ROCm and sliding windows fixes (#2033)

* update vllm commit & fix models using sliding window

* update

* update commit

* fix bug where tunableop is bound to cuda graph even when cuda graph are disabled

* enable tunableop by default

* fix sliding window

* address review

* dead code

* precise comment

* is it flaky?

launcher/src/main.rs

@@ -481,6 +481,7 @@ fn shard_manager(
     rope_factor: Option<f32>,
     max_total_tokens: usize,
     max_batch_size: Option<usize>,
+    max_input_tokens: usize,
     otlp_endpoint: Option<String>,
     log_level: LevelFilter,
     status_sender: mpsc::Sender<ShardStatus>,
@@ -553,6 +554,10 @@ fn shard_manager(
         shard_args.push(otlp_endpoint);
     }
 
+    // In case we use sliding window, we may ignore the sliding in flash for some backends depending on the parameter.
+    shard_args.push("--max-input-tokens".to_string());
+    shard_args.push(max_input_tokens.to_string());
+
     // Copy current process env
     let mut envs: Vec<(OsString, OsString)> = env::vars_os().collect();
 
@@ -1009,6 +1014,7 @@ fn spawn_shards(
     args: &Args,
     cuda_graphs: Vec<usize>,
     max_total_tokens: usize,
+    max_input_tokens: usize,
     max_log_level: LevelFilter,
     shutdown: Arc<AtomicBool>,
     shutdown_receiver: &mpsc::Receiver<()>,
@@ -1066,6 +1072,7 @@ fn spawn_shards(
                 rope_factor,
                 max_total_tokens,
                 max_batch_size,
+                max_input_tokens,
                 otlp_endpoint,
                 max_log_level,
                 status_sender,
@@ -1540,6 +1547,7 @@ fn main() -> Result<(), LauncherError> {
         &args,
         cuda_graphs,
         max_total_tokens,
+        max_input_tokens,
         max_log_level,
         shutdown.clone(),
         &shutdown_receiver,

server/Makefile-vllm

@@ -1,5 +1,5 @@
 commit_cuda := b5dfc61db88a81069e45b44f7cc99bd9e62a60fa
-commit_rocm := ca6913b3c2ffacdcb7d15e914dc34adbc6c89479
+commit_rocm := 559200c1a028de990c1ddea761b0ccd62109e3a0
 build-vllm-cuda:
 	if [ ! -d 'vllm' ]; then \
 		pip install -U ninja packaging --no-cache-dir && \

server/text_generation_server/cli.py

@@ -42,6 +42,7 @@ def serve(
     logger_level: str = "INFO",
     json_output: bool = False,
     otlp_endpoint: Optional[str] = None,
+    max_input_tokens: Optional[int] = None,
 ):
     if sharded:
         assert (
@@ -98,6 +99,7 @@ def serve(
         dtype,
         trust_remote_code,
         uds_path,
+        max_input_tokens,
     )
 
 

server/text_generation_server/layers/attention/rocm.py

@@ -169,10 +169,8 @@ if ENGINE == "ck":
     ):
         if window_size_left <= 0 and window_size_left != -1:
             raise ValueError("`window_size_left` must be > 0 or -1")
-        if window_size_left != -1:
-            raise ValueError(
-                f"ROCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
-            )
+
+        # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
         return flash_attn_2_cuda.varlen_fwd(
             q,
             k,
@@ -204,10 +202,7 @@ elif ENGINE == "triton":
         window_size_left=-1,
         causal=True,
     ):
-        if window_size_left != -1:
-            raise ValueError(
-                f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
-            )
+        # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
         output, _ = triton_attention(
             q,
             k,

server/text_generation_server/layers/attention/xpu.py

@@ -14,10 +14,7 @@ def attention(
     softmax_scale,
     window_size_left=-1,
 ):
-    if window_size_left != -1:
-        raise ValueError(
-            f"XPU version of Flash Attention does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
-        )
+    # We do not need to check window_size_left (not supported) here, so it is already checked ahead of time at model load.
     return ipex.llm.functional.varlen_attention(
         q,
         k,

server/text_generation_server/models/__init__.py

@@ -24,6 +24,8 @@ from text_generation_server.models.t5 import T5Sharded
 from text_generation_server.models.gpt_neox import GPTNeoxSharded
 from text_generation_server.models.phi import Phi
 
