Update by abstracting away text model.

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -281,9 +281,8 @@ class LlamaMLP(nn.Module):
 
 
 class FlashLlamaLayer(nn.Module):
-    def __init__(self, layer_id, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
-        prefix = f"model.layers.{layer_id}"
         self.self_attn = FlashLlamaAttention(
             prefix=f"{prefix}.self_attn", config=config, weights=weights
         )
@@ -337,27 +336,36 @@ class FlashLlamaLayer(nn.Module):
 
 
 class FlashLlamaModel(torch.nn.Module):
-    def __init__(self, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
 
         process_group = weights.process_group
         self.tp_rank = process_group.rank()
         self.tp_world_size = process_group.size()
         self.embed_tokens = TensorParallelEmbedding(
-            prefix="model.embed_tokens", weights=weights
+            prefix=(
+                "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
+            ),
+            weights=weights,
         )
         self.layers = nn.ModuleList(
             [
                 FlashLlamaLayer(
-                    layer_id,
+                    prefix=(
-                    config,
+                        f"model.layers.{layer_id}"
-                    weights,
+                        if not prefix
+                        else f"{prefix}.model.layers.{layer_id}"
+                    ),
+                    config=config,
+                    weights=weights,
                 )
                 for layer_id in range(config.num_hidden_layers)
             ]
         )
         self.norm = FastRMSNorm.load(
-            prefix="model.norm", weights=weights, eps=config.rms_norm_eps
+            prefix="model.norm" if not prefix else f"{prefix}.model.norm",
+            weights=weights,
+            eps=config.rms_norm_eps,
         )
 
         self.gradient_checkpointing = False
@@ -406,13 +414,13 @@ class FlashLlamaModel(torch.nn.Module):
 
 
 class FlashLlamaForCausalLM(torch.nn.Module):
-    def __init__(self, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
 
-        self.model = FlashLlamaModel(config, weights)
+        self.model = FlashLlamaModel(prefix, config, weights)
         self.lm_head = SpeculativeHead.load(
             config,
-            prefix="lm_head",
+            prefix="lm_head" if not prefix else f"{prefix}.lm_head",
             weights=weights,
         )
 
@@ -426,6 +434,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
         slots: torch.Tensor,
         input_lengths: torch.Tensor,
         max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor] = None,
         lm_head_indices: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
         hidden_states = self.model(

server/text_generation_server/models/custom_modeling/llava_next.py

@@ -113,7 +113,13 @@ def load_vision_model(prefix, config, weights):
 
 
 def load_text_model(prefix, config, weights):
-    if config.model_type == "mistral":
+    if config.model_type == "llama":
+        from text_generation_server.models.custom_modeling.flash_llama_modeling import (
+            FlashLlamaForCausalLM,
+        )
+
+        return FlashLlamaForCausalLM(prefix, config, weights)
+    elif config.model_type == "mistral":
         from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
             FlashMistralForCausalLM,
         )
@@ -124,22 +130,25 @@ def load_text_model(prefix, config, weights):
 
 
 class LlavaNextForConditionalGeneration(nn.Module):
-    def __init__(self, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
         config.vision_config.quantize = config.quantize
-        self.vision_tower = load_vision_model(
+        # self.vision_tower = load_vision_model(
-            prefix="vision_tower", config=config.vision_config, weights=weights
+        #     prefix="vision_tower" if not prefix else f"{prefix}.vision_tower", config=config.vision_config, weights=weights
-        )
+        # )
 
-        self.multi_modal_projector = LlavaNextMultiModalProjector(config)
+        # self.multi_modal_projector = LlavaNextMultiModalProjector(config)
 
         self.image_newline = weights.get_tensor("image_newline")
 
         self.vocab_size = config.text_config.vocab_size
+        self.config = config
         config.text_config.quantize = config.quantize
         config.text_config.use_medusa = config.use_medusa
         self.language_model = load_text_model(
-            prefix="language_model", config=config.text_config, weights=weights
+            prefix="language_model" if not prefix else f"{prefix}.language_model",
+            config=config.text_config,
+            weights=weights,
         )
         self.pad_token_id = (
             config.pad_token_id if config.pad_token_id is not None else -1
@@ -257,168 +266,141 @@ class LlavaNextForConditionalGeneration(nn.Module):
 
     def forward(
         self,
-        input_ids: torch.LongTensor = None,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
+        lm_head_indices: Optional[torch.Tensor] = None,
         pixel_values: torch.FloatTensor = None,
         image_sizes: Optional[torch.LongTensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
         past_key_values: Optional[List[torch.FloatTensor]] = None,
         vision_feature_layer: Optional[int] = None,
         vision_feature_select_strategy: Optional[str] = None,
     ):
 
