[Model] Make llama3.2 support multiple and interleaved images (#9095)

examples/offline_inference_vision_language_multi_image.py

@@ -234,12 +234,35 @@ def load_qwen2_vl(question, image_urls: List[str]) -> ModelRequestData:
     )
 
 
+def load_mllama(question, image_urls: List[str]) -> ModelRequestData:
+    model_name = "meta-llama/Llama-3.2-11B-Vision-Instruct"
+
+    # The configuration below has been confirmed to launch on a single L40 GPU.
+    llm = LLM(
+        model=model_name,
+        max_model_len=4096,
+        max_num_seqs=16,
+        enforce_eager=True,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    prompt = f"<|image|><|image|><|begin_of_text|>{question}"
+    return ModelRequestData(
+        llm=llm,
+        prompt=prompt,
+        stop_token_ids=None,
+        image_data=[fetch_image(url) for url in image_urls],
+        chat_template=None,
+    )
+
+
 model_example_map = {
     "phi3_v": load_phi3v,
     "internvl_chat": load_internvl,
     "NVLM_D": load_nvlm_d,
     "qwen2_vl": load_qwen2_vl,
     "qwen_vl_chat": load_qwenvl_chat,
+    "mllama": load_mllama,
 }
 
 

tests/models/encoder_decoder/vision_language/test_mllama.py

@@ -12,7 +12,7 @@ from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
 from ....utils import large_gpu_test
 from ...utils import check_logprobs_close
 
-_LIMIT_IMAGE_PER_PROMPT = 1
+_LIMIT_IMAGE_PER_PROMPT = 3
 
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "stop_sign":
@@ -244,8 +244,9 @@ def _run_test(
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [128])
 @pytest.mark.parametrize("num_logprobs", [5])
-def test_models(hf_runner, vllm_runner, image_assets, model, sizes, dtype,
-                max_tokens, num_logprobs) -> None:
+def test_models_single_leading_image(hf_runner, vllm_runner, image_assets,
+                                     model, sizes, dtype, max_tokens,
+                                     num_logprobs) -> None:
     run_test(
         hf_runner,
         vllm_runner,
@@ -257,3 +258,81 @@ def test_models(hf_runner, vllm_runner, image_assets, model, sizes, dtype,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+@large_gpu_test(min_gb=48)
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models_multi_leading_images(hf_runner, vllm_runner, image_assets,
+                                     model, dtype, max_tokens,
+                                     num_logprobs) -> None:
+
+    stop_sign = image_assets[0].pil_image
+    cherry_blossom = image_assets[1].pil_image
+
+    inputs = [(
+        [
+            "<|image|><|image|><|begin_of_text|>Describe 2 images.",  # noqa: E501
+            "<|image|><|image|><|begin_of_text|>Describe 2 images.",  # noqa: E501
+            "<|image|><|image|><|image|><|begin_of_text|>Describe 3 images.",  # noqa: E501
+        ],
+        [
+            [stop_sign, cherry_blossom],
+            # Images with different sizes.
+            [
+                stop_sign.resize((512, 512)),
+                stop_sign,
+            ],
+            [
+                stop_sign,
+                stop_sign.resize((512, 1536)),
+                cherry_blossom.resize((512, 1024)),
+            ],
+        ])]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        inputs,
+        model,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )
+
+
+@large_gpu_test(min_gb=48)
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models_interleaved_images(hf_runner, vllm_runner, image_assets, model,
+                                   dtype, max_tokens, num_logprobs) -> None:
+
+    stop_sign = image_assets[0].pil_image
+    cherry_blossom = image_assets[1].pil_image
+
+    inputs = [(
+        [
+            "<|begin_of_text|>The content of the image <|image|> is",  # noqa: E501
+            "<|begin_of_text|>Between the first image <|image|> and the second image<|image|>, "  # noqa: E501
+            "which is a stop sign and which is a cherry blossom?",  # noqa: E501
+        ],
+        [
+            [stop_sign],
+            [stop_sign, cherry_blossom],
+        ])]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        inputs,
+        model,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

vllm/model_executor/models/mllama.py

@@ -18,6 +18,7 @@ from array import array
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
 
