feat(server): Support AutoModelForSeq2SeqLM

README.md

@@ -15,12 +15,20 @@ A Rust and gRPC server for text generation inference.
 - [Safetensors](https://github.com/huggingface/safetensors) weight loading
 - 45ms per token generation for BLOOM with 8xA100 80GB
 
-## Supported models
+## Officialy supported models
 
 - BLOOM
 - BLOOMZ
 - BLOOM-560m
 
+Other models are supported on a best effort basis using:
+
+`AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")`
+
+or
+
+`AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")`
+
 ## Load Tests for BLOOM
 
 See `k6/load_test.js`
@@ -81,7 +89,6 @@ make router-dev
 
 ## TODO:
 
-- [ ] Support AutoModelForSeq2SeqLM
 - [ ] Add tests for the `server/model` logic
 - [ ] Backport custom CUDA kernels to Transformers
 - [ ] Install safetensors with pip

proto/generate.proto

@@ -54,8 +54,6 @@ message Batch {
     repeated Request requests = 2;
     /// Batch size (==len(requests))
     uint32 size = 3;
-    /// Length of the longest sequence within the batch (used for padding)
-    uint32 max_sequence_length = 4;
 }
 
 message GeneratedText {

router/src/db.rs

@@ -142,14 +142,10 @@ impl Db {
 
             // Batch size
             let size = requests.len();
-            // Longest input length for all requests in batch size
-            // Used for padding inside the inference server
-            let max_sequence_length = requests.iter().map(|r| r.input_length).max().unwrap();
             let batch = Batch {
                 id: state.next_batch_id,
                 requests,
                 size: size as u32,
-                max_sequence_length,
             };
             // Update next_batch_start_id to the last id in the batch + 1
             state.next_batch_start_id = ids.last().unwrap() + 1;

server/text_generation/cache.py

@@ -1,16 +1,18 @@
-from typing import Dict, Optional
+from typing import Dict, Optional, TypeVar
 
 from text_generation.models.types import Batch
 
+B = TypeVar("B", bound=Batch)
+
 
 class Cache:
     def __init__(self):
-        self.cache: Dict[int, Batch] = {}
+        self.cache: Dict[int, B] = {}
 
-    def pop(self, batch_id: int) -> Optional[Batch]:
+    def pop(self, batch_id: int) -> Optional[B]:
         return self.cache.pop(batch_id, None)
 
-    def set(self, entry: Batch):
+    def set(self, entry: B):
         if entry is not None:
             self.cache[entry.batch_id] = entry
 

server/text_generation/models/__init__.py

@@ -1,8 +1,9 @@
 from text_generation.models.model import Model
-from text_generation.models.bloom import BLOOMSharded
 from text_generation.models.causal_lm import CausalLM
+from text_generation.models.bloom import BLOOMSharded
+from text_generation.models.seq2seq_lm import Seq2SeqLM
 
-__all__ = ["Model", "BLOOMSharded", "CausalLM"]
+__all__ = ["Model", "BLOOMSharded", "CausalLM", "Seq2SeqLM"]
 
 
 def get_model(model_name: str, sharded: bool, quantize: bool) -> Model:
@@ -18,4 +19,7 @@ def get_model(model_name: str, sharded: bool, quantize: bool) -> Model:
             raise ValueError("sharded is not supported for AutoModel")
         if quantize:
             raise ValueError("quantize is not supported for AutoModel")
+        try:
             return CausalLM(model_name)
+        except Exception as e:
+            return Seq2SeqLM(model_name)

server/text_generation/models/bloom.py

@@ -12,7 +12,7 @@ from transformers.models.bloom.parallel_layers import (
     TensorParallelRowLinear,
 )
 
-from text_generation.models import Model
+from text_generation.models import CausalLM
 from text_generation.utils import (
     initialize_torch_distributed,
     weight_files,
@@ -29,7 +29,7 @@ except Exception as e:
 torch.manual_seed(0)
 
 
-class BLOOMSharded(Model):
+class BLOOMSharded(CausalLM):
     def __init__(self, model_name: str, quantize: bool = False):
         if not model_name.startswith("bigscience/bloom"):
             raise ValueError(f"Model {model_name} is not supported")
@@ -78,8 +78,11 @@ class BLOOMSharded(Model):
         )
         self.model = model.eval().to(dtype)
         torch.distributed.barrier(group=self.process_group)
-        super(BLOOMSharded, self).__init__(tokenizer=tokenizer, num_heads=config.n_head // self.process_group.size(),
+        super(CausalLM, self).__init__(
-                                           device=device)
+            tokenizer=tokenizer,
+            num_heads=config.n_head // self.process_group.size(),
+            device=device,
+        )
 
     @staticmethod
     def load_weights(

server/text_generation/models/causal_lm.py

@@ -1,9 +1,211 @@
 import torch
 
+from dataclasses import dataclass
 from transformers import AutoTokenizer, AutoModelForCausalLM
-from typing import Optional, Tuple, List
+from typing import Optional, Tuple, List, Dict, Type
 
