llama.cpp: add scripts to read tensor values

fundamentals/llama.cpp/scripts/read-pre-pos-emb.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+import logging
+import sys
+from pathlib import Path
+from gguf.gguf_reader import GGUFReader
+import numpy as np
+
+logger = logging.getLogger("reader")
+
+def read_position_embeddings(gguf_file_path, tensor_name, row_idx=6, num_values=10):
+    """
+    Reads and prints information about position embeddings from a GGUF file.
+
+    Parameters:
+    - gguf_file_path: Path to the GGUF file
+    - tensor_name: Name of the tensor to inspect
+    - row_idx: Which row to read (default 6)
+    - num_values: How many values to print (default 10)
+    """
+    reader = GGUFReader(gguf_file_path)
+
+    # Find the specified tensor
+    target_tensor = None
+    for tensor in reader.tensors:
+        if tensor.name == tensor_name:
+            target_tensor = tensor
+            break
+
+    if target_tensor is None:
+        print(f"Tensor '{tensor_name}' not found in the model")
+        return
+
+    # Print tensor information
+    print(f"\nTensor Information:")
+    print(f"Name: {target_tensor.name}")
+    print(f"Shape: {target_tensor.shape}")  # This should already be correct
+    print(f"Type: {target_tensor.tensor_type.name}")
+    print(f"Total elements: {target_tensor.n_elements}")
+
+    # Get the tensor data
+    data = target_tensor.data
+    if isinstance(data, np.ndarray):
+        # Print the actual shape we got
+        print(f"Actual data shape: {data.shape}")
+        print(f"Number of rows: {data.shape[0]}")
+        print(f"Elements per row: {data.shape[1]}")
+
+        # Get the specified row
+        row_data = data[row_idx]
+
+        print(f"\nFirst {num_values} values from row {row_idx}:")
+        for i in range(num_values):
+            print(f"[{i}] = {row_data[i]}")
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print("Usage: reader.py <path_to_gguf_file> [row_index] [num_values]")
+        sys.exit(1)
+
+    gguf_file_path = sys.argv[1]
+    row_idx = int(sys.argv[2]) if len(sys.argv) > 2 else 6
+    num_values = int(sys.argv[3]) if len(sys.argv) > 3 else 10
+
+    read_position_embeddings(
+        gguf_file_path,
+        #"v.enc.pre_tile_pos_embd.weight",
+        "v.pre_tile_position_embd.weight",
+        row_idx,
+        num_values
+    )

fundamentals/llama.cpp/scripts/read-safetensor.py

@@ -0,0 +1,33 @@
+from safetensors import safe_open
+import numpy as np
+
+# Load the tensor
+tensors = [
+    "vision_model.gated_positional_embedding.tile_embedding.weight",
+    "vision_model.gated_positional_embedding.tile_embedding.weight",
+    "vision_model.gated_positional_embedding.gate",
+    "vision_model.gated_positional_embedding.embedding",
+    "vision_model.post_tile_positional_embedding.embedding.weight",
+    "vision_model.post_tile_positional_embedding.gate",
+    "vision_model.pre_tile_positional_embedding.embedding.weight",
+    "vision_model.pre_tile_positional_embedding.gate",
+    "vision_model.class_embedding",
+    "vision_model.patch_embedding.weight",
+]
+
+with safe_open("/home/danbev/work/ai/llama-models/Llama-3.2-11B-Vision-Instruct/model-00001-of-00005.safetensors", framework="pt") as f:
+#with safe_open("/home/danbev/Downloads/model-00001-of-00005.safetensors", framework="pt") as f:
+    # Get the tensor
+
+    for tensor_name in tensors:
+        tensor = f.get_tensor(tensor_name)
+
+        # Print tensor info
+        print(f"\nTensor Information:")
+        print(f"Name: {tensor_name}")
+        print(f"Shape: {tensor.shape}")
+        print(f"Type: {tensor.dtype}")
+        print(f"First 10 values:")
+        flattened = tensor.flatten()
+        for i, val in enumerate(flattened[:50]):
+            print(f"[{i}] = {val}")

fundamentals/llama.cpp/scripts/read-tensor.py

@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+import logging
+import sys
+from pathlib import Path
+from gguf.gguf_reader import GGUFReader
+import numpy as np
+
+logger = logging.getLogger("reader")
+
+def read_tensor_values(gguf_file_path, tensor_name):
+    """
+    Reads and prints information about a specific tensor from a GGUF file.
+
+    Parameters:
+    - gguf_file_path: Path to the GGUF file
+    - tensor_name: Name of the tensor to inspect
+    """
+    reader = GGUFReader(gguf_file_path)
+
+    # Find the specified tensor
+    target_tensor = None
+    for tensor in reader.tensors:
+        if tensor.name == tensor_name:
+            target_tensor = tensor
+            break
+
+    if target_tensor is None:
+        print(f"Tensor '{tensor_name}' not found in the model")
+        return
+
+    # Print tensor information
+    print(f"\nTensor Information:")
+    print(f"Name: {target_tensor.name}")
+    print(f"Shape: {' x '.join(map(str, target_tensor.shape))}")
+    print(f"Type: {target_tensor.tensor_type.name}")
+    print(f"Total elements: {target_tensor.n_elements}")
+
+    # Get the tensor data
+    data = target_tensor.data
+    if isinstance(data, np.ndarray):
+        print("\nFirst 10 values:")
+        values = data.flatten()[:100]
+        for i, value in enumerate(values):
+            print(f"[{i}] = {value}")
+
+if __name__ == '__main__':
+    if len(sys.argv) < 3:
+        print("Usage: reader.py <path_to_gguf_file> <tensor_name>")
+        sys.exit(1)
+
+    gguf_file_path = sys.argv[1]
+    tensor_name = sys.argv[2]
+    read_tensor_values(gguf_file_path, tensor_name)