feat: format code (#1070)

clients/python/text_generation/client.py

@@ -137,7 +137,7 @@ class Client:
             typical_p=typical_p,
             watermark=watermark,
             decoder_input_details=decoder_input_details,
-            top_n_tokens=top_n_tokens
+            top_n_tokens=top_n_tokens,
         )
         request = Request(inputs=prompt, stream=False, parameters=parameters)
 

clients/python/text_generation/types.py

@@ -133,7 +133,9 @@ class Request(BaseModel):
             and parameters.best_of > 1
             and field_value
         ):
-            raise ValidationError("`best_of` != 1 is not supported when `stream` == True")
+            raise ValidationError(
+                "`best_of` != 1 is not supported when `stream` == True"
+            )
         return field_value
 
 

integration-tests/models/test_flash_awq.py

@@ -3,7 +3,11 @@ import pytest
 
 @pytest.fixture(scope="module")
 def flash_llama_awq_handle(launcher):
-    with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=1, quantize="awq") as handle:
+    with launcher(
+        "abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
+        num_shard=1,
+        quantize="awq",
+    ) as handle:
         yield handle
 
 
@@ -12,6 +16,7 @@ async def flash_llama_awq(flash_llama_awq_handle):
     await flash_llama_awq_handle.health(300)
     return flash_llama_awq_handle.client
 
+
 @pytest.mark.asyncio
 @pytest.mark.private
 async def test_flash_llama_awq(flash_llama_awq, response_snapshot):
@@ -20,11 +25,13 @@ async def test_flash_llama_awq(flash_llama_awq, response_snapshot):
     )
 
     assert response.details.generated_tokens == 10
-    assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
+    assert (
+        response.generated_text
+        == "\nWhat is the difference between Deep Learning and Machine"
+    )
     assert response == response_snapshot
 
 
-
 @pytest.mark.asyncio
 @pytest.mark.private
 async def test_flash_llama_awq_all_params(flash_llama_awq, response_snapshot):
@@ -49,16 +56,18 @@ async def test_flash_llama_awq_all_params(flash_llama_awq, response_snapshot):
 
 @pytest.mark.asyncio
 @pytest.mark.private
-async def test_flash_llama_awq_load(
-    flash_llama_awq, generate_load, response_snapshot
-):
+async def test_flash_llama_awq_load(flash_llama_awq, generate_load, response_snapshot):
     responses = await generate_load(
         flash_llama_awq, "What is Deep Learning?", max_new_tokens=10, n=4
     )
 
     assert len(responses) == 4
-    assert all([r.generated_text ==  "\nWhat is the difference between Deep Learning and Machine" for r in responses])
+    assert all(
+        [
+            r.generated_text
+            == "\nWhat is the difference between Deep Learning and Machine"
+            for r in responses
+        ]
+    )
 
     assert responses == response_snapshot
-
-

integration-tests/models/test_flash_awq_sharded.py

@@ -1,15 +1,22 @@
 import pytest
 
+
 @pytest.fixture(scope="module")
 def flash_llama_awq_handle_sharded(launcher):
-    with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=2, quantize="awq") as handle:
+    with launcher(
+        "abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq",
+        num_shard=2,
+        quantize="awq",
+    ) as handle:
         yield handle
 
+
 @pytest.fixture(scope="module")
 async def flash_llama_awq_sharded(flash_llama_awq_handle_sharded):
     await flash_llama_awq_handle_sharded.health(300)
     return flash_llama_awq_handle_sharded.client
 
+
 @pytest.mark.asyncio
 @pytest.mark.private
 async def test_flash_llama_awq_sharded(flash_llama_awq_sharded, response_snapshot):
@@ -18,9 +25,13 @@ async def test_flash_llama_awq_sharded(flash_llama_awq_sharded, response_snapsho
     )
 
     assert response.details.generated_tokens == 10
-    assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
+    assert (
+        response.generated_text
+        == "\nWhat is the difference between Deep Learning and Machine"
+    )
     assert response == response_snapshot
 
+
 @pytest.mark.asyncio
 @pytest.mark.private
 async def test_flash_llama_awq_load_sharded(
@@ -31,6 +42,12 @@ async def test_flash_llama_awq_load_sharded(
     )
 
     assert len(responses) == 4
-    assert all([r.generated_text ==  "\nWhat is the difference between Deep Learning and Machine" for r in responses])
+    assert all(
+        [
+            r.generated_text
+            == "\nWhat is the difference between Deep Learning and Machine"
+            for r in responses
+        ]
+    )
 
     assert responses == response_snapshot

integration-tests/models/test_idefics.py

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

server/tests/utils/test_tokens.py

@@ -45,12 +45,15 @@ def test_stopping_criteria_max():
     assert criteria(1, "") == (False, None)
     assert criteria(1, "") == (True, FinishReason.FINISH_REASON_LENGTH)
 
+
 def test_batch_top_tokens():
     top_n_tokens = [0, 2, 3, 4, 5]
     top_n_tokens_tensor = torch.tensor(top_n_tokens)
-    inp_logprobs = torch.tensor([[-1., -3., -4., -2., -3.]] * 5)
+    inp_logprobs = torch.tensor([[-1.0, -3.0, -4.0, -2.0, -3.0]] * 5)
 
-    topn_tok_ids, topn_tok_logprobs = batch_top_tokens(top_n_tokens, top_n_tokens_tensor, inp_logprobs)
+    topn_tok_ids, topn_tok_logprobs = batch_top_tokens(
+        top_n_tokens, top_n_tokens_tensor, inp_logprobs
+    )
 
     assert topn_tok_ids[0] == []
     assert topn_tok_ids[1] == [0, 3]

server/text_generation_server/cli.py

@@ -125,8 +125,12 @@ def download_weights(
 
     if not is_local_model:
         try:
-            adapter_config_filename = hf_hub_download(model_id, revision=revision, filename="adapter_config.json")
-            utils.download_and_unload_peft(model_id, revision, trust_remote_code=trust_remote_code)
+            adapter_config_filename = hf_hub_download(
+                model_id, revision=revision, filename="adapter_config.json"
+            )
+            utils.download_and_unload_peft(
+                model_id, revision, trust_remote_code=trust_remote_code
+            )
             is_local_model = True
             utils.weight_files(model_id, revision, extension)
             return
@@ -179,11 +183,12 @@ def download_weights(
             import transformers
             import json
 
-
             if is_local_model:
                 config_filename = os.path.join(model_id, "config.json")
             else:
-                config_filename = hf_hub_download(model_id, revision=revision, filename="config.json")
+                config_filename = hf_hub_download(
+                    model_id, revision=revision, filename="config.json"
+                )
             with open(config_filename, "r") as f:
                 config = json.load(f)
             architecture = config["architectures"][0]

server/text_generation_server/models/__init__.py

@@ -153,7 +153,11 @@ def get_model(
         )
     elif model_type == "mpt":
         return MPTSharded(
-            model_id, revision, quantize=quantize, dtype=dtype, trust_remote_code=trust_remote_code
+            model_id,
+            revision,
+            quantize=quantize,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
         )
 
     elif model_type == "gpt_neox":
@@ -269,13 +273,9 @@ def get_model(
             "gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
         )
     if quantize == "awq":
-        raise ValueError(
-            "awq quantization is not supported for AutoModel"
-        )
+        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"
-        )
+        raise ValueError("4bit quantization is not supported for AutoModel")
     if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
         return CausalLM(
             model_id,

server/text_generation_server/models/causal_lm.py

@@ -643,9 +643,12 @@ class CausalLM(Model):
                     # Decode generated tokens
                     output_text, _, _ = self.decode_token(
                         all_input_ids[:, 0],
-                        prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
-                        read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
-                        skip_special_tokens=True
+                        prefix_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens
+                        - 1,
+                        read_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens,
+                        skip_special_tokens=True,
                     )
                     # Get seed
                     if isinstance(next_token_chooser.choice, Sampling):

server/text_generation_server/models/custom_modeling/bloom_modeling.py

@@ -40,7 +40,10 @@ from text_generation_server.utils.layers import (
 )
 
 CUSTOM_KERNELS_ENABLED = False
-if torch.cuda.is_available() and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True":
+if (
+    torch.cuda.is_available()
+    and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True"
+):
     try:
         from custom_kernels import fused_bloom_attention_cuda
 

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -169,6 +169,7 @@ def load_attention(config, prefix, weights):
                 bias=False,
             )
 
