[TRTLLM-9089][chore] Port prepare_dataset into trtllm-bench

[FrankD412]

Summary by CodeRabbit

• New Features

  • Added dataset preparation CLI command with configurable tokenizer, output path, and random seed options.
  • New real dataset tool for extracting and preparing HuggingFace datasets with text and multimodal support.
  • New synthetic dataset generators: token lengths drawn from normal and uniform distributions.
  • Added support for LoRA configuration and randomized task IDs across datasets.

• Chores

  • Updated .gitignore to exclude macOS metadata files.

Description

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[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request adds dataset preparation infrastructure to the TensorRT LLM benchmarking suite, introducing CLI commands to generate synthetic datasets and prepare real HuggingFace datasets, along with supporting data models and utilities. It also updates the main bench CLI to register the new prepare-dataset command and adds .DS_Store to .gitignore.

Changes

Cohort / File(s)	Summary
Configuration .gitignore	Adds .DS_Store to ignore macOS Finder metadata files
Dataset Preparation CLI Entry Point tensorrt_llm/bench/dataset/prepare_dataset.py	Introduces Click group prepare_dataset with CLI options (output, random-seed, task-id, rand-task-id, lora-dir, log-level, trust-remote-code). Defines Pydantic model RootArgs for storing parsed arguments including tokenizer validation and loading. Registers subcommands for real, synthetic (normal and uniform distribution) dataset generation
Real Dataset Preparation tensorrt_llm/bench/dataset/prepare_real_data.py	Adds real-dataset CLI command with DatasetConfig model for HuggingFace dataset configuration. Provides utilities to load datasets, handle text-only and multimodal (image) inputs, support configurable output length distributions, and serialize to JSON. Includes validation for prompt key/source specifications
Synthetic Dataset Generation tensorrt_llm/bench/dataset/prepare_synthetic_data.py	Implements token_norm_dist and token_unif_dist CLI commands to generate synthetic token datasets using normal and uniform distributions respectively. Handles task ID generation, LoRA configuration, and structured dataset output with metadata
Dataset Utilities & Models tensorrt_llm/bench/dataset/utils.py	Defines TextSample, MultimodalSample, and Workload Pydantic models. Provides serialization (text_dataset_dump, multimodal_dataset_dump), JSON streaming (print_text_dataset, print_multimodal_dataset), distribution sampling (get_norm_dist_lengths, get_unif_dist_lengths, get_exponential_dist_delays), and random token generation utilities with deterministic seeding
CLI Registration tensorrt_llm/commands/bench.py	Imports and registers prepare_dataset as a subcommand in the trtllm-bench CLI group

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant PrepareDataset as prepare_dataset<br/>(CLI Group)
    participant RootArgs as RootArgs<br/>(Validation)
    participant Subcommands as Subcommands<br/>(real-dataset,<br/>token_norm_dist,<br/>token_unif_dist)
    participant Utils as Dataset Utils<br/>(Serialization &<br/>Sampling)
    participant Output as Output File

    User->>PrepareDataset: Run CLI with options
    PrepareDataset->>RootArgs: Parse CLI args & build model
    activate RootArgs
    RootArgs->>RootArgs: Load & validate tokenizer
    RootArgs-->>PrepareDataset: Return validated args
    deactivate RootArgs
    
    PrepareDataset->>Subcommands: Invoke subcommand<br/>(real-dataset or synthetic)
    activate Subcommands
    alt Real Dataset Path
        Subcommands->>Subcommands: Load HF dataset<br/>& DatasetConfig
        Subcommands->>Utils: Generate samples<br/>(text or multimodal)
    else Synthetic Dataset Path
        Subcommands->>Utils: Sample lengths from<br/>distribution
        Subcommands->>Utils: Generate random tokens
    end
    Subcommands->>Utils: Serialize Workload<br/>(TextSample/MultimodalSample)
    Utils->>Output: Write JSON to file
    Subcommands-->>User: Complete
    deactivate Subcommands

