同步 b7516

examples/model-conversion/scripts/utils/common.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+import torch
+
+
+def get_model_name_from_env_path(env_path_name):
+    model_path = os.getenv(env_path_name)
+    if not model_path:
+        print(f"Error: {env_path_name} environment variable not set")
+        sys.exit(1)
+
+    if not os.path.exists(model_path):
+        print(f"Error: Model file not found: {model_path}")
+        sys.exit(1)
+
+    name = os.path.basename(os.path.normpath(model_path))
+    if name.endswith(".gguf"):
+        name = name[:-5]
+
+    return name
+
+
+def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
+    """
+    Print a tensor in llama.cpp debug style.
+
+    Supports:
+    - 2D tensors (seq, hidden)
+    - 3D tensors (batch, seq, hidden)
+    - 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
+
+    Shows first and last max_vals of each vector per sequence position.
+    """
+    t = tensor.detach().to(torch.float32).cpu()
+
+    # Determine dimensions
+    if t.ndim == 3:
+        _, s, _ = t.shape
+    elif t.ndim == 2:
+        _, s = 1, t.shape[0]
+        t = t.unsqueeze(0)
+    elif t.ndim == 4:
+        _, s, _, _ = t.shape
+    else:
+        print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
+        return
+
+    ten_shape = t.shape
+
+    print(f"ggml_debug: {name} = (f32)  ... = {{{ten_shape}}}")
+    print("                                     [")
+    print("                                      [")
+
+    # Determine indices for first and last sequences
+    first_indices = list(range(min(s, max_seq)))
+    last_indices = list(range(max(0, s - max_seq), s))
+
+    # Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
+    has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
+
+    # Combine indices
+    if has_overlap:
+        # If there's overlap, just use the combined unique indices
+        indices = sorted(list(set(first_indices + last_indices)))
+        separator_index = None
+    else:
+        # If no overlap, we'll add a separator between first and last sequences
+        indices = first_indices + last_indices
+        separator_index = len(first_indices)
+
+    for i, si in enumerate(indices):
+        # Add separator if needed
+        if separator_index is not None and i == separator_index:
+            print("                                       ...")
+
+        # Extract appropriate slice
+        vec = t[0, si]
+        if vec.ndim == 2:  # 4D case: flatten heads × dim_per_head
+            flat = vec.flatten().tolist()
+        else:  # 2D or 3D case
+            flat = vec.tolist()
+
+        # First and last slices
+        first = flat[:max_vals]
+        last = flat[-max_vals:] if len(flat) >= max_vals else flat
+        first_str = ", ".join(f"{v:12.4f}" for v in first)
+        last_str = ", ".join(f"{v:12.4f}" for v in last)
+
+        print(f"                                       [{first_str}, ..., {last_str}]")
+
+    print("                                      ],")
+    print("                                     ]")
+    print(f"                                     sum = {t.sum().item():.6f}\n")
+
+
+def debug_hook(name):
+    def fn(_m, input, output):
+        if isinstance(input, torch.Tensor):
+            summarize(input, name + "_in")
+        elif isinstance(input, (tuple, list)) and len(input) > 0 and isinstance(input[0], torch.Tensor):
+            summarize(input[0], name + "_in")
+        if isinstance(output, torch.Tensor):
+            summarize(output, name + "_out")
+        elif isinstance(output, (tuple, list)) and len(output) > 0 and isinstance(output[0], torch.Tensor):
+            summarize(output[0], name + "_out")
+
+    return fn
+
+
+def setup_rope_debug(model_module_path: str, function_name: str = "apply_rotary_pos_emb"):
+    """
+    Apply monkey patch to dump RoPE activations for debugging.
+
+    Args:
+        model_module_path: Path to the model module (e.g., "transformers.models.apertus.modeling_apertus")
+        function_name: Name of the RoPE function to patch (default: "apply_rotary_pos_emb")
+
+    Example:
+        from utils.common import setup_rope_debug
+        setup_rope_debug("transformers.models.apertus.modeling_apertus")
+    """
+    import importlib
+
+    # Import the module and get the original function
+    module = importlib.import_module(model_module_path)
+    orig_rope = getattr(module, function_name)
+
+    # Set torch print options for better debugging
+    torch.set_printoptions(threshold=float('inf'))
+    torch.set_printoptions(precision=6, sci_mode=False)
+
+    def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+        # log inputs
+        summarize(q, "RoPE.q_in")
+        summarize(k, "RoPE.k_in")
+
+        # call original
+        q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
+
+        # log outputs
+        summarize(q_out, "RoPE.q_out")
+        summarize(k_out, "RoPE.k_out")
+
+        return q_out, k_out
+
+    # Patch it
+    setattr(module, function_name, debug_rope)
+    print(f"RoPE debug patching applied to {model_module_path}.{function_name}")