[TRTLLM-7958][doc] add 1.0 release notes

docs/source/release-notes.md

@@ -4,6 +4,181 @@
 
 All published functionality in the Release Notes has been fully tested and verified with known limitations documented. To share feedback about this release, access our [NVIDIA Developer Forum](https://forums.developer.nvidia.com/).
 
+## TensorRT-LLM Release 1.0
+
+TensorRT LLM 1.0 brings 2 major changes: the PyTorch-based architecture is now stable and the default experience, and the LLM API is now stable. For more details on new developments in 1.0, please see below.
+
+### Key Features and Enhancements
+- **Model Support**
+  - Add Mistral3.1 VLM model support
+  - Add TensorRT-Engine Qwen3 (dense) model support
+  - Add phi-4-multimodal model support
+  - Add EXAONE 4.0 model support
+  - Add Qwen3 MoE support to TensorRT backend
+
+- **Features**
+  - Add support for sm121
+  - Add LoRA support for Gemma3
+  - Support PyTorch LoRA adapter eviction
+  - Add LoRA support for PyTorch backend in trtllm-serve 
+  - Add support of scheduling attention dp request
+  - Remove padding of FusedMoE in attention DP
+  - Support torch compile for attention dp
+  - Add KV events support for sliding window attention
+  - Add TRTLLM MoE nvfp4 cubins for mid-high concurrency; attention_dp for TRTLLM MoE
+  - Add Piecewise CUDA Graph support for MLA
+  - Support mutliCtasKvMode for high-throughput MLA kernels
+  - Enable kvcache to be reused during request generation
+  - Add ADP schedule balance optimization
+  - Add chunked prefill support for MLA (Blackwell)
+  - Enable Multi-block mode for Hopper spec dec XQA kernel
+  - Add vLLM KV Pool support for XQA kernel
+  - Allow sending more than 2GiB through MPI by using mpi4py.util.pkl5
+  - Add support for fused gate_up_proj scales for FP8 blockwise
+  - Support FP8 row-wise dense GEMM in torch flow
+  - Enable fp8 SwiGLU to minimize host overhead
+  - Add Deepseek R1 FP8 Support on Blackwell
+  - Add support for MXFP8xMXFP4 in pytorch
+  - Support nvfp4 model and fp8 kv cache for MLA chunked prefill (Blackwell)
+  - Opensource MOE MXFP8-MXFP4 implementation
+  - Add support for Modelopt fp8_pb_wo quantization scheme
+  - Support deepEP fp4 post quant all2all dispatch
+  - Fuse w4a8 moe pre-quant scale on Hopper
+  - Support Weight-Only-Quantization in PyTorch Workflow
+  - Add support for per expert activation scaling factors
+  - Add ReDrafter support for Qwen
+  - Enable CUDA Graph for Nemotron-H
+  - Add support for YARN in NemotronNAS models
+  - Switch to internal version of MMProjector in Gemma3
+  - Disable add special tokens for Llama3.3 70B
+  - Auto-enable ngram with concurrency <= 32
+  - Support turning on/off spec decoding dynamically
+  - Support structural tag in C++ runtime and upgrade xgrammar to 0.1.21
+  - Add support for external multimodal embeddings
+  - Add support for disaggregation with pp with pytorch backend
+  - Add status tags to LLM API reference
+  - Support JSON Schema in OpenAI-Compatible API
+  - Support chunked prefill on spec decode 2 model
+  - Add KV cache reuse support for multimodal models 
+  - Support nanobind bindings
+  - Add support for two-model engine KV cache reuse
+  - Add Eagle-3 support for qwen3 dense model
+  - Migrate Eagle-3 and draft/target speculation to Drafter
+  - Enable guided decoding with overlap scheduler
+  - Support n-gram speculative decoding with disagg
+  - Add beam search support to the PyTorch Workflow
+  - Add LLGuidance Support for PyTorch Backend
+  - Add NGrams V2 support
+  - Add MTP support for Online EPLB
+  - Support disaggregated serving in TRTLLM Sampler
+  - Add core infrastructure to enable loading of custom checkpoint formats
+  - Support TRTLLM_DEEP_EP_TOKEN_LIMIT to allow run deep-ep on memory-constrained GPUs
+  - Use huge page mapping for host accessible memory on GB200
+  - Add user-provided speculative decoding support
+  - Add streaming scaffolding_llm.generate_async support
+  - Detokenize option in /v1/completions request
+  - Integrate TRT-LLM Gen FP4 block scale MoE with Pytorch workflow kernel autotuner
+  - Remove support for llmapi + TRT backend in Triton
+  - Add request_perf_metrics to triton LLMAPI backend 
+  - Add support for Triton request cancellation
+
+- Benchmark:
+  - Add support for benchmarking individual gemms in MOE benchmark (#6080)
+  - Add speculative metrics for trtllm-bench
+  - Add the ability to write a request timeline for trtllm-bench
+  - Add no_kv_cache_reuse option and streaming support for trtllm-serve bench
+  - Add latency support for trtllm-bench
+  - Add Acceptance Rate calculation to benchmark_serving 
+  - Add wide-ep benchmarking scripts
+  - Update trtllm-bench to support new Pytorch default
+  - Add support for TRTLLM CustomDataset
+  - Make benchmark_serving part of the library
+
+- Documentation:
+  - Refactored the doc structure to focus on the PyTorch workflow.
