stuff

Dockerfile

@@ -177,7 +177,8 @@ ENTRYPOINT ["./entrypoint.sh"]
 # Final image
 FROM base
 
-
+RUN git clone https://github.com/bigcode-project/bigcode-inference-benchmark.git && \
+    cd bigcode-inference-benchmark && git checkout text_gen_inference
 
 ENV HUGGINGFACE_HUB_CACHE=/usr/data/.hf_cache/
 ENV PYTHONPATH=/usr/src/server/

server/text_generation_server/models/__init__.py

@@ -8,6 +8,7 @@ from typing import Optional
 
 from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
+from text_generation_server.models.vectorized_causal_lm import VectorizedCausalLM
 from text_generation_server.models.flash_causal_lm import FlashCausalLM
 from text_generation_server.models.bloom import BLOOM, BLOOMSharded
 from text_generation_server.models.seq2seq_lm import Seq2SeqLM
@@ -155,6 +156,8 @@ def get_model(
         raise ValueError("sharded is not supported for AutoModel")
 
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
+        if os.environ.get("VECTORIZED_LM") is not None:
+            return VectorizedCausalLM(model_id, revision, quantize=quantize)
         return CausalLM(model_id, revision, quantize=quantize)
     if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
         return Seq2SeqLM(model_id, revision, quantize=quantize)

server/text_generation_server/models/causal_lm.py

@@ -4,7 +4,6 @@ from dataclasses import dataclass
 from opentelemetry import trace
 from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Dict
-from loguru import logger
 
 from text_generation_server.models import Model
 from text_generation_server.models.types import (
@@ -54,7 +53,6 @@ class CausalLMBatch(Batch):
     keys_head_dim_last: bool = True
 
     def to_pb(self) -> generate_pb2.Batch:
-        #logger.info(f"to_pb, id={self.batch_id}, requests={self.requests}, size={len(self)}, max_tokens={self.max_tokens}")
         return generate_pb2.Batch(
             id=self.batch_id,
             requests=self.requests,
@@ -69,7 +67,6 @@ class CausalLMBatch(Batch):
         tokenizer: PreTrainedTokenizerBase,
         device: torch.device,
     ) -> "CausalLMBatch":
-        #logger.info(f"from_pb, pb={pb}, tokenizer={tokenizer}, device={device}")
         inputs = []
         next_token_choosers = []
         stopping_criterias = []
@@ -144,7 +141,6 @@ class CausalLMBatch(Batch):
 
     @tracer.start_as_current_span("filter")
     def filter(self, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]:
-        logger.info(f"filter, requests={requests}")
         if len(requests) == 0:
             raise ValueError("Batch must have at least one request")
         if len(requests) == len(self):
@@ -242,7 +238,6 @@ class CausalLMBatch(Batch):
     @classmethod
     @tracer.start_as_current_span("concatenate")
     def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
-        logger.info(f"concatenate, batches={batches}")
         # Used for padding
         total_batch_size = 0
         max_input_length = 0

server/text_generation_server/models/vectorized_causal_lm.py

@@ -20,19 +20,18 @@ tracer = trace.get_tracer(__name__)
 
 
 @dataclass
-class CausalLMBatch(Batch):
+class VectorizedCausalLMBatch(Batch):
     batch_id: int
     requests: List[generate_pb2.Request]
     requests_idx_mapping: Dict[int, int]
 
     # Decoder values
-    input_ids: torch.Tensor
     attention_mask: torch.Tensor
     position_ids: torch.Tensor
     past_key_values: Optional[List[Tuple]]
 
     # All tokens
-    all_input_ids: List[torch.Tensor]
+    input_ids: torch.Tensor
 
     # Lengths of all generations present in the batch
     input_lengths: List[int]
@@ -45,16 +44,11 @@ class CausalLMBatch(Batch):
 