+
 def _load_gqa(config, prefix: str, weights):
     assert config.hidden_size % config.num_attention_heads == 0
     assert config.num_attention_heads % weights.process_group.size() == 0
@@ -211,7 +212,10 @@ class FlashLlamaAttention(torch.nn.Module):
         #     config=config, prefix=f"{prefix}.rotary_emb", weights=weights
         # )
         self.rotary_emb = PositionRotaryEmbedding.static(
-            config=config, dim=self.head_size, base=config.rope_theta, device=weights.device
+            config=config,
+            dim=self.head_size,
+            base=config.rope_theta,
+            device=weights.device,
         )
 
         self.softmax_scale = self.head_size**-0.5

server/text_generation_server/models/custom_modeling/idefics_image_processing.py

@@ -20,7 +20,12 @@ import numpy as np
 from PIL import Image
 
 from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
-from transformers.image_transforms import resize, to_channel_dimension_format, rescale, normalize
+from transformers.image_transforms import (
+    resize,
+    to_channel_dimension_format,
+    rescale,
+    normalize,
+)
 from transformers.image_utils import (
     ChannelDimension,
     ImageInput,
@@ -121,7 +126,11 @@ class IdeficsImageProcessor(BaseImageProcessor):
             a PyTorch tensor of the processed images
         """
         image_size = image_size if image_size is not None else self.image_size
-        image_num_channels = image_num_channels if image_num_channels is not None else self.image_num_channels
+        image_num_channels = (
+            image_num_channels
+            if image_num_channels is not None
+            else self.image_num_channels
+        )
         image_mean = image_mean if image_mean is not None else self.image_mean
         image_std = image_std if image_std is not None else self.image_std
         size = (image_size, image_size)
@@ -160,9 +169,13 @@ class IdeficsImageProcessor(BaseImageProcessor):
         images = [resize(x, size, resample=PILImageResampling.BICUBIC) for x in images]
         images = [self.rescale(image=image, scale=1 / 255) for image in images]
         images = [self.normalize(x, mean=image_mean, std=image_std) for x in images]
-        images = [to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images]
+        images = [
+            to_channel_dimension_format(x, ChannelDimension.FIRST) for x in images
+        ]
         # TODO: this converts to torch tensors - switch to convert_to_tensors once it becomes available
-        images = BatchFeature(data={"pixel_values": images}, tensor_type=TensorType.PYTORCH)["pixel_values"]
+        images = BatchFeature(
+            data={"pixel_values": images}, tensor_type=TensorType.PYTORCH
+        )["pixel_values"]
 
         return images
 
@@ -185,7 +198,9 @@ class IdeficsImageProcessor(BaseImageProcessor):
             response.raise_for_status()
             return Image.open(BytesIO(response.content))
         else:
-            raise ValueError(f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}")
+            raise ValueError(
+                f"only a single or a list of entries is supported but got type={type(image_url_or_urls)}"
+            )
 
     def rescale(
         self,
@@ -255,10 +270,9 @@ class IdeficsImageProcessor(BaseImageProcessor):
             `np.ndarray`: The normalized image.
         """
         # TODO 4.32
-        return normalize(
-            image, mean=mean, std=std, data_format=data_format, **kwargs
-        )
+        return normalize(image, mean=mean, std=std, data_format=data_format, **kwargs)
 
 
 import transformers
+
 transformers.IdeficsImageProcessor = IdeficsImageProcessor

server/text_generation_server/models/custom_modeling/idefics_modeling.py

@@ -28,7 +28,11 @@ from torch.nn import CrossEntropyLoss
 
 from transformers import PreTrainedModel
 from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, dataclass
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    dataclass,
+)
 from transformers.modeling_utils import PretrainedConfig
 from transformers.utils import (
     add_start_docstrings,
@@ -37,8 +41,12 @@ from transformers.utils import (
     replace_return_docstrings,
 )
 from text_generation_server.models.custom_modeling.idefics_config import IdeficsConfig
-from text_generation_server.models.custom_modeling.idefics_vision import IdeficsVisionTransformer
-from text_generation_server.models.custom_modeling.idefics_perceiver import IdeficsPerceiverResampler
+from text_generation_server.models.custom_modeling.idefics_vision import (
+    IdeficsVisionTransformer,
+)
+from text_generation_server.models.custom_modeling.idefics_perceiver import (
+    IdeficsPerceiverResampler,
+)
 from text_generation_server.utils.layers import (
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
@@ -49,10 +57,12 @@ from text_generation_server.utils.layers import (
 )
 import dropout_layer_norm
 
+
 @dataclass
 class BaseModelOutputWithPastImage(BaseModelOutputWithPast):
     image_hidden_states: Optional[torch.FloatTensor] = None
 
+
 @dataclass
 class CausalLMOutputWithPastImage(CausalLMOutputWithPast):
     image_hidden_states: Optional[torch.FloatTensor] = None
@@ -78,25 +88,39 @@ def expand_inputs_for_generation(
     **model_kwargs,
 ):
     expanded_return_idx = (
-        torch.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1).to(input_ids.device)
+        torch.arange(input_ids.shape[0])
+        .view(-1, 1)
+        .repeat(1, expand_size)
+        .view(-1)
+        .to(input_ids.device)
     )
     input_ids = input_ids.index_select(0, expanded_return_idx)
 
     if "token_type_ids" in model_kwargs:
         token_type_ids = model_kwargs["token_type_ids"]
-        model_kwargs["token_type_ids"] = token_type_ids.index_select(0, expanded_return_idx)
-
-    if attention_mask is not None:
-        model_kwargs["attention_mask"] = attention_mask.index_select(0, expanded_return_idx)
-        model_kwargs["image_attention_mask"] = model_kwargs["image_attention_mask"].index_select(
+        model_kwargs["token_type_ids"] = token_type_ids.index_select(
+            0, expanded_return_idx
+        )
+
+    if attention_mask is not None:
+        model_kwargs["attention_mask"] = attention_mask.index_select(
+            0, expanded_return_idx
+        )
+        model_kwargs["image_attention_mask"] = model_kwargs[
+            "image_attention_mask"
+        ].index_select(0, expanded_return_idx)
+        model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(
             0, expanded_return_idx
         )
-        model_kwargs["pixel_values"] = model_kwargs["pixel_values"].index_select(0, expanded_return_idx)
 
     if is_encoder_decoder:
         if encoder_outputs is None:
-            raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
-        encoder_outputs["last_hidden_state"] = encoder_outputs.last_hidden_state.index_select(
+            raise ValueError(
+                "If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined."
+            )
+        encoder_outputs[
+            "last_hidden_state"
+        ] = encoder_outputs.last_hidden_state.index_select(
             0, expanded_return_idx.to(encoder_outputs.last_hidden_state.device)
         )
         model_kwargs["encoder_outputs"] = encoder_outputs
@@ -120,14 +144,17 @@ def update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder
     # update token_type_ids with last value
     if "token_type_ids" in model_kwargs:
         token_type_ids = model_kwargs["token_type_ids"]
-        model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
+        model_kwargs["token_type_ids"] = torch.cat(
+            [token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1
+        )
 
     # update attention masks
     if not is_encoder_decoder:
         if "attention_mask" in model_kwargs:
             attention_mask = model_kwargs["attention_mask"]
             model_kwargs["attention_mask"] = torch.cat(
-                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))],
+                dim=-1,
             )
         if "image_attention_mask" in model_kwargs:
             image_attention_mask = model_kwargs["image_attention_mask"]
@@ -180,8 +207,12 @@ def freeze_model(model, module_exceptions=[]):
     }
     module_exceptions_mapped = [mapping[m] for m in module_exceptions]
     for module in model.modules():
-        if module_exceptions and any([isinstance(module, t) for t in module_exceptions_mapped]):
-            module.requires_grad_(True)  # Explicitely setting it to true to avoid any mistakes
+        if module_exceptions and any(
+            [isinstance(module, t) for t in module_exceptions_mapped]
+        ):
+            module.requires_grad_(
+                True
+            )  # Explicitely setting it to true to avoid any mistakes
         else:
             module.requires_grad_(False)
     return model
@@ -195,15 +226,21 @@ class IdeficsDecoupledPartialTPEmbedding(nn.Module):
     ):
         super().__init__()
         self.num_embeddings = config.vocab_size
-        self.weight = TensorParallelEmbedding(prefix="model.embed_tokens", weights=weights)
-        self.additional_weight = nn.Parameter(weights.get_tensor(f"model.embed_tokens.additional_embedding.weight"))
+        self.weight = TensorParallelEmbedding(
+            prefix="model.embed_tokens", weights=weights
+        )
+        self.additional_weight = nn.Parameter(
+            weights.get_tensor(f"model.embed_tokens.additional_embedding.weight")
+        )
 
     def forward(self, input_ids):
         # Clone so that we don't modify the original input_ids later on
         input_ids = input_ids.clone()
         additional_vocab_indices = torch.where(input_ids >= self.num_embeddings)
         input_ids_additional_vocab = input_ids[additional_vocab_indices]
-        additional_embeddings = torch.nn.functional.embedding(input_ids_additional_vocab - self.num_embeddings, self.additional_weight)
+        additional_embeddings = torch.nn.functional.embedding(
+            input_ids_additional_vocab - self.num_embeddings, self.additional_weight
+        )
 
