!pip install transformers==4.52.3

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import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

import numpy as np
import torch

!pip install lz4
!pip install graphviz

Requirement already satisfied: lz4 in /usr/local/lib/python3.12/dist-packages (4.4.5)

Requirement already satisfied: graphviz in /usr/local/lib/python3.12/dist-packages (0.21)

import torch
import torch.nn as nn
from torch.export import Dim
from transformers import AutoTokenizer, AutoModelForCausalLM
from threadpoolctl import threadpool_limits
from dl_backtrace.pytorch_backtrace import DLBacktrace

import warnings
warnings.filterwarnings("ignore")

# --- Environment Setup ---
os.environ["TORCH_LOGS"] = "+dynamic"
num_cores = os.cpu_count()
if num_cores: # Ensure num_cores is not None
    torch.set_num_threads(num_cores)
    print(f"PyTorch num_threads set to: {torch.get_num_threads()}") # Verify
else:
    print("Could not determine number of CPU cores for PyTorch.")

Using /root/.cache/torch_extensions/py312_cu126 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py312_cu126/linear_layer_cuda_v3...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py312_cu126/linear_layer_cuda_v3/build.ninja...
Building extension module linear_layer_cuda_v3...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)

Loading extension module linear_layer_cuda_v3...
Using /root/.cache/torch_extensions/py312_cu126 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py312_cu126/custom_embedding_layer_cuda_v2...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py312_cu126/custom_embedding_layer_cuda_v2/build.ninja...
Building extension module custom_embedding_layer_cuda_v2...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)

Loading extension module custom_embedding_layer_cuda_v2...
Using /root/.cache/torch_extensions/py312_cu126 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py312_cu126/fused_softmax...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py312_cu126/fused_softmax/build.ninja...
Building extension module fused_softmax...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)

Loading extension module fused_softmax...
Using /root/.cache/torch_extensions/py312_cu126 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py312_cu126/fused_stabilize_normalize...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py312_cu126/fused_stabilize_normalize/build.ninja...
Building extension module fused_stabilize_normalize...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)

Loading extension module fused_stabilize_normalize...
Using /root/.cache/torch_extensions/py312_cu126 as PyTorch extensions root...
Creating extension directory /root/.cache/torch_extensions/py312_cu126/fused_conservation...
Detected CUDA files, patching ldflags
Emitting ninja build file /root/.cache/torch_extensions/py312_cu126/fused_conservation/build.ninja...
Building extension module fused_conservation...
Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N)

PyTorch num_threads set to: 12

Loading extension module fused_conservation...

# --- Model and Tokenizer Setup ---
model_id = "Qwen/Qwen3-0.6B"    #"Qwen/Qwen3-4B"
auth_token = "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"

class QwenWrapper(nn.Module):
    def __init__(self, model_id, token):
        super().__init__()
        self.model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float32,
            token=token
        ).eval()

    def forward(self, input_ids, attention_mask):
        return self.model(input_ids=input_ids, attention_mask=attention_mask).logits

model = QwenWrapper(model_id, auth_token)
tokenizer = AutoTokenizer.from_pretrained(model_id, token=auth_token)
tokenizer.pad_token = tokenizer.eos_token

# --- Input Sentences ---
sentences = ["What is the capital of France?",]
    # "Hello"]  #,
    # "What's 2+2 ?"]

tokens = tokenizer(sentences, return_tensors="pt", padding=True, truncation=True)
input_ids = tokens["input_ids"]
attention_mask = tokens["attention_mask"]

# --- Dynamic Shapes ---
if len(sentences) > 1:
    batch_dim = Dim("batch", min=1, max=len(sentences))
else:
    batch_dim = 1  # Static dimension

seq_dim = Dim("seq", min=1, max=input_ids.shape[1])
dynamic_shapes = {
    "input_ids": {0: batch_dim, 1: seq_dim},
    "attention_mask": {0: batch_dim, 1: seq_dim},
}

# --- Create an instance of DLBacktrace ---
ir = DLBacktrace(
    model,
    (input_ids, attention_mask),
    dynamic_shapes=dynamic_shapes,
    device='cuda',    # 'cpu',
    verbose=False
)

Setting up DL-Bactrace

✅ Model exported deterministically

out = ir.sample_auto(
    tokenizer, input_ids, attention_mask,
    temperature=None, top_k=None, top_p=None,
    max_new_tokens=1, min_new_tokens=None,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True,
    return_scores=True,
    return_relevance=True,
    return_layerwise_output=True,
)

The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.

