Adapt the change of brevitas custom op name (#1772)

Once the latest works with LORA train, unpin it.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>

.flake8

@@ -2,4 +2,4 @@
 count = 1
 show-source = 1
 select = E9,F63,F7,F82
-exclude = lit.cfg.py, apps/language_models/scripts/vicuna.py
+exclude = lit.cfg.py, apps/language_models/scripts/vicuna.py, apps/language_models/src/pipelines/minigpt4_pipeline.py, apps/language_models/langchain/h2oai_pipeline.py

.github/workflows/nightly.yml

@@ -51,11 +51,11 @@ jobs:
       run: |
         ./setup_venv.ps1
         $env:SHARK_PACKAGE_VERSION=${{ env.package_version }}
-        pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+        pip wheel -v -w dist . --pre -f https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html
         python process_skipfiles.py
         pyinstaller .\apps\stable_diffusion\shark_sd.spec
         mv ./dist/nodai_shark_studio.exe ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
-        signtool sign /f c:\g\shark_02152023.cer /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
+        signtool sign /f c:\g\shark_02152023.cer /fd certHash /csp "eToken Base Cryptographic Provider" /k "${{ secrets.CI_CERT }}" ./dist/nodai_shark_studio_${{ env.package_version_ }}.exe
   
     - name: Upload Release Assets
       id: upload-release-assets
@@ -104,7 +104,7 @@ jobs:
         echo "DATE=$(date +'%Y-%m-%d')" >> $GITHUB_ENV
         python -m pip install --upgrade pip
         python -m pip install flake8 pytest toml
-        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html; fi
+        if [ -f requirements.txt ]; then pip install -r requirements.txt -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html; fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names
@@ -144,7 +144,7 @@ jobs:
         source shark.venv/bin/activate
         package_version="$(printf '%(%Y%m%d)T.${{ github.run_number }}')"
         SHARK_PACKAGE_VERSION=${package_version} \
-        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+        pip wheel -v -w wheelhouse . --pre -f https://download.pytorch.org/whl/nightly/torch -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html
         # Install the built wheel
         pip install ./wheelhouse/nodai*
         # Validate the Models

.github/workflows/test-models.yml

@@ -115,6 +115,7 @@ jobs:
         pytest --forked --benchmark=native --ci --ci_sha=${SHORT_SHA} --update_tank --tank_url="gs://shark_tank/nightly/" -k cpu 
         gsutil cp ./bench_results.csv gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv
         gsutil cp gs://shark-public/builder/bench_results/${DATE}/bench_results_cpu_${SHORT_SHA}.csv gs://shark-public/builder/bench_results/latest/bench_results_cpu_latest.csv
+        python build_tools/vicuna_testing.py
 
     - name: Validate Models on NVIDIA GPU
       if: matrix.suite == 'cuda'

.gitmodules

@@ -1,4 +1,4 @@
 [submodule "inference/thirdparty/shark-runtime"]
 	path = inference/thirdparty/shark-runtime
-	url =https://github.com/nod-ai/SHARK-Runtime.git
+	url =https://github.com/nod-ai/SRT.git
 	branch = shark-06032022

README.md

@@ -170,7 +170,7 @@ python -m pip install --upgrade pip
 This step pip installs SHARK and related packages on Linux Python 3.8, 3.10 and 3.11 and macOS / Windows Python 3.11
 
 ```shell
-pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+pip install nodai-shark -f https://nod-ai.github.io/SHARK/package-index/ -f https://llvm.github.io/torch-mlir/package-index/ -f  https://nod-ai.github.io/SRT/pip-release-links.html --extra-index-url https://download.pytorch.org/whl/nightly/cpu
 ```
 
 ### Run shark tank model tests.

apps/language_models/langchain/README.md

@@ -5,6 +5,7 @@
 1.) Install all the dependencies by running:
 ```shell
 pip install -r apps/language_models/langchain/langchain_requirements.txt
+sudo apt-get install -y libmagic-dev poppler-utils tesseract-ocr libtesseract-dev libreoffice
 ```
 
 2.) Create a folder named `user_path` in `apps/language_models/langchain/` directory.

apps/language_models/langchain/cli.py

@@ -2,7 +2,7 @@ import copy
 import torch
 
 from evaluate_params import eval_func_param_names
-from gen import get_score_model, get_model, evaluate, check_locals
+from gen import Langchain
 from prompter import non_hf_types
 from utils import clear_torch_cache, NullContext, get_kwargs
 
@@ -87,7 +87,7 @@ def run_cli(  # for local function:
     # unique to this function:
     cli_loop=None,
 ):
-    check_locals(**locals())
+    Langchain.check_locals(**locals())
 
     score_model = ""  # FIXME: For now, so user doesn't have to pass
     n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
@@ -98,16 +98,20 @@ def run_cli(  # for local function:
         from functools import partial
 
         # get score model
-        smodel, stokenizer, sdevice = get_score_model(
+        smodel, stokenizer, sdevice = Langchain.get_score_model(
             reward_type=True,
             **get_kwargs(
-                get_score_model, exclude_names=["reward_type"], **locals()
+                Langchain.get_score_model,
+                exclude_names=["reward_type"],
+                **locals()
             )
         )
 
-        model, tokenizer, device = get_model(
+        model, tokenizer, device = Langchain.get_model(
             reward_type=False,
-            **get_kwargs(get_model, exclude_names=["reward_type"], **locals())
+            **get_kwargs(
+                Langchain.get_model, exclude_names=["reward_type"], **locals()
+            )
         )
         model_dict = dict(
             base_model=base_model,
@@ -121,11 +125,11 @@ def run_cli(  # for local function:
         model_state.update(model_dict)
         my_db_state = [None]
         fun = partial(
-            evaluate,
+            Langchain.evaluate,
             model_state,
             my_db_state,
             **get_kwargs(
-                evaluate,
+                Langchain.evaluate,
                 exclude_names=["model_state", "my_db_state"]
                 + eval_func_param_names,
                 **locals()

