[None][chore] Bump version to 1.2.0rc4.post1

[yiqingy0]

Summary by CodeRabbit

• Chores
  • Updated version from 1.2.0rc5 to 1.2.0rc4.post1 across the project.

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Description

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Please review the following before submitting your PR:

• PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.

• PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.

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[coderabbitai]

📝 Walkthrough

Walkthrough

This PR updates the project version from 1.2.0rc5 to 1.2.0rc4.post1 across all version-related files including the public version constant, README badge, and dependency constraints.

Changes

Cohort / File(s)	Summary
Version metadata updates README.md, examples/constraints.txt, tensorrt_llm/version.py	Updated version references from 1.2.0rc5 to 1.2.0rc4.post1 across version badge, dependency specification, and public version constant

Estimated code review effort

🎯 1 (Trivial) | ⏱️ ~2 minutes

Possibly related PRs

• [None][chore] Bump version to 1.2.0rc4 #9363: Version bump PR affecting tensorrt_llm/version.py, README, and examples/constraints.txt with the same set of version-related files.
• [None][chore] Bump version to 1.2.0rc5 #9455: Version update PR modifying package release string across the same files.
• [None][chore] Bump version to 1.2.0rc2 #8562: Direct modification of tensorrt_llm/version.py, README.md, and examples/constraints.txt for version updates.

Suggested reviewers

• chzblych
• nv-guomingz
• kaiyux

Pre-merge checks and finishing touches

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Description check	⚠️ Warning	The PR description is largely empty with only the template structure present. Required sections like 'Description' and 'Test Coverage' contain no actual content explaining the change or test strategy.	Fill in the 'Description' section explaining why the version bump is necessary and complete the 'Test Coverage' section listing relevant tests that validate the version change.

✅ Passed checks (2 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately describes the main change: bumping the version to 1.2.0rc4.post1, which is confirmed by changes to README.md, examples/constraints.txt, and tensorrt_llm/version.py.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.

✨ Finishing touches

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📥 Commits

Reviewing files that changed from the base of the PR and between fbcf030 and c0a8eed.

📒 Files selected for processing (3)

• README.md (1 hunks)
• examples/constraints.txt (1 hunks)
• tensorrt_llm/version.py (1 hunks)

🧰 Additional context used

📓 Path-based instructions (2)

**/*.py

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

**/*.py: The code developed for TensorRT-LLM should conform to Python 3.8+
Indent Python code with 4 spaces; do not use tabs
Always maintain the namespace when importing in Python, even if only one class or function from a module is used (e.g., use from package.subpackage import foo and then foo.SomeClass() instead of from package.subpackage.foo import SomeClass)
Python filenames should use snake_case (e.g., some_file.py)
Python class names should use PascalCase (e.g., class SomeClass)
Python function and method names should use snake_case (e.g., def my_awesome_function():)
Python local variable names should use snake_case, with prefix k for variable names that start with a number (e.g., k_99th_percentile = ...)
Python global variables should use upper snake_case with prefix G (e.g., G_MY_GLOBAL = ...)
Python constants should use upper snake_case (e.g., MY_CONSTANT = ...)
Avoid shadowing variables declared in an outer scope in Python
Initialize all externally visible members of a Python class in the constructor
For Python interfaces that may be used outside a file, prefer docstrings over comments
Python comments should be reserved for code within a function, or interfaces that are local to a file
Use Google style docstrings for Python classes and functions, which can be parsed by Sphinx
Python attributes and variables can be documented inline with type and description (e.g., self.x = 5 followed by """<type>: Description of 'x'""" )
Avoid using reflection in Python when functionality can be easily achieved without reflection
When using try-except blocks in Python, limit the except clause to the smallest set of specific errors possible instead of catching all exceptions
When using try-except blocks in Python to handle multiple possible variable types (duck-typing), keep the body of the try as small as possible and use the else block to implement the logic

Files:

• tensorrt_llm/version.py

**/*.{cpp,h,cu,py}

📄 CodeRabbit inference engine (CODING_GUIDELINES.md)

All TensorRT-LLM Open Source Software code files should contain an NVIDIA copyright header that includes the current year at the top

Files:

• tensorrt_llm/version.py

🧠 Learnings (13)

📓 Common learnings

Learnt from: venkywonka
Repo: NVIDIA/TensorRT-LLM PR: 6029
File: .github/pull_request_template.md:45-53
Timestamp: 2025-08-27T17:50:13.264Z
Learning: For PR templates in TensorRT-LLM, avoid suggesting changes that would increase developer overhead, such as converting plain bullets to mandatory checkboxes. The team prefers guidance-style bullets that don't require explicit interaction to reduce friction in the PR creation process.