         # for successful lookup replace input_ids with 0, the results of these will be discarded anyway
         input_ids[additional_vocab_indices] = 0
@@ -234,7 +271,10 @@ class IdeficsDecoupledTensorParallelLinear(nn.Module):
             config=config, prefix="lm_head", weights=weights
         )
         self.additional_fc = FastLinear.load(
-            config=config, prefix="lm_head.additional_fc", weights=weights, bias=False,
+            config=config,
+            prefix="lm_head.additional_fc",
+            weights=weights,
+            bias=False,
         )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
@@ -257,7 +297,10 @@ class IdeficsDecoupledTensorParallelLinear(nn.Module):
 
 # Copied from transformers.models.bart.modeling_bart._make_causal_mask
 def _make_causal_mask(
-    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
 ):
     """
     Make causal mask used for bi-directional self-attention.
@@ -269,8 +312,18 @@ def _make_causal_mask(
     mask = mask.to(dtype)
 
     if past_key_values_length > 0:
-        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
-    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
 
 
 def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
@@ -284,7 +337,9 @@ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int]
 
     inverted_mask = 1.0 - expanded_mask
 
-    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
 
 
 class IdeficsRMSNorm(nn.Module):
@@ -346,7 +401,6 @@ class IdeficsRMSNorm(nn.Module):
             if unwrap:
                 normed_hidden_states = normed_hidden_states.view(*shape)
 
-
             return normed_hidden_states
 
 
@@ -367,7 +421,10 @@ class IdeficsMLP(nn.Module):
             bias=False,
         )
         self.down_proj = TensorParallelRowLinear.load(
-            config, prefix=f"{prefix}.down_proj", weights=weights, bias=False,
+            config,
+            prefix=f"{prefix}.down_proj",
+            weights=weights,
+            bias=False,
         )
         self.act_fn = ACT2FN[config.hidden_act]
 
@@ -375,7 +432,9 @@ class IdeficsMLP(nn.Module):
         gate_up_states = self.gate_up_proj(hidden_states)
         shape = gate_up_states.shape
         gate_up_states = gate_up_states.view(*shape[:-1], 2, shape[-1] // 2)
-        return self.down_proj(self.act_fn(gate_up_states[:, :, 0]) * gate_up_states[:, :, 1])
+        return self.down_proj(
+            self.act_fn(gate_up_states[:, :, 0]) * gate_up_states[:, :, 1]
+        )
 
 
 # this was adapted from LlamaAttention
@@ -445,14 +504,22 @@ class IdeficsAttention(nn.Module):
         self.qk_layer_norms = qk_layer_norms
         if self.qk_layer_norms:
             self.q_layer_norm = IdeficsRMSNorm(
-            prefix=f"{prefix}.q_layer_norm", weights=weights, eps=config.rms_norm_eps
+                prefix=f"{prefix}.q_layer_norm",
+                weights=weights,
+                eps=config.rms_norm_eps,
             )
             self.k_layer_norm = IdeficsRMSNorm(
-            prefix=f"{prefix}.q_layer_norm", weights=weights, eps=config.rms_norm_eps
+                prefix=f"{prefix}.q_layer_norm",
+                weights=weights,
+                eps=config.rms_norm_eps,
             )
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
 
     def forward(
         self,
@@ -470,20 +537,42 @@ class IdeficsAttention(nn.Module):
         bsz, q_len, _ = hidden_states.size()
 
         if is_cross_attention:
-            query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
+            query_states = self.q_proj(hidden_states).view(
+                bsz, q_len, self.num_heads, self.head_dim
+            )  # .transpose(1, 2)
             query_states = query_states.transpose(1, 2)
-            _, kv_len, _ = key_value_states.size()  # Note that, in this case, `kv_len` == `kv_seq_len`
-            key_states = self.k_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
+            (
+                _,
+                kv_len,
+                _,
+            ) = (
+                key_value_states.size()
+            )  # Note that, in this case, `kv_len` == `kv_seq_len`
+            key_states = (
+                self.k_proj(key_value_states)
+                .view(bsz, kv_len, self.num_heads, self.head_dim)
+                .transpose(1, 2)
+            )
             value_states = (
-                self.v_proj(key_value_states).view(bsz, kv_len, self.num_heads, self.head_dim).transpose(1, 2)
+                self.v_proj(key_value_states)
+                .view(bsz, kv_len, self.num_heads, self.head_dim)
+                .transpose(1, 2)
             )
         else:
             qkv = self.qkv(hidden_states)
-            query_states, key_states, value_states = qkv.split(self.num_heads * self.head_dim, dim=2)
+            query_states, key_states, value_states = qkv.split(
+                self.num_heads * self.head_dim, dim=2
+            )
 
-            query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
-            key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim)# . transpose(1, 2)
-            value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim)# .transpose(1, 2)
+            query_states = query_states.view(
+                bsz, q_len, self.num_heads, self.head_dim
+            )  # .transpose(1, 2)
+            key_states = key_states.view(
+                bsz, q_len, self.num_heads, self.head_dim
+            )  # . transpose(1, 2)
+            value_states = value_states.view(
+                bsz, q_len, self.num_heads, self.head_dim
+            )  # .transpose(1, 2)
             kv_seq_len = q_len
             if past_key_value is not None:
                 kv_seq_len += past_key_value[0].shape[-2]
@@ -493,10 +582,14 @@ class IdeficsAttention(nn.Module):
             )
 
             shape = query_states.shape
-            query_states = self.rotary_emb(query_states.view(-1, *shape[2:]), cos, sin).view(shape)
+            query_states = self.rotary_emb(
+                query_states.view(-1, *shape[2:]), cos, sin
+            ).view(shape)
 
             shape = key_states.shape
-            key_states = self.rotary_emb(key_states.reshape(-1, *shape[2:]), cos, sin).view(shape)
+            key_states = self.rotary_emb(
+                key_states.reshape(-1, *shape[2:]), cos, sin
+            ).view(shape)
 
             query_states = query_states.transpose(1, 2)
             key_states = key_states.transpose(1, 2)
@@ -571,8 +664,14 @@ class IdeficsDecoderLayer(nn.Module):
             prefix=f"{prefix}.mlp",
             weights=weights,
         )
-        self.input_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=config.rms_norm_eps)
+        self.input_layernorm = IdeficsRMSNorm(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = IdeficsRMSNorm(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
         self.dropout = config.dropout
 
     def forward(
@@ -583,7 +682,9 @@ class IdeficsDecoderLayer(nn.Module):
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         output_attentions: Optional[bool] = False,
         use_cache: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
@@ -650,14 +751,22 @@ class IdeficsGatedCrossAttentionLayer(nn.Module):
             prefix=f"{prefix}.mlp",
             weights=weights,
         )
-        self.input_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = IdeficsRMSNorm(prefix=f"{prefix}.post_attention_layernorm", weights=weights, eps=config.rms_norm_eps)
+        self.input_layernorm = IdeficsRMSNorm(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = IdeficsRMSNorm(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
         self.config = config.dropout
 
         self.act_cross_attn = nn.Tanh()
         self.act_dense = nn.Tanh()
 
-        self.alpha_cross_attn = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_cross_attn"))
+        self.alpha_cross_attn = nn.Parameter(
+            weights.get_tensor(f"{prefix}.alpha_cross_attn")
+        )
         self.alpha_dense = nn.Parameter(weights.get_tensor(f"{prefix}.alpha_dense"))
 
         if not (hasattr(self, "alpha_cross_attn") and hasattr(self, "alpha_dense")):
@@ -673,7 +782,9 @@ class IdeficsGatedCrossAttentionLayer(nn.Module):
         use_cache: Optional[bool] = False,
         past_key_value: Optional[Tuple[torch.Tensor]] = None,
         no_images: Optional[bool] = False,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
         """
         Args:
             hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
@@ -695,7 +806,9 @@ class IdeficsGatedCrossAttentionLayer(nn.Module):
             )
 
         if past_key_value is not None:
-            raise NotImplementedError("Past key value states are not implemented for Idefics cross attention module.")
+            raise NotImplementedError(
+                "Past key value states are not implemented for Idefics cross attention module."
+            )
 
         residual = hidden_states
 
@@ -711,7 +824,9 @@ class IdeficsGatedCrossAttentionLayer(nn.Module):
         # hidden_states = nn.functional.dropout(hidden_states, p=self.config, training=self.training)
         # when there are no images the model is used in pure language mode
         gate = 0 if no_images else 1
-        hidden_states = residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states
+        hidden_states = (
+            residual + gate * self.act_cross_attn(self.alpha_cross_attn) * hidden_states
+        )
 