apps/language_models/langchain/eval.py

@@ -1,402 +0,0 @@
-import inspect
-import os
-import traceback
-import numpy as np
-import pandas as pd
-import torch
-from matplotlib import pyplot as plt
-
-from evaluate_params import eval_func_param_names, eval_extra_columns
-from gen import get_context, get_score_model, get_model, evaluate, check_locals
-from prompter import Prompter
-from utils import clear_torch_cache, NullContext, get_kwargs
-
-
-def run_eval(  # for local function:
-    base_model=None,
-    lora_weights=None,
-    inference_server=None,
-    prompt_type=None,
-    prompt_dict=None,
-    debug=None,
-    chat=False,
-    chat_context=None,
-    stream_output=None,
-    eval_filename=None,
-    eval_prompts_only_num=None,
-    eval_prompts_only_seed=None,
-    eval_as_output=None,
-    examples=None,
-    memory_restriction_level=None,
-    # for get_model:
-    score_model=None,
-    load_8bit=None,
-    load_4bit=None,
-    load_half=None,
-    load_gptq=None,
-    use_safetensors=None,
-    infer_devices=None,
-    tokenizer_base_model=None,
-    gpu_id=None,
-    local_files_only=None,
-    resume_download=None,
-    use_auth_token=None,
-    trust_remote_code=None,
-    offload_folder=None,
-    compile_model=None,
-    # for evaluate args beyond what's already above, or things that are always dynamic and locally created
-    temperature=None,
-    top_p=None,
-    top_k=None,
-    num_beams=None,
-    max_new_tokens=None,
-    min_new_tokens=None,
-    early_stopping=None,
-    max_time=None,
-    repetition_penalty=None,
-    num_return_sequences=None,
-    do_sample=None,
-    langchain_mode=None,
-    langchain_action=None,
-    top_k_docs=None,
-    chunk=None,
-    chunk_size=None,
-    document_choice=None,
-    # for evaluate kwargs:
-    src_lang=None,
-    tgt_lang=None,
-    concurrency_count=None,
-    save_dir=None,
-    sanitize_bot_response=None,
-    model_state0=None,
-    max_max_new_tokens=None,
-    is_public=None,
-    max_max_time=None,
-    raise_generate_gpu_exceptions=None,
-    load_db_if_exists=None,
-    dbs=None,
-    user_path=None,
-    detect_user_path_changes_every_query=None,
-    use_openai_embedding=None,
-    use_openai_model=None,
-    hf_embedding_model=None,
-    db_type=None,
-    n_jobs=None,
-    first_para=None,
-    text_limit=None,
-    verbose=None,
-    cli=None,
-    reverse_docs=None,
-    use_cache=None,
-    auto_reduce_chunks=None,
-    max_chunks=None,
-    model_lock=None,
-    force_langchain_evaluate=None,
-    model_state_none=None,
-):
-    check_locals(**locals())
-
-    if eval_prompts_only_num > 0:
-        np.random.seed(eval_prompts_only_seed)
-        example1 = examples[-1]  # pick reference example
-        examples = []
-        responses = []
-        if eval_filename is None:
-            # override default examples with shareGPT ones for human-level eval purposes only
-            eval_filename = (
-                "ShareGPT_V3_unfiltered_cleaned_split_no_imsorry.json"
-            )
-            if not os.path.isfile(eval_filename):
-                os.system(
-                    "wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s"
-                    % eval_filename
-                )
-            import json
-
-            data = json.load(open(eval_filename, "rt"))
-            # focus on data that starts with human, else likely chopped from other data
-            turn_start = 0  # odd in general
-            data = [
-                x
-                for x in data
-                if len(x["conversations"]) > turn_start + 1
-                and x["conversations"][turn_start]["from"] == "human"
-                and x["conversations"][turn_start + 1]["from"] == "gpt"
-            ]
-            for i in sorted(
-                np.random.randint(0, len(data), size=eval_prompts_only_num)
-            ):
-                assert data[i]["conversations"][turn_start]["from"] == "human"
-                instruction = data[i]["conversations"][turn_start]["value"]
-                assert (
-                    data[i]["conversations"][turn_start + 1]["from"] == "gpt"
-                )
-                output = data[i]["conversations"][turn_start + 1]["value"]
-                examplenew = example1.copy()
-                assert (
-                    not chat
-                ), "No gradio must use chat=False, uses nochat instruct"
-                examplenew[
-                    eval_func_param_names.index("instruction_nochat")
-                ] = instruction
-                examplenew[
-                    eval_func_param_names.index("iinput_nochat")
-                ] = ""  # no input
-                examplenew[
-                    eval_func_param_names.index("context")
-                ] = get_context(chat_context, prompt_type)
-                examples.append(examplenew)
-                responses.append(output)
-        else:
-            # get data, assume in correct format: json of rows of dict of instruction and output
-            # only instruction is required
-            import json
-
-            data = json.load(open(eval_filename, "rt"))
-            for i in sorted(
-                np.random.randint(0, len(data), size=eval_prompts_only_num)
-            ):
-                examplenew = example1.copy()
-                instruction = data[i]["instruction"]
-                output = data[i].get("output", "")  # not required
-                assert (
-                    not chat
-                ), "No gradio must use chat=False, uses nochat instruct"
-                examplenew[
-                    eval_func_param_names.index("instruction_nochat")
-                ] = instruction
-                examplenew[
-                    eval_func_param_names.index("iinput_nochat")
-                ] = ""  # no input
-                examplenew[
-                    eval_func_param_names.index("context")
-                ] = get_context(chat_context, prompt_type)
-                examples.append(examplenew)
-                responses.append(output)
-
-    num_examples = len(examples)
-    scoring_path = "scoring"
-    os.makedirs(scoring_path, exist_ok=True)
-    if eval_as_output:
-        used_base_model = "gpt35"
-        used_lora_weights = ""
-        used_inference_server = ""
-    else:
-        used_base_model = str(base_model.split("/")[-1])
-        used_lora_weights = str(lora_weights.split("/")[-1])
-        used_inference_server = str(inference_server.split("/")[-1])
-    eval_out_filename = "df_scores_%s_%s_%s_%s_%s_%s_%s.parquet" % (
-        num_examples,
-        eval_prompts_only_num,
-        eval_prompts_only_seed,
-        eval_as_output,
-        used_base_model,
-        used_lora_weights,
-        used_inference_server,
-    )
-    eval_out_filename = os.path.join(scoring_path, eval_out_filename)
-
-    # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
-    n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
-    device = "cpu" if n_gpus == 0 else "cuda"
-    context_class = NullContext if n_gpus > 1 or n_gpus == 0 else torch.device
-
-    with context_class(device):
-        # ensure was set right above before examples generated
-        assert (
-            not stream_output
-        ), "stream_output=True does not make sense with example loop"
-        import time
-        from functools import partial
-
-        # get score model
-        smodel, stokenizer, sdevice = get_score_model(
-            reward_type=True,
-            **get_kwargs(
-                get_score_model, exclude_names=["reward_type"], **locals()
-            )
-        )
-
-        if not eval_as_output:
-            model, tokenizer, device = get_model(
-                reward_type=False,
-                **get_kwargs(
-                    get_model, exclude_names=["reward_type"], **locals()
-                )
-            )
-            model_dict = dict(
-                base_model=base_model,
-                tokenizer_base_model=tokenizer_base_model,
-                lora_weights=lora_weights,
-                inference_server=inference_server,
-                prompt_type=prompt_type,
-                prompt_dict=prompt_dict,
-            )
-            model_state = dict(model=model, tokenizer=tokenizer, device=device)
-            model_state.update(model_dict)
-            my_db_state = [None]
-            fun = partial(
-                evaluate,
-                model_state,
-                my_db_state,
-                **get_kwargs(
-                    evaluate,
-                    exclude_names=["model_state", "my_db_state"]
-                    + eval_func_param_names,
-                    **locals()
-                )
-            )
-        else:
-            assert eval_prompts_only_num > 0
-
-            def get_response(*args, exi=0):
-                # assumes same ordering of examples and responses
-                yield responses[exi]
-
-            fun = get_response
-        t0 = time.time()
-        score_dump = []
-        score_avg = 0
-        score_median = 0
-
-        for exi, ex in enumerate(examples):
-            clear_torch_cache()
-
-            instruction = ex[eval_func_param_names.index("instruction_nochat")]
-            iinput = ex[eval_func_param_names.index("iinput_nochat")]
-            context = ex[eval_func_param_names.index("context")]
-            clear_torch_cache()
-            print("")
-            print("START" + "=" * 100)
-            print(
-                "Question: %s %s"
-                % (instruction, ("input=%s" % iinput if iinput else ""))
-            )
-            print("-" * 105)
-            # fun yields as generator, so have to iterate over it
-            # Also means likely do NOT want --stream_output=True, else would show all generations
-            t1 = time.time()
-            gener = (
-                fun(*tuple(ex), exi=exi) if eval_as_output else fun(*tuple(ex))
-            )
-            for res_fun in gener:
-                res = res_fun["response"]
-                extra = res_fun["sources"]
-                print(res)
-                if smodel:
-                    score_with_prompt = False
-                    if score_with_prompt:
-                        data_point = dict(
-                            instruction=instruction,
-                            input=iinput,
-                            context=context,
-                        )
-                        prompter = Prompter(
-                            prompt_type,
-                            prompt_dict,
-                            debug=debug,
-                            chat=chat,
-                            stream_output=stream_output,
-                        )
-                        prompt = prompter.generate_prompt(data_point)
-                    else:
-                        # just raw input and output
-                        if eval_prompts_only_num > 0:
-                            # only our own examples have this filled at moment
-                            assert iinput in [
-                                None,
-                                "",
-                            ], iinput  # should be no iinput
-                        if not (chat_context and prompt_type == "human_bot"):
-                            assert context in [
-                                None,
-                                "",
-                            ], context  # should be no context
-                        prompt = instruction
-                    if memory_restriction_level > 0:
-                        cutoff_len = (
-                            768 if memory_restriction_level <= 2 else 512
-                        )
-                    else:
-                        cutoff_len = tokenizer.model_max_length
-                    inputs = stokenizer(
-                        prompt,
-                        res,
-                        return_tensors="pt",
-                        truncation=True,
-                        max_length=cutoff_len,
-                    )
-                    try:
-                        score = (
-                            torch.sigmoid(smodel(**inputs).logits[0].float())
-                            .cpu()
-                            .detach()
-                            .numpy()[0]
-                        )
-                    except torch.cuda.OutOfMemoryError as e:
-                        print(
-                            "GPU OOM 1: question: %s answer: %s exception: %s"
-                            % (prompt, res, str(e)),
-                            flush=True,
-                        )
-                        traceback.print_exc()
-                        score = 0.0
-                        clear_torch_cache()
-                    except (Exception, RuntimeError) as e:
-                        if (
-                            "Expected all tensors to be on the same device"
-                            in str(e)
-                            or "expected scalar type Half but found Float"
-                            in str(e)
-                            or "probability tensor contains either" in str(e)
-                            or "cublasLt ran into an error!" in str(e)
-                        ):
-                            print(
-                                "GPU error: question: %s answer: %s exception: %s"
-                                % (prompt, res, str(e)),
-                                flush=True,
-                            )
-                            traceback.print_exc()
-                            score = 0.0
-                            clear_torch_cache()
-                        else:
-                            raise
-                    score_dump.append(ex + [prompt, res, score])
-                    # dump every score in case abort
-                    df_scores = pd.DataFrame(
-                        score_dump,
-                        columns=eval_func_param_names + eval_extra_columns,
-                    )
-                    df_scores.to_parquet(eval_out_filename, index=False)
-                    # plot histogram so far
-                    plt.figure(figsize=(10, 10))
-                    plt.hist(df_scores["score"], bins=20)
-                    score_avg = np.mean(df_scores["score"])
-                    score_median = np.median(df_scores["score"])
-                    print(
-                        "SCORE %s: %s  So far: AVG: %s MEDIAN: %s"
-                        % (exi, score, score_avg, score_median),
-                        flush=True,
-                    )
-                    plt.title(
-                        "Score avg: %s median: %s" % (score_avg, score_median)
-                    )
-                    plt.savefig(eval_out_filename.replace(".parquet", ".png"))
-                    plt.close()
-
-            print("END" + "=" * 102)
-            print("")
-            t2 = time.time()
-            print(
-                "Time taken for example: %s Time taken so far: %.4f about %.4g per example"
-                % (t2 - t1, t2 - t0, (t2 - t0) / (1 + exi))
-            )
-        t1 = time.time()
-        print(
-            "Total time taken: %.4f about %.4g per example"
-            % (t1 - t0, (t1 - t0) / num_examples)
-        )
-        print(
-            "Score avg: %s median: %s" % (score_avg, score_median), flush=True
-        )
-    return eval_out_filename

