refactor model_id, make tunableop default

server/text_generation_server/models/bloom.py

@@ -46,7 +46,6 @@ class BLOOMSharded(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_causal_lm.py

@@ -31,6 +31,7 @@ from text_generation_server.models.cache_manager import (
 )
 from text_generation_server.pb import generate_pb2
 from text_generation_server.models.globals import MEM_POOL, CUDA_GRAPHS
+import text_generation_server.models.globals as tgi_globals
 from text_generation_server.utils import StoppingCriteria, HeterogeneousNextTokenChooser
 from text_generation_server.utils.dist import MEMORY_FRACTION
 
@@ -827,11 +828,14 @@ class FlashCausalLM(Model):
         )
 
         if SYSTEM == "rocm":
-            if os.environ.get("PYTORCH_TUNABLEOP_ENABLED", False):
-                if os.environ.get("PYTORCH_TUNABLEOP_TUNING", "1"):
+            if (
+                os.environ.get("PYTORCH_TUNABLEOP_ENABLED") is None
+                or os.environ.get("PYTORCH_TUNABLEOP_ENABLED") == "1"
+            ):
+                if os.environ.get("PYTORCH_TUNABLEOP_TUNING") != "0":
                     torch.cuda.tunable.tuning_enable(True)
 
-                if os.environ.get("PYTORCH_TUNABLEOP_SEQLENS", False):
+                if os.environ.get("PYTORCH_TUNABLEOP_SEQLENS") is not None:
                     tuning_sequences = [
                         int(val)
                         for val in os.environ["PYTORCH_TUNABLEOP_SEQLENS"].split(",")
@@ -841,11 +845,11 @@ class FlashCausalLM(Model):
 
                 tunableop_filepath = os.path.join(
                     HUGGINGFACE_HUB_CACHE,
-                    f"tunableop_{self.model_id.replace('/', '-')}_tp{self.world_size}_rank{self.rank}.csv",
+                    f"tunableop_{tgi_globals.MODEL_ID.replace('/', '-')}_tp{self.world_size}_rank{self.rank}.csv",
                 )
 
                 logger.info(
-                    f"PyTorch TunableOp (https://github.com/fxmarty/pytorch/tree/2.3-patched/aten/src/ATen/cuda/tunable) is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])} (typical decoding lengths). The picked GEMMs are saved in the file {tunableop_filepath}."
+                    f"PyTorch TunableOp (https://github.com/fxmarty/pytorch/tree/2.3-patched/aten/src/ATen/cuda/tunable) is enabled. The warmup may take several minutes, picking the ROCm optimal matrix multiplication kernel for the target lengths {', '.join([str(seqlen) for seqlen in tuning_sequences])}, with typical 5-8% latency improvement for small sequence lengths. The picked GEMMs are saved in the file {tunableop_filepath}. To disable TunableOp, please launch TGI with `PYTORCH_TUNABLEOP_ENABLED=0`."
                 )
 
                 if os.path.isfile(tunableop_filepath):

server/text_generation_server/models/flash_cohere.py

@@ -28,7 +28,6 @@ class FlashCohere(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_dbrx.py

@@ -30,7 +30,6 @@ class FlashDbrx(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_gemma.py

@@ -28,7 +28,6 @@ class FlashGemma(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_gpt2.py

@@ -15,11 +15,11 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
+from text_generation_server.models import CausalLM
+from text_generation_server.utils.import_utils import SYSTEM
 
 tracer = trace.get_tracer(__name__)
 
-from text_generation_server.utils.import_utils import SYSTEM
-
 
 class FlashGPT2(FlashCausalLM):
     def __init__(
@@ -31,6 +31,7 @@ class FlashGPT2(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
             device = torch.device(f"cuda:{rank}")

server/text_generation_server/models/flash_llama.py

@@ -10,6 +10,7 @@ from text_generation_server.models import FlashCausalLM
 from text_generation_server.models.custom_modeling.flash_llama_modeling import (
     FlashLlamaForCausalLM,
 )
+
 from text_generation_server.utils import (
     initialize_torch_distributed,
     weight_files,
@@ -31,7 +32,6 @@ class FlashLlama(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_mistral.py

@@ -27,6 +27,7 @@ from text_generation_server.utils import (
     HeterogeneousNextTokenChooser,
     StoppingCriteria,
 )
+from text_generation_server.models import CausalLM
 
 tracer = trace.get_tracer(__name__)
 