+import numpy as np
 import torch
 import torch.nn.functional as F
 import torch.utils.checkpoint
@@ -28,9 +29,12 @@ from transformers.modeling_outputs import (BaseModelOutput,
                                            CausalLMOutputWithPast)
 from transformers.models.mllama.image_processing_mllama import (
     get_optimal_tiled_canvas)
+from transformers.models.mllama.processing_mllama import (
+    get_cross_attention_token_mask)
 
 import vllm.distributed.parallel_state as ps
 from vllm.attention import Attention, AttentionMetadata, AttentionType
+from vllm.attention.ops.paged_attn import PagedAttention
 from vllm.config import CacheConfig, MultiModalConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import INPUT_REGISTRY, InputContext, LLMInputs
@@ -72,6 +76,16 @@ class MllamaImagePixelInputs(TypedDict):
 # TODO: support LlamaImageEmbeddingInputs
 
 
+def _get_num_image_in_last_group(prompt_token_ids: List[int]) -> int:
+    num_images = 0
+    for token_id in prompt_token_ids[::-1]:
+        if token_id == MLLAMA_IMAGE_TOKEN_ID:
+            num_images += 1
+        elif num_images > 0:
+            break
+    return num_images
+
+
 def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
     # move encoder_prompt to prompt
     if llm_inputs.get("prompt") is None:
@@ -91,12 +105,16 @@ def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
         llm_inputs["encoder_multi_modal_data"] = {}
         return llm_inputs
 
-    # get num_tiles
     if isinstance(multi_modal_data['image'], Image.Image):
         multi_modal_data['image'] = [multi_modal_data['image']]
+    # Since only the last group of consecutive images
+    # are attended by the decoded tokens, we only need to
+    # get the number of tiles for those images.
+    num_decode_images = _get_num_image_in_last_group(
+        llm_inputs["prompt_token_ids"])
     hf_config = ctx.model_config.hf_config
     num_tiles = 0
-    for image in multi_modal_data["image"]:
+    for image in multi_modal_data["image"][::-1]:
         width, height = image.size
         tile_size = hf_config.vision_config.image_size
         canvas_height, canvas_width = get_optimal_tiled_canvas(
@@ -108,8 +126,13 @@ def input_processor_for_mllama(ctx: InputContext, llm_inputs: LLMInputs):
         num_tiles_height = canvas_height // tile_size
         num_tiles_width = canvas_width // tile_size
         num_tiles += num_tiles_height * num_tiles_width
+        num_decode_images -= 1
+        if num_decode_images == 0:
+            break
 
-    # set encoder prompt based on num_tiles
+    # Set encoder prompt length based on the number of tiles.
+    # This tells the block manager to allocate correct number
+    # of slots for encoder tokens.
     assert hf_config.vision_config.image_size % 14 == 0, \
         "chunk size should be multiple of 14"
     token_per_chunk = (hf_config.vision_config.image_size // 14)**2 + 1
@@ -675,6 +698,7 @@ class MllamaTextCrossAttention(nn.Module):
         self,
         hidden_states: torch.Tensor,
         attention_mask: Optional[torch.Tensor],
+        kv_range_for_decode: Optional[List[Tuple[int, int]]],
         cross_attention_states: Optional[torch.Tensor],
         kv_cache: torch.Tensor,
         attn_metadata: AttentionMetadata,
@@ -697,15 +721,71 @@ class MllamaTextCrossAttention(nn.Module):
         q = q.view(-1, self.num_local_heads, self.head_dim)
         q = self.q_norm(q)
 