-        vision_feature_layer = (
+        # vision_feature_layer = (
-            vision_feature_layer
+        #     vision_feature_layer
-            if vision_feature_layer is not None
+        #     if vision_feature_layer is not None
-            else self.config.vision_feature_layer
+        #     else self.config.vision_feature_layer
+        # )
+        # vision_feature_select_strategy = (
+        #     vision_feature_select_strategy
+        #     if vision_feature_select_strategy is not None
+        #     else self.config.vision_feature_select_strategy
+        # )
+
+        # if cu_seqlen_prefill is not None:
+        #     pass
+        #     # # 1. Extract the input embeddings
+        #     # inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        #     # # 2. Merge text and images
+        #     # if pixel_values is not None and input_ids.shape[1] != 1:
+        #     #     batch_size, num_patches, num_channels, height, width = (
+        #     #         pixel_values.shape
+        #     #     )
+        #     #     reshaped_pixel_values = pixel_values.view(
+        #     #         batch_size * num_patches, num_channels, height, width
+        #     #     )
+        #     #     image_features = self.vision_tower(
+        #     #         reshaped_pixel_values, output_hidden_states=True
+        #     #     )
+
+        #     #     selected_image_feature = image_features.hidden_states[
+        #     #         vision_feature_layer
+        #     #     ]
+
+        #     #     if vision_feature_select_strategy == "default":
+        #     #         selected_image_feature = selected_image_feature[:, 1:]
+        #     #     elif vision_feature_select_strategy == "full":
+        #     #         selected_image_feature = selected_image_feature
+
+        #     #     image_features = self.multi_modal_projector(selected_image_feature)
+
+        #     #     # split up image_features for each of the individual images
+        #     #     # hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
+        #     #     # if we assume each image has 5 image features (base image + 4 patches)
+        #     #     split_sizes = [image.shape[0] for image in pixel_values]
+        #     #     image_features = torch.split(image_features, split_sizes, dim=0)
+
+        #     #     # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
+        #     #     height = width = (
+        #     #         self.config.vision_config.image_size
+        #     #         // self.config.vision_config.patch_size
+        #     #     )
+
+        #     #     new_image_features = []
+        #     #     for image_idx, image_feature in enumerate(image_features):
+        #     #         if image_feature.shape[0] > 1:
+        #     #             base_image_feature = image_feature[0]
+        #     #             image_feature = image_feature[1:]
+
+        #     #             if height * width != base_image_feature.shape[0]:
+        #     #                 raise ValueError(
+        #     #                     "The number of patches is not consistent with the image size."
+        #     #                 )
+        #     #             num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+        #     #                 image_sizes[image_idx],
+        #     #                 self.config.image_grid_pinpoints,
+        #     #                 self.config.vision_config.image_size,
+        #     #             )
+        #     #             image_feature = image_feature.view(
+        #     #                 num_patch_height, num_patch_width, height, width, -1
+        #     #             )
+        #     #             image_feature = image_feature.permute(
+        #     #                 4, 0, 2, 1, 3
+        #     #             ).contiguous()
+        #     #             image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+        #     #             image_feature = unpad_image(
+        #     #                 image_feature, image_sizes[image_idx]
+        #     #             )
+        #     #             image_feature = torch.cat(
+        #     #                 (
+        #     #                     image_feature,
+        #     #                     self.image_newline[:, None, None].expand(
+        #     #                         *image_feature.shape[:-1], 1
+        #     #                     ),
+        #     #                 ),
+        #     #                 dim=-1,
+        #     #             )
+        #     #             image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+        #     #             image_feature = torch.cat(
+        #     #                 (base_image_feature, image_feature), dim=0
+        #     #             )
+        #     #         else:
+        #     #             image_feature = image_feature[0]
+        #     #             image_feature = torch.cat(
+        #     #                 (image_feature, self.image_newline[None]), dim=0
+        #     #             )
+        #     #         new_image_features.append(image_feature)
+        #     #     image_features = torch.stack(new_image_features, dim=0)
+
+        #     #     inputs_embeds, attention_mask, labels, position_ids = (
+        #     #         self._merge_input_ids_with_image_features(
+        #     #             image_features, inputs_embeds, input_ids, attention_mask, labels
+        #     #         )
+        #     #     )
+        #     #     if labels is None:
+        #     #         labels = torch.full_like(
+        #     #             attention_mask, self.config.ignore_index
+        #     #         ).to(torch.long)
+
+        logits = self.language_model(
+            input_ids,
+            position_ids,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
+            max_s,
+            prefill_cache_indices,
+            lm_head_indices,
         )
-        vision_feature_select_strategy = (
-            vision_feature_select_strategy
-            if vision_feature_select_strategy is not None
-            else self.config.vision_feature_select_strategy
-        )
-
-        if inputs_embeds is None:
-            # 1. Extract the input embeddings
-            inputs_embeds = self.get_input_embeddings()(input_ids)
-
-            # 2. Merge text and images
-            if pixel_values is not None and input_ids.shape[1] != 1:
-                batch_size, num_patches, num_channels, height, width = (
-                    pixel_values.shape
-                )
-                reshaped_pixel_values = pixel_values.view(
-                    batch_size * num_patches, num_channels, height, width
-                )
-                image_features = self.vision_tower(
-                    reshaped_pixel_values, output_hidden_states=True
-                )
-
-                selected_image_feature = image_features.hidden_states[
-                    vision_feature_layer
-                ]
-
-                if vision_feature_select_strategy == "default":
-                    selected_image_feature = selected_image_feature[:, 1:]
-                elif vision_feature_select_strategy == "full":
-                    selected_image_feature = selected_image_feature
-
-                image_features = self.multi_modal_projector(selected_image_feature)
-
-                # split up image_features for each of the individual images
-                # hence we get a list of image_features, each of shape (5, num_patches, hidden_size)
-                # if we assume each image has 5 image features (base image + 4 patches)
-                split_sizes = [image.shape[0] for image in pixel_values]
-                image_features = torch.split(image_features, split_sizes, dim=0)
-
-                # NOTE we only support multimodal_patch_merge_type == "spatial_unpad"
-                height = width = (
-                    self.config.vision_config.image_size
-                    // self.config.vision_config.patch_size
-                )
-
-                new_image_features = []
-                for image_idx, image_feature in enumerate(image_features):
-                    if image_feature.shape[0] > 1:
-                        base_image_feature = image_feature[0]
-                        image_feature = image_feature[1:]
-
-                        if height * width != base_image_feature.shape[0]:
-                            raise ValueError(
-                                "The number of patches is not consistent with the image size."
-                            )
-                        num_patch_height, num_patch_width = get_anyres_image_grid_shape(
-                            image_sizes[image_idx],
-                            self.config.image_grid_pinpoints,
-                            self.config.vision_config.image_size,
-                        )
-                        image_feature = image_feature.view(
-                            num_patch_height, num_patch_width, height, width, -1
-                        )
-                        image_feature = image_feature.permute(
-                            4, 0, 2, 1, 3
-                        ).contiguous()
-                        image_feature = image_feature.flatten(1, 2).flatten(2, 3)
-                        image_feature = unpad_image(
-                            image_feature, image_sizes[image_idx]
-                        )
-                        image_feature = torch.cat(
-                            (
-                                image_feature,
-                                self.image_newline[:, None, None].expand(
-                                    *image_feature.shape[:-1], 1
-                                ),
-                            ),
-                            dim=-1,
-                        )
-                        image_feature = image_feature.flatten(1, 2).transpose(0, 1)
-                        image_feature = torch.cat(
-                            (base_image_feature, image_feature), dim=0
-                        )
-                    else:
-                        image_feature = image_feature[0]
-                        image_feature = torch.cat(
-                            (image_feature, self.image_newline[None]), dim=0
-                        )
-                    new_image_features.append(image_feature)
-                image_features = torch.stack(new_image_features, dim=0)
-
-                inputs_embeds, attention_mask, labels, position_ids = (
-                    self._merge_input_ids_with_image_features(
-                        image_features, inputs_embeds, input_ids, attention_mask, labels
-                    )
-                )
-                if labels is None:
-                    labels = torch.full_like(
-                        attention_mask, self.config.ignore_index
-                    ).to(torch.long)
-
-            # In case input_ids.shape[1] == 1 & pixel_values==None & past_key_values != None, we are in the case of
-            # generation with cache
-            elif (
-                past_key_values is not None
-                and pixel_values is not None
-                and input_ids.shape[1] == 1
-            ):
-                # Retrieve the first layer to inspect the logits and mask out the hidden states
-                # that are set to 0
-                first_layer_past_key_value = past_key_values[0][0][:, :, :, 0]
-
-                # Sum all dimensions of head_dim (-2) to avoid random errors such as: https://github.com/huggingface/transformers/pull/28032#issuecomment-1863691941
-                batch_index, non_attended_tokens = torch.where(
-                    first_layer_past_key_value.float().sum(-2) == 0
-                )
-
-                # Get the target length
-                target_seqlen = first_layer_past_key_value.shape[-1] + 1
-
-                extended_attention_mask = torch.ones(
-                    (attention_mask.shape[0], target_seqlen - attention_mask.shape[1]),
-                    dtype=attention_mask.dtype,
-                    device=attention_mask.device,
-                )
-
-                # Filter out only the tokens that can be un-attended, this can happen
-                # if one uses Llava + Fused modules where the cache on the
-                # first iteration is already big enough, or if one passes custom cache
-                valid_indices = non_attended_tokens < extended_attention_mask.size(-1)
-                new_batch_index = batch_index[valid_indices]
-                new_non_attended_tokens = non_attended_tokens[valid_indices]
-
-                # Zero-out the places where we don't need to attend
-                extended_attention_mask[new_batch_index, new_non_attended_tokens] = 0
-
-                attention_mask = torch.cat(
-                    (attention_mask, extended_attention_mask), dim=1
-                )
-                position_ids = torch.sum(attention_mask, dim=1).unsqueeze(-1) - 1
-
-        outputs = self.language_model(
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-        )
-
-        logits = outputs[0]
         return logits

