[API] Add GenerationConfig #1024

davidpissarra · 2023-10-05T03:08:21Z

This PR adds a new dataclass GenerationConfig to allow specific generation settings only throughout one generation. Recently, #800 came up with a solution that updated the ChatConfig for every different request, overwriting the previous settings. Unlike, GenerationConfig is stateless and will partially or fully override ChatConfig if provided, but will not overwrite the original settings, and it will be discarded by the end of the generation. Therefore, concurrent requests cannot override each other. GenerationConfig only covers the available generation settings: temperature, repetition_penalty, top_p, max_gen_len and mean_gen_len. GenerationConfig is only available for the python API, and does not affect the CLI usage. This PR is part of the effort to bring more OpenAI parameters to the REST API #683 .

Usage:

output = cm.generate(
    prompt,
    GenerationConfig(
        temperature=0.7,
        repetition_penalty=1.0,
    ),
)

cc @MasterJH5574

* [API] Add GenerationConfig (mlc-ai#1024) * Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break * [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. * [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) * Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model * [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 * [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) * Update compile_models.rst (mlc-ai#1038) fix permission issue * Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model * [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix * Update README.md * [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. * Update README.md (mlc-ai#1045) Update README.md * add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs * [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary * [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) * [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> * [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. * [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. * Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig * [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. * [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. * Minor typo fix (mlc-ai#1064) * Add links to Python API Reference (mlc-ai#1068) * [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. * [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) * [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. * [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. * Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. * Update README.md for Multi-GPU (mlc-ai#1090) * Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path * StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> * Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` * Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) * [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. * More formatting (mlc-ai#1099) * Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work * Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. * [CI] Add clang-format (mlc-ai#1103) * [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) * [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. * [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation * [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) * [WINDOWS] reduce noise in windows build (mlc-ai#1115) * Add CLI commands for compilation (mlc-ai#1109) * Auto updated submodule references * fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 * [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs * Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) * Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. * [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path * Add --opt flag parsing to CLI (mlc-ai#1123) * [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. * added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. * Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst * Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. * Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. * Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... * Support parameter packing (mlc-ai#1146) * [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. * Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) * Migrate Compiler Passes (mlc-ai#1150) * Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. * Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. * Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring * [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. * Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. * [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) * [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. * [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. * [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) * sync llama def * fi --------- Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Co-authored-by: Roee Shenberg <shenberg@gmail.com> Co-authored-by: Eric Lunderberg <Lunderberg@users.noreply.github.com> Co-authored-by: Yaxing Cai <caiyaxing666@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu> Co-authored-by: yongjer <54315206+yongjer@users.noreply.github.com> Co-authored-by: Jeethu Rao <jeethu@jeethurao.com> Co-authored-by: Junru Shao <junrushao@apache.org> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Denise Kutnick <boca.denise@gmail.com> Co-authored-by: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> Co-authored-by: Sunghyun Park <sunggg@umich.edu> Co-authored-by: “Sunghyun <sunggg@umich.com> Co-authored-by: Rick Zhou <riczhou@linkedin.com> Co-authored-by: Varshith Bathini <varshith15@gmail.com> Co-authored-by: Varshith <varshith.bathini@sprinklr.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Git bot <bot@noreply.github.com> Co-authored-by: SingLi <Sing-Li@users.noreply.github.com> Co-authored-by: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Co-authored-by: Goutham Tamilselvan <goutham2688@gmail.com> Co-authored-by: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Co-authored-by: Yuchen Jin <yuchenj@cs.washington.edu> Co-authored-by: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Co-authored-by: fennecJ <hwahwa649@gmail.com> Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com>

* [API] Add GenerationConfig (mlc-ai#1024) * Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break * [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. * [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) * Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model * [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 * [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) * Update compile_models.rst (mlc-ai#1038) fix permission issue * Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model * [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix * Update README.md * [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. * Update README.md (mlc-ai#1045) Update README.md * add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs * [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary * [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) * [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> * [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. * [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. * Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig * [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. * [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. * Minor typo fix (mlc-ai#1064) * Add links to Python API Reference (mlc-ai#1068) * [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. * [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) * [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. * [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. * Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. * Update README.md for Multi-GPU (mlc-ai#1090) * Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path * StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> * Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` * Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) * [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. * More formatting (mlc-ai#1099) * Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work * Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. * [CI] Add clang-format (mlc-ai#1103) * [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) * [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. * [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation * [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) * [WINDOWS] reduce noise in windows build (mlc-ai#1115) * Add CLI commands for compilation (mlc-ai#1109) * Auto updated submodule references * fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 * [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs * Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) * Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. * [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path * Add --opt flag parsing to CLI (mlc-ai#1123) * [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. * added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. * Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst * Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. * Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. * Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... * Support parameter packing (mlc-ai#1146) * [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. * Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) * Migrate Compiler Passes (mlc-ai#1150) * Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. * Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. * Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring * [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. * Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. * [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) * [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. * [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. * [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) * [Rest] Document emoji handling (mlc-ai#1160) Followup PR of mlc-ai#1142 to document the emoji handling. * Enable group quant transform with nn.Module (mlc-ai#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> * Misc Cleanups of Compilation Pipeline (mlc-ai#1165) * Support CUDA Multi-Arch Compilation (mlc-ai#1166) * [Bugfix] Cannot find global function `mlc.llm_chat_create` (mlc-ai#1167) * Fix RWKV Support (mlc-ai#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) * Auto updated submodule references * Fix Android app Permission denied error on Android 10 (mlc-ai#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [SLM] Fix group quantization (mlc-ai#1172) This PR fixes the group quantization and add related unit tests. * [Fix] TIR block name of dequantization (mlc-ai#1177) * [SLM][AutoLLM] Enable Command Line Weight Conversion (mlc-ai#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` * [Fix][SLM] Update q4f16 quantization with the new mutator name rule (mlc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule * [Model Support][SWA] Add support for sliding window attention for Mistral (mlc-ai#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> * Add Python API for Weight Conversion (mlc-ai#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. * Merge `llama_config.CONFIG` into `MODEL_PRESETS` (mlc-ai#1188) * Merge llama_config.py into llama_model.py (mlc-ai#1189) * Add CodeLlama as part of model presets (mlc-ai#1190) * [Docs] Clarify zstd installation on Windows (mlc-ai#1191) * [Docs] Clarify zstd installation on Windows (mlc-ai#1196) Update zstd installation * Support overriding `--max-sequence-length` in command line (mlc-ai#1197) * [RestAPI] Added docs (mlc-ai#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [API] ```llm-vscode``` extension support (mlc-ai#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - huggingface/llm-vscode#103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot * [Fix] Use `fabs` as floating point abs function in C++ (mlc-ai#1202) * Integrating MLC runtime with the new compilation workflow (mlc-ai#1203) * [Fix] Remove Redundant Warnings (mlc-ai#1204) PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. * Try fix macOS build with picojson (mlc-ai#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. * Try fix macOS build with picojson again (mlc-ai#1207) Try fix macOS build with picojson * Auto updated submodule references * [Fix] Keep update-to-date with upstream API change (mlc-ai#1209) * Detect `mtriple` via LLVM (mlc-ai#1211) * Fix Python3.8 compatibility breakage (mlc-ai#1210) The breakage was resulting from newer syntax being used for type annotations, as part of mlc-ai#592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In mlc-ai#1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. * [Slim-LM] Enable loading from AWQ pre-quantized weight. (mlc-ai#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ * fix * rm --------- Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Co-authored-by: Roee Shenberg <shenberg@gmail.com> Co-authored-by: Eric Lunderberg <Lunderberg@users.noreply.github.com> Co-authored-by: Yaxing Cai <caiyaxing666@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu> Co-authored-by: yongjer <54315206+yongjer@users.noreply.github.com> Co-authored-by: Jeethu Rao <jeethu@jeethurao.com> Co-authored-by: Junru Shao <junrushao@apache.org> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Denise Kutnick <boca.denise@gmail.com> Co-authored-by: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> Co-authored-by: Sunghyun Park <sunggg@umich.edu> Co-authored-by: “Sunghyun <sunggg@umich.com> Co-authored-by: Rick Zhou <riczhou@linkedin.com> Co-authored-by: Varshith Bathini <varshith15@gmail.com> Co-authored-by: Varshith <varshith.bathini@sprinklr.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Git bot <bot@noreply.github.com> Co-authored-by: SingLi <Sing-Li@users.noreply.github.com> Co-authored-by: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Co-authored-by: Goutham Tamilselvan <goutham2688@gmail.com> Co-authored-by: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Co-authored-by: Yuchen Jin <yuchenj@cs.washington.edu> Co-authored-by: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Co-authored-by: fennecJ <hwahwa649@gmail.com> Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com> Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Co-authored-by: Animesh Bohara <ani.bohara@gmail.com> Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt>

