[None][fix] Honor deterministic mode in PyTorch backend

[achartier]

@coderabbitai summary

Description

Rebased on NVIDIA/TensorRT-LLM main at f7dd7ec54.

Propagate FORCE_DETERMINISTIC=1 through PyTorch backend settings that were not previously covered:

• Disable overlap scheduling when deterministic mode is requested.
• Disable PyTorch MoE finalize fusion when deterministic mode is requested.
• Pass the deterministic flag into vanilla FlashAttention no-KV-cache attention.

This narrows batch-composition-dependent execution differences in deterministic PyTorch generation while keeping the changes guarded behind the existing env var.

Test Coverage

• Added unit coverage for vanilla attention forwarding the env-controlled deterministic flag to FlashAttention.
• python3 -m py_compile tensorrt_llm/_torch/pyexecutor/py_executor_creator.py tensorrt_llm/_torch/attention_backend/vanilla.py tests/unittest/_torch/attention/test_vanilla_attention.py
• Targeted runtime test was previously attempted but blocked in this cloud image because pytest and torch are not installed:
  • python3 -m pytest tests/unittest/_torch/attention/test_vanilla_attention.py::TestVanillaAttention::test_no_kv_cache_forward_honors_force_deterministic
  • python3 -m unittest tests.unittest._torch.attention.test_vanilla_attention.TestVanillaAttention.test_no_kv_cache_forward_honors_force_deterministic

PR Checklist

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

• Test cases are provided for new code paths (see test instructions)

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• Documentation updated as needed

• Update tava architecture diagram if there is a significant design change in PR.

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• Please check this after reviewing the above items as appropriate for this PR.

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