server/text_generation_server/models/flash_causal_lm.py

@@ -1043,7 +1043,12 @@ class FlashCausalLM(Model):
         batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
         batch.speculative_ids = speculative_ids
         batch.position_ids = next_position_ids + accepted_ids
-        batch.input_lengths_tensor += accepted_ids
+        try:
+            batch.input_lengths_tensor += accepted_ids
+        except Exception:
+            import ipdb
+
+            ipdb.set_trace()
         batch.slot_indices += accepted_ids
 
         if prefill and prefill_logprobs:

server/text_generation_server/models/flash_llama.py

@@ -67,7 +67,8 @@ class FlashLlama(FlashCausalLM):
         if config.quantize in ["gptq", "awq"]:
             weights._set_gptq_params(model_id, revision)
 
-        model = FlashLlamaForCausalLM(config, weights)
+        prefix = ""
+        model = FlashLlamaForCausalLM(prefix, config, weights)
         torch.distributed.barrier(group=self.process_group)
         super(FlashLlama, self).__init__(
             model=model,

server/text_generation_server/models/flash_mistral.py

@@ -6,7 +6,7 @@ import numpy as np
 
 from dataclasses import dataclass
 from opentelemetry import trace
-from transformers import PreTrainedTokenizerBase, AutoTokenizer
+from transformers import PreTrainedTokenizerBase, AutoTokenizer, AutoConfig
 from transformers.models.llama import LlamaTokenizerFast
 from typing import Optional, Tuple, Type
 
@@ -301,14 +301,15 @@ class FlashMistralBatch(FlashCausalLMBatch):
 class BaseFlashMistral(FlashCausalLM):
     def __init__(
         self,
-        config_cls,
         model_cls,
         model_id: str,
+        config_cls=AutoConfig,
         revision: Optional[str] = None,
         quantize: Optional[str] = None,
         use_medusa: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
+        tokenizer_class=AutoTokenizer,
     ):
         self.process_group, rank, world_size = initialize_torch_distributed()
         if torch.cuda.is_available():
@@ -317,22 +318,13 @@ class BaseFlashMistral(FlashCausalLM):
         else:
             raise NotImplementedError("FlashMistral is only available on GPU")
 
-        try:
+        tokenizer = tokenizer_class.from_pretrained(
-            tokenizer = LlamaTokenizerFast.from_pretrained(
+            model_id,
-                model_id,
+            revision=revision,
-                revision=revision,
+            padding_side="left",
-                padding_side="left",
+            truncation_side="left",
-                truncation_side="left",
+            trust_remote_code=trust_remote_code,
-                trust_remote_code=trust_remote_code,
+        )
-            )
-        except Exception:
-            tokenizer = AutoTokenizer.from_pretrained(
-                model_id,
-                revision=revision,
-                padding_side="left",
-                truncation_side="left",
-                trust_remote_code=trust_remote_code,
-            )
 
         config = config_cls.from_pretrained(
             model_id, revision=revision, trust_remote_code=trust_remote_code
@@ -341,10 +333,12 @@ class BaseFlashMistral(FlashCausalLM):
         config.use_medusa = use_medusa
 
         # Set context windows
-        if config.sliding_window is not None:
+        if getattr(config, "sliding_window", None) is not None:
             set_sliding_window(
                 config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
             )
+        else:
+            config.sliding_window = None
 
         torch.distributed.barrier(group=self.process_group)
 
@@ -353,17 +347,19 @@ class BaseFlashMistral(FlashCausalLM):
         if config.quantize in ["gptq", "awq"]:
             weights._set_gptq_params(model_id, revision)
 
-        model = model_cls(config, weights)
+        prefix = ""
+        model = model_cls(prefix, config, weights)
 
         self.cuda_graphs = {}
 
         torch.distributed.barrier(group=self.process_group)
-        super(BaseFlashMistral, self).__init__(
+        num_layers, num_kv_heads, head_size = self.get_layer_config(model)
+        super().__init__(
             model=model,
             tokenizer=tokenizer,
-            num_layers=len(model.model.layers),
+            num_layers=num_layers,
-            num_kv_heads=model.model.num_key_value_heads,
+            num_kv_heads=num_kv_heads,
-            head_size=model.model.head_size,
+            head_size=head_size,
             dtype=dtype,
             device=device,
             rank=rank,
@@ -371,6 +367,16 @@ class BaseFlashMistral(FlashCausalLM):
             sliding_window=config.sliding_window,
         )
 