📚 Learning: 2025-08-21T00:16:56.457Z

Learnt from: farshadghodsian
Repo: NVIDIA/TensorRT-LLM PR: 7101
File: docs/source/blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.md:36-36
Timestamp: 2025-08-21T00:16:56.457Z
Learning: TensorRT-LLM container release tags in documentation should only reference published NGC container images. The README badge version may be ahead of the actual published container versions.

Applied to files:

• README.md
• tensorrt_llm/version.py
• examples/constraints.txt

📚 Learning: 2025-09-23T15:12:38.312Z

Learnt from: nv-lschneider
Repo: NVIDIA/TensorRT-LLM PR: 7910
File: cpp/tensorrt_llm/thop/allreduceOp.cpp:352-446
Timestamp: 2025-09-23T15:12:38.312Z
Learning: In TensorRT-LLM NCCL device implementation, NCCL version 2.28+ requirements are handled at runtime in the nccl_device/config layer rather than with compile-time guards. This allows the allreduceOp to remain version-agnostic and delegates version compatibility validation to the appropriate lower-level components that can gracefully handle unsupported configurations.

Applied to files:

• README.md
• tensorrt_llm/version.py
• examples/constraints.txt

📚 Learning: 2025-08-27T14:23:55.566Z

Learnt from: ixlmar
Repo: NVIDIA/TensorRT-LLM PR: 7294
File: tensorrt_llm/_torch/modules/rms_norm.py:17-17
Timestamp: 2025-08-27T14:23:55.566Z
Learning: The TensorRT-LLM project requires Python 3.10+ as evidenced by the use of TypeAlias from typing module, match/case statements, and union type | syntax throughout the codebase, despite some documentation still mentioning Python 3.8+.

Applied to files:

• README.md
• examples/constraints.txt

📚 Learning: 2025-08-01T15:14:45.673Z

Learnt from: yibinl-nvidia
Repo: NVIDIA/TensorRT-LLM PR: 6506
File: examples/models/core/mixtral/requirements.txt:3-3
Timestamp: 2025-08-01T15:14:45.673Z
Learning: In TensorRT-LLM, examples directory can have different dependency versions than the root requirements.txt file. Version conflicts between root and examples dependencies are acceptable because examples are designed to be standalone and self-contained.

Applied to files:

• README.md
• examples/constraints.txt

📚 Learning: 2025-08-06T13:58:07.506Z

Learnt from: galagam
Repo: NVIDIA/TensorRT-LLM PR: 6487
File: tests/unittest/_torch/auto_deploy/unit/singlegpu/test_ad_trtllm_bench.py:1-12
Timestamp: 2025-08-06T13:58:07.506Z
Learning: In TensorRT-LLM, test files (files under tests/ directories) do not require NVIDIA copyright headers, unlike production source code files. Test files typically start directly with imports, docstrings, or code.

Applied to files:

• README.md
• examples/constraints.txt

📚 Learning: 2025-08-11T20:09:24.389Z

Learnt from: achartier
Repo: NVIDIA/TensorRT-LLM PR: 6763
File: tests/integration/defs/triton_server/conftest.py:16-22
Timestamp: 2025-08-11T20:09:24.389Z
Learning: In the TensorRT-LLM test infrastructure, the team prefers simple, direct solutions (like hard-coding directory traversal counts) over more complex but robust approaches when dealing with stable directory structures. They accept the maintenance cost of updating tests if the layout changes.

Applied to files:

• README.md

📚 Learning: 2025-07-28T17:06:08.621Z

Learnt from: moraxu
Repo: NVIDIA/TensorRT-LLM PR: 6303
File: tests/integration/test_lists/qa/examples_test_list.txt:494-494
Timestamp: 2025-07-28T17:06:08.621Z
Learning: In TensorRT-LLM testing, it's common to have both CLI flow tests (test_cli_flow.py) and PyTorch API tests (test_llm_api_pytorch.py) for the same model. These serve different purposes: CLI flow tests validate the traditional command-line workflow, while PyTorch API tests validate the newer LLM API backend. Both are legitimate and should coexist.