     # Metadata used for padding
     max_input_length: int
-    padding_right_offset: int
 
     # Maximum number of tokens this batch will grow to
     max_tokens: int
 
-    # Past metadata
-    keys_head_dim_last: bool = True
-
     def to_pb(self) -> generate_pb2.Batch:
-        #logger.info(f"to_pb, id={self.batch_id}, requests={self.requests}, size={len(self)}, max_tokens={self.max_tokens}")
         return generate_pb2.Batch(
             id=self.batch_id,
             requests=self.requests,
@@ -68,8 +62,7 @@ class CausalLMBatch(Batch):
         pb: generate_pb2.Batch,
         tokenizer: PreTrainedTokenizerBase,
         device: torch.device,
-    ) -> "CausalLMBatch":
+    ) -> "VectorizedCausalLMBatch":
-        #logger.info(f"from_pb, pb={pb}, tokenizer={tokenizer}, device={device}")
         inputs = []
         next_token_choosers = []
         stopping_criterias = []
@@ -82,11 +75,14 @@ class CausalLMBatch(Batch):
         padding_right_offset = 0
         max_decode_tokens = 0
         for i, r in enumerate(pb.requests):
+            next_token_chooser=NextTokenChooser.from_pb(r.parameters, device)
+            # TODO: Implement
+            assert len(next_token_chooser.warpers)==0
             requests_idx_mapping[r.id] = i
             inputs.append(r.inputs)
             offsets.append(None)
             token_offsets.append(None)
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+            next_token_choosers.append(next_token_chooser)
             stopping_criteria = StoppingCriteria.from_pb(
                 r.stopping_parameters, tokenizer
             )
@@ -109,17 +105,19 @@ class CausalLMBatch(Batch):
         input_lengths = tokenized_inputs["attention_mask"].sum(1)
         max_input_length = input_lengths.max()
 
-        input_ids = tokenized_inputs["input_ids"]
+        input_shape=(pb.size, max_input_length + padding_right_offset)
-        # Allocate maximum attention_mask
-        attention_mask = input_ids.new_zeros(
-            (pb.size, max_input_length + padding_right_offset)
-        )
-        # Copy tokenizer attention_mask into fully allocated attention_mask
-        attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
 
-        position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
+        # Allocate maximum attention_mask
-        position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
+        attention_mask = torch.empty(input_shape, dtype=torch.bool, device=device)
-        all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
+        # Copy tokenizer attention_mask into fully allocated attention_mask
+        attention_mask[:, :max_input_length].copy_(tokenized_inputs["attention_mask"])
+        attention_mask[:, max_input_length:].fill_(1)
+
+        position_ids = attention_mask.cumsum(-1).sub_(1)
+        position_ids[:, :max_input_length].relu_()
+
+        input_ids = torch.empty(input_shape, dtype=torch.int64, device=device)
+        input_ids[:, :max_input_length].copy_(tokenized_inputs["input_ids"])
 
         max_tokens = len(inputs) * max_input_length + max_decode_tokens
 
@@ -127,327 +125,148 @@ class CausalLMBatch(Batch):
             batch_id=pb.id,
             requests=pb.requests,
             requests_idx_mapping=requests_idx_mapping,
-            input_ids=input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
             past_key_values=None,
-            all_input_ids=list(all_input_ids),
+            input_ids=input_ids,
             input_lengths=input_lengths.tolist(),
             offsets=offsets,
             token_offsets=token_offsets,
             next_token_choosers=next_token_choosers,
             stopping_criterias=stopping_criterias,
             max_input_length=max_input_length.item(),
-            padding_right_offset=padding_right_offset,
             max_tokens=max_tokens,
         )
 
