✈️ Introduce Jetstream/Pytorch in TGI

.github/workflows/test-pytorch-xla-tpu-tgi.yml

@@ -31,3 +31,13 @@ jobs:
       - name: Build and test TGI server
         run: |
           HF_TOKEN=${{ secrets.HF_TOKEN_OPTIMUM_TPU_CI }} make tgi_test
+
+      # Use a different step to test the Jetstream Pytorch version, to avoid conflicts with torch-xla[tpu]
+      - name: Install and test TGI server (Jetstream Pytorch)
+        run: |
+          pip install -U .[jetstream-pt] \
+            -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
+            -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html \
+            -f https://storage.googleapis.com/libtpu-releases/index.html
+          JETSTREAM_PT=1 HF_TOKEN=${{ secrets.HF_TOKEN_OPTIMUM_TPU_CI }} python -m \
+            pytest -sv text-generation-inference/tests -k jetstream

Makefile

@@ -40,6 +40,7 @@ $(PACKAGE_DIST) $(PACKAGE_WHEEL): $(PACKAGE_FILES)
 
 clean:
 	rm -rf dist
+	make -C text-generation-inference/server/ clean
 
 tpu-tgi:
 	docker build --rm -f text-generation-inference/docker/Dockerfile \

README.md

@@ -45,7 +45,20 @@ Other TPU versions will be supported along the way.
 As part of the integration, we do support a [text-generation-inference (TGI)](https://github.com/huggingface/optimum-tpu/tree/main/text-generation-inference) backend allowing to deploy and serve
 incoming HTTP requests and execute them on Cloud TPUs.
 
-Please see the [TGI specific documentation]() on how to get started
+Please see the [TGI specific documentation](text-generation-inference) on how to get started.
+
+### JetStream Pytorch Engine
+
+`optimum-tpu` provides an optional support of JetStream Pytorch engine inside of TGI. This support can be installed using the dedicated command:
+
+```shell
+pip install "optimum-tpu[jetstream-pt]" \
+  -f https://storage.googleapis.com/jax-releases/jax_nightly_releases.html \
+  -f https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html \
+  -f https://storage.googleapis.com/libtpu-releases/index.html
+```
+
+To enable the support, export the environment variable `JETSTREAM_PT=1`.
 
 ## Training
 

optimum/tpu/__init__.py

@@ -12,6 +12,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+from .jetstream_pt_support import jetstream_pt_available # isort:skip
 from .fsdp_v2 import get_fsdp_config, use_fsdp_v2
 from .modeling import AutoModelForCausalLM
 from .version import VERSION, __version__

optimum/tpu/fsdp_v2.py

@@ -15,12 +15,16 @@
 """
 Utility functions to provide FSDPv2 configuration for TPU training.
 """
-from typing import Dict, List, Union
+from typing import Any, Dict, List, Union
 
-from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import logging
 
 
+PreTrainedModel = Any
+# NOTE: instead of the above, modeling_utils.PreTrainedModel should be used, but since the usage is only for type
+# hinting, it is not imported here, so to avoid pulling imports of torch_xla.
+
+
 def use_fsdp_v2():
     """
     Enable FSDPv2 for TPU training.
@@ -61,6 +65,7 @@ def _unwrap_model(model: PreTrainedModel) -> PreTrainedModel:
     """
     try:
         from peft.peft_model import LoraModel, PeftModel
+
         if isinstance(model, PeftModel) and isinstance(model.base_model, LoraModel):
             return model.base_model.model
         return model
@@ -89,10 +94,13 @@ def get_fsdp_training_args(model: PreTrainedModel) -> Dict:
         if isinstance(model, GemmaForCausalLM) or isinstance(model, HFGemmaForCausalLLM):
             logger = logging.get_logger(__name__)
             from torch_xla import __version__ as xla_version
+
             if xla_version == "2.3.0":
-                logger.warning_once("Fine-tuning Gemma on Pytorch XLA 2.3.0 might raise some issues. In case of any "
-                                "issues consider using the nightly version, and report the issue on the optimum-tpu "
-                                "GitHub repository: https://github.com/huggingface/optimum-tpu/issues/new.")
+                logger.warning_once(
+                    "Fine-tuning Gemma on Pytorch XLA 2.3.0 might raise some issues. In case of any "
+                    "issues consider using the nightly version, and report the issue on the optimum-tpu "
+                    "GitHub repository: https://github.com/huggingface/optimum-tpu/issues/new."
+                )
             cls_to_wrap = "GemmaDecoderLayer"
             matched_model = True
     elif model_type == "llama":