apps/language_models/langchain/expanded_pipelines.py

@@ -0,0 +1,846 @@
+from __future__ import annotations
+from typing import (
+    Any,
+    Mapping,
+    Optional,
+    Dict,
+    List,
+    Sequence,
+    Tuple,
+    Union,
+    Protocol,
+)
+import inspect
+import json
+import warnings
+from pathlib import Path
+import yaml
+from abc import ABC, abstractmethod
+import langchain
+from langchain.base_language import BaseLanguageModel
+from langchain.callbacks.base import BaseCallbackManager
+from langchain.chains.question_answering import stuff_prompt
+from langchain.prompts.base import BasePromptTemplate
+from langchain.docstore.document import Document
+from langchain.callbacks.manager import (
+    CallbackManager,
+    CallbackManagerForChainRun,
+    Callbacks,
+)
+from langchain.load.serializable import Serializable
+from langchain.schema import RUN_KEY, BaseMemory, RunInfo
+from langchain.input import get_colored_text
+from langchain.load.dump import dumpd
+from langchain.prompts.prompt import PromptTemplate
+from langchain.schema import LLMResult, PromptValue
+from pydantic import Extra, Field, root_validator, validator
+
+
+def _get_verbosity() -> bool:
+    return langchain.verbose
+
+
+def format_document(doc: Document, prompt: BasePromptTemplate) -> str:
+    """Format a document into a string based on a prompt template."""
+    base_info = {"page_content": doc.page_content}
+    base_info.update(doc.metadata)
+    missing_metadata = set(prompt.input_variables).difference(base_info)
+    if len(missing_metadata) > 0:
+        required_metadata = [
+            iv for iv in prompt.input_variables if iv != "page_content"
+        ]
+        raise ValueError(
+            f"Document prompt requires documents to have metadata variables: "
+            f"{required_metadata}. Received document with missing metadata: "
+            f"{list(missing_metadata)}."
+        )
+    document_info = {k: base_info[k] for k in prompt.input_variables}
+    return prompt.format(**document_info)
+
+
+class Chain(Serializable, ABC):
+    """Base interface that all chains should implement."""
+
+    memory: Optional[BaseMemory] = None
+    callbacks: Callbacks = Field(default=None, exclude=True)
+    callback_manager: Optional[BaseCallbackManager] = Field(
+        default=None, exclude=True
+    )
+    verbose: bool = Field(
+        default_factory=_get_verbosity
+    )  # Whether to print the response text
+    tags: Optional[List[str]] = None
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        arbitrary_types_allowed = True
+
+    @property
+    def _chain_type(self) -> str:
+        raise NotImplementedError("Saving not supported for this chain type.")
+
+    @root_validator()
+    def raise_deprecation(cls, values: Dict) -> Dict:
+        """Raise deprecation warning if callback_manager is used."""
+        if values.get("callback_manager") is not None:
+            warnings.warn(
+                "callback_manager is deprecated. Please use callbacks instead.",
+                DeprecationWarning,
+            )
+            values["callbacks"] = values.pop("callback_manager", None)
+        return values
+
+    @validator("verbose", pre=True, always=True)
+    def set_verbose(cls, verbose: Optional[bool]) -> bool:
+        """If verbose is None, set it.
+
+        This allows users to pass in None as verbose to access the global setting.
+        """
+        if verbose is None:
+            return _get_verbosity()
+        else:
+            return verbose
+
+    @property
+    @abstractmethod
+    def input_keys(self) -> List[str]:
+        """Input keys this chain expects."""
+
+    @property
+    @abstractmethod
+    def output_keys(self) -> List[str]:
+        """Output keys this chain expects."""
+
+    def _validate_inputs(self, inputs: Dict[str, Any]) -> None:
+        """Check that all inputs are present."""
+        missing_keys = set(self.input_keys).difference(inputs)
+        if missing_keys:
+            raise ValueError(f"Missing some input keys: {missing_keys}")
+
+    def _validate_outputs(self, outputs: Dict[str, Any]) -> None:
+        missing_keys = set(self.output_keys).difference(outputs)
+        if missing_keys:
+            raise ValueError(f"Missing some output keys: {missing_keys}")
+
+    @abstractmethod
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, Any]:
+        """Run the logic of this chain and return the output."""
+
+    def __call__(
+        self,
+        inputs: Union[Dict[str, Any], Any],
+        return_only_outputs: bool = False,
+        callbacks: Callbacks = None,
+        *,
+        tags: Optional[List[str]] = None,
+        include_run_info: bool = False,
+    ) -> Dict[str, Any]:
+        """Run the logic of this chain and add to output if desired.
+
+        Args:
+            inputs: Dictionary of inputs, or single input if chain expects
+                only one param.
+            return_only_outputs: boolean for whether to return only outputs in the
+                response. If True, only new keys generated by this chain will be
+                returned. If False, both input keys and new keys generated by this
+                chain will be returned. Defaults to False.
+            callbacks: Callbacks to use for this chain run. If not provided, will
+                use the callbacks provided to the chain.
+            include_run_info: Whether to include run info in the response. Defaults
+                to False.
+        """
+        input_docs = inputs["input_documents"]
+        missing_keys = set(self.input_keys).difference(inputs)
+        if missing_keys:
+            raise ValueError(f"Missing some input keys: {missing_keys}")
+
+        callback_manager = CallbackManager.configure(
+            callbacks, self.callbacks, self.verbose, tags, self.tags
+        )
+        run_manager = callback_manager.on_chain_start(
+            dumpd(self),
+            inputs,
+        )
+
+        if "is_first" in inputs.keys() and not inputs["is_first"]:
+            run_manager_ = run_manager
+            input_list = [inputs]
+            stop = None
+            prompts = []
+            for inputs in input_list:
+                selected_inputs = {
+                    k: inputs[k] for k in self.prompt.input_variables
+                }
+                prompt = self.prompt.format_prompt(**selected_inputs)
+                _colored_text = get_colored_text(prompt.to_string(), "green")
+                _text = "Prompt after formatting:\n" + _colored_text
+                if run_manager_:
+                    run_manager_.on_text(_text, end="\n", verbose=self.verbose)
+                if "stop" in inputs and inputs["stop"] != stop:
+                    raise ValueError(
+                        "If `stop` is present in any inputs, should be present in all."
+                    )
+                prompts.append(prompt)
+
+            prompt_strings = [p.to_string() for p in prompts]
+            prompts = prompt_strings
+            callbacks = run_manager_.get_child() if run_manager_ else None
+            tags = None
+
+            """Run the LLM on the given prompt and input."""
+            # If string is passed in directly no errors will be raised but outputs will
+            # not make sense.
+            if not isinstance(prompts, list):
+                raise ValueError(
+                    "Argument 'prompts' is expected to be of type List[str], received"
+                    f" argument of type {type(prompts)}."
+                )
+            params = self.llm.dict()
+            params["stop"] = stop
+            options = {"stop": stop}
+            disregard_cache = self.llm.cache is not None and not self.llm.cache
+            callback_manager = CallbackManager.configure(
+                callbacks,
+                self.llm.callbacks,
+                self.llm.verbose,
+                tags,
+                self.llm.tags,
+            )
+            if langchain.llm_cache is None or disregard_cache:
+                # This happens when langchain.cache is None, but self.cache is True
+                if self.llm.cache is not None and self.cache:
+                    raise ValueError(
+                        "Asked to cache, but no cache found at `langchain.cache`."
+                    )
+                run_manager_ = callback_manager.on_llm_start(
+                    dumpd(self),
+                    prompts,
+                    invocation_params=params,
+                    options=options,
+                )
+
+                generations = []
+                for prompt in prompts:
+                    inputs_ = prompt
+                    num_workers = None
+                    batch_size = None
+
+                    if num_workers is None:
+                        if self.llm.pipeline._num_workers is None:
+                            num_workers = 0
+                        else:
+                            num_workers = self.llm.pipeline._num_workers
+                    if batch_size is None:
+                        if self.llm.pipeline._batch_size is None:
+                            batch_size = 1
+                        else:
+                            batch_size = self.llm.pipeline._batch_size
+
+                    preprocess_params = {}
+                    generate_kwargs = {}
+                    preprocess_params.update(generate_kwargs)
+                    forward_params = generate_kwargs
+                    postprocess_params = {}
+                    # Fuse __init__ params and __call__ params without modifying the __init__ ones.
+                    preprocess_params = {
+                        **self.llm.pipeline._preprocess_params,
+                        **preprocess_params,
+                    }
+                    forward_params = {
+                        **self.llm.pipeline._forward_params,
+                        **forward_params,
+                    }
+                    postprocess_params = {
+                        **self.llm.pipeline._postprocess_params,
+                        **postprocess_params,
+                    }
+
+                    self.llm.pipeline.call_count += 1
+                    if (
+                        self.llm.pipeline.call_count > 10
+                        and self.llm.pipeline.framework == "pt"
+                        and self.llm.pipeline.device.type == "cuda"
+                    ):
+                        warnings.warn(
+                            "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a"
+                            " dataset",
+                            UserWarning,
+                        )
+
+                    model_inputs = self.llm.pipeline.preprocess(
+                        inputs_, **preprocess_params
+                    )
+                    model_outputs = self.llm.pipeline.forward(
+                        model_inputs, **forward_params
+                    )
+                    model_outputs["process"] = False
+                    return model_outputs
+                output = LLMResult(generations=generations)
+                run_manager_.on_llm_end(output)
+                if run_manager_:
+                    output.run = RunInfo(run_id=run_manager_.run_id)
+                response = output
+
+            outputs = [
+                # Get the text of the top generated string.
+                {self.output_key: generation[0].text}
+                for generation in response.generations
+            ][0]
+            run_manager.on_chain_end(outputs)
+            final_outputs: Dict[str, Any] = self.prep_outputs(
+                inputs, outputs, return_only_outputs
+            )
+            if include_run_info:
+                final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
+            return final_outputs
+        else:
+            _run_manager = (
+                run_manager or CallbackManagerForChainRun.get_noop_manager()
+            )
+            docs = inputs[self.input_key]
+            # Other keys are assumed to be needed for LLM prediction
+            other_keys = {
+                k: v for k, v in inputs.items() if k != self.input_key
+            }
+            doc_strings = [
+                format_document(doc, self.document_prompt) for doc in docs
+            ]
+            # Join the documents together to put them in the prompt.
+            inputs = {
+                k: v
+                for k, v in other_keys.items()
+                if k in self.llm_chain.prompt.input_variables
+            }
+            inputs[self.document_variable_name] = self.document_separator.join(
+                doc_strings
+            )
+            inputs["is_first"] = False
+            inputs["input_documents"] = input_docs
+
+            # Call predict on the LLM.
+            output = self.llm_chain(inputs, callbacks=_run_manager.get_child())
+            if "process" in output.keys() and not output["process"]:
+                return output
+            output = output[self.llm_chain.output_key]
+            extra_return_dict = {}
+        extra_return_dict[self.output_key] = output
+        outputs = extra_return_dict
+        run_manager.on_chain_end(outputs)
+        final_outputs: Dict[str, Any] = self.prep_outputs(
+            inputs, outputs, return_only_outputs
+        )
+        if include_run_info:
+            final_outputs[RUN_KEY] = RunInfo(run_id=run_manager.run_id)
+        return final_outputs
+
+    def prep_outputs(
+        self,
+        inputs: Dict[str, str],
+        outputs: Dict[str, str],
+        return_only_outputs: bool = False,
+    ) -> Dict[str, str]:
+        """Validate and prep outputs."""
+        self._validate_outputs(outputs)
+        if self.memory is not None:
+            self.memory.save_context(inputs, outputs)
+        if return_only_outputs:
+            return outputs
+        else:
+            return {**inputs, **outputs}
+
+    def prep_inputs(
+        self, inputs: Union[Dict[str, Any], Any]
+    ) -> Dict[str, str]:
+        """Validate and prep inputs."""
+        if not isinstance(inputs, dict):
+            _input_keys = set(self.input_keys)
+            if self.memory is not None:
+                # If there are multiple input keys, but some get set by memory so that
+                # only one is not set, we can still figure out which key it is.
+                _input_keys = _input_keys.difference(
+                    self.memory.memory_variables
+                )
+            if len(_input_keys) != 1:
+                raise ValueError(
+                    f"A single string input was passed in, but this chain expects "
+                    f"multiple inputs ({_input_keys}). When a chain expects "
+                    f"multiple inputs, please call it by passing in a dictionary, "
+                    "eg `chain({'foo': 1, 'bar': 2})`"
+                )
+            inputs = {list(_input_keys)[0]: inputs}
+        if self.memory is not None:
+            external_context = self.memory.load_memory_variables(inputs)
+            inputs = dict(inputs, **external_context)
+        self._validate_inputs(inputs)
+        return inputs
+
+    def apply(
+        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
+    ) -> List[Dict[str, str]]:
+        """Call the chain on all inputs in the list."""
