chore: formatting

integration-tests/conftest.py

@@ -25,6 +25,7 @@ DOCKER_VOLUME = os.getenv("DOCKER_VOLUME", "/data")
 
 class ResponseComparator(JSONSnapshotExtension):
     rtol = 0.2
+
     def serialize(
         self,
         data,
@@ -69,7 +70,9 @@ class ResponseComparator(JSONSnapshotExtension):
                     prefill_token.id == other.id
                     and prefill_token.text == other.text
                     and (
-                        math.isclose(prefill_token.logprob, other.logprob, rel_tol=self.rtol)
+                        math.isclose(
+                            prefill_token.logprob, other.logprob, rel_tol=self.rtol
+                        )
                         if prefill_token.logprob is not None
                         else prefill_token.logprob == other.logprob
                     )
@@ -153,6 +156,7 @@ class GenerousResponseComparator(ResponseComparator):
     # Needed for GPTQ with exllama which has serious numerical fluctuations.
     rtol = 0.75
 
+
 class LauncherHandle:
     def __init__(self, port: int):
         self.client = AsyncClient(f"http://localhost:{port}")
@@ -198,6 +202,7 @@ class ProcessLauncherHandle(LauncherHandle):
 def response_snapshot(snapshot):
     return snapshot.use_extension(ResponseComparator)
 
+
 @pytest.fixture
 def generous_response_snapshot(snapshot):
     return snapshot.use_extension(GenerousResponseComparator)
@@ -219,7 +224,7 @@ def launcher(event_loop):
         quantize: Optional[str] = None,
         trust_remote_code: bool = False,
         use_flash_attention: bool = True,
-        dtype: Optional[str] = None
+        dtype: Optional[str] = None,
     ):
         port = random.randint(8000, 10_000)
         master_port = random.randint(10_000, 20_000)
@@ -282,7 +287,7 @@ def launcher(event_loop):
         quantize: Optional[str] = None,
         trust_remote_code: bool = False,
         use_flash_attention: bool = True,
-        dtype: Optional[str] = None
+        dtype: Optional[str] = None,
     ):
         port = random.randint(8000, 10_000)
 
@@ -335,7 +340,7 @@ def launcher(event_loop):
             ],
             volumes=volumes,
             ports={"80/tcp": port},
-            shm_size="1G"
+            shm_size="1G",
         )
 
         yield ContainerLauncherHandle(client, container.name, port)

integration-tests/models/test_flash_medusa.py

@@ -50,10 +50,16 @@ async def test_flash_medusa_all_params(flash_medusa, response_snapshot):
 @pytest.mark.asyncio
 @pytest.mark.private
 async def test_flash_medusa_load(flash_medusa, generate_load, response_snapshot):
-    responses = await generate_load(flash_medusa, "What is Deep Learning?", max_new_tokens=10, n=4)
+    responses = await generate_load(
+        flash_medusa, "What is Deep Learning?", max_new_tokens=10, n=4
+    )
 
     assert len(responses) == 4
-    assert all([r.generated_text == responses[0].generated_text for r in responses]), f"{[r.generated_text for r in responses]}"
-    assert responses[0].generated_text == '\nDeep learning is a subset of machine learning' 
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text for r in responses]}"
+    assert (
+        responses[0].generated_text == "\nDeep learning is a subset of machine learning"
+    )
 
     assert responses == response_snapshot

integration-tests/models/test_flash_mistral.py

@@ -56,7 +56,9 @@ async def test_flash_mistral_load(flash_mistral, generate_load, response_snapsho
     )
 
     assert len(responses) == 4
-    assert all([r.generated_text == responses[0].generated_text for r in responses]), f"{[r.generated_text  for r in responses]}"
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text  for r in responses]}"
     assert responses[0].generated_text == ": Let n = 10 - 1"
 
     assert responses == response_snapshot

integration-tests/models/test_idefics.py

@@ -3,7 +3,9 @@ import pytest
 
 @pytest.fixture(scope="module")
 def idefics_handle(launcher):
-    with launcher("HuggingFaceM4/idefics-9b-instruct", num_shard=2, dtype="float16") as handle:
+    with launcher(
+        "HuggingFaceM4/idefics-9b-instruct", num_shard=2, dtype="float16"
+    ) as handle:
         yield handle
 
 

server/tests/models/test_bloom.py

@@ -133,8 +133,20 @@ def test_causal_lm_generate_token(default_bloom, default_bloom_batch):
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([token_id.item() == 10264 for generation in generations for token_id in generation.tokens.token_ids])
-    assert all([token_text == "Test" for generation in generations for token_text in generation.tokens.texts])
+    assert all(
+        [
+            token_id.item() == 10264
+            for generation in generations
+            for token_id in generation.tokens.token_ids
+        ]
+    )
+    assert all(
+        [
+            token_text == "Test"
+            for generation in generations
+            for token_text in generation.tokens.texts
+        ]
+    )
     assert generations[0].request_id == 0
 
 

server/tests/models/test_causal_lm.py

@@ -129,8 +129,20 @@ def test_causal_lm_generate_token(default_causal_lm, default_causal_lm_batch):
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([token_id.item() == 13 for generation in generations for token_id in generation.tokens.token_ids])
-    assert all([token_text == "." for generation in generations for token_text in generation.tokens.texts])
+    assert all(
+        [
+            token_id.item() == 13
+            for generation in generations
+            for token_id in generation.tokens.token_ids
+        ]
+    )
+    assert all(
+        [
+            token_text == "."
+            for generation in generations
+            for token_text in generation.tokens.texts
+        ]
+    )
     assert generations[0].request_id == 0
 