     @tracer.start_as_current_span("filter")
-    def filter(self, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]:
+    def filter(self, requests: List[generate_pb2.Request]) -> Optional["VectorizedCausalLMBatch"]:
-        logger.info(f"filter, requests={requests}")
+        raise NotImplementedError()
-        if len(requests) == 0:
-            raise ValueError("Batch must have at least one request")
-        if len(requests) == len(self):
-            return self
-
-        keep_indices = []
-
-        # New values after filtering
-        requests_idx_mapping = {}
-        input_lengths = []
-        offsets = []
-        token_offsets = []
-        all_input_ids = []
-        max_input_length = 0
-
-        next_token_choosers = []
-        stopping_criterias = []
-
-        total_remaining_decode_tokens = 0
-        new_padding_right_offset = 0
-
-        for i, r in enumerate(requests):
-            idx = self.requests_idx_mapping[r.id]
-            requests_idx_mapping[r.id] = i
-            keep_indices.append(idx)
-
-            offsets.append(self.offsets[idx])
-            token_offsets.append(self.token_offsets[idx])
-            all_input_ids.append(self.all_input_ids[idx])
-
-            request_input_length = self.input_lengths[idx]
-            input_lengths.append(request_input_length)
-            max_input_length = max(max_input_length, request_input_length)
-
-            next_token_choosers.append(self.next_token_choosers[idx])
-            stopping_criteria = self.stopping_criterias[idx]
-            stopping_criterias.append(stopping_criteria)
-            remaining_decode_tokens = (
-                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
-            )
-            total_remaining_decode_tokens += remaining_decode_tokens
-            new_padding_right_offset = max(
-                new_padding_right_offset, remaining_decode_tokens
-            )
-
-        # Apply indices to input_ids, attention mask, past key values and other items that need to be cached
-        input_ids = self.input_ids[keep_indices]
-        position_ids = self.position_ids[keep_indices]
-        self.attention_mask = self.attention_mask[
-            keep_indices,
-            -(self.padding_right_offset + max_input_length) : (
-                self.attention_mask.shape[1] - self.padding_right_offset
-            )
-            + new_padding_right_offset,
-        ]
-
-        # Ensure that past_key_values tensors can be updated in-place
-        if type(self.past_key_values[0]) == tuple:
-            self.past_key_values = [list(layer) for layer in self.past_key_values]
-
-        # Update tensors in-place to allow incremental garbage collection
-        past_kv_length = max_input_length - 1
-        for layer in self.past_key_values:
-            past_keys, past_values = layer
-            if len(past_keys.shape) == 3:
-                # Force past to be of dim [self_size, num_heads, ...] for easy indexing
-                past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
-                past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
-            if self.keys_head_dim_last:
-                layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
-            else:
-                layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
-            del past_keys
-            layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
-            del past_values
-
-        max_tokens = len(requests) * max_input_length + total_remaining_decode_tokens
-
-        self.requests = requests
-        self.requests_idx_mapping = requests_idx_mapping
-        self.input_ids = input_ids
-        self.position_ids = position_ids
-        self.all_input_ids = all_input_ids
-        self.input_lengths = input_lengths
-        self.offsets = offsets
-        self.token_offsets = token_offsets
-        self.next_token_choosers = next_token_choosers
-        self.stopping_criterias = stopping_criterias
-        self.max_input_length = max_input_length
-        self.padding_right_offset = new_padding_right_offset
-        self.max_tokens = max_tokens
-
-        return self
 