optimum/tpu/generation/token_selector.py

@@ -3,7 +3,6 @@
 from typing import List, Optional, Union
 
 import torch
-import torch_xla.core.xla_model as xm
 from transformers.generation import (
     GenerationConfig,
     GenerationMixin,
@@ -53,7 +52,6 @@ def __init__(
         self.eos_token_ids = eos_token_ids
         self.pad_token_id = pad_token_id
         self.logits_warper = logits_warper
-        xm.set_rng_state(seed)
         self.generator = torch.Generator()
         self.generator.manual_seed(seed)
 

optimum/tpu/jetstream_pt_support.py

@@ -0,0 +1,26 @@
+import os
+import sys
+
+from loguru import logger
+
+
+def jetstream_pt_available() -> bool:
+    """Check if the necessary imports to use jetstream_pt are available.
+    """
+    try:
+        # For now Jetstream Pytorch is opt-in, it can be enabled with an ENV variable.
+        jetstream_pt_enabled = os.environ.get("JETSTREAM_PT", False) == "1"
+        if not jetstream_pt_enabled:
+            return False
+        # Torch XLA should not be imported before torch_xla2 to avoid conflicts.
+        if 'torch_xla2' not in sys.modules and 'torch_xla.core' in sys.modules:
+            logger.warning("torch_xla2 cannot be imported after torch_xla, disabling Jetstream PyTorch support.")
+            return False
+        # Import torch_xla2 first!
+        import torch_xla2  # noqa: F401, isort:skip
+
+        import jetstream_pt  # noqa: F401
+
+        return True
+    except ImportError:
+        return False

optimum/tpu/version.py

@@ -12,8 +12,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-from pkg_resources import parse_version
+from packaging.version import parse
 
 
 __version__ = "0.1.5"
-VERSION = parse_version(__version__)
+VERSION = parse(__version__)

pyproject.toml

@@ -43,8 +43,8 @@ keywords = [
 
 dependencies = [
     "transformers == 4.41.1",
-    "torch >= 2.3.0, <= 2.4.0",
-    "torch-xla[tpu] >= 2.3.0, <= 2.4.0",
+    "torch == 2.4.0",
+    "torch-xla[tpu] == 2.4.0",
     "loguru == 0.6.0",
     "sentencepiece == 0.2.0",
 ]
@@ -58,6 +58,12 @@ build-backend = "setuptools.build_meta"
 [project.optional-dependencies]
 tests = ["pytest", "safetensors"]
 quality = ["black", "ruff", "isort"]
+# Jetstream/Pytorch support is experimental for now, requires installation from fixed commit.
+# Pallas is pulled because it will install a compatible version of jax[tpu].
+jetstream-pt = [
+    "jetstream-pt @ git+https://github.com/google/jetstream-pytorch.git#df92015289953c506004e674d57651b03e4e89f2",
+    "torch-xla[pallas] == 2.4.0"
+]
 
 [project.urls]
 Homepage = "https://hf.co/hardware"

text-generation-inference/server/Makefile

@@ -12,12 +12,14 @@ clean:
 
 # List static sources to be deployed in the package
 src_dir := $(mkfile_dir)/$(pkg_name)
-sources := $(wildcard $(src_dir)/*.py)
+rwildcard_py = $(wildcard $(1)/*.py) $(foreach d,$(wildcard $(1)/*),$(call rwildcard_py,$d))
+sources := $(call rwildcard_py,$(src_dir))
 deployed_sources := $(subst $(src_dir), $(pkg_dir), $(sources))
 
 # Static files are just copied
 
 define COPY
+	mkdir -p $(dir $@)
 	cp -f $< $@
 endef
 

text-generation-inference/server/pyproject.toml

@@ -22,7 +22,7 @@ dependencies = [
 ]
 
 [tool.setuptools]
-packages = ["text_generation_server", "text_generation_server.pb"]
+packages = ["text_generation_server", "text_generation_server.pb", "text_generation_server.jetstream_pt_support"]
 