 

server/tests/models/test_seq2seq_lm.py

@@ -151,8 +151,20 @@ def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch)
     )
     assert all([generation.generated_text is None for generation in generations])
     assert all([len(generation.prefill_tokens) == 1 for generation in generations])
-    assert all([token_id.item() == 259 for generation in generations for token_id in generation.tokens.token_ids])
-    assert all([token_text == " " for generation in generations for token_text in generation.tokens.texts])
+    assert all(
+        [
+            token_id.item() == 259
+            for generation in generations
+            for token_id in generation.tokens.token_ids
+        ]
+    )
+    assert all(
+        [
+            token_text == " "
+            for generation in generations
+            for token_text in generation.tokens.texts
+        ]
+    )
     assert generations[0].request_id == 0
 
 

server/text_generation_server/cli.py

@@ -77,12 +77,24 @@ def serve(
     # Downgrade enum into str for easier management later on
     quantize = None if quantize is None else quantize.value
     dtype = None if dtype is None else dtype.value
-    if dtype is not None and quantize not in {None, "bitsandbytes", "bitsandbytes-nf4", "bitsandbytes-fp4"}:
+    if dtype is not None and quantize not in {
+        None,
+        "bitsandbytes",
+        "bitsandbytes-nf4",
+        "bitsandbytes-fp4",
+    }:
         raise RuntimeError(
             "Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
         )
     server.serve(
-        model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code, uds_path
+        model_id,
+        revision,
+        sharded,
+        quantize,
+        speculate,
+        dtype,
+        trust_remote_code,
+        uds_path,
     )
 
 
@@ -140,12 +152,17 @@ def download_weights(
 
         try:
             import json
-            medusa_head = hf_hub_download(model_id, revision=revision, filename="medusa_lm_head.pt")
+
+            medusa_head = hf_hub_download(
+                model_id, revision=revision, filename="medusa_lm_head.pt"
+            )
             if auto_convert:
-                medusa_sf = Path(medusa_head[:-len(".pt")] + ".safetensors")
+                medusa_sf = Path(medusa_head[: -len(".pt")] + ".safetensors")
                 if not medusa_sf.exists():
                     utils.convert_files([Path(medusa_head)], [medusa_sf], [])
-            medusa_config = hf_hub_download(model_id, revision=revision, filename="config.json")
+            medusa_config = hf_hub_download(
+                model_id, revision=revision, filename="config.json"
+            )
             with open(medusa_config, "r") as f:
                 config = json.load(f)
 
@@ -153,10 +170,17 @@ def download_weights(
             revision = "main"
             try:
                 utils.weight_files(model_id, revision, extension)
-                logger.info(f"Files for parent {model_id} are already present on the host. " "Skipping download.")
+                logger.info(
+                    f"Files for parent {model_id} are already present on the host. "
+                    "Skipping download."
+                )
                 return
             # Local files not found
-            except (utils.LocalEntryNotFoundError, FileNotFoundError, utils.EntryNotFoundError):
+            except (
+                utils.LocalEntryNotFoundError,
+                FileNotFoundError,
+                utils.EntryNotFoundError,
+            ):
                 pass
         except (utils.LocalEntryNotFoundError, utils.EntryNotFoundError):
             pass

server/text_generation_server/models/__init__.py

@@ -88,7 +88,6 @@ if MIXTRAL:
     __all__.append(FlashMixtral)
 
 
-
 def get_model(
     model_id: str,
     revision: Optional[str],
@@ -157,7 +156,9 @@ def get_model(
         speculate_medusa = config_dict["medusa_num_heads"]
         if speculate is not None:
             if speculate > speculate_medusa:
-                raise RuntimeError("Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match")
+                raise RuntimeError(
+                    "Speculate is set to `{speculate}` but this medusa models only has `{speculate_medusa}` heads, please make them match"
+                )
             else:
                 set_speculate(speculate)
         else:
@@ -249,7 +250,7 @@ def get_model(
                 quantize=quantize,
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
-                use_medusa=use_medusa
+                use_medusa=use_medusa,
             )
         elif sharded:
             raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
@@ -313,7 +314,9 @@ def get_model(
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
-        raise NotImplementedError("Mixtral models requires flash attention v2, stk and megablocks")
+        raise NotImplementedError(
+            "Mixtral models requires flash attention v2, stk and megablocks"
+        )
 
     if model_type == "opt":
         return OPTSharded(
@@ -354,7 +357,7 @@ def get_model(
         raise ValueError("awq quantization is not supported for AutoModel")
     elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
         raise ValueError("4bit quantization is not supported for AutoModel")
-    elif (quantize == "eetq"):
+    elif quantize == "eetq":
         raise ValueError("Eetq quantization is not supported for AutoModel")
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
         return CausalLM(