     @classmethod
     @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
+    def concatenate(cls, batches: List["VectorizedCausalLMBatch"]) -> "VectorizedCausalLMBatch":
-        logger.info(f"concatenate, batches={batches}")
+        raise NotImplementedError()
-        # Used for padding
-        total_batch_size = 0
-        max_input_length = 0
-        padding_right_offset = 0
-        for batch in batches:
-            total_batch_size += len(batch)
-            max_input_length = max(max_input_length, batch.max_input_length)
-            padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
-
-        # Batch attributes
-        requests = []
-        requests_idx_mapping = {}
-        input_lengths = []
-        offsets = []
-        token_offsets = []
-        all_input_ids = []
-        next_token_choosers = []
-        stopping_criterias = []
-        max_tokens = 0
-
-        # Batch tensors
-        input_ids = None
-        attention_mask = None
-        position_ids = None
-        past_key_values = []
-
-        # Used for slicing correctly inside the tensors
-        # Equivalent to a cumsum on batch sizes
-        start_index = 0
-        for i, batch in enumerate(batches):
-            requests.extend(batch.requests)
-            input_lengths.extend(batch.input_lengths)
-            offsets.extend(batch.offsets)
-            token_offsets.extend(batch.token_offsets)
-            all_input_ids.extend(batch.all_input_ids)
-            next_token_choosers.extend(batch.next_token_choosers)
-            stopping_criterias.extend(batch.stopping_criterias)
-
-            if i == 0:
-                requests_idx_mapping = batch.requests_idx_mapping
-            else:
-                # We need to offset the mapping for each batch by the cumulative batch size
-                for k, v in batch.requests_idx_mapping.items():
-                    requests_idx_mapping[k] = v + start_index
-
-            # Slicing end index for this batch
-            end_index = start_index + len(batch)
-
-            # We only concatenate batches that did at least one step
-            if batch.past_key_values is None:
-                raise ValueError("only concatenate prefilled batches")
-
-            # Create empty tensor
-            # input_ids is always of shape [batch_size, 1]
-            # We do not need to pad it
-            if input_ids is None:
-                input_ids = batch.input_ids.new_empty((total_batch_size, 1))
-            # Copy to correct indices
-            input_ids[start_index:end_index] = batch.input_ids
-
-            # Create padded tensor
-            if attention_mask is None:
-                attention_mask = batch.attention_mask.new_zeros(
-                    (total_batch_size, max_input_length + padding_right_offset),
-                )
-
-            # We need to slice the attention mask to remove padding from previous steps
-            # and to remove unused allocated space
-            left_offset = max_input_length - batch.max_input_length
-            batch_left_offset = (
-                batch.attention_mask.shape[1]
-                - batch.max_input_length
-                - batch.padding_right_offset
-            )
-            attention_mask[
-                start_index:end_index,
-                left_offset:-padding_right_offset,
-            ] = batch.attention_mask[
-                :,
-                batch_left_offset : -batch.padding_right_offset,
-            ]
-
-            # Create empty tensor
-            # position_ids is always of shape [batch_size, 1]
-            if position_ids is None:
-                position_ids = batch.position_ids.new_empty((total_batch_size, 1))
-            position_ids[start_index:end_index] = batch.position_ids
-
-            # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
-            # BLOOM Keys:   [batch_size * num_heads, head_dim, seq_length]
-            # BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
-            # And ensure that we can update tensors in-place
-            if type(batch.past_key_values[0]) == tuple:
-                batch.past_key_values = [
-                    [t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
-                    for layer in batch.past_key_values
-                ]
-            elif len(batch.past_key_values[0][0].shape) == 3:
-                for layer in batch.past_key_values:
-                    for k, t in enumerate(layer):
-                        layer[k] = t.view(len(batch), -1, *t.shape[-2:])
-
-            # Add eventual padding tokens that were added while concatenating
-            max_tokens += batch.max_tokens + (
-                max_input_length - batch.max_input_length
-            ) * len(batch)
-
-            start_index = end_index
-
-        first_past_kvs = batches[0].past_key_values
-        _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
-
-        padded_past_values_shape = (
-            total_batch_size,
-            num_heads,
-            max_input_length - 1,
-            head_dim,
-        )
-
-        if batches[0].keys_head_dim_last:
-            padded_past_keys_shape = padded_past_values_shape
-        else:
-            # seq_length is last for BLOOM
-            padded_past_keys_shape = (
-                total_batch_size,
-                num_heads,
-                head_dim,
-                max_input_length - 1,
-            )
-
-        # Iterate over attention layers
-        # Concatenate past key values layer by layer to allow incremental garbage collection
-        for j in range(len(first_past_kvs)):
-            padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
-            start_index = 0
-            for batch in batches:
-                past_keys = batch.past_key_values[j][0]
-                # Clear reference to the original tensor
-                batch.past_key_values[j][0] = None
-
-                # Slicing end index for this batch
-                end_index = start_index + len(batch)
-                # We slice the keys to remove the padding from previous batches
-                past_seq_len = batch.max_input_length - 1
-                if batch.keys_head_dim_last:
-                    padded_past_keys[
-                        start_index:end_index, :, -past_seq_len:, :
-                    ] = past_keys[:, :, -past_seq_len:, :]
-                else:
-                    # BLOOM case
-                    padded_past_keys[
-                        start_index:end_index, :, :, -past_seq_len:
-                    ] = past_keys[:, :, :, -past_seq_len:]
-                del past_keys
-
-                start_index = end_index
-
-            padded_past_values = first_past_kvs[j][1].new_zeros(
-                padded_past_values_shape
-            )
-            start_index = 0
-            for batch in batches:
-                past_values = batch.past_key_values[j][1]
-                # Clear reference to the original tensor
-                batch.past_key_values[j][1] = None
-
-                # Slicing end index for this batch
-                end_index = start_index + len(batch)
-                # We slice the past values to remove the padding from previous batches
-                past_seq_len = batch.max_input_length - 1
-                padded_past_values[
-                    start_index:end_index, :, -past_seq_len:, :
-                ] = past_values[:, :, -past_seq_len:, :]
-                del past_values
-
-                # Update values
-                start_index = end_index
-
-            past_key_values.append([padded_past_keys, padded_past_values])
-
-        return cls(
-            batch_id=batches[0].batch_id,
-            requests=requests,
-            requests_idx_mapping=requests_idx_mapping,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            all_input_ids=all_input_ids,
-            input_lengths=input_lengths,
-            offsets=offsets,
-            token_offsets=token_offsets,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-            max_input_length=max_input_length,
-            padding_right_offset=padding_right_offset,
-            keys_head_dim_last=batches[0].keys_head_dim_last,
-            max_tokens=max_tokens,
-        )
 
