Upgrade tests (still missing load tests for some reason).

integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_all_params.json

@@ -0,0 +1,65 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "stop_sequence",
+    "generated_tokens": 6,
+    "prefill": [
+      {
+        "id": 1,
+        "logprob": null,
+        "text": "<s>"
+      },
+      {
+        "id": 3735,
+        "logprob": -10.5,
+        "text": "Test"
+      },
+      {
+        "id": 2159,
+        "logprob": -12.140625,
+        "text": "request"
+      }
+    ],
+    "seed": 0,
+    "tokens": [
+      {
+        "id": 13,
+        "logprob": -1.0654297,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 1014,
+        "logprob": -2.7460938,
+        "special": false,
+        "text": "The"
+      },
+      {
+        "id": 6032,
+        "logprob": -1.359375,
+        "special": false,
+        "text": " purpose"
+      },
+      {
+        "id": 302,
+        "logprob": 0.0,
+        "special": false,
+        "text": " of"
+      },
+      {
+        "id": 456,
+        "logprob": 0.0,
+        "special": false,
+        "text": " this"
+      },
+      {
+        "id": 1369,
+        "logprob": -0.40063477,
+        "special": false,
+        "text": " test"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "Test request\nThe purpose of this test"
+}

integration-tests/models/__snapshots__/test_llava_next/test_flash_llava_next_simple.json

@@ -0,0 +1,73 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 13,
+        "logprob": -0.00756073,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 13,
+        "logprob": -0.20117188,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 16114,
+        "logprob": -1.2597656,
+        "special": false,
+        "text": "Once"
+      },
+      {
+        "id": 3714,
+        "logprob": -0.20825195,
+        "special": false,
+        "text": " upon"
+      },
+      {
+        "id": 264,
+        "logprob": -0.00178051,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 727,
+        "logprob": -0.011955261,
+        "special": false,
+        "text": " time"
+      },
+      {
+        "id": 28725,
+        "logprob": -0.17541504,
+        "special": false,
+        "text": ","
+      },
+      {
+        "id": 736,
+        "logprob": -0.91308594,
+        "special": false,
+        "text": " there"
+      },
+      {
+        "id": 403,
+        "logprob": -0.058410645,
+        "special": false,
+        "text": " was"
+      },
+      {
+        "id": 264,
+        "logprob": -0.009689331,
+        "special": false,
+        "text": " a"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "\n\nOnce upon a time, there was a"
+}

integration-tests/models/test_idefics.py

@@ -33,7 +33,9 @@ async def test_idefics(idefics, response_snapshot):
     )
 
     assert response.details.generated_tokens == 10
-    assert response.generated_text == "\n\nDeep learning is a new type of machine"
+    assert (
+        response.generated_text == " \nAssistant: A rooster stands"
+    ), f"{repr(response.generated_text)}"
     assert response == response_snapshot
 
 
@@ -49,7 +51,9 @@ async def test_idefics_load(idefics, generate_load, response_snapshot):
 
     generated_texts = [r.generated_text for r in responses]
 
-    assert generated_texts[0] == "\n\nDeep learning is a new type of machine"
+    assert (
+        generated_texts[0] == " \nAssistant: A rooster stands"
+    ), f"{response.generated_text}"
     assert len(generated_texts) == 4
     assert generated_texts, all(
         [text == generated_texts[0] for text in generated_texts]

integration-tests/models/test_llava_next.py

@@ -13,7 +13,7 @@ def get_chicken():
 def flash_llava_next_handle(launcher):
     with launcher(
         "llava-hf/llava-v1.6-mistral-7b-hf",
-        num_shard=4,
+        num_shard=1,
         max_input_length=4000,
         max_total_tokens=4096,
     ) as handle:
@@ -34,7 +34,9 @@ async def test_flash_llava_next_simple(flash_llava_next, response_snapshot):
         f"User:![]({chicken})Can you tell me a very short story based on the image?",
         max_new_tokens=10,
     )
-    assert response.generated_text == "toto"
+    assert (
+        response.generated_text == "\n\nOnce upon a time, there was a"
+    ), f"{repr(response.generated_text)}"
     assert response.details.generated_tokens == 10
     assert response == response_snapshot
 
@@ -58,7 +60,7 @@ async def test_flash_llava_next_all_params(flash_llava_next, response_snapshot):
         seed=0,
     )
 
-    assert response.details.generated_tokens == 5
+    assert response.details.generated_tokens == 6
     assert response == response_snapshot
 
 
@@ -75,7 +77,7 @@ async def test_flash_llava_next_load(
         n=4,
     )
     generated_texts = [r.generated_text for r in responses]
-    assert generated_texts[0] == "\n\nDeep learning is a new type of machine"
+    assert generated_texts[0] == "\n\nOnce upon a time, there was a"
     assert len(generated_texts) == 4
     assert all([r.generated_text == generated_texts[0] for r in responses])
 

server/text_generation_server/models/custom_modeling/flash_mistral_modeling.py

@@ -413,7 +413,10 @@ class FlashMistralForCausalLM(torch.nn.Module):
         super().__init__()
 
         self.embed_tokens = TensorParallelEmbedding(
-            prefix=f"{prefix}.model.embed_tokens", weights=weights
+            prefix=(
+                "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
+            ),
+            weights=weights,
         )
         self.model = MistralModel(
             prefix="model" if not prefix else f"{prefix}.model",

server/text_generation_server/models/flash_causal_lm.py

@@ -1047,12 +1047,7 @@ 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
-        try:
+        batch.input_lengths_tensor += accepted_ids
-            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/idefics.py