2025-11-04 11:49:41 - dlbacktrace.execution_engine - INFO - ExecutionEngineNoCache initialized with debug=False, log_level=INFO

2025-11-04 11:49:41 - dlbacktrace.execution_engine - INFO - Starting execution with 2 inputs

2025-11-04 11:49:41 - dlbacktrace.execution_engine - INFO - Executing `run_execution_nocache` ...!

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_13` produced extreme values → max: 4.20e+07

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_16` produced extreme values → max: 4.20e+07

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_17` produced extreme values → max: 4.20e+07

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_20` produced extreme values → max: 4.20e+07

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_21` produced extreme values → max: 4.20e+07

2025-11-04 11:49:43 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_24` produced extreme values → max: 4.20e+07

generated, info = out

print(f"Input prompt: {tokenizer.batch_decode(input_ids, skip_special_tokens=True)}")
print(f"Prompt after Generated Tokens: {tokenizer.batch_decode(generated, skip_special_tokens=True)}")

Input prompt: ['What is the capital of France?']
Prompt after Generated Tokens: ['What is the capital of France? The']

timewise_relevance_out = info['relevance_trace']
len(timewise_relevance_out)

1

timewise_layerwise_output = info['layerwise_output_trace']
len(timewise_layerwise_output)

1

# Token-wise Relevance Map
ir.visualize_tokenwise_relevance_map(timewise_relevance_out, input_ids, tokenizer, generated_ids=generated)

# Input Heatmap for n-th Token
ir.visualize_input_heatmap_for_token(timewise_relevance_out, n=0, input_ids=input_ids, tokenizer=tokenizer, generated_ids=generated)

print("\n===== Visualizing DL-Backtrace =====")
ir.visualize_dlbacktrace(output_path="qwen3_single_token", engine_auto_threshold=2500)

===== Visualizing DL-Backtrace =====
num_nodes: 2877
big graph → collapsing it ...
Calculate relevance using `visualize_relevance_fast(...)`

✅ Fast graph saved → backtrace_collapsed_fast.svg (nodes=1313, engine=dot)

# Greedy (no knobs) + stop on EOS as soon as allowed by min_new_tokens
out1 = ir.sample_auto(
    tokenizer, input_ids, attention_mask,
    temperature=None, top_k=None, top_p=None,
    max_new_tokens=1, min_new_tokens=None,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True,
    return_scores=True,
    return_relevance=True,
    return_layerwise_output=True,
)

The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.

2025-11-04 11:53:33 - dlbacktrace.execution_engine - INFO - ExecutionEngineNoCache initialized with debug=False, log_level=INFO

2025-11-04 11:53:33 - dlbacktrace.execution_engine - INFO - Starting execution with 2 inputs

2025-11-04 11:53:33 - dlbacktrace.execution_engine - INFO - Executing `run_execution_nocache` ...!

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_13` produced extreme values → max: 4.20e+07

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_16` produced extreme values → max: 4.20e+07

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_17` produced extreme values → max: 4.20e+07

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_20` produced extreme values → max: 4.20e+07

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_21` produced extreme values → max: 4.20e+07

2025-11-04 11:53:35 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_24` produced extreme values → max: 4.20e+07

generated1, info1 = out1

print(f"Input prompt: {tokenizer.batch_decode(input_ids, skip_special_tokens=True)}")
print(f"Prompt after Generated Tokens: {tokenizer.batch_decode(generated1, skip_special_tokens=True)}")

Input prompt: ['What is the capital of France?']
Prompt after Generated Tokens: ['What is the capital of France? The']

info1.keys()

dict_keys(['scores_trace', 'relevance_trace', 'layerwise_output_trace'])

timewise_relevance_out1 = info1['relevance_trace']
len(timewise_relevance_out1)

1

timewise_layerwise_output1 = info1['layerwise_output_trace']
len(timewise_layerwise_output1)

1

# Token-wise Relevance Map
ir.visualize_tokenwise_relevance_map(timewise_relevance_out1, input_ids, tokenizer, generated_ids=generated1)

