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model : Granite docling + Idefics3 preprocessing (SmolVLM) (#16206)
* feat: Add granite-docling conversion using trillion pretokenizer Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add granite-docling vocab pre enum Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use granite-docling pre Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add clip_is_idefics3 Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow multi-token boundary sequences for image templating Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add tiling support for idefices3 in clip.cpp This should likely be moved into llava_uhd::get_slice_instructions, but for now this avoids disrupting the logic there. Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Partial support for full templating for idefics3 in mtmd There are still errors encoding some of the image chunks, but the token sequence now matches transformers _almost_ perfectly, except for the double newline before the global image which shows up as two consecutive newline tokens instead of a single double-newline token. I think this is happening because the blocks are tokenized separately then concatenated. Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Fully working image preprocessing for idefics3 w/ resize and slicing Branch: gabe-l-hart/GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Parse the preprocessor config's longest side and add it to the mmproj hparams Branch: GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use the longest side instead of size * scale_factor For Granite Docling, these come out to the same value, but that was just a conicidence. Branch: GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Allow batch encoding and remove clip_is_idefics3 Branch: GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove unnecessary conditionals for empty token vectors Branch: GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use image_manipulation util Branch: GraniteDocling Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * add test model --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
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@@ -891,6 +891,9 @@ class TextModel(ModelBase):
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if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
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# ref: https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base
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res = "llada-moe"
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if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
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# ref: https://huggingface.co/ibm-granite/granite-docling-258M
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res = "granite-docling"
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if res is None:
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logger.warning("\n")
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@@ -1325,6 +1328,7 @@ class MmprojModel(ModelBase):
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self.tensor_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.MMPROJ, self.block_count)
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# load preprocessor config
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self.preprocessor_config = {}
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if not self.is_mistral_format:
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with open(self.dir_model / "preprocessor_config.json", "r", encoding="utf-8") as f:
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self.preprocessor_config = json.load(f)
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@@ -1347,7 +1351,8 @@ class MmprojModel(ModelBase):
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self.gguf_writer.add_vision_projection_dim(self.n_embd_text)
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# vision config
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self.gguf_writer.add_vision_image_size(self.find_vparam(["image_size"]))
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self.image_size = self.find_vparam(["image_size"])
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self.gguf_writer.add_vision_image_size(self.image_size)
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self.gguf_writer.add_vision_patch_size(self.find_vparam(["patch_size"]))
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self.gguf_writer.add_vision_embedding_length(self.find_vparam(["hidden_size"]))
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self.gguf_writer.add_vision_feed_forward_length(self.find_vparam(["intermediate_size"]))
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@@ -2378,6 +2383,10 @@ class SmolVLMModel(MmprojModel):
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self.gguf_writer.add_vision_projector_scale_factor(self.global_config.get("scale_factor", 2))
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self.gguf_writer.add_vision_use_gelu(True)
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# Add the preprocessor longest edge size
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preproc_image_size = self.preprocessor_config.get("size", {}).get("longest_edge", self.image_size)
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self.gguf_writer.add_vision_preproc_image_size(preproc_image_size)
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def tensor_force_quant(self, name, new_name, bid, n_dims):
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if ".embeddings." in name:
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return gguf.GGMLQuantizationType.F32
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