commit 44f4cbdfed7941e7ed060d74b23d870d026a57c9 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 20 13:41:40 2023 +0000 Support execution using pre-sharded weights commit 35644870e2daf829c29ebb5431d357ceaa4e2793 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 20:35:39 2023 +0000 Support writing of pre-sharded weights commit 97572226d331ebd9ef49e4a2c1dad42344d09bac Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 20:55:34 2023 +0000 Extract ParamManager.create_parameter_transformation call from convert_weights commit e1d3217f7b0c87c49ba3721567cb24df436618a3 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 22:39:00 2023 +0000 Extract a ParamManager.optimize_transform_param_order method commit b2a9e1c7e83c0886e3a0ebed02f4c8416dfbfb5f Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 18:30:59 2023 +0000 [MultiGPU] Cleanup create_shard_info_func - De-duplicate the `if param.shard_strategy == foo` if/else chain - Return a `tvm.IRModule` instead of modifying an existing module commit f67d47a57fbca21df48867a9dfc10e430a3a3b04 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Mon Oct 16 16:59:43 2023 +0000 [Bugfix] Include LegalizeOps in utils.convert_weights Prior to this commit, `utils.convert_weights` assumes that the parameter transformation module is already legalized, and uses no relax operations that require legalization. This commit adds a call to `relax.transform.LegalizeOps` to remove this assumption. commit a98f9cf45a4cc7a2412f68a4f4306d97e7239a13 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Mon Oct 16 16:49:44 2023 +0000 [Bugfix] Correct input shape for shard info function Prior to this commit, the sharding functions sharded axis converted from `orig_size * num_shards` to `orig_size // num_shards`. This commit updates the sharding functions to instead convert from `orig_size` to `orig_size // num_shards`. commit 4042626 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Mon Nov 6 15:43:21 2023 -0800 [Slim-LM] Enable loading from AWQ pre-quantized weight. (mlc-ai#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ commit 9869ca6 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Nov 6 16:03:12 2023 -0600 Fix Python3.8 compatibility breakage (mlc-ai#1210) The breakage was resulting from newer syntax being used for type annotations, as part of mlc-ai#592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In mlc-ai#1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. commit e00220c Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 13:04:36 2023 -0800 Detect `mtriple` via LLVM (mlc-ai#1211) commit e2c99a8 Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 12:01:51 2023 -0800 [Fix] Keep update-to-date with upstream API change (mlc-ai#1209) commit a7f1183 Author: Git bot <bot@noreply.github.com> Date: Mon Nov 6 18:53:07 2023 +0000 Auto updated submodule references commit 51d6f9c Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 09:28:57 2023 -0800 Try fix macOS build with picojson again (mlc-ai#1207) Try fix macOS build with picojson commit 01d4339 Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 09:08:58 2023 -0800 Try fix macOS build with picojson (mlc-ai#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. commit 65478c8 Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 19:52:53 2023 -0800 [Fix] Remove Redundant Warnings (mlc-ai#1204) PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. commit 7ccb51a Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 18:33:49 2023 -0800 Integrating MLC runtime with the new compilation workflow (mlc-ai#1203) commit 3413d17 Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 12:03:33 2023 -0800 [Fix] Use `fabs` as floating point abs function in C++ (mlc-ai#1202) commit 145a984 Author: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Date: Sun Nov 5 06:18:47 2023 +0000 [API] ```llm-vscode``` extension support (mlc-ai#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - huggingface/llm-vscode#103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot commit 0e08845 Author: Animesh Bohara <ani.bohara@gmail.com> Date: Sun Nov 5 01:01:26 2023 -0400 [RestAPI] Added docs (mlc-ai#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> commit 3417505 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 19:44:25 2023 -0700 Support overriding `--max-sequence-length` in command line (mlc-ai#1197) commit 5d63f7e Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 15:42:19 2023 -0700 [Docs] Clarify zstd installation on Windows (mlc-ai#1196) Update zstd installation commit 78424f0 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 02:13:21 2023 -0700 [Docs] Clarify zstd installation on Windows (mlc-ai#1191) commit 4832c2f Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:58:55 2023 -0700 Add CodeLlama as part of model presets (mlc-ai#1190) commit 5d1dc34 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:46:19 2023 -0700 Merge llama_config.py into llama_model.py (mlc-ai#1189) commit 9d20575 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:30:15 2023 -0700 Merge `llama_config.CONFIG` into `MODEL_PRESETS` (mlc-ai#1188) commit 4716704 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:20:43 2023 -0700 Add Python API for Weight Conversion (mlc-ai#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. commit 6ae02dd Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Fri Nov 3 15:34:29 2023 -0400 [Model Support][SWA] Add support for sliding window attention for Mistral (mlc-ai#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> commit 2dc8183 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Fri Nov 3 00:36:52 2023 -0700 [Fix][SLM] Update q4f16 quantization with the new mutator name rule (mlc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule commit 53060af Author: Xiyou Zhou <xiyou.zhou@gmail.com> Date: Thu Nov 2 13:08:11 2023 -0700 [SLM][AutoLLM] Enable Command Line Weight Conversion (mlc-ai#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` commit 2ca7d15 Author: Junru Shao <junrushao@apache.org> Date: Thu Nov 2 11:30:28 2023 -0700 [Fix] TIR block name of dequantization (mlc-ai#1177) commit 1757777 Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Wed Nov 1 15:52:19 2023 -0700 [SLM] Fix group quantization (mlc-ai#1172) This PR fixes the group quantization and add related unit tests. commit 9831135 Author: Animesh Bohara <ani.bohara@gmail.com> Date: Wed Nov 1 15:16:09 2023 -0400 Fix Android app Permission denied error on Android 10 (mlc-ai#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> commit 200653a Author: Git bot <bot@noreply.github.com> Date: Wed Nov 1 14:53:54 2023 +0000 Auto updated submodule references commit f5b2e88 Author: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Date: Wed Nov 1 12:23:40 2023 +0800 Fix RWKV Support (mlc-ai#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) commit e0cd3f6 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:56:28 2023 -0700 [Bugfix] Cannot find global function `mlc.llm_chat_create` (mlc-ai#1167) commit 02d1e57 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:43:17 2023 -0700 Support CUDA Multi-Arch Compilation (mlc-ai#1166) commit 8438b27 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:09:16 2023 -0700 Misc Cleanups of Compilation Pipeline (mlc-ai#1165) commit b5bfa5b Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Tue Oct 31 11:39:44 2023 -0700 Enable group quant transform with nn.Module (mlc-ai#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> commit 9076d01 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Mon Oct 30 22:40:53 2023 -0700 [Rest] Document emoji handling (mlc-ai#1160) Followup PR of mlc-ai#1142 to document the emoji handling. commit 425a2cb Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 30 15:00:24 2023 -0700 [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) commit 0a9d6c7 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 14:44:44 2023 -0500 [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. commit 3cf5605 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 14:43:51 2023 -0500 [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. commit 8ca0176 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Mon Oct 30 12:26:58 2023 -0700 [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) commit b190578 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 13:58:18 2023 -0500 Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. commit ece97b1 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 13:58:08 2023 -0500 [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. commit fee2cb5 Author: masahi <masahi129@gmail.com> Date: Tue Oct 31 01:32:06 2023 +0900 Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring commit ba67835 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 23:54:12 2023 -0700 Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. commit 1a79a53 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 21:51:36 2023 -0700 Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. commit 0a25374 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 21:17:38 2023 -0700 Migrate Compiler Passes (mlc-ai#1150) commit 2193767 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 16:35:07 2023 -0700 Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) commit c0c3a8d Author: Xiyou Zhou <xiyou.zhou@gmail.com> Date: Sun Oct 29 13:16:46 2023 -0700 [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. commit 878ae84 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 00:19:20 2023 -0700 Support parameter packing (mlc-ai#1146) commit 2b6d832 Author: fennecJ <hwahwa649@gmail.com> Date: Sun Oct 29 14:59:10 2023 +0800 Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... commit 27ac5ac Author: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Date: Sat Oct 28 20:18:16 2023 -0400 Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. commit 2ec0cc8 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Sat Oct 28 15:13:48 2023 -0700 Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. commit 2c492e5 Author: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Date: Sun Oct 29 03:43:15 2023 +0530 Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst commit 24f795e Author: Goutham Tamilselvan <goutham2688@gmail.com> Date: Fri Oct 27 03:25:59 2023 -0400 added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. commit 973f9fc Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Wed Oct 25 10:14:46 2023 -0500 [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. commit a4279e3 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 21:05:24 2023 -0700 Add --opt flag parsing to CLI (mlc-ai#1123) commit 9166edb Author: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Date: Tue Oct 24 15:07:23 2023 -0400 [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path commit 9cb8e8e Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 09:04:45 2023 -0700 Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. commit 2aa6809 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 09:03:38 2023 -0700 Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) commit 206103b Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Tue Oct 24 11:54:01 2023 -0400 [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs commit 488017d Author: SingLi <Sing-Li@users.noreply.github.com> Date: Tue Oct 24 08:19:31 2023 -0500 fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 commit 8ce7793 Author: Git bot <bot@noreply.github.com> Date: Tue Oct 24 07:30:53 2023 +0000 Auto updated submodule references commit 61179a0 Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 23 23:58:01 2023 -0700 Add CLI commands for compilation (mlc-ai#1109) commit 5a7dcd8 Author: Tianqi Chen <tqchen@users.noreply.github.com> Date: Tue Oct 24 00:00:41 2023 -0400 [WINDOWS] reduce noise in windows build (mlc-ai#1115) commit 7ae8c6d Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Mon Oct 23 15:33:00 2023 -0700 [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) commit e5927ce Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 23 13:31:44 2023 -0500 [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation commit f57c9c9 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 23 13:31:24 2023 -0500 [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. commit 16dd2ae Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Sun Oct 22 19:51:10 2023 -0700 [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) commit 6159cc4 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 22 02:22:55 2023 -0700 [CI] Add clang-format (mlc-ai#1103) commit 46d11e6 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 23:39:28 2023 -0700 Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. commit 03c641a Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 21:51:51 2023 -0700 Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work commit e9b85ce Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 21:36:28 2023 -0700 More formatting (mlc-ai#1099) commit cf39bf6 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 15:38:49 2023 -0700 [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. commit 62d0c03 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Fri Oct 20 15:33:51 2023 -0700 Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) commit 9bf5723 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 15:49:40 2023 -0700 Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` commit 830656f Author: Varshith Bathini <varshith15@gmail.com> Date: Fri Oct 20 00:40:14 2023 +0530 StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> commit b0373d1 Author: Rick Zhou <riczhou@linkedin.com> Date: Thu Oct 19 12:09:12 2023 -0700 Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path commit 56a8004 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 10:37:24 2023 -0700 Update README.md for Multi-GPU (mlc-ai#1090) commit 2625945 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 08:57:50 2023 -0700 Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. commit 3aefd9f Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 16 21:16:27 2023 -0700 [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. commit 9872c48 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Mon Oct 16 14:56:24 2023 -0400 [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. commit d202077 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 16 08:06:26 2023 -0500 [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) commit 204860b Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Sun Oct 15 14:02:12 2023 -0400 [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. commit b0bfc88 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 15 00:24:24 2023 -0700 Add links to Python API Reference (mlc-ai#1068) commit 9010d48 Author: Jeethu Rao <jeethu@jeethurao.com> Date: Sun Oct 15 06:42:24 2023 +0100 Minor typo fix (mlc-ai#1064) commit 8184431 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 14 00:33:15 2023 -0500 [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. commit 481cd92 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 14 00:32:36 2023 -0500 [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. commit c2b8cbc Author: Jeethu Rao <jeethu@jeethurao.com> Date: Sat Oct 14 06:32:05 2023 +0100 Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig commit d854105 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Fri Oct 13 20:45:58 2023 -0400 [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. commit edab9b5 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 13 09:57:46 2023 -0700 [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. commit ca8c11b Author: Sunghyun Park <sunggg@umich.edu> Date: Fri Oct 13 09:00:21 2023 -0700 [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> commit bfaa5b9 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Thu Oct 12 17:40:54 2023 -0400 Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) commit 98ebd28 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Thu Oct 12 13:24:10 2023 -0700 [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> commit b9179cf Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Thu Oct 12 12:15:44 2023 -0500 [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary commit 1e6fb11 Author: Denise Kutnick <boca.denise@gmail.com> Date: Wed Oct 11 00:06:46 2023 -0700 add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs commit 20131fb Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 9 16:53:56 2023 -0700 Update README.md (mlc-ai#1045) Update README.md commit bdd9d9b Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Mon Oct 9 19:08:14 2023 -0400 [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. commit a58605f Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 9 15:05:34 2023 -0700 Update README.md commit a032d40 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 18:03:24 2023 -0400 [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix commit 85001ed Author: Jeethu Rao <jeethu@jeethurao.com> Date: Mon Oct 9 20:40:52 2023 +0100 Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model commit c02fdaf Author: yongjer <54315206+yongjer@users.noreply.github.com> Date: Tue Oct 10 00:58:51 2023 +0800 Update compile_models.rst (mlc-ai#1038) fix permission issue commit bed9e60 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 12:58:36 2023 -0400 [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) commit 3a9849a Author: Bohan Hou <bohanhou@andrew.cmu.edu> Date: Mon Oct 9 12:27:58 2023 -0400 [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 commit b44f679 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 11:35:58 2023 -0400 Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model commit bae37b3 Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Sun Oct 8 16:55:16 2023 -0700 [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) commit 6e40c21 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 7 22:07:09 2023 -0500 [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. commit ad3a6b9 Author: Roee Shenberg <shenberg@gmail.com> Date: Sun Oct 8 04:59:15 2023 +0200 Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break commit 898db76 Author: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Date: Sun Oct 8 03:36:19 2023 +0100 [API] Add GenerationConfig (mlc-ai#1024)