         # Fully Connected
         residual = hidden_states
@@ -896,11 +1011,14 @@ class IdeficsModel(IdeficsPreTrainedModel):
         self.gated_cross_attn_layers = nn.ModuleList(
             [
                 IdeficsGatedCrossAttentionLayer(layer_id, config, weights)
-                for layer_id in range(num_cross_layers)]
+                for layer_id in range(num_cross_layers)
+            ]
         )
         # self.gradient_checkpointing = False
 
-        self.norm = IdeficsRMSNorm(prefix=f"model.norm", weights=weights, eps=config.rms_norm_eps)
+        self.norm = IdeficsRMSNorm(
+            prefix=f"model.norm", weights=weights, eps=config.rms_norm_eps
+        )
 
         # self.gradient_checkpointing = False
         # Initialize weights and apply final processing
@@ -932,7 +1050,9 @@ class IdeficsModel(IdeficsPreTrainedModel):
     #     self.embed_tokens = value
 
     # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
-    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+    def _prepare_decoder_attention_mask(
+        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
         # create causal mask
         # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
         combined_attention_mask = None
@@ -946,11 +1066,13 @@ class IdeficsModel(IdeficsPreTrainedModel):
 
         if attention_mask is not None:
             # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
-            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
-                inputs_embeds.device
-            )
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
             combined_attention_mask = (
-                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
             )
 
         return combined_attention_mask
@@ -974,23 +1096,35 @@ class IdeficsModel(IdeficsPreTrainedModel):
     ) -> Union[Tuple, BaseModelOutputWithPastImage]:
         device = input_ids.device if input_ids is not None else inputs_embeds.device
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
         output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
 
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         # retrieve input_ids and inputs_embeds
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
         elif input_ids is not None:
             batch_size, seq_length = input_ids.shape
         elif inputs_embeds is not None:
             batch_size, seq_length, _ = inputs_embeds.shape
         else:
-            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
 
         seq_length_with_past = seq_length
         past_key_values_length = 0
@@ -1006,7 +1140,10 @@ class IdeficsModel(IdeficsPreTrainedModel):
         elif position_ids is None:
             device = input_ids.device if input_ids is not None else inputs_embeds.device
             position_ids = torch.arange(
-                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
             )
             position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
         else:
@@ -1016,29 +1153,52 @@ class IdeficsModel(IdeficsPreTrainedModel):
 
         if image_hidden_states is None:
             if pixel_values is None and image_embeddings is None:
-                raise ValueError("Either pixel_values and image_embeddings have to be not-None.")
+                raise ValueError(
+                    "Either pixel_values and image_embeddings have to be not-None."
+                )
 
             elif pixel_values is not None and image_embeddings is not None:
-                raise ValueError("You cannot specify both pixel_values and image_embeddings at the same time")
+                raise ValueError(
+                    "You cannot specify both pixel_values and image_embeddings at the same time"
+                )
 
             elif pixel_values is not None:
                 no_images = len(torch.nonzero(pixel_values)) == 0
-                pixel_values = pixel_values.to(dtype=self.dtype, device=device)  # fp16 compatibility
+                pixel_values = pixel_values.to(
+                    dtype=self.dtype, device=device
+                )  # fp16 compatibility
                 batch_size, num_images = pixel_values.shape[:2]
-                pixel_values = pixel_values.contiguous().view(batch_size * num_images, *pixel_values.shape[2:])
+                pixel_values = pixel_values.contiguous().view(
+                    batch_size * num_images, *pixel_values.shape[2:]
+                )
 
                 # Get sequence from the vision encoder
-                image_hidden_states = self.vision_model(pixel_values=pixel_values).last_hidden_state
+                image_hidden_states = self.vision_model(
+                    pixel_values=pixel_values
+                ).last_hidden_state
 
             elif image_embeddings is not None:
-                batch_size, num_images, image_seq_len, image_hidden_size = image_embeddings.size()
-                image_hidden_states = image_embeddings.to(dtype=self.dtype, device=input_ids.device)
-                image_hidden_states = image_hidden_states.view(batch_size * num_images, image_seq_len, image_hidden_size)
+                (
+                    batch_size,
+                    num_images,
+                    image_seq_len,
+                    image_hidden_size,
+                ) = image_embeddings.size()
+                image_hidden_states = image_embeddings.to(
+                    dtype=self.dtype, device=input_ids.device
+                )
+                image_hidden_states = image_hidden_states.view(
+                    batch_size * num_images, image_seq_len, image_hidden_size
+                )
 
             if self.config.use_resampler:
                 image_hidden_states = self.perceiver_resampler(image_hidden_states)
-            image_seq_len, image_hidden_size = image_hidden_states.size(1), image_hidden_states.size(2)
-            image_hidden_states = image_hidden_states.view(batch_size, num_images * image_seq_len, image_hidden_size)
+            image_seq_len, image_hidden_size = image_hidden_states.size(
+                1
+            ), image_hidden_states.size(2)
+            image_hidden_states = image_hidden_states.view(
+                batch_size, num_images * image_seq_len, image_hidden_size
+            )
         else:
             no_images = False
             num_images = pixel_values.shape[1]
@@ -1050,7 +1210,9 @@ class IdeficsModel(IdeficsPreTrainedModel):
         text_seq_len = image_attention_mask.size(1)
         image_attention_mask = image_attention_mask.unsqueeze(-1)
         image_attention_mask = image_attention_mask.repeat(1, 1, 1, image_seq_len)
-        image_attention_mask = image_attention_mask.view(batch_size, text_seq_len, num_images * image_seq_len)
+        image_attention_mask = image_attention_mask.view(
+            batch_size, text_seq_len, num_images * image_seq_len
+        )
         image_batch_size, image_sequence_length, _ = image_hidden_states.size()
         image_hidden_shape = (image_batch_size, image_sequence_length)
         if image_attention_mask is None:
@@ -1060,7 +1222,6 @@ class IdeficsModel(IdeficsPreTrainedModel):
         # if list(image_attention_mask.shape) != [4, 1, 1024, 64]:
         #     raise ValueError(f"Image hidden_states {image_hidden_states.shape} - mask {image_attention_mask.shape} {num_images} {image_seq_len} {text_seq_len}")
 
-
         # if image_hidden_states is not None:
         # else:
         #     image_attention_mask = None
@@ -1070,10 +1231,15 @@ class IdeficsModel(IdeficsPreTrainedModel):
         # embed positions
         if attention_mask is None:
             attention_mask = torch.ones(
-                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
             )
         attention_mask = self._prepare_decoder_attention_mask(
-            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
         )
 
         hidden_states = inputs_embeds
@@ -1094,7 +1260,9 @@ class IdeficsModel(IdeficsPreTrainedModel):
             if output_hidden_states:
                 all_hidden_states += (hidden_states,)
 
-            past_key_value = past_key_values[idx] if past_key_values is not None else None
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
 
             def vblock(
                 main_block,
@@ -1194,7 +1362,11 @@ class IdeficsModel(IdeficsPreTrainedModel):
 
         next_cache = next_decoder_cache if use_cache else None
         if not return_dict:
-            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
         return BaseModelOutputWithPastImage(
             last_hidden_state=hidden_states,
             past_key_values=next_cache,
@@ -1264,11 +1436,19 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
         "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
         ```"""
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
         outputs = self.model(
@@ -1298,7 +1478,7 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
-            image_hidden_states=outputs.image_hidden_states
+            image_hidden_states=outputs.image_hidden_states,
         )
 
     def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
@@ -1316,12 +1496,20 @@ class IdeficsForVisionText2Text(IdeficsPreTrainedModel):
         return expand_inputs_for_generation(*args, **model_kwargs)
 
     @staticmethod
-    def _update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=False):
-        return update_model_kwargs_for_generation(outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder)
+    def _update_model_kwargs_for_generation(
+        outputs, model_kwargs, is_encoder_decoder=False
+    ):
+        return update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder
+        )
 
     @staticmethod
     def _reorder_cache(past, beam_idx):
         reordered_past = ()
         for layer_past in past:
-            reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx) for past_state in layer_past
+                ),
+            )
         return reordered_past

server/text_generation_server/models/custom_modeling/idefics_perceiver.py

@@ -48,6 +48,7 @@ from text_generation_server.utils.layers import (
 
 EPS = 1e-5
 
+
 class IdeficsPerceiverResampler(nn.Module):
     def __init__(
         self,
@@ -78,7 +79,12 @@ class IdeficsPerceiverResampler(nn.Module):
 
         """
         super().__init__()
-        self.embed_dim, self.n_heads, self.head_dim, self.n_latents = embed_dim, n_heads, head_dim, n_latents
+        self.embed_dim, self.n_heads, self.head_dim, self.n_latents = (
+            embed_dim,
+            n_heads,
+            head_dim,
+            n_latents,
+        )
         self.qk_layer_norms = config.perceiver_config.qk_layer_norms_perceiver
 