-        output = self.attn(q,
-                           k,
-                           v,
-                           kv_cache,
-                           attn_metadata,
-                           attn_type=AttentionType.ENCODER_DECODER)
+        if attention_mask is not None:
+            output = self.attention_with_mask(q, k, v, kv_cache,
+                                              attention_mask,
+                                              kv_range_for_decode,
+                                              attn_metadata)
+        else:
+            output = self.attn(q,
+                               k,
+                               v,
+                               kv_cache,
+                               attn_metadata,
+                               attn_type=AttentionType.ENCODER_DECODER)
         out, _ = self.o_proj(output)
         return out
 
+    def attention_with_mask(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attention_mask: torch.Tensor,
+        kv_range_for_decode: List[Tuple[int, int]],
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        # Skip writing kv-cache for the initial profiling run.
+        if len(kv_cache.shape) == 3:
+            key_cache, value_cache = PagedAttention.split_kv_cache(
+                kv_cache, self.num_local_key_value_heads, self.head_dim)
+            cached_k = torch.cat([k[s:e] for s, e in kv_range_for_decode])
+            cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
+            PagedAttention.write_to_paged_cache(
+                cached_k, cached_v, key_cache, value_cache,
+                attn_metadata.cross_slot_mapping, "auto", 1.0, 1.0)
+        # We have to call torch.sdpa for prefill when using a
+        # custom cross-attention mask. Because the mask is not a
+        # standard causal mask, neither a block diagonal mask which
+        # can be optimized by xformers.BlockDiagonalMask.
+        # The mask is specially calculated for supporting multi
+        # images and interleaved images.
+        q_len = q.shape[0]
+        kv_len = k.shape[0]
+        q = q.transpose(0, 1).view(self.num_local_key_value_heads,
+                                   self.num_key_value_groups, q_len,
+                                   self.head_dim)
+        k = k.transpose(0,
+                        1)[:,
+                           None, :, :].expand(self.num_local_key_value_heads,
+                                              self.num_key_value_groups,
+                                              kv_len, self.head_dim)
+        v = v.transpose(0,
+                        1)[:,
+                           None, :, :].expand(self.num_local_key_value_heads,
+                                              self.num_key_value_groups,
+                                              kv_len, self.head_dim)
+        attention_mask = attention_mask.view(1, 1, q_len, kv_len)
+        output = F.scaled_dot_product_attention(q,
+                                                k,
+                                                v,
+                                                attn_mask=attention_mask,
+                                                is_causal=False)
+        output = output.permute(2, 0, 1, 3).reshape(
+            q_len, self.num_local_heads * self.head_dim)
+        return output
+
 
 class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
     """Cross-attention transformer block with tanh-gated attention
@@ -741,6 +821,7 @@ class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
         hidden_states: torch.Tensor,
         cross_attention_states: torch.Tensor,
         cross_attention_mask: torch.Tensor,
+        kv_range_for_decode: Optional[List[Tuple[int, int]]],
         full_text_row_masked_out_mask: torch.Tensor,
         kv_cache: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
@@ -751,6 +832,7 @@ class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
         hidden_states = self.cross_attn(
             hidden_states=hidden_states,
             attention_mask=cross_attention_mask,
+            kv_range_for_decode=kv_range_for_decode,
             cross_attention_states=cross_attention_states,
             kv_cache=kv_cache,
             attn_metadata=attn_metadata,
@@ -804,6 +886,7 @@ class MllamaTextModel(nn.Module):
         positions: Optional[torch.LongTensor],
         cross_attention_states: Optional[torch.LongTensor],
         cross_attention_mask: Optional[torch.LongTensor],
+        kv_range_for_decode: Optional[List[Tuple[int, int]]],
         full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor,
                                                       torch.Tensor]],
         kv_caches: List[torch.Tensor],
@@ -820,6 +903,7 @@ class MllamaTextModel(nn.Module):
                         hidden_states=hidden_states,
                         cross_attention_states=cross_attention_states,
                         cross_attention_mask=cross_attention_mask,
+                        kv_range_for_decode=kv_range_for_decode,
                         full_text_row_masked_out_mask=
                         full_text_row_masked_out_mask,
                         kv_cache=kv_caches[idx],
@@ -868,6 +952,7 @@ class MllamaForCausalLM(nn.Module):
         positions: Optional[torch.LongTensor],
         cross_attention_states: Optional[torch.LongTensor],
         cross_attention_mask: Optional[torch.LongTensor],
+        kv_range_for_decode: Optional[List[Tuple[int, int]]],
         full_text_row_masked_out_mask: Optional[Tuple[torch.Tensor,
                                                       torch.Tensor]],
         kv_caches: List[torch.Tensor],
@@ -879,6 +964,7 @@ class MllamaForCausalLM(nn.Module):
             positions=positions,
             cross_attention_states=cross_attention_states,
             cross_attention_mask=cross_attention_mask,
+            kv_range_for_decode=kv_range_for_decode,
             full_text_row_masked_out_mask=full_text_row_masked_out_mask,
             kv_caches=kv_caches,
             attn_metadata=attn_metadata,
@@ -1026,36 +1112,102 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
         raise AssertionError("This line should be unreachable.")
 
