More Jetstream Pytorch fixes, prepare for release

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

@@ -1,6 +1,10 @@
 name: Optimum TPU / Test TGI on TPU / Jetstream Pytorch
 
 on:
+  push:
+    branches: [ main ]
+    paths:
+      - "text-generation-inference/**"
   pull_request:
     branches: [ main ]
     paths:

optimum/tpu/version.py

@@ -15,5 +15,5 @@
 from packaging.version import parse
 
 
-__version__ = "0.1.5"
+__version__ = "0.1.6"
 VERSION = parse(__version__)

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

@@ -19,12 +19,35 @@
 
 if TYPE_CHECKING:
     from transformers import PretrainedConfig
+
 from transformers import AutoConfig
 
 from .compatibility import model_can_use_jetstream_pt
 from .models import GemmaModel, LlamaModel, MixtralModel
 
 
+class OptimumJetstreamEngine(PyTorchEngine):
+    """This is essentially the same as the PytorchEngine, but it also supports a callback for sampling in prefill and
+    generation that can change on each request while not needing to be JIT'ed.
+    """
+    prefill_ex = PyTorchEngine.prefill
+
+    def __init__(
+        self,
+        pt_model: torch.nn.Module,
+        env: JetEngineEnvironment,
+        weights=None,
+    ):
+        super().__init__(pt_model, env, weights)
+        # Call model prefill and generate needs to be JIT'ed, because it is called with sharded notations, and it would
+        # otherwise not work for some models.
+        self._call_model_prefill = jax.jit(
+            self._call_model_prefill,
+        )
+        self._call_model_generate = jax.jit(
+            self._call_model_generate,
+        )
+
 def _get_head_dim(config: "PretrainedConfig") -> int:
     if hasattr(config, "head_dim"):
         return config.head_dim
@@ -174,8 +197,9 @@ def create_engine(
         logger.info(f"Quantization took {end - start:.2f} seconds")
     model_weights = model.state_dict()
     sharded_weights = shard_weights(env, model_weights, weight_shardings)
-    return PyTorchEngine(
+    engine = OptimumJetstreamEngine(
         pt_model=model,
         env=env,
         weights=torchjax.from_torch_with_copy(sharded_weights),
     )
+    return engine

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

@@ -55,7 +55,6 @@ def clear(self):
         self._state = Slot.State.EMPTY
         self._batch_id = None
         self._request_id = None
-        self._inputs = ""
         self._generation_config = None
         self._tokens = []
         self._selector = None
@@ -263,9 +262,8 @@ def __init__(
         tokenizer.truncation_side = "left"
         self.tokenizer = tokenizer
         self.special_tokens = self.tokenizer.all_special_ids
-
-        # Slots are empty to begin with, they will be populated as new batches arrive
-        self.slots = []
+        # Slots number is static, it cannot grow over the size of the batch
+        self.slots = [Slot(i, tokenizer) for i in range(self.model.config.batch_size)]
         self.batch_id = 0
         # Note: this index will _never_ be decremented, and that's fine.
         self.slot_index = 0
@@ -363,13 +361,11 @@ def warmup(self, batch: Batch) -> int:
         seq_len = self.engine.env.seq_len
         return batch_size * seq_len
 
-    def _get_slot_id(self):
-        """Get the next available slot id."""
-        batch_size = self.engine.env.batch_size
-        used_ids = [slot.id for slot in self.slots if slot.state != Slot.State.EMPTY]
-        for i in range(batch_size):
-            if i not in used_ids:
-                return i
+    def _get_slot(self):
+        """Get the next available slot."""
+        for slot in self.slots:
+            if slot.state == Slot.State.EMPTY:
+                return slot
         # if we reach this point, all slots were used - this should not happen
         raise ValueError("All slots are used, but we should have stopped earlier")
 