+        return [self(inputs, callbacks=callbacks) for inputs in input_list]
+
+    def run(
+        self,
+        *args: Any,
+        callbacks: Callbacks = None,
+        tags: Optional[List[str]] = None,
+        **kwargs: Any,
+    ) -> str:
+        """Run the chain as text in, text out or multiple variables, text out."""
+        if len(self.output_keys) != 1:
+            raise ValueError(
+                f"`run` not supported when there is not exactly "
+                f"one output key. Got {self.output_keys}."
+            )
+
+        if args and not kwargs:
+            if len(args) != 1:
+                raise ValueError(
+                    "`run` supports only one positional argument."
+                )
+            return self(args[0], callbacks=callbacks, tags=tags)[
+                self.output_keys[0]
+            ]
+
+        if kwargs and not args:
+            return self(kwargs, callbacks=callbacks, tags=tags)[
+                self.output_keys[0]
+            ]
+
+        if not kwargs and not args:
+            raise ValueError(
+                "`run` supported with either positional arguments or keyword arguments,"
+                " but none were provided."
+            )
+
+        raise ValueError(
+            f"`run` supported with either positional arguments or keyword arguments"
+            f" but not both. Got args: {args} and kwargs: {kwargs}."
+        )
+
+    def dict(self, **kwargs: Any) -> Dict:
+        """Return dictionary representation of chain."""
+        if self.memory is not None:
+            raise ValueError("Saving of memory is not yet supported.")
+        _dict = super().dict()
+        _dict["_type"] = self._chain_type
+        return _dict
+
+    def save(self, file_path: Union[Path, str]) -> None:
+        """Save the chain.
+
+        Args:
+            file_path: Path to file to save the chain to.
+
+        Example:
+        .. code-block:: python
+
+            chain.save(file_path="path/chain.yaml")
+        """
+        # Convert file to Path object.
+        if isinstance(file_path, str):
+            save_path = Path(file_path)
+        else:
+            save_path = file_path
+
+        directory_path = save_path.parent
+        directory_path.mkdir(parents=True, exist_ok=True)
+
+        # Fetch dictionary to save
+        chain_dict = self.dict()
+
+        if save_path.suffix == ".json":
+            with open(file_path, "w") as f:
+                json.dump(chain_dict, f, indent=4)
+        elif save_path.suffix == ".yaml":
+            with open(file_path, "w") as f:
+                yaml.dump(chain_dict, f, default_flow_style=False)
+        else:
+            raise ValueError(f"{save_path} must be json or yaml")
+
+
+class BaseCombineDocumentsChain(Chain, ABC):
+    """Base interface for chains combining documents."""
+
+    input_key: str = "input_documents"  #: :meta private:
+    output_key: str = "output_text"  #: :meta private:
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Expect input key.
+
+        :meta private:
+        """
+        return [self.input_key]
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Return output key.
+
+        :meta private:
+        """
+        return [self.output_key]
+
+    def prompt_length(
+        self, docs: List[Document], **kwargs: Any
+    ) -> Optional[int]:
+        """Return the prompt length given the documents passed in.
+
+        Returns None if the method does not depend on the prompt length.
+        """
+        return None
+
+    def _call(
+        self,
+        inputs: Dict[str, List[Document]],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, str]:
+        _run_manager = (
+            run_manager or CallbackManagerForChainRun.get_noop_manager()
+        )
+        docs = inputs[self.input_key]
+        # Other keys are assumed to be needed for LLM prediction
+        other_keys = {k: v for k, v in inputs.items() if k != self.input_key}
+        doc_strings = [
+            format_document(doc, self.document_prompt) for doc in docs
+        ]
+        # Join the documents together to put them in the prompt.
+        inputs = {
+            k: v
+            for k, v in other_keys.items()
+            if k in self.llm_chain.prompt.input_variables
+        }
+        inputs[self.document_variable_name] = self.document_separator.join(
+            doc_strings
+        )
+
+        # Call predict on the LLM.
+        output, extra_return_dict = (
+            self.llm_chain(inputs, callbacks=_run_manager.get_child())[
+                self.llm_chain.output_key
+            ],
+            {},
+        )
+
+        extra_return_dict[self.output_key] = output
+        return extra_return_dict
+
+
+from pydantic import BaseModel
+
+
+class Generation(Serializable):
+    """Output of a single generation."""
+
+    text: str
+    """Generated text output."""
+
+    generation_info: Optional[Dict[str, Any]] = None
+    """Raw generation info response from the provider"""
+    """May include things like reason for finishing (e.g. in OpenAI)"""
+    # TODO: add log probs
+
+
+VALID_TASKS = ("text2text-generation", "text-generation", "summarization")
+
+
+class LLMChain(Chain):
+    """Chain to run queries against LLMs.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import LLMChain, OpenAI, PromptTemplate
+            prompt_template = "Tell me a {adjective} joke"
+            prompt = PromptTemplate(
+                input_variables=["adjective"], template=prompt_template
+            )
+            llm = LLMChain(llm=OpenAI(), prompt=prompt)
+    """
+
+    @property
+    def lc_serializable(self) -> bool:
+        return True
+
+    prompt: BasePromptTemplate
+    """Prompt object to use."""
+    llm: BaseLanguageModel
+    output_key: str = "text"  #: :meta private:
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @property
+    def input_keys(self) -> List[str]:
+        """Will be whatever keys the prompt expects.
+
+        :meta private:
+        """
+        return self.prompt.input_variables
+
+    @property
+    def output_keys(self) -> List[str]:
+        """Will always return text key.
+
+        :meta private:
+        """
+        return [self.output_key]
+
+    def _call(
+        self,
+        inputs: Dict[str, Any],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Dict[str, str]:
+        prompts, stop = self.prep_prompts([inputs], run_manager=run_manager)
+        response = self.llm.generate_prompt(
+            prompts,
+            stop,
+            callbacks=run_manager.get_child() if run_manager else None,
+        )
+        return self.create_outputs(response)[0]
+
+    def prep_prompts(
+        self,
+        input_list: List[Dict[str, Any]],
+        run_manager: Optional[CallbackManagerForChainRun] = None,
+    ) -> Tuple[List[PromptValue], Optional[List[str]]]:
+        """Prepare prompts from inputs."""
+        stop = None
+        if "stop" in input_list[0]:
+            stop = input_list[0]["stop"]
+        prompts = []
+        for inputs in input_list:
+            selected_inputs = {
+                k: inputs[k] for k in self.prompt.input_variables
+            }
+            prompt = self.prompt.format_prompt(**selected_inputs)
+            _colored_text = get_colored_text(prompt.to_string(), "green")
+            _text = "Prompt after formatting:\n" + _colored_text
+            if run_manager:
+                run_manager.on_text(_text, end="\n", verbose=self.verbose)
+            if "stop" in inputs and inputs["stop"] != stop:
+                raise ValueError(
+                    "If `stop` is present in any inputs, should be present in all."
+                )
+            prompts.append(prompt)
+        return prompts, stop
+
+    def apply(
+        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
+    ) -> List[Dict[str, str]]:
+        """Utilize the LLM generate method for speed gains."""
+        callback_manager = CallbackManager.configure(
+            callbacks, self.callbacks, self.verbose
+        )
+        run_manager = callback_manager.on_chain_start(
+            dumpd(self),
+            {"input_list": input_list},
+        )
+        try:
+            response = self.generate(input_list, run_manager=run_manager)
+        except (KeyboardInterrupt, Exception) as e:
+            run_manager.on_chain_error(e)
+            raise e
+        outputs = self.create_outputs(response)
+        run_manager.on_chain_end({"outputs": outputs})
+        return outputs
+
+    def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
+        """Create outputs from response."""
+        return [
+            # Get the text of the top generated string.
+            {self.output_key: generation[0].text}
+            for generation in response.generations
+        ]
+
+    def predict_and_parse(
+        self, callbacks: Callbacks = None, **kwargs: Any
+    ) -> Union[str, List[str], Dict[str, Any]]:
+        """Call predict and then parse the results."""
+        result = self.predict(callbacks=callbacks, **kwargs)
+        if self.prompt.output_parser is not None:
+            return self.prompt.output_parser.parse(result)
+        else:
+            return result
+
+    def apply_and_parse(
+        self, input_list: List[Dict[str, Any]], callbacks: Callbacks = None
+    ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
+        """Call apply and then parse the results."""
+        result = self.apply(input_list, callbacks=callbacks)
+        return self._parse_result(result)
+
+    def _parse_result(
+        self, result: List[Dict[str, str]]
+    ) -> Sequence[Union[str, List[str], Dict[str, str]]]:
+        if self.prompt.output_parser is not None:
+            return [
+                self.prompt.output_parser.parse(res[self.output_key])
+                for res in result
+            ]
+        else:
+            return result
+
+    @property
+    def _chain_type(self) -> str:
+        return "llm_chain"
+
+    @classmethod
+    def from_string(cls, llm: BaseLanguageModel, template: str) -> LLMChain:
+        """Create LLMChain from LLM and template."""
+        prompt_template = PromptTemplate.from_template(template)
+        return cls(llm=llm, prompt=prompt_template)
+
+
+def _get_default_document_prompt() -> PromptTemplate:
+    return PromptTemplate(
+        input_variables=["page_content"], template="{page_content}"
+    )
+
+
+class StuffDocumentsChain(BaseCombineDocumentsChain):
+    """Chain that combines documents by stuffing into context."""
+
+    llm_chain: LLMChain
+    """LLM wrapper to use after formatting documents."""
+    document_prompt: BasePromptTemplate = Field(
+        default_factory=_get_default_document_prompt
+    )
+    """Prompt to use to format each document."""
+    document_variable_name: str
+    """The variable name in the llm_chain to put the documents in.
+    If only one variable in the llm_chain, this need not be provided."""
+    document_separator: str = "\n\n"
+    """The string with which to join the formatted documents"""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @root_validator(pre=True)
+    def get_default_document_variable_name(cls, values: Dict) -> Dict:
+        """Get default document variable name, if not provided."""
+        llm_chain_variables = values["llm_chain"].prompt.input_variables
+        if "document_variable_name" not in values:
+            if len(llm_chain_variables) == 1:
+                values["document_variable_name"] = llm_chain_variables[0]
+            else:
+                raise ValueError(
+                    "document_variable_name must be provided if there are "
+                    "multiple llm_chain_variables"
+                )
+        else:
+            if values["document_variable_name"] not in llm_chain_variables:
+                raise ValueError(
+                    f"document_variable_name {values['document_variable_name']} was "
+                    f"not found in llm_chain input_variables: {llm_chain_variables}"
+                )
+        return values
+
+    def _get_inputs(self, docs: List[Document], **kwargs: Any) -> dict:
+        # Format each document according to the prompt
+        doc_strings = [
+            format_document(doc, self.document_prompt) for doc in docs
+        ]
+        # Join the documents together to put them in the prompt.
+        inputs = {
+            k: v
+            for k, v in kwargs.items()
+            if k in self.llm_chain.prompt.input_variables
+        }
+        inputs[self.document_variable_name] = self.document_separator.join(
+            doc_strings
+        )
+        return inputs
+
+    def prompt_length(
+        self, docs: List[Document], **kwargs: Any
+    ) -> Optional[int]:
+        """Get the prompt length by formatting the prompt."""
+        inputs = self._get_inputs(docs, **kwargs)
+        prompt = self.llm_chain.prompt.format(**inputs)
+        return self.llm_chain.llm.get_num_tokens(prompt)
+
+    @property
+    def _chain_type(self) -> str:
+        return "stuff_documents_chain"
+
+
+class LoadingCallable(Protocol):
+    """Interface for loading the combine documents chain."""
+
+    def __call__(
+        self, llm: BaseLanguageModel, **kwargs: Any
+    ) -> BaseCombineDocumentsChain:
+        """Callable to load the combine documents chain."""
+
+
+def _load_stuff_chain(
+    llm: BaseLanguageModel,
+    prompt: Optional[BasePromptTemplate] = None,
+    document_variable_name: str = "context",
+    verbose: Optional[bool] = None,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    callbacks: Callbacks = None,
+    **kwargs: Any,
+) -> StuffDocumentsChain:
+    _prompt = prompt or stuff_prompt.PROMPT_SELECTOR.get_prompt(llm)
+    llm_chain = LLMChain(
+        llm=llm,
+        prompt=_prompt,
+        verbose=verbose,
+        callback_manager=callback_manager,
+        callbacks=callbacks,
+    )
+    # TODO: document prompt
+    return StuffDocumentsChain(
+        llm_chain=llm_chain,
+        document_variable_name=document_variable_name,
+        verbose=verbose,
+        callback_manager=callback_manager,
+        **kwargs,
+    )
+
+
+def load_qa_chain(
+    llm: BaseLanguageModel,
+    chain_type: str = "stuff",
+    verbose: Optional[bool] = None,
+    callback_manager: Optional[BaseCallbackManager] = None,
+    **kwargs: Any,
+) -> BaseCombineDocumentsChain:
+    """Load question answering chain.
+
+    Args:
+        llm: Language Model to use in the chain.
+        chain_type: Type of document combining chain to use. Should be one of "stuff",
+            "map_reduce", "map_rerank", and "refine".
+        verbose: Whether chains should be run in verbose mode or not. Note that this
+            applies to all chains that make up the final chain.
+        callback_manager: Callback manager to use for the chain.
+
+    Returns:
+        A chain to use for question answering.
+    """
+    loader_mapping: Mapping[str, LoadingCallable] = {
+        "stuff": _load_stuff_chain,
+    }
+    if chain_type not in loader_mapping:
+        raise ValueError(
+            f"Got unsupported chain type: {chain_type}. "
+            f"Should be one of {loader_mapping.keys()}"
+        )
+    return loader_mapping[chain_type](
+        llm, verbose=verbose, callback_manager=callback_manager, **kwargs
+    )

