fix(models): Revert buggy support for AutoModel

README.md

@@ -15,13 +15,11 @@ 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
 
-## Officially supported models
+## Supported models
 
 - BLOOM
 - BLOOM-560m
 
-Other models are supported on a best-effort basis using `AutoModelForCausalLM.from_pretrained(<model>, torch_dtype=torch.float16, device_map="auto")`.
-
 ## Load Tests for BLOOM
 
 See `k6/load_test.js`
@@ -82,6 +80,7 @@ make router-dev
 
 ## TODO:
 
+- [ ] Support AutoModelForSeq2SeqLM
 - [ ] Add tests for the `server/model` logic
 - [ ] Backport custom CUDA kernels to Transformers
 - [ ] Install safetensors with pip

server/Makefile

@@ -9,11 +9,11 @@ gen-server:
 install-transformers:
 	# Install specific version of transformers
 	rm transformers || true
-	rm transformers-46d37bece7d3ffdef97b1ee4a3170c0a0627d921 || true
+	rm transformers-7302a24535e8dc5637ea5b4e4572fc971d404098 || true
-	curl -L -O https://github.com/huggingface/transformers/archive/46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
+	curl -L -O https://github.com/OlivierDehaene/transformers/archive/7302a24535e8dc5637ea5b4e4572fc971d404098.zip
-	unzip 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
+	unzip 7302a24535e8dc5637ea5b4e4572fc971d404098.zip
-	rm 46d37bece7d3ffdef97b1ee4a3170c0a0627d921.zip
+	rm 7302a24535e8dc5637ea5b4e4572fc971d404098.zip
-	mv transformers-46d37bece7d3ffdef97b1ee4a3170c0a0627d921 transformers
+	mv transformers-7302a24535e8dc5637ea5b4e4572fc971d404098 transformers
 	cd transformers && python setup.py install
 
 install-safetensors:

server/text_generation/models/__init__.py

@@ -1,22 +1,16 @@
 from text_generation.models.model import Model
-from text_generation.models.bloom import BLOOMSharded
+from text_generation.models.bloom import BLOOM, BLOOMSharded
 
-__all__ = ["Model", "BLOOMSharded"]
+__all__ = ["Model", "BLOOM", "BLOOMSharded"]
 
 
 def get_model(model_name: str, sharded: bool, quantize: bool) -> Model:
-
     if model_name.startswith("bigscience/bloom"):
         if sharded:
             return BLOOMSharded(model_name, quantize)
         else:
             if quantize:
                 raise ValueError("quantization is not supported for non-sharded BLOOM")
-            return Model(model_name)
+            return BLOOM(model_name)
     else:
-        if sharded:
+        raise ValueError(f"model {model_name} is not supported yet")
-            raise ValueError("sharded is only supported for BLOOM models")
-        if quantize:
-            raise ValueError("Quantization is only supported for BLOOM models")
-
-        return Model(model_name)

server/text_generation/models/bloom.py

@@ -1,7 +1,7 @@
 import torch
 import torch.distributed
 
-from typing import List, Optional
+from typing import List, Optional, Tuple, Type
 
 from accelerate import init_empty_weights
 from safetensors import safe_open
@@ -11,8 +11,10 @@ from transformers.models.bloom.parallel_layers import (
     TensorParallelEmbedding,
     TensorParallelRowLinear,
 )
+from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from text_generation.models import Model
+from text_generation.models.types import Batch, GeneratedText
 from text_generation.utils import (
     initialize_torch_distributed,
     weight_files,
@@ -29,9 +31,306 @@ except Exception as e:
 torch.manual_seed(0)
 
