Merge upstream nov7 #52
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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
…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.
…ai#1040) Add doc for ChatConfig, ConvConfig, GenerationConfig, BuildArgs, build model
llama2 q4f160
fix permission issue
Support for the stablelm-3b-4e1t model
* Iterate model prebuilts docs * small fix
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
* add verbose stats to mlc-chat REST API * update docs
* [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
…i#1055) Co-authored-by: Junru Shao <junrushao1994@gmail.com>
…i#1033)" (mlc-ai#1058) This reverts commit b9179cf as elaborated here mlc-ai#1033 (comment)
…ma-2 families (mlc-ai#1032) * fix * reflect feedback --------- Co-authored-by: “Sunghyun <sunggg@umich.com>
`--force-reinstall` will reinstall all dependencies to a python package, which is unnecessary. `-U` is a better choice in this case.
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.
* [stablelm 3b] Rename dynamic vocab size from "v" to "vocab_size" * Add get_num_key_value_heads method to StableLM3bConfig
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.
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.
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.
…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.
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.
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.
Use scoped storage instead of Downloads directory Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu>
This PR fixes the group quantization and add related unit tests.
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/`
…lc-ai#1178) [Fix] Update q4f16 quantization with the new mutator name rule
…tral (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>
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.
Update zstd installation
Add docs for RestAPI Co-authored-by: Animesh Bohara <abohara@cs.cmu.edu>
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
PR mlc-ai#1203 introduces some unnecessary and redundant logging messages. This PR gets them removed.
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
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.
* [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
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