apps/language_models/langchain/export_hf_checkpoint.py

@@ -1,283 +0,0 @@
-import os
-import json
-import shutil
-import subprocess
-
-import torch
-from peft import PeftModel
-from transformers import PreTrainedModel
-
-
-def do_export():
-    BASE_MODEL = "h2oai/h2ogpt-oasst1-512-12b"
-    LORA_WEIGHTS = "h2ogpt-oasst1-512-12b.h2oaih2ogpt-oig-oasst1-instruct-cleaned-v3.1_epochs.805b8e8eff369207340a5a6f90f3c833f9731254.2"
-    OUTPUT_NAME = "h2ogpt-oig-oasst1-512-12b"
-
-    BASE_MODEL = "EleutherAI/pythia-12b-deduped"
-    LORA_WEIGHTS = "pythia-12b-deduped.h2oaiopenassistant_oasst1_h2ogpt_graded.3_epochs.2ccf687ea3f3f3775a501838e81c1a0066430455.4"
-    OUTPUT_NAME = "h2ogpt-oasst1-512-12b"
-
-    BASE_MODEL = "tiiuae/falcon-40b"
-    LORA_WEIGHTS = "falcon-40b.h2oaiopenassistant_oasst1_h2ogpt.1_epochs.894d8450d35c180cd03222a45658d04c15b78d4b.9"
-    OUTPUT_NAME = "h2ogpt-oasst1-2048-falcon-40b"
-
-    # BASE_MODEL = 'decapoda-research/llama-65b-hf'
-    # LORA_WEIGHTS = 'llama-65b-hf.h2oaiopenassistant_oasst1_h2ogpt_graded.1_epochs.113510499324f0f007cbec9d9f1f8091441f2469.3'
-    # OUTPUT_NAME = "h2ogpt-research-oasst1-llama-65b"
-
-    model = os.getenv("MODEL")
-    # for testing
-    if model:
-        BASE_MODEL = "tiiuae/falcon-7b"
-        LORA_WEIGHTS = model + ".lora"
-        OUTPUT_NAME = model
-
-    llama_type = "llama" in BASE_MODEL
-    as_pytorch = False  # False -> HF
-
-    from loaders import get_loaders
-
-    model_loader, tokenizer_loader = get_loaders(
-        model_name=BASE_MODEL, reward_type=False, llama_type=llama_type
-    )
-
-    tokenizer = tokenizer_loader.from_pretrained(
-        BASE_MODEL,
-        local_files_only=False,
-        resume_download=True,
-    )
-    tokenizer.save_pretrained(OUTPUT_NAME)
-
-    base_model = model_loader(
-        BASE_MODEL,
-        load_in_8bit=False,
-        trust_remote_code=True,
-        torch_dtype=torch.float16,
-        device_map={"": "cpu"},
-    )
-
-    print(base_model)
-    if llama_type:
-        layers = base_model.model.layers
-        first_weight = layers[0].self_attn.q_proj.weight
-    else:
-        if any(
-            [x in BASE_MODEL.lower() for x in ["pythia", "h2ogpt", "gpt-neox"]]
-        ):
-            layers = base_model.gpt_neox.base_model.layers
-            first_weight = layers[0].attention.query_key_value.weight
-        elif any([x in BASE_MODEL.lower() for x in ["falcon"]]):
-            first_weight = base_model.transformer.h._modules[
-                "0"
-            ].self_attention.query_key_value.weight
-        else:
-            layers = base_model.transformer.base_model.h
-            first_weight = layers[0].attn.q_proj.weight
-    first_weight_old = first_weight.clone()
-
-    lora_model = PeftModel.from_pretrained(
-        base_model,
-        LORA_WEIGHTS,
-        device_map={"": "cpu"},
-        torch_dtype=torch.float16,
-    )
-
-    assert torch.allclose(first_weight_old, first_weight)
-
-    # merge weights TODO: include all lora_target_modules, not just default ones
-    if llama_type:
-        lora_model = lora_model.merge_and_unload()
-        # for layer in lora_model.base_model.model.model.layers:
-        #     layer.self_attn.q_proj.merge_weights = True
-        #     layer.self_attn.k_proj.merge_weights = True
-        #     layer.self_attn.v_proj.merge_weights = True
-        #     layer.self_attn.o_proj.merge_weights = True
-    else:
-        if any(
-            [x in BASE_MODEL.lower() for x in ["pythia", "h2ogpt", "gpt-neox"]]
-        ):
-            for layer in lora_model.base_model.gpt_neox.base_model.layers:
-                layer.attention.query_key_value.merge_weights = True
-        else:
-            lora_model.merge_and_unload()
-            # for layer in lora_model.base_model.transformer.base_model.h:
-            #     layer.attn.q_proj.merge_weights = True
-            #     layer.attn.v_proj.merge_weights = True
-
-    lora_model.train(False)
-
-    # did we do anything?
-    assert not torch.allclose(first_weight_old, first_weight)
-
-    lora_model_sd = lora_model.state_dict()
-
-    if as_pytorch:
-        # FIXME - might not be generic enough still
-        params = {
-            "dim": base_model.config.hidden_size,
-            "n_heads": base_model.config.num_attention_heads,
-            "n_layers": base_model.config.num_hidden_layers,
-            "norm_eps": base_model.config.layer_norm_eps,
-            "vocab_size": base_model.config.vocab_size,
-        }
-        n_layers = params["n_layers"]
-        n_heads = params["n_heads"]
-        dim = params["dim"]
-        dims_per_head = dim // n_heads
-        base = 10000.0
-        inv_freq = 1.0 / (
-            base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head)
-        )
-
-        def permute(w):
-            return (
-                w.view(n_heads, dim // n_heads // 2, 2, dim)
-                .transpose(1, 2)
-                .reshape(dim, dim)
-            )
-
-        def unpermute(w):
-            return (
-                w.view(n_heads, 2, dim // n_heads // 2, dim)
-                .transpose(1, 2)
-                .reshape(dim, dim)
-            )
-
-        def translate_state_dict_key(k):
-            if "gpt-neoxt" in BASE_MODEL.lower():
-                k = k.replace("gpt_neox.model.", "")
-            else:
-                k = k.replace("base_model.model.", "")
-            if k == "model.embed_tokens.weight":
-                return "tok_embeddings.weight"
-            elif k == "model.norm.weight":
-                return "norm.weight"
-            elif k == "lm_head.weight":
-                return "output.weight"
-            elif k.startswith("model.layers."):
-                layer = k.split(".")[2]
-                if k.endswith(".self_attn.q_proj.weight"):
-                    return f"layers.{layer}.attention.wq.weight"
-                elif k.endswith(".self_attn.k_proj.weight"):
-                    return f"layers.{layer}.attention.wk.weight"
-                elif k.endswith(".self_attn.v_proj.weight"):
-                    return f"layers.{layer}.attention.wv.weight"
-                elif k.endswith(".self_attn.o_proj.weight"):
-                    return f"layers.{layer}.attention.wo.weight"
-                elif k.endswith(".mlp.gate_proj.weight"):
-                    return f"layers.{layer}.feed_forward.w1.weight"
-                elif k.endswith(".mlp.down_proj.weight"):
-                    return f"layers.{layer}.feed_forward.w2.weight"
-                elif k.endswith(".mlp.up_proj.weight"):
-                    return f"layers.{layer}.feed_forward.w3.weight"
-                elif k.endswith(".input_layernorm.weight"):
-                    return f"layers.{layer}.attention_norm.weight"
-                elif k.endswith(".post_attention_layernorm.weight"):
-                    return f"layers.{layer}.ffn_norm.weight"
-                elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
-                    return None
-                else:
-                    print(layer, k)
-                    raise NotImplementedError
-            else:
-                print(k)
-                raise NotImplementedError
-
-        new_state_dict = {}
-        for k, v in lora_model_sd.items():
-            new_k = translate_state_dict_key(k)
-            if new_k is not None:
-                if "wq" in new_k or "wk" in new_k:
-                    new_state_dict[new_k] = unpermute(v)
-                else:
-                    new_state_dict[new_k] = v
-
-        os.makedirs("./ckpt", exist_ok=True)
-
-        torch.save(new_state_dict, "./ckpt/consolidated.00.pth")
-
-        with open("./ckpt/params.json", "w") as f:
-            json.dump(params, f)
-    else:
-        deloreanized_sd = {
-            k.replace("base_model.model.", ""): v
-            for k, v in lora_model_sd.items()
-            if "lora" not in k
-        }
-        base_model.config.custom_pipelines = {
-            "text-generation": {
-                "impl": "h2oai_pipeline.H2OTextGenerationPipeline",
-                "pt": "AutoModelForCausalLM",
-            }
-        }
-        PreTrainedModel.save_pretrained(
-            base_model,
-            OUTPUT_NAME,
-            state_dict=deloreanized_sd,
-            # max_shard_size="5GB",
-        )
-
-    do_copy(OUTPUT_NAME)
-    test_copy()
-
-
-def do_copy(OUTPUT_NAME):
-    dest_file = os.path.join(OUTPUT_NAME, "h2oai_pipeline.py")
-    shutil.copyfile("src/h2oai_pipeline.py", dest_file)
-    os.system("""sed -i 's/from enums.*//g' %s""" % dest_file)
-    os.system("""sed -i 's/from stopping.*//g' %s""" % dest_file)
-    os.system("""sed -i 's/from prompter.*//g' %s""" % dest_file)
-    os.system(
-        """cat %s|grep -v "from enums import PromptType" >> %s"""
-        % ("src/enums.py", dest_file)
-    )
-    os.system(
-        """cat %s|grep -v "from enums import PromptType" >> %s"""
-        % ("src/prompter.py", dest_file)
-    )
-    os.system(
-        """cat %s|grep -v "from enums import PromptType" >> %s"""
-        % ("src/stopping.py", dest_file)
-    )
-
-
-TEST_OUTPUT_NAME = "test_output"
-
-
-def test_copy():
-    if os.path.isdir(TEST_OUTPUT_NAME):
-        shutil.rmtree(TEST_OUTPUT_NAME)
-    os.makedirs(TEST_OUTPUT_NAME, exist_ok=False)
-    do_copy(TEST_OUTPUT_NAME)
-    shutil.copy("src/export_hf_checkpoint.py", TEST_OUTPUT_NAME)
-    os.environ["DO_COPY_TEST"] = "1"
-    os.chdir(TEST_OUTPUT_NAME)
-    output = subprocess.check_output(["python", "export_hf_checkpoint.py"])
-    print(output)
-
-
-def inner_test_copy():
-    """
-    pytest -s -v export_hf_checkpoint.py::test_copy
-    :return:
-    """
-    # test imports
-    # below supposed to look bad in pycharm, don't fix!
-    from h2oai_pipeline import (
-        get_stopping,
-        get_prompt,
-        H2OTextGenerationPipeline,
-    )
-
-    assert get_stopping
-    assert get_prompt
-    assert H2OTextGenerationPipeline
-
-
-if __name__ == "__main__":
-    if os.getenv("DO_COPY_TEST"):
-        inner_test_copy()
-    else:
-        do_export()
-    # uncomment for raw isolated test, but test is done every time for each export now
-    # test_copy()

