feat(server): Support all AutoModelForCausalLM on a best effort basis

server/text_generation/models/model.py

+import torch
+import torch.distributed
+
+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
+
+
+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")
+        self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name, torch_dtype=dtype, device_map="auto"
+        ).eval()
+
+        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,
+        )
+
+    def generate_token(
+        self, batch: Batch
+    ) -> Tuple[List[GeneratedText], Optional[Batch]]:
+        # 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 = 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

+import torch
+
+from dataclasses import dataclass
+from typing import List, Dict
+
+from transformers import AutoTokenizer
+
+from text_generation.pb import generate_pb2
+from text_generation.utils import NextTokenChooser, StoppingCriteria
+
+
+@dataclass
+class Batch:
+    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,
+            max_sequence_length=self.max_sequence_length,
+        )
+
+    @classmethod
+    def from_pb(
+        cls, pb: generate_pb2.Batch, tokenizer: AutoTokenizer, device: torch.device
+    ) -> "Batch":
+        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(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
+    def concatenate(cls, batches: List["Batch"]) -> "Batch":
+        # 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,
+        )
+
+
+@dataclass
+class GeneratedText:
+    request: generate_pb2.Request
+    output: str
+
+    def to_pb(self) -> generate_pb2.GeneratedText:
+        return generate_pb2.GeneratedText(request=self.request, output=self.output)

server/bloom_inference/server.py → server/text_generation/server.py

@@ -5,15 +5,16 @@ from grpc import aio
 
 from grpc_reflection.v1alpha import reflection
 from pathlib import Path
-from typing import Optional, List
+from typing import List
 
-from bloom_inference.cache import Cache
-from bloom_inference.model import BLOOM, Batch, BLOOMSharded
-from bloom_inference.pb import generate_pb2_grpc, generate_pb2
+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
 
 
 class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
-    def __init__(self, model: BLOOM, cache: Cache, server_urls: List[str]):
+    def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
         self.cache = cache
         self.model = model
         self.server_urls = server_urls
@@ -78,21 +79,17 @@ def serve(
     ):
         unix_socket_template = "unix://{}-{}"
         if sharded:
-            model = BLOOMSharded(model_name, quantize)
             server_urls = [
                 unix_socket_template.format(uds_path, rank)
-                for rank in range(model.world_size)
+                for rank in range(int(os.environ["WORLD_SIZE"]))
             ]
-            local_url = server_urls[model.rank]
+            local_url = server_urls[int(os.environ["RANK"])]
         else:
-            if quantize:
-                raise ValueError(
-                    "bitsandbytes quantization is only available when running in `sharded` mode."
-                )
-            model = BLOOM(model_name)
             local_url = unix_socket_template.format(uds_path, 0)
             server_urls = [local_url]
 
+        model = get_model(model_name, sharded, quantize)
+
         server = aio.server()
         generate_pb2_grpc.add_TextGenerationServiceServicer_to_server(
             TextGenerationService(model, Cache(), server_urls), server

server/bloom_inference/utils.py → server/text_generation/utils.py

@@ -92,19 +92,17 @@ def initialize_torch_distributed():
     return torch.distributed.distributed_c10d._get_default_group(), rank, world_size
 
 
-def weight_hub_files(model_name):
+def weight_hub_files(model_name, extension=".safetensors"):
     """Get the safetensors filenames on the hub"""
     api = HfApi()
     info = api.model_info(model_name)
-    filenames = [
-        s.rfilename for s in info.siblings if s.rfilename.endswith(".safetensors")
-    ]
+    filenames = [s.rfilename for s in info.siblings if s.rfilename.endswith(extension)]
     return filenames
 
 
-def weight_files(model_name):
+def weight_files(model_name, extension=".safetensors"):
     """Get the local safetensors filenames"""
-    filenames = weight_hub_files(model_name)
+    filenames = weight_hub_files(model_name, extension)
     files = []
     for filename in filenames:
         cache_file = try_to_load_from_cache(model_name, filename=filename)
@@ -112,16 +110,16 @@ def weight_files(model_name):
             raise LocalEntryNotFoundError(
                 f"File {filename} of model {model_name} not found in "
                 f"{os.getenv('HUGGINGFACE_HUB_CACHE', 'the local cache')}. "
-                f"Please run `bloom-inference-server download-weights {model_name}` first."
+                f"Please run `text-generation-server download-weights {model_name}` first."
             )
         files.append(cache_file)
 
     return files
 
 
-def download_weights(model_name):
+def download_weights(model_name, extension=".safetensors"):
     """Download the safetensors files from the hub"""
-    filenames = weight_hub_files(model_name)
+    filenames = weight_hub_files(model_name, extension)
 
     download_function = partial(
         hf_hub_download, repo_id=model_name, local_files_only=False