model-conversion : add verbose flag in run-org-model.py (#18194)
This commit adds a --verbose flag to the run-org-model.py script to enable or disable detailed debug output, such as input and output tensors for each layer. Debug utilities (summarize, debug_hook, setup_rope_debug) have been moved to utils/common.py. The motivation for this is that the detailed debug output can be useful for diagnosing issues with model conversion or execution, but it can also produce a large amount of output that may not always be needed. The script will also be further cleaned/refactored in follow-up commits.
This commit is contained in:
Daniel Bevenius
GitHub
parent
52fc7fee8a
commit
0a271d82b4
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@ -2,135 +2,22 @@
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import argparse
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import os
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import sys
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import importlib
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from pathlib import Path
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# Add parent directory to path for imports
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig
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import torch
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import numpy as np
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### If you want to dump RoPE activations, apply this monkey patch to the model
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### class from Transformers that you are running (replace apertus.modeling_apertus
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### with the proper package and class for your model
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### === START ROPE DEBUG ===
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# from transformers.models.apertus.modeling_apertus import apply_rotary_pos_emb
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# orig_rope = apply_rotary_pos_emb
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# torch.set_printoptions(threshold=float('inf'))
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# torch.set_printoptions(precision=6, sci_mode=False)
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# def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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# # log inputs
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# summarize(q, "RoPE.q_in")
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# summarize(k, "RoPE.k_in")
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# # call original
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# q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
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# # log outputs
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# summarize(q_out, "RoPE.q_out")
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# summarize(k_out, "RoPE.k_out")
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# return q_out, k_out
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# # Patch it
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# import transformers.models.apertus.modeling_apertus as apertus_mod # noqa: E402
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# apertus_mod.apply_rotary_pos_emb = debug_rope
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### == END ROPE DEBUG ===
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def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
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"""
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Print a tensor in llama.cpp debug style.
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Supports:
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- 2D tensors (seq, hidden)
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- 3D tensors (batch, seq, hidden)
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- 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
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Shows first and last max_vals of each vector per sequence position.
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"""
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t = tensor.detach().to(torch.float32).cpu()
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# Determine dimensions
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if t.ndim == 3:
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_, s, _ = t.shape
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elif t.ndim == 2:
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_, s = 1, t.shape[0]
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t = t.unsqueeze(0)
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elif t.ndim == 4:
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_, s, _, _ = t.shape
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else:
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print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
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return
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ten_shape = t.shape
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print(f"ggml_debug: {name} = (f32) ... = {{{ten_shape}}}")
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print(" [")
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print(" [")
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# Determine indices for first and last sequences
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first_indices = list(range(min(s, max_seq)))
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last_indices = list(range(max(0, s - max_seq), s))
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# Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
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has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
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# Combine indices
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if has_overlap:
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# If there's overlap, just use the combined unique indices
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indices = sorted(list(set(first_indices + last_indices)))
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separator_index = None
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else:
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# If no overlap, we'll add a separator between first and last sequences
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indices = first_indices + last_indices
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separator_index = len(first_indices)
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for i, si in enumerate(indices):
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# Add separator if needed
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if separator_index is not None and i == separator_index:
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print(" ...")
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# Extract appropriate slice
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vec = t[0, si]
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if vec.ndim == 2: # 4D case: flatten heads × dim_per_head
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flat = vec.flatten().tolist()
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else: # 2D or 3D case
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flat = vec.tolist()
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# First and last slices
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first = flat[:max_vals]
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last = flat[-max_vals:] if len(flat) >= max_vals else flat
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first_str = ", ".join(f"{v:12.4f}" for v in first)
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last_str = ", ".join(f"{v:12.4f}" for v in last)
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print(f" [{first_str}, ..., {last_str}]")
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print(" ],")
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print(" ]")
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print(f" sum = {t.sum().item():.6f}\n")
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def debug_hook(name):
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def fn(_m, input, output):
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if isinstance(input, torch.Tensor):
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summarize(input, name + "_in")
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elif isinstance(input, (tuple, list)) and len(input) > 0 and isinstance(input[0], torch.Tensor):
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summarize(input[0], name + "_in")
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if isinstance(output, torch.Tensor):
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summarize(output, name + "_out")
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elif isinstance(output, (tuple, list)) and len(output) > 0 and isinstance(output[0], torch.Tensor):
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summarize(output[0], name + "_out")
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return fn
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unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
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from utils.common import debug_hook
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parser = argparse.ArgumentParser(description="Process model with specified path")
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parser.add_argument("--model-path", "-m", help="Path to the model")
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parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False)
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parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose debug output")
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args = parser.parse_args()
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model_path = os.environ.get("MODEL_PATH", args.model_path)
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@ -139,6 +26,12 @@ if model_path is None:
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"Model path must be specified either via --model-path argument or MODEL_PATH environment variable"
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)
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### If you want to dump RoPE activations, uncomment the following lines:
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### === START ROPE DEBUG ===
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# from utils.common import setup_rope_debug
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# setup_rope_debug("transformers.models.apertus.modeling_apertus")
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### == END ROPE DEBUG ===
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print("Loading model and tokenizer using AutoTokenizer:", model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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@ -156,6 +49,7 @@ print("Number of layers: ", config.num_hidden_layers)
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print("BOS token id: ", config.bos_token_id)
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print("EOS token id: ", config.eos_token_id)
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unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
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if unreleased_model_name:
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model_name_lower = unreleased_model_name.lower()
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unreleased_module_path = (
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@ -184,7 +78,8 @@ else:
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model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
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)
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for name, module in model.named_modules():
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if args.verbose:
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for name, module in model.named_modules():
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if len(list(module.children())) == 0: # only leaf modules
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module.register_forward_hook(debug_hook(name))
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@ -2,6 +2,8 @@
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import os
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import sys
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import torch
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def get_model_name_from_env_path(env_path_name):
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model_path = os.getenv(env_path_name)
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@ -18,3 +20,131 @@ def get_model_name_from_env_path(env_path_name):
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name = name[:-5]
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return name
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def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
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"""
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Print a tensor in llama.cpp debug style.