apps/language_models/langchain/gpt_langchain.py

@@ -34,7 +34,7 @@ from enums import (
     LangChainMode,
 )
 from evaluate_params import gen_hyper
-from gen import get_model, SEED
+from gen import Langchain, SEED
 from prompter import non_hf_types, PromptType, Prompter
 from utils import (
     wrapped_partial,
@@ -44,7 +44,6 @@ from utils import (
     makedirs,
     get_url,
     flatten_list,
-    get_device,
     ProgressParallel,
     remove,
     hash_file,
@@ -88,10 +87,11 @@ from langchain.document_loaders import (
     UnstructuredExcelLoader,
 )
 from langchain.text_splitter import RecursiveCharacterTextSplitter, Language
-from langchain.chains.question_answering import load_qa_chain
+from expanded_pipelines import load_qa_chain
 from langchain.docstore.document import Document
 from langchain import PromptTemplate, HuggingFaceTextGenInference
 from langchain.vectorstores import Chroma
+from apps.stable_diffusion.src import args
 
 
 def get_db(
@@ -371,8 +371,8 @@ def get_embedding(
         # to ensure can fork without deadlock
         from langchain.embeddings import HuggingFaceEmbeddings
 
-        device, torch_dtype, context_class = get_device_dtype()
-        model_kwargs = dict(device=device)
+        torch_dtype, context_class = get_dtype()
+        model_kwargs = dict(device=args.device)
         if "instructor" in hf_embedding_model:
             encode_kwargs = {"normalize_embeddings": True}
             embedding = HuggingFaceInstructEmbeddings(
@@ -436,7 +436,7 @@ class GradioInference(LLM):
     chat_client: bool = False
 
     return_full_text: bool = True
-    stream: bool = False
+    stream_output: bool = Field(False, alias="stream")
     sanitize_bot_response: bool = False
 
     prompter: Any = None
@@ -481,7 +481,7 @@ class GradioInference(LLM):
         # so server should get prompt_type or '', not plain
         # This is good, so gradio server can also handle stopping.py conditions
         # this is different than TGI server that uses prompter to inject prompt_type prompting
-        stream_output = self.stream
+        stream_output = self.stream_output
         gr_client = self.client
         client_langchain_mode = "Disabled"
         client_langchain_action = LangChainAction.QUERY.value
@@ -596,7 +596,7 @@ class H2OHuggingFaceTextGenInference(HuggingFaceTextGenInference):
     inference_server_url: str = ""
     timeout: int = 300
     headers: dict = None
-    stream: bool = False
+    stream_output: bool = Field(False, alias="stream")
     sanitize_bot_response: bool = False
     prompter: Any = None
     tokenizer: Any = None
@@ -663,7 +663,7 @@ class H2OHuggingFaceTextGenInference(HuggingFaceTextGenInference):
         # lower bound because client is re-used if multi-threading
         self.client.timeout = max(300, self.timeout)
 
-        if not self.stream:
+        if not self.stream_output:
             res = self.client.generate(
                 prompt,
                 **gen_server_kwargs,
@@ -852,7 +852,7 @@ def get_llm(
                 top_p=top_p,
                 # typical_p=top_p,
                 callbacks=callbacks if stream_output else None,
-                stream=stream_output,
+                stream_output=stream_output,
                 prompter=prompter,
                 tokenizer=tokenizer,
                 client=hf_client,
@@ -907,7 +907,7 @@ def get_llm(
                 # model_name = 'h2oai/h2ogpt-oig-oasst1-512-6_9b'
                 # model_name = 'h2oai/h2ogpt-oasst1-512-20b'
             inference_server = ""
-            model, tokenizer, device = get_model(
+            model, tokenizer, _ = Langchain.get_model(
                 load_8bit=True,
                 base_model=model_name,
                 inference_server=inference_server,
@@ -974,17 +974,15 @@ def get_llm(
     return llm, model_name, streamer, prompt_type
 
 
-def get_device_dtype():
+def get_dtype():
     # torch.device("cuda") leads to cuda:x cuda:y mismatches for multi-GPU consistently
     import torch
 
-    n_gpus = torch.cuda.device_count() if torch.cuda.is_available else 0
-    device = "cpu" if n_gpus == 0 else "cuda"
     # from utils import NullContext
     # context_class = NullContext if n_gpus > 1 or n_gpus == 0 else context_class
     context_class = torch.device
-    torch_dtype = torch.float16 if device == "cuda" else torch.float32
-    return device, torch_dtype, context_class
+    torch_dtype = torch.float16 if args.device == "cuda" else torch.float32
+    return torch_dtype, context_class
 
 
 def get_wiki_data(
@@ -1715,7 +1713,7 @@ def path_to_docs(
         caption_loader
         and not isinstance(caption_loader, (bool, str))
         and caption_loader.device != "cpu"
-        or get_device() == "cuda"
+        or args.device == "cuda"
     ):
         # to avoid deadlocks, presume was preloaded and so can't fork due to cuda context
         n_jobs_image = 1
@@ -2512,8 +2510,7 @@ def _run_qa_db(
         formatted_doc_chunks = "\n\n".join(
             [get_url(x) + "\n\n" + x.page_content for x in docs]
         )
-        yield formatted_doc_chunks, ""
-        return
+        return formatted_doc_chunks, ""
     if not docs and langchain_action in [
         LangChainAction.SUMMARIZE_MAP.value,
         LangChainAction.SUMMARIZE_ALL.value,
@@ -2525,8 +2522,7 @@ def _run_qa_db(
             else "No documents to summarize."
         )
         extra = ""
-        yield ret, extra
-        return
+        return ret, extra
     if not docs and langchain_mode not in [
         LangChainMode.DISABLED.value,
         LangChainMode.CHAT_LLM.value,
@@ -2538,8 +2534,7 @@ def _run_qa_db(
             else "No documents to query."
         )
         extra = ""
-        yield ret, extra
-        return
+        return ret, extra
 
     if chain is None and model_name not in non_hf_types:
         # here if no docs at all and not HF type
@@ -2549,32 +2544,17 @@ def _run_qa_db(
     # context stuff similar to used in evaluate()
     import torch
 
-    device, torch_dtype, context_class = get_device_dtype()
+    torch_dtype, context_class = get_dtype()
     with torch.no_grad():
         have_lora_weights = lora_weights not in [no_lora_str, "", None]
         context_class_cast = (
             NullContext
-            if device == "cpu" or have_lora_weights
+            if args.device == "cpu" or have_lora_weights
             else torch.autocast
         )
-        with context_class_cast(device):
+        with context_class_cast(args.device):
             answer = chain()
-
-    if not use_context:
-        ret = answer["output_text"]
-        extra = ""
-        yield ret, extra
-    elif answer is not None:
-        ret, extra = get_sources_answer(
-            query,
-            answer,
-            scores,
-            show_rank,
-            answer_with_sources,
-            verbose=verbose,
-        )
-        yield ret, extra
-    return
+            return answer
 
 
 def get_similarity_chain(
@@ -2960,56 +2940,8 @@ def get_similarity_chain(
                 template=template,
             )
             chain = load_qa_chain(llm, prompt=prompt)
-        else:
-            # only if use_openai_model = True, unused normally except in testing
-            chain = load_qa_with_sources_chain(llm)
-        if not use_context:
-            chain_kwargs = dict(input_documents=[], question=query)
-        else:
-            chain_kwargs = dict(input_documents=docs, question=query)
+        chain_kwargs = dict(input_documents=docs, question=query)
         target = wrapped_partial(chain, chain_kwargs)
-    elif langchain_action in [
-        LangChainAction.SUMMARIZE_MAP.value,
-        LangChainAction.SUMMARIZE_REFINE,
-        LangChainAction.SUMMARIZE_ALL.value,
-    ]:
-        from langchain.chains.summarize import load_summarize_chain
-
-        if langchain_action == LangChainAction.SUMMARIZE_MAP.value:
-            prompt = PromptTemplate(
-                input_variables=["text"], template=template
-            )
-            chain = load_summarize_chain(
-                llm,
-                chain_type="map_reduce",
-                map_prompt=prompt,
-                combine_prompt=prompt,
-                return_intermediate_steps=True,
-            )
-            target = wrapped_partial(
-                chain, {"input_documents": docs}
-            )  # , return_only_outputs=True)
-        elif langchain_action == LangChainAction.SUMMARIZE_ALL.value:
-            assert use_template
-            prompt = PromptTemplate(
-                input_variables=["text"], template=template
-            )
-            chain = load_summarize_chain(
-                llm,
-                chain_type="stuff",
-                prompt=prompt,
-                return_intermediate_steps=True,
-            )
-            target = wrapped_partial(chain)
-        elif langchain_action == LangChainAction.SUMMARIZE_REFINE.value:
-            chain = load_summarize_chain(
-                llm, chain_type="refine", return_intermediate_steps=True
-            )
-            target = wrapped_partial(chain)
-        else:
-            raise RuntimeError(
-                "No such langchain_action=%s" % langchain_action
-            )
     else:
         raise RuntimeError("No such langchain_action=%s" % langchain_action)
 

