support tarsier2

Author: yihuiwen

lightllm/models/tarsier2/__init__.py

@@ -0,0 +1,38 @@
+import torch
+import numpy as np
+from lightllm.models.llama.layer_weights.pre_and_post_layer_weight import LlamaPreAndPostLayerWeight
+
+from lightllm.models.internlm2.layer_weights.pre_and_post_layer_weight import Internlm2PreAndPostLayerWeight
+from lightllm.models.qwen2.layer_weights.pre_and_post_layer_weight import Qwen2PreAndPostLayerWeight
+
+
+# add key: language_model.xxx -> xxx
+# only change keys at PreAndPostLayerWeight load, TransformLayerWeight is correct now
+def rename_weight_keys(weights):
+    prefix = "language_model."
+    keys = list(weights.keys())
+    for k in keys:
+        if prefix in k:
+            weights[k[len(prefix) :]] = weights[k]
+
+
+class Tarsier2Qwen2PreAndPostLayerWeight(Qwen2PreAndPostLayerWeight):
+    def __init__(self, data_type, network_config, mode):
+        super().__init__(data_type, network_config, mode)
+        return
+
+    def load_hf_weights(self, weights):
+        rename_weight_keys(weights)
+        super().load_hf_weights(weights)
+        return
+
+
+class Tarsier2LlamaPreAndPostLayerWeight(LlamaPreAndPostLayerWeight):
+    def __init__(self, data_type, network_config, mode):
+        super().__init__(data_type, network_config, mode)
+        return
+
+    def load_hf_weights(self, weights):
+        rename_weight_keys(weights)
+        super().load_hf_weights(weights)
+        return

lightllm/models/tarsier2/layer_weights/__init__.py

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+import json
+import os
+
+from lightllm.common.build_utils import repair_config
+from lightllm.models.llama.model import LlamaTpPartModel
+from lightllm.models.qwen2.model import Qwen2TpPartModel
+from lightllm.models.qwen2_vl.model import Qwen2VLTpPartModel
+from lightllm.models.qwen2_vl.vision_process import smart_resize
+from lightllm.models.qwen_vl.layer_infer.pre_layer_infer import LlamaMultimodalPreLayerInfer
+from lightllm.models.tarsier2.layer_weights.pre_and_post_layer_weight import Tarsier2Qwen2PreAndPostLayerWeight, Tarsier2LlamaPreAndPostLayerWeight
+from lightllm.server.multimodal_params import MultimodalParams, ImageItem
+from lightllm.server.core.objs import SamplingParams
+
+
+class Tarsier2Tokenizer:
+
+    def __init__(self, tokenizer=None, image_processor=None, **kwargs):
+        self.tokenizer = tokenizer
+        self.image_processor = image_processor
+        self.image_start_id = kwargs["model_cfg"]["text_config"]["vision_start_token_id"]
+        self.image_end_id = kwargs["model_cfg"]["text_config"]["vision_end_token_id"]
+        self.image_token_id = kwargs["model_cfg"]["text_config"]["image_token_id"]
+
+    def init_imageItem_extral_params(
+        self, img: ImageItem, multi_params: MultimodalParams, sampling_params: SamplingParams
+    ):
+        return
+
+    def get_image_token_length(self, img: ImageItem):
+        width = img.image_w
+        height = img.image_h
+        resized_height, resized_width = smart_resize(height=height, width=width)
+        self.patch_size = self.image_processor.patch_size
+        self.merge_size = self.image_processor.merge_size
+        grid_t = 1
+        grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
+        merge_length = self.merge_size ** 2
+        self.token_num = (grid_t * grid_h * grid_w) // merge_length
+        self.image_length = self.token_num
+        return self.image_length
+
+    def encode(self, prompt, multimodal_params: MultimodalParams = None, **kwargs):
+
+        origin_ids = self.tokenizer.encode(prompt)
+
+        # <img><image_pad></img> -> <img></img>
+        origin_ids = [token for token in origin_ids if token != self.image_token_id]
+        # <img></img> --> <img>id,id+1...id+num</img>
+        input_ids = []
+        image_id = 0
+        start_idx = 0
+        while True:
+            try:
+                start_idx = origin_ids.index(self.image_start_id, start_idx)
+                if start_idx + 1 >= len(origin_ids):
+                    break
+                if origin_ids[start_idx + 1] == self.image_end_id:
+                    input_ids.extend(origin_ids[: start_idx + 1])
+                    token_id = multimodal_params.images[image_id].token_id
+                    token_num = multimodal_params.images[image_id].token_num
+                    input_ids.extend(range(token_id, token_id + token_num))
+                    input_ids.append(self.image_end_id)
+                    origin_ids = origin_ids[start_idx + 2 :]
+                    start_idx = 0
+                    image_id += 1
+                else:
+                    raise ValueError("image token error")
+            except ValueError:
+                break
+        input_ids.extend(origin_ids[start_idx:])
+        return input_ids
+
+    def __getattr__(self, name):
+        if name != "encode":
+            return getattr(self.tokenizer, name)
+        return self.encode
+
+
+    pass
+
+class Tarsier2Qwen2TpPartModel(Qwen2TpPartModel):
+    # weight class
+    pre_and_post_weight_class = Tarsier2Qwen2PreAndPostLayerWeight
+
+    # infer class
+    pre_layer_infer_class = LlamaMultimodalPreLayerInfer
+
+    def __init__(self, kvargs):
+        super().__init__(kvargs)
+        return
+
+    def _init_config(self):
+        with open(os.path.join(self.weight_dir_, "config.json"), "r") as json_file:
+            self.config = json.load(json_file)["text_config"]
+        # rename keys
+        repair_config(self.config, same_names=["num_attention_heads", "n_head"])
+        repair_config(self.config, same_names=["hidden_size", "n_embd", "n_embed"])
+        repair_config(self.config, same_names=["num_hidden_layers", "n_layer"])
+        return
+
+
+class Tarsier2Qwen2VLTpPartModel(Qwen2VLTpPartModel):
+    # weight class
+    pre_and_post_weight_class = Tarsier2Qwen2PreAndPostLayerWeight
+
+    # infer class
+    pre_layer_infer_class = LlamaMultimodalPreLayerInfer
+
+    def __init__(self, kvargs):
+        super().__init__(kvargs)
+        return
+
+    def _init_config(self):
+        with open(os.path.join(self.weight_dir_, "config.json"), "r") as json_file:
+            self.config = json.load(json_file)["text_config"]
+        # rename keys
+        repair_config(self.config, same_names=["num_attention_heads", "n_head"])
+        repair_config(self.config, same_names=["hidden_size", "n_embd", "n_embed"])
+        repair_config(self.config, same_names=["num_hidden_layers", "n_layer"])
+        return
+
+class Tarsier2LlamaTpPartModel(LlamaTpPartModel):
+
+    pre_and_post_weight_class = Tarsier2LlamaPreAndPostLayerWeight
+
+    # infer class
+    pre_layer_infer_class = LlamaMultimodalPreLayerInfer
+
+    def __init__(self, kvargs):
+        super().__init__(kvargs)
+        return
+
+    def _init_config(self):
+        with open(os.path.join(self.weight_dir_, "config.json"), "r") as json_file:
+            self.config = json.load(json_file)["text_config"]
+        # rename keys
+        repair_config(self.config, same_names=["num_attention_heads", "n_head"])
+        repair_config(self.config, same_names=["hidden_size", "n_embd", "n_embed"])
+        repair_config(self.config, same_names=["num_hidden_layers", "n_layer"])
+        return