+    def get_layer_config(self, model) -> Tuple[int, int, int]:
+        return (
+            len(model.model.layers),
+            model.model.num_key_value_heads,
+            model.model.head_size,
+        )
+
+    def max_past(self) -> int:
+        return self.model.max_past
+
     @property
     def batch_type(self) -> Type[FlashMistralBatch]:
         return FlashMistralBatch
@@ -485,11 +491,11 @@ class BaseFlashMistral(FlashCausalLM):
             max_s = batch.max_seqlen
             lm_head_indices = batch.prefill_head_indices
 
-        if cu_seqlen_prefill is None and self.model.max_past is not None:
+        if cu_seqlen_prefill is None and self.max_past() is not None:
             # In decode, not prefill, we're actually overwriting the KV-cache
             # in a circular buffer mode.
             # This makes sure the max_s for the decode pass is correct.
-            max_s = min(self.model.max_past, max_s)
+            max_s = min(self.max_past(), max_s)
 
         bs = input_ids.shape[0]
         padded_bs = bs

server/text_generation_server/models/idefics.py

@@ -17,7 +17,7 @@ from transformers import LlamaTokenizerFast
 from text_generation_server.models.custom_modeling.idefics_modeling import (
     IdeficsForVisionText2Text,
 )
-from text_generation_server.models.vlm_causal_lm import VlmCausalLM
+from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
 from text_generation_server.utils import (
     initialize_torch_distributed,
     weight_files,
@@ -25,7 +25,7 @@ from text_generation_server.utils import (
 )
 
 
-class IDEFICSSharded(VlmCausalLM):
+class IDEFICSSharded(IdeficsCausalLM):
     def __init__(
         self,
         model_id: str,

server/text_generation_server/models/llava_next.py

@@ -1,24 +1,15 @@
 import torch
-import torch.distributed
 
-from typing import List, Optional, Tuple
+from typing import Optional
 
 from transformers import (
-    AutoTokenizer,
-    AutoConfig,
     AutoProcessor,
 )
 from text_generation_server.models.custom_modeling.llava_next import (
     LlavaNextForConditionalGeneration,
 )
 
-# from transformers import AutoConfig, AutoTokenizer, AutoProcessor
 from text_generation_server.models.vlm_causal_lm import VlmCausalLM
-from text_generation_server.utils import (
-    initialize_torch_distributed,
-    weight_files,
-    Weights,
-)
 
 
 class LlavaNext(VlmCausalLM):
@@ -31,59 +22,15 @@ class LlavaNext(VlmCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        self.process_group, rank, world_size = initialize_torch_distributed()
-        if torch.cuda.is_available():
-            device = torch.device(f"cuda:{rank}")
-            # 9b seems to work correctly enough in float16, but 80b seems
-            # to be really saturating for f16.
-            dtype = torch.float16 if dtype is None else dtype
-        else:
-            device = torch.device("cpu")
-            dtype = torch.float32 if dtype is None else dtype
-        self.device, self.dtype = device, dtype
-
-        config = AutoConfig.from_pretrained(
-            model_id,
-            revision=revision,
-            trust_remote_code=trust_remote_code,
-        )
-        config.quantize = quantize
-        config.use_medusa = use_medusa
-        config.vision_config.quantize = quantize
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
         self.processor = AutoProcessor.from_pretrained(
-            model_id,
+            model_id, revision=revision, trust_remote_code=trust_remote_code
+        )
+        super().__init__(
+            model_cls=LlavaNextForConditionalGeneration,
+            model_id=model_id,
             revision=revision,
-            padding_side="left",
+            quantize=quantize,
-            truncation_side="left",
+            use_medusa=use_medusa,
+            dtype=dtype,
             trust_remote_code=trust_remote_code,
         )
-
-        torch.distributed.barrier(group=self.process_group)
-        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
-        weights = Weights(
-            filenames,
-            device=device,
-            dtype=dtype,
-            process_group=self.process_group,
-        )
-
-        model = LlavaNextForConditionalGeneration(config, weights)
-
-        torch.distributed.barrier(group=self.process_group)
-        super(VlmCausalLM, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-            rank=rank,
-            world_size=world_size,
-        )

server/text_generation_server/models/vlm_causal_lm.py

@@ -1,869 +1,50 @@
-import torch
+import re
-import time
 
-from dataclasses import dataclass
 from opentelemetry import trace
-from transformers import (
-    AutoProcessor,
-    AutoTokenizer,
-    PreTrainedTokenizerBase,
-    ProcessorMixin,
-)
 from typing import Optional, Tuple, List, Type, Dict
 
-from text_generation_server.models import Model
+from text_generation_server.models.flash_mistral import (
-from text_generation_server.models.types import (
+    BaseFlashMistral,
-    Batch,
+    FlashMistralBatch,
-    Tokens,
-    Generation,
-    GeneratedText,
 )
-from text_generation_server.pb import generate_pb2
-from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
 
-import re
+tracer = trace.get_tracer(__name__)
 
 IMAGES = re.compile(r"!\[[^\]]*\]\((.*?)\s*(\"(?:.*[^\"])\")?\s*\)")
 
 
-def split(string):
+def split(string) -> List[Dict[str, str]]:
     parts = []
     cursor = 0
     for pattern in IMAGES.finditer(string):
         start = pattern.start()
         if start != cursor:
-            parts.append(string[cursor:start])
+            parts.append({"type": "text", "content": string[cursor:start]})
 
-        parts.append(pattern.group(1))
+        parts.append({"type": "image", "content": pattern.group(1)})
         cursor = pattern.end()
 
     if cursor != len(string):
-        parts.append(string[cursor:])
+        parts.append({"type": "text", "content": string[cursor:]})
 
     return parts
 
 
-tracer = trace.get_tracer(__name__)
+class VlmCausalLMBatch(FlashMistralBatch):
+    pass
 