@@ -82,7 +82,7 @@ class IDEFICSSharded(IdeficsCausalLM):
         model = IdeficsForVisionText2Text(config, weights)
 
         torch.distributed.barrier(group=self.process_group)
-        super(VlmCausalLM, self).__init__(
+        super(IdeficsCausalLM, self).__init__(
             model=model,
             tokenizer=tokenizer,
             requires_padding=True,

server/text_generation_server/models/idefics_causal_lm.py

@@ -81,850 +81,10 @@ class IdeficsCausalLMBatch(Batch):
         cls,
         pb: generate_pb2.Batch,
         tokenizer: PreTrainedTokenizerBase,
-        processor: ProcessorMixin,  # Hack
         dtype: torch.dtype,
         device: torch.device,
     ) -> "IdeficsCausalLMBatch":
-        inputs = []
+        raise NotImplementedError
-        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
-            )
-
-        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(
-            prompts,
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=max_truncation,
-            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["IdeficsCausalLMBatch"]:
-        # 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["IdeficsCausalLMBatch"]
-    ) -> "IdeficsCausalLMBatch":
-        # 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 IdeficsCausalLM(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 (
-            IdeficsForVisionText2Text,
-        )
-
-        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 = IdeficsForVisionText2Text.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(IdeficsCausalLM, self).__init__(
-            model=model,
-            tokenizer=tokenizer,
-            requires_padding=True,
-            dtype=dtype,
-            device=device,
-        )
-
-    @property
-    def batch_type(self) -> Type[IdeficsCausalLMBatch]:
-        return IdeficsCausalLMBatch
-
-    def forward(
-        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,
-            speculative_logits,
-            outputs.past_key_values,
-            outputs.image_hidden_states,
-        )
-
-    @tracer.start_as_current_span("generate_token")
-    def generate_token(
-        self, batch: IdeficsCausalLMBatch
-    ) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch], 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)
-
-
-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.types import (
-    Batch,
-    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__)
-
-
-@dataclass
-class IdeficsCausalLMBatch(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_processor(
@@ -932,6 +92,7 @@ class IdeficsCausalLMBatch(Batch):
         pb: generate_pb2.Batch,
         tokenizer: PreTrainedTokenizerBase,
         processor: ProcessorMixin,  # Hack
+        config,
         dtype: torch.dtype,
         device: torch.device,
     ) -> "IdeficsCausalLMBatch":
@@ -966,7 +127,10 @@ class IdeficsCausalLMBatch(Batch):
         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))
+            prompt = []
+            for chunk in split(inp):
+                prompt.append(chunk["content"])
+            prompts.append(prompt)
 
         # The processor replaces the call to tokenizer, and
         # a/ takes care of fetching images from the URL

server/text_generation_server/models/vlm_causal_lm.py

@@ -1,5 +1,8 @@
 import re
 import torch
+from PIL import Image
+from io import BytesIO
+import base64
 
 from opentelemetry import trace
 from typing import Optional, Tuple, List, Type, Dict
@@ -92,6 +95,13 @@ def get_number_of_features(height: int, width: int, config) -> int:
     return 2634
 
 
+def load_data_uri(image_uri: str) -> Image.Image:
+    image_uri = image_uri.split(",")[-1]
+    content = base64.b64decode(image_uri)
+    image = Image.open(BytesIO(content))
+    return image
+
+
 # assert get_number_of_features(889, 1024) == 2634, f"{get_number_of_features(889, 1024)}"
 # assert get_number_of_features(640, 640) == 2928
 
@@ -100,6 +110,21 @@ class VlmCausalLMBatch(FlashMistralBatch):
     pixel_values: Optional[List[torch.Tensor]]
     image_sizes: Optional[List[Tuple[int, int]]]
 
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches):
+        batch = super(VlmCausalLMBatch, cls).concatenate(batches)
+        batch.pixel_values = None
+        batch.image_sizes = None
+        return batch
+
+    @tracer.start_as_current_span("filter")
+    def filter(self, request_ids: List[int]):
+        batch = super().filter(request_ids)
+        batch.pixel_values = None
+        batch.image_sizes = None
+        return batch
+
     @classmethod
     def batch_tokenized_inputs(cls, requests, tokenizer, processor, config):
         batch_inputs = []
@@ -115,6 +140,12 @@ class VlmCausalLMBatch(FlashMistralBatch):
                     image = chunk["content"]
                     if image.startswith("https://") or image.startswith("http://"):
                         image = processor.image_processor.fetch_images(image)
+                    elif image.startswith("data:"):
+                        image = load_data_uri(image)
+                    else:
+                        raise RuntimeError(
+                            "Cannot process input image not starting with http(s):// nor data:"
+                        )
                     image_input = processor.image_processor(image, return_tensors="pt")
                     height, width = image_input["image_sizes"][0]
                     num_features = get_number_of_features(height, width, config)