* Merge Eric PR commit 44f4cbdfed7941e7ed060d74b23d870d026a57c9 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 20 13:41:40 2023 +0000 Support execution using pre-sharded weights commit 35644870e2daf829c29ebb5431d357ceaa4e2793 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 20:35:39 2023 +0000 Support writing of pre-sharded weights commit 97572226d331ebd9ef49e4a2c1dad42344d09bac Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 20:55:34 2023 +0000 Extract ParamManager.create_parameter_transformation call from convert_weights commit e1d3217f7b0c87c49ba3721567cb24df436618a3 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 22:39:00 2023 +0000 Extract a ParamManager.optimize_transform_param_order method commit b2a9e1c7e83c0886e3a0ebed02f4c8416dfbfb5f Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Fri Oct 13 18:30:59 2023 +0000 [MultiGPU] Cleanup create_shard_info_func - De-duplicate the `if param.shard_strategy == foo` if/else chain - Return a `tvm.IRModule` instead of modifying an existing module commit f67d47a57fbca21df48867a9dfc10e430a3a3b04 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Mon Oct 16 16:59:43 2023 +0000 [Bugfix] Include LegalizeOps in utils.convert_weights Prior to this commit, `utils.convert_weights` assumes that the parameter transformation module is already legalized, and uses no relax operations that require legalization. This commit adds a call to `relax.transform.LegalizeOps` to remove this assumption. commit a98f9cf45a4cc7a2412f68a4f4306d97e7239a13 Author: Eric Lunderberg <elunderberg@octoml.ai> Date: Mon Oct 16 16:49:44 2023 +0000 [Bugfix] Correct input shape for shard info function Prior to this commit, the sharding functions sharded axis converted from `orig_size * num_shards` to `orig_size // num_shards`. This commit updates the sharding functions to instead convert from `orig_size` to `orig_size // num_shards`. commit 4042626 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Mon Nov 6 15:43:21 2023 -0800 [Slim-LM] Enable loading from AWQ pre-quantized weight. (mlc-ai#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ commit 9869ca6 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Nov 6 16:03:12 2023 -0600 Fix Python3.8 compatibility breakage (mlc-ai#1210) The breakage was resulting from newer syntax being used for type annotations, as part of mlc-ai#592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In mlc-ai#1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. commit e00220c Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 13:04:36 2023 -0800 Detect `mtriple` via LLVM (mlc-ai#1211) commit e2c99a8 Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 12:01:51 2023 -0800 [Fix] Keep update-to-date with upstream API change (mlc-ai#1209) commit a7f1183 Author: Git bot <bot@noreply.github.com> Date: Mon Nov 6 18:53:07 2023 +0000 Auto updated submodule references commit 51d6f9c Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 09:28:57 2023 -0800 Try fix macOS build with picojson again (mlc-ai#1207) Try fix macOS build with picojson commit 01d4339 Author: Junru Shao <junrushao@apache.org> Date: Mon Nov 6 09:08:58 2023 -0800 Try fix macOS build with picojson (mlc-ai#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. commit 65478c8 Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 19:52:53 2023 -0800 [Fix] Remove Redundant Warnings (mlc-ai#1204) PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. commit 7ccb51a Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 18:33:49 2023 -0800 Integrating MLC runtime with the new compilation workflow (mlc-ai#1203) commit 3413d17 Author: Junru Shao <junrushao@apache.org> Date: Sun Nov 5 12:03:33 2023 -0800 [Fix] Use `fabs` as floating point abs function in C++ (mlc-ai#1202) commit 145a984 Author: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Date: Sun Nov 5 06:18:47 2023 +0000 [API] ```llm-vscode``` extension support (mlc-ai#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - huggingface/llm-vscode#103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot commit 0e08845 Author: Animesh Bohara <ani.bohara@gmail.com> Date: Sun Nov 5 01:01:26 2023 -0400 [RestAPI] Added docs (mlc-ai#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> commit 3417505 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 19:44:25 2023 -0700 Support overriding `--max-sequence-length` in command line (mlc-ai#1197) commit 5d63f7e Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 15:42:19 2023 -0700 [Docs] Clarify zstd installation on Windows (mlc-ai#1196) Update zstd installation commit 78424f0 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 02:13:21 2023 -0700 [Docs] Clarify zstd installation on Windows (mlc-ai#1191) commit 4832c2f Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:58:55 2023 -0700 Add CodeLlama as part of model presets (mlc-ai#1190) commit 5d1dc34 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:46:19 2023 -0700 Merge llama_config.py into llama_model.py (mlc-ai#1189) commit 9d20575 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:30:15 2023 -0700 Merge `llama_config.CONFIG` into `MODEL_PRESETS` (mlc-ai#1188) commit 4716704 Author: Junru Shao <junrushao@apache.org> Date: Sat Nov 4 01:20:43 2023 -0700 Add Python API for Weight Conversion (mlc-ai#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. commit 6ae02dd Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Fri Nov 3 15:34:29 2023 -0400 [Model Support][SWA] Add support for sliding window attention for Mistral (mlc-ai#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> commit 2dc8183 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Fri Nov 3 00:36:52 2023 -0700 [Fix][SLM] Update q4f16 quantization with the new mutator name rule (mlc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule commit 53060af Author: Xiyou Zhou <xiyou.zhou@gmail.com> Date: Thu Nov 2 13:08:11 2023 -0700 [SLM][AutoLLM] Enable Command Line Weight Conversion (mlc-ai#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` commit 2ca7d15 Author: Junru Shao <junrushao@apache.org> Date: Thu Nov 2 11:30:28 2023 -0700 [Fix] TIR block name of dequantization (mlc-ai#1177) commit 1757777 Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Wed Nov 1 15:52:19 2023 -0700 [SLM] Fix group quantization (mlc-ai#1172) This PR fixes the group quantization and add related unit tests. commit 9831135 Author: Animesh Bohara <ani.bohara@gmail.com> Date: Wed Nov 1 15:16:09 2023 -0400 Fix Android app Permission denied error on Android 10 (mlc-ai#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> commit 200653a Author: Git bot <bot@noreply.github.com> Date: Wed Nov 1 14:53:54 2023 +0000 Auto updated submodule references commit f5b2e88 Author: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Date: Wed Nov 1 12:23:40 2023 +0800 Fix RWKV Support (mlc-ai#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) commit e0cd3f6 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:56:28 2023 -0700 [Bugfix] Cannot find global function `mlc.llm_chat_create` (mlc-ai#1167) commit 02d1e57 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:43:17 2023 -0700 Support CUDA Multi-Arch Compilation (mlc-ai#1166) commit 8438b27 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 31 12:09:16 2023 -0700 Misc Cleanups of Compilation Pipeline (mlc-ai#1165) commit b5bfa5b Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Tue Oct 31 11:39:44 2023 -0700 Enable group quant transform with nn.Module (mlc-ai#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> commit 9076d01 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Mon Oct 30 22:40:53 2023 -0700 [Rest] Document emoji handling (mlc-ai#1160) Followup PR of mlc-ai#1142 to document the emoji handling. commit 425a2cb Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 30 15:00:24 2023 -0700 [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) commit 0a9d6c7 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 14:44:44 2023 -0500 [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. commit 3cf5605 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 14:43:51 2023 -0500 [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. commit 8ca0176 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Mon Oct 30 12:26:58 2023 -0700 [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) commit b190578 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 13:58:18 2023 -0500 Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. commit ece97b1 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 30 13:58:08 2023 -0500 [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. commit fee2cb5 Author: masahi <masahi129@gmail.com> Date: Tue Oct 31 01:32:06 2023 +0900 Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring commit ba67835 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 23:54:12 2023 -0700 Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. commit 1a79a53 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 21:51:36 2023 -0700 Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. commit 0a25374 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 21:17:38 2023 -0700 Migrate Compiler Passes (mlc-ai#1150) commit 2193767 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 16:35:07 2023 -0700 Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) commit c0c3a8d Author: Xiyou Zhou <xiyou.zhou@gmail.com> Date: Sun Oct 29 13:16:46 2023 -0700 [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. commit 878ae84 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 29 00:19:20 2023 -0700 Support parameter packing (mlc-ai#1146) commit 2b6d832 Author: fennecJ <hwahwa649@gmail.com> Date: Sun Oct 29 14:59:10 2023 +0800 Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... commit 27ac5ac Author: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Date: Sat Oct 28 20:18:16 2023 -0400 Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. commit 2ec0cc8 Author: Yuchen Jin <yuchenj@cs.washington.edu> Date: Sat Oct 28 15:13:48 2023 -0700 Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. commit 2c492e5 Author: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Date: Sun Oct 29 03:43:15 2023 +0530 Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst commit 24f795e Author: Goutham Tamilselvan <goutham2688@gmail.com> Date: Fri Oct 27 03:25:59 2023 -0400 added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. commit 973f9fc Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Wed Oct 25 10:14:46 2023 -0500 [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. commit a4279e3 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 21:05:24 2023 -0700 Add --opt flag parsing to CLI (mlc-ai#1123) commit 9166edb Author: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Date: Tue Oct 24 15:07:23 2023 -0400 [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path commit 9cb8e8e Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 09:04:45 2023 -0700 Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. commit 2aa6809 Author: Junru Shao <junrushao@apache.org> Date: Tue Oct 24 09:03:38 2023 -0700 Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) commit 206103b Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Tue Oct 24 11:54:01 2023 -0400 [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs commit 488017d Author: SingLi <Sing-Li@users.noreply.github.com> Date: Tue Oct 24 08:19:31 2023 -0500 fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 commit 8ce7793 Author: Git bot <bot@noreply.github.com> Date: Tue Oct 24 07:30:53 2023 +0000 Auto updated submodule references commit 61179a0 Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 23 23:58:01 2023 -0700 Add CLI commands for compilation (mlc-ai#1109) commit 5a7dcd8 Author: Tianqi Chen <tqchen@users.noreply.github.com> Date: Tue Oct 24 00:00:41 2023 -0400 [WINDOWS] reduce noise in windows build (mlc-ai#1115) commit 7ae8c6d Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Mon Oct 23 15:33:00 2023 -0700 [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) commit e5927ce Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 23 13:31:44 2023 -0500 [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation commit f57c9c9 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 23 13:31:24 2023 -0500 [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. commit 16dd2ae Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Sun Oct 22 19:51:10 2023 -0700 [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) commit 6159cc4 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 22 02:22:55 2023 -0700 [CI] Add clang-format (mlc-ai#1103) commit 46d11e6 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 23:39:28 2023 -0700 Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. commit 03c641a Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 21:51:51 2023 -0700 Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work commit e9b85ce Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 21:36:28 2023 -0700 More formatting (mlc-ai#1099) commit cf39bf6 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 20 15:38:49 2023 -0700 [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. commit 62d0c03 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Fri Oct 20 15:33:51 2023 -0700 Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) commit 9bf5723 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 15:49:40 2023 -0700 Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` commit 830656f Author: Varshith Bathini <varshith15@gmail.com> Date: Fri Oct 20 00:40:14 2023 +0530 StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> commit b0373d1 Author: Rick Zhou <riczhou@linkedin.com> Date: Thu Oct 19 12:09:12 2023 -0700 Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path commit 56a8004 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 10:37:24 2023 -0700 Update README.md for Multi-GPU (mlc-ai#1090) commit 2625945 Author: Junru Shao <junrushao@apache.org> Date: Thu Oct 19 08:57:50 2023 -0700 Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. commit 3aefd9f Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 16 21:16:27 2023 -0700 [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. commit 9872c48 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Mon Oct 16 14:56:24 2023 -0400 [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. commit d202077 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Mon Oct 16 08:06:26 2023 -0500 [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) commit 204860b Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Sun Oct 15 14:02:12 2023 -0400 [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. commit b0bfc88 Author: Junru Shao <junrushao@apache.org> Date: Sun Oct 15 00:24:24 2023 -0700 Add links to Python API Reference (mlc-ai#1068) commit 9010d48 Author: Jeethu Rao <jeethu@jeethurao.com> Date: Sun Oct 15 06:42:24 2023 +0100 Minor typo fix (mlc-ai#1064) commit 8184431 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 14 00:33:15 2023 -0500 [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. commit 481cd92 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 14 00:32:36 2023 -0500 [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. commit c2b8cbc Author: Jeethu Rao <jeethu@jeethurao.com> Date: Sat Oct 14 06:32:05 2023 +0100 Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig commit d854105 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Fri Oct 13 20:45:58 2023 -0400 [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. commit edab9b5 Author: Junru Shao <junrushao@apache.org> Date: Fri Oct 13 09:57:46 2023 -0700 [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. commit ca8c11b Author: Sunghyun Park <sunggg@umich.edu> Date: Fri Oct 13 09:00:21 2023 -0700 [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> commit bfaa5b9 Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Thu Oct 12 17:40:54 2023 -0400 Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) commit 98ebd28 Author: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Date: Thu Oct 12 13:24:10 2023 -0700 [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> commit b9179cf Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Thu Oct 12 12:15:44 2023 -0500 [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary commit 1e6fb11 Author: Denise Kutnick <boca.denise@gmail.com> Date: Wed Oct 11 00:06:46 2023 -0700 add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs commit 20131fb Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 9 16:53:56 2023 -0700 Update README.md (mlc-ai#1045) Update README.md commit bdd9d9b Author: Ruihang Lai <ruihangl@cs.cmu.edu> Date: Mon Oct 9 19:08:14 2023 -0400 [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. commit a58605f Author: Junru Shao <junrushao@apache.org> Date: Mon Oct 9 15:05:34 2023 -0700 Update README.md commit a032d40 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 18:03:24 2023 -0400 [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix commit 85001ed Author: Jeethu Rao <jeethu@jeethurao.com> Date: Mon Oct 9 20:40:52 2023 +0100 Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model commit c02fdaf Author: yongjer <54315206+yongjer@users.noreply.github.com> Date: Tue Oct 10 00:58:51 2023 +0800 Update compile_models.rst (mlc-ai#1038) fix permission issue commit bed9e60 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 12:58:36 2023 -0400 [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) commit 3a9849a Author: Bohan Hou <bohanhou@andrew.cmu.edu> Date: Mon Oct 9 12:27:58 2023 -0400 [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 commit b44f679 Author: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Date: Mon Oct 9 11:35:58 2023 -0400 Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model commit bae37b3 Author: Yaxing Cai <caiyaxing666@gmail.com> Date: Sun Oct 8 16:55:16 2023 -0700 [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) commit 6e40c21 Author: Eric Lunderberg <Lunderberg@users.noreply.github.com> Date: Sat Oct 7 22:07:09 2023 -0500 [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. commit ad3a6b9 Author: Roee Shenberg <shenberg@gmail.com> Date: Sun Oct 8 04:59:15 2023 +0200 Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break commit 898db76 Author: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Date: Sun Oct 8 03:36:19 2023 +0100 [API] Add GenerationConfig (mlc-ai#1024) * clean

* [API] Add GenerationConfig (mlc-ai#1024) * Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break * [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. * [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) * Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model * [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 * [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) * Update compile_models.rst (mlc-ai#1038) fix permission issue * Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model * [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix * Update README.md * [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. * Update README.md (mlc-ai#1045) Update README.md * add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs * [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary * [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) * [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> * [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. * [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. * Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig * [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. * [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. * Minor typo fix (mlc-ai#1064) * Add links to Python API Reference (mlc-ai#1068) * [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. * [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) * [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. * [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. * Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. * Update README.md for Multi-GPU (mlc-ai#1090) * Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path * StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> * Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` * Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) * [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. * More formatting (mlc-ai#1099) * Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work * Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. * [CI] Add clang-format (mlc-ai#1103) * [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) * [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. * [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation * [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) * [WINDOWS] reduce noise in windows build (mlc-ai#1115) * Add CLI commands for compilation (mlc-ai#1109) * Auto updated submodule references * fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 * [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs * Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) * Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. * [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path * Add --opt flag parsing to CLI (mlc-ai#1123) * [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. * added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. * Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst * Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. * Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. * Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... * Support parameter packing (mlc-ai#1146) * [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. * Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) * Migrate Compiler Passes (mlc-ai#1150) * Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. * Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. * Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring * [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. * Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. * [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) * [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. * [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. * [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) * [Rest] Document emoji handling (mlc-ai#1160) Followup PR of mlc-ai#1142 to document the emoji handling. * Enable group quant transform with nn.Module (mlc-ai#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> * Misc Cleanups of Compilation Pipeline (mlc-ai#1165) * Support CUDA Multi-Arch Compilation (mlc-ai#1166) * [Bugfix] Cannot find global function `mlc.llm_chat_create` (mlc-ai#1167) * Fix RWKV Support (mlc-ai#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) * Auto updated submodule references * Fix Android app Permission denied error on Android 10 (mlc-ai#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [SLM] Fix group quantization (mlc-ai#1172) This PR fixes the group quantization and add related unit tests. * [Fix] TIR block name of dequantization (mlc-ai#1177) * [SLM][AutoLLM] Enable Command Line Weight Conversion (mlc-ai#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` * [Fix][SLM] Update q4f16 quantization with the new mutator name rule (mlc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule * [Model Support][SWA] Add support for sliding window attention for Mistral (mlc-ai#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> * Add Python API for Weight Conversion (mlc-ai#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. * Merge `llama_config.CONFIG` into `MODEL_PRESETS` (mlc-ai#1188) * Merge llama_config.py into llama_model.py (mlc-ai#1189) * Add CodeLlama as part of model presets (mlc-ai#1190) * [Docs] Clarify zstd installation on Windows (mlc-ai#1191) * [Docs] Clarify zstd installation on Windows (mlc-ai#1196) Update zstd installation * Support overriding `--max-sequence-length` in command line (mlc-ai#1197) * [RestAPI] Added docs (mlc-ai#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [API] ```llm-vscode``` extension support (mlc-ai#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - huggingface/llm-vscode#103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot * [Fix] Use `fabs` as floating point abs function in C++ (mlc-ai#1202) * Integrating MLC runtime with the new compilation workflow (mlc-ai#1203) * [Fix] Remove Redundant Warnings (mlc-ai#1204) PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. * Try fix macOS build with picojson (mlc-ai#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. * Try fix macOS build with picojson again (mlc-ai#1207) Try fix macOS build with picojson * Auto updated submodule references * [Fix] Keep update-to-date with upstream API change (mlc-ai#1209) * Detect `mtriple` via LLVM (mlc-ai#1211) * Fix Python3.8 compatibility breakage (mlc-ai#1210) The breakage was resulting from newer syntax being used for type annotations, as part of mlc-ai#592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In mlc-ai#1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. * [Slim-LM] Enable loading from AWQ pre-quantized weight. (mlc-ai#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ * [Bugfix] Fix Cannot import name '_LIB' from 'mlc_chat.base' (mlc-ai#1214) Fix Python API doc * [SLM] Support `q3f16_1` and `q4f32_1` (mlc-ai#1215) This PR supports the int3 and float32 group quantization, and fixes some minor issue in quantization impl and tests. * Make the Compilation Working E2E (mlc-ai#1218) * [Mistral][SWA] Add sliding window to metadata (mlc-ai#1217) Add sliding window to metadata, make smalle changes to invariants in runtime * Support for `chatml` format conversation (for TinyLlama-1.1B-Chat-v0.2) (mlc-ai#956) * added support for chatml format conversation * added template to factory * Add Rust Support for MLC-LLM (mlc-ai#1213) This PR introduces Rust language support for the MLC-LLM project, specifically targeting supporting the `ChatModule` interface. It utilizes the existing C++ implementation of MLC-LLM and leverages both TVM's C API and its Rust bindings. The `rust/examples/mlc_chat.rs` gives an example of how to create a `chat_module` and serve user prompts in Rust. The primary goal of this PR is to enrich the MLC-LLM ecosystem by offering a Rust interface that aligns with the current Python API. This enhancement will empower Rust developers to integrate MLC-LLM into their codebase and applications. **Followup PRs**: - Extend the feature set to achieve parity with the C++/Python interface. - Refine the Rust API, ensuring robustness. - Set up Rust CI if needed. * [Bugfix] Remove dependency on openai_api in chat module (mlc-ai#1222) Remove dependency on openai_api * Bake in RAM Usage in the Generated DSO (mlc-ai#1224) With this PR, the metadata in a DSO file using `vm["_metadata"]()` now have information about the upper bound RAM estimate on each function. As an example, the JSON string now is: ```json { "quantization": "q4f16_1", "model_type": "llama", "memory_usage": { "_initialize_effect": 0, "prefill": 136192, "softmax_with_temperature": 0, "decode": 218624 }, "params": [ {"name": "model.embed_tokens.q_weight", "shape": [32000, 512], "dtype": "uint32"}, {"name": "model.embed_tokens.q_scale", "shape": [32000, 128], "dtype": "float16"}, ... ] } ``` This helps the MLC runtime to better determine if a method is going to OOM and plan ahead, e.g. plan pre-allocated KVCache, accordingly. The idea originates from Ruihang's ancient PR that prints memory usage estimate as debugging information for demo purposes, and this PR further enhances it to IRModule-level attribute that can be used by the runtime. * [Fix] ChatModule python messages and offset types (mlc-ai#1220) small fixes * [Fix] Variable Upperbound Should be Injected before Build Pipeline (mlc-ai#1225) Now it shows a more reasonable upper bound for sequence length = 4096. ```json { "_initialize_effect": 0, "prefill": 3479311360, "softmax_with_temperature": 0, "decode": 34531840 } ``` Thanks Ruihang for helping with the fix! * [MultiGPU] Support pre-sharded model weights (mlc-ai#1096) * [Bugfix] Correct input shape for shard info function Prior to this commit, the sharding functions sharded axis converted from `orig_size * num_shards` to `orig_size // num_shards`. This commit updates the sharding functions to instead convert from `orig_size` to `orig_size // num_shards`. * [Bugfix] Include LegalizeOps in utils.convert_weights Prior to this commit, `utils.convert_weights` assumes that the parameter transformation module is already legalized, and uses no relax operations that require legalization. This commit adds a call to `relax.transform.LegalizeOps` to remove this assumption. * [MultiGPU] Cleanup create_shard_info_func - De-duplicate the `if param.shard_strategy == foo` if/else chain - Return a `tvm.IRModule` instead of modifying an existing module * Extract a ParamManager.optimize_transform_param_order method * Extract ParamManager.create_parameter_transformation call from convert_weights * Support writing of pre-sharded weights * Support execution using pre-sharded weights * Updating for review comments * fix typo * fix --------- Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Co-authored-by: Roee Shenberg <shenberg@gmail.com> Co-authored-by: Eric Lunderberg <Lunderberg@users.noreply.github.com> Co-authored-by: Yaxing Cai <caiyaxing666@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu> Co-authored-by: yongjer <54315206+yongjer@users.noreply.github.com> Co-authored-by: Jeethu Rao <jeethu@jeethurao.com> Co-authored-by: Junru Shao <junrushao@apache.org> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Denise Kutnick <boca.denise@gmail.com> Co-authored-by: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> Co-authored-by: Sunghyun Park <sunggg@umich.edu> Co-authored-by: “Sunghyun <sunggg@umich.com> Co-authored-by: Rick Zhou <riczhou@linkedin.com> Co-authored-by: Varshith Bathini <varshith15@gmail.com> Co-authored-by: Varshith <varshith.bathini@sprinklr.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Git bot <bot@noreply.github.com> Co-authored-by: SingLi <Sing-Li@users.noreply.github.com> Co-authored-by: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Co-authored-by: Goutham Tamilselvan <goutham2688@gmail.com> Co-authored-by: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Co-authored-by: Yuchen Jin <yuchenj@cs.washington.edu> Co-authored-by: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Co-authored-by: fennecJ <hwahwa649@gmail.com> Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com> Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Co-authored-by: Animesh Bohara <ani.bohara@gmail.com> Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: Antonio Calatrava <antonio@antoniocalatrava.com>

