[Fix] Fix llava on multi images (#1247)
This commit is contained in:
Lianmin Zheng
@@ -26,11 +26,6 @@ from vllm.config import CacheConfig
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from sglang.srt.mm_utils import (
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get_anyres_image_grid_shape,
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unpad_image,
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unpad_image_shape,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
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from sglang.srt.models.llama2 import LlamaForCausalLM
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@@ -59,23 +54,14 @@ class LlavaVidForCausalLM(nn.Module):
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torch.empty(config.text_config.hidden_size, dtype=torch.float16)
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)
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def pad_input_ids(self, input_ids, pad_value, pt_shape=None, image_size=None):
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def pad_input_ids(
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self,
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input_ids: List[int],
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pad_value: List[int],
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pixel_values: List,
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image_sizes: List[List[int]],
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):
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new_image_feature_len = self.image_feature_len
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# now only support spatial_unpad + anyres
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# if self.mm_patch_merge_type.startswith("spatial"):
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# height = width = self.num_patches_per_side
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# if pt_shape[0] > 1:
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# if self.image_aspect_ratio == "anyres":
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# num_patch_width, num_patch_height = get_anyres_image_grid_shape(
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# image_size,
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# self.image_grid_pinpoints,
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# self.vision_tower.config.image_size,
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# )
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# if "unpad" in self.mm_patch_merge_type:
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# h = num_patch_height * height
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# w = num_patch_width * width
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# new_h, new_w = unpad_image_shape(h, w, image_size)
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# new_image_feature_len += new_h * (new_w + 1)
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pad_ids = pad_value * (
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(new_image_feature_len + len(pad_value)) // len(pad_value)
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@@ -87,7 +73,7 @@ class LlavaVidForCausalLM(nn.Module):
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+ pad_ids[:new_image_feature_len]
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+ input_ids[offset + 1 :]
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)
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return new_input_ids, offset
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return new_input_ids, [offset]
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def encode_images(self, pixel_values: torch.Tensor) -> torch.Tensor:
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image_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
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@@ -133,22 +119,18 @@ class LlavaVidForCausalLM(nn.Module):
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if input_metadata.forward_mode == ForwardMode.EXTEND:
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bs = input_metadata.batch_size
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# Embed text input
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# Embed text inputs
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input_embeds = self.language_model.model.embed_tokens(input_ids)
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# Embed vision input
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need_vision = (
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(positions[input_metadata.extend_start_loc] < self.image_feature_len)
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.cpu()
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.numpy()
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# Whether the requests need vision inputs
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max_image_offset = np.array(
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[max(image_offsets[i]) if image_offsets[i] else -1 for i in range(bs)]
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)
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# FIXME: We need to substract the length of the system prompt
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has_pixel = np.array([pixel_values[i] is not None for i in range(bs)])
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need_vision = need_vision & has_pixel
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start_positions = positions[input_metadata.extend_start_loc].cpu().numpy()
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need_vision = start_positions <= max_image_offset
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if need_vision.any():
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pixel_values = [pixel_values[i] for i in range(bs) if need_vision[i]]
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image_sizes = [image_sizes[i] for i in range(bs) if need_vision[i]]
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########## Encode Image ########
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@@ -183,31 +165,36 @@ class LlavaVidForCausalLM(nn.Module):
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new_image_features.append(image_feature.flatten(0, 1))
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image_features = new_image_features
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# Fill in the placeholder for the image
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extend_start_loc_cpu = input_metadata.extend_start_loc.cpu().numpy()
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prefix_lens_cpu = input_metadata.extend_prefix_lens.cpu().numpy()
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pt = 0
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for i in range(bs):
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if not need_vision[i]:
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continue
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start_idx = extend_start_loc_cpu[i]
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pad_len, pad_dim = image_features[pt].shape # 576, 4096
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dim = input_embeds.shape[1]
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assert (
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pad_dim == dim
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), "invalid pad_dim={}, input_embed_dim={}!".format(pad_dim, dim)
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# Fill in the placeholder for the image
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try:
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input_embeds[
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start_idx
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+ image_offsets[i] : start_idx
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+ image_offsets[i]
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+ pad_len
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] = image_features[pt]
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except RuntimeError as e:
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print(f"RuntimeError in llava image encoding: {e}")
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print(input_embeds.shape)
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print(start_idx, image_offsets[i])
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pt += 1
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prefix_len = prefix_lens_cpu[i]
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# Multiple images
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for image_offset in image_offsets[i]:
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if image_offset < prefix_len:
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continue
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tmp_image_feature = image_features[pt]
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pad_len = tmp_image_feature.shape[0]
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left_idx = start_idx + (image_offset - prefix_len)
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right_idx = start_idx + (image_offset - prefix_len) + pad_len
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try:
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input_embeds[left_idx:right_idx] = tmp_image_feature
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except RuntimeError as e:
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print(f"RuntimeError in image encoding: {e}")
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print(f"{input_embeds.shape=}, {tmp_image_feature.shape=}")
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print(
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f"{start_idx=}, {image_offset=}, {prefix_len=}, {pad_len=}"
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)
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pt += 1
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return self.language_model(
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input_ids, positions, input_metadata, input_embeds=input_embeds
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@@ -216,8 +203,9 @@ class LlavaVidForCausalLM(nn.Module):
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return self.language_model(input_ids, positions, input_metadata)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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# load clip vision model by cfg['mm_vision_tower']:
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# huggingface_name or path_of_clip_relative_to_llava_model_dir
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# Load clip vision model by cfg['mm_vision_tower']:
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# huggingface_name or path_of_clip_relative_to_llava_model_dir
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# We put the initialization here instead of __init__ to allow it being reused by other subclasses.
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vision_path = self.config.mm_vision_tower
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self.vision_tower = CLIPVisionModel.from_pretrained(
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vision_path, torch_dtype=torch.float16
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@@ -271,43 +259,9 @@ class LlavaVidForCausalLM(nn.Module):
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# load language model
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self.language_model.load_weights(weights)
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monkey_path_clip_vision_embed_forward()
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@property
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def num_patches_per_side(self):
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return self.image_size // self.patch_size
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first_call = True
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def clip_vision_embed_forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
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batch_size = pixel_values.shape[0]
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# Move this conv layer to CPU to avoid a bug in torch >= 2.1 on A10G.
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global first_call
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if first_call:
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self.patch_embedding.cpu().float()
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first_call = False
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pixel_values = pixel_values.to(dtype=torch.float32, device="cpu")
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patch_embeds = self.patch_embedding(pixel_values).cuda().half()
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patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
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class_embeds = self.class_embedding.expand(batch_size, 1, -1)
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embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
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embeddings = embeddings + self.position_embedding(self.position_ids)
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return embeddings
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def monkey_path_clip_vision_embed_forward():
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import transformers
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setattr(
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transformers.models.clip.modeling_clip.CLIPVisionEmbeddings,
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"forward",
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clip_vision_embed_forward,
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)
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EntryClass = LlavaVidForCausalLM
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