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Support precomputed_embeddings for Llama 4 (#8156)

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Signed-off-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
Co-authored-by: Xiang (Kevin) Li <lik@nvidia.com>
Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>
Co-authored-by: Xinyuan Tong <xinyuantong.cs@gmail.com>
This commit is contained in:
Kevin Xiang Li
2025-07-27 01:14:49 -07:00
committed by GitHub
parent 5c9c275bc8
commit 44d600cd67
6 changed files with 449 additions and 123 deletions
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19
python/sglang/srt/layers/multimodal.py
View File

@@ -55,14 +55,17 @@ def gpu_tensor_hash(tensor: torch.Tensor) -> int:
intermediate_hashes = torch.empty(n, dtype=torch.int64, device=tensor.device)
hash_kernel[grid](
tensor,
intermediate_hashes,
n,
BLOCK_SIZE=BLOCK_SIZE,
PRIME=PRIME_1,
XCONST=PRIME_2,
)
# Set cuda device to prevent ValueError: Pointer argument (at 0) cannot be accessed from Triton (cpu tensor?)
# Solution from Tri: https://github.com/Dao-AILab/flash-attention/issues/523#issuecomment-1707611579
with torch.cuda.device(tensor.device):
hash_kernel[grid](
tensor,
intermediate_hashes,
n,
BLOCK_SIZE=BLOCK_SIZE,
PRIME=PRIME_1,
XCONST=PRIME_2,
)
# TODO: threads can't be synced on triton kernel
final_hash = intermediate_hashes.sum().item()

125
python/sglang/srt/multimodal/processors/mllama4.py
View File

@@ -22,12 +22,12 @@ class Mllama4ImageProcessor(BaseMultimodalProcessor):
super().__init__(hf_config, server_args, _processor, *args, **kwargs)
self.vision_config = hf_config.vision_config
self.text_config = hf_config.text_config
self.boi_token_index = hf_config.boi_token_index
self.eoi_token_index = hf_config.eoi_token_index
self.image_token_index = hf_config.image_token_index
self.multimodal_tokens = MultimodalSpecialTokens(
self.IM_START_TOKEN_ID = hf_config.boi_token_index
self.IM_END_TOKEN_ID = hf_config.eoi_token_index
self.IM_TOKEN_ID = hf_config.image_token_index
self.mm_tokens = MultimodalSpecialTokens(
image_token=_processor.image_token,
image_token_id=self.image_token_index,
image_token_id=self.IM_TOKEN_ID,
).build(_processor)
async def process_mm_data_async(
@@ -37,114 +37,21 @@ class Mllama4ImageProcessor(BaseMultimodalProcessor):
*args,
**kwargs,
):
if isinstance(input_text, list):
assert len(input_text) and isinstance(input_text[0], int)
input_text = self._processor.tokenizer.decode(input_text)
# Process images and text using the base processor's load_mm_data method
processed_data = self.load_mm_data(
base_output = self.load_mm_data(
prompt=input_text,
multimodal_tokens=self.multimodal_tokens,
image_data=image_data,
return_text=True,
multimodal_tokens=self.mm_tokens,
)
# Process the images using the processor
processor = self._processor
# Process the prompt and images
processor_output = self.process_mm_data(
input_text=processed_data.input_text,
images=processed_data.images,
mm_items, input_ids, _ = self.process_and_combine_mm_data(
base_output, self.mm_tokens
)
# Handle image resolutions and aspect ratios
if "pixel_values" not in processor_output: # no image processed
return None
image_processor = processor.image_processor
tokenizer = self._processor.tokenizer
# Calculate tile size and find supported resolutions
tile_size = self.vision_config.image_size
max_num_tiles = getattr(self.vision_config, "max_patches", 1)
possible_resolutions = find_supported_resolutions(
max_num_chunks=max_num_tiles,
patch_size=SizeDict(height=tile_size, width=tile_size),
)
# Find best fit for each image
best_fit_sizes = [
get_best_fit(
(image.size[1], image.size[0]), # (height, width)
torch.tensor(possible_resolutions),
resize_to_max_canvas=image_processor.resize_to_max_canvas,
)
for image in processed_data.images
]
# Calculate aspect ratios and patches per image
aspect_ratios = [
(image_size[0] // tile_size, image_size[1] // tile_size)
for image_size in best_fit_sizes
]
patches_per_image = [
1 if r_h * r_w == 1 else 1 + r_h * r_w for (r_h, r_w) in aspect_ratios
]
# Add to image_inputs
processor_output["aspect_ratios"] = aspect_ratios
processor_output["patches_per_image"] = torch.tensor(patches_per_image)
# Process embed_is_patch
vocab = tokenizer.get_vocab()
patch_id = vocab.get(processor.img_patch_token, -1)
image_end_id = vocab.get(processor.end_of_img_token, -1)
if patch_id != -1 and image_end_id != -1:
input_ids = processor_output["input_ids"].view(-1)
# Remove BOS token if present
if input_ids.size(0) > 0 and input_ids[0] == tokenizer.bos_token_id:
input_ids = input_ids[1:]
# Find image end indices and split input_ids
image_end_indices = (input_ids == image_end_id).nonzero().view(-1)
if image_end_indices.size(0) > 0:
# Split at image boundaries
split_indices = (image_end_indices + 1)[:-1]
split_input_ids = torch.tensor_split(input_ids, split_indices)
split_input_ids = [x for x in split_input_ids if x.numel() > 0]
# Create embed_is_patch for each image
embed_is_patch = []
for per_image_input_ids in split_input_ids:
embed_is_patch.append(per_image_input_ids == patch_id)
processor_output["embed_is_patch"] = embed_is_patch
# Convert to the format expected by SGLang
processor_output["input_ids"] = processor_output["input_ids"].tolist()[0]
processor_output["im_start_id"] = self.boi_token_index
processor_output["im_end_id"] = self.eoi_token_index
processor_output["im_token_id"] = self.image_token_index
image_offsets = self.get_mm_items_offset(
input_ids=torch.tensor(processor_output["input_ids"]),
mm_token_id=self.image_token_index,
)
# Add metadata for image processing
processor_output["mm_items"] = [
MultimodalDataItem(
feature=processor_output["pixel_values"],
modality=Modality.IMAGE,
offsets=image_offsets,
)
]
return processor_output
return {
"input_ids": input_ids.tolist(),
"mm_items": mm_items,
"im_start_id": self.IM_START_TOKEN_ID,
"im_end_id": self.IM_END_TOKEN_ID,
"im_token_id": self.IM_TOKEN_ID,
}