 
-class BLOOMSharded(Model):
+class BloomBatch(Batch):
+    @classmethod
+    def concatenate(cls, batches: List["Batch"]) -> "BloomBatch":
+        # 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"]):
+                past_keys = past[0]
+                past_values = past[1]
+
+                _, head_dim, padded_sequence_length = past_keys.shape
+
+                # Reshape the tensors to make slicing easier
+                past_keys = past_keys.view(
+                    batch.size, -1, head_dim, padded_sequence_length
+                )
+                past_values = past_values.view(
+                    batch.size, -1, padded_sequence_length, head_dim
+                )
+                num_heads = past_keys.shape[1]
+
+                # Initialize tensors
+                # This will run only once per layer
+                if j == len(input_ids["past_key_values"]):
+                    padded_past_keys = torch.zeros(
+                        (
+                            total_batch_size,
+                            num_heads,
+                            head_dim,
+                            max_sequence_length - 1,
+                        ),
+                        dtype=past_keys.dtype,
+                        device=past_keys.device,
+                    )
+                    padded_past_values = torch.zeros(
+                        (
+                            total_batch_size,
+                            num_heads,
+                            max_sequence_length - 1,
+                            head_dim,
+                        ),
+                        dtype=past_values.dtype,
+                        device=past_values.device,
+                    )
+                    input_ids["past_key_values"].append(
+                        [padded_past_keys, padded_past_values]
+                    )
+
+                # We slice the past keys and values to remove the padding from previous batches
+                input_ids["past_key_values"][j][0][
+                start_index:end_index, :, :, -(batch.max_sequence_length - 1):
+                ] = past_keys[:, :, :, -(batch.max_sequence_length - 1):]
+
+                input_ids["past_key_values"][j][1][
+                start_index:end_index, :, -(batch.max_sequence_length - 1):, :
+                ] = past_values[:, :, -(batch.max_sequence_length - 1):, :]
+
+                # If we are on the last batch, we need to reshape the tensors
+                if (i + 1) == len(batches):
+                    input_ids["past_key_values"][j][0] = input_ids["past_key_values"][
+                        j
+                    ][0].view(total_batch_size * num_heads, head_dim, -1)
+                    input_ids["past_key_values"][j][1] = input_ids["past_key_values"][
+                        j
+                    ][1].view(total_batch_size * num_heads, -1, head_dim)
+
+            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 BLOOM(Model):
+    def __init__(self, model_name: str):
+        if not model_name.startswith("bigscience/bloom"):
+            raise ValueError(f"Model {model_name} is not supported")
+
+        if torch.cuda.is_available():
+            self.device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            self.device = torch.device("cpu")
+            dtype = torch.float32
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+        self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name, torch_dtype=dtype, device_map="auto" if torch.cuda.is_available() else None
+        ).eval()
+
+        self.num_heads = self.model.config.num_attention_heads
+
+    @property
+    def batch_type(self) -> Type[BloomBatch]:
+        return BloomBatch
+
+    def forward(
+            self, input_ids, attention_mask, past_key_values: Optional = None
+    ) -> CausalLMOutputWithPast:
+        # Model Forward
+        return self.model.forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=True,
+        )
+
+    def generate_token(
+            self, batch: BloomBatch
+    ) -> Tuple[List[GeneratedText], Optional[BloomBatch]]:
+        # 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():
+            outputs = self.forward(**batch.input_ids)
+
+        # List of indices to cache
+        next_batch_keep_indices = []
+        next_batch_past_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,
+            outputs.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))
+            # add to the next batch
+            else:
+                next_batch_keep_indices.append(i)
+                # past_key_values is of shape [batch_size * num_heads, ...]
+                # so we need to take into account the `num_heads` stride here
+                next_batch_past_keep_indices.extend(
+                    [j for j in range(i * self.num_heads, (i + 1) * self.num_heads)]
+                )
+                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
+            ]
+            next_batch_input_ids["past_key_values"] = [
+                (
+                    keys[next_batch_past_keep_indices],
+                    values[next_batch_past_keep_indices],
+                )
+                for keys, values in outputs["past_key_values"]
+            ]
+            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"] = outputs["past_key_values"]
+            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 = BloomBatch(
+            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
+
+
+class BLOOMSharded(BLOOM):
     def __init__(self, model_name: str, quantize: bool = False):
         super(Model, self).__init__()
+        if not model_name.startswith("bigscience/bloom"):
+            raise ValueError(f"Model {model_name} is not supported")
+
         self.process_group, self.rank, self.world_size = initialize_torch_distributed()
         self.master = self.rank == 0
         if torch.cuda.is_available():
@@ -80,17 +379,17 @@ class BLOOMSharded(Model):
 
     @staticmethod
     def load_weights(
-        model,
+            model,
-        filenames: List[str],
+            filenames: List[str],
-        quantize: bool,
+            quantize: bool,
-        device: torch.device,
+            device: torch.device,
-        rank: int,
+            rank: int,
-        world_size: int,
+            world_size: int,
     ):
         parameters = dict(model.named_parameters())
         for file in filenames:
             with safe_open(
-                file, framework="pt", device=str(device) if not quantize else "cpu"
+                    file, framework="pt", device=str(device) if not quantize else "cpu"
             ) as f:
                 for name in f.keys():
                     full_name = f"transformer.{name}"
@@ -153,9 +452,9 @@ class BLOOMSharded(Model):
                             )
 
                         if (
-                            type(module)
+                                type(module)
-                            in [TensorParallelRowLinear, TensorParallelColumnLinear]
+                                in [TensorParallelRowLinear, TensorParallelColumnLinear]
-                            and param_name == "weight"
+                                and param_name == "weight"
                         ):
                             tensor = Int8Params(
                                 tensor.transpose(1, 0),

server/text_generation/models/model.py

@@ -1,166 +1,19 @@
-import torch
+from abc import ABC, abstractmethod
-import torch.distributed
+from typing import List, Tuple, Optional, TypeVar, Type
-
-from typing import List, Tuple, Optional
-from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
-from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from text_generation.models.types import Batch, GeneratedText
 