 
-@dataclass
+class VlmCausalLM(BaseFlashMistral):
-class VlmCausalLMBatch(Batch):
-    batch_id: int
-    requests: List[generate_pb2.Request]
-    requests_idx_mapping: Dict[int, int]
-
-    # Decoder values
-    input_ids: torch.Tensor
-    attention_mask: torch.Tensor
-    position_ids: torch.Tensor
-    pixel_values: Optional[torch.Tensor]
-    image_hidden_states: Optional[torch.Tensor]
-    image_attention_mask: Optional[torch.Tensor]
-    past_key_values: Optional[List[Tuple]]
-
-    # All tokens
-    all_input_ids: List[torch.Tensor]
-
-    # Lengths of all generations present in the batch
-    input_lengths: List[int]
-    prefix_offsets: List[int]
-    read_offsets: List[int]
-
-    # Generation helpers
-    next_token_choosers: List[NextTokenChooser]
-    stopping_criterias: List[StoppingCriteria]
-
-    # Metadata used for padding
-    max_input_length: int
-    padding_right_offset: int
-
-    # Maximum number of tokens this batch will grow to
-    max_tokens: int
-
-    # Past metadata
-    keys_head_dim_last: bool = True
-
-    def to_pb(self) -> generate_pb2.CachedBatch:
-        return generate_pb2.CachedBatch(
-            id=self.batch_id,
-            request_ids=[r.id for r in self.requests],
-            size=len(self),
-            max_tokens=self.max_tokens,
-        )
-
-    @classmethod
-    def from_pb(
-        cls,
-        pb: generate_pb2.Batch,
-        tokenizer: PreTrainedTokenizerBase,
-        processor: ProcessorMixin,  # Hack
-        dtype: torch.dtype,
-        device: torch.device,
-    ) -> "VlmCausalLMBatch":
-        inputs = []
-        next_token_choosers = []
-        stopping_criterias = []
-        prefix_offsets = []
-        read_offsets = []
-        requests_idx_mapping = {}
-
-        # Parse batch
-        max_truncation = 0
-        padding_right_offset = 0
-        max_decode_tokens = 0
-        for i, r in enumerate(pb.requests):
-            requests_idx_mapping[r.id] = i
-            inputs.append(r.inputs)
-            next_token_choosers.append(
-                NextTokenChooser.from_pb(r.parameters, device, tokenizer)
-            )
-            stopping_criteria = StoppingCriteria.from_pb(
-                r.stopping_parameters, tokenizer
-            )
-            stopping_criterias.append(stopping_criteria)
-            max_truncation = max(max_truncation, r.truncate)
-            max_decode_tokens += stopping_criteria.max_new_tokens
-            padding_right_offset = max(
-                padding_right_offset, stopping_criteria.max_new_tokens
-            )
-
-        # TODO Check impact on idefics
-        # prompts = []
-        # for inp in inputs:
-        #     # Each input is encoded into a list, where each element of this input list is either a string or a URL
-        #     prompts.append(split(inp))
-
-        # The processor replaces the call to tokenizer, and
-        # a/ takes care of fetching images from the URL
-        # b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
-        tokenized_inputs = processor(
-            inputs,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=max_truncation,
-            # TODO Check impact on idefics
-            # add_end_of_utterance_token=False,  # Already taken care of inside the prompts, so bypassing the processor's handling of this token
-        ).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
-
-        input_lengths = tokenized_inputs["attention_mask"].sum(1)
-        max_input_length = input_lengths.max()
-
-        input_ids = tokenized_inputs["input_ids"]
-        pixel_values = tokenized_inputs.get("pixel_values", None)
-        image_hidden_states = None
-        # Allocate maximum attention_mask
-        attention_mask = input_ids.new_zeros(
-            (pb.size, max_input_length + padding_right_offset)
-        )
-        # Copy tokenizer attention_mask into fully allocated attention_mask
-        attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
-        # Do the same for image_attention_mask
-        if pixel_values is None:
-            image_attention_mask = None
-        else:
-            image_attention_mask = input_ids.new_zeros(
-                (
-                    pb.size,
-                    max_input_length + padding_right_offset,
-                    pixel_values.size(1),
-                )
-            )
-            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
-
-        max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
-
-        return cls(
-            batch_id=pb.id,
-            requests=pb.requests,
-            requests_idx_mapping=requests_idx_mapping,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            pixel_values=pixel_values,
-            image_hidden_states=image_hidden_states,
-            image_attention_mask=image_attention_mask,
-            past_key_values=None,
-            all_input_ids=list(all_input_ids),
-            input_lengths=input_lengths.tolist(),
-            prefix_offsets=prefix_offsets,
-            read_offsets=read_offsets,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-            max_input_length=max_input_length.item(),
-            padding_right_offset=padding_right_offset,
-            max_tokens=max_tokens,
-        )
-
-    @tracer.start_as_current_span("filter")
-    def filter(self, request_ids: List[int]) -> Optional["VlmCausalLMBatch"]:
-        # It deletes requests from the batch. For instance when client lost connection
-        if len(request_ids) == 0:
-            raise ValueError("Batch must have at least one request")
-        if len(request_ids) == len(self):
-            return self
-
-        keep_indices = []
-
-        # New values after filtering
-        requests_idx_mapping = {}
-        requests = []
-        input_lengths = []
-        prefix_offsets = []
-        read_offsets = []
-        all_input_ids = []
-        max_input_length = 0
-
-        next_token_choosers = []
-        stopping_criterias = []
-
-        total_remaining_decode_tokens = 0
-        new_padding_right_offset = 0
-
-        for i, request_id in enumerate(request_ids):
-            idx = self.requests_idx_mapping[request_id]
-            requests_idx_mapping[request_id] = i
-            keep_indices.append(idx)
-
-            requests.append(self.requests[idx])
-            prefix_offsets.append(self.prefix_offsets[idx])
-            read_offsets.append(self.read_offsets[idx])
-            all_input_ids.append(self.all_input_ids[idx])
-
-            request_input_length = self.input_lengths[idx]
-            input_lengths.append(request_input_length)
-            max_input_length = max(max_input_length, request_input_length)
-
-            next_token_choosers.append(self.next_token_choosers[idx])
-            stopping_criteria = self.stopping_criterias[idx]
-            stopping_criterias.append(stopping_criteria)
-            remaining_decode_tokens = (
-                stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
-            )
-            total_remaining_decode_tokens += remaining_decode_tokens
-            new_padding_right_offset = max(
-                new_padding_right_offset, remaining_decode_tokens
-            )
-
-        # Apply indices to input_ids, attention mask, past key values and other items that need to be cached
-        input_ids = self.input_ids[keep_indices]
-        position_ids = self.position_ids[keep_indices]
-        self.attention_mask = self.attention_mask[
-            keep_indices,
-            -(self.padding_right_offset + max_input_length) : (
-                self.attention_mask.shape[1] - self.padding_right_offset
-            )
-            + new_padding_right_offset,
-        ]
-        # Do the same for pixel_values and image_attention_mask
-        pixel_values = self.pixel_values[keep_indices]
-        self.image_attention_mask = self.image_attention_mask[
-            keep_indices,
-            -(self.padding_right_offset + max_input_length) : (
-                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
-        else:
-            image_hidden_states = self.image_hidden_states[keep_indices]
-
-        # Ensure that past_key_values tensors can be updated in-place
-        if type(self.past_key_values[0]) == tuple:
-            self.past_key_values = [list(layer) for layer in self.past_key_values]
-
-        # Update tensors in-place to allow incremental garbage collection
-        past_kv_length = max_input_length - 1
-        for layer in self.past_key_values:
-            past_keys, past_values = layer
-            if len(past_keys.shape) == 3:
-                # Force past to be of dim [self_size, num_heads, ...] for easy indexing
-                past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
-                past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
-            if self.keys_head_dim_last:
-                layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
-            else:
-                layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
-            del past_keys
-            layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
-            del past_values
-
-        max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
-
-        self.requests = requests
-        self.requests_idx_mapping = requests_idx_mapping
-        self.input_ids = input_ids
-        self.pixel_values = pixel_values
-        self.image_hidden_states = image_hidden_states
-        self.position_ids = position_ids
-        self.all_input_ids = all_input_ids
-        self.input_lengths = input_lengths
-        self.prefix_offsets = prefix_offsets
-        self.read_offsets = read_offsets
-        self.next_token_choosers = next_token_choosers
-        self.stopping_criterias = stopping_criterias
-        self.max_input_length = max_input_length
-        self.padding_right_offset = new_padding_right_offset
-        self.max_tokens = max_tokens
-
-        return self
-
-    @classmethod
-    @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["VlmCausalLMBatch"]) -> "VlmCausalLMBatch":
-        # It adds new requests to the batch
-        # Used for padding
-        total_batch_size = 0
-        max_input_length = 0
-        max_num_images = 0
-        padding_right_offset = 0
-        for batch in batches:
-            total_batch_size += len(batch)
-            max_input_length = max(max_input_length, batch.max_input_length)
-            max_num_images = max(max_num_images, batch.pixel_values.size(1))
-            padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
-
-        # Batch attributes
-        requests = []
-        requests_idx_mapping = {}
