtype
         else:
             raise ValueError(
                 "Flash `Transformers` modeling backend is not available on cpu."
             )
 
-        tokenizer = AutoTokenizer.from_pretrained(
+        tokenizer = tokenizer_class.from_pretrained(
             model_id,
             revision=revision,
             padding_side="left",
@@ -126,10 +123,10 @@ def __init__(
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto",
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
             attn_implementation="tgi",
+            device_map=device if world_size == 1 else None,
             tp_plan="auto" if world_size > 1 else None,
         )
 
@@ -261,6 +258,6 @@ def _model_forward(
         # To update with full Transformers support asap
         if lm_head_indices is not None:
             hidden_states = hidden_states[lm_head_indices]
-        logits = self.model.lm_head.forward(hidden_states)
+        logits = self.model.lm_head(hidden_states)
 
         return logits, None

server/text_generation_server/server.py

@@ -68,9 +68,9 @@ def __init__(
         self.quantize = model.quantize
         self.server_urls = server_urls
         # For some reason, inference_mode does not work well with GLOO which we use on CPU
-        if model.device.type == "cuda":
-            # Force inference mode for the lifetime of TextGenerationService
-            self._inference_mode_raii_guard = torch._C._InferenceMode(True)
+        # if model.device.type == "cuda":
+        #     # Force inference mode for the lifetime of TextGenerationService
+        #     self._inference_mode_raii_guard = torch._C._InferenceMode(True)
 
     async def Info(self, request, context):
         return self.model.info