+B = TypeVar("B", bound=Batch)
 
-class Model:
-    def __init__(self, model_name: str):
-        if torch.cuda.is_available():
-            self.device = torch.device("cuda")
-            dtype = torch.float16
-        else:
-            self.device = torch.device("cpu")
-            dtype = torch.float32
 
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
+class Model(ABC):
-        self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+    @property
-        self.model = AutoModelForCausalLM.from_pretrained(
+    @abstractmethod
-            model_name, torch_dtype=dtype, device_map="auto" if torch.cuda.is_available() else None
+    def batch_type(self) -> Type[B]:
-        ).eval()
+        raise NotImplementedError
-
-        self.num_heads = self.model.config.num_attention_heads
-
-    def forward(
-        self, input_ids, attention_mask, past_key_values: Optional = None
-    ) -> CausalLMOutputWithPast:
-        # Model Forward
-        return self.model.forward(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            use_cache=True,
-        )
 
+    @abstractmethod
     def generate_token(
-        self, batch: Batch
+            self, batch: B
-    ) -> Tuple[List[GeneratedText], Optional[Batch]]:
+    ) -> Tuple[List[GeneratedText], Optional[B]]:
-        # For some reason, inference_mode does not work well with GLOO which we use on CPU
+        raise NotImplementedError
-        context_manager = (
-            torch.no_grad if self.device.type == "cpu" else torch.inference_mode
-        )
-        with context_manager():
-            outputs = self.forward(**batch.input_ids)
-
-        # List of indices to cache
-        next_batch_keep_indices = []
-        next_batch_past_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,
-            outputs.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))
-            # add to the next batch
-            else:
-                next_batch_keep_indices.append(i)
-                # past_key_values is of shape [batch_size * num_heads, ...]
-                # so we need to take into account the `num_heads` stride here
-                next_batch_past_keep_indices.extend(
-                    [j for j in range(i * self.num_heads, (i + 1) * self.num_heads)]
-                )
-                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
-            ]
-            next_batch_input_ids["past_key_values"] = [
-                (
-                    keys[next_batch_past_keep_indices],
-                    values[next_batch_past_keep_indices],
-                )
-                for keys, values in outputs["past_key_values"]
-            ]
-            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"] = outputs["past_key_values"]
-            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/types.py

@@ -1,5 +1,6 @@
 import torch
 
+from abc import abstractmethod
 from dataclasses import dataclass
 from typing import List, Dict
 
@@ -70,131 +71,9 @@ class Batch:
         )
 
     @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)
-
-        # 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"]):
-                past_keys = past[0]
-                past_values = past[1]
-
-                _, head_dim, padded_sequence_length = past_keys.shape
-
-                # Reshape the tensors to make slicing easier
-                past_keys = past_keys.view(
-                    batch.size, -1, head_dim, padded_sequence_length
-                )
-                past_values = past_values.view(
-                    batch.size, -1, padded_sequence_length, head_dim
-                )
-                num_heads = past_keys.shape[1]
-
-                # Initialize tensors
-                # This will run only once per layer
-                if j == len(input_ids["past_key_values"]):
-                    padded_past_keys = torch.zeros(
-                        (
-                            total_batch_size,
-                            num_heads,
-                            head_dim,
-                            max_sequence_length - 1,
-                        ),
-                        dtype=past_keys.dtype,
-                        device=past_keys.device,
-                    )
-                    padded_past_values = torch.zeros(
-                        (
-                            total_batch_size,
-                            num_heads,
-                            max_sequence_length - 1,
-                            head_dim,
-                        ),
-                        dtype=past_values.dtype,
-                        device=past_values.device,
-                    )
-                    input_ids["past_key_values"].append(
-                        [padded_past_keys, padded_past_values]
-                    )
-
-                # We slice the past keys and values to remove the padding from previous batches
-                input_ids["past_key_values"][j][0][
-                    start_index:end_index, :, :, -(batch.max_sequence_length - 1) :
-                ] = past_keys[:, :, :, -(batch.max_sequence_length - 1) :]
-
-                input_ids["past_key_values"][j][1][
-                    start_index:end_index, :, -(batch.max_sequence_length - 1) :, :
-                ] = past_values[:, :, -(batch.max_sequence_length - 1) :, :]
-
-                # If we are on the last batch, we need to reshape the tensors
-                if (i + 1) == len(batches):
-                    input_ids["past_key_values"][j][0] = input_ids["past_key_values"][
-                        j
-                    ][0].view(total_batch_size * num_heads, head_dim, -1)
-                    input_ids["past_key_values"][j][1] = input_ids["past_key_values"][
-                        j
-                    ][1].view(total_batch_size * num_heads, -1, head_dim)
-
-            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

server/text_generation/server.py

@@ -27,7 +27,7 @@ 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 +51,7 @@ 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]