     def __len__(self):
         return len(self.requests)
 
 
-class CausalLM(Model):
+class VectorizedNextTokenChooser:
+    def __init__(
+        self,
+        batch_size:int,
+        watermark=None,
+        temperature=None,
+        repetition_penalty=None,
+        top_k=None,
+        top_p=None,
+        typical_p=None,
+        do_sample=None,
+        seed:int=0,
+        device="cpu",
+    ):
+        self.batch_size=batch_size
+
+        do_sample=self._standardize(do_sample, False)
+
+        watermark=self._standardize(watermark, False)
+        if any(watermark):
+            raise NotImplementedError("Watermarking not implemented")
+
+        repetition_penalty=self._standardize(repetition_penalty, 1.0)
+        if any([x!=1.0 for x in repetition_penalty]):
+            self.repetition_penalty=torch.tensor([repetition_penalty], dtype=torch.float32, device=device).unsqueeze(1)
+        else:
+            self.repetition_penalty=None
+
+        temperature=self._standardize(temperature, 1.0)
+        if any([x!=1.0 for x in temperature]):
+            do_sample=[sample or x!=1.0 for x, sample in zip(temperature, do_sample)]
+            self.temperature=torch.tensor([temperature], dtype=torch.float32, device=device).unsqueeze(1)
+        else:
+            self.temperature=None
+
+        top_k=self._standardize(top_k, 0)
+        if any([x!=0 for x in top_k]):
+            do_sample=[sample or x!=0 for x, sample in zip(top_k, do_sample)]
+            self.top_k=torch.tensor([top_k], dtype=torch.float32, device=device).unsqueeze(1)
+        else:
+            self.top_k=None
+
+
+        top_p=self._standardize(top_p, 1.0)
+        if any([x<1.0 for x in top_p]):
+            raise NotImplementedError("Top P not implemented")
+
+        typical_p=self._standardize(typical_p, 1.0)
+        if any([x<1.0 for x in typical_p]):
+            raise NotImplementedError("Typical P not implemented")
+
+        self.do_sample = any(do_sample)
+        if self.do_sample and not all(do_sample):
+            raise NotImplementedError("Mixed greedy and probabilistic sampling not supported")
+
+    def _standardize(self, values, default):
+        if isinstance(values, list):
+            values=values.copy()
+        else:
+            values=[values]*self.batch_size
+        assert len(values)==self.batch_size
+        for i, v in enumerate(values):
+            if v is None:
+                values[i]=default
+        return values
+
+    def __call__(self, input_ids, scores):
+        # Only process the last token
+        scores=scores[: -1, :]
+
+        if self.repetition_penalty is not None:
+            score = torch.gather(scores, 1, input_ids)
+            # if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
+            score = torch.where(score < 0, score * self.repetition_penalty, score / self.repetition_penalty)
+            scores.scatter_(1, input_ids, score)
+
+        if self.temperature is not None:
+            scores.div_(self.temperature)
+
+        if self.top_k is not None:
+            top_k = min(self.top_k, scores.size(-1))  # Safety check
+            # Remove all tokens with a probability less than the last token of the top-k
+            indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]
+            scores = scores.masked_fill(indices_to_remove, self.filter_value)
+
+        # Compute logprobs
+        logprobs = torch.log_softmax(scores, dim=-1)
+
+        if self.do_sample:
+            raise NotImplementedError()
+        else:
+            next_token_ids = torch.argmax(scores, dim=-1)
+
+        return next_token_ids, logprobs
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: List[generate_pb2.NextTokenChooserParameters],
+        device: torch.device,
+    ) -> "VectorizedNextTokenChooser":
+        # TODO: Seeds are ignored
+        return VectorizedNextTokenChooser(
+            watermark=[pb_.watermark for pb_ in pb],
+            temperature=[pb_.temperature for pb_ in pb],
+            repetition_penalty=[pb_.repetition_penalty for pb_ in pb],
+            top_k=[pb_.top_k for pb_ in pb],
+            top_p=[pb_.top_p for pb_ in pb],
+            typical_p=[pb_.typical_p for pb_ in pb],
+            do_sample=[pb_.do_sample for pb_ in pb],
+            seed=0,
+            device=device,
+        )
+
+
+class VectorizedCausalLM(Model):
     def __init__(
         self,
         model_id: str,
@@ -457,6 +276,7 @@ class CausalLM(Model):
     ):
         if torch.cuda.is_available():
             device = torch.device("cuda")
+            # TODO: Choose dtype (fp16?)
             dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
         else:
             if quantize:
@@ -482,7 +302,7 @@ class CausalLM(Model):
             else self.model.config.eos_token_id
         )
 