 from text_generation.models import Model
+from text_generation.models.types import GeneratedText
+from text_generation.pb import generate_pb2
+from text_generation.utils import NextTokenChooser, StoppingCriteria
+
+
+@dataclass
+class CausalLMBatch:
+    batch_id: int
+    requests: List[generate_pb2.Request]
+    all_input_lengths: List[int]
+    input_ids: Dict[str, torch.Tensor]
+    all_input_ids: List[torch.Tensor]
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+    size: int
+    max_sequence_length: int
+
+    def to_pb(self):
+        return generate_pb2.Batch(
+            id=self.batch_id,
+            requests=self.requests,
+            size=self.size,
+        )
+
+    @classmethod
+    def from_pb(
+        cls, pb: generate_pb2.Batch, tokenizer: AutoTokenizer, device: torch.device
+    ) -> "CausalLMBatch":
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+        all_input_lengths = []
+
+        # Parse batch
+        for r in pb.requests:
+            inputs.append(r.inputs)
+            all_input_lengths.append(r.input_length)
+            next_token_choosers.append(
+                NextTokenChooser(
+                    temperature=r.parameters.temperature,
+                    top_k=r.parameters.top_k,
+                    top_p=r.parameters.top_p,
+                    do_sample=r.parameters.do_sample,
+                )
+            )
+            stopping_criterias.append(
+                StoppingCriteria(
+                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
+                )
+            )
+
+        input_ids = tokenizer(
+            inputs, return_tensors="pt", padding=True, pad_to_multiple_of=8
+        ).to(device)
+        all_input_ids = input_ids["input_ids"].unsqueeze(-1)
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            all_input_lengths=all_input_lengths,
+            input_ids=input_ids,
+            all_input_ids=all_input_ids,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            size=pb.size,
+            max_sequence_length=max(all_input_lengths),
+        )
+
+    @classmethod
+    def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
+        # Used for padding
+        total_batch_size = sum(batch.size for batch in batches)
+        max_sequence_length = max(batch.max_sequence_length for batch in batches)
+
+        # Batch attributes
+        input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
+        requests = []
+        all_input_lengths = []
+        all_input_ids = []
+        next_token_choosers = []
+        stopping_criterias = []
+
+        # 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)
+            all_input_lengths.extend(batch.all_input_lengths)
+            all_input_ids.extend(batch.all_input_ids)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            # Slicing end index for this batch
+            end_index = start_index + batch.size
+
+            # We only concatenate batches that did at least one step
+            if batch.input_ids["input_ids"].shape[1] > 1:
+                raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
+
+            # Initialize tensors
+            if i == 0:
+                input_ids["input_ids"] = torch.empty(
+                    (total_batch_size, 1),
+                    dtype=batch.input_ids["input_ids"].dtype,
+                    device=batch.input_ids["input_ids"].device,
+                )
+                input_ids["attention_mask"] = torch.zeros(
+                    (total_batch_size, max_sequence_length),
+                    dtype=batch.input_ids["attention_mask"].dtype,
+                    device=batch.input_ids["attention_mask"].device,
+                )
+
+            # input_ids["input_ids"] is always of shape [batch_size, 1]
+            # We do not need to pad it
+            input_ids["input_ids"][start_index:end_index] = batch.input_ids["input_ids"]
+
+            # We need to slice the attention mask to remove padding from previous steps
+            input_ids["attention_mask"][
+                start_index:end_index, -batch.max_sequence_length :
+            ] = batch.input_ids["attention_mask"][:, -batch.max_sequence_length :]
+
+            for j, past in enumerate(batch.input_ids["past_key_values"]):
+                # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
+                # BLOOM: [batch_size * num_heads, ...] vs [batch_size, num_heads, ...]
+                head_dim, padded_sequence_length = past[0].shape[-2:]
+                num_heads = (
+                    past[0]
+                    .view(batch.size, -1, head_dim, padded_sequence_length)
+                    .shape[1]
+                )
+
+                # This will run only once per layer
+                if j == len(input_ids["past_key_values"]):
+                    input_ids["past_key_values"].append([])
+
+                # Decoder past
+                for k, t in enumerate(past):
+                    # Needed because BLOOM past shapes are not the same for keys and values
+                    # Keys:   [batch_size * num_heads, head_dim, seq_length]
+                    # Values: [batch_size * num_heads, seq_length, head_dim]
+                    head_dim_last = False
+                    if t.shape[-2] == head_dim:
+                        t = t.view(
+                            batch.size, num_heads, head_dim, padded_sequence_length
+                        )
+                        padded_t_shape = (
+                            total_batch_size,
+                            num_heads,
+                            head_dim,
+                            max_sequence_length - 1,
+                        )
+                    elif t.shape[-1] == head_dim:
+                        head_dim_last = True
+                        t = t.view(
+                            batch.size, num_heads, padded_sequence_length, head_dim
+                        )
+                        padded_t_shape = (
+                            total_batch_size,
+                            num_heads,
+                            max_sequence_length - 1,
+                            head_dim,
+                        )
+                    else:
+                        raise ValueError(f"shape {t.shape} is not valid")
+
+                    # Initialize tensors
+                    # This will run only once per layer and per past tensor
+                    if k == len(input_ids["past_key_values"][j]):
+                        input_ids["past_key_values"][j].append(
+                            torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
+                        )
+
+                    # We slice the past keys and values to remove the padding from previous batches
+                    if not head_dim_last:
+                        input_ids["past_key_values"][j][k][
+                            start_index:end_index,
+                            :,
+                            :,
+                            -(batch.max_sequence_length - 1) :,
+                        ] = t[:, :, :, -(batch.max_sequence_length - 1) :]
+                    else:
+                        input_ids["past_key_values"][j][k][
+                            start_index:end_index,
+                            :,
+                            -(batch.max_sequence_length - 1) :,
+                            :,
+                        ] = t[:, :, -(batch.max_sequence_length - 1) :, :]
+
+            start_index += batch.size
+
+        return cls(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            all_input_lengths=all_input_lengths,
+            input_ids=input_ids,
+            all_input_ids=all_input_ids,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            size=total_batch_size,
+            max_sequence_length=max_sequence_length,
+        )
 
 
 class CausalLM(Model):
@@ -23,7 +225,15 @@ class CausalLM(Model):
             device_map="auto" if torch.cuda.is_available() else None,
         ).eval()
 