+  - Improved the LLMAPI and API reference documentation. Stable APIs are now protected and will remain consistent in subsequent versions following v1.0.
+  - Removed legacy documentation related to the TensorRT workflow.
+
+### Infrastructure Changes
+- The base Docker image for TensorRT-LLM is updated to `nvcr.io/nvidia/pytorch:25.06-py3`.
+- The base Docker image for TensorRT-LLM Backend is updated to `nvcr.io/nvidia/tritonserver:25.06-py3`.
+- The dependent NVIDIA ModelOpt version is updated to 0.33.
+- The dependent xgrammar version is updated to 0.1.21.
+- The dependent transformers version is updated to 4.53.1.
+
+### API Changes
+- **BREAKING CHANGE** Promote PyTorch to be the default LLM backend
+- **BREAKING CHANGE** Change default backend to PyTorch in trtllm-serve
+- **BREAKING CHANGE** Unify KvCacheConfig in LLM class for pytorch backend
+- **BREAKING CHANGE** Rename cuda_graph_config padding_enabled field
+- **BREAKING CHANGE** Rename mixed_sampler to enable_mixed_sampler
+- **BREAKING CHANGE** Rename LLM.autotuner_enabled to enable_autotuner
+- Add back allreduce_strategy parameter into TorchLlmArgs
+- Add LLmArgs option to force using dynamic quantization
+- Change default LoRA cache sizes and change peft_cache_config cache size fields to take effect when not explicitly set in lora_config
+- Remove deprecated LoRA LLM args, that are already specified in lora_config
+- Add request_perf_metrics to LLMAPI
+- Remove batch_manager::KvCacheConfig and use executor::KvCacheConfig instead 
+- Remove TrtGptModelOptionalParams 
+- Remove ptuning knobs from TorchLlmArgs
+
+
+### Fixed Issues
+- Fix illegal memory access in MLA (#6437)
+- Fix nemotronNAS loading for TP>1 (#6447)
+- Fix wide EP when using DeepEP with online EPLB (#6429)
+- Fix bugs caused by None attention_bias during Qwen3 model convert engine (#6344)
+- Fix PD + MTP + overlap scheduler accuracy issue (#6136)
+- Fix bug of Qwen3 when using fp4 on sm120 (#6065)
+- Fix TMA error with GEMM+AR on TP=2 (#6075)
+- Fix scaffolding aime test in test_e2e (#6140)
+- Fix KV Cache overrides in trtllm-bench (#6103)
+- Fix MOE benchmark to rotate buffers to prevent L2 cache reuse (#4135)
+- Fix eagle3 two model disaggregated serving test (#6014)
+- Fix chunked prefill + overlap scheduling (#5761)
+- Fix mgmn postprocess error (#5835)
+- Fallback to cubins for fp8 fmha kernels on Ada (#5779)
+- Fix disagg + speculative decoding (#5558)
+- Fix test_generate_with_seed CI failure. (#5772)
+- Fix prompt adapter TP2 case (#5782)
+- Fix disaggregate serving with attention DP (#4993)
+- Fix a quote error introduced in #5534 (#5816)
+- Fix the accuracy issue when reduce_fusion is enabled for GEMMA model. (#5801)
+- Fix lost requests for disaggregated serving (#5815)
+- Update unit tests: skip all_close assert for dropout in attention, increase tolerance for rope op test (#5855)
+- Fix GEMM+AR fusion on blackwell (#5563)
+- Fix llama4 multimodal support (#5809)
+- Fix Llama4 Scout FP4 crash issue (#5925)
+- Fix max batch size and max tokens in kv cache estimations for Nemotron-H (#5371)
+- Fix moe regression for sm120 (#5823)
+- Fix Qwen2.5VL FP8 support (#5029)
+- Fix the illegal memory access issue in moe gemm on SM120 (#5636)
+- Fix tileN cannot % 16==0 & support sm89 deepgemm bmm (#5531)
+- Fix incremental detokenization (#5825)
+- Fix MoE workspace info by storing Torch tensor itself instead of data_ptr (#5900)
+- Fix mistral unit tests due to transformers upgrade (#5904)
+- Fix the Llama3.1 405B hanging issue. (#5698) (#5925)
+- Fix Gemma3 unit tests due to transformers upgrade (#5921)
+- Fix alltoall for llama4 (apply_router_weight_on_input=True) (#5902)
+- Remove SpecConfig and fix thread leak issues (#5931)
+- Fast redux detection in trtllm gen routing kernel (#5941)
+- Fix cancel request logic (#5800)
+- Fix errors in wide-ep scripts (#5992)
+- Fix error in post-merge-tests (#5949)
+- Fix missing arg to alltoall_prepare_maybe_dispatch (#5669)
+- Fix attention DP doesn't work with embedding TP (#5642)
+- Fix broken cyclic reference detect (#5417) 
+- Fix permission for local user issues in NGC docker container. (#5373)
+- Fix mtp vanilla draft inputs (#5568) 
+- Fix mPtrExpertCounts allocation in MoE TRT-LLM backend (nvfp4) (#5519) 
+- Fix block scale fp8 support for deepseek v3 on Blackwell. (#5514)
+- Fix the issue MoE autotune fallback failed to query default heuristic (#5520) 
+- Fix the unexpected keyword argument 'streaming' (#5436)
+
+### Known Issues
+- When using disaggregated serving with pipeline parallelism and KV cache reuse, a hang can occur. This will be fixed in a future release. In the meantime, disabling KV cache reuse will fix this issue.
+- Running multi-node cases where each node has just a single GPU is known to fail. This will be addressed in a future release. 
+
 ## TensorRT-LLM Release 0.21.0
 
 ### Key Features and Enhancements