Loading

Estimated code review effort

🎯 4 (Complex) | ⏱️ ~45 minutes

Areas requiring extra attention:

• prepare_real_data.py: HuggingFace dataset loading and multimodal (image) handling logic with PIL Image to file conversion; validation of dataset configuration options
• utils.py: Deterministic randomization logic across multiple distribution functions; JSON serialization via Pydantic models; handling of both text and multimodal sample paths
• prepare_synthetic_data.py: Token generation logic ensuring EOS token exclusion; task ID and LoRA configuration derivation
• prepare_dataset.py: Tokenizer loading and validation in Pydantic model hook; context object wiring across CLI subcommands
• Cross-file integration: Interaction between RootArgs, subcommands, and utility functions; correct threading of metadata, seeds, and configuration through the pipeline

Pre-merge checks and finishing touches

❌ Failed checks (2 warnings)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 50.00% which is insufficient. The required threshold is 80.00%.	You can run @coderabbitai generate docstrings to improve docstring coverage.
Description check	⚠️ Warning	PR description is entirely empty. Required sections (title format, description, test coverage) are not filled in, only the template structure remains.	Fill in the PR title following the template (e.g., [TRTLLM-9089][feat] Port prepare_dataset into trtllm-bench), add a clear description of changes, and document test coverage for the new dataset preparation features.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely summarizes the main change: porting the prepare_dataset functionality into the trtllm-bench command group, with specific reference to the ticket TRTLLM-9089.

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[coderabbitai]

Actionable comments posted: 6

🧹 Nitpick comments (8)

tensorrt_llm/bench/dataset/prepare_dataset.py (1)

34-49: Improve exception chaining for better debugging.

The exception handling would benefit from using raise ... from e to preserve the original traceback context.

Apply this diff:

         except EnvironmentError as e:
-            raise ValueError(
+            raise ValueError(
                 "Cannot find a tokenizer from the given string because of "
                 f"{e}\nPlease set tokenizer to the directory that contains "
                 "the tokenizer, or set to a model name in HuggingFace."
-            )
+            ) from e

tensorrt_llm/bench/dataset/prepare_synthetic_data.py (3)

15-38: Add stacklevel to warning for better debugging.

The warning should include stacklevel=2 to point to the caller's code rather than this helper function.

Apply this diff:

     if use_task_ids and not use_lora:
         warnings.warn(
-            "Task IDs require LoRA directory. Use --lora-dir or omit task IDs.", UserWarning
+            "Task IDs require LoRA directory. Use --lora-dir or omit task IDs.", 
+            UserWarning, 
+            stacklevel=2
         )

---

50-52: Remove unused variable initializations.

These variables are immediately overwritten and never used in their initial state.

Apply this diff:

-    input_ids = []
-    input_lens = []
-    output_lens = []
-
     input_lens = get_norm_dist_lengths(

---

110-112: Remove unused variable initializations.

These variables are immediately overwritten and never used in their initial state.

Apply this diff:

-    input_ids = []
-    input_lens = []
-    output_lens = []
-
     input_lens = get_unif_dist_lengths(

tensorrt_llm/bench/dataset/prepare_real_data.py (2)

21-32: Use Click's BadParameter exception for better error reporting.

Using click.BadParameter provides better integration with Click's error handling and gives users clearer feedback about which parameter failed.

Apply this diff:

 def validate_output_len_dist(ctx, param, value):
     """Validate the --output-len-dist option."""
     if value is None:
         return value
     m = re.match(r"(\d+),(\d+)", value)
     if m:
         return int(m.group(1)), int(m.group(2))
     else:
-        raise AssertionError(
+        raise click.BadParameter(
             "Incorrect specification for --output-len-dist. Correct format: "
             "--output-len-dist <output_len_mean>,<output_len_stdev>"
         )

---

55-61: Use ValueError instead of AssertionError in validator.

Validators should raise ValueError for invalid data rather than AssertionError, which is typically reserved for internal invariants.