     def flat_encoder_result(self, cross_attention_states: torch.Tensor,
-                            attn_metadata: AttentionMetadata):
+                            attn_metadata: AttentionMetadata,
+                            actual_encoder_seq_lens: List[int]):
 
         cross_attention_states_flat = torch.zeros(
-            sum(attn_metadata.encoder_seq_lens),
+            sum(actual_encoder_seq_lens),
             cross_attention_states.shape[-1],
             device=cross_attention_states.device,
             dtype=cross_attention_states.dtype)
         start_pos = 0
-        for seq_len, vision_token_in_batch in zip(
-                attn_metadata.encoder_seq_lens, cross_attention_states):
+        for seq_len, vision_token_in_batch in zip(actual_encoder_seq_lens,
+                                                  cross_attention_states):
             end_pos = start_pos + seq_len
             cross_attention_states_flat[
                 start_pos:end_pos] = vision_token_in_batch[:seq_len]
             start_pos = end_pos
         cross_attention_states = cross_attention_states_flat
+        return cross_attention_states
+
+    def get_cross_attention_states(
+        self,
+        image_inputs: MllamaImagePixelInputs,
+        attn_metadata: AttentionMetadata,
+        actual_encoder_seq_lens: List[int],
+    ) -> Tuple[torch.Tensor]:
+        # NOTE: llama's reference implementation runs vision model on CPU
+        pixel_values = image_inputs['data']
+        aspect_ratio_ids = image_inputs['aspect_ratio_ids']
+        aspect_ratio_mask = image_inputs['aspect_ratio_mask']
+        cross_attention_states = self.vision_model(pixel_values,
+                                                   aspect_ratio_ids,
+                                                   aspect_ratio_mask)
+        cross_attention_states = self.multi_modal_projector(
+            cross_attention_states)
+
+        bsz, _, _, _, image_token_dim = tuple(cross_attention_states.shape)
+        cross_attention_states = cross_attention_states.view(
+            bsz, -1, image_token_dim)
+
+        cross_attention_states = self.flat_encoder_result(
+            cross_attention_states, attn_metadata, actual_encoder_seq_lens)
+
+        return cross_attention_states
+
+    def get_cross_attention_mask(
+        self,
+        input_ids: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+        num_tiles: List[List[int]],
+        num_tokens_per_tile: int,
+        dtype: torch.dtype,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        token_ids = input_ids.tolist()
+        start = 0
+        batch_token_ids = []
+        for seq_len in attn_metadata.seq_lens:
+            batch_token_ids.append(token_ids[start:start + seq_len])
+            start += seq_len
+        sparse_mask = [
+            get_cross_attention_token_mask(t, MLLAMA_IMAGE_TOKEN_ID)
+            for t in batch_token_ids
+        ]
 