-        super(CausalLM, self).__init__(tokenizer=tokenizer, num_heads=self.model.config.num_attention_heads, device=device)
+        super(CausalLM, self).__init__(
+            tokenizer=tokenizer,
+            num_heads=self.model.config.num_attention_heads,
+            device=device,
+        )
+
+    @property
+    def batch_type(self) -> Type[CausalLMBatch]:
+        return CausalLMBatch
 
     def forward(
         self, input_ids, attention_mask, past_key_values: Optional = None
@@ -36,3 +246,129 @@ class CausalLM(Model):
             use_cache=True,
         )
         return outputs.logits, outputs.past_key_values
+
+    def generate_token(
+        self, batch: CausalLMBatch
+    ) -> Tuple[List[GeneratedText], Optional[CausalLMBatch]]:
+        # For some reason, inference_mode does not work well with GLOO which we use on CPU
+        context_manager = (
+            torch.no_grad if self.device.type == "cpu" else torch.inference_mode
+        )
+        with context_manager():
+            logits, past = self.forward(**batch.input_ids)
+
+        # List of indices to cache
+        next_batch_keep_indices = []
+
+        # New input_ids for next forward
+        next_batch_input_ids = []
+        next_batch_all_input_ids = []
+        next_all_input_lengths = []
+
+        next_batch_size = 0
+        next_batch_max_sequence_length = 0
+
+        # Finished requests
+        generated_texts: List[GeneratedText] = []
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.all_input_lengths,
+            logits,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_input_ids,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            logits,
+            next_token_chooser,
+            stopping_criteria,
+            all_tokens,
+        ) in enumerate(iterator):
+            # Select next token
+            next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
+
+            # Append next token to all tokens
+            all_tokens = torch.cat([all_tokens, next_token])
+
+            # Evaluate stopping criteria
+            if stopping_criteria(all_tokens):
+                # Decode all tokens
+                output = self.tokenizer.decode(
+                    all_tokens.squeeze(-1), skip_special_tokens=True
+                )
+                # Add to the list of finished generations with the original request
+                generated_texts.append(
+                    GeneratedText(request, output, stopping_criteria.current_tokens)
+                )
+            # add to the next batch
+            else:
+                next_batch_keep_indices.append(i)
+                next_batch_input_ids.append(next_token)
+                next_batch_all_input_ids.append(all_tokens)
+                next_batch_size += 1
+                new_input_length = input_length + 1
+                next_all_input_lengths.append(new_input_length)
+                next_batch_max_sequence_length = max(
+                    next_batch_max_sequence_length, new_input_length
+                )
+
+        # We finished all generations in the batch; there is no next batch
+        if not next_batch_keep_indices:
+            return generated_texts, None
+
+        # If we finished at least one generation
+        next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
+        if generated_texts:
+            # Apply indices to attention mask, past key values and other items that need to be cached
+            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
+                next_batch_keep_indices
+            ]
+            # Force past to be of dim [batch_size, num_heads, ...] for easy indexing
+            next_batch_input_ids["past_key_values"] = [
+                [
+                    t.view(-1, self.num_heads, *t.shape[-2:])[next_batch_keep_indices]
+                    for t in layer
+                ]
+                for layer in past
+            ]
+            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
+            next_batch_next_token_choosers = [
+                batch.next_token_choosers[i] for i in next_batch_keep_indices
+            ]
+            next_batch_stopping_criterias = [
+                batch.stopping_criterias[i] for i in next_batch_keep_indices
+            ]
+        else:
+            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
+            next_batch_input_ids["past_key_values"] = past
+            next_batch_requests = batch.requests
+            next_batch_next_token_choosers = batch.next_token_choosers
+            next_batch_stopping_criterias = batch.stopping_criterias
+
+        # Update attention_mask with padding as we added a new token to input_ids
+        next_batch_input_ids["attention_mask"] = torch.cat(
+            [
+                next_batch_input_ids["attention_mask"],
+                torch.ones((next_batch_size, 1)).to(self.device),
+            ],
+            dim=1,
+        )
+
+        next_batch = CausalLMBatch(
+            batch_id=batch.batch_id,
+            requests=next_batch_requests,
+            all_input_lengths=next_all_input_lengths,
+            input_ids=next_batch_input_ids,
+            all_input_ids=next_batch_all_input_ids,
+            next_token_choosers=next_batch_next_token_choosers,
+            stopping_criterias=next_batch_stopping_criterias,
+            size=next_batch_size,
+            max_sequence_length=next_batch_max_sequence_length,
+        )
+        return generated_texts, next_batch

server/text_generation/models/model.py

@@ -1,11 +1,13 @@
 import torch
 
 from abc import ABC, abstractmethod
-from typing import List, Tuple, Optional
+from typing import List, Tuple, Optional, TypeVar, Type
 from tokenizers import Tokenizer
 
 from text_generation.models.types import Batch, GeneratedText
 
+B = TypeVar("B", bound=Batch)
+
 
 class Model(ABC):
     def __init__(self, tokenizer: Tokenizer, num_heads: int, device: torch.device):
@@ -13,127 +15,11 @@ class Model(ABC):
         self.num_heads = num_heads
         self.device = device
 
+    @property
     @abstractmethod
-    def forward(self, input_ids, attention_mask, past_key_values: Optional = None) -> Tuple[torch.Tensor, List[Tuple]]:
+    def batch_type(self) -> Type[B]:
         raise NotImplementedError
 