Apply this diff:

     @model_validator(mode="after")
     def check_prompt(self) -> "DatasetConfig":
         if self.prompt_key and self.prompt:
-            raise AssertionError("--prompt-key and --prompt cannot be set at the same time.")
+            raise ValueError("--prompt-key and --prompt cannot be set at the same time.")
         if (not self.prompt_key) and (not self.prompt):
-            raise AssertionError("Either --prompt-key or --prompt must be set.")
+            raise ValueError("Either --prompt-key or --prompt must be set.")
         return self

tensorrt_llm/bench/dataset/utils.py (2)

29-40: Consider using Pydantic's standard patterns.

The custom __init__ could be replaced with a @model_validator(mode='after') or model_post_init hook for better alignment with Pydantic conventions.

Example using @model_validator:

 class Workload(BaseModel):
     metadata: dict
     samples: List[Union[TextSample, MultimodalSample]] = []
 
-    def __init__(self, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.setup_workload_name()
-
-    def setup_workload_name(self):
+    @model_validator(mode='after')
+    def setup_workload_name(self):
         # Keys to ignore
         ignore_keys = ["tokenizer"]
         # Create a string by concatenating keys and values with "__"
         workload_name = "__".join(
             f"{key}:{value}" for key, value in self.metadata.items() if key not in ignore_keys
         )
         self.metadata.setdefault("workload_name", workload_name)
+        return self

---

96-104: Add strict parameter to zip for safety.

Without strict=True, mismatched list lengths would be silently truncated, potentially hiding bugs.

Apply this diff:

 def print_multimodal_dataset(multimodal_texts, multimodal_image_paths, output_lens):
-    for i, (text, image_paths) in enumerate(zip(multimodal_texts, multimodal_image_paths)):
+    for i, (text, image_paths) in enumerate(zip(multimodal_texts, multimodal_image_paths, strict=True)):
         d = {
             "task_id": i,
             "prompt": text,
             "media_paths": image_paths,
             "output_tokens": output_lens[i],
         }
         yield json.dumps(d, separators=(",", ":"), ensure_ascii=False)

📜 Review details

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Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 96cfdd8 and 5910dfe.

📒 Files selected for processing (6)

• .gitignore (1 hunks)
• tensorrt_llm/bench/dataset/prepare_dataset.py (1 hunks)
• tensorrt_llm/bench/dataset/prepare_real_data.py (1 hunks)
• tensorrt_llm/bench/dataset/prepare_synthetic_data.py (1 hunks)
• tensorrt_llm/bench/dataset/utils.py (1 hunks)
• tensorrt_llm/commands/bench.py (2 hunks)

🧰 Additional context used

🧠 Learnings (1)

📚 Learning: 2025-08-18T08:42:02.640Z

Learnt from: samuellees
Repo: NVIDIA/TensorRT-LLM PR: 6974
File: tensorrt_llm/serve/scripts/benchmark_dataset.py:558-566
Timestamp: 2025-08-18T08:42:02.640Z
Learning: In TensorRT-LLM's RandomDataset (tensorrt_llm/serve/scripts/benchmark_dataset.py), when using --random-token-ids option, sequence length accuracy is prioritized over semantic correctness for benchmarking purposes. The encode/decode operations should use skip_special_tokens=True and add_special_tokens=False to ensure exact target token lengths.

Applied to files:

• tensorrt_llm/bench/dataset/prepare_dataset.py
• tensorrt_llm/bench/dataset/utils.py
• tensorrt_llm/bench/dataset/prepare_synthetic_data.py

🧬 Code graph analysis (4)

tensorrt_llm/commands/bench.py (1)

tensorrt_llm/bench/dataset/prepare_dataset.py (1)

• prepare_dataset (73-84)

tensorrt_llm/bench/dataset/prepare_dataset.py (4)

tensorrt_llm/bench/dataset/prepare_real_data.py (1)

• real_dataset (209-314)

tensorrt_llm/bench/dataset/prepare_synthetic_data.py (2)