 [tool.setuptools.dynamic]
 version = {attr = "text_generation_server.version.__version__"}

text-generation-inference/server/text_generation_server/auto_generator.py

@@ -0,0 +1,39 @@
+from loguru import logger
+
+from .generator_base import Generator
+from .jetstream_pt_support import model_can_use_jetstream_pt
+
+
+class AutoGenerator:
+
+    @staticmethod
+    def from_pretrained(
+        model_path: str, revision: str, max_batch_size: int, max_sequence_length: int
+    ) -> Generator:
+        """Instantiate a Generator for TPU using Jetstream Pytorch or Pytorch/XLA.
+
+        Args:
+            model_path (`str`):
+                The path to a local model. This path must also contain a Tokenizer.
+            revision (`str`):
+                The revision of the model.
+            max_batch_size (`int`):
+                The maximum batch size.
+            max_sequence_length (`int`):
+                The maximum sequence length.
+
+        Returns:
+            A TpuGenerator.
+        """
+        if model_can_use_jetstream_pt(model_path):
+            logger.debug("Using Jetstream PyTorch generator.")
+            from .jetstream_pt_support.generator import TpuGeneratorJetStream
+            return TpuGeneratorJetStream.from_pretrained(
+                model_path, revision=revision, max_batch_size=max_batch_size, max_sequence_length=max_sequence_length
+            )
+        else:
+            logger.debug("Using PyTorch/XLA generator.")
+            from .generator import TpuGenerator
+            return TpuGenerator.from_pretrained(
+                model_path, revision=revision, max_batch_size=max_batch_size, max_sequence_length=max_sequence_length
+            )

text-generation-inference/server/text_generation_server/generator.py

@@ -590,6 +590,19 @@ def decode(self, batches: List[CachedBatch]) -> Tuple[List[Generation], CachedBa
         # just carry on with decoding. We adopt the id of the first
         # batch in the list as our next batch id.
         next_batch_id = batches[0].id
+        request_ids = []
+        for batch in batches:
+            request_ids += batch.request_ids
+        cleared_request_ids = []
+        for slot in self.slots:
+            if slot.state == slot.State.READY and slot.request_id not in request_ids:
+                cleared_request_ids.append(slot.request_id)
+                slot.clear()
+        if len(cleared_request_ids) > 0:
+            logger.info(f"Clearing slot for requests {cleared_request_ids} as they are not requested.")
+        active_slots = [slot for slot in self.slots if slot.state == slot.State.READY]
+        if len(active_slots) < len(request_ids):
+            logger.error("Unable to decode tokens for non-prefilled batches (probably due to a previous failure)")
         # Reconstruct input_ids and attention_mask from slots
         input_ids = None
         attention_mask = None
@@ -608,7 +621,7 @@ def decode(self, batches: List[CachedBatch]) -> Tuple[List[Generation], CachedBa
         # Create blank inputs covering all slots (even empty ones)
         input_ids = torch.full(
             [batch_size, 1],
-            fill_value=self.tokenizer.eos_token_id,
+            fill_value=pad_token_id,
             dtype=torch.int64,
         )
         cache_position = torch.zeros([1], dtype=torch.int64)

text-generation-inference/server/text_generation_server/jetstream_pt_support/__init__.py

@@ -0,0 +1,15 @@
+#  Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from .compatibility import create_engine, model_can_use_jetstream_pt

text-generation-inference/server/text_generation_server/jetstream_pt_support/compatibility.py

@@ -0,0 +1,53 @@
+#  Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import os
+from typing import Any
+
+from transformers import AutoConfig
+
+from optimum.tpu import jetstream_pt_available
+
+
+def model_can_use_jetstream_pt(model_path: str) -> bool:
+    """Checks if the model is supported by Jetstream Pytorch on Optimum TPU and if the required dependencies to provide
+    the engine are installed.
+    """
+    config = AutoConfig.from_pretrained(model_path)
+    # For now only Llama 2 with tokenizer.model is supported
+    if config.model_type != "llama" or not os.path.exists(
+        os.path.join(model_path, "tokenizer.model")
+    ):
+        return False
+    if jetstream_pt_available():
+        return True
+    return False
+
+
+def create_engine(
+    model_path: str,
+    batch_size: int,
+    sequence_length: int,
+    max_input_tokens: int,
+    max_output_tokens: int,
+) -> Any:
+    if not model_can_use_jetstream_pt(model_path):
+        # The model is not compatible with Jetstream PyTorch, just exit
+        return None
+
+    # Now import engine_loader to prevent importing it at the top when not supported
+    from .engine_loader import create_engine
+    return create_engine(
+        model_path, batch_size, sequence_length, max_input_tokens, max_output_tokens
+    )