+        # Skip generating cross-attention mask if all samples
+        # are text-only or have only 1 leading image.
+        if skip_attention_mask(sparse_mask):
+            return None, None
+
+        dense_mask, tile_range_for_decode = \
+            convert_sparse_cross_attention_mask_to_dense(
+                sparse_mask, num_tiles, attn_metadata.seq_lens)
+        cross_attention_mask = \
+            convert_dense_cross_attention_mask_to_tensor(
+                dense_mask, num_tokens_per_tile, input_ids.device, dtype)
+        kv_range_for_decode = [[
+            t[0] * num_tokens_per_tile, t[1] * num_tokens_per_tile
+        ] for t in tile_range_for_decode]
+
+        return cross_attention_mask, kv_range_for_decode
+
+    def get_full_text_row_masked_out_mask(
+        self,
+        attn_metadata: AttentionMetadata,
+        device: torch.device,
+    ) -> torch.Tensor:
         full_text_row_masked_out_mask = torch.ones(
             (attn_metadata.num_prefill_tokens, 1), dtype=torch.bool)
         start_pos = 0
-        for seq_len, encoder_seq_len in zip(
-                attn_metadata.seq_lens_tensor.cpu(),
-                attn_metadata.encoder_seq_lens):
+        for seq_len, encoder_seq_len in zip(attn_metadata.seq_lens,
+                                            attn_metadata.encoder_seq_lens):
             if encoder_seq_len == 0:
                 full_text_row_masked_out_mask[start_pos:start_pos +
                                               seq_len] = False
             start_pos += seq_len
         full_text_row_masked_out_mask = full_text_row_masked_out_mask.to(
-            cross_attention_states.device)
-
-        return cross_attention_states, full_text_row_masked_out_mask
+            device)
+        return full_text_row_masked_out_mask
 
     def forward(
         self,
@@ -1069,39 +1221,54 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
             attn_metadata.num_decode_tokens > 0:
             raise ValueError("Chunk prefill not supported")
         image_inputs = self._parse_and_validate_image_input(**kwargs)
+        cross_attention_states = None
+        cross_attention_mask = None
+        kv_range_for_decode = None
+
+        # For 1) text-only prefill and decode, 2) image-present decode.
         if image_inputs is None:
-            cross_attention_mask = None
             full_text_row_masked_out_mask = (
                 attn_metadata.encoder_seq_lens_tensor != 0).reshape(-1, 1).to(
                     input_ids.device)
-            cross_attention_states = None
             skip_cross_attention = max(attn_metadata.encoder_seq_lens) == 0
+
+        # For image-present prefill.
         else:
-            # NOTE: llama's reference implementation runs vision model on CPU
-            pixel_values = image_inputs['data']
-            aspect_ratio_ids = image_inputs['aspect_ratio_ids']
-            aspect_ratio_mask = image_inputs['aspect_ratio_mask']
-            cross_attention_states = self.vision_model(pixel_values,
-                                                       aspect_ratio_ids,
-                                                       aspect_ratio_mask)
-            cross_attention_states = self.multi_modal_projector(
-                cross_attention_states)
-
-            bsz, _, _, _, image_token_dim = tuple(cross_attention_states.shape)
-            cross_attention_states = cross_attention_states.view(
-                bsz, -1, image_token_dim)
-
-            cross_attention_states, full_text_row_masked_out_mask = \
-                self.flat_encoder_result(cross_attention_states, attn_metadata)
             skip_cross_attention = False
-            # TODO: support multi-image by this mask
-            cross_attention_mask = None
+
+            # Get the actual number of encoder tokens for each sample.
+            # Because attn_metadata.encoder_seq_lens only counts the last
+            # group of images for each sample, which is used to cheat the
+            # block manager to allocate blocks for those images only.
+            # See input_processor_for_mllama() for more details.
+            num_tiles_tensor = kwargs.pop("num_tiles")
+            num_tiles = [t[0].tolist() for t in num_tiles_tensor]
+            num_tokens_per_tile = (self.image_size // 14)**2 + 1
+            actual_encoder_seq_lens = [
+                sum(num_tile) * num_tokens_per_tile for num_tile in num_tiles
+            ]
+            for actual_len, last_group_len in zip(
+                    actual_encoder_seq_lens, attn_metadata.encoder_seq_lens):
+                assert actual_len >= last_group_len
+
+            cross_attention_states = self.get_cross_attention_states(
+                image_inputs, attn_metadata, actual_encoder_seq_lens)
+
+            full_text_row_masked_out_mask = \
+                self.get_full_text_row_masked_out_mask(
+                    attn_metadata, input_ids.device)
+
+            cross_attention_mask, kv_range_for_decode = \
+                self.get_cross_attention_mask(
+                    input_ids, attn_metadata, num_tiles,
+                    num_tokens_per_tile, cross_attention_states.dtype)
 