-    def generate_token(
+    @abstractmethod
-            self, batch: Batch
+    def generate_token(self, batch: B) -> Tuple[List[GeneratedText], Optional[B]]:
-    ) -> Tuple[List[GeneratedText], Optional[Batch]]:
+        raise NotImplementedError
-        # For some reason, inference_mode does not work well with GLOO which we use on CPU
-        context_manager = (
-            torch.no_grad if self.device.type == "cpu" else torch.inference_mode
-        )
-        with context_manager():
-            logits, past = self.forward(**batch.input_ids)
-
-        # List of indices to cache
-        next_batch_keep_indices = []
-
-        # New input_ids for next forward
-        next_batch_input_ids = []
-        next_batch_all_input_ids = []
-        next_all_input_lengths = []
-
-        next_batch_size = 0
-        next_batch_max_sequence_length = 0
-
-        # Finished requests
-        generated_texts: List[GeneratedText] = []
-
-        # Zipped iterator
-        iterator = zip(
-            batch.requests,
-            batch.all_input_lengths,
-            logits,
-            batch.next_token_choosers,
-            batch.stopping_criterias,
-            batch.all_input_ids,
-        )
-
-        # For each member of the batch
-        for i, (
-                request,
-                input_length,
-                logits,
-                next_token_chooser,
-                stopping_criteria,
-                all_tokens,
-        ) in enumerate(iterator):
-            # Select next token
-            next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
-
-            # Append next token to all tokens
-            all_tokens = torch.cat([all_tokens, next_token])
-
-            # Evaluate stopping criteria
-            if stopping_criteria(all_tokens):
-                # Decode all tokens
-                output = self.tokenizer.decode(
-                    all_tokens.squeeze(-1), skip_special_tokens=True
-                )
-                # Add to the list of finished generations with the original request
-                generated_texts.append(GeneratedText(request, output, stopping_criteria.current_tokens))
-            # add to the next batch
-            else:
-                next_batch_keep_indices.append(i)
-                next_batch_input_ids.append(next_token)
-                next_batch_all_input_ids.append(all_tokens)
-                next_batch_size += 1
-                new_input_length = input_length + 1
-                next_all_input_lengths.append(new_input_length)
-                next_batch_max_sequence_length = max(
-                    next_batch_max_sequence_length, new_input_length
-                )
-
-        # We finished all generations in the batch; there is no next batch
-        if not next_batch_keep_indices:
-            return generated_texts, None
-
-        # If we finished at least one generation
-        next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
-        if generated_texts:
-            # Apply indices to attention mask, past key values and other items that need to be cached
-            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
-                next_batch_keep_indices
-            ]
-            # Force past to be of dim [batch_size, num_heads, ...] for easy indexing
-            next_batch_input_ids["past_key_values"] = [
-                [t.view(-1, self.num_heads, *t.shape[-2:])[next_batch_keep_indices] for t in layer]
-                for layer in past
-            ]
-            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
-            next_batch_next_token_choosers = [
-                batch.next_token_choosers[i] for i in next_batch_keep_indices
-            ]
-            next_batch_stopping_criterias = [
-                batch.stopping_criterias[i] for i in next_batch_keep_indices
-            ]
-        else:
-            next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
-            next_batch_input_ids["past_key_values"] = past
-            next_batch_requests = batch.requests
-            next_batch_next_token_choosers = batch.next_token_choosers
-            next_batch_stopping_criterias = batch.stopping_criterias
-
-        # Update attention_mask with padding as we added a new token to input_ids
-        next_batch_input_ids["attention_mask"] = torch.cat(
-            [
-                next_batch_input_ids["attention_mask"],
-                torch.ones((next_batch_size, 1)).to(self.device),
-            ],
-            dim=1,
-        )
-
-        next_batch = Batch(
-            batch_id=batch.batch_id,
-            requests=next_batch_requests,
-            all_input_lengths=next_all_input_lengths,
-            input_ids=next_batch_input_ids,
-            all_input_ids=next_batch_all_input_ids,
-            next_token_choosers=next_batch_next_token_choosers,
-            stopping_criterias=next_batch_stopping_criterias,
-            size=next_batch_size,
-            max_sequence_length=next_batch_max_sequence_length,
-        )
-        return generated_texts, next_batch