server/text_generation_server/models/bloom.py

@@ -74,7 +74,11 @@ class BLOOMSharded(CausalLM):
         torch.distributed.barrier(group=self.process_group)
         filenames = weight_files(model_id, revision=revision, extension=".safetensors")
         weights = Weights(
-            filenames, device=device, dtype=dtype, process_group=self.process_group, prefix="transformer",
+            filenames,
+            device=device,
+            dtype=dtype,
+            process_group=self.process_group,
+            prefix="transformer",
         )
         if config.quantize == "gptq":
             weights._set_gptq_params(model_id)

server/text_generation_server/models/causal_lm.py

@@ -510,7 +510,11 @@ class CausalLM(Model):
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
         )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1 and quantize != "bitsandbytes":
+        if (
+            torch.cuda.is_available()
+            and torch.cuda.device_count() == 1
+            and quantize != "bitsandbytes"
+        ):
             model = model.cuda()
 
         if tokenizer.pad_token_id is None:
@@ -676,7 +680,10 @@ class CausalLM(Model):
                         skip_special_tokens=False,
                     )
                     prefill_tokens = Tokens(
-                        prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
+                        prefill_token_ids,
+                        prefill_logprobs,
+                        prefill_texts,
+                        is_special=[],
                     )
                 else:
                     prefill_tokens = None
@@ -703,11 +710,11 @@ class CausalLM(Model):
                     request.id,
                     prefill_tokens,
                     Tokens(
-                    [next_token_id_squeezed],
-                    [next_token_logprob],
-                    [next_token_text],
-                    [next_token_id_squeezed.item() in self.all_special_ids],
-                     ),
+                        [next_token_id_squeezed],
+                        [next_token_logprob],
+                        [next_token_text],
+                        [next_token_id_squeezed.item() in self.all_special_ids],
+                    ),
                     generated_text,
                     top_tokens,
                 )

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -34,9 +34,10 @@ from text_generation_server.utils.layers import (
     PositionRotaryEmbedding,
     TensorParallelHead,
     get_linear,
-    FastRMSNorm
+    FastRMSNorm,
 )
 
+
 class LlamaConfig(PretrainedConfig):
     def __init__(
         self,
@@ -202,7 +203,7 @@ class FlashLlamaAttention(torch.nn.Module):
         )
         query = query.view(-1, self.num_heads, self.head_size)
         kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
-        
+
         self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
 
         paged_attention.reshape_and_cache(
@@ -237,7 +238,7 @@ class FlashLlamaAttention(torch.nn.Module):
                 input_lengths,
                 max_s,
             )
-        
+
         return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
 
 
@@ -288,7 +289,9 @@ class FlashLlamaLayer(nn.Module):
         )
         self.mlp = LlamaMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
 
-        self.input_layernorm = FastRMSNorm.load(prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps)
+        self.input_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
         self.post_attention_layernorm = FastRMSNorm.load(
             prefix=f"{prefix}.post_attention_layernorm",
             weights=weights,

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -27,7 +27,11 @@ from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 
 from text_generation_server.utils import paged_attention, flash_attn
-from text_generation_server.utils.flash_attn import attention, HAS_FLASH_ATTN_V2_ROCM, HAS_FLASH_ATTN_V2_CUDA
+from text_generation_server.utils.flash_attn import (
+    attention,
+    HAS_FLASH_ATTN_V2_ROCM,
+    HAS_FLASH_ATTN_V2_CUDA,
+)
 from text_generation_server.utils.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -35,7 +39,7 @@ from text_generation_server.utils.layers import (
     PositionRotaryEmbedding,
     TensorParallelHead,
     get_linear,
-    FastRMSNorm
+    FastRMSNorm,
 )
 
 
@@ -96,6 +100,7 @@ class MistralConfig(PretrainedConfig):
             **kwargs,
         )
 
+
 def load_attention(config, prefix, weights):
     if config.num_attention_heads != config.num_key_value_heads:
         return _load_gqa(config, prefix, weights)

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -29,7 +29,10 @@ from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 
 from text_generation_server.utils import paged_attention, flash_attn
-from text_generation_server.utils.flash_attn import HAS_FLASH_ATTN_V2_ROCM, HAS_FLASH_ATTN_V2_CUDA
+from text_generation_server.utils.flash_attn import (
+    HAS_FLASH_ATTN_V2_ROCM,
+    HAS_FLASH_ATTN_V2_CUDA,
+)
 from text_generation_server.utils.layers import (
     FastLinear,
     FastRMSNorm,
@@ -59,28 +62,28 @@ class MixtralConfig(PretrainedConfig):
     model_type = "mixtral"
 
     def __init__(
-            self,
-            vocab_size=32000,
-            hidden_size=4096,
-            intermediate_size=14336,
-            num_hidden_layers=32,
-            num_attention_heads=32,
-            num_key_value_heads=8,
-            hidden_act="silu",
-            max_position_embeddings=4096 * 32,
-            initializer_range=0.02,
-            rms_norm_eps=1e-05,
-            use_cache=True,
-            pad_token_id=None,
-            bos_token_id=1,
-            eos_token_id=2,
-            pretraining_tp=1,
-            tie_word_embeddings=False,
-            rope_theta=10000.0,
-            sliding_window=4096,
-            num_experts_per_tok=2,
-            num_local_experts=8,
-            **kwargs,
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-05,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        **kwargs,
     ):
         self.vocab_size = vocab_size
         self.max_position_embeddings = max_position_embeddings
@@ -166,16 +169,18 @@ def _load_experts(config, prefix, mat, weights):
     rank = weights.process_group.rank()
 
     assert (
-            config.intermediate_size % world_size == 0
+        config.intermediate_size % world_size == 0
     ), f"The chosen size {config.intermediate_size} is not compatible with sharding on {world_size} shards"
 
     block_size = config.intermediate_size // world_size
     start = rank * block_size
     stop = (rank + 1) * block_size
 