@@ -388,6 +384,8 @@ def _token_encode(self, text: str, max_length: int) -> Tuple[jnp.ndarray, int]:
         """
         if max_length == 0:
             max_length = self.model.config.sequence_length
+        # Remove one to max_length because BOS is going to be added when padding
+        max_length -= 1
         input_ids = self.tokenizer.encode(
             text,
             return_tensors="np",
@@ -417,14 +415,9 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
             A list of `Generation` for each request and a `CachedBatch` containing all pending requests.
         """
 
-        slots = {state: [] for state in Slot.State}
-        for slot in self.slots:
-            slots[slot.state].append(slot)
-        len_active_slots = len(slots[Slot.State.READY])
-        # Delete all empty slots, no need to have them anymore
-        empty_slots = slots[Slot.State.EMPTY]
-        for slot in empty_slots:
-            self.slots.remove(slot)
+        active_slots = [slot for slot in self.slots if slot.state == Slot.State.READY]
+        len_active_slots = len(active_slots)
+
         len_requests = len(batch.requests)
         model_batch_size = self.model.config.batch_size
         if model_batch_size is not None and model_batch_size < len_active_slots + len_requests:
@@ -439,10 +432,10 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
         # Assign each request to an empty slot
         logger.debug(f"Prefilling {len_requests} new request(s) adding to {len_active_slots} active slot(s)")
         generations = []
-
+        prefilled_active_slots = []
         for request in batch.requests:
             # Dynamically create a new slot for each request
-            slot = Slot(self._get_slot_id(), self.tokenizer)
+            slot = self._get_slot()
             self.prefill_slot.set(slot)
             self.slot_index += 1
             slot.assign(self.batch_id, request, self.model.generation_config)
@@ -462,7 +455,7 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
             # To allow jit'ing the select function, we need to wrap it in a partial
             slot_select = jax.tree_util.Partial(self.prefill_slot.select)
             # Ask for prefill and insert
-            prefill_results, _result_tokens = self.engine.prefill(
+            prefill_results, _result_tokens = self.engine.prefill_ex(
                 params=self.params,
                 padded_tokens=input_ids,
                 true_length=true_lengths,
@@ -473,28 +466,20 @@ def prefill(self, batch: Batch) -> Tuple[List[Generation], CachedBatch]:
 
             self._post_generate(slot, next_token, generations)
             if not slot.empty:
-                # append current to list of active slots
-                self.slots.append(slot)
-                len_active_slots += 1
-
-        batch = None
-        if len_active_slots > 0:
-            # Whatever initial batch these requests came from, we always return all pending requests in a single batch
-            request_ids = [slot.request_id for slot in self.slots if slot.state == Slot.State.READY]
-            batch = self._cached_batch(self.batch_id, request_ids)
-        else:
-            logger.debug("No more pending requests")
+                prefilled_active_slots.append(slot)
+
+        cached_batch = self._cached_batch(self.batch_id, prefilled_active_slots)
         self.batch_id += 1
         logger.debug("Model ready for decoding")
-        return generations, batch
+        return generations, cached_batch
 
     def _select_from_slots(self, logits: jnp.ndarray, batch_size: int=0) -> jnp.ndarray:
         pad_token_id = self.tokenizer.pad_token_id
         batch_size = logits.shape[0]
         tokens = jnp.full((batch_size, 1), pad_token_id)
         for slot in filter(lambda slot: slot.state == slot.State.READY, self.slots):
             # Every slot might have a different selection criteria, so we are obliged to call select in a loop
-            next_token = slot.select(logits)
+            next_token = slot.select(logits[slot.id : slot.id + 1, :])
             tokens = tokens.at[slot.id].set(next_token)
         return tokens
 
@@ -539,10 +524,9 @@ def decode(self, batches: List[CachedBatch]) -> Tuple[List[Generation], CachedBa
             raise ValueError("Unable to decode tokens for non-prefilled batches (probably due to a previous failure)")
 