# Input Heatmap for n-th Token
ir.visualize_input_heatmap_for_token(timewise_relevance_out1, n=0, input_ids=input_ids, tokenizer=tokenizer, generated_ids=generated1)

print("\n===== Visualizing DL-Backtrace =====")
ir.visualize_dlbacktrace(output_path="qwen3_greedy", engine_auto_threshold=2500)

===== Visualizing DL-Backtrace =====
num_nodes: 2877
big graph → collapsing it ...
Calculate relevance using `visualize_relevance_fast(...)`

✅ Fast graph saved → backtrace_collapsed_fast.svg (nodes=1313, engine=dot)

# Temperature + Top-p + max_time guard (all warpers applied together)
out2 = ir.sample_auto(
    tokenizer, input_ids, attention_mask,
    temperature=0.7, top_p=0.9, top_k=None,
    max_new_tokens=5, min_new_tokens=1,
    eos_token_id=tokenizer.eos_token_id,
    early_stopping=True,
    return_scores=True,
    return_relevance=True,
    return_layerwise_output=True,
)

2025-11-04 11:57:23 - dlbacktrace.execution_engine - INFO - ExecutionEngineNoCache initialized with debug=False, log_level=INFO

2025-11-04 11:57:23 - dlbacktrace.execution_engine - INFO - Starting execution with 2 inputs

2025-11-04 11:57:23 - dlbacktrace.execution_engine - INFO - Executing `run_execution_nocache` ...!

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_13` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_16` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_17` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_20` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_21` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_24` produced extreme values → max: 4.20e+07

2025-11-04 11:57:25 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_25` produced extreme values → max: 4.20e+07

generated2, info2 = out2

print(f"Input prompt: {tokenizer.batch_decode(input_ids, skip_special_tokens=True)}")
print(f"Prompt after Generated Tokens: {tokenizer.batch_decode(generated2, skip_special_tokens=True)}")

Input prompt: ['What is the capital of France?']
Prompt after Generated Tokens: ['What is the capital of France? The capital of France is']

timewise_relevance_out2 = info2['relevance_trace']
len(timewise_relevance_out2)

5

timewise_layerwise_output2 = info2['layerwise_output_trace']
len(timewise_layerwise_output2)

5

# Token-wise Relevance Map
ir.visualize_tokenwise_relevance_map(timewise_relevance_out2, input_ids, tokenizer, generated_ids=generated2)

# Input Heatmap for n-th Token
ir.visualize_input_heatmap_for_token(timewise_relevance_out2, n=1, input_ids=input_ids, tokenizer=tokenizer, generated_ids=generated2)

print("\n===== Visualizing DL-Backtrace =====")
ir.visualize_dlbacktrace(output_path="qwen3_temp+top-p", engine_auto_threshold=2500)

===== Visualizing DL-Backtrace =====
num_nodes: 2877
big graph → collapsing it ...
Calculate relevance using `visualize_relevance_fast(...)`

✅ Fast graph saved → backtrace_collapsed_fast.svg (nodes=1313, engine=dot)

# Temperature + Top-k + max_time guard (all warpers applied together)
out3 = ir.sample_auto(
    tokenizer, input_ids, attention_mask,
    temperature=0.7, top_p=None, top_k=40,
    max_new_tokens=5, min_new_tokens=1,
    eos_token_id=tokenizer.eos_token_id,
    return_scores=True,
    return_relevance=True,
    return_layerwise_output=True,
)

2025-11-04 12:01:35 - dlbacktrace.execution_engine - INFO - ExecutionEngineNoCache initialized with debug=False, log_level=INFO

2025-11-04 12:01:35 - dlbacktrace.execution_engine - INFO - Starting execution with 2 inputs

2025-11-04 12:01:35 - dlbacktrace.execution_engine - INFO - Executing `run_execution_nocache` ...!

2025-11-04 12:01:37 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_13` produced extreme values → max: 4.20e+07

2025-11-04 12:01:37 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_16` produced extreme values → max: 4.20e+07

2025-11-04 12:01:37 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_17` produced extreme values → max: 4.20e+07

2025-11-04 12:01:38 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_20` produced extreme values → max: 4.20e+07

2025-11-04 12:01:38 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_21` produced extreme values → max: 4.20e+07

2025-11-04 12:01:38 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_24` produced extreme values → max: 4.20e+07