-    tensor = torch.empty((config.num_local_experts * block_size, config.hidden_size),
-                         dtype=weights.dtype,
-                         device=weights.device)
+    tensor = torch.empty(
+        (config.num_local_experts * block_size, config.hidden_size),
+        dtype=weights.dtype,
+        device=weights.device,
+    )
 
     for i in range(config.num_local_experts):
         slice_ = weights._get_slice(f"{prefix}.{i}.{mat}.weight")
@@ -184,16 +189,18 @@ def _load_experts(config, prefix, mat, weights):
             expert_slice = slice_[:, start:stop].t().contiguous()
         else:
             expert_slice = slice_[start:stop]
-        tensor[i * block_size:(i + 1) * block_size] = expert_slice.to(dtype=weights.dtype).to(device=weights.device)
+        tensor[i * block_size : (i + 1) * block_size] = expert_slice.to(
+            dtype=weights.dtype
+        ).to(device=weights.device)
     return tensor
 
 
 class MixtralAttention(torch.nn.Module):
     def __init__(
-            self,
-            prefix: str,
-            config,
-            weights,
+        self,
+        prefix: str,
+        config,
+        weights,
     ):
         super().__init__()
         self.max_past = (
@@ -210,7 +217,7 @@ class MixtralAttention(torch.nn.Module):
             device=weights.device,
         )
 
-        self.softmax_scale = self.head_size ** -0.5
+        self.softmax_scale = self.head_size**-0.5
 
         if self.num_heads % weights.process_group.size() != 0:
             raise ValueError(
@@ -219,7 +226,7 @@ class MixtralAttention(torch.nn.Module):
             )
         self.num_heads = self.num_heads // weights.process_group.size()
         self.num_key_value_heads = (
-                config.num_key_value_heads // weights.process_group.size()
+            config.num_key_value_heads // weights.process_group.size()
         )
 
         self.query_key_value = load_attention(config, prefix, weights)
@@ -236,17 +243,17 @@ class MixtralAttention(torch.nn.Module):
         ).repeat_interleave(self.num_groups)
 
     def forward(
-            self,
-            hidden_states,
-            cos,
-            sin,
-            cu_seqlen_prefill,
-            kv_cache,
-            block_tables,
-            slots,
-            input_lengths,
-            max_s,
-            prefill_cache_indices,
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
+        max_s,
+        prefill_cache_indices,
     ):
         qkv = self.query_key_value(hidden_states)
         query, kv = qkv.split(
@@ -399,8 +406,9 @@ class BlockSparseMoE(nn.Module):
         # Indices for the sparse matrix. The indices for
         # the intermediate matrix are dynamic depending
         # on the mapping of tokens to experts.
-        column_indices = ops.topology(padded_bins, self.blocking, block_rows,
-                                      blocks_per_row)
+        column_indices = ops.topology(
+            padded_bins, self.blocking, block_rows, blocks_per_row
+        )
 
         # For now, use meta init to save the device memory.
         data = torch.empty(
@@ -444,8 +452,7 @@ class BlockSparseMoE(nn.Module):
         # position of each bin.
 
         # List of size num_experts
-        padded_tokens_per_expert = round_up(tokens_per_expert,
-                                            self.blocking)
+        padded_tokens_per_expert = round_up(tokens_per_expert, self.blocking)
         # padded_tokens_per_expert => [128, O, 128, ...]
 
         # Cumulative selected experts per token
@@ -484,8 +491,7 @@ class BlockSparseMoE(nn.Module):
 
         # Permute tokens and pad to prepare expert computation
         # (top_k * sequence_length + padding, model_dim)
-        x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins,
-                              self.top_k)
+        x = ops.padded_gather(x, indices, bin_ids, bins, padded_bins, self.top_k)
 
         # Create the sparse matrix topology
         with torch.no_grad():
@@ -496,8 +502,8 @@ class BlockSparseMoE(nn.Module):
         # (top_k * sequence_length + padding, ffn_dim * n_experts)
         x = stk.Matrix(
             topo.size(),
-            self.act(stk.ops.sdd(x, self.w1, topo).data) *
-            stk.ops.sdd(x, self.w3, topo).data,
+            self.act(stk.ops.sdd(x, self.w1, topo).data)
+            * stk.ops.sdd(x, self.w3, topo).data,
             topo.row_indices,
             topo.column_indices,
             topo.offsets,
@@ -537,7 +543,9 @@ class MixtralLayer(nn.Module):
         self.self_attn = MixtralAttention(
             prefix=f"{prefix}.self_attn", config=config, weights=weights
         )
-        self.block_sparse_moe = BlockSparseMoE(f"{prefix}.block_sparse_moe", config, weights)
+        self.block_sparse_moe = BlockSparseMoE(
+            f"{prefix}.block_sparse_moe", config, weights
+        )
 
         self.input_layernorm = FastRMSNorm.load(
             prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
@@ -549,18 +557,18 @@ class MixtralLayer(nn.Module):
         )
 
     def forward(
-            self,
-            hidden_states,
-            residual,
-            cos,
-            sin,
-            cu_seqlen_prefill,
-            kv_cache,
-            block_tables,
-            slots,
-            input_lengths,
-            max_s,
-            prefill_cache_indices,
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
+        max_s,
+        prefill_cache_indices,
     ):
         normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
 