-        input_lengths = []
-        prefix_offsets = []
-        read_offsets = []
-        all_input_ids = []
-        next_token_choosers = []
-        stopping_criterias = []
-        max_tokens = 0
-
-        # Batch tensors
-        input_ids = None
-        attention_mask = None
-        position_ids = None
-        pixel_values = None
-        image_hidden_states = None
-        image_attention_mask = None
-        past_key_values = []
-
-        # Used for slicing correctly inside the tensors
-        # Equivalent to a cumsum on batch sizes
-        start_index = 0
-        for i, batch in enumerate(batches):
-            requests.extend(batch.requests)
-            input_lengths.extend(batch.input_lengths)
-            prefix_offsets.extend(batch.prefix_offsets)
-            read_offsets.extend(batch.read_offsets)
-            all_input_ids.extend(batch.all_input_ids)
-            next_token_choosers.extend(batch.next_token_choosers)
-            stopping_criterias.extend(batch.stopping_criterias)
-
-            if i == 0:
-                requests_idx_mapping = batch.requests_idx_mapping
-            else:
-                # We need to offset the mapping for each batch by the cumulative batch size
-                for k, v in batch.requests_idx_mapping.items():
-                    requests_idx_mapping[k] = v + start_index
-
-            # Slicing end index for this batch
-            end_index = start_index + len(batch)
-
-            # We only concatenate batches that did at least one step
-            if batch.past_key_values is None:
-                raise ValueError("only concatenate prefilled batches")
-
-            # Create empty tensor
-            # input_ids is always of shape [batch_size, 1]
-            # We do not need to pad it
-            if input_ids is None:
-                input_ids = batch.input_ids.new_empty((total_batch_size, 1))
-            # Copy to correct indices
-            input_ids[start_index:end_index] = batch.input_ids
-
-            # Create padded tensor
-            if attention_mask is None:
-                attention_mask = batch.attention_mask.new_zeros(
-                    (total_batch_size, max_input_length + padding_right_offset),
-                )
-
-            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
-            )
-
-            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,
-                    )
-                )
-
-            # We need to slice the attention mask to remove padding from previous steps
-            # and to remove unused allocated space
-            left_offset = max_input_length - batch.max_input_length
-            batch_left_offset = (
-                batch.attention_mask.shape[1]
-                - batch.max_input_length
-                - batch.padding_right_offset
-            )
-            attention_mask[
-                start_index:end_index,
-                left_offset:-padding_right_offset,
-            ] = batch.attention_mask[
-                :,
-                batch_left_offset : -batch.padding_right_offset,
-            ]
-            image_attention_mask[
-                start_index:end_index,
-                left_offset:-padding_right_offset,
-                :curr_batch_max_num_images,
-            ] = batch.image_attention_mask[
-                :, batch_left_offset : -batch.padding_right_offset, :
-            ]
-
-            # Create empty tensor
-            # position_ids is always of shape [batch_size, 1]
-            if position_ids is None:
-                position_ids = batch.position_ids.new_empty((total_batch_size, 1))
-            position_ids[start_index:end_index] = batch.position_ids
-
-            # Shenanigans to get dimensions because BLOOM outputs a past with a different shape
-            # BLOOM Keys:   [batch_size * num_heads, head_dim, seq_length]
-            # BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
-            # And ensure that we can update tensors in-place
-            if type(batch.past_key_values[0]) == tuple:
-                batch.past_key_values = [
-                    [t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
-                    for layer in batch.past_key_values
-                ]
-            elif len(batch.past_key_values[0][0].shape) == 3:
-                for layer in batch.past_key_values:
-                    for k, t in enumerate(layer):
-                        layer[k] = t.view(len(batch), -1, *t.shape[-2:])
-
-            # Add eventual padding tokens that were added while concatenating
-            max_tokens += batch.max_tokens + (
-                max_input_length - batch.max_input_length
-            ) * len(batch)
-
-            start_index = end_index
-
-        first_past_kvs = batches[0].past_key_values
-        _, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
-
-        padded_past_values_shape = (
-            total_batch_size,
-            num_heads,
-            max_input_length - 1,
-            head_dim,
-        )
-
-        if batches[0].keys_head_dim_last:
-            padded_past_keys_shape = padded_past_values_shape
-        else:
-            # seq_length is last for BLOOM
-            padded_past_keys_shape = (
-                total_batch_size,
-                num_heads,
-                head_dim,
-                max_input_length - 1,
-            )
-
-        # Iterate over attention layers
-        # Concatenate past key values layer by layer to allow incremental garbage collection
-        for j in range(len(first_past_kvs)):
-            padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
-            start_index = 0
-            for batch in batches:
-                past_keys = batch.past_key_values[j][0]
-                # Clear reference to the original tensor
-                batch.past_key_values[j][0] = None
-
-                # Slicing end index for this batch
-                end_index = start_index + len(batch)
-                # We slice the keys to remove the padding from previous batches
-                past_seq_len = batch.max_input_length - 1
-                if batch.keys_head_dim_last:
-                    padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
-                        past_keys[:, :, -past_seq_len:, :]
-                    )
-                else:
-                    # BLOOM case
-                    padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
-                        past_keys[:, :, :, -past_seq_len:]
-                    )
-                del past_keys
-
-                start_index = end_index
-
-            padded_past_values = first_past_kvs[j][1].new_zeros(
-                padded_past_values_shape
-            )
-            start_index = 0
-            for batch in batches:
-                past_values = batch.past_key_values[j][1]
-                # Clear reference to the original tensor
-                batch.past_key_values[j][1] = None
-
-                # Slicing end index for this batch
-                end_index = start_index + len(batch)
-                # We slice the past values to remove the padding from previous batches
-                past_seq_len = batch.max_input_length - 1
-                padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
-                    past_values[:, :, -past_seq_len:, :]
-                )
-                del past_values
-
-                # Update values
-                start_index = end_index
-
-            past_key_values.append([padded_past_keys, padded_past_values])
-
-        return cls(
-            batch_id=batches[0].batch_id,
-            requests=requests,
-            requests_idx_mapping=requests_idx_mapping,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            pixel_values=pixel_values,
-            image_hidden_states=image_hidden_states,
-            image_attention_mask=image_attention_mask,
-            past_key_values=past_key_values,
-            all_input_ids=all_input_ids,
-            input_lengths=input_lengths,
-            prefix_offsets=prefix_offsets,
-            read_offsets=read_offsets,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-            max_input_length=max_input_length,
-            padding_right_offset=padding_right_offset,
-            keys_head_dim_last=batches[0].keys_head_dim_last,
-            max_tokens=max_tokens,
-        )
-
-    def __len__(self):
-        return len(self.requests)
-
-
-class VlmCausalLM(Model):
-    def __init__(
-        self,
-        model_id: str,
-        revision: Optional[str] = None,
-        quantize: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-        trust_remote_code: bool = False,
-    ):
-        from text_generation_server.models.custom_modeling.idefics_modeling import (
-            VlmForVisionText2Text,
-        )
-
-        if torch.cuda.is_available():
-            device = torch.device("cuda")
-            dtype = torch.bfloat16 if dtype is None else dtype
-        else:
-            if quantize:
-                raise ValueError("quantization is not available on CPU")
-
-            device = torch.device("cpu")
-            dtype = torch.float32 if dtype is None else dtype
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        self.processor = AutoProcessor.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
-        model = VlmForVisionText2Text.from_pretrained(
-            model_id,
-            revision=revision,
-            torch_dtype=dtype,
-            device_map=(
-                "auto"
-                if torch.cuda.is_available() and torch.cuda.device_count() > 1
-                else None
-            ),
-            load_in_8bit=quantize == "bitsandbytes",
-            trust_remote_code=trust_remote_code,
-        )
-        if torch.cuda.is_available() and torch.cuda.device_count() == 1:
-            model = model.cuda()
-
-        if tokenizer.pad_token_id is None:
-            if model.config.pad_token_id is not None:
-                tokenizer.pad_token_id = model.config.pad_token_id
-            elif model.config.eos_token_id is not None:
-                tokenizer.pad_token_id = model.config.eos_token_id
-            elif tokenizer.eos_token_id is not None:
-                tokenizer.pad_token_id = tokenizer.eos_token_id
-            else:
-                tokenizer.add_special_tokens({"pad_token": "<unk>"})
-
-        super(VlmCausalLM, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-        )
-
     @property
-    def batch_type(self) -> Type[VlmCausalLMBatch]:
+    def batch_type(self) -> Type[FlashMistralBatch]:
-        return VlmCausalLMBatch
+        return FlashMistralBatch
 