server/text_generation/models/seq2seq_lm.py

@@ -0,0 +1,488 @@
+import torch
+
+from dataclasses import dataclass
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from typing import Optional, Tuple, List, Type
+
+from text_generation.models import Model
+from text_generation.models.types import GeneratedText
+from text_generation.pb import generate_pb2
+from text_generation.utils import NextTokenChooser, StoppingCriteria
+
+
+@dataclass
+class Seq2SeqLMBatch:
+    batch_id: int
+    requests: List[generate_pb2.Request]
+
+    input_ids: torch.Tensor
+    attention_mask: torch.Tensor
+
+    decoder_input_ids: torch.Tensor
+    decoder_attention_mask: Optional[torch.Tensor]
+    encoder_last_hidden_state: Optional[torch.Tensor]
+
+    past_key_values: Optional[List[Tuple]]
+
+    input_lengths: List[int]
+    decoder_input_lengths: List[int]
+
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+
+    size: int
+    max_input_length: int
+    max_decoder_input_length: int
+
+    def to_pb(self):
+        return generate_pb2.Batch(
+            id=self.batch_id,
+            requests=self.requests,
+            size=self.size,
+        )
+
+    @classmethod
+    def from_pb(
+        cls, pb: generate_pb2.Batch, tokenizer: AutoTokenizer, device: torch.device
+    ) -> "Seq2SeqLMBatch":
+        inputs = []
+        next_token_choosers = []
+        stopping_criterias = []
+        input_lengths = []
+
+        decoder_input_ids = []
+        decoder_input_lengths = []
+
+        # Parse batch
+        for r in pb.requests:
+            inputs.append(r.inputs)
+            input_lengths.append(r.input_length)
+            decoder_input_ids.append(tokenizer.bos_token_id)
+            decoder_input_lengths.append(1)
+            next_token_choosers.append(
+                NextTokenChooser(
+                    temperature=r.parameters.temperature,
+                    top_k=r.parameters.top_k,
+                    top_p=r.parameters.top_p,
+                    do_sample=r.parameters.do_sample,
+                )
+            )
+            stopping_criterias.append(
+                StoppingCriteria(
+                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
+                )
+            )
+
+        tokenized_inputs = tokenizer(
+            inputs, return_tensors="pt", padding=True, pad_to_multiple_of=8
+        ).to(device)
+        decoder_input_ids = torch.tensor(decoder_input_ids).to(device).unsqueeze(-1)
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            input_ids=tokenized_inputs["input_ids"],
+            attention_mask=tokenized_inputs["attention_mask"],
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=None,
+            encoder_last_hidden_state=None,
+            past_key_values=None,
+            input_lengths=input_lengths,
+            decoder_input_lengths=decoder_input_lengths,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            size=len(pb.requests),
+            max_input_length=max(input_lengths),
+            max_decoder_input_length=1,
+        )
+
+    @classmethod
+    def concatenate(cls, batches: List["Seq2SeqLMBatch"]) -> "Seq2SeqLMBatch":
+        # Used for padding
+        total_batch_size = sum(batch.size for batch in batches)
+        max_input_length = max(batch.max_input_length for batch in batches)
+        max_decoder_input_length = max(
+            batch.max_decoder_input_length for batch in batches
+        )
+
+        # Batch attributes
+        requests = []
+        input_lengths = []
+        decoder_input_lengths = []
+        next_token_choosers = []
+        stopping_criterias = []
+
+        input_ids = None
+        attention_mask = None
+        decoder_input_ids = None
+        decoder_attention_mask = None
+        encoder_last_hidden_state = 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)
+            decoder_input_lengths.extend(batch.decoder_input_lengths)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            # Slicing end index for this batch
+            end_index = start_index + batch.size
+
+            # We only concatenate batches that did at least one step
+            if batch.encoder_last_hidden_state is None:
+                raise ValueError("Batch encoder_last_hidden_state cannot be None")
+
+            if input_ids is None:
+                input_ids = torch.zeros(
+                    (total_batch_size, max_input_length),
+                    dtype=batch.input_ids.dtype,
+                    device=batch.input_ids.device,
+                )
+            input_ids[
+                start_index:end_index, -batch.max_input_length :
+            ] = batch.input_ids[:, -batch.max_input_length :]
+
+            if attention_mask is None:
+                attention_mask = torch.zeros(
+                    (total_batch_size, max_input_length),
+                    dtype=batch.attention_mask.dtype,
+                    device=batch.attention_mask.device,
+                )
+            attention_mask[
+                start_index:end_index, -batch.max_input_length :
+            ] = batch.attention_mask[:, -batch.max_input_length :]
+
+            if decoder_input_ids is None:
+                decoder_input_ids = torch.zeros(
+                    (total_batch_size, max_decoder_input_length),
+                    dtype=batch.decoder_input_ids.dtype,
+                    device=batch.decoder_input_ids.device,
+                )
+            decoder_input_ids[
+                start_index:end_index, -batch.max_decoder_input_length :
+            ] = batch.decoder_input_ids[:, -batch.max_decoder_input_length :]
+
+            if decoder_attention_mask is None:
+                decoder_attention_mask = torch.zeros(
+                    (total_batch_size, max_decoder_input_length),
+                    dtype=batch.attention_mask.dtype,
+                    device=batch.attention_mask.device,
+                )
+            if batch.decoder_attention_mask is None:
+                decoder_attention_mask[
+                    start_index:end_index, -batch.max_decoder_input_length :
+                ] = 1
+            else:
+                decoder_attention_mask[
+                    start_index:end_index, -batch.max_decoder_input_length :
+                ] = batch.decoder_attention_mask[:, -batch.max_decoder_input_length :]
+
+            if encoder_last_hidden_state is None:
+                encoder_last_hidden_state = torch.zeros(
+                    (
+                        total_batch_size,
+                        max_input_length,
+                        batch.encoder_last_hidden_state.shape[-1],
+                    ),
+                    dtype=batch.encoder_last_hidden_state.dtype,
+                    device=batch.encoder_last_hidden_state.device,
+                )
+
+            encoder_last_hidden_state[
+                start_index:end_index, -batch.max_decoder_input_length :, :
+            ] = batch.encoder_last_hidden_state[:, -batch.max_decoder_input_length :, :]
+
+            for j, past in enumerate(batch.past_key_values):
+                _, num_heads, _, head_dim = past[0].shape
+
+                # This will run only once per layer
+                if j == len(past_key_values):
+                    past_key_values.append([])
+
+                # Decoder past
+                for k, t in enumerate(past[:2]):
+                    padded_t_shape = (
+                        total_batch_size,
+                        num_heads,
+                        (max_decoder_input_length - 1),
+                        head_dim,
+                    )
+
+                    # Initialize tensors
+                    # This will run only once per layer and per past tensor
+                    if k == len(past_key_values[j]):
+                        past_key_values[j].append(
+                            torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
+                        )
+
+                    # We slice the past keys and values to remove the padding from previous batches
+                    past_key_values[j][k][
+                        start_index:end_index,
+                        :,
+                        -(batch.max_decoder_input_length - 1) :,
+                        :,
+                    ] = t[:, :, -(batch.max_decoder_input_length - 1) :, :]
+
+                # encoder past
+                for k, t in enumerate(past[2:]):
+                    padded_t_shape = (
+                        total_batch_size,
+                        num_heads,
+                        max_input_length,
+                        head_dim,
+                    )
+
+                    idx = k + 2
+
+                    # Initialize tensors
+                    # This will run only once per layer and per past tensor
+                    if idx == len(past_key_values[j]):
+                        past_key_values[j].append(