         # Create Latents for Perceiver
@@ -107,14 +113,16 @@ class IdeficsPerceiverResampler(nn.Module):
                             prefix=f"{prefix}.blocks.{layer_id}.1",
                             intermediate_size=self.intermediate_dim,
                             config=config,
-                            weights=weights
+                            weights=weights,
                         ),
                     ]
                 )
                 for layer_id in range(depth)
             ]
         )
-        self.layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.layer_norm", weights=weights, eps=EPS)
+        self.layer_norm = nn.LayerNorm.load(
+            prefix=f"{prefix}.layer_norm", weights=weights, eps=EPS
+        )
 
     def forward(self, context: torch.Tensor) -> torch.Tensor:
         """Resample arbitrary length context & *compress* down to self.n_latents latent embeddings"""
@@ -130,25 +138,34 @@ class IdeficsPerceiverResampler(nn.Module):
 
 
 class IdeficsPerceiverAttention(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         prefix,
         config,
         embed_dim: int,
         n_heads: int,
         head_dim: int,
         qk_layer_norms: bool,
-            weights
+        weights,
     ) -> None:
         """Perceiver Cross-Attention Module --> let long-form inputs be `context`, resampled embeddings be `latents`"""
         super().__init__()
         self.embed_dim, self.n_heads, self.head_dim = embed_dim, n_heads, head_dim
         self.qk_layer_norms = qk_layer_norms
         # Normalization & Scaling
-        self.context_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.context_layer_norm", weights=weights, eps=EPS)
-        self.latents_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.latents_layer_norm", weights=weights, eps=EPS)
+        self.context_layer_norm = nn.LayerNorm.load(
+            prefix=f"{prefix}.context_layer_norm", weights=weights, eps=EPS
+        )
+        self.latents_layer_norm = nn.LayerNorm.load(
+            prefix=f"{prefix}.latents_layer_norm", weights=weights, eps=EPS
+        )
         if self.qk_layer_norms:
-            self.q_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.q_layer_norm", weights=weights, eps=EPS)
-            self.k_layer_norm = nn.LayerNorm.load(prefix=f"{prefix}.k_layer_norm", weights=weights, eps=EPS)
+            self.q_layer_norm = nn.LayerNorm.load(
+                prefix=f"{prefix}.q_layer_norm", weights=weights, eps=EPS
+            )
+            self.k_layer_norm = nn.LayerNorm.load(
+                prefix=f"{prefix}.k_layer_norm", weights=weights, eps=EPS
+            )
 
         self.qk_scale = self.head_dim**-0.5
 
@@ -202,7 +219,12 @@ class IdeficsPerceiverAttention(nn.Module):
         # Multiheaded Self-Attention w/ stable softmax (subtract per-row max -- `amax` -- before softmax call)
         #   =>> `attn` should be a 2D matrix of shape [n_latents x (context + n_latents)]
         # einsum.rearrange(x, "bsz seq (heads embed) -> bsz heads seq embed", heads=self.n_heads)
-        q, k, v = [x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(1, 2) for x in (q, k, v)]
+        q, k, v = [
+            x.reshape(batch_size, x.shape[1], self.n_heads, self.head_dim).transpose(
+                1, 2
+            )
+            for x in (q, k, v)
+        ]
 
         if self.qk_layer_norms:
             q = self.q_layer_norm(q)
@@ -219,7 +241,8 @@ class IdeficsPerceiverAttention(nn.Module):
 
 
 class IdeficsMLP(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         prefix,
         intermediate_size,
         config,
@@ -230,14 +253,22 @@ class IdeficsMLP(nn.Module):
         self.embed_dim = config.vision_config.embed_dim
         self.ln = nn.LayerNorm.load(prefix=f"{prefix}.ln", weights=weights, eps=EPS)
         self.fc = TensorParallelColumnLinear.load(
-            config=config, prefix=f"{prefix}.fc", weights=weights, bias=False,
+            config=config,
+            prefix=f"{prefix}.fc",
+            weights=weights,
+            bias=False,
         )
         self.act = nn.ReLU()
         self.c_proj = TensorParallelRowLinear.load(
-            config=config, prefix=f"{prefix}.c_proj", weights=weights, bias=False,
+            config=config,
+            prefix=f"{prefix}.c_proj",
+            weights=weights,
+            bias=False,
         )
 
-    def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.FloatTensor:
+    def forward(
+        self, hidden_states: Optional[Tuple[torch.FloatTensor]]
+    ) -> torch.FloatTensor:
         hidden_states = self.ln(hidden_states)
         hidden_states = self.fc(hidden_states)
         hidden_states = self.act(hidden_states)

server/text_generation_server/models/custom_modeling/idefics_processing.py

@@ -21,9 +21,16 @@ from urllib.parse import urlparse
 
 from transformers.feature_extraction_utils import BatchFeature
 from transformers.processing_utils import ProcessorMixin
-from transformers.tokenization_utils_base import BatchEncoding, PaddingStrategy, TextInput, TruncationStrategy
+from transformers.tokenization_utils_base import (
+    BatchEncoding,
+    PaddingStrategy,
+    TextInput,
+    TruncationStrategy,
+)
 from transformers.utils import TensorType, is_torch_available
-from text_generation_server.models.custom_modeling.idefics_image_processing import IdeficsImageProcessor
+from text_generation_server.models.custom_modeling.idefics_image_processing import (
+    IdeficsImageProcessor,
+)
 
 
 if is_torch_available():
@@ -124,7 +131,14 @@ class IdeficsProcessor(ProcessorMixin):
     image_processor_class = "IdeficsImageProcessor"
     tokenizer_class = "LlamaTokenizerFast"
 
-    def __init__(self, image_processor, tokenizer=None, image_size=224, add_end_of_utterance_token=None, **kwargs):
+    def __init__(
+        self,
+        image_processor,
+        tokenizer=None,
+        image_size=224,
+        add_end_of_utterance_token=None,
+        **kwargs,
+    ):
         if image_processor is None:
             raise ValueError("You need to specify an `image_processor`.")
         if tokenizer is None:
@@ -142,7 +156,8 @@ class IdeficsProcessor(ProcessorMixin):
 
         self.tokenizer_was_trained_with_end_of_utterance_token = (
             True
-            if "<end_of_utterance>" in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
+            if "<end_of_utterance>"
+            in self.tokenizer.special_tokens_map.get("additional_special_tokens", [])
             else False
         )
 
@@ -265,7 +280,9 @@ class IdeficsProcessor(ProcessorMixin):
 
         # if the value isn't overriden by the user, check if the tokenizer was trained with this token and then use it
         if add_end_of_utterance_token is None:
-            add_end_of_utterance_token = self.tokenizer_was_trained_with_end_of_utterance_token
+            add_end_of_utterance_token = (
+                self.tokenizer_was_trained_with_end_of_utterance_token
+            )
 
         # turn non-batched prompts into batched
         if not any(isinstance(i, list) for i in prompts):
@@ -358,10 +375,14 @@ class IdeficsProcessor(ProcessorMixin):
             current_images = images[:local_max_num_images]
 
             if len(current_images) > 0:
-                padded_image_tensor = torch.zeros(max_num_images, *current_images.size()[1:])
+                padded_image_tensor = torch.zeros(
+                    max_num_images, *current_images.size()[1:]
+                )
                 padded_image_tensor[: current_images.size(0)] = current_images
             else:
-                padded_image_tensor = torch.zeros(max_num_images, *self.default_image_dims)
+                padded_image_tensor = torch.zeros(
+                    max_num_images, *self.default_image_dims
+                )
 
             output_images.append(padded_image_tensor)
             output_input_ids.append(torch.tensor(padded_input_ids))
@@ -373,14 +394,19 @@ class IdeficsProcessor(ProcessorMixin):
         output_attention_masks = torch.stack(output_attention_masks)
 
         if at_least_one_image:
-            image_attention_mask, _ = image_attention_mask_for_packed_input_ids(output_input_ids, self.tokenizer)
+            image_attention_mask, _ = image_attention_mask_for_packed_input_ids(
+                output_input_ids, self.tokenizer
+            )
             image_attention_mask = incremental_to_binary_attention_mask(
                 image_attention_mask, num_classes=max_num_images
             )
         else:
             # in full language mode we set the image mask to all-0s
             image_attention_mask = torch.zeros(
-                output_input_ids.shape[0], output_input_ids.shape[1], 1, dtype=torch.bool
+                output_input_ids.shape[0],
+                output_input_ids.shape[1],
+                1,
+                dtype=torch.bool,
             )
 
         return BatchFeature(

server/text_generation_server/models/custom_modeling/idefics_vision.py

@@ -75,7 +75,9 @@ class IdeficsVisionEmbeddings(nn.Module):
         self.image_size = config.image_size
         self.patch_size = config.patch_size
 
