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

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 -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 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

server/Makefile-flashinfer

@@ -2,4 +2,4 @@ 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 flashinfer-python==0.2.0.post1

server/text_generation_server/models/__init__.py

@@ -79,7 +79,7 @@ __all__ = [
 
 FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
 
-FLASH_ATTENTION = True
+FLASH_ATTENTION = False
 
 try:
     from text_generation_server.models.flash_causal_lm import FlashCausalLM
@@ -931,10 +931,10 @@ def get_model(
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
-        elif sharded:
+        # elif sharded:
-            raise NotImplementedError(
+        #     raise NotImplementedError(
-                FLASH_ATT_ERROR_MESSAGE.format(f"Sharded {model_type}")
+        #         FLASH_ATT_ERROR_MESSAGE.format(f"Sharded {model_type}")
-            )
+        #     )
         else:
             return transformers_causal_lm_class.fallback(
                 model_id,

server/text_generation_server/models/transformers_flash_causal_lm.py

@@ -3,7 +3,7 @@ from typing import List, Optional
 
 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 +36,11 @@ 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]
 
+    import ipdb
+
+    ipdb.set_trace()
     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 +97,6 @@ class TransformersFlashCausalLM(FlashCausalLM):
         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 +106,17 @@ class TransformersFlashCausalLM(FlashCausalLM):
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
         if torch.cuda.is_available():
-            device = torch.device("cuda:0")
+            device = torch.device(f"cuda:{rank}")
-            dtype = torch.float16 if dtype is None else dtype
+            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 +127,10 @@ class TransformersFlashCausalLM(FlashCausalLM):
             model_id,
             revision=revision,
             torch_dtype=dtype,
-            device_map="auto",
+            # load_in_8bit=quantize == "bitsandbytes",
-            load_in_8bit=quantize == "bitsandbytes",
+            # trust_remote_code=trust_remote_code,
-            trust_remote_code=trust_remote_code,
+            # attn_implementation="tgi",
-            attn_implementation="tgi",
+            device_map=device if world_size == 1 else None,
             tp_plan="auto" if world_size > 1 else None,
         )