@@ -615,16 +623,16 @@ class MixtralModel(torch.nn.Module):
         self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
 
     def forward(
-            self,
-            input_ids: torch.Tensor,
-            position_ids: torch.Tensor,
-            cu_seqlen_prefill: Optional[torch.Tensor],
-            kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
-            block_tables: torch.Tensor,
-            slots: torch.Tensor,
-            input_lengths: torch.Tensor,
-            max_s: int,
-            prefill_cache_indices: Optional[torch.Tensor],
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
     ) -> torch.Tensor:
         hidden_states = self.embed_tokens(input_ids)
 
@@ -670,17 +678,17 @@ class FlashMixtralForCausalLM(torch.nn.Module):
             raise ValueError("max_past cannot be None")
 
     def forward(
-            self,
-            input_ids: torch.Tensor,
-            position_ids: torch.Tensor,
-            cu_seqlen_prefill: Optional[torch.Tensor],
-            kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
-            block_tables: torch.Tensor,
-            slots: torch.Tensor,
-            input_lengths: torch.Tensor,
-            max_s: int,
-            prefill_cache_indices: Optional[torch.Tensor],
-            lm_head_indices: Optional[torch.Tensor] = None,
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
+        lm_head_indices: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if prefill_cache_indices is not None:
             # Slots also need to be sliced as it has the same size as the whole kv tensor

server/text_generation_server/models/custom_modeling/idefics_image_processing.py

@@ -198,7 +198,9 @@ class IdeficsImageProcessor(BaseImageProcessor):
             image = image_url_or_urls
 
             if image.startswith("http://") or image.startswith("https://"):
-                response = requests.get(image_url_or_urls, stream=True, headers=headers, timeout=(1, 5))
+                response = requests.get(
+                    image_url_or_urls, stream=True, headers=headers, timeout=(1, 5)
+                )
                 response.raise_for_status()
                 content = response.content
             elif image.startswith("data:"):
@@ -213,7 +215,7 @@ class IdeficsImageProcessor(BaseImageProcessor):
                 image = Image.open(BytesIO(content))
                 # image.verify()
             except Exception:
-                raise ValueError(f"Could not load image from url {image_url_or_urls}")    
+                raise ValueError(f"Could not load image from url {image_url_or_urls}")
             return image
         else:
             raise ValueError(

server/text_generation_server/models/custom_modeling/idefics_modeling.py

@@ -62,6 +62,7 @@ if IS_CUDA_SYSTEM:
 elif IS_ROCM_SYSTEM:
     from vllm import layernorm_ops
 
+
 @dataclass
 class BaseModelOutputWithPastImage(BaseModelOutputWithPast):
     image_hidden_states: Optional[torch.FloatTensor] = None
@@ -431,7 +432,9 @@ class IdeficsRMSNorm(nn.Module):
 
             return out
         else:
-            raise ValueError("Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction.")
+            raise ValueError(
+                "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
+            )
 
 
 # this was adapted from LlamaMLP
@@ -613,8 +616,13 @@ class IdeficsAttention(nn.Module):
 
             query_shape = query_states.shape
             key_shape = key_states.shape
-            self.rotary_emb(query_states.view(-1, *query_shape[2:]), key_states.reshape(-1, *key_shape[2:]), cos, sin)
-            
+            self.rotary_emb(
+                query_states.view(-1, *query_shape[2:]),
+                key_states.reshape(-1, *key_shape[2:]),
+                cos,
+                sin,
+            )
+
             query_states = query_states.view(query_shape)
             key_states = key_states.view(key_shape)
 

server/text_generation_server/models/custom_modeling/idefics_processing.py

@@ -112,6 +112,7 @@ def is_url(string):
     result = urlparse(string)
     return all([result.scheme, result.netloc])
 
+
 def is_image(string):
     """Checks if the passed string contains a valid url and nothing else. e.g. if space is included it's immediately
     invalidated the url"""
@@ -344,7 +345,6 @@ class IdeficsProcessor(ProcessorMixin):
 
             image_objects = self.image_processor(image_objects, transform=transform)
 
-
             text_encoding = self.tokenizer(
                 text=full_text,
                 add_special_tokens=False,

server/text_generation_server/models/flash_causal_lm.py

@@ -11,7 +11,7 @@ from opentelemetry import trace
 from transformers import PreTrainedTokenizerBase
 from typing import Optional, Tuple, List, Type, Union, Dict
 
-from text_generation_server.models import Model 
+from text_generation_server.models import Model
 from text_generation_server.utils.speculate import get_speculate
 from text_generation_server.models.types import (
     Batch,
@@ -165,8 +165,6 @@ class FlashCausalLMBatch(Batch):
             input_length = len(tokenized_input)
             input_lengths.append(input_length)
 
-
-
             prefix_offsets.append(input_length - 5)
             read_offsets.append(input_length)
 