-        self.class_embedding = nn.Parameter(weights.get_tensor(f"{prefix}.class_embedding"))
+        self.class_embedding = nn.Parameter(
+            weights.get_tensor(f"{prefix}.class_embedding")
+        )
 
         self.patch_embedding = nn.Conv2d.load_no_bias(
             prefix=f"{prefix}.patch_embedding",
@@ -91,12 +93,16 @@ class IdeficsVisionEmbeddings(nn.Module):
         self.position_embedding = TensorParallelEmbedding(
             prefix="model.vision_model.embeddings.position_embedding", weights=weights
         )
-        self.position_ids = torch.arange(self.num_positions).expand((1, -1)).to(device=weights.device)
+        self.position_ids = (
+            torch.arange(self.num_positions).expand((1, -1)).to(device=weights.device)
+        )
 
     def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
         batch_size = pixel_values.shape[0]
         target_dtype = self.patch_embedding.weight.dtype
-        patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype))  # shape = [*, width, grid, grid]
+        patch_embeds = self.patch_embedding(
+            pixel_values.to(dtype=target_dtype)
+        )  # shape = [*, width, grid, grid]
         patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
 
         class_embeds = self.class_embedding.expand(batch_size, 1, -1)
@@ -132,7 +138,6 @@ class IdeficsVisionAttention(nn.Module):
         self.num_heads = self.num_heads // weights.process_group.size()
         self.embed_dim = self.embed_dim // weights.process_group.size()
 
-
         self.k_proj = TensorParallelColumnLinear.load(
             config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
         )
@@ -147,7 +152,11 @@ class IdeficsVisionAttention(nn.Module):
         )
 
     def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
 
     def forward(
         self,
@@ -186,7 +195,10 @@ class IdeficsVisionAttention(nn.Module):
                     f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
                     f" {causal_attention_mask.size()}"
                 )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + causal_attention_mask
+            attn_weights = (
+                attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+                + causal_attention_mask
+            )
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         if attention_mask is not None:
@@ -194,7 +206,10 @@ class IdeficsVisionAttention(nn.Module):
                 raise ValueError(
                     f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                 )
-            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            attn_weights = (
+                attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+                + attention_mask
+            )
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
         attn_weights = nn.functional.softmax(attn_weights, dim=-1)
@@ -204,12 +219,18 @@ class IdeficsVisionAttention(nn.Module):
             # make sure that attn_weights keeps its gradient.
             # In order to do so, attn_weights have to reshaped
             # twice and have to be reused in the following
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
+            attn_weights_reshaped = attn_weights.view(
+                bsz, self.num_heads, tgt_len, src_len
+            )
+            attn_weights = attn_weights_reshaped.view(
+                bsz * self.num_heads, tgt_len, src_len
+            )
         else:
             attn_weights_reshaped = None
 
-        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+        attn_probs = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )
 
         attn_output = torch.bmm(attn_probs, value_states)
 
@@ -253,11 +274,15 @@ class IdeficsVisionEncoderLayer(nn.Module):
     def __init__(self, prefix, config, weights):
         super().__init__()
         self.embed_dim = config.hidden_size
-        self.self_attn = IdeficsVisionAttention(prefix=f"{prefix}.self_attn", config=config, weights=weights)
+        self.self_attn = IdeficsVisionAttention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
         self.layer_norm1 = nn.LayerNorm.load(
             prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
         )
-        self.mlp = IdeficsVisionMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.mlp = IdeficsVisionMLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights
+        )
         self.layer_norm2 = nn.LayerNorm.load(
             prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
         )
@@ -318,7 +343,11 @@ class IdeficsVisionEncoder(nn.Module):
         self.config = config
         self.layers = nn.ModuleList(
             [
-                IdeficsVisionEncoderLayer(prefix=f"{prefix}.encoder.layers.{layer_id}", config=config, weights=weights)
+                IdeficsVisionEncoderLayer(
+                    prefix=f"{prefix}.encoder.layers.{layer_id}",
+                    config=config,
+                    weights=weights,
+                )
                 for layer_id in range(config.num_hidden_layers)
             ]
         )
@@ -362,11 +391,19 @@ class IdeficsVisionEncoder(nn.Module):
             return_dict (`bool`, *optional*):
                 Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         encoder_states = () if output_hidden_states else None
         all_attentions = () if output_attentions else None
@@ -406,9 +443,15 @@ class IdeficsVisionEncoder(nn.Module):
             encoder_states = encoder_states + (hidden_states,)
 
         if not return_dict:
-            return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
+            return tuple(
+                v
+                for v in [hidden_states, encoder_states, all_attentions]
+                if v is not None
+            )
         return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
+            last_hidden_state=hidden_states,
+            hidden_states=encoder_states,
+            attentions=all_attentions,
         )
 
 
@@ -419,13 +462,19 @@ class IdeficsVisionTransformer(nn.Module):
         self.config = config
         embed_dim = config.hidden_size
 
-        self.embeddings = IdeficsVisionEmbeddings(prefix=f"{prefix}.embeddings", config=config, weights=weights)
+        self.embeddings = IdeficsVisionEmbeddings(
+            prefix=f"{prefix}.embeddings", config=config, weights=weights
+        )
         self.pre_layrnorm = nn.LayerNorm.load(
             prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps
         )
-        self.encoder = IdeficsVisionEncoder(prefix=prefix, config=config, weights=weights)
+        self.encoder = IdeficsVisionEncoder(
+            prefix=prefix, config=config, weights=weights
+        )
         self.post_layernorm = nn.LayerNorm.load(
-            prefix=f"{prefix}.post_layernorm", weights=weights, eps=config.layer_norm_eps
+            prefix=f"{prefix}.post_layernorm",
+            weights=weights,
+            eps=config.layer_norm_eps,
         )
 
     # copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
@@ -440,11 +489,19 @@ class IdeficsVisionTransformer(nn.Module):
         Returns:
 
         """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")

server/text_generation_server/models/custom_modeling/neox_modeling.py

@@ -49,7 +49,10 @@ from text_generation_server.utils.layers import (
 
 
 CUSTOM_KERNELS_ENABLED = False
-if torch.cuda.is_available() and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True":
+if (
+    torch.cuda.is_available()
+    and not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True"
+):
     try:
         from custom_kernels import fused_attention_cuda
 

server/text_generation_server/models/flash_causal_lm.py

@@ -1005,9 +1005,12 @@ class FlashCausalLM(Model):
                     # Decode generated tokens
                     output_text, _, _ = self.decode_token(
                         all_input_ids,
-                        prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
-                        read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
-                        skip_special_tokens=True
+                        prefix_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens
+                        - 1,
+                        read_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens,
+                        skip_special_tokens=True,
                     )
                     generated_text = GeneratedText(
                         output_text,

server/text_generation_server/models/idefics_causal_lm.py

@@ -8,7 +8,13 @@ import re
 
 from dataclasses import dataclass
 from opentelemetry import trace
-from transformers import AutoProcessor, AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase, ProcessorMixin
+from transformers import (
+    AutoProcessor,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    PreTrainedTokenizerBase,
+    ProcessorMixin,
+)
 from typing import Optional, Tuple, List, Type, Dict
 
 from text_generation_server.models import Model
@@ -23,7 +29,8 @@ from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sam
 
 import re
 
-IMAGES = re.compile(r'!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)')
+IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
+
 
 def split(string):
     parts = []
@@ -41,6 +48,7 @@ def split(string):
 
     return parts
 
+
 tracer = trace.get_tracer(__name__)
 
 
@@ -141,8 +149,12 @@ class IdeficsCausalLMBatch(Batch):
         ).to(device)
         for _ in pb.requests:
             input_len = tokenized_inputs["input_ids"].shape[1]
-            prefix_offsets.append(input_len - 5) # To decode without potential fallbacks errors
-            read_offsets.append(input_len) # To decode without potential fallbacks errors
+            prefix_offsets.append(
+                input_len - 5
+            )  # To decode without potential fallbacks errors
+            read_offsets.append(
+                input_len
+            )  # To decode without potential fallbacks errors
 
         input_lengths = tokenized_inputs["attention_mask"].sum(1)
         max_input_length = input_lengths.max()
@@ -158,14 +170,21 @@ class IdeficsCausalLMBatch(Batch):
         attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
         # Do the same for image_attention_mask
         image_attention_mask = input_ids.new_zeros(
-            (pb.size, max_input_length + padding_right_offset, tokenized_inputs["pixel_values"].size(1))
+            (
+                pb.size,
+                max_input_length + padding_right_offset,
+                tokenized_inputs["pixel_values"].size(1),
             )
-        image_attention_mask[:, :max_input_length, :] = tokenized_inputs["image_attention_mask"]
-
+        )
+        image_attention_mask[:, :max_input_length, :] = tokenized_inputs[
+            "image_attention_mask"
+        ]
 
         position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
         position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
-        all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
+        all_input_ids = tokenized_inputs["input_ids"].T.split(
+            1, dim=1
+        )  # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
 
         max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
 
@@ -259,7 +278,7 @@ class IdeficsCausalLMBatch(Batch):
                 self.image_attention_mask.shape[1] - self.padding_right_offset
             )
             + new_padding_right_offset,
-            :
+            :,
         ]
         if self.image_hidden_states is None:
             image_hidden_states = None
@@ -308,7 +327,9 @@ class IdeficsCausalLMBatch(Batch):
 