• token_norm_dist (48-90)
• token_unif_dist (108-150)

tensorrt_llm/builder.py (1)

• default (45-50)

tensorrt_llm/bench/benchmark/__init__.py (1)

• checkpoint_path (75-76)

tensorrt_llm/bench/dataset/prepare_real_data.py (1)

tensorrt_llm/bench/dataset/utils.py (3)

• get_norm_dist_lengths (122-126)
• print_multimodal_dataset (96-104)
• print_text_dataset (79-93)

tensorrt_llm/bench/dataset/prepare_synthetic_data.py (1)

tensorrt_llm/bench/dataset/utils.py (5)

• gen_random_tokens (136-157)
• get_norm_dist_lengths (122-126)
• get_unif_dist_lengths (129-133)
• print_text_dataset (79-93)
• text_dataset_dump (43-57)

🪛 Ruff (0.14.5)

tensorrt_llm/bench/dataset/prepare_dataset.py

41-45: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

---

41-45: Avoid specifying long messages outside the exception class

(TRY003)

tensorrt_llm/bench/dataset/utils.py

97-97: zip() without an explicit strict= parameter

Add explicit value for parameter strict=

(B905)

---

142-142: Standard pseudo-random generators are not suitable for cryptographic purposes

(S311)

tensorrt_llm/bench/dataset/prepare_real_data.py

21-21: Unused function argument: ctx

(ARG001)

---

21-21: Unused function argument: param

(ARG001)

---

29-32: Avoid specifying long messages outside the exception class

(TRY003)

---

58-58: Avoid specifying long messages outside the exception class

(TRY003)

---

60-60: Avoid specifying long messages outside the exception class

(TRY003)

---

109-115: Avoid specifying long messages outside the exception class

(TRY003)

---

150-150: Within an except clause, raise exceptions with raise ... from err or raise ... from None to distinguish them from errors in exception handling

(B904)

---

247-247: Avoid specifying long messages outside the exception class

(TRY003)

---

268-268: Standard pseudo-random generators are not suitable for cryptographic purposes

(S311)

tensorrt_llm/bench/dataset/prepare_synthetic_data.py

21-21: Standard pseudo-random generators are not suitable for cryptographic purposes

(S311)

---

30-30: No explicit stacklevel keyword argument found

Set stacklevel=2

(B028)

⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (1)

• GitHub Check: Pre-commit Check

🔇 Additional comments (6)

.gitignore (1)

88-90: ✓ Sensible addition to ignore macOS system files.

Adding .DS_Store to .gitignore is a standard best practice that prevents macOS Finder metadata from being committed. The comment clearly delineates the macOS-specific section, and the placement is logical.

tensorrt_llm/commands/bench.py (1)

9-9: LGTM! Clean integration of the new dataset preparation command.

The import and command registration follow the existing pattern consistently.

Also applies to: 61-61

tensorrt_llm/bench/dataset/prepare_dataset.py (2)

25-32: LGTM! Well-structured data model.

The RootArgs model cleanly captures all necessary configuration parameters for dataset preparation.

---

87-89: LGTM! Command registration follows Click conventions.

tensorrt_llm/bench/dataset/utils.py (2)

43-76: LGTM! Clean serialization functions.

Both dump functions properly handle directory creation and JSON serialization.

---

136-157: LGTM! Robust random token generation.

The function correctly handles EOS token exclusion and uses deterministic seeding for reproducibility. Based on learnings, this aligns with TensorRT-LLM's approach of prioritizing sequence length accuracy over semantic correctness for benchmarking.

[FrankD412]

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PR_Github #27338 Bot args parsing error: usage: /bot [-h]
{run,kill,skip,submit,reviewers,reuse-pipeline,reuse-review} ...
/bot: error: unrecognized arguments: 20733

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/bot skip --comment "Passed previously, rebased to fix conflict in docs."

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PR_Github #27341 [ skip ] triggered by Bot. Commit: e8b221f

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PR_Github #27341 [ skip ] completed with state SUCCESS. Commit: e8b221f
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