+                            torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
+                        )
+
+                    past_key_values[j][idx][
+                        start_index:end_index, :, -batch.max_input_length :, :
+                    ] = t[:, :, -batch.max_input_length :, :]
+
+            start_index += batch.size
+
+        return cls(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_last_hidden_state=encoder_last_hidden_state,
+            past_key_values=past_key_values,
+            input_lengths=input_lengths,
+            decoder_input_lengths=decoder_input_lengths,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+            size=total_batch_size,
+            max_input_length=max_input_length,
+            max_decoder_input_length=max_decoder_input_length,
+        )
+
+
+class Seq2SeqLM(Model):
+    def __init__(self, model_name: str):
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
+        else:
+            device = torch.device("cpu")
+            dtype = torch.float32
+
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(
+            model_name,
+            torch_dtype=dtype,
+            device_map="auto" if torch.cuda.is_available() else None,
+        ).eval()
+        tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        tokenizer.bos_token_id = self.model.config.decoder_start_token_id
+
+        super(Seq2SeqLM, self).__init__(
+            tokenizer=tokenizer,
+            num_heads=self.model.config.num_attention_heads,
+            device=device,
+        )
+
+    @property
+    def batch_type(self) -> Type[Seq2SeqLMBatch]:
+        return Seq2SeqLMBatch
+
+    def forward(
+        self,
+        input_ids,
+        attention_mask,
+        decoder_input_ids,
+        decoder_attention_mask: Optional,
+        encoder_last_hidden_state: Optional,
+        past_key_values: Optional = None,
+    ) -> Tuple[
+        torch.Tensor,
+        torch.Tensor,
+        List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]],
+    ]:
+        # Model Forward
+        if past_key_values is not None:
+            decoder_input_ids = decoder_input_ids[:, -1].unsqueeze(-1)
+
+        outputs = self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            encoder_outputs=[encoder_last_hidden_state]
+            if encoder_last_hidden_state is not None
+            else None,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+        return (
+            outputs.logits,
+            outputs.encoder_last_hidden_state,
+            outputs.past_key_values,
+        )
+
+    def generate_token(
+        self, batch: Seq2SeqLMBatch
+    ) -> Tuple[List[GeneratedText], Optional[Seq2SeqLMBatch]]:
+        # For some reason, inference_mode does not work well with GLOO which we use on CPU
+        context_manager = (
+            torch.no_grad if self.device.type == "cpu" else torch.inference_mode
+        )
+        with context_manager():
+            logits, encoder_last_hidden_state, past = self.forward(
+                batch.input_ids,
+                batch.attention_mask,
+                batch.decoder_input_ids,
+                batch.decoder_attention_mask,
+                batch.encoder_last_hidden_state,
+                batch.past_key_values,
+            )
+
+        # List of indices to cache
+        next_batch_keep_indices = []
+
+        # New input_ids for next forward
+        next_batch_input_lengths = []
+        next_batch_decoder_input_ids = []
+        next_batch_decoder_input_lengths = []
+
+        next_batch_size = 0
+        next_batch_max_input_length = 0
+        next_batch_max_decoder_input_length = 0
+
+        # Finished requests
+        generated_texts: List[GeneratedText] = []
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.decoder_input_lengths,
+            logits,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.input_ids,
+            batch.decoder_input_ids,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            decoder_input_length,
+            logits,
+            next_token_chooser,
+            stopping_criteria,
+            input_tokens,
+            decoder_tokens,
+        ) in enumerate(iterator):
+            all_tokens = torch.cat([input_tokens, decoder_tokens])
+            # Select next token
+            next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
+
+            # Append next token to decoder tokens
+            decoder_tokens = torch.cat([decoder_tokens, next_token.squeeze(1)])
+
+            # Evaluate stopping criteria
+            if stopping_criteria(decoder_tokens):
+                # Decode all tokens
+                output = self.tokenizer.decode(decoder_tokens, skip_special_tokens=True)
+                # Add to the list of finished generations with the original request
+                generated_texts.append(
+                    GeneratedText(request, output, stopping_criteria.current_tokens)
+                )
+            # add to the next batch
+            else:
+                next_batch_keep_indices.append(i)
+                next_batch_decoder_input_ids.append(decoder_tokens.unsqueeze(0))
+                next_batch_size += 1
+                new_decoder_input_length = decoder_input_length + 1
+                next_batch_input_lengths.append(input_length)
+                next_batch_decoder_input_lengths.append(new_decoder_input_length)
+                next_batch_max_input_length = max(
+                    next_batch_max_input_length, input_length
+                )
+                next_batch_max_decoder_input_length = max(
+                    next_batch_max_decoder_input_length, new_decoder_input_length
+                )
+
+        # We finished all generations in the batch; there is no next batch
+        if not next_batch_keep_indices:
+            return generated_texts, None
+
+        # If we finished at least one generation
+        next_batch_decoder_input_ids = torch.cat(next_batch_decoder_input_ids)
+        if generated_texts:
+            next_batch_input_ids = batch.input_ids[next_batch_keep_indices]
+            next_batch_attention_mask = batch.attention_mask[next_batch_keep_indices]
+
+            if batch.decoder_attention_mask is not None:
+                next_batch_decoder_attention_mask = batch.decoder_attention_mask[
+                    next_batch_keep_indices
+                ]
+            else:
+                next_batch_decoder_attention_mask = None
+
+            next_batch_encoder_last_hidden_state = encoder_last_hidden_state[
+                next_batch_keep_indices
+            ]
+
+            next_batch_past_key_values = [
+                [t[next_batch_keep_indices] for t in layer] for layer in past
+            ]
+            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
+            next_batch_next_token_choosers = [
+                batch.next_token_choosers[i] for i in next_batch_keep_indices
+            ]
+            next_batch_stopping_criterias = [
+                batch.stopping_criterias[i] for i in next_batch_keep_indices
+            ]
+        else:
+            next_batch_input_ids = batch.input_ids
+            next_batch_attention_mask = batch.attention_mask
+            next_batch_decoder_attention_mask = batch.decoder_attention_mask
+            next_batch_encoder_last_hidden_state = encoder_last_hidden_state
+            next_batch_past_key_values = past
+
+            next_batch_requests = batch.requests
+            next_batch_next_token_choosers = batch.next_token_choosers
+            next_batch_stopping_criterias = batch.stopping_criterias
+
+        # Update attention_mask with padding as we added a new token to input_ids
+        if next_batch_decoder_attention_mask is not None:
+            next_batch_decoder_attention_mask = torch.cat(
+                [
+                    next_batch_decoder_attention_mask,
+                    torch.ones((next_batch_size, 1)).to(self.device),
+                ],
+                dim=1,
+            )
+
+        next_batch = Seq2SeqLMBatch(
+            batch_id=batch.batch_id,
+            requests=next_batch_requests,
+            input_ids=next_batch_input_ids,
+            attention_mask=next_batch_attention_mask,
+            decoder_input_ids=next_batch_decoder_input_ids,
+            decoder_attention_mask=next_batch_decoder_attention_mask,
+            encoder_last_hidden_state=next_batch_encoder_last_hidden_state,
+            past_key_values=next_batch_past_key_values,
+            input_lengths=next_batch_input_lengths,
+            decoder_input_lengths=next_batch_decoder_input_lengths,
+            next_token_choosers=next_batch_next_token_choosers,
+            stopping_criterias=next_batch_stopping_criterias,
+            size=next_batch_size,
+            max_input_length=next_batch_max_input_length,
+            max_decoder_input_length=next_batch_max_decoder_input_length,
+        )
+        return generated_texts, next_batch