     @classmethod
     @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["IdeficsCausalLMBatch"]) -> "IdeficsCausalLMBatch":
+    def concatenate(
+        cls, batches: List["IdeficsCausalLMBatch"]
+    ) -> "IdeficsCausalLMBatch":
         # It adds new requests to the batch
         # Used for padding
         total_batch_size = 0
@@ -383,12 +404,20 @@ class IdeficsCausalLMBatch(Batch):
 
             curr_batch_max_num_images = batch.pixel_values.size(1)
             if pixel_values is None:
-                pixel_values = batch.pixel_values.new_zeros((total_batch_size, max_num_images, 3, 224, 224))
-            pixel_values[start_index:end_index, :curr_batch_max_num_images] = batch.pixel_values
+                pixel_values = batch.pixel_values.new_zeros(
+                    (total_batch_size, max_num_images, 3, 224, 224)
+                )
+            pixel_values[
+                start_index:end_index, :curr_batch_max_num_images
+            ] = batch.pixel_values
 
             if image_attention_mask is None:
                 image_attention_mask = batch.image_attention_mask.new_zeros(
-                    (total_batch_size, max_input_length + padding_right_offset, max_num_images)
+                    (
+                        total_batch_size,
+                        max_input_length + padding_right_offset,
+                        max_num_images,
+                    )
                 )
 
             # We need to slice the attention mask to remove padding from previous steps
@@ -409,11 +438,9 @@ class IdeficsCausalLMBatch(Batch):
             image_attention_mask[
                 start_index:end_index,
                 left_offset:-padding_right_offset,
-                :curr_batch_max_num_images
+                :curr_batch_max_num_images,
             ] = batch.image_attention_mask[
-                :,
-                batch_left_offset : - batch.padding_right_offset,
-                :
+                :, batch_left_offset : -batch.padding_right_offset, :
             ]
 
             # Create empty tensor
@@ -550,7 +577,9 @@ class IdeficsCausalLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        from text_generation_server.models.custom_modeling.idefics_modeling import IdeficsForVisionText2Text
+        from text_generation_server.models.custom_modeling.idefics_modeling import (
+            IdeficsForVisionText2Text,
+        )
 
         if torch.cuda.is_available():
             device = torch.device("cuda")
@@ -650,9 +679,13 @@ class IdeficsCausalLM(Model):
             # this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
             # token need to attend to the encoder hidden states (i.e. the vision encoder)
             # Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
-            image_attention_mask = batch.image_attention_mask[:, -(batch.padding_right_offset+1)].unsqueeze(1)
+            image_attention_mask = batch.image_attention_mask[
+                :, -(batch.padding_right_offset + 1)
+            ].unsqueeze(1)
         else:
-            image_attention_mask = batch.image_attention_mask[:, : -batch.padding_right_offset]
+            image_attention_mask = batch.image_attention_mask[
+                :, : -batch.padding_right_offset
+            ]
 
         logits, past, image_hidden_states = self.forward(
             input_ids=batch.input_ids,
@@ -725,9 +758,12 @@ class IdeficsCausalLM(Model):
                     # Decode generated tokens
                     output_text, _, _ = self.decode_token(
                         all_input_ids[:, 0],
-                        prefix_offset=len(all_input_ids) - stopping_criteria.current_tokens - 1,
-                        read_offset=len(all_input_ids) - stopping_criteria.current_tokens,
-                        skip_special_tokens=True
+                        prefix_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens
+                        - 1,
+                        read_offset=len(all_input_ids)
+                        - stopping_criteria.current_tokens,
+                        skip_special_tokens=True,
                     )
                     # Get seed
                     if isinstance(next_token_chooser.choice, Sampling):
@@ -771,7 +807,7 @@ class IdeficsCausalLM(Model):
                     next_token_text,
                     next_token_id_squeezed.item() in self.all_special_ids,
                     generated_text,
-                    top_tokens
+                    top_tokens,
                 )
 
                 generations.append(generation)
@@ -793,7 +829,9 @@ class IdeficsCausalLM(Model):
 
         # Update attention_mask as we added a new token to input_ids
         batch.attention_mask[:, -batch.padding_right_offset] = 1
-        batch.image_attention_mask[:, -batch.padding_right_offset, :] = batch.image_attention_mask[:, -(batch.padding_right_offset+1), :]
+        batch.image_attention_mask[
+            :, -batch.padding_right_offset, :
+        ] = batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
         # Decrease right offset
         batch.padding_right_offset -= 1
 

server/text_generation_server/models/model.py

@@ -71,7 +71,8 @@ class Model(ABC):
         # The prefix text is necessary only to defeat cleanup algorithms in the decode
         # which decide to add a space or not depending on the surrounding ids.
         prefix_text = self.tokenizer.decode(
-            all_input_ids[prefix_offset:read_offset], skip_special_tokens=skip_special_tokens
+            all_input_ids[prefix_offset:read_offset],
+            skip_special_tokens=skip_special_tokens,
         )
         new_text = self.tokenizer.decode(
             all_input_ids[prefix_offset:], skip_special_tokens=skip_special_tokens

server/text_generation_server/models/seq2seq_lm.py

@@ -712,9 +712,11 @@ class Seq2SeqLM(Model):
                     # Decode all tokens
                     output_text, _, _ = self.decode_token(
                         all_decoder_input_ids,
-                        prefix_offset=len(all_decoder_input_ids) - decoder_input_length - 1,
+                        prefix_offset=len(all_decoder_input_ids)
+                        - decoder_input_length
+                        - 1,
                         read_offset=len(all_decoder_input_ids) - decoder_input_length,
-                        skip_special_tokens=True
+                        skip_special_tokens=True,
                     )
 
                     # Get seed

server/text_generation_server/server.py

@@ -16,6 +16,7 @@ from text_generation_server.pb import generate_pb2_grpc, generate_pb2
 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
 from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
 
+
 class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
     def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
         self.cache = cache
@@ -26,7 +27,6 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
             # Force inference mode for the lifetime of TextGenerationService
             self._inference_mode_raii_guard = torch._C._InferenceMode(True)
 
-
     async def Info(self, request, context):
         return self.model.info
 
@@ -55,9 +55,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
 
     async def Warmup(self, request, context):
-        if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
+        if (
+            self.model.batch_type == IdeficsCausalLMBatch
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
-                request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.dtype,
+                self.model.device,
             )
         else:
             batch = self.model.batch_type.from_pb(
@@ -70,9 +76,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         )
 
     async def Prefill(self, request, context):
-        if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
+        if (
+            self.model.batch_type == IdeficsCausalLMBatch
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
-                request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.dtype,
+                self.model.device,
             )
         else:
             batch = self.model.batch_type.from_pb(

server/text_generation_server/utils/awq/quantize/qmodule.py

@@ -43,6 +43,8 @@ class WQLinear(nn.Module):
     @torch.no_grad()
     def forward(self, x):
         out_shape = x.shape[:-1] + (self.out_features,)
-        out = awq_inference_engine.gemm_forward_cuda(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8)
+        out = awq_inference_engine.gemm_forward_cuda(
+            x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8
+        )
         out = out + self.bias if self.bias is not None else out
         return out.reshape(out_shape)

server/text_generation_server/utils/gptq/quantize.py

@@ -578,7 +578,9 @@ def get_c4_new(nsamples, seed, seqlen, model_id, trust_remote_code):
     return trainloader, valenc
 
 
-def get_loaders(name, nsamples=128, seed=0, seqlen=2048, model_id="", trust_remote_code=False):
+def get_loaders(
+    name, nsamples=128, seed=0, seqlen=2048, model_id="", trust_remote_code=False
+):
     if "wikitext2" in name:
         return get_wikitext2(nsamples, seed, seqlen, model_id, trust_remote_code)
     if "ptb" in name:
@@ -927,7 +929,7 @@ def quantize(
         seed=seed,
         model_id=model_id,
         seqlen=model.seqlen,
-        trust_remote_code=trust_remote_code
+        trust_remote_code=trust_remote_code,
     )
 
     tick = time.time()

server/text_generation_server/utils/layers.py

@@ -38,6 +38,7 @@ if os.getenv("DISABLE_EXLLAMA") == "True":
 elif CAN_EXLLAMA:
     try:
         from text_generation_server.utils.gptq.exllama import Ex4bitLinear
+
         HAS_EXLLAMA = True
     except ImportError:
         pass
@@ -47,6 +48,7 @@ from typing import Optional
 HAS_EETQ = False
 try:
     from EETQ import quant_weights, w8_a16_gemm
+
     HAS_EETQ = True
 except ImportError:
     pass
@@ -74,12 +76,18 @@ def load_layer_norm_no_bias(cls, prefix, weights, eps):
     ln.bias = None
     return ln
 