@@ -318,7 +319,6 @@ class BaseFlashMistral(FlashCausalLM):
         trust_remote_code: bool = False,
         tokenizer_class=AutoTokenizer,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_neox.py

@@ -15,6 +15,7 @@ from text_generation_server.utils import (
     Weights,
 )
 from text_generation_server.utils.import_utils import SYSTEM
+from text_generation_server.models import CausalLM
 
 tracer = trace.get_tracer(__name__)
 
@@ -29,7 +30,6 @@ class FlashNeoXSharded(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_phi.py

@@ -15,6 +15,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
+from text_generation_server.models import CausalLM
 
 tracer = trace.get_tracer(__name__)
 
@@ -29,7 +30,6 @@ class FlashPhi(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_qwen2.py

@@ -20,6 +20,7 @@ from text_generation_server.utils import (
     weight_files,
     Weights,
 )
+from text_generation_server.models import CausalLM
 
 tracer = trace.get_tracer(__name__)
 
@@ -34,7 +35,6 @@ class FlashQwen2(BaseFlashMistral):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_rw.py

@@ -30,7 +30,6 @@ class FlashRWSharded(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_santacoder.py

@@ -33,7 +33,6 @@ class FlashSantacoderSharded(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/flash_starcoder2.py

@@ -33,7 +33,6 @@ class FlashStarcoder2(BaseFlashMistral):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/galactica.py

@@ -21,6 +21,7 @@ from text_generation_server.utils import (
     Weights,
 )
 
+
 # CREDIT: Papers with code => https://github.com/paperswithcode/galai/blob/main/galai/utils.py
 
 # we split individual characters inside special tokens like [START_DNA]
@@ -171,7 +172,6 @@ class GalacticaSharded(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/globals.py

@@ -15,3 +15,12 @@ else:
     cuda_graphs = None
 
 CUDA_GRAPHS = cuda_graphs
+
+# This is overridden at model loading.
+global MODEL_ID
+MODEL_ID = None
+
+
+def set_model_id(model_id: str):
+    global MODEL_ID
+    MODEL_ID = model_id

server/text_generation_server/models/gpt_neox.py

@@ -28,7 +28,6 @@ class GPTNeoxSharded(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/idefics.py

@@ -35,7 +35,6 @@ class IDEFICSSharded(IdeficsCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/idefics_causal_lm.py

@@ -22,7 +22,6 @@ from text_generation_server.models.types import (
 from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
 from text_generation_server.models.vlm_causal_lm import split
-
 import re
 
 IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
@@ -577,6 +576,7 @@ class IdeficsCausalLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+
         from text_generation_server.models.custom_modeling.idefics_modeling import (
             IdeficsForVisionText2Text,
         )

server/text_generation_server/models/mamba.py

@@ -412,7 +412,6 @@ class Mamba(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, _rank, world_size = initialize_torch_distributed()
         if world_size > 1:

server/text_generation_server/models/mpt.py

@@ -47,7 +47,6 @@ class MPTSharded(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/opt.py

@@ -26,7 +26,6 @@ class OPTSharded(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/phi.py

@@ -26,7 +26,6 @@ class Phi(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, _rank, _world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/models/rw.py

@@ -16,6 +16,7 @@ class RW(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+
         if speculator:
             raise RuntimeError("Medusa decoding is not enabled for AutoModel")
 

server/text_generation_server/models/santacoder.py

@@ -23,7 +23,6 @@ class SantaCoder(CausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         if torch.cuda.is_available():
             device = torch.device("cuda")

server/text_generation_server/models/seq2seq_lm.py

@@ -17,6 +17,7 @@ from text_generation_server.models.types import (
 from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
 
+
 tracer = trace.get_tracer(__name__)
 
 
@@ -536,7 +537,7 @@ class Seq2SeqLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
+
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 

server/text_generation_server/models/t5.py

@@ -29,7 +29,6 @@ class T5Sharded(Seq2SeqLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.model_id = model_id
 
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():

server/text_generation_server/server.py

@@ -21,6 +21,7 @@ from text_generation_server.models.vlm_causal_lm import (
 from text_generation_server.pb import generate_pb2_grpc, generate_pb2
 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
 from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
+from text_generation_server.models.globals import set_model_id
 
 
 class SignalHandler:
@@ -255,6 +256,7 @@ def serve(
         while signal_handler.KEEP_PROCESSING:
             await asyncio.sleep(0.5)
 
+    set_model_id(model_id)
     asyncio.run(
         serve_inner(
             model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code