* [API] Add GenerationConfig (mlc-ai#1024) * Fix two bugs in kv-cache backtrack loop (mlc-ai#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break * [Build] Added --pdb flag to build.py, drop into pdb on error (mlc-ai#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. * [Android] Use `AlertDialog` instead of `Toast` (mlc-ai#1039) * Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (mlc-ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model * [Android] Add Llama2 q4f16_0 (mlc-ai#1041) llama2 q4f160 * [Docs] Model prebuilts tracking page revamp (mlc-ai#1000) * Update compile_models.rst (mlc-ai#1038) fix permission issue * Support for the Stable LM 3B model (mlc-ai#1008) Support for the stablelm-3b-4e1t model * [Docs] Iterate model prebuilts docs (mlc-ai#1043) * Iterate model prebuilts docs * small fix * Update README.md * [CPP] Separate common utils out from llm_chat.cc (mlc-ai#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. * Update README.md (mlc-ai#1045) Update README.md * add verbose stats to mlc-chat REST API (mlc-ai#1049) * add verbose stats to mlc-chat REST API * update docs * [Transform] Apply split_rotary optimization on prefill (mlc-ai#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary * [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (mlc-ai#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Revert "[Transform] Apply split_rotary optimization on prefill (mlc-ai#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment) * [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> * [Doc] Use -U instead of --force-reinstall (mlc-ai#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. * [Model] Initial batching support for Llama (mlc-ai#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. * Fix Stable LM 3B build (mlc-ai#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig * [Core] Remove duplication in MODEL.get_model calls (mlc-ai#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. * [ParamManager] Cleanup creation of quantization IRModule (mlc-ai#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. * Minor typo fix (mlc-ai#1064) * Add links to Python API Reference (mlc-ai#1068) * [Fix] ChatModule incorrect temperature buffer shape (mlc-ai#1070) PR mlc-ai#1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. * [ParamManager] Added progress bar for get_item/set_item (mlc-ai#1063) * [Python] Extract common device str parse function in ChatModule (mlc-ai#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. * [Bugfix] Compilation Error in q4f32_1 (mlc-ai#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. * Establish `mlc_chat.compiler` (mlc-ai#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. * Update README.md for Multi-GPU (mlc-ai#1090) * Support lib_path override in C++. Improvements on docs and error messages (mlc-ai#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path * StreamIterator (mlc-ai#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> * Update `benchmark.py` according to mlc-ai#1086 (mlc-ai#1091) Update `benchmark.py` * Disable Disco for q4f16_ft and q8f16_ft quantization (mlc-ai#1094) * [Format] Apply isort and black for `python/` (mlc-ai#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. * More formatting (mlc-ai#1099) * Enable Python Linter (mlc-ai#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work * Add Basic Pylint and Mypy Tooling (mlc-ai#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in mlc-ai#1101. * [CI] Add clang-format (mlc-ai#1103) * [Slim-LM] Smart path finding for config and weight (mlc-ai#1088) * [Transform] Provide IRModule transform for rewrite_attention (mlc-ai#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. * [ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation * [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (mlc-ai#1113) * [WINDOWS] reduce noise in windows build (mlc-ai#1115) * Add CLI commands for compilation (mlc-ai#1109) * Auto updated submodule references * fix mismatched argument name (mlc-ai#1117) fix error introduced by recent code changes fixes mlc-ai#1116 * [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (mlc-ai#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs * Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (mlc-ai#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (mlc-ai#1056)" This reverts commit e5927ce. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: mlc-ai#1060 (comment) * Remove inaccurate warning message (mlc-ai#1121) This PR removes an inaccurate warning from mlc-ai#1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. * [REST] OpenAI compatible Rest API (mlc-ai#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path * Add --opt flag parsing to CLI (mlc-ai#1123) * [ParamManager][Redo] Use BundleModelParams for transform_dequantize (mlc-ai#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of apache/tvm#15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR mlc-ai#1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. * added details to windows installation (mlc-ai#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. * Grammatical and Typographical improvements (mlc-ai#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst * Minor enhancements to `ChatModule` (mlc-ai#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. * Updating tvm install docs (mlc-ai#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. * Make the help info consistent with program name (mlc-ai#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... * Support parameter packing (mlc-ai#1146) * [Slim-LM] Enable Group Quant (mlc-ai#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. * Enable Mypy and Pylint in mlc_chat Python Package (mlc-ai#1149) * Migrate Compiler Passes (mlc-ai#1150) * Compile Model Preset without External `config.json` (mlc-ai#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. * Update attention layer (mlc-ai#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. * Add batched Llama model definition using vLLM paged attention (mlc-ai#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring * [Transform][Redo] Apply split_rotary optimization on prefill (mlc-ai#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit mlc-ai#1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](mlc-ai#1058 (comment)), as these two sequence lengths are identical for the first call. * Apply rewrite for normal attention and MQA (mlc-ai#1138) Fixes a bug introduced in mlc-ai#1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. * [Rest] Fix emoji handling in Rest API. (mlc-ai#1142) * [Utility] Check for isinstance(exc, Exception) before entering pdb (mlc-ai#1095) This is a follow-up to mlc-ai#1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. * [Utils] Remove conversion to numpy array in utils.save_params (mlc-ai#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. * [Fix][REST] Use lowered-cased "app" (mlc-ai#1159) * [Rest] Document emoji handling (mlc-ai#1160) Followup PR of mlc-ai#1142 to document the emoji handling. * Enable group quant transform with nn.Module (mlc-ai#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> * Misc Cleanups of Compilation Pipeline (mlc-ai#1165) * Support CUDA Multi-Arch Compilation (mlc-ai#1166) * [Bugfix] Cannot find global function `mlc.llm_chat_create` (mlc-ai#1167) * Fix RWKV Support (mlc-ai#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) * Auto updated submodule references * Fix Android app Permission denied error on Android 10 (mlc-ai#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [SLM] Fix group quantization (mlc-ai#1172) This PR fixes the group quantization and add related unit tests. * [Fix] TIR block name of dequantization (mlc-ai#1177) * [SLM][AutoLLM] Enable Command Line Weight Conversion (mlc-ai#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` * [Fix][SLM] Update q4f16 quantization with the new mutator name rule (mlc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule * [Model Support][SWA] Add support for sliding window attention for Mistral (mlc-ai#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> * Add Python API for Weight Conversion (mlc-ai#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. * Merge `llama_config.CONFIG` into `MODEL_PRESETS` (mlc-ai#1188) * Merge llama_config.py into llama_model.py (mlc-ai#1189) * Add CodeLlama as part of model presets (mlc-ai#1190) * [Docs] Clarify zstd installation on Windows (mlc-ai#1191) * [Docs] Clarify zstd installation on Windows (mlc-ai#1196) Update zstd installation * Support overriding `--max-sequence-length` in command line (mlc-ai#1197) * [RestAPI] Added docs (mlc-ai#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [API] ```llm-vscode``` extension support (mlc-ai#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - huggingface/llm-vscode#103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot * [Fix] Use `fabs` as floating point abs function in C++ (mlc-ai#1202) * Integrating MLC runtime with the new compilation workflow (mlc-ai#1203) * [Fix] Remove Redundant Warnings (mlc-ai#1204) PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. * Try fix macOS build with picojson (mlc-ai#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. * Try fix macOS build with picojson again (mlc-ai#1207) Try fix macOS build with picojson * Auto updated submodule references * [Fix] Keep update-to-date with upstream API change (mlc-ai#1209) * Detect `mtriple` via LLVM (mlc-ai#1211) * Fix Python3.8 compatibility breakage (mlc-ai#1210) The breakage was resulting from newer syntax being used for type annotations, as part of mlc-ai#592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In mlc-ai#1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. * [Slim-LM] Enable loading from AWQ pre-quantized weight. (mlc-ai#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ * [Bugfix] Fix Cannot import name '_LIB' from 'mlc_chat.base' (mlc-ai#1214) Fix Python API doc * [SLM] Support `q3f16_1` and `q4f32_1` (mlc-ai#1215) This PR supports the int3 and float32 group quantization, and fixes some minor issue in quantization impl and tests. * Make the Compilation Working E2E (mlc-ai#1218) * [Mistral][SWA] Add sliding window to metadata (mlc-ai#1217) Add sliding window to metadata, make smalle changes to invariants in runtime * Support for `chatml` format conversation (for TinyLlama-1.1B-Chat-v0.2) (mlc-ai#956) * added support for chatml format conversation * added template to factory * Add Rust Support for MLC-LLM (mlc-ai#1213) This PR introduces Rust language support for the MLC-LLM project, specifically targeting supporting the `ChatModule` interface. It utilizes the existing C++ implementation of MLC-LLM and leverages both TVM's C API and its Rust bindings. The `rust/examples/mlc_chat.rs` gives an example of how to create a `chat_module` and serve user prompts in Rust. The primary goal of this PR is to enrich the MLC-LLM ecosystem by offering a Rust interface that aligns with the current Python API. This enhancement will empower Rust developers to integrate MLC-LLM into their codebase and applications. **Followup PRs**: - Extend the feature set to achieve parity with the C++/Python interface. - Refine the Rust API, ensuring robustness. - Set up Rust CI if needed. * [Bugfix] Remove dependency on openai_api in chat module (mlc-ai#1222) Remove dependency on openai_api * Bake in RAM Usage in the Generated DSO (mlc-ai#1224) With this PR, the metadata in a DSO file using `vm["_metadata"]()` now have information about the upper bound RAM estimate on each function. As an example, the JSON string now is: ```json { "quantization": "q4f16_1", "model_type": "llama", "memory_usage": { "_initialize_effect": 0, "prefill": 136192, "softmax_with_temperature": 0, "decode": 218624 }, "params": [ {"name": "model.embed_tokens.q_weight", "shape": [32000, 512], "dtype": "uint32"}, {"name": "model.embed_tokens.q_scale", "shape": [32000, 128], "dtype": "float16"}, ... ] } ``` This helps the MLC runtime to better determine if a method is going to OOM and plan ahead, e.g. plan pre-allocated KVCache, accordingly. The idea originates from Ruihang's ancient PR that prints memory usage estimate as debugging information for demo purposes, and this PR further enhances it to IRModule-level attribute that can be used by the runtime. * [Fix] ChatModule python messages and offset types (mlc-ai#1220) small fixes * [Fix] Variable Upperbound Should be Injected before Build Pipeline (mlc-ai#1225) Now it shows a more reasonable upper bound for sequence length = 4096. ```json { "_initialize_effect": 0, "prefill": 3479311360, "softmax_with_temperature": 0, "decode": 34531840 } ``` Thanks Ruihang for helping with the fix! * [MultiGPU] Support pre-sharded model weights (mlc-ai#1096) * [Bugfix] Correct input shape for shard info function Prior to this commit, the sharding functions sharded axis converted from `orig_size * num_shards` to `orig_size // num_shards`. This commit updates the sharding functions to instead convert from `orig_size` to `orig_size // num_shards`. * [Bugfix] Include LegalizeOps in utils.convert_weights Prior to this commit, `utils.convert_weights` assumes that the parameter transformation module is already legalized, and uses no relax operations that require legalization. This commit adds a call to `relax.transform.LegalizeOps` to remove this assumption. * [MultiGPU] Cleanup create_shard_info_func - De-duplicate the `if param.shard_strategy == foo` if/else chain - Return a `tvm.IRModule` instead of modifying an existing module * Extract a ParamManager.optimize_transform_param_order method * Extract ParamManager.create_parameter_transformation call from convert_weights * Support writing of pre-sharded weights * Support execution using pre-sharded weights * Updating for review comments * fix typo * [AWQ] e2e awq-quantized model (mlc-ai#1229) * [SLM] Support `q0f16` and `q0f32` (mlc-ai#1228) This PR adds the support of `q0f16` and `q0f32`, and change `RMSNorm` to `nn.RMSNorm`. * [Core][Llama] Argument `max_vocab_size` and `max_batch_size` (mlc-ai#1076) This PR introduces the `max_vocab_size` and `max_batch_size` as two new compile arguments. The purpose is for better memory planning. Besides, this PR updates llama to make use of the two arguments. Other models are not changed yet. The default value for `max_vocab_size` is set to 40000, which I think is larger than the values of most models. The default value for `max_batch_size` is currently set as 256. It is possible that we update this value in the future to have a good default number. * [Llama] Support batched prefill (mlc-ai#1233) This PR supports the Llama modeling with batched prefill, which can bring higher throughput for the overall prefill process in serving. Besides, the PR splits the attention function used in batching settings into two separate ones, so that we do not dispatch to the prefill/decode attention functions at runtime. * [Core] Skip PrimExpr index int32 downcasting for batching (mlc-ai#1234) This PR makes the ForceNarrowIndexToInt32 to skip application when batching is enabled. The reason is because the flattened index of the KV cache append function may exceed the range of int32 when the cache is large. For example, in Llama-7b, when a KV cache supports more than 8192 tokens, the total cache size will be at least ``` 8192 * 2 (K/V) * 32 (layers) * 4096 = 2147483648, ``` which reaches the maximum int32 value. * Auto updated submodule references * Update index.rst (mlc-ai#1236) Fixed typo on tab:: Android * Update android.rst (mlc-ai#1237) On linux, TVM_NDK_CC environment variable should contain linux-x86_64 * Correct typo in cuda device name for rust chat model (mlc-ai#1241) * Generating mlc-chat-config.json (mlc-ai#1238) This PR finishes the last piece of new compilation pipeline, i.e. generation of `mlc-chat-config.json` and other configuration files. * Rename `--config` to `--model` and Consolidate CLI Messages (mlc-ai#1244) * Specify argument "dest" in argparse (mlc-ai#1245) * Add more stats during quantization (mlc-ai#1246) * ensure that max_gen_len is set properly in mlc_chat_config (mlc-ai#1249) Currently, `max_gen_len` defaults to 512 in `dump_mlc_chat_config`. However, the instantiations of `dump_mlc_chat_config` within `mlc-llm.build` currently omit the `max_gen_len` argument (even when it's specified in the HF config), so the default of 512 gets set for every `mlc-chat-config.json` that is created by `mlc-llm.build`. This PR fixes the issue. * [Fix] Memory usage statistics (mlc-ai#1252) * Introduce mlc_chat subcommands (mlc-ai#1251) This PR makes it possible to use subcommands of `mlc_chat` package to control quantization and compilation. Example: ```bash python -m mlc_chat convert_weight \ --model /models/Llama-2/hf/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ -o ./dist/new-llama/ python -m mlc_chat gen_mlc_chat_config \ --model ./dist/models/Llama-2-7b-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ --conv-template LM \ -o ./dist/new-llama \ python -m mlc_chat compile \ --model ./dist/models/Llama-2-7b-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./dist/new-llama/llama.so ``` It slightly simplifies the workflow. * Update mlc-chat-config.json (mlc-ai#1254) This PR updates two fields: * `tokenizer_files`, which now non-existent files are removed from this list; * `model_preset_tag` added to `model_config`, which helps the system to conveniently identify if a model configuration is already part of the system's built-in model preset. * [Rust] Support multiple prompts (mlc-ai#1253) This PR introduces `Prompt` and `ChatMessage` structures, and enhances the `ChatModule` to generate tokens using either a single string (via `Prompt::String`) or a vector of `ChatMessage` (via `Prompt::MessageList`). An example is provided in [rust/examples/mlc_chat.rs](https://github.com/mlc-ai/mlc-llm/compare/main...YuchenJin:mlc-llm:multi-prompts?expand=1#diff-4ffa9349207c1df6ceeebe06a9afc8f2015000e031b39d677bbbe7e85ae2819b). Here is a snippet demonstrating the interface: ```rust let message1 = ChatMessage { role: "user".to_owned(), content: "suppose we already have projects llama, alpaca and vicuna, what do you think would be a great name for the next project?".to_string(), }; let message2 = ChatMessage { role: "assistant".to_owned(), content: "based on the previous projects, a possible name for the next project could be \"cervidae\" which is the scientific name for deer family. this name reflects the collaboration and teamwork involved in the development of the project, and also nods to the previous projects that have been developed by the team.".to_string(), }; let message3 = ChatMessage { role: "user".to_owned(), content: "Summarize our conversations".to_string(), }; let messages = vec![message1, message2, message3]; let prompt = Prompt::MessageList(messages); let output = cm.generate(&prompt, None).unwrap(); ``` * [UI] Correct "convert_weight_only" to "convert_weights_only" (mlc-ai#1227) * [UI] Correct "convert_weight_only" to "convert_weights_only" This is a frequent typo among multiple developers, as "weights" is typically plural. This commit updates the command-line-argument from `--convert-weight-only` to `--convert-weights-only`. For backwards compatibility, the original spelling is kept as an equivalent usage. * Update all use of "convert_weight_only" to "convert_weights_only" * Add a downloader from HuggingFace (mlc-ai#1258) This PR allows programmably downloading from HuggingFace to MLC's cache directory, which locates in `$HOME/.cache/mlc_chat/model_weights/` by default. This PR relies on Git to clone the metadata, and Python's requests library to fetch concrete weights as large files instead of the less reliable Git LFS. The example demonstrates downloading the 4-bit quantized Llama2-7B model: ```python from mlc_chat.support.download import download_mlc_weights download_mlc_weights("HF://mlc-ai/mlc-chat-Llama-2-7b-chat-hf-q4f16_1") ``` Screenshot: <img width="1913" alt="image" src="https://github.com/mlc-ai/mlc-llm/assets/22515877/3ac50594-4971-4216-bb17-47710b4af1dd"> * [Fix] Add prefix_tokens to `ConvConfig` in Python to match C++ implementation (mlc-ai#1256) During my Rust implementation of the project, I noticed an inconsistency between the Python and C++ implementations of `ConvConfig`. Specifically, the Python version lacks the `prefix_tokens` field, which is present in the C++ version.: https://github.com/mlc-ai/mlc-llm/blob/5e02cacd8ebba2e0206d5a447225a137de2dac0d/cpp/conversation.h#L69-L70. This can cause the [`_load_json_override`](https://github.com/mlc-ai/mlc-llm/blob/5e02cacd8ebba2e0206d5a447225a137de2dac0d/python/mlc_chat/chat_module.py#L1062C26-L1062C26) fails to work in the `_prefill` function. I think a simple unit test would help, I'd like to add a regression test if the CI has been set up. * [nn.Module] Mistral implementation (mlc-ai#1230) * Add mistral high level structure * Small config change * Now work with compile, mistral inference logic left * Add masking, cache_len, kv_seq_len; only attention forward left * fix mistral override naming * `interleave_kv` implementation * lint fix * move rolling buffer cache impl to mlc-llm * isort fix * nn.module implementation - reorganize structure * Update python/mlc_chat/cli/compile.py Co-authored-by: Junru Shao <junrushao1994@gmail.com> * lint fix --------- Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Add `mlc_chat.__main__` as command line entrypoint (mlc-ai#1263) This PR makes it possible to invoke mlc_chat subcommands directly. Previously one has to use `python -m` as the prefix to invoke `mlc_chat`: ```bash python -m mlc_chat compile \ --model /models/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./llama.so ``` This PR makes is possible to use it without the `python -m` prefix: ```bash mlc_chat compile \ --model /models/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./llama.so ``` * [Rust] Improve ergonomics of `generate` function in `ChatModule` (mlc-ai#1262) Following PR mlc-ai#1253, I think ergonomics of the `generate` function of `ChatModule` can be improved (given it's an important public-facing API). This PR simplifies the function's usage by implementing the `From` trait for the `Prompt` enum. Also updated the example code. Now the interface changes to: ```rust /// Single prompt case: cm.generate("what is the meaning of life?", None) /// Multiple prompt case: let messages: Vec<ChatMessage> = vec![message1, message2, message3]; let output = cm.generate(messages, None).unwrap(); ``` * [Fix] mistral `max_gen_len` (mlc-ai#1264) * Rename `max-sequence-length` to `context-window-size` (mlc-ai#1265) "Context window" is a terminology better aligned with LLM world. Whenever a new model is trained, it is one of the most important metrics that people care about. Therefore, I'd love to switch it over sooner than later, before "mlc_chat compile" becomes mature and documented. * Auto updated submodule references * Fix group quantization shape infer (mlc-ai#1273) This PR fixes the shape infer for group quantization. * Continuous Model Delivery (mlc-ai#1272) This PR provides a script that automatically quantizes models from HuggingFace using various quantization formats as specified. Example: When being provided the following JSON file: ```json { "destination": "{username}/{model_id}-{quantization}", # Name of HF repo "default_quantization": ["q0f16", "q0f32", "q3f16_1", "q4f16_1", "q4f32_1"], "tasks": [ { "model_id": "Llama-2-7b-hf", "model": "/models/Llama-2-7b-hf", # Can be HF URL or a local path "context_window_size": 4096, "conv_template": "LM", "quantization": [ { "format": "q4f16_awq", "model": "https://huggingface.co/TheBloke/Llama-2-7B-AWQ", # Overriding default `source` "source_format": "awq" } ] } ] } ``` The script will automatically run quantization and upload them to the following repos: - https://huggingface.co/junrushao/Llama-2-7b-hf-q0f16 - https://huggingface.co/junrushao/Llama-2-7b-hf-q0f32 - https://huggingface.co/junrushao/Llama-2-7b-hf-q3f16_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f16_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f32_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f16_awq * Auto updated submodule references * Enhance Model Delivery (mlc-ai#1283) This PR introduces a few enhancements: - Allow to override temporary path via environment variable `MLC_TEMP_DIR`; - Add a 10-time retry when uploading the quantized weights to HuggingFace Hub. It could fail at times; - Echo the commands being used to quantize the models in `logs.txt`; - Fix a compatibility issue when pulling individual weights down from HuggingFace Hub in Git LFS. * add python, rest api test (mlc-ai#1278) * add python, rest api test * remove mistral, fix pylint * fix pylint requests import error * Enable Jenkins CI (mlc-ai#1292) * fix * Update android.rst (mlc-ai#1289) This fix enables default models in app-config.json to get shown "downloaded" in model list via with adb push method for the default models * more fix * Consolidate Logics for GPU Detection (mlc-ai#1297) This PR unifies automatic device detection logic by using `mlc_chat.support.auto_device`, which comes with detailed logging and fallback mechanisms. * [CI] Fix lint concurrent clone issue (mlc-ai#1299) This PR fixes the broken CI due to different tasks sharing the same workspace. * Auto updated submodule references * [Feature] Prefill chunking for non-SWA models (mlc-ai#1280) * generalize `prefill-chunk-size` * renaming `cache_len` to `rolling_cache_len` * [nn.Module] generalize `prefill_chunk_size` * quick fix * lint fix * check sw with chunking * fix `_attach_variable_bounds` * update config from lib metadata * cleanup cleanup * metadata fix * Compatible with chatglm (mlc-ai#979) compatible for chatglm * Add q4/q8_ft_group quantization mode (mlc-ai#1284) * Add q4/q8_ft_group quantization mode * Update submodule * fix * restore multi gpu support for FT quant --------- Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Co-authored-by: Roee Shenberg <shenberg@gmail.com> Co-authored-by: Eric Lunderberg <Lunderberg@users.noreply.github.com> Co-authored-by: Yaxing Cai <caiyaxing666@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu> Co-authored-by: yongjer <54315206+yongjer@users.noreply.github.com> Co-authored-by: Jeethu Rao <jeethu@jeethurao.com> Co-authored-by: Junru Shao <junrushao@apache.org> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Denise Kutnick <boca.denise@gmail.com> Co-authored-by: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> Co-authored-by: Sunghyun Park <sunggg@umich.edu> Co-authored-by: “Sunghyun <sunggg@umich.com> Co-authored-by: Rick Zhou <riczhou@linkedin.com> Co-authored-by: Varshith Bathini <varshith15@gmail.com> Co-authored-by: Varshith <varshith.bathini@sprinklr.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Git bot <bot@noreply.github.com> Co-authored-by: SingLi <Sing-Li@users.noreply.github.com> Co-authored-by: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Co-authored-by: Goutham Tamilselvan <goutham2688@gmail.com> Co-authored-by: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Co-authored-by: Yuchen Jin <yuchenj@cs.washington.edu> Co-authored-by: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Co-authored-by: fennecJ <hwahwa649@gmail.com> Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com> Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Co-authored-by: Animesh Bohara <ani.bohara@gmail.com> Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: Antonio Calatrava <antonio@antoniocalatrava.com> Co-authored-by: Aman Kushwaha <aman.kushwaha.inbox@gmail.com> Co-authored-by: Malcolm Ramsay <m@malramsay.com> Co-authored-by: Denise Kutnick <denise@octoml.ai> Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> Co-authored-by: Siyuan Feng <Hzfengsy@sjtu.edu.cn> Co-authored-by: Masahiro Masuda <maasahi129@gmail.com> Co-authored-by: ChaoQin <qc903113684@163.com> Co-authored-by: Wuwei Lin <wuwei@apache.org>