apps/language_models/langchain/gradio_themes.py

@@ -1,225 +0,0 @@
-from __future__ import annotations
-
-from typing import Iterable
-
-from gradio.themes.soft import Soft
-from gradio.themes import Color, Size
-from gradio.themes.utils import colors, sizes, fonts
-
-h2o_yellow = Color(
-    name="yellow",
-    c50="#fffef2",
-    c100="#fff9e6",
-    c200="#ffecb3",
-    c300="#ffe28c",
-    c400="#ffd659",
-    c500="#fec925",
-    c600="#e6ac00",
-    c700="#bf8f00",
-    c800="#a67c00",
-    c900="#664d00",
-    c950="#403000",
-)
-h2o_gray = Color(
-    name="gray",
-    c50="#f8f8f8",
-    c100="#e5e5e5",
-    c200="#cccccc",
-    c300="#b2b2b2",
-    c400="#999999",
-    c500="#7f7f7f",
-    c600="#666666",
-    c700="#4c4c4c",
-    c800="#333333",
-    c900="#191919",
-    c950="#0d0d0d",
-)
-
-
-text_xsm = Size(
-    name="text_xsm",
-    xxs="4px",
-    xs="5px",
-    sm="6px",
-    md="7px",
-    lg="8px",
-    xl="10px",
-    xxl="12px",
-)
-
-
-spacing_xsm = Size(
-    name="spacing_xsm",
-    xxs="1px",
-    xs="1px",
-    sm="1px",
-    md="2px",
-    lg="3px",
-    xl="5px",
-    xxl="7px",
-)
-
-
-radius_xsm = Size(
-    name="radius_xsm",
-    xxs="1px",
-    xs="1px",
-    sm="1px",
-    md="2px",
-    lg="3px",
-    xl="5px",
-    xxl="7px",
-)
-
-
-class H2oTheme(Soft):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = h2o_yellow,
-        secondary_hue: colors.Color | str = h2o_yellow,
-        neutral_hue: colors.Color | str = h2o_gray,
-        spacing_size: sizes.Size | str = sizes.spacing_md,
-        radius_size: sizes.Size | str = sizes.radius_md,
-        text_size: sizes.Size | str = sizes.text_lg,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Montserrat"),
-            "ui-sans-serif",
-            "system-ui",
-            "sans-serif",
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"),
-            "ui-monospace",
-            "Consolas",
-            "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-        super().set(
-            link_text_color="#3344DD",
-            link_text_color_hover="#3344DD",
-            link_text_color_visited="#3344DD",
-            link_text_color_dark="#74abff",
-            link_text_color_hover_dark="#a3c8ff",
-            link_text_color_active_dark="#a3c8ff",
-            link_text_color_visited_dark="#74abff",
-            button_primary_text_color="*neutral_950",
-            button_primary_text_color_dark="*neutral_950",
-            button_primary_background_fill="*primary_500",
-            button_primary_background_fill_dark="*primary_500",
-            block_label_background_fill="*primary_500",
-            block_label_background_fill_dark="*primary_500",
-            block_label_text_color="*neutral_950",
-            block_label_text_color_dark="*neutral_950",
-            block_title_text_color="*neutral_950",
-            block_title_text_color_dark="*neutral_950",
-            block_background_fill_dark="*neutral_950",
-            body_background_fill="*neutral_50",
-            body_background_fill_dark="*neutral_900",
-            background_fill_primary_dark="*block_background_fill",
-            block_radius="0 0 8px 8px",
-            checkbox_label_text_color_selected_dark="#000000",
-        )
-
-
-class SoftTheme(Soft):
-    def __init__(
-        self,
-        *,
-        primary_hue: colors.Color | str = colors.indigo,
-        secondary_hue: colors.Color | str = colors.indigo,
-        neutral_hue: colors.Color | str = colors.gray,
-        spacing_size: sizes.Size | str = sizes.spacing_md,
-        radius_size: sizes.Size | str = sizes.radius_md,
-        text_size: sizes.Size | str = sizes.text_md,
-        font: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("Montserrat"),
-            "ui-sans-serif",
-            "system-ui",
-            "sans-serif",
-        ),
-        font_mono: fonts.Font
-        | str
-        | Iterable[fonts.Font | str] = (
-            fonts.GoogleFont("IBM Plex Mono"),
-            "ui-monospace",
-            "Consolas",
-            "monospace",
-        ),
-    ):
-        super().__init__(
-            primary_hue=primary_hue,
-            secondary_hue=secondary_hue,
-            neutral_hue=neutral_hue,
-            spacing_size=spacing_size,
-            radius_size=radius_size,
-            text_size=text_size,
-            font=font,
-            font_mono=font_mono,
-        )
-
-
-h2o_logo = (
-    '<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="100%" height="100%"'
-    ' viewBox="0 0 600.28 600.28"><defs><style>.cls-1{fill:#fec925;}.cls-2{fill:#161616;}.cls-3{fill:'
-    '#54585a;}</style></defs><g id="Fill-1"><rect class="cls-1" width="600.28" height="600.28" '
-    'rx="23.24"/></g><path class="cls-2" d="M174.33,246.06v92.78H152.86v-38H110.71v38H89.24V246.06h21.'
-    '47v36.58h42.15V246.06Z"/><path class="cls-2" d="M259.81,321.34v17.5H189.7V324.92l35.78-33.8c8.22-7.'
-    "82,9.68-12.59,9.68-17.09,0-7.29-5-11.53-14.85-11.53-7.95,0-14.71,3-19.21,9.27L185.46,261.7c7.15-10"
-    '.47,20.14-17.23,36.84-17.23,20.68,0,34.46,10.6,34.46,27.44,0,9-2.52,17.22-15.51,29.29l-21.33,20.14Z"'
-    '/><path class="cls-2" d="M268.69,292.45c0-27.57,21.47-48,50.76-48s50.76,20.28,50.76,48-21.6,48-50.'
-    "76,48S268.69,320,268.69,292.45Zm79.78,0c0-17.63-12.46-29.69-29-29.69s-29,12.06-29,29.69,12.46,29.69"
-    ',29,29.69S348.47,310.08,348.47,292.45Z"/><path class="cls-3" d="M377.23,326.91c0-7.69,5.7-12.73,12.'
-    '85-12.73s12.86,5,12.86,12.73a12.86,12.86,0,1,1-25.71,0Z"/><path class="cls-3" d="M481.4,298.15v40.'
-    "69H462.05V330c-3.84,6.49-11.27,9.94-21.74,9.94-16.7,0-26.64-9.28-26.64-21.61,0-12.59,8.88-21.34,30."
-    "62-21.34h16.43c0-8.87-5.3-14-16.43-14-7.55,0-15.37,2.51-20.54,6.62l-7.43-14.44c7.82-5.57,19.35-8."
-    "62,30.75-8.62C468.81,266.47,481.4,276.54,481.4,298.15Zm-20.68,18.16V309H446.54c-9.67,0-12.72,3.57-"
-    '12.72,8.35,0,5.16,4.37,8.61,11.66,8.61C452.37,326,458.34,322.8,460.72,316.31Z"/><path class="cls-3"'
-    ' d="M497.56,246.06c0-6.49,5.17-11.53,12.86-11.53s12.86,4.77,12.86,11.13c0,6.89-5.17,11.93-12.86,'
-    '11.93S497.56,252.55,497.56,246.06Zm2.52,21.47h20.68v71.31H500.08Z"/></svg>'
-)
-
-
-def get_h2o_title(title, description):
-    # NOTE: Check full width desktop, smallest width browser desktop, iPhone browsers to ensure no overlap etc.
-    return f"""<div style="float:left; justify-content:left; height: 80px; width: 195px; margin-top:0px">
-                    {description}
-                </div>
-                <div style="display:flex; justify-content:center; margin-bottom:30px; margin-right:330px;">
-                    <div style="height: 60px; width: 60px; margin-right:20px;">{h2o_logo}</div>
-                    <h1 style="line-height:60px">{title}</h1>
-                </div>
-                <div style="float:right; height: 80px; width: 80px; margin-top:-100px">
-                    <img src="https://raw.githubusercontent.com/h2oai/h2ogpt/main/docs/h2o-qr.png">
-                </div>
-                """
-
-
-def get_simple_title(title, description):
-    return f"""{description}<h1 align="center"> {title}</h1>"""
-
-
-def get_dark_js():
-    return """() => {
-        if (document.querySelectorAll('.dark').length) {
-            document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
-        } else {
-            document.querySelector('body').classList.add('dark');
-        }
-    }"""

apps/language_models/langchain/gradio_utils/css.py

@@ -1,53 +0,0 @@
-def get_css(kwargs) -> str:
-    if kwargs["h2ocolors"]:
-        css_code = """footer {visibility: hidden;}
-        body{background:linear-gradient(#f5f5f5,#e5e5e5);}
-        body.dark{background:linear-gradient(#000000,#0d0d0d);}
-        """
-    else:
-        css_code = """footer {visibility: hidden}"""
-
-    css_code += make_css_base()
-    return css_code
-
-
-def make_css_base() -> str:
-    return """
-    @import url('https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@400;600&display=swap');
-    
-    body.dark{#warning {background-color: #555555};}
-    
-    #small_btn {
-        margin: 0.6em 0em 0.55em 0;
-        max-width: 20em;
-        min-width: 5em !important;
-        height: 5em;
-        font-size: 14px !important;
-    }
-    
-    #prompt-form {
-        border: 1px solid var(--primary-500) !important;
-    }
-    
-    #prompt-form.block {
-        border-radius: var(--block-radius) !important;
-    }
-    
-    #prompt-form textarea {
-        border: 1px solid rgb(209, 213, 219);
-    }
-    
-    #prompt-form label > div {
-        margin-top: 4px;
-    }
-    
-    button.primary:hover {
-        background-color: var(--primary-600) !important;
-        transition: .2s;
-    }
-    
-    #prompt-form-area {
-        margin-bottom: 2.5rem;
-    }
-    .chatsmall chatbot {font-size: 10px !important}
-    """