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Supports:
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- 2D tensors (seq, hidden)
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- 3D tensors (batch, seq, hidden)
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- 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
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Shows first and last max_vals of each vector per sequence position.
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"""
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t = tensor.detach().to(torch.float32).cpu()
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# Determine dimensions
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if t.ndim == 3:
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_, s, _ = t.shape
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elif t.ndim == 2:
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_, s = 1, t.shape[0]
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t = t.unsqueeze(0)
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elif t.ndim == 4:
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_, s, _, _ = t.shape
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else:
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print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
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return
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ten_shape = t.shape
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print(f"ggml_debug: {name} = (f32) ... = {{{ten_shape}}}")
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print(" [")
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print(" [")
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# Determine indices for first and last sequences
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first_indices = list(range(min(s, max_seq)))
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last_indices = list(range(max(0, s - max_seq), s))
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# Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
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has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
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# Combine indices
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if has_overlap:
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# If there's overlap, just use the combined unique indices
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indices = sorted(list(set(first_indices + last_indices)))
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separator_index = None
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else:
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# If no overlap, we'll add a separator between first and last sequences
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indices = first_indices + last_indices
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separator_index = len(first_indices)
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for i, si in enumerate(indices):
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# Add separator if needed
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if separator_index is not None and i == separator_index:
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print(" ...")
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# Extract appropriate slice
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vec = t[0, si]
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if vec.ndim == 2: # 4D case: flatten heads × dim_per_head
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flat = vec.flatten().tolist()
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else: # 2D or 3D case
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flat = vec.tolist()
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# First and last slices
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first = flat[:max_vals]
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last = flat[-max_vals:] if len(flat) >= max_vals else flat
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first_str = ", ".join(f"{v:12.4f}" for v in first)
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last_str = ", ".join(f"{v:12.4f}" for v in last)
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print(f" [{first_str}, ..., {last_str}]")
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print(" ],")
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print(" ]")
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print(f" sum = {t.sum().item():.6f}\n")
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def debug_hook(name):
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def fn(_m, input, output):
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if isinstance(input, torch.Tensor):
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summarize(input, name + "_in")
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elif isinstance(input, (tuple, list)) and len(input) > 0 and isinstance(input[0], torch.Tensor):
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summarize(input[0], name + "_in")
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if isinstance(output, torch.Tensor):
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summarize(output, name + "_out")
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elif isinstance(output, (tuple, list)) and len(output) > 0 and isinstance(output[0], torch.Tensor):
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summarize(output[0], name + "_out")
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return fn
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def setup_rope_debug(model_module_path: str, function_name: str = "apply_rotary_pos_emb"):
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"""
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Apply monkey patch to dump RoPE activations for debugging.
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Args:
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model_module_path: Path to the model module (e.g., "transformers.models.apertus.modeling_apertus")
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function_name: Name of the RoPE function to patch (default: "apply_rotary_pos_emb")
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Example:
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from utils.common import setup_rope_debug
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setup_rope_debug("transformers.models.apertus.modeling_apertus")
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"""
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import importlib
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# Import the module and get the original function
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module = importlib.import_module(model_module_path)
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orig_rope = getattr(module, function_name)
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# Set torch print options for better debugging
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torch.set_printoptions(threshold=float('inf'))
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torch.set_printoptions(precision=6, sci_mode=False)
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def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
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# log inputs
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summarize(q, "RoPE.q_in")
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summarize(k, "RoPE.k_in")
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# call original
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q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
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# log outputs
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summarize(q_out, "RoPE.q_out")
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summarize(k_out, "RoPE.k_out")
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return q_out, k_out
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# Patch it
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setattr(module, function_name, debug_rope)
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print(f"RoPE debug patching applied to {model_module_path}.{function_name}")
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