@@ -229,7 +227,9 @@ class FlashCausalLMBatch(Batch):
             cumulative_max_length += total_tokens
             max_seqlen = max(max_seqlen, input_length)
             max_blocks = max(max_blocks, needed_blocks)
-            max_length = max(max_length, input_length + max_new_tokens + speculative_length)
+            max_length = max(
+                max_length, input_length + max_new_tokens + speculative_length
+            )
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
             next_token_chooser_parameters, dtype, device
@@ -424,7 +424,9 @@ class FlashCausalLMBatch(Batch):
         slots = self.slots[slot_filtering_indices]
         next_token_chooser = self.next_token_chooser.filter(indices)
         top_n_tokens_tensor = self.top_n_tokens_tensor[indices]
-        speculative_ids = self.speculative_ids[indices] if self.speculative_ids is not None else None
+        speculative_ids = (
+            self.speculative_ids[indices] if self.speculative_ids is not None else None
+        )
 
         start_slots = torch.tensor(start_slots, dtype=torch.int64)
 
@@ -480,7 +482,9 @@ class FlashCausalLMBatch(Batch):
             total_batch_size += len(b)
             total_slots += len(b.slots)
             blocks += b.blocks
-            speculative_length = b.speculative_ids.shape[1] if b.speculative_ids is not None else 0
+            speculative_length = (
+                b.speculative_ids.shape[1] if b.speculative_ids is not None else 0
+            )
             max_blocks = max(max_blocks, b.max_blocks)
             max_seqlen = max(max_seqlen, b.max_seqlen)
             max_length = max(
@@ -586,7 +590,11 @@ class FlashCausalLMBatch(Batch):
             device=batches[0].next_token_chooser.device,
         )
 
-        speculative_ids = torch.cat([b.speculative_ids for b in batches], dim=0) if batches[0].speculative_ids is not None else None
+        speculative_ids = (
+            torch.cat([b.speculative_ids for b in batches], dim=0)
+            if batches[0].speculative_ids is not None
+            else None
+        )
 
         # Needed to avoid dropping blocks when the batches will go out of scope
         for b in batches:
@@ -622,7 +630,7 @@ class FlashCausalLMBatch(Batch):
             top_n_tokens_tensor=top_n_tokens_tensor,
             blocks=blocks,
             max_blocks=max_blocks,
-            speculative_ids=speculative_ids
+            speculative_ids=speculative_ids,
         )
 
     def __del__(self):
@@ -727,43 +735,54 @@ class FlashCausalLM(Model):
     def forward(self, batch: FlashCausalLMBatch) -> Tuple[torch.Tensor, torch.Tensor]:
         # Model Forward
         if batch.speculative_ids is not None:
-            input_ids=batch.input_ids
-            position_ids=batch.position_ids
-            cu_seqlen_prefill=batch.cu_seqlen_prefill
-            kv_cache=get_cache_manager().kv_cache
-            block_tables=batch.block_tables_tensor
-            slots=batch.slots[batch.slot_indices]
-            input_lengths=batch.input_lengths_tensor
-            max_s=batch.max_seqlen
-            lm_head_indices=batch.prefill_head_indices
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
 
             speculative_ids = batch.speculative_ids
 
-            B, speculative_length = speculative_ids.shape 
+            B, speculative_length = speculative_ids.shape
             new_length = speculative_length + 1
-            new_input_ids = torch.cat([input_ids.unsqueeze(-1), speculative_ids], dim=1).reshape(-1)
+            new_input_ids = torch.cat(
+                [input_ids.unsqueeze(-1), speculative_ids], dim=1
+            ).reshape(-1)
             arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
             arange_int = arange.to(dtype=torch.int32)
-            new_position_ids = (position_ids.unsqueeze(-1).expand(B, new_length) + arange).view(-1)
+            new_position_ids = (
+                position_ids.unsqueeze(-1).expand(B, new_length) + arange
+            ).view(-1)
             slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
-            input_lengths = (input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
+            input_lengths = (
+                input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
+            ).view(-1)
 
             # Add Copy the block tables for all members
-            block_tables = block_tables.unsqueeze(1).expand(B, new_length, -1).reshape(B* new_length, -1).contiguous()
+            block_tables = (
+                block_tables.unsqueeze(1)
+                .expand(B, new_length, -1)
+                .reshape(B * new_length, -1)
+                .contiguous()
+            )
             max_s = max_s + speculative_length
 
             input_ids = new_input_ids
             position_ids = new_position_ids
         else:
-            input_ids=batch.input_ids
-            position_ids=batch.position_ids
-            cu_seqlen_prefill=batch.cu_seqlen_prefill
-            kv_cache=get_cache_manager().kv_cache
-            block_tables=batch.block_tables_tensor
-            slots=batch.slots[batch.slot_indices]
-            input_lengths=batch.input_lengths_tensor
-            max_s=batch.max_seqlen
-            lm_head_indices=batch.prefill_head_indices
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
 
         return self.model.forward(
             input_ids=input_ids,
@@ -808,20 +827,31 @@ class FlashCausalLM(Model):
         else:
             speculative_logits = None
 
-
         if prefill:
             next_token_logits = (
                 out[batch.prefill_next_token_indices] if prefill_logprobs else out
             )
             if speculative_logits is not None:
                 speculative_logits = (
-                    speculative_logits[batch.prefill_next_token_indices] if prefill_logprobs else speculative_logits
+                    speculative_logits[batch.prefill_next_token_indices]
+                    if prefill_logprobs
+                    else speculative_logits
                 )
         else:
             next_token_logits = out
 