Applied to files:

• README.md
• examples/constraints.txt

📚 Learning: 2025-09-09T09:40:45.658Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 7645
File: tests/integration/test_lists/qa/llm_function_core.txt:648-648
Timestamp: 2025-09-09T09:40:45.658Z
Learning: In TensorRT-LLM test lists, it's common and intentional for the same test to appear in multiple test list files when they serve different purposes (e.g., llm_function_core.txt for comprehensive core functionality testing and llm_function_core_sanity.txt for quick sanity checks). This duplication allows tests to be run in different testing contexts.

Applied to files:

• README.md
• examples/constraints.txt

📚 Learning: 2025-09-16T09:30:09.716Z

Learnt from: tongyuantongyu
Repo: NVIDIA/TensorRT-LLM PR: 7763
File: cpp/tensorrt_llm/CMakeLists.txt:297-301
Timestamp: 2025-09-16T09:30:09.716Z
Learning: In the TensorRT-LLM project, NCCL libraries are loaded earlier by PyTorch libraries or the bindings library, so the main shared library doesn't need NCCL paths in its RPATH - the libraries will already be available in the process address space when needed.

Applied to files:

• README.md

📚 Learning: 2025-11-24T17:09:17.870Z

Learnt from: CR
Repo: NVIDIA/TensorRT-LLM PR: 0
File: CODING_GUIDELINES.md:0-0
Timestamp: 2025-11-24T17:09:17.870Z
Learning: Applies to **/*.py : The code developed for TensorRT-LLM should conform to Python 3.8+

Applied to files:

• README.md

📚 Learning: 2025-08-14T15:38:01.771Z

Learnt from: MatthiasKohl
Repo: NVIDIA/TensorRT-LLM PR: 6904
File: cpp/tensorrt_llm/pybind/thop/bindings.cpp:55-57
Timestamp: 2025-08-14T15:38:01.771Z
Learning: In TensorRT-LLM Python bindings, tensor parameter collections like mla_tensor_params and spec_decoding_tensor_params are kept as required parameters without defaults to maintain API consistency, even when it might affect backward compatibility.

Applied to files:

• examples/constraints.txt

📚 Learning: 2025-11-27T09:23:18.742Z

Learnt from: fredricz-20070104
Repo: NVIDIA/TensorRT-LLM PR: 9511
File: tests/integration/defs/examples/serve/test_serve.py:136-186
Timestamp: 2025-11-27T09:23:18.742Z
Learning: In TensorRT-LLM testing, when adding test cases based on RCCA commands, the command format should be copied exactly as it appears in the RCCA case, even if it differs from existing tests. For example, some RCCA commands for trtllm-serve may omit the "serve" subcommand while others include it.

Applied to files:

• examples/constraints.txt

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• GitHub Check: Pre-commit Check

🔇 Additional comments (3)

examples/constraints.txt (1)

1-1: Dependency constraint updated consistently with other version files.

The tensorrt_llm dependency has been updated to match the new version. This change is consistent with the version updates in tensorrt_llm/version.py and README.md. However, the same backward versioning concern applies (see the comment on README.md line 13).

tensorrt_llm/version.py (1)

15-15: Package version updated consistently across the codebase.

The version constant has been updated to match the changes in README.md and examples/constraints.txt. However, the same backward versioning concern applies (see the comment on README.md line 13).

README.md (1)

13-13: Verify that the backward version change from rc5 to rc4.post1 is intentional.

This PR changes the version from 1.2.0rc5 to 1.2.0rc4.post1, which is indeed a backward version change according to PEP 440 (1.2.0rc4.post1 < 1.2.0rc5). This could cause issues with package managers and users who have already installed rc5, as they won't be able to "upgrade" to the lower version without force-reinstalling.

Please confirm:

1. Is this intentional (e.g., was rc5 pulled/cancelled)?
2. Should this be 1.2.0rc5.post1 instead?
3. Have any rc5 packages been published that users might have installed?

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