* [API] Add GenerationConfig (#1024) * Fix two bugs in kv-cache backtrack loop (#856) Fix two bugs in kv-cache pop loop Bug 1: old code would stop early because output_ids was shortened in-place during the loop Bug 2: off-by-one in backoff size due to break * [Build] Added --pdb flag to build.py, drop into pdb on error (#1017) This commit adds an optional `--pdb` flag to the `build.py` script. If passed, any exception raised that would otherwise terminate the script will first enter a pdb post-mortem, allowing the error to be inspected. * [Android] Use `AlertDialog` instead of `Toast` (#1039) * Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs (#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model * [Android] Add Llama2 q4f16_0 (#1041) llama2 q4f160 * [Docs] Model prebuilts tracking page revamp (#1000) * Update compile_models.rst (#1038) fix permission issue * Support for the Stable LM 3B model (#1008) Support for the stablelm-3b-4e1t model * [Docs] Iterate model prebuilts docs (#1043) * Iterate model prebuilts docs * small fix * Update README.md * [CPP] Separate common utils out from llm_chat.cc (#1044) This PR separates out the tokenizer creation function, the random number generator out from `llm_chat.cc` as a preparation step for batching inference support, since these functions/modules are also used in the same way in batching inference. * Update README.md (#1045) Update README.md * add verbose stats to mlc-chat REST API (#1049) * add verbose stats to mlc-chat REST API * update docs * [Transform] Apply split_rotary optimization on prefill (#1033) * [Transform] Apply split_rotary optimization on prefill Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. * Avoid multiple kernel launches for split_rotary * [Docs] Add `mlc.ai/package` to `DEPENDENCY INSTALLATION` group (#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Revert "[Transform] Apply split_rotary optimization on prefill (#1033)" (#1058) This reverts commit b9179cfdf02e041be15871b36e74400ab9001921 as elaborated here https://github.com/mlc-ai/mlc-llm/pull/1033#issuecomment-1760386712 * [BugFix] Set the right `max_sequence_length` for both Llama-1 and Llama-2 families (#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com> * [Doc] Use -U instead of --force-reinstall (#1062) `--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case. * [Model] Initial batching support for Llama (#1048) This PR introduces the initial batched input support for llama models. To make the code managable, we keep both the single-sequence handling flow and the batching handling flow in the Llama modeling. Now, with `--enable-batching` as a build argument, we build Llama for the batched version. NOTE: The paged attention kernel/TIR func are not included in this PR, so currently the built library with batching enabled is not runnable. We will follow up with the attention kernel in the future. This PR guarantees that the existing single-sequence inference (Python API, CLI, etc.) is not broken. P.S.. The batching flow is subject to bug fixes as we integrate with the attention function and run the e2e flow in the future. * Fix Stable LM 3B build (#1061) * [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig * [Core] Remove duplication in MODEL.get_model calls (#1054) This commit removes the `if`/`elif` chain in `core.py`, where the body of each conditional assigns the same `mod, param_manager, params, model_config`, and is identical except for the choice of model being built. * [ParamManager] Cleanup creation of quantization IRModule (#1053) This commit replaces the single-parameter `relax_model.param_manager.create_quantize_func` function with a method on the `ParamManager`, `create_parameter_transformation`. This avoids potential typos between `param_manager` as the imported Python module `mlc_llm.relax_model.param_manager` and an instance of the `ParamManager` class named `param_manager`, and makes the functionality easier to find. This function also takes an optional `optimize_parameter_order` flag, defaulting to `True`, which applies the `ReorderTransformFunc` pass. Since the `ReorderTransformFunc` is intended to be used with several configuration objects owned by `ParamManager`, this simplifies the common path of producing an optimally-ordered parameter transformation module. * Minor typo fix (#1064) * Add links to Python API Reference (#1068) * [Fix] ChatModule incorrect temperature buffer shape (#1070) PR #1048 updated the signature of softmax in the built model library and changed the temperature buffer shape in ChatModule. This causes some existing demo unable to run since we did not do a round of model library update. This PR reverts the ChatModule change, and adds back the softmax function in non-batching case. With this PR, the regression should be fixed. * [ParamManager] Added progress bar for get_item/set_item (#1063) * [Python] Extract common device str parse function in ChatModule (#1074) This PR lifts the device string parsing (just a few of lines) to a standalone function, so that on the serving side the serving can make use of this function as well. Tested Python API and it does not seem to incur regression. * [Bugfix] Compilation Error in q4f32_1 (#1078) The pass `fuse-split-rotary` assumes the compute dtype is fp16, which usually is, but in certain cases, e.g. `q0f32` and `q4f32_1`, the compute is based on fp32 instead. This PR strengthens the check guard. * Establish `mlc_chat.compiler` (#1082) This PR establishes the compiler components in MLC-Chat Python API, which currently includes two primary components: models and parameters. The models are `nn.Module`-based definition of an LLM, which, as the very first stab, contains only `LlamaForCasualLM`. It is decomposed into three files: - `llama_config.py`: common configurations for Llama, where we define relevant configurations of its architecture, as well as include standard config file for Llama2-7B/13B/70B for convenient testing; - `llama.py`: the model architecture of Llama, based on the PyTorch-like `nn.Module` API; - `llama_parameter.py`: defines the mapping between MLC parameters and pytorch parameters. The parameters contains the basic functionality of parameter mapping, and the loaders that effectively convert parameters from PyTorch to MLC according to the mapping specified. Currently, only `HFTorchLoader` is implemented, but loaders like SafeTensor, GPTQ or AWQ should be quite straightforward according to the existing design. On top of this PR, on-the-fly quantization could be defined as a loading time transformation on MLC parameters, while pre-quantized parameter loading is effectively parameter loading after MLC's `nn.Module` is quantized. Two unittests examplify how the infrastructure works: - `./tests/python/model/test_llama.py` shows how to create an `nn.Module` using the new infra, and then convert it to TVM IRModule; - `./tests/python/parameter/hf_torch_loader.py` shows how to load parameters from HuggingFace PyTorch format. Besides, `mlc_chat.support` is established for utility functions, which now contains two utils: - `config.py` which supports reading configurations into dataclasses from JSON file or Python dict. On top of Python dataclass, it throws irrelevant fields into `cls.kwargs`, which is helpful when loading HuggingFace configuration file; - `tqdm.py` which contains tqdm-related utilities, primarily redirecting logging and printing to work nicely with tqdm. * Update README.md for Multi-GPU (#1090) * Support lib_path override in C++. Improvements on docs and error messages (#1086) * Support lib_path option in C++ CLI. Disable ChatConfig.model_lib override in Python API. Improvements on helper messages and error messages * Update docs * Rename lib_path -> model_lib_path * StreamIterator (#1057) Co-authored-by: Varshith <varshith.bathini@sprinklr.com> * Update `benchmark.py` according to #1086 (#1091) Update `benchmark.py` * Disable Disco for q4f16_ft and q8f16_ft quantization (#1094) * [Format] Apply isort and black for `python/` (#1097) [Format] Apply isort and black on `python/` The commands I am using are: ``` isort --profile black python/ black python/ ``` It is always recommended to format the code before submission, given we don't have a linter CI yet. * More formatting (#1099) * Enable Python Linter (#1098) This PR enables two Python formatters "black" and "isort" on the following directory: - `./python/` - `./tests/python/` Enabling pylint and mypy is left for future work * Add Basic Pylint and Mypy Tooling (#1100) Add pylint/mypy tooling into pyproject.toml This PR establishes the initial Python tooling infra with Pylint and Mypy. Currently only the newest modules, i.e. `mlc_chat.support` and `mlc_chat.compiler` are covered, and we expect to cover the entire package, as being tracked in #1101. * [CI] Add clang-format (#1103) * [Slim-LM] Smart path finding for config and weight (#1088) * [Transform] Provide IRModule transform for rewrite_attention (#1052) Prior to this commit, `mlc_llm.transform.rewrite_attention` updated a single function. This commit modifies it to instead be a transform operating on any pattern matches within an `IRModule`. * [ParamManager] Use BundleModelParams for transform_dequantize (#1056) * [ParamManager] Use BundleModelParams for transform_quantize Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of https://github.com/apache/tvm/pull/15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. * Correct type annotation * [Slim-LM] Introduce HFLoad for loading Pytorch and SafeTensor weights (#1113) * [WINDOWS] reduce noise in windows build (#1115) * Add CLI commands for compilation (#1109) * Auto updated submodule references * fix mismatched argument name (#1117) fix error introduced by recent code changes fixes #1116 * [Docs] Add doc for max and mean gen len, shift factor; and buildArgs (#1119) * Add doc for max and mean gen len, shift factor * Update python docs for BuildArgs * Revert "[ParamManager] Use BundleModelParams for transform_dequantize" (#1120) Revert "[ParamManager] Use BundleModelParams for transform_dequantize (#1056)" This reverts commit e5927cee3b932b6e3116b43778008a3aa11ef0a3. This causes a regression impacting all MLC LLM nightlies as it violates the existing calling convention in MLC Chat runtime. An example: https://github.com/mlc-ai/mlc-llm/issues/1060#issuecomment-1776761032 * Remove inaccurate warning message (#1121) This PR removes an inaccurate warning from #1086, which warns about `model_lib` overriding regardless of whether or not it's actually overridden. With this commit, we only warn if its value is not None. * [REST] OpenAI compatible Rest API (#1107) * add presence and frequency penalty * Added support for passing conversation history in /v1/chat/completions endpoint * Added support for RestAPI parameters max_gen_len, n, and stop_str * * add presence and frequency penalty to generation config * refactor generation config * Added documentation for parameters * replace lib_path with model_lib_path in rest.py * fixed black isort issues * fix lib_path * Add --opt flag parsing to CLI (#1123) * [ParamManager][Redo] Use BundleModelParams for transform_dequantize (#1127) Prior to this commit, `ParamManager.transform_quantize` function took as input functions with separate parameters for each weight tensor, and produced output functions with a tuple parameter for all weights. Because `LiftTransformParams` had the same convention, neither could be applied as part of the same build flow. This commit updates `ParamManager.transform_quantize` pass to produce outputs with separate tensor parameters, using the `BundleModelParams` transform to later combine them into a single tuple parameter. The analogous change was also performed for `LiftTransformParams` as part of https://github.com/apache/tvm/pull/15657. In addition, prior to this commit, the `ParamManager.transform_dequantize` function operated directly on a `IRModule` object. As a result, any debug instrumentation (e.g. before/after printouts for each pass, before/after verification with `relax.analysis.well_formed`, etc.) did not apply to this `transform_dequantize`. This commit updates `ParamManager.transform_dequantize` to return a `ir.transform.Pass`. This commit is a repeat of the reverted PR https://github.com/mlc-ai/mlc-llm/pull/1056. This PR resolves the bug in the earlier implementation by removing the call to `.without_attr("num_input")` in `ParamReplacer.rewrite_func`. This follows an analogous update in `LiftTransformParams`, preserving the `"num_input"` attribute for use in `BundleModelParams`. * added details to windows installation (#1133) 32bit version of the zstd.dll library was causing issues, so updated the doc to be more specific and download the 64bit version. * Grammatical and Typographical improvements (#1139) * Update faq.rst * Update guideline.rst * Update compile_models.rst * Update distribute_compiled_models.rst * Update get-vicuna-weight.rst * Update python.rst * Update android.rst * Update cli.rst * Update ios.rst * Update javascript.rst * Update python.rst * Update rest.rst * Minor enhancements to `ChatModule` (#1132) Some minor enhancements to `ChatModule`, mainly handle the device parsing solely in `_parse_device_str` instead of handling it both in the member function and the `__init__` function to avoid redundancy; and some type annotation fix. * Updating tvm install docs (#1143) Updating the tvm install docs to assist a user in finding and copying zstd.dll to the correct folder. * Make the help info consistent with program name (#1137) When user use command `mlc_chat_cli --help`, the output will be something like Usage: mlc_chat [--help] ... That's because the program name specified in `cli_main.cc` is "mlc_chat". It will be less confusing if the output of help info shows Usage: mlc_chat_cli [--help] ... * Support parameter packing (#1146) * [Slim-LM] Enable Group Quant (#1129) * Enable group quant via new interface. * Minor fix. * Linting. * Fix isort. * Fix mypy. * TE compute working. * Skip embed. * Support cpu+gpu quantization. * Add target option to tests. * Linting. * Enable Mypy and Pylint in mlc_chat Python Package (#1149) * Migrate Compiler Passes (#1150) * Compile Model Preset without External `config.json` (#1151) This PR adds support for compiling a preset of models without having to provide a `config.json` on disk using the commands below: ```diff python -m mlc_chat.cli.compile \ --quantization q4f16_1 -o /tmp/1.so \ - --config /models/Llama-2-7b-chat-hf + --config llama2_7b ``` This allows easier testing and binary distribution without having to depend on external model directory. * Update attention layer (#1153) Existing dlight optimization only works for NT matmul, but not NN. As a result, the new `nn.Module`-based implementation, which uses NN matmul, fails compilation at HEAD for now. This PR fixes this issue by tweaking `k` to the preferred layout. The following commands now work with the new compilation pipeline: ```bash python -m mlc_chat.cli.compile --config llama2_7b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_13b --quantization q4f16_1 -o /tmp/1.so python -m mlc_chat.cli.compile --config llama2_70b --quantization q4f16_1 -o /tmp/1.so ``` Note that the quantization algorithm per se, `q4f16_1`, has not been implemented yet, meaning this code path is not yet ready for use so far. * Add batched Llama model definition using vLLM paged attention (#1134) * Add batched Llama model with vllm paged attention * update core.py * doc * minor * add e2e test * mv file * clean * Check if TVM has been built with USE_VLLM * update BuildArgs docstring * [Transform][Redo] Apply split_rotary optimization on prefill (#1125) Prior to this commit, the `transform.fuse_split_rotary_embedding` function was only applicable to the `decode` function of a Llama-type model. This was due to the sequence length being restricted to one, both in the pattern-match rule and in the `split_rotary` function, and the function being restricted to operate