-        next_input_ids, next_token_logprobs, logprobs, accepted_ids, speculative_ids = batch.next_token_chooser(
-            batch.all_input_ids_tensor[:, : batch.max_seqlen], next_token_logits, get_speculate(), batch.speculative_ids, speculative_logits
+        (
+            next_input_ids,
+            next_token_logprobs,
+            logprobs,
+            accepted_ids,
+            speculative_ids,
+        ) = batch.next_token_chooser(
+            batch.all_input_ids_tensor[:, : batch.max_seqlen],
+            next_token_logits,
+            get_speculate(),
+            batch.speculative_ids,
+            speculative_logits,
         )
 
         batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
@@ -851,11 +881,7 @@ class FlashCausalLM(Model):
         stopped = True
 
         # Zipped iterator
-        iterator = zip(
-            batch.input_lengths,
-            batch.all_input_ids,
-            accepted_ids
-        )
+        iterator = zip(batch.input_lengths, batch.all_input_ids, accepted_ids)
 
         # We do two for loops as the first one can run completely asynchronously from the GPU while for the second
         # one, we need to first do a GPU <-> CPU sync
@@ -863,11 +889,7 @@ class FlashCausalLM(Model):
 
         # For each member of the batch
         index = 0
-        for i, (
-            input_length,
-            all_input_ids,
-            n_accepted_ids
-        ) in enumerate(iterator):
+        for i, (input_length, all_input_ids, n_accepted_ids) in enumerate(iterator):
             # Indexing metadata
             start_index = cumulative_length
             end_index = cumulative_length + input_length
@@ -901,7 +923,6 @@ class FlashCausalLM(Model):
 
             cumulative_length += input_length
 
-
         batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
         batch.speculative_ids = speculative_ids
         batch.position_ids = next_position_ids + accepted_ids
@@ -983,8 +1004,10 @@ class FlashCausalLM(Model):
                     current_stopped = False
             stopped = stopped and current_stopped
 
-            _next_token_ids = next_token_ids[index: index+n_accepted_ids - left]
-            _next_token_logprobs = next_token_logprobs[index: index+n_accepted_ids - left]
+            _next_token_ids = next_token_ids[index : index + n_accepted_ids - left]
+            _next_token_logprobs = next_token_logprobs[
+                index : index + n_accepted_ids - left
+            ]
             index += n_accepted_ids
 
             # Shard generations
@@ -1027,7 +1050,10 @@ class FlashCausalLM(Model):
                     )
 
                     prefill_tokens = Tokens(
-                        prefill_token_ids, request_prefill_logprobs, prefill_texts, is_special = []
+                        prefill_token_ids,
+                        request_prefill_logprobs,
+                        prefill_texts,
+                        is_special=[],
                     )
                 else:
                     prefill_tokens = None

server/text_generation_server/models/flash_llama.py

@@ -71,12 +71,19 @@ class FlashLlama(FlashCausalLM):
             from text_generation_server.utils.medusa import MedusaModel
             from huggingface_hub import hf_hub_download
             import json
-            medusa_config = hf_hub_download(use_medusa, revision=revision, filename="config.json")
+
+            medusa_config = hf_hub_download(
+                use_medusa, revision=revision, filename="config.json"
+            )
             with open(medusa_config, "r") as f:
                 config = json.load(f)
-            medusa_head = hf_hub_download(use_medusa, revision=revision, filename="medusa_lm_head.pt")
-            medusa_sf = medusa_head[:-len(".pt")] + ".safetensors"
-            weights = Weights([medusa_sf], device, dtype, process_group=self.process_group)
+            medusa_head = hf_hub_download(
+                use_medusa, revision=revision, filename="medusa_lm_head.pt"
+            )
+            medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
+            weights = Weights(
+                [medusa_sf], device, dtype, process_group=self.process_group
+            )
             lm_head = model.lm_head
             model.lm_head = MedusaModel(config, weights, lm_head)
 

server/text_generation_server/models/flash_mistral.py

@@ -45,11 +45,11 @@ class FlashMistralBatch(FlashCausalLMBatch):
 
     @classmethod
     def from_pb(
-            cls,
-            pb: generate_pb2.Batch,
-            tokenizer: PreTrainedTokenizerBase,
-            dtype: torch.dtype,
-            device: torch.device,
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        dtype: torch.dtype,
+        device: torch.device,
     ) -> "FlashCausalLMBatch":
         global SLIDING_WINDOW
         global SLIDING_WINDOW_BLOCKS
@@ -99,12 +99,12 @@ class FlashMistralBatch(FlashCausalLMBatch):
 
         # Parse batch
         for i, (r, tokenized_input) in enumerate(
-                zip(pb.requests, batch_tokenized_inputs)
+            zip(pb.requests, batch_tokenized_inputs)
         ):
             # request id -> idx in list mapping
             requests_idx_mapping[r.id] = i
 