-        super(CausalLM, self).__init__(
+        super().__init__(
             tokenizer=tokenizer,
             requires_padding=True,
             dtype=dtype,
@@ -491,94 +311,58 @@ class CausalLM(Model):
         )
 
     @property
-    def batch_type(self) -> Type[CausalLMBatch]:
+    def batch_type(self) -> Type[VectorizedCausalLMBatch]:
-        return CausalLMBatch
+        return VectorizedCausalLMBatch
 
     def decode(self, generated_ids: List[int]) -> str:
         return self.tokenizer.decode(
             generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
         )
 
-    def forward(
-        self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
-    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
-        # Model Forward
-        outputs = self.model.forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            use_cache=True,
-        )
-        return outputs.logits, outputs.past_key_values
-
     @tracer.start_as_current_span("generate_token")
     def generate_token(
-        self, batch: CausalLMBatch
+        self, batch: VectorizedCausalLMBatch
-    ) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
+    ) -> Tuple[List[Generation], Optional[VectorizedCausalLMBatch]]:
-        # slice the attention mask to the correct shape
+        key_length=batch.max_input_length
-        attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
+        query_length=key_length if batch.past_key_values is None else 1
 
-        logits, past = self.forward(
+        outputs = self.model.forward(
-            batch.input_ids,
+            input_ids=batch.input_ids[:, key_length-query_length: key_length],
-            attention_mask,
+            attention_mask=batch.attention_mask[:, : key_length],
-            batch.position_ids,
+            position_ids=batch.position_ids[:, key_length-query_length: key_length],
-            batch.past_key_values,
+            past_key_values=batch.past_key_values,
         )
+        # TODO: Post-processing
+        next_token_ids = torch.argmax(outputs.logits[:, -1, :], dim=-1)
+
+        # Update batch
+        # TODO: Why do we need all input ids?
+        batch.input_ids[:, key_length].copy_(next_token_ids)
+        batch.past_key_values=outputs.past_key_values
+        batch.input_lengths=[length+1 for length in batch.input_lengths]
+        batch.max_input_length+=1
+
+        # TODO: self.decode_token, offsets?
+        next_token_ids=next_token_ids.cpu().tolist()
+        next_token_texts=self.tokenizer.batch_decode(next_token_ids)
+
+        # TODO: Vectorize some of this?
 