         outputs = self.language_model(
             input_ids=input_ids,
             positions=positions,
             cross_attention_states=cross_attention_states,
             cross_attention_mask=cross_attention_mask,
+            kv_range_for_decode=kv_range_for_decode,
             full_text_row_masked_out_mask=full_text_row_masked_out_mask,
             kv_caches=kv_caches,
             attn_metadata=attn_metadata,
@@ -1140,3 +1307,76 @@ class MllamaForConditionalGeneration(nn.Module, SupportsMultiModal):
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)
                 weight_loader(param, loaded_weight)
+
+
+def skip_attention_mask(sparse_mask: List[List[int]]) -> bool:
+    for mask in sparse_mask:
+        # Skip text-only samples.
+        if len(mask) == 0:
+            continue
+        # If the sample contains more than 1 images,
+        # we can't skip mask.
+        if len(mask) != 1:
+            return False
+        # If the sample contains only 1 image,
+        # but the image is not the leading one,
+        # we can't skip mask.
+        if mask[0][0] != 0 or mask[0][1] != -1:
+            return False
+    return True
+
+
+def convert_sparse_cross_attention_mask_to_dense(
+    sparse_mask: List[List[List[int]]],
+    num_tiles: List[List[int]],
+    lengths: List[int],
+) -> Tuple[np.ndarray, List[Tuple[int, int]]]:
+    total_length = sum(lengths)
+    total_tiles = sum([sum(tiles) for tiles in num_tiles])
+    dense_mask = np.zeros(shape=(total_length, total_tiles), dtype=np.int64)
+    # A list of ranges, range[i] = [start, end] means
+    # if the i-th sample has N tiles in total, the tiles[start, end]
+    # will be used for cross-attention decoding.
+    tile_range_for_decode = []
+
+    seq_start = 0
+    tile_start = 0
+    for masks, tiles, length in zip(sparse_mask, num_tiles, lengths):
+        ts, td = -1, 0
+        for mask, tile in zip(masks, tiles):
+            if len(mask) != 2:
+                continue
+            start, end = mask
+            end = min(end, length)
+            if end == -1:
+                end = length
+            if end == length:
+                if ts == -1:
+                    ts = tile_start
+                td += tile
+            dense_mask[seq_start + start:seq_start + end,
+                       tile_start:tile_start + tile] = 1
+            tile_start += tile
+        tile_range_for_decode.append((ts, ts + td))
+        seq_start += length
+
+    return dense_mask, tile_range_for_decode
+
+
+def convert_dense_cross_attention_mask_to_tensor(
+    cross_attention_token_mask: np.ndarray,
+    num_tokens_per_tile: int,
+    device: torch.device,
+    dtype: torch.dtype,
+) -> torch.Tensor:
+    mask = torch.tensor(cross_attention_token_mask, dtype=dtype, device=device)
+    mask = mask.repeat_interleave(num_tokens_per_tile, dim=1)
+
+    mask = 1.0 - mask
+    mask = mask.masked_fill(mask.to(torch.bool), torch.finfo(dtype).min)
+
+    ninf = torch.finfo(dtype).min
+    full_text_mask = ((mask != ninf).any(dim=-1).type_as(mask)[..., None])
+    mask *= full_text_mask
+    # (num_prompt_tokens, num_encoder_tokens)
+    return mask