server/text_generation/models/types.py

@@ -1,237 +1,30 @@
 import torch
 
+from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import List, Dict
+from typing import List
 
 from transformers import AutoTokenizer
 
 from text_generation.pb import generate_pb2
-from text_generation.utils import NextTokenChooser, StoppingCriteria
 
 
-@dataclass
+class Batch(ABC):
-class Batch:
+    @abstractmethod
-    batch_id: int
+    def to_pb(self) -> generate_pb2.Batch:
-    requests: List[generate_pb2.Request]
+        raise NotImplementedError
-    all_input_lengths: List[int]
-    input_ids: Dict[str, torch.Tensor]
-    all_input_ids: List[torch.Tensor]
-    next_token_choosers: List[NextTokenChooser]
-    stopping_criterias: List[StoppingCriteria]
-    size: int
-    max_sequence_length: int
-
-    def to_pb(self):
-        return generate_pb2.Batch(
-            id=self.batch_id,
-            requests=self.requests,
-            size=self.size,
-            max_sequence_length=self.max_sequence_length,
-        )
 
     @classmethod
+    @abstractmethod
     def from_pb(
         cls, pb: generate_pb2.Batch, tokenizer: AutoTokenizer, device: torch.device
     ) -> "Batch":
-        inputs = []
+        raise NotImplementedError
-        next_token_choosers = []
-        stopping_criterias = []
-        all_input_lengths = []
-
-        # Parse batch
-        for r in pb.requests:
-            inputs.append(r.inputs)
-            all_input_lengths.append(r.input_length)
-            next_token_choosers.append(
-                NextTokenChooser(
-                    temperature=r.parameters.temperature,
-                    top_k=r.parameters.top_k,
-                    top_p=r.parameters.top_p,
-                    do_sample=r.parameters.do_sample,
-                )
-            )
-            stopping_criterias.append(
-                StoppingCriteria(
-                    eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
-                )
-            )
-
-        input_ids = tokenizer(
-            inputs, return_tensors="pt", padding=True, pad_to_multiple_of=8
-        ).to(device)
-        all_input_ids = input_ids["input_ids"].unsqueeze(-1)
-
-        return cls(
-            batch_id=pb.id,
-            requests=pb.requests,
-            all_input_lengths=all_input_lengths,
-            input_ids=input_ids,
-            all_input_ids=all_input_ids,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-            size=pb.size,
-            max_sequence_length=pb.max_sequence_length,
-        )
 