+from text_generation_server.utils.import_utils import SYSTEM
+
 # The flag below controls whether to allow TF32 on matmul. This flag defaults to False
 # in PyTorch 1.12 and later.
 torch.backends.cuda.matmul.allow_tf32 = True
@@ -257,6 +259,7 @@ def get_model(
     speculate: Optional[int],
     dtype: Optional[str],
     trust_remote_code: bool,
+    max_input_tokens: int,
 ) -> Model:
     global FLASH_ATTENTION
     if dtype is None:
@@ -410,11 +413,15 @@ def get_model(
             "Sharding is currently not supported with `exl2` quantization"
         )
     sliding_window = config_dict.get("sliding_window", -1)
-    if sliding_window != -1 and not SUPPORTS_WINDOWING:
-        logger.warning(
-            f"Flash attention is available, but doesn't support windowing which is required by model {model_id}"
+
+    if (
+        (sliding_window is not None and sliding_window != -1)
+        and not SUPPORTS_WINDOWING
+        and max_input_tokens > sliding_window
+    ):
+        raise ValueError(
+            f"The backend {SYSTEM} does not support sliding window attention that is used by the model type {model_type}. To use this model nonetheless with the {SYSTEM} backend, please launch TGI with the argument `--max-input-tokens` smaller than sliding_window={sliding_window} (got here max_input_tokens={max_input_tokens})."
         )
-        FLASH_ATTENTION = False
 
     if model_type == MAMBA:
         return Mamba(
@@ -701,7 +708,6 @@ def get_model(
                 )
 
     if model_type == MISTRAL:
-        sliding_window = config_dict.get("sliding_window", -1)
         if FLASH_ATTENTION:
             return FlashMistral(
                 model_id,
@@ -724,7 +730,6 @@ def get_model(
             )
 
     if model_type == MIXTRAL:
-        sliding_window = config_dict.get("sliding_window", -1)
         if FLASH_ATTENTION:
             return FlashMixtral(
                 model_id,
@@ -747,7 +752,6 @@ def get_model(
             )
 
     if model_type == STARCODER2:
-        sliding_window = config_dict.get("sliding_window", -1)
         if FLASH_ATTENTION:
             return FlashStarcoder2(
                 model_id,
@@ -771,8 +775,7 @@ def get_model(
             )
 
     if model_type == QWEN2:
-        sliding_window = config_dict.get("sliding_window", -1)
-        if (sliding_window is None or sliding_window != -1) and SUPPORTS_WINDOWING:
+        if FLASH_ATTENTION:
             return FlashQwen2(
                 model_id,
                 revision,

server/text_generation_server/models/flash_causal_lm.py

@@ -902,6 +902,8 @@ class FlashCausalLM(Model):
                 os.environ.get("PYTORCH_TUNABLEOP_ENABLED") is None
                 or os.environ.get("PYTORCH_TUNABLEOP_ENABLED") == "1"
             ):
+                torch.cuda.tunable.enable()
+
                 if os.environ.get("PYTORCH_TUNABLEOP_TUNING") != "0":
                     torch.cuda.tunable.tuning_enable(True)
 
@@ -910,8 +912,11 @@ class FlashCausalLM(Model):
                         int(val)
                         for val in os.environ["PYTORCH_TUNABLEOP_SEQLENS"].split(",")
                     ]
-                else:
+                elif CUDA_GRAPHS is not None:
                     tuning_sequences = CUDA_GRAPHS
+                else:
+                    # For seqlen = 1, we dispatch to LLMM1 kernel.
+                    tuning_sequences = [2, 3, 4, 5, 6, 7]
 
                 tunableop_filepath = os.path.join(
                     HUGGINGFACE_HUB_CACHE,

server/text_generation_server/server.py

@@ -199,6 +199,7 @@ def serve(
     dtype: Optional[str],
     trust_remote_code: bool,
     uds_path: Path,
+    max_input_tokens: int,
 ):
     async def serve_inner(
         model_id: str,
@@ -229,6 +230,7 @@ def serve(
                 speculate,
                 dtype,
                 trust_remote_code,
+                max_input_tokens,
             )
         except Exception:
             logger.exception("Error when initializing model")