+
 @classmethod
 def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
     weight = weights.get_tensor(f"{prefix}.weight")
     bias = weights.get_tensor(f"{prefix}.bias")
     with init_empty_weights():
-        conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
+        conv2d = cls(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+        )
 
     conv2d.weight = nn.Parameter(weight)
     conv2d.bias = nn.Parameter(bias)
@@ -87,10 +95,17 @@ def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, st
 
 
 @classmethod
-def load_conv2d_no_bias(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
+def load_conv2d_no_bias(
+    cls, prefix, weights, in_channels, out_channels, kernel_size, stride
+):
     weight = weights.get_tensor(f"{prefix}.weight")
     with init_empty_weights():
-        conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
+        conv2d = cls(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+        )
 
     conv2d.weight = nn.Parameter(weight)
     conv2d.bias = None
@@ -215,7 +230,10 @@ class Linear4bit(nn.Module):
     def __init__(self, weight, bias, quant_type):
         super().__init__()
         self.weight = Params4bit(
-            weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
+            weight.data,
+            requires_grad=False,
+            compress_statistics=True,
+            quant_type=quant_type,
         )
         self.compute_dtype = None
         self.weight.cuda(weight.device)
@@ -246,7 +264,10 @@ class Linear4bit(nn.Module):
 
 @lru_cache(1)
 def warn_deprecate_bnb():
-    logger.warning("Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce")
+    logger.warning(
+        "Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce"
+    )
+
 
 def get_linear(weight, bias, quantize):
     if quantize is None:
@@ -255,7 +276,9 @@ def get_linear(weight, bias, quantize):
         if HAS_EETQ:
             linear = EETQLinear(weight, bias)
         else:
-            raise ImportError("Please install EETQ from https://github.com/NetEase-FuXi/EETQ")
+            raise ImportError(
+                "Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
+            )
     elif quantize == "bitsandbytes":
         warn_deprecate_bnb()
         linear = Linear8bitLt(
@@ -305,7 +328,14 @@ def get_linear(weight, bias, quantize):
             raise NotImplementedError(
                 f"The passed weight is not `awq` compatible, loader needs to be updated."
             )
-        linear = WQLinear(w_bit=bits, group_size=groupsize, qweight=qweight, qzeros=qzeros, scales=scales, bias=bias is not None)
+        linear = WQLinear(
+            w_bit=bits,
+            group_size=groupsize,
+            qweight=qweight,
+            qzeros=qzeros,
+            scales=scales,
+            bias=bias is not None,
+        )
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear
@@ -392,9 +422,7 @@ class TensorParallelColumnLinear(SuperLayer):
     @classmethod
     def load_qkv(cls, config, prefix: str, weights, bias: bool):
         """Specific method when the QKV was joined after the fact"""
-        weight = weights.get_weights_col_packed_qkv(
-            prefix, quantize=config.quantize
-        )
+        weight = weights.get_weights_col_packed_qkv(prefix, quantize=config.quantize)
         if bias:
             raise NotImplementedError("packed_qkv only implemented for baichuan")
         else:
@@ -530,14 +558,16 @@ try:
 
     def _create_inv_freq(dim, base, device):
         inv_freq = 1.0 / (
-            base
-            ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
+            base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
         )
         return inv_freq
 
     def _get_rope_config(config):
         if os.getenv("ROPE_SCALING", None) is not None:
-            rope_scaling = {"type": os.environ["ROPE_SCALING"], "factor": float(os.environ["ROPE_FACTOR"])}
+            rope_scaling = {
+                "type": os.environ["ROPE_SCALING"],
+                "factor": float(os.environ["ROPE_FACTOR"]),
+            }
             return rope_scaling
         return getattr(config, "rope_scaling", None)
 
@@ -563,9 +593,17 @@ try:
                 if rope_scaling["type"] == "linear":
                     pass
                 elif rope_scaling["type"] == "dynamic":
-                    return DynamicPositionRotaryEmbedding(dim=dim, max_position_embeddings=config.max_position_embeddings, base=base, device=inv_freq.device, scaling_factor=scaling_factor)
+                    return DynamicPositionRotaryEmbedding(
+                        dim=dim,
+                        max_position_embeddings=config.max_position_embeddings,
+                        base=base,
+                        device=inv_freq.device,
+                        scaling_factor=scaling_factor,
+                    )
                 else:
-                    raise NotImplementedError(f"rope scaling type {rope_scaling['type']} is not implemented or invalid")
+                    raise NotImplementedError(
+                        f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
+                    )
             return cls(inv_freq, scaling_factor)
 
         @classmethod
@@ -583,9 +621,17 @@ try:
                 if rope_scaling["type"] == "linear":
                     pass
                 elif rope_scaling["type"] == "dynamic":
-                    return DynamicPositionRotaryEmbedding(dim=2*inv_freq.shape[0], max_position_embeddings=config.max_position_embeddings, base=10000.0, device=inv_freq.device, scaling_factor=scaling_factor)
+                    return DynamicPositionRotaryEmbedding(
+                        dim=2 * inv_freq.shape[0],
+                        max_position_embeddings=config.max_position_embeddings,
+                        base=10000.0,
+                        device=inv_freq.device,
+                        scaling_factor=scaling_factor,
+                    )
                 else:
-                    raise NotImplementedError(f"rope scaling type {rope_scaling['type']} is not implemented or invalid")
+                    raise NotImplementedError(
+                        f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
+                    )
             return cls(inv_freq, scaling_factor)
 
         def _update_cos_sin_cache(self, dtype, device, seqlen):
@@ -645,8 +691,13 @@ try:
                 or self._cos_cached.dtype != dtype
             ):
                 if seqlen > self.max_position_embeddings:
-                    newbase = self.base * ((self.scaling_factor * seqlen / self.max_position_embeddings) - (self.scaling_factor - 1)) ** (self.dim / (self.dim - 2))
-                    self.inv_freq = _create_inv_freq(self.dim, newbase, self.inv_freq.device)
+                    newbase = self.base * (
+                        (self.scaling_factor * seqlen / self.max_position_embeddings)
+                        - (self.scaling_factor - 1)
+                    ) ** (self.dim / (self.dim - 2))
+                    self.inv_freq = _create_inv_freq(
+                        self.dim, newbase, self.inv_freq.device
+                    )
                 self._seq_len_cached = seqlen
                 t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
                 # Don't do einsum, it converts fp32 to fp16
@@ -656,6 +707,5 @@ try:
                 self._cos_cached = torch.cos(freqs).to(dtype)
                 self._sin_cached = torch.sin(freqs).to(dtype)
 
-
 except ImportError:
     pass

server/text_generation_server/utils/peft.py

@@ -6,6 +6,7 @@ import torch
 from transformers import AutoTokenizer
 from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
 
+
 def download_and_unload_peft(model_id, revision, trust_remote_code):
     torch_dtype = torch.float16
 
@@ -41,6 +42,3 @@ def download_and_unload_peft(model_id, revision, trust_remote_code):
     model.save_pretrained(cache_dir, safe_serialization=True)
     model.config.save_pretrained(cache_dir)
     tokenizer.save_pretrained(cache_dir)
-
-
-

server/text_generation_server/utils/weights.py

@@ -110,7 +110,6 @@ class Weights:
         ), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
         return self.get_partial_sharded(tensor_name, dim)
 
-
     def _get_qweight(self, name: str):
         slice_ = self._get_slice(name)
         total_size = slice_.get_shape()[1]
@@ -119,7 +118,9 @@ class Weights:
         world_size = self.process_group.size()
         rank = self.process_group.rank()
 
-        assert single_size % world_size == 0, f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
+        assert (
+            single_size % world_size == 0
+        ), f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
         block_size = single_size // world_size
         start = rank * block_size
         stop = (rank + 1) * block_size
@@ -161,7 +162,9 @@ class Weights:
             world_size = self.process_group.size()
             rank = self.process_group.rank()
 
-            assert single_size % world_size == 0, f"Prepacked qkv cannot be sharded across {world_size} shards"
+            assert (
+                single_size % world_size == 0
+            ), f"Prepacked qkv cannot be sharded across {world_size} shards"
             block_size = single_size // world_size
             start = rank * block_size
             stop = (rank + 1) * block_size