     @classmethod
+    @abstractmethod
     def concatenate(cls, batches: List["Batch"]) -> "Batch":
-        # Used for padding
+        raise NotImplementedError
-        total_batch_size = sum(batch.size for batch in batches)
-        max_sequence_length = max(batch.max_sequence_length for batch in batches)
-        # Only needed for Seq2SeqLM
-        max_encoded_sequence_length = None
-
-        # Batch attributes
-        input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
-        requests = []
-        all_input_lengths = []
-        all_input_ids = []
-        next_token_choosers = []
-        stopping_criterias = []
-
-        # 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)
-            all_input_lengths.extend(batch.all_input_lengths)
-            all_input_ids.extend(batch.all_input_ids)
-            next_token_choosers.extend(batch.next_token_choosers)
-            stopping_criterias.extend(batch.stopping_criterias)
-
-            # Slicing end index for this batch
-            end_index = start_index + batch.size
-
-            # We only concatenate batches that did at least one step
-            if batch.input_ids["input_ids"].shape[1] > 1:
-                raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
-
-            # Initialize tensors
-            if i == 0:
-                input_ids["input_ids"] = torch.empty(
-                    (total_batch_size, 1),
-                    dtype=batch.input_ids["input_ids"].dtype,
-                    device=batch.input_ids["input_ids"].device,
-                )
-                input_ids["attention_mask"] = torch.zeros(
-                    (total_batch_size, max_sequence_length),
-                    dtype=batch.input_ids["attention_mask"].dtype,
-                    device=batch.input_ids["attention_mask"].device,
-                )
-
-            # input_ids["input_ids"] is always of shape [batch_size, 1]
-            # We do not need to pad it
-            input_ids["input_ids"][start_index:end_index] = batch.input_ids["input_ids"]
-
-            # We need to slice the attention mask to remove padding from previous steps
-            input_ids["attention_mask"][
-            start_index:end_index, -batch.max_sequence_length:
-            ] = batch.input_ids["attention_mask"][:, -batch.max_sequence_length:]
-
-            for j, past in enumerate(batch.input_ids["past_key_values"]):
-                # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
-                # BLOOM: [batch_size * num_heads, ...] vs [batch_size, num_heads, ...]
-                head_dim, padded_sequence_length = past[0].shape[-2:]
-                num_heads = (
-                    past[0]
-                    .view(batch.size, -1, head_dim, padded_sequence_length)
-                    .shape[1]
-                )
-
-                # This will run only once per layer
-                if j == len(input_ids["past_key_values"]):
-                    input_ids["past_key_values"].append([])
-
-                # Decoder past
-                for k, t in enumerate(past[:2]):
-                    # Needed because BLOOM past shapes are not the same for keys and values
-                    # Keys:   [batch_size * num_heads, head_dim, seq_length]
-                    # Values: [batch_size * num_heads, seq_length, head_dim]
-                    head_dim_last = False
-                    if t.shape[-2] == head_dim:
-                        t = t.view(
-                            batch.size, num_heads, head_dim, padded_sequence_length
-                        )
-                        padded_t_shape = (
-                            total_batch_size,
-                            num_heads,
-                            head_dim,
-                            max_sequence_length - 1,
-                        )
-                    elif t.shape[-1] == head_dim:
-                        head_dim_last = True
-                        t = t.view(
-                            batch.size, num_heads, padded_sequence_length, head_dim
-                        )
-                        padded_t_shape = (
-                            total_batch_size,
-                            num_heads,
-                            max_sequence_length - 1,
-                            head_dim,
-                        )
-                    else:
-                        raise ValueError(f"shape {t.shape} is not valid")
-
-                    # Initialize tensors
-                    # This will run only once per layer and per past tensor
-                    if k == len(input_ids["past_key_values"][j]):
-                        input_ids["past_key_values"][j].append(
-                            torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
-                        )
-
-                    # We slice the past keys and values to remove the padding from previous batches
-                    if not head_dim_last:
-                        input_ids["past_key_values"][j][k][
-                        start_index:end_index,
-                        :,
-                        :,
-                        -(batch.max_sequence_length - 1):,
-                        ] = t[:, :, :, -(batch.max_sequence_length - 1):]
-                    else:
-                        input_ids["past_key_values"][j][k][
-                        start_index:end_index,
-                        :,
-                        -(batch.max_sequence_length - 1):,
-                        :,
-                        ] = t[:, :, -(batch.max_sequence_length - 1):, :]
-
-                # Seq2SeqLM specific past (encoder past)
-                for k, t in enumerate(past[2:]):
-                    if max_encoded_sequence_length is None:
-                        max_encoded_sequence_length = max(max(batch.all_input_lengths) for batch in batches)
-                    batch_max_encoded_sequence_length = max(batch.all_input_lengths)
-
-                    padded_t_shape = (total_batch_size, num_heads, max_encoded_sequence_length, head_dim)
-
-                    idx = k + 2
-
-                    # Initialize tensors
-                    # This will run only once per layer and per past tensor
-                    if idx == len(input_ids["past_key_values"][j]):
-                        input_ids["past_key_values"][j].append(
-                            torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
-                        )
-
-                    input_ids["past_key_values"][j][idx][
-                    start_index:end_index,
-                    :,
-                    -batch_max_encoded_sequence_length:,
-                    :
-                    ] = t[:, :, -batch_max_encoded_sequence_length:, :]
-
-            start_index += batch.size
-
-        return cls(
-            batch_id=batches[0].batch_id,
-            requests=requests,
-            all_input_lengths=all_input_lengths,
-            input_ids=input_ids,
-            all_input_ids=all_input_ids,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-            size=total_batch_size,
-            max_sequence_length=max_sequence_length,
-        )
 
 
 @dataclass
@@ -241,4 +34,6 @@ class GeneratedText:
     tokens: int
 
     def to_pb(self) -> generate_pb2.GeneratedText:
-        return generate_pb2.GeneratedText(request=self.request, output=self.output, tokens=self.tokens)
+        return generate_pb2.GeneratedText(
+            request=self.request, output=self.output, tokens=self.tokens
+        )

server/text_generation/server.py

@@ -9,7 +9,6 @@ from typing import List
 
 from text_generation.cache import Cache
 from text_generation.models import Model, get_model
-from text_generation.models.types import Batch
 from text_generation.pb import generate_pb2_grpc, generate_pb2
 
 
@@ -27,7 +26,9 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         return generate_pb2.ClearCacheResponse()
 
     async def Generate(self, request, context):
-        batch = Batch.from_pb(request.batch, self.model.tokenizer, self.model.device)
+        batch = self.model.batch_type.from_pb(
+            request.batch, self.model.tokenizer, self.model.device
+        )
 
         generated_texts, next_batch = self.model.generate_token(batch)
         self.cache.set(next_batch)
@@ -51,7 +52,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
             batches.append(batch)
 
         if len(batches) > 1:
-            batch = Batch.concatenate(batches)
+            batch = self.model.batch_type.concatenate(batches)
         else:
             batch = batches[0]