only on the `decode` function. This commit updates the `transform.fuse_split_rotary_embedding` pass to be a `tvm.ir.transform.Pass`, operating on all applicable matched in the `IRModule`. The `split_rotary` function is now produced as a fully-generic function, with static parameters substituted in afterwards. At this stage, the sequence length is retained as a dynamic parameter, such that it can be used by the `prefill` function. This commit reapplies the reverted commit https://github.com/mlc-ai/mlc-llm/pull/1033. The error in the previous implementation was in the definition of `rotary_embedding_offset`, which provided the `query_sequence_length` instead of `kv_sequence_length`. This was able to pass the validity tests described [here](https://github.com/mlc-ai/mlc-llm/pull/1058#issuecomment-1761622534), as these two sequence lengths are identical for the first call. * Apply rewrite for normal attention and MQA (#1138) Fixes a bug introduced in https://github.com/mlc-ai/mlc-llm/pull/1052, where use of the `--use-flash-attn-mqa` flag on a model that doesn't use MQA would prevent the use of CUTLASS attention at all. * [Rest] Fix emoji handling in Rest API. (#1142) * [Utility] Check for isinstance(exc, Exception) before entering pdb (#1095) This is a follow-up to #1017, which added a `--pdb` flag to enter a debugger on exit. This commit checks the type of the raised exception, and only enters the debugger if it is a subclass of `Exception`. This ensures that implementation-details, such as a thrown `SystemExit` or `KeyboardInterrupt`, do not cause an erroneous entry to pdb. * [Utils] Remove conversion to numpy array in utils.save_params (#1083) Prior to this commit, each parameter was converted to a numpy-owned array as part of a total size computation. This commit computes the size directly, removing the conversion. * [Fix][REST] Use lowered-cased "app" (#1159) * [Rest] Document emoji handling (#1160) Followup PR of #1142 to document the emoji handling. * Enable group quant transform with nn.Module (#1154) * Enable group quant transform with nn.Module This PR completes the group quantization support for `nn.Module` based model. * remove deprecated tests * Update * wip * remove deprecated test * fix lint * fix lint * fix lint --------- Co-authored-by: Junru Shao <junrushao@apache.org> * Misc Cleanups of Compilation Pipeline (#1165) * Support CUDA Multi-Arch Compilation (#1166) * [Bugfix] Cannot find global function `mlc.llm_chat_create` (#1167) * Fix RWKV Support (#1136) I successfully ran the rwkv-world-3b fp16 model on my Xiaomi phone. This PR is to fix a bug on the main branch where the rwkv model outputs only one word and then stop. ![image](https://github.com/mlc-ai/mlc-llm/assets/35585791/6514d6ef-c93c-4ad2-8e76-8ffa0663080f) * Auto updated submodule references * Fix Android app Permission denied error on Android 10 (#1175) Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [SLM] Fix group quantization (#1172) This PR fixes the group quantization and add related unit tests. * [Fix] TIR block name of dequantization (#1177) * [SLM][AutoLLM] Enable Command Line Weight Conversion (#1170) This PR enables weight conversion in command line. Sample command: `python3 -m mlc_chat.cli.convert_weight --config dist/models/llama-2-13b-chat-hf/ --quantization "q4f16_1" --output dist/test/` * [Fix][SLM] Update q4f16 quantization with the new mutator name rule (#1178) [Fix] Update q4f16 quantization with the new mutator name rule * [Model Support][SWA] Add support for sliding window attention for Mistral (#1087) * mistral base * Add sliding window mask making and its tests * Small changes for sliding window mask * Clean up mask making * Remove kv_seq_len * Add prefill chunking, handle max window size in SWA * Add interleave kv * Temporary fix for kv seq len * Pass in more shapes to SWA prefill and decode in runtime * mistral var fix * Small changes regarding shape passing * Small fix on chunk size * Add build args, fix mlc chat config dump * mistral system prompt --------- Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> * Add Python API for Weight Conversion (#1182) This PR primarily does a major refactoring to introduce Python API that is consistent with the CLI API. Besides, it includes the following fixes and enhancements: - More info provided to `isort` for better formatting in `pyproject.toml`; - Print out the default value of all arguments in argparse command line; - Ensure `--device` is always available locally when doing weight conversion; - Add argument echoing in weight conversion to be consistent with its counterpart in compilation; - Add a consistency checker to make sure the shapes/dtypes of all tensors from weight conversion is consistent with compilation; - Echo the total size of parameters; - Better logging of each parameter's shape and dtype, and either or not its quantized; - More structure robustification, renaming `parameter/` to `loader/` to be more explicit about its intention; - Inline and remove `ParamQuantizer` into the loader to improve logging and the logic flow; - Always add instructions "Use `--xxx` to override" for any options that are auto detected to be more informative to end users; - Fix wrong shape calculation when quantizing `nn.Embedding`; - Fix wrong dtype calculation in group quantization when the input dtype is different from model dtype (e.g. "float32" in torch, but the model dtype in quantization is fp16 in `q4f16_1`); - Fix inconsistent param names in layers such as `GroupQuantizeLinear`; - Fix dtype inconsistency when a parameter is not quantized; - Fix existing unittests. * Merge `llama_config.CONFIG` into `MODEL_PRESETS` (#1188) * Merge llama_config.py into llama_model.py (#1189) * Add CodeLlama as part of model presets (#1190) * [Docs] Clarify zstd installation on Windows (#1191) * [Docs] Clarify zstd installation on Windows (#1196) Update zstd installation * Support overriding `--max-sequence-length` in command line (#1197) * [RestAPI] Added docs (#1193) Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * [API] ```llm-vscode``` extension support (#1198) This PR enables ```llm-vscode``` extension API support for copilot-like code completion, following [HF's LSP](https://github.com/huggingface/llm-ls). Fully compatible with ```CodeLlama``` and ```starcoder``` on mlc-llm. - https://github.com/huggingface/llm-vscode/pull/103 enhances extension user experience when used with mlc-llm rest api. Thanks @ pacman100, who came up with this on his latest blogpost: https://huggingface.co/blog/personal-copilot * [Fix] Use `fabs` as floating point abs function in C++ (#1202) * Integrating MLC runtime with the new compilation workflow (#1203) * [Fix] Remove Redundant Warnings (#1204) PR #1203 introduces some unnecessary and redundant logging messages. This PR gets them removed. * Try fix macOS build with picojson (#1206) The error message below ``` /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h: In member function 'std::string picojson::value::to_str() const': /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:494:37: error: expected ')' before 'PRId64' 494 | SNPRINTF(buf, sizeof(buf), "%" PRId64, u_.int64_); | ~ ^~~~~~~ | ) /usr/share/miniconda/envs/mlc-llm-build/conda-bld/mlc-chat-cli-nightly-package_1699286394016/work/3rdparty/tvm/3rdparty/picojson/picojson.h:81:1: note: 'PRId64' is defined in header '<cinttypes>'; did you forget to '#include <cinttypes>'? 80 | #include <errno.h> +++ |+#include <cinttypes> 81 | #include <inttypes.h> ``` indicates that the `__STDC_FORMAT_MACROS` flag is not turned on for some reason. * Try fix macOS build with picojson again (#1207) Try fix macOS build with picojson * Auto updated submodule references * [Fix] Keep update-to-date with upstream API change (#1209) * Detect `mtriple` via LLVM (#1211) * Fix Python3.8 compatibility breakage (#1210) The breakage was resulting from newer syntax being used for type annotations, as part of https://github.com/mlc-ai/mlc-llm/pull/592. So long as `mlc_chat.interface.openai_api` wasn't imported, the breaking changes were not encountered. In https://github.com/mlc-ai/mlc-llm/pull/1107, the addition of `from .interface.openai_api import ChatMessage` caused this module to be imported, breaking compatibility of `mlc_chat.ChatModule` with Python3.8. This commit updates the type annotations to the supported syntax. * [Slim-LM] Enable loading from AWQ pre-quantized weight. (#1114) * [SLM] Enable loading from AWQ pre-quantized weight. * remove awq_loader.py * Update to the latest commit * Delete llama_parameter.py * update unittest * fix lint * upd * add Llama-2-7B-AWQ * [Bugfix] Fix Cannot import name '_LIB' from 'mlc_chat.base' (#1214) Fix Python API doc * [SLM] Support `q3f16_1` and `q4f32_1` (#1215) This PR supports the int3 and float32 group quantization, and fixes some minor issue in quantization impl and tests. * Make the Compilation Working E2E (#1218) * [Mistral][SWA] Add sliding window to metadata (#1217) Add sliding window to metadata, make smalle changes to invariants in runtime * Support for `chatml` format conversation (for TinyLlama-1.1B-Chat-v0.2) (#956) * added support for chatml format conversation * added template to factory * Add Rust Support for MLC-LLM (#1213) This PR introduces Rust language support for the MLC-LLM project, specifically targeting supporting the `ChatModule` interface. It utilizes the existing C++ implementation of MLC-LLM and leverages both TVM's C API and its Rust bindings. The `rust/examples/mlc_chat.rs` gives an example of how to create a `chat_module` and serve user prompts in Rust. The primary goal of this PR is to enrich the MLC-LLM ecosystem by offering a Rust interface that aligns with the current Python API. This enhancement will empower Rust developers to integrate MLC-LLM into their codebase and applications. **Followup PRs**: - Extend the feature set to achieve parity with the C++/Python interface. - Refine the Rust API, ensuring robustness. - Set up Rust CI if needed. * [Bugfix] Remove dependency on openai_api in chat module (#1222) Remove dependency on openai_api * Bake in RAM Usage in the Generated DSO (#1224) With this PR, the metadata in a DSO file using `vm["_metadata"]()` now have information about the upper bound RAM estimate on each function. As an example, the JSON string now is: ```json { "quantization": "q4f16_1", "model_type": "llama", "memory_usage": { "_initialize_effect": 0, "prefill": 136192, "softmax_with_temperature": 0, "decode": 218624 }, "params": [ {"name": "model.embed_tokens.q_weight", "shape": [32000, 512], "dtype": "uint32"}, {"name": "model.embed_tokens.q_scale", "shape": [32000, 128], "dtype": "float16"}, ... ] } ``` This helps the MLC runtime to better determine if a method is going to OOM and plan ahead, e.g. plan pre-allocated KVCache, accordingly. The idea originates from Ruihang's ancient PR that prints memory usage estimate as debugging information for demo purposes, and this PR further enhances it to IRModule-level attribute that can be used by the runtime. * [Fix] ChatModule python messages and offset types (#1220) small fixes * [Fix] Variable Upperbound Should be Injected before Build Pipeline (#1225) Now it shows a more reasonable upper bound for sequence length = 4096. ```json { "_initialize_effect": 0, "prefill": 3479311360, "softmax_with_temperature": 0, "decode": 34531840 } ``` Thanks Ruihang for helping with the fix! * [MultiGPU] Support pre-sharded model weights (#1096) * [Bugfix] Correct input shape for shard info function Prior to this commit, the sharding functions sharded axis converted from `orig_size * num_shards` to `orig_size // num_shards`. This commit updates the sharding functions to instead convert from `orig_size` to `orig_size // num_shards`. * [Bugfix] Include LegalizeOps in utils.convert_weights Prior to this commit, `utils.convert_weights` assumes that the parameter transformation module is already legalized, and uses no relax operations that require legalization. This commit adds a call to `relax.transform.LegalizeOps` to remove this assumption. * [MultiGPU] Cleanup create_shard_info_func - De-duplicate the `if param.shard_strategy == foo` if/else chain - Return a `tvm.IRModule` instead of modifying an existing module * Extract a ParamManager.optimize_transform_param_order method * Extract ParamManager.create_parameter_transformation call from convert_weights * Support writing of pre-sharded weights * Support execution using pre-sharded weights * Updating for review comments * fix typo * [AWQ] e2e awq-quantized model (#1229) * [SLM] Support `q0f16` and `q0f32` (#1228) This PR adds the support of `q0f16` and `q0f32`, and change `RMSNorm` to `nn.RMSNorm`. * [Core][Llama] Argument `max_vocab_size` and `max_batch_size` (#1076) This PR introduces the `max_vocab_size` and `max_batch_size` as two new compile arguments. The purpose is for better memory planning. Besides, this PR updates llama to make use of the two arguments. Other models are not changed yet. The default value for `max_vocab_size` is set to 40000, which I think is larger than the values of most models. The default value for `max_batch_size` is currently set as 256. It is possible that we update this value in the future to have a good default number. * [Llama] Support batched prefill (#1233) This PR supports the Llama modeling with batched prefill, which can bring higher throughput for the overall prefill process in serving. Besides, the PR splits the attention function used in batching settings into two separate ones, so that we do not dispatch to the prefill/decode attention functions at runtime. * [Core] Skip PrimExpr index int32 downcasting for batching (#1234) This PR makes the ForceNarrowIndexToInt32 to skip application when batching is enabled. The reason is because the flattened index of the KV cache append function may exceed the range of int32 when the cache is large. For example, in Llama-7b, when a KV cache supports more than 8192 tokens, the total cache size will be at least ``` 8192 * 2 (K/V) * 32 (layers) * 4096 = 2147483648, ``` which reaches the maximum int32 value. * Auto updated submodule references * Update index.rst (#1236) Fixed typo on tab:: Android * Update android.rst (#1237) On linux, TVM_NDK_CC environment variable should contain linux-x86_64 * Correct typo in cuda device name for rust chat model (#1241) * Generating mlc-chat-config.json (#1238) This PR finishes the last piece of new compilation pipeline, i.e. generation of `mlc-chat-config.json` and other configuration files. * Rename `--config` to `--model` and Consolidate CLI Messages (#1244) * Specify argument "dest" in argparse (#1245) * Add more stats during quantization (#1246) * ensure that max_gen_len is set properly in mlc_chat_config (#1249) Currently, `max_gen_len` defaults to 512 in `dump_mlc_chat_config`. However, the instantiations of `dump_mlc_chat_config` within `mlc-llm.build` currently omit the `max_gen_len` argument (even when it's specified in the HF config), so the default of 512 gets set for every `mlc-chat-config.json` that is created by `mlc-llm.build`. This PR fixes the issue. * [Fix] Memory usage statistics (#1252) * Introduce mlc_chat subcommands (#1251) This PR makes it possible to use subcommands of `mlc_chat` package to control quantization and compilation. Example: ```bash python -m mlc_chat convert_weight \ --model /models/Llama-2/hf/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ -o ./dist/new-llama/ python -m mlc_chat gen_mlc_chat_config \ --model ./dist/models/Llama-2-7b-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ --conv-template LM \ -o ./dist/new-llama \ python -m mlc_chat compile \ --model ./dist/models/Llama-2-7b-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./dist/new-llama/llama.so ``` It slightly simplifies the workflow. * Update mlc-chat-config.json (#1254) This PR updates two fields: * `tokenizer_files`, which now non-existent files are removed from this list; * `model_preset_tag` added to `model_config`, which helps