         # Use a custom function to select the next token for each slot
-        select_fn = jax.tree_util.Partial(self._select_from_slots)
-        self.decode_state, result_tokens = self.engine.generate(self.params, self.decode_state, select_fn)
+        # select_fn = jax.tree_util.Partial(self._select_from_slots)
+        self.decode_state, result_tokens = self.engine.generate_impl(self.params, self.decode_state, self._select_from_slots)
 
-        newly_empty = []
         generations = []
         for slot in active_slots:
             # Get the next token.
@@ -555,20 +539,9 @@ def decode(self, batches: List[CachedBatch]) -> Tuple[List[Generation], CachedBa
                 raise ValueError("Unexpected Slot is not ready for decoding")
 
             self._post_generate(slot, next_token, generations)
-            if slot.empty:
-                newly_empty.append(slot)
-
-        # Remove empty slots
-        for slot in newly_empty:
-            self.slots.remove(slot)
-        batch = None
-        if len(self.slots) > 0:
-            # Whatever initial batch these requests came from, we always return all pending requests in a single batch
-            request_ids = [slot.request_id for slot in self.slots if slot.state == Slot.State.READY]
-            batch = self._cached_batch(next_batch_id, request_ids)
-        else:
-            logger.debug("No more pending requests")
-        return generations, batch
+
+        cached_batch = self._cached_batch(next_batch_id, active_slots)
+        return generations, cached_batch
 
     def _post_generate(self, slot: Slot, next_token: int, generations: List[Generation]) -> None:
         """Post-generate a slot after the generation has been completed.
@@ -616,7 +589,13 @@ def _post_generate(self, slot: Slot, next_token: int, generations: List[Generati
             )
         )
 
-    def _cached_batch(self, batch_id: int, request_ids: List):
+    def _cached_batch(self, batch_id: int, active_slots: List):
+        """Create a CachedBatch from the active slots.
+        """
+        request_ids = [slot.request_id for slot in active_slots if slot.state == Slot.State.READY]
+        if len(request_ids) == 0:
+            logger.debug("No more pending requests")
+            return None
         size = len(request_ids)
         max_tokens = size * self.model.config.sequence_length
         return CachedBatch(id=batch_id, request_ids=request_ids, size=size, max_tokens=max_tokens)

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

@@ -175,11 +175,13 @@ def select(self, input_ids: jnp.ndarray, logits: jnp.ndarray) -> jnp.ndarray:
         """
         scores = self.logits_processor(input_ids, logits)
         if self.mode == GenerationMode.SAMPLE:
-            return self._sample(scores)
+            # split the key to avoid reusing the same key for multiple samples
+            subkey, self.key = jax.random.split(self.key)
+            return self._sample(scores, subkey)
         else:
             return jnp.argmax(scores, axis=-1)
 
-    def _sample(self, scores: jnp.ndarray) -> jnp.ndarray:
+    def _sample(self, scores: jnp.ndarray, key) -> jnp.ndarray:
         do_top_k = self.logits_warper.top_k > 0 and self.logits_warper.top_k < scores.shape[-1]
         do_top_p = self.logits_warper.top_p < 1.0 and self.logits_warper.top_p > 0.0
 
@@ -188,14 +190,14 @@ def _sample(self, scores: jnp.ndarray) -> jnp.ndarray:
                 scores,
                 self.logits_warper.top_k,
                 self.logits_warper.temperature,
-                self.key,
+                key,
             )
         elif do_top_p:
             return sampling_utils.sample_nucleus_topp_logits(
                 scores,
                 self.logits_warper.top_p,
                 self.logits_warper.temperature,
-                self.key,
+                key,
             )
 
-        return jax.random.categorical(self.key, scores / self.logits_warper.temperature)
+        return jax.random.categorical(key, scores / self.logits_warper.temperature)

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

@@ -1,5 +1,5 @@
 from pkg_resources import parse_version
 
 
-__version__ = "0.1.5"
+__version__ = "0.1.6"
 VERSION = parse_version(__version__)