-        # Results
         generations: List[Generation] = []
-        stopped = True
+        next_batch=None
 
-        # Zipped iterator
+        for i, (next_token_id, next_token_text) in enumerate(zip(next_token_ids, next_token_texts)):
-        iterator = zip(
+            stopping_criterias=batch.stopping_criterias[i]
-            batch.requests,
+            next_token_chooser=batch.next_token_choosers[i]
-            batch.input_lengths,
+            stop, reason = stopping_criterias(
-            batch.offsets,
+                next_token_id,
-            batch.token_offsets,
-            logits,
-            batch.next_token_choosers,
-            batch.stopping_criterias,
-            batch.all_input_ids,
-        )
-
-        # For each member of the batch
-        for i, (
-            request,
-            input_length,
-            offset,
-            token_offset,
-            logits,
-            next_token_chooser,
-            stopping_criteria,
-            all_input_ids,
-        ) in enumerate(iterator):
-            # Select next token
-            next_token_id, logprobs = next_token_chooser(
-                all_input_ids.view(1, -1), logits
-            )
-
-            # Append next token to all tokens
-            all_input_ids = torch.cat([all_input_ids, next_token_id])
-            new_input_length = input_length + 1
-
-            # Generated token
-            next_token_logprob = logprobs[-1, next_token_id]
-            next_token_id_squeezed = next_token_id.squeeze()
-            next_token_text, offset, token_offset = self.decode_token(
-                all_input_ids[:, 0], offset, token_offset
-            )
-
-            # Evaluate stopping criteria
-            stop, reason = stopping_criteria(
-                next_token_id_squeezed,
                 next_token_text,
             )
-
             if stop:
                 # Decode generated tokens
+                # TODO: Same as stopping_criteria.current_output?
                 output_text = self.decode(
-                    all_input_ids[-stopping_criteria.current_tokens :, 0]
+                    batch.input_ids[i, -stopping_criterias.current_tokens :]
                 )
                 # Get seed
                 if isinstance(next_token_chooser.choice, Sampling):
@@ -587,67 +371,24 @@ class CausalLM(Model):
                     seed = None
 
                 generated_text = GeneratedText(
-                    output_text, stopping_criteria.current_tokens, reason, seed
+                    output_text, stopping_criterias.current_tokens, reason, seed
                 )
             else:
                 # Keep request in the batch
                 generated_text = None
-                stopped = False
+                next_batch = batch
 
-            # Prefill
-            if stopping_criteria.current_tokens == 1:
-                # Remove generated token to only have prefill and add nan for first prompt token
-                prefill_logprobs = [float("nan")] + logprobs.gather(
-                    1, all_input_ids[1:]
-                ).squeeze(1)[-new_input_length:-1].tolist()
-                prefill_token_ids = all_input_ids[-new_input_length:-1]
-                prefill_texts = self.tokenizer.batch_decode(
-                    prefill_token_ids,
-                    clean_up_tokenization_spaces=False,
-                    skip_special_tokens=False,
-                )
-                prefill_tokens = PrefillTokens(
-                    prefill_token_ids, prefill_logprobs, prefill_texts
-                )
-            else:
-                prefill_tokens = None
 
             generation = Generation(
-                request.id,
+                batch.requests[i].id,
-                prefill_tokens,
+                None,
-                next_token_id_squeezed,
+                next_token_id,
-                next_token_logprob,
+                0,
                 next_token_text,
-                next_token_id_squeezed.item() in self.all_special_ids,
+                next_token_id in self.all_special_ids,
                 generated_text,
             )
 
             generations.append(generation)
 
-            # Update values
+        return generations, next_batch
-            batch.input_ids[i, 0] = next_token_id
-            batch.all_input_ids[i] = all_input_ids
-            batch.input_lengths[i] = new_input_length
-            batch.offsets[i] = offset
-            batch.token_offsets[i] = token_offset
-            batch.max_input_length = max(batch.max_input_length, new_input_length)
-
-        # We finished all generations in the batch; there is no next batch
-        if stopped:
-            return generations, None
-
-        # Slice unused values from prefill
-        batch.input_ids = batch.input_ids[:, :1]
-
-        # Update attention_mask as we added a new token to input_ids
-        batch.attention_mask[:, -batch.padding_right_offset] = 1
-        # Decrease right offset
-        batch.padding_right_offset -= 1
-
-        # Update position_ids
-        batch.position_ids = batch.position_ids[:, -1:] + 1
-
-        # Update past key values
-        batch.past_key_values = past
-
-        return generations, batch