-            tokenized_input = tokenized_input[-r.truncate:]
+            tokenized_input = tokenized_input[-r.truncate :]
 
             input_length = len(tokenized_input)
             input_lengths.append(input_length)
@@ -184,7 +184,9 @@ class FlashMistralBatch(FlashCausalLMBatch):
             cumulative_max_length += total_tokens
             max_seqlen = max(max_seqlen, input_length)
             max_blocks = max(max_blocks, needed_blocks)
-            max_length = max(max_length, input_length + max_new_tokens + speculative_length)
+            max_length = max(
+                max_length, input_length + max_new_tokens + speculative_length
+            )
 
         next_token_chooser = HeterogeneousNextTokenChooser.from_pb(
             next_token_chooser_parameters, dtype, device
@@ -273,20 +275,20 @@ class FlashMistralBatch(FlashCausalLMBatch):
             blocks=blocks,
             max_blocks=max_blocks,
             prefill_cache_indices=prefill_cache_indices,
-            speculative_ids=None
+            speculative_ids=None,
         )
 
 
 class BaseFlashMistral(FlashCausalLM):
     def __init__(
-            self,
-            config_cls,
-            model_cls,
-            model_id: str,
-            revision: Optional[str] = None,
-            quantize: Optional[str] = None,
-            dtype: Optional[torch.dtype] = None,
-            trust_remote_code: bool = False,
+        self,
+        config_cls,
+        model_cls,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        trust_remote_code: bool = False,
     ):
         global SLIDING_WINDOW
         global SLIDING_WINDOW_BLOCKS
@@ -345,43 +347,54 @@ class BaseFlashMistral(FlashCausalLM):
     def forward(self, batch: FlashMistralBatch) -> Tuple[torch.Tensor, torch.Tensor]:
         # Model Forward
         if batch.speculative_ids is not None:
-            input_ids=batch.input_ids
-            position_ids=batch.position_ids
-            cu_seqlen_prefill=batch.cu_seqlen_prefill
-            kv_cache=get_cache_manager().kv_cache
-            block_tables=batch.block_tables_tensor
-            slots=batch.slots[batch.slot_indices]
-            input_lengths=batch.input_lengths_tensor
-            max_s=batch.max_seqlen
-            lm_head_indices=batch.prefill_head_indices
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
 
             speculative_ids = batch.speculative_ids
 
-            B, speculative_length = speculative_ids.shape 
+            B, speculative_length = speculative_ids.shape
             new_length = speculative_length + 1
-            new_input_ids = torch.cat([input_ids.unsqueeze(-1), speculative_ids], dim=1).reshape(-1)
+            new_input_ids = torch.cat(
+                [input_ids.unsqueeze(-1), speculative_ids], dim=1
+            ).reshape(-1)
             arange = torch.arange(new_length, device=position_ids.device).unsqueeze(0)
             arange_int = arange.to(dtype=torch.int32)
-            new_position_ids = (position_ids.unsqueeze(-1).expand(B, new_length) + arange).view(-1)
+            new_position_ids = (
+                position_ids.unsqueeze(-1).expand(B, new_length) + arange
+            ).view(-1)
             slots = (slots.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
-            input_lengths = (input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int).view(-1)
+            input_lengths = (
+                input_lengths.unsqueeze(-1).expand(B, new_length) + arange_int
+            ).view(-1)
 
             # Add Copy the block tables for all members
-            block_tables = block_tables.unsqueeze(1).expand(B, new_length, -1).reshape(B* new_length, -1).contiguous()
+            block_tables = (
+                block_tables.unsqueeze(1)
+                .expand(B, new_length, -1)
+                .reshape(B * new_length, -1)
+                .contiguous()
+            )
             max_s = max_s + speculative_length
 
             input_ids = new_input_ids
             position_ids = new_position_ids
         else:
-            input_ids=batch.input_ids
-            position_ids=batch.position_ids
-            cu_seqlen_prefill=batch.cu_seqlen_prefill
-            kv_cache=get_cache_manager().kv_cache
-            block_tables=batch.block_tables_tensor
-            slots=batch.slots[batch.slot_indices]
-            input_lengths=batch.input_lengths_tensor
-            max_s=batch.max_seqlen
-            lm_head_indices=batch.prefill_head_indices
+            input_ids = batch.input_ids
+            position_ids = batch.position_ids
+            cu_seqlen_prefill = batch.cu_seqlen_prefill
+            kv_cache = get_cache_manager().kv_cache
+            block_tables = batch.block_tables_tensor
+            slots = batch.slots[batch.slot_indices]
+            input_lengths = batch.input_lengths_tensor
+            max_s = batch.max_seqlen
+            lm_head_indices = batch.prefill_head_indices
         logits = self.model.forward(
             input_ids=input_ids,
             position_ids=position_ids,
@@ -401,12 +414,12 @@ class BaseFlashMistral(FlashCausalLM):
 
 class FlashMistral(BaseFlashMistral):
     def __init__(
-            self,
-            model_id: str,
-            revision: Optional[str] = None,
-            quantize: Optional[str] = None,
-            dtype: Optional[torch.dtype] = None,
-            trust_remote_code: bool = False,
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        trust_remote_code: bool = False,
     ):
         super(FlashMistral, self).__init__(
             config_cls=MistralConfig,
@@ -415,5 +428,5 @@ class FlashMistral(BaseFlashMistral):
             revision=revision,
             quantize=quantize,
             dtype=dtype,
-            trust_remote_code=trust_remote_code
+            trust_remote_code=trust_remote_code,
         )