-    def forward(
+    def get_layer_config(self, model) -> Tuple[int, int, int]:
-        self,
-        input_ids,
-        attention_mask,
-        position_ids,
-        pixel_values,
-        image_hidden_states,
-        image_attention_mask,
-        past_key_values: Optional = None,
-    ) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
-        # Model Forward
-        kwargs = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "pixel_values": pixel_values,
-            "image_hidden_states": image_hidden_states,
-            "image_attention_mask": image_attention_mask,
-            "past_key_values": past_key_values,
-            "use_cache": True,
-            "return_dict": True,
-        }
-        if self.has_position_ids:
-            kwargs["position_ids"] = position_ids
-
-        outputs, speculative_logits = self.model.forward(**kwargs)
         return (
-            outputs.logits,
+            len(model.language_model.model.layers),
-            speculative_logits,
+            model.language_model.model.num_key_value_heads,
-            outputs.past_key_values,
+            model.language_model.model.head_size,
-            outputs.image_hidden_states,
         )
 
-    @tracer.start_as_current_span("generate_token")
+    def max_past(self) -> Optional[int]:
-    def generate_token(
+        return getattr(self.model.language_model, "max_past", None)
-        self, batch: VlmCausalLMBatch
-    ) -> Tuple[List[Generation], Optional[VlmCausalLMBatch], Tuple[int, int]]:
-        start = time.time_ns()
-        # slice the attention mask to the correct shape
-        attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
-        if batch.image_attention_mask is None:
-            image_attention_mask = None
-        else:
-            if batch.input_ids.size(1) == 1:
-                # THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
-                # but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
-                # 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)
-            else:
-                image_attention_mask = batch.image_attention_mask[
-                    :, : -batch.padding_right_offset
-                ]
-
-        logits, speculative_logits, past, image_hidden_states = self.forward(
-            input_ids=batch.input_ids,
-            attention_mask=attention_mask,
-            position_ids=batch.position_ids,
-            pixel_values=batch.pixel_values,
-            image_hidden_states=batch.image_hidden_states,
-            image_attention_mask=image_attention_mask,
-            past_key_values=batch.past_key_values,
-        )
-        # Hardcoded remove image tokens
-        logits[:, 32000:32001] = torch.finfo(logits.dtype).min
-
-        start_decode = time.time_ns()
-
-        # Results
-        generations: List[Generation] = []
-        stopped = True
-
-        # Zipped iterator
-        iterator = zip(
-            batch.requests,
-            batch.input_lengths,
-            batch.prefix_offsets,
-            batch.read_offsets,
-            logits,
-            batch.next_token_choosers,
-            batch.stopping_criterias,
-            batch.all_input_ids,
-        )
-
-        # For each member of the batch
-        for i, (
-            request,
-            input_length,
-            prefix_offset,
-            read_offset,
-            logits,
-            next_token_chooser,
-            stopping_criteria,
-            all_input_ids,
-        ) in enumerate(iterator):
-            # Select next token
-            next_token_id, logprobs = next_token_chooser(
-                all_input_ids.view(1, -1), logits[-1:, :]
-            )
-
-            # Append next token to all tokens
-            all_input_ids = torch.cat([all_input_ids, next_token_id])
-            new_input_length = input_length + 1
-
-            # Generated token
-            next_token_logprob = logprobs[-1, next_token_id]
-            next_token_id_squeezed = next_token_id.squeeze()
-            next_token_text, prefix_offset, read_offset = self.decode_token(
-                all_input_ids[:, 0], prefix_offset, read_offset
-            )
-
-            # Evaluate stopping criteria
-            stop, reason = stopping_criteria(
-                next_token_id_squeezed,
-                next_token_text,
-            )
-
-            if not stop:
-                stopped = False
-
-            # Shard generations
-            # All generations will be appended in the rust sharded client
-            if i % self.world_size == self.rank:
-                if stop:
-                    # 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,
-                    )
-                    # Get seed
-                    if isinstance(next_token_chooser.choice, Sampling):
-                        seed = next_token_chooser.choice.seed
-                    else:
-                        seed = None
-
-                    generated_text = GeneratedText(
-                        output_text, stopping_criteria.current_tokens, reason, seed
-                    )
-                else:
-                    generated_text = None
-
-                # Prefill
-                if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
-                    # Remove generated token to only have prefill and add nan for first prompt token
-                    prefill_logprobs = [float("nan")] + torch.log_softmax(
-                        logits, -1
-                    ).gather(1, all_input_ids[1:]).squeeze(1)[
-                        -new_input_length:-1
-                    ].tolist()
-                    prefill_token_ids = all_input_ids[-new_input_length:-1]
-                    prefill_texts = self.tokenizer.batch_decode(
-                        prefill_token_ids,
-                        clean_up_tokenization_spaces=False,
-                        skip_special_tokens=False,
-                    )
-                    prefill_tokens = Tokens(
-                        prefill_token_ids,
-                        prefill_logprobs,
-                        prefill_texts,
-                        is_special=[],
-                    )
-                else:
-                    prefill_tokens = None
-
-                top_tokens = None
-
-                generation = Generation(
-                    request.id,
-                    prefill_tokens,
-                    Tokens(
-                        [next_token_id_squeezed],
-                        [next_token_logprob],
-                        [next_token_text],
-                        [next_token_id_squeezed.item() in self.all_special_ids],
-                    ),
-                    generated_text,
-                    top_tokens,
-                )
-
-                generations.append(generation)
-
-            # Update values
-            batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
-                next_token_id_squeezed.item()
-            )
-            batch.input_ids[i, 0] = next_token_id
-            batch.all_input_ids[i] = all_input_ids
-            batch.input_lengths[i] = new_input_length
-            batch.prefix_offsets[i] = prefix_offset
-            batch.read_offsets[i] = read_offset
-            batch.max_input_length = max(batch.max_input_length, new_input_length)
-
-        # We finished all generations in the batch; there is no next batch
-        if stopped:
-            forward_ns = start_decode - start
-            decode_ns = time.time_ns() - start_decode
-            return generations, None, (forward_ns, decode_ns)
-
-        # Slice unused values from prefill
-        batch.input_ids = batch.input_ids[:, :1]
-
-        # 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), :]
-        )
-        # Decrease right offset
-        batch.padding_right_offset -= 1
-
-        # Update position_ids
-        batch.position_ids = batch.position_ids[:, -1:] + 1
-
-        # Update past key values
-        batch.past_key_values = past
-        batch.image_hidden_states = image_hidden_states
-
-        forward_ns = start_decode - start
-        decode_ns = time.time_ns() - start_decode
-        return generations, batch, (forward_ns, decode_ns)

