wip minicpmv

convert_hf_to_gguf.py

@@ -204,9 +204,10 @@ class Model:
                                  f"Missing tensors: {missing}\n"
                                  f"Extra tensors: {extra}")
 
-    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str:
-        if key not in gguf.MODEL_TENSORS[self.model_arch]:
-            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}")
+    def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight", is_vision = False) -> str:
+        arch = self.vision_arch if is_vision and self.vision_arch is not None else self.model_arch
+        if key not in gguf.MODEL_TENSORS[arch]:
+            raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {arch!r}")
         name: str = gguf.TENSOR_NAMES[key]
         if "{bid}" in name:
             assert bid is not None
@@ -2144,6 +2145,7 @@ class DbrxModel(Model):
 class MiniCPMModel(Model):
     model_arch = gguf.MODEL_ARCH.MINICPM
     proj_type: gguf.constants.CLIPProjectorType | None
+    resampler_n_embd = 0
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -2162,6 +2164,12 @@ class MiniCPMModel(Model):
                 self.proj_type = gguf.constants.CLIPProjectorType.MINICPMV_2_6
             else:
                 raise ValueError(f"Unsupported MiniCPM-V version: {version}")
+            # TODO: how to do this without reading the whole safetensor file?
+            for tname, tensor in self.get_tensors():
+                if tname == "resampler.ln_post.bias":
+                    self.resampler_n_embd = tensor.shape[0]
+            if self.resampler_n_embd < 2:
+                raise ValueError("Failed to detect resampler embedding size")
 
         if self.vparams is not None and self.vision_arch is not None and self.preprocessor_config is not None:
             self.preprocessor_config["image_mean"] = [0.5, 0.5, 0.5]
@@ -2220,6 +2228,12 @@ class MiniCPMModel(Model):
             yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_LONG), torch.tensor(long_factors, dtype=torch.float32))
             yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT), torch.tensor(short_factors, dtype=torch.float32))
 
+        if self.vision_arch == gguf.MODEL_ARCH.VISION_MINICPMV:
+            yield (
+                self.format_tensor_name(gguf.MODEL_TENSOR.V_RESMPL_POS_EMBD_K, is_vision=True),
+                torch.from_numpy(self._get_2d_sincos_pos_embed(self.resampler_n_embd, (70, 70)))
+            )
+
     def set_vocab(self):
         if self.vision_arch == gguf.MODEL_ARCH.VISION_MINICPMV:
             # undocumented anywhere, I only found this thanks to https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf
@@ -2233,11 +2247,23 @@ class MiniCPMModel(Model):
         # For vision model
         if name.startswith("llm."):
             name = name.replace("llm.", "")
-        # attention, someone mess up and use underscore instead of dot
-        if name.endswith("in_proj_weight"):
-            name = name.replace("_weight", ".weight")
-        if name.endswith("in_proj_bias"):
-            name = name.replace("_bias", ".bias")
+
+        # split the resampler.attn.in_proj_(weight|bias) tensors into q, k, v
+        if name.endswith("in_proj_weight") or name.endswith("in_proj_bias"):
+            assert data_torch.shape[0] == 3 * self.resampler_n_embd
+            split_tensor = data_torch.chunk(3, dim=0)
+            name_q = name.replace("in_proj_", "in_proj_q.") # in_proj_q.(weight|bias)
+            name_k = name.replace("in_proj_", "in_proj_k.") # in_proj_k.(weight|bias)
+            name_v = name.replace("in_proj_", "in_proj_v.") # in_proj_v.(weight|bias)
+            return [
+                (self.map_tensor_name(name_q), split_tensor[0]),
+                (self.map_tensor_name(name_k), split_tensor[1]),
+                (self.map_tensor_name(name_v), split_tensor[2]),
+            ]
+
+        if name == "resampler.proj" or name == "resampler.query":
+            name += ".weight"
+
         if "post_layernorm" in name:
             return [] # skip post_layernorm
 
@@ -2251,6 +2277,69 @@ class MiniCPMModel(Model):
             data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head)
 
         return [(self.map_tensor_name(name), data_torch)]
+    
+    def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
+        del name, bid  # unused
+        if "v.resmpl.query" in new_name or "v.resmpl.pos_embd_k" in new_name:
+            return gguf.GGMLQuantizationType.F32
+        if "v.resmpl." in new_name:
+            return gguf.GGMLQuantizationType.F32 if n_dims == 1 else gguf.GGMLQuantizationType.F16
+        return False
+    
+    # utils to work with MiniCPM-V resampler
+
+    # https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
+    def _get_2d_sincos_pos_embed(self, embed_dim: int, grid_size: tuple[int, int] | int, cls_token=False) -> np.ndarray:
+        """
+        grid_size: int of the grid height and width
+        return:
+        pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+        """
+        if isinstance(grid_size, int):
+            grid_h_size, grid_w_size = grid_size, grid_size
+        else:
+            grid_h_size, grid_w_size = grid_size[0], grid_size[1]
+
+        grid_h = np.arange(grid_h_size, dtype=np.float32)
+        grid_w = np.arange(grid_w_size, dtype=np.float32)
+        grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+        grid = np.stack(grid, axis=0)
+
+        grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
+        pos_embed = self._get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+        if cls_token:
+            pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+        return pos_embed
+
+    def _get_2d_sincos_pos_embed_from_grid(self, embed_dim: int, grid: np.ndarray) -> np.ndarray:
+        assert embed_dim % 2 == 0
+
+        # use half of dimensions to encode grid_h
+        emb_h = self._get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+        emb_w = self._get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+        emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+        return emb
+
+    def _get_1d_sincos_pos_embed_from_grid(self, embed_dim: int, pos: np.ndarray) -> np.ndarray:
+        """
+        embed_dim: output dimension for each position
+        pos: a list of positions to be encoded: size (M,)
+        out: (M, D)
+        """
+        assert embed_dim % 2 == 0
+        omega = np.arange(embed_dim // 2, dtype=np.float32)
+        omega /= embed_dim / 2.
+        omega = 1. / 10000 ** omega  # (D/2,)
+
+        pos = pos.reshape(-1)  # (M,)
+        out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+        emb_sin = np.sin(out)  # (M, D/2)
+        emb_cos = np.cos(out)  # (M, D/2)
+
+        emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+        return emb
 
 
 @Model.register("MiniCPM3ForCausalLM")