the system to conveniently identify if a model configuration is already part of the system's built-in model preset. * [Rust] Support multiple prompts (#1253) This PR introduces `Prompt` and `ChatMessage` structures, and enhances the `ChatModule` to generate tokens using either a single string (via `Prompt::String`) or a vector of `ChatMessage` (via `Prompt::MessageList`). An example is provided in [rust/examples/mlc_chat.rs](https://github.com/mlc-ai/mlc-llm/compare/main...YuchenJin:mlc-llm:multi-prompts?expand=1#diff-4ffa9349207c1df6ceeebe06a9afc8f2015000e031b39d677bbbe7e85ae2819b). Here is a snippet demonstrating the interface: ```rust let message1 = ChatMessage { role: "user".to_owned(), content: "suppose we already have projects llama, alpaca and vicuna, what do you think would be a great name for the next project?".to_string(), }; let message2 = ChatMessage { role: "assistant".to_owned(), content: "based on the previous projects, a possible name for the next project could be \"cervidae\" which is the scientific name for deer family. this name reflects the collaboration and teamwork involved in the development of the project, and also nods to the previous projects that have been developed by the team.".to_string(), }; let message3 = ChatMessage { role: "user".to_owned(), content: "Summarize our conversations".to_string(), }; let messages = vec![message1, message2, message3]; let prompt = Prompt::MessageList(messages); let output = cm.generate(&prompt, None).unwrap(); ``` * [UI] Correct "convert_weight_only" to "convert_weights_only" (#1227) * [UI] Correct "convert_weight_only" to "convert_weights_only" This is a frequent typo among multiple developers, as "weights" is typically plural. This commit updates the command-line-argument from `--convert-weight-only` to `--convert-weights-only`. For backwards compatibility, the original spelling is kept as an equivalent usage. * Update all use of "convert_weight_only" to "convert_weights_only" * Add a downloader from HuggingFace (#1258) This PR allows programmably downloading from HuggingFace to MLC's cache directory, which locates in `$HOME/.cache/mlc_chat/model_weights/` by default. This PR relies on Git to clone the metadata, and Python's requests library to fetch concrete weights as large files instead of the less reliable Git LFS. The example demonstrates downloading the 4-bit quantized Llama2-7B model: ```python from mlc_chat.support.download import download_mlc_weights download_mlc_weights("HF://mlc-ai/mlc-chat-Llama-2-7b-chat-hf-q4f16_1") ``` Screenshot: <img width="1913" alt="image" src="https://github.com/mlc-ai/mlc-llm/assets/22515877/3ac50594-4971-4216-bb17-47710b4af1dd"> * [Fix] Add prefix_tokens to `ConvConfig` in Python to match C++ implementation (#1256) During my Rust implementation of the project, I noticed an inconsistency between the Python and C++ implementations of `ConvConfig`. Specifically, the Python version lacks the `prefix_tokens` field, which is present in the C++ version.: https://github.com/mlc-ai/mlc-llm/blob/5e02cacd8ebba2e0206d5a447225a137de2dac0d/cpp/conversation.h#L69-L70. This can cause the [`_load_json_override`](https://github.com/mlc-ai/mlc-llm/blob/5e02cacd8ebba2e0206d5a447225a137de2dac0d/python/mlc_chat/chat_module.py#L1062C26-L1062C26) fails to work in the `_prefill` function. I think a simple unit test would help, I'd like to add a regression test if the CI has been set up. * [nn.Module] Mistral implementation (#1230) * Add mistral high level structure * Small config change * Now work with compile, mistral inference logic left * Add masking, cache_len, kv_seq_len; only attention forward left * fix mistral override naming * `interleave_kv` implementation * lint fix * move rolling buffer cache impl to mlc-llm * isort fix * nn.module implementation - reorganize structure * Update python/mlc_chat/cli/compile.py Co-authored-by: Junru Shao <junrushao1994@gmail.com> * lint fix --------- Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> * Add `mlc_chat.__main__` as command line entrypoint (#1263) This PR makes it possible to invoke mlc_chat subcommands directly. Previously one has to use `python -m` as the prefix to invoke `mlc_chat`: ```bash python -m mlc_chat compile \ --model /models/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./llama.so ``` This PR makes is possible to use it without the `python -m` prefix: ```bash mlc_chat compile \ --model /models/Llama-2-7b-chat-hf \ --quantization q4f16_1 \ --max-sequence-length 4096 \ -o ./llama.so ``` * [Rust] Improve ergonomics of `generate` function in `ChatModule` (#1262) Following PR #1253, I think ergonomics of the `generate` function of `ChatModule` can be improved (given it's an important public-facing API). This PR simplifies the function's usage by implementing the `From` trait for the `Prompt` enum. Also updated the example code. Now the interface changes to: ```rust /// Single prompt case: cm.generate("what is the meaning of life?", None) /// Multiple prompt case: let messages: Vec<ChatMessage> = vec![message1, message2, message3]; let output = cm.generate(messages, None).unwrap(); ``` * [Fix] mistral `max_gen_len` (#1264) * Rename `max-sequence-length` to `context-window-size` (#1265) "Context window" is a terminology better aligned with LLM world. Whenever a new model is trained, it is one of the most important metrics that people care about. Therefore, I'd love to switch it over sooner than later, before "mlc_chat compile" becomes mature and documented. * Auto updated submodule references * Fix group quantization shape infer (#1273) This PR fixes the shape infer for group quantization. * Continuous Model Delivery (#1272) This PR provides a script that automatically quantizes models from HuggingFace using various quantization formats as specified. Example: When being provided the following JSON file: ```json { "destination": "{username}/{model_id}-{quantization}", # Name of HF repo "default_quantization": ["q0f16", "q0f32", "q3f16_1", "q4f16_1", "q4f32_1"], "tasks": [ { "model_id": "Llama-2-7b-hf", "model": "/models/Llama-2-7b-hf", # Can be HF URL or a local path "context_window_size": 4096, "conv_template": "LM", "quantization": [ { "format": "q4f16_awq", "model": "https://huggingface.co/TheBloke/Llama-2-7B-AWQ", # Overriding default `source` "source_format": "awq" } ] } ] } ``` The script will automatically run quantization and upload them to the following repos: - https://huggingface.co/junrushao/Llama-2-7b-hf-q0f16 - https://huggingface.co/junrushao/Llama-2-7b-hf-q0f32 - https://huggingface.co/junrushao/Llama-2-7b-hf-q3f16_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f16_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f32_1 - https://huggingface.co/junrushao/Llama-2-7b-hf-q4f16_awq * Auto updated submodule references * Enhance Model Delivery (#1283) This PR introduces a few enhancements: - Allow to override temporary path via environment variable `MLC_TEMP_DIR`; - Add a 10-time retry when uploading the quantized weights to HuggingFace Hub. It could fail at times; - Echo the commands being used to quantize the models in `logs.txt`; - Fix a compatibility issue when pulling individual weights down from HuggingFace Hub in Git LFS. * add python, rest api test (#1278) * add python, rest api test * remove mistral, fix pylint * fix pylint requests import error * Enable Jenkins CI (#1292) * Update android.rst (#1289) This fix enables default models in app-config.json to get shown "downloaded" in model list via with adb push method for the default models * Consolidate Logics for GPU Detection (#1297) This PR unifies automatic device detection logic by using `mlc_chat.support.auto_device`, which comes with detailed logging and fallback mechanisms. * [CI] Fix lint concurrent clone issue (#1299) This PR fixes the broken CI due to different tasks sharing the same workspace. * Auto updated submodule references * [Feature] Prefill chunking for non-SWA models (#1280) * generalize `prefill-chunk-size` * renaming `cache_len` to `rolling_cache_len` * [nn.Module] generalize `prefill_chunk_size` * quick fix * lint fix * check sw with chunking * fix `_attach_variable_bounds` * update config from lib metadata * cleanup cleanup * metadata fix * Compatible with chatglm (#979) compatible for chatglm * Add q4/q8_ft_group quantization mode (#1284) * Add q4/q8_ft_group quantization mode * Update submodule * [CI] Clean workspace before build (#1304) Cleaning the workspace before building, so that the previous corrupted directory will not affect the current CI build. Used the `cleanWS` from here https://www.jenkins.io/doc/pipeline/steps/ws-cleanup/ * [Python] Detect Driver/Device in a Separate Process (#1311) This PR separates the device detection into separate subprocesses. The change is because the device detection will setup the driver, which consumes some GPU VRAM (for example, `tvm.device("opencl", 0).exist` consumes 390MB of VRAM on RTX 4090). Consider the case we detect if CUDA, Vulkan and OpenCL are available. When they are all available, each detection holds some VRAM, larger than 430MB altogether. If the device detection is in the same process as the main process, the VRAM consumed by device detection will never be released. This means that in the example above, we detect CUDA, Vulkan and OpenCL, while in the end we prioritize the CUDA device. Consequently, the memory held by Vulkan and OpenCL detection will never be released. Motivated by this issue, we separate the detection into subprocess, so that the held VRAM can be successfully released after detection. * add chatglm3 support (#1313) * feat: add chatglm3 support * fix: remove duplicate code --------- Co-authored-by: Max Lin <jason.max.lin@outlook.com> * [SLIM] Skip None param when loading rather than failing (#1308) * Auto updated submodule references * [nn.Module] Implement GPT-2 Model Support (#1314) Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> * remove ndk referencce from mali build target (#1312) * remove ndk referencce from mali target This removes the ndk reference for mali targets when building * adding check for NDK environment variable --------- Co-authored-by: x <x@xs-MacBook-Pro.local> * [Rust] A few enhancements (#1310) a few enhancements. * [iOS] Mistral support (#1320) * Add terminator for streaming REST API (#1325) * Add terminator for streaming REST API Add compatibility with OpenAI's streaming API. fixes #1300 * make black happy * read CUDA_ARCH_LIST to set CUDA capability versions for nvcc (#1326) read {TORCH_,}CUDA_ARCH_LIST from environment and use it to set compute arch versions * Update emcc.rst * [AUTO-DEVICE] In process early exit device detection (#1333) This PR updates the device auto detection to use in process early exit. This hopefully will address some of the windows issues in multi-process based approach while preserving memory without allocating in each during auto mode. * [RestAPI] Update parameters for /v1/completions and add tests (#1335) Add n and stop for /v1/completions endpoint Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> * fix broken REST examples due to recent compatibility change (#1345) fix broken restapi examples due to recent OpenAI API compatibility changes * [Bugfix] Ignore exit code in device detection (#1350) TVM's Vulkan runtime emits a non-zero exit code on certain Windows drivers on DLL offloading. While there is definitely a way to fix this, for now, we quickly get around this by not checking the exit code in device detection. This PR also improves clarify when multiple GPUs presents by emitting logging messages on all GPUs available, rather than only GPU 0. * [OpenHermes] Add conversation template for OpenHermes Mistral (#1354) Add conversation template for open hermes mistral Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> * [Tokenizer] Prioritize huggingface tokenizer.json, generate one if not included (#1352) * Prioritize tokenzer json, generate one if not included * [Rust] Prepare for publishing (#1342) Prepare for publishing. * Fix gen_mlc_chat_config for mistral (#1353) * fix gen config for mistral * fix pylint * Allow override of sw and chunk size in config gen * lint fix --------- Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> * Fix ft quantization scale computation (#1321) * fix * fix --------- Co-authored-by: David Pissarra <61968959+davidpissarra@users.noreply.github.com> Co-authored-by: Roee Shenberg <shenberg@gmail.com> Co-authored-by: Eric Lunderberg <Lunderberg@users.noreply.github.com> Co-authored-by: Yaxing Cai <caiyaxing666@gmail.com> Co-authored-by: Charlie Ruan <53290280+CharlieFRuan@users.noreply.github.com> Co-authored-by: Bohan Hou <bohanhou@andrew.cmu.edu> Co-authored-by: yongjer <54315206+yongjer@users.noreply.github.com> Co-authored-by: Jeethu Rao <jeethu@jeethurao.com> Co-authored-by: Junru Shao <junrushao@apache.org> Co-authored-by: Ruihang Lai <ruihangl@cs.cmu.edu> Co-authored-by: Denise Kutnick <boca.denise@gmail.com> Co-authored-by: Lesheng Jin <34279105+LeshengJin@users.noreply.github.com> Co-authored-by: Junru Shao <junrushao1994@gmail.com> Co-authored-by: Sunghyun Park <sunggg@umich.edu> Co-authored-by: “Sunghyun <sunggg@umich.com> Co-authored-by: Rick Zhou <riczhou@linkedin.com> Co-authored-by: Varshith Bathini <varshith15@gmail.com> Co-authored-by: Varshith <varshith.bathini@sprinklr.com> Co-authored-by: Tianqi Chen <tqchen@users.noreply.github.com> Co-authored-by: Git bot <bot@noreply.github.com> Co-authored-by: SingLi <Sing-Li@users.noreply.github.com> Co-authored-by: Kartik Khandelwal <kartikkhandelwal1998@gmail.com> Co-authored-by: Goutham Tamilselvan <goutham2688@gmail.com> Co-authored-by: S A G A R <110724849+tmsagarofficial@users.noreply.github.com> Co-authored-by: Yuchen Jin <yuchenj@cs.washington.edu> Co-authored-by: DavidSharma <68979667+David-Sharma@users.noreply.github.com> Co-authored-by: fennecJ <hwahwa649@gmail.com> Co-authored-by: Xiyou Zhou <xiyou.zhou@gmail.com> Co-authored-by: Xiaoyu Zhang <35585791+BBuf@users.noreply.github.com> Co-authored-by: Animesh Bohara <ani.bohara@gmail.com> Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu> Co-authored-by: David Pissarra <david.pissarra@tecnico.ulisboa.pt> Co-authored-by: Antonio Calatrava <antonio@antoniocalatrava.com> Co-authored-by: Aman Kushwaha <aman.kushwaha.inbox@gmail.com> Co-authored-by: Malcolm Ramsay <m@malramsay.com> Co-authored-by: Denise Kutnick <denise@octoml.ai> Co-authored-by: Charlie Ruan <charlieruan2001130@gmail.com> Co-authored-by: Siyuan Feng <Hzfengsy@sjtu.edu.cn> Co-authored-by: ChaoQin <qc903113684@163.com> Co-authored-by: Wuwei Lin <wuwei@apache.org> Co-authored-by: Jason Lin <2627866800@qq.com> Co-authored-by: Max Lin <jason.max.lin@outlook.com> Co-authored-by: Rick Zhou <rickzhoucmu@gmail.com> Co-authored-by: Shankar Ambady <shanbady@gmail.com> Co-authored-by: x <x@xs-MacBook-Pro.local> Co-authored-by: technillogue <wisepoison@gmail.com> Co-authored-by: Hongyi Jin <jinhongyi02@gmail.com>

davidpissarra and others added 9 commits October 4, 2023 01:07

[API] add GenerationConfig dataclass

da5304d

Merge branch 'mlc-ai:main' into pr-generationconfig-api

171b7d5

pass GenerationConfig into prefill and decode

5653fef

GenerationConfig rest API

f7a5136

add GenerationConfig to embed function

64a489f

align existing generation settings

b8d4066

cleanup

afa5d1c

Merge branch 'mlc-ai:main' into pr-generationconfig-api

ed48160

remove update config test

95db062

davidpissarra mentioned this pull request Oct 5, 2023

[Tracking] Support more OpenAI parameters for chat completion endpoint in REST API #683

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tqchen merged commit 898db76 into mlc-ai:main Oct 8, 2023

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[API] Add GenerationConfig #1024

[API] Add GenerationConfig #1024

davidpissarra commented Oct 5, 2023

[API] Add GenerationConfig #1024

[API] Add GenerationConfig #1024

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davidpissarra commented Oct 5, 2023