server/text_generation_server/server.py

@@ -13,9 +13,10 @@ from typing import List, Optional
 from text_generation_server.cache import Cache
 from text_generation_server.interceptor import ExceptionInterceptor
 from text_generation_server.models import Model, get_model
+from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch
 from text_generation_server.pb import generate_pb2_grpc, generate_pb2
 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
-from text_generation_server.models.vlm_causal_lm import VlmCausalLMBatch
+from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
 
 
 class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
@@ -78,9 +79,10 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
             except ImportError:
                 pass
 
-        if (
+        if self.model.batch_type in {
-            self.model.batch_type == VlmCausalLMBatch
+            IdeficsCausalLMBatch,
-        ):  # Hack, i would rather use kwargs in the `from_pb` call
+            VlmCausalLMBatch,
+        }:  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
                 request.batch,
                 self.model.tokenizer,
@@ -100,9 +102,10 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
 
     async def Prefill(self, request, context):
         start = time.time_ns()
-        if (
+        if self.model.batch_type in {
-            self.model.batch_type == VlmCausalLMBatch
+            IdeficsCausalLMBatch,
-        ):  # Hack, i would rather use kwargs in the `from_pb` call
+            VlmCausalLMBatch,
+        }:  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
                 request.batch,
                 self.model.tokenizer,