2025-11-04 12:01:38 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_25` produced extreme values → max: 4.20e+07

generated3, info3 = out3

print(f"Input prompt: {tokenizer.batch_decode(input_ids, skip_special_tokens=True)}")
print(f"Prompt after Generated Tokens: {tokenizer.batch_decode(generated3, skip_special_tokens=True)}")

Input prompt: ['What is the capital of France?']
Prompt after Generated Tokens: ['What is the capital of France? Also, what is the']

timewise_relevance_out3 = info3['relevance_trace']
len(timewise_relevance_out3)

5

timewise_layerwise_output3 = info3['layerwise_output_trace']
len(timewise_layerwise_output3)

5

# Token-wise Relevance Map
ir.visualize_tokenwise_relevance_map(timewise_relevance_out3, input_ids, tokenizer, generated_ids=generated3)

# Input Heatmap for n-th Token
ir.visualize_input_heatmap_for_token(timewise_relevance_out3, n=0, input_ids=input_ids, tokenizer=tokenizer, generated_ids=generated3)

print("\n===== Visualizing DL-Backtrace =====")
ir.visualize_dlbacktrace(output_path="qwen3_temp+top-k", engine_auto_threshold=2500)

===== Visualizing DL-Backtrace =====
num_nodes: 2877
big graph → collapsing it ...
Calculate relevance using `visualize_relevance_fast(...)`

✅ Fast graph saved → backtrace_collapsed_fast.svg (nodes=1313, engine=dot)

# Beam search now works without batch-dynamic export
eos_id = tokenizer.eos_token_id
pad_id = tokenizer.pad_token_id or eos_id
result = ir.sample_auto(
    tokenizer, input_ids, attention_mask,
    num_beams=2,
    num_return_sequences=2,
    length_penalty=1.0,
    early_stopping=True,            # optional; semantics implemented here
    temperature=None, top_k=None, top_p=None,
    max_new_tokens=1, min_new_tokens=1,
    eos_token_id=eos_id,
    pad_token_id=pad_id,
    return_scores=True,
    return_relevance=True,
    return_layerwise_output=True,
)

The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.

2025-11-04 12:05:45 - dlbacktrace.execution_engine - INFO - ExecutionEngineNoCache initialized with debug=False, log_level=INFO

2025-11-04 12:05:45 - dlbacktrace.execution_engine - INFO - Starting execution with 2 inputs

2025-11-04 12:05:45 - dlbacktrace.execution_engine - INFO - Executing `run_execution_nocache` ...!

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_13` produced extreme values → max: 4.20e+07

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_16` produced extreme values → max: 4.20e+07

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_17` produced extreme values → max: 4.20e+07

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_20` produced extreme values → max: 4.20e+07

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_21` produced extreme values → max: 4.20e+07

2025-11-04 12:05:47 - dlbacktrace.execution_engine - WARNING - [WARNING:EXTREME] Node `pow_24` produced extreme values → max: 4.20e+07

out_top1, info_beam = result

print(f"Input prompt: {tokenizer.batch_decode(input_ids, skip_special_tokens=True)}")
print(f"Prompt after Generated Tokens: {tokenizer.batch_decode(out_top1, skip_special_tokens=True)}")

Input prompt: ['What is the capital of France?']
Prompt after Generated Tokens: ['What is the capital of France? The']

timewise_beamlevel_relevance = info_beam["relevance_trace"]
len(timewise_beamlevel_relevance)

1

timewise_beamlevel_layerwise_output = info_beam['layerwise_output_trace']
len(timewise_beamlevel_layerwise_output)

1

ir.visualize_tokenwise_relevance_map(timewise_beamlevel_relevance, input_ids, tokenizer, generated_ids=out_top1)

# Input Heatmap for n-th Token
ir.visualize_input_heatmap_for_token(timewise_beamlevel_relevance, n=0, input_ids=input_ids, tokenizer=tokenizer, generated_ids=out_top1)

print("\n===== Visualizing DL-Backtrace =====")
ir.visualize_dlbacktrace(output_path="qwen3_beam_search", engine_auto_threshold=2500)

===== Visualizing DL-Backtrace =====
num_nodes: 2877
big graph → collapsing it ...
Calculate relevance using `visualize_relevance_fast(...)`

✅ Fast graph saved → backtrace_collapsed_fast.svg (nodes=1313, engine=dot)

Single Token¶

Multi-Token¶

1. Greedy Approach¶

2. Temp + Top-p¶

3. Temp + Top-k¶

4. Beam Search¶