apps/language_models/langchain/gradio_utils/prompt_form.py

@@ -1,185 +0,0 @@
-import os
-import math
-
-import gradio as gr
-
-
-def make_chatbots(output_label0, output_label0_model2, **kwargs):
-    text_outputs = []
-    chat_kwargs = []
-    for model_state_lock in kwargs["model_states"]:
-        if os.environ.get("DEBUG_MODEL_LOCK"):
-            model_name = (
-                model_state_lock["base_model"]
-                + " : "
-                + model_state_lock["inference_server"]
-            )
-        else:
-            model_name = model_state_lock["base_model"]
-        output_label = f"h2oGPT [{model_name}]"
-        min_width = (
-            250
-            if kwargs["gradio_size"] in ["small", "large", "medium"]
-            else 160
-        )
-        chat_kwargs.append(
-            dict(
-                label=output_label,
-                visible=kwargs["model_lock"],
-                elem_classes="chatsmall",
-                height=kwargs["height"] or 400,
-                min_width=min_width,
-            )
-        )
-
-    if kwargs["model_lock_columns"] == -1:
-        kwargs["model_lock_columns"] = len(kwargs["model_states"])
-    if kwargs["model_lock_columns"] is None:
-        kwargs["model_lock_columns"] = 3
-
-    ncols = kwargs["model_lock_columns"]
-    if kwargs["model_states"] == 0:
-        nrows = 0
-    else:
-        nrows = math.ceil(
-            len(kwargs["model_states"]) / kwargs["model_lock_columns"]
-        )
-
-    if kwargs["model_lock_columns"] == 0:
-        # not using model_lock
-        pass
-    elif nrows <= 1:
-        with gr.Row():
-            for chat_kwargs1, model_state_lock in zip(
-                chat_kwargs, kwargs["model_states"]
-            ):
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-    elif nrows == kwargs["model_states"]:
-        with gr.Row():
-            for chat_kwargs1, model_state_lock in zip(
-                chat_kwargs, kwargs["model_states"]
-            ):
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-    elif nrows == 2:
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii >= len(kwargs["model_states"]) / 2:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii < len(kwargs["model_states"]) / 2:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-    elif nrows == 3:
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii >= 1 * len(kwargs["model_states"]) / 3:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if (
-                    mii < 1 * len(kwargs["model_states"]) / 3
-                    or mii >= 2 * len(kwargs["model_states"]) / 3
-                ):
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii < 2 * len(kwargs["model_states"]) / 3:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-    elif nrows >= 4:
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii >= 1 * len(kwargs["model_states"]) / 4:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if (
-                    mii < 1 * len(kwargs["model_states"]) / 4
-                    or mii >= 2 * len(kwargs["model_states"]) / 4
-                ):
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if (
-                    mii < 2 * len(kwargs["model_states"]) / 4
-                    or mii >= 3 * len(kwargs["model_states"]) / 4
-                ):
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-        with gr.Row():
-            for mii, (chat_kwargs1, model_state_lock) in enumerate(
-                zip(chat_kwargs, kwargs["model_states"])
-            ):
-                if mii < 3 * len(kwargs["model_states"]) / 4:
-                    continue
-                text_outputs.append(gr.Chatbot(**chat_kwargs1))
-
-    with gr.Row():
-        text_output = gr.Chatbot(
-            label=output_label0,
-            visible=not kwargs["model_lock"],
-            height=kwargs["height"] or 400,
-        )
-        text_output2 = gr.Chatbot(
-            label=output_label0_model2,
-            visible=False and not kwargs["model_lock"],
-            height=kwargs["height"] or 400,
-        )
-    return text_output, text_output2, text_outputs
-
-
-def make_prompt_form(kwargs, LangChainMode):
-    if kwargs["langchain_mode"] != LangChainMode.DISABLED.value:
-        extra_prompt_form = ".  For summarization, empty submission uses first top_k_docs documents."
-    else:
-        extra_prompt_form = ""
-    if kwargs["input_lines"] > 1:
-        instruction_label = (
-            "Shift-Enter to Submit, Enter for more lines%s" % extra_prompt_form
-        )
-    else:
-        instruction_label = (
-            "Enter to Submit, Shift-Enter for more lines%s" % extra_prompt_form
-        )
-
-    with gr.Row():  # elem_id='prompt-form-area'):
-        with gr.Column(scale=50):
-            instruction = gr.Textbox(
-                lines=kwargs["input_lines"],
-                label="Ask anything",
-                placeholder=instruction_label,
-                info=None,
-                elem_id="prompt-form",
-                container=True,
-            )
-        with gr.Row():
-            submit = gr.Button(
-                value="Submit", variant="primary", scale=0, size="sm"
-            )
-            stop_btn = gr.Button(
-                value="Stop", variant="secondary", scale=0, size="sm"
-            )
-
-    return instruction, submit, stop_btn

apps/language_models/langchain/h2oai_pipeline.py

@@ -1,13 +1,13 @@
 import os
 from apps.stable_diffusion.src.utils.utils import _compile_module
-
-from transformers import TextGenerationPipeline
-from transformers.pipelines.text_generation import ReturnType
+from io import BytesIO
+import torch_mlir
 
 from stopping import get_stopping
 from prompter import Prompter, PromptType
 
-
+from transformers import TextGenerationPipeline
+from transformers.pipelines.text_generation import ReturnType
 from transformers.generation import (
     GenerationConfig,
     LogitsProcessorList,
@@ -20,93 +20,463 @@ import gc
 from pathlib import Path
 from shark.shark_inference import SharkInference
 from shark.shark_downloader import download_public_file
+from shark.shark_importer import import_with_fx
 from apps.stable_diffusion.src import args
 
+# Brevitas
+from typing import List, Tuple
+from brevitas_examples.llm.llm_quant.quantize import quantize_model
+from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
+
+
+# fmt: off
+def quant〇matmul_rhs_group_quant〡shape(lhs: List[int], rhs: List[int], rhs_scale: List[int], rhs_zero_point: List[int], rhs_bit_width: int, rhs_group_size: int) -> List[int]:
+    if len(lhs) == 3 and len(rhs) == 2:
+        return [lhs[0], lhs[1], rhs[0]]
+    elif len(lhs) == 2 and len(rhs) == 2:
+        return [lhs[0], rhs[0]]
+    else:
+        raise ValueError("Input shapes not supported.")
+
+
+def quant〇matmul_rhs_group_quant〡dtype(lhs_rank_dtype: Tuple[int, int], rhs_rank_dtype: Tuple[int, int], rhs_scale_rank_dtype: Tuple[int, int], rhs_zero_point_rank_dtype: Tuple[int, int], rhs_bit_width: int, rhs_group_size: int) -> int:
+    # output dtype is the dtype of the lhs float input
+    lhs_rank, lhs_dtype = lhs_rank_dtype
+    return lhs_dtype
+
+
+def quant〇matmul_rhs_group_quant〡has_value_semantics(lhs, rhs, rhs_scale, rhs_zero_point, rhs_bit_width, rhs_group_size) -> None:
+    return
+
+
+brevitas_matmul_rhs_group_quant_library = [
+    quant〇matmul_rhs_group_quant〡shape,
+    quant〇matmul_rhs_group_quant〡dtype,
+    quant〇matmul_rhs_group_quant〡has_value_semantics]
+# fmt: on
+
 global_device = "cuda"
 global_precision = "fp16"
 
 if not args.run_docuchat_web:
     args.device = global_device
     args.precision = global_precision
+tensor_device = "cpu" if args.device == "cpu" else "cuda"
+
+
+class H2OGPTModel(torch.nn.Module):
+    def __init__(self, device, precision):
+        super().__init__()
+        torch_dtype = (
+            torch.float32
+            if precision == "fp32" or device == "cpu"
+            else torch.float16
+        )
+        device_map = {"": "cpu"} if device == "cpu" else {"": 0}
+        model_kwargs = {
+            "local_files_only": False,
+            "torch_dtype": torch_dtype,
+            "resume_download": True,
+            "use_auth_token": False,
+            "trust_remote_code": True,
+            "offload_folder": "offline_folder",
+            "device_map": device_map,
+        }
+        config = AutoConfig.from_pretrained(
+            "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            use_auth_token=False,
+            trust_remote_code=True,
+            offload_folder="offline_folder",
+        )
+        self.model = AutoModelForCausalLM.from_pretrained(
+            "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            config=config,
+            **model_kwargs,
+        )
+        if precision in ["int4", "int8"]:
+            print("Applying weight quantization..")
+            weight_bit_width = 4 if precision == "int4" else 8
+            quantize_model(
+                self.model.transformer.h,
+                dtype=torch.float32,
+                weight_bit_width=weight_bit_width,
+                weight_param_method="stats",
+                weight_scale_precision="float",
+                weight_quant_type="asym",
+                weight_quant_granularity="per_group",
+                weight_group_size=128,
+                quantize_weight_zero_point=False,
+            )
+            print("Weight quantization applied.")
+
+    def forward(self, input_ids, attention_mask):
+        input_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "past_key_values": None,
+            "use_cache": True,
+        }
+        output = self.model(
+            **input_dict,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+        return output.logits[:, -1, :]
 
 
 class H2OGPTSHARKModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
         model_name = "h2ogpt_falcon_7b"
-        path_str = (
-            model_name + "_" + args.precision + "_" + args.device + ".vmfb"
+        extended_model_name = (
+            model_name + "_" + args.precision + "_" + args.device
         )
-        vmfb_path = Path(path_str)
-        path_str = model_name + "_" + args.precision + ".mlir"
-        mlir_path = Path(path_str)
+        vmfb_path = Path(extended_model_name + ".vmfb")
+        mlir_path = Path(model_name + "_" + args.precision + ".mlir")
         shark_module = None
 
+        need_to_compile = False
         if not vmfb_path.exists():
-            if args.device == "cuda" and args.precision in ["fp16", "fp32"]:
-                # Downloading VMFB from shark_tank
-                print("Downloading vmfb from shark tank.")
+            need_to_compile = True
+            # Downloading VMFB from shark_tank
+            print("Trying to download pre-compiled vmfb from shark tank.")
+            download_public_file(
+                "gs://shark_tank/langchain/" + str(vmfb_path),
+                vmfb_path.absolute(),
+                single_file=True,
+            )
+            if vmfb_path.exists():
+                print(
+                    "Pre-compiled vmfb downloaded from shark tank successfully."
+                )
+                need_to_compile = False
+
+        if need_to_compile:
+            if not mlir_path.exists():
+                print("Trying to download pre-generated mlir from shark tank.")
+                # Downloading MLIR from shark_tank
                 download_public_file(
-                    "gs://shark_tank/langchain/" + path_str,
-                    vmfb_path.absolute(),
+                    "gs://shark_tank/langchain/" + str(mlir_path),
+                    mlir_path.absolute(),
                     single_file=True,
                 )
+            if mlir_path.exists():
+                with open(mlir_path, "rb") as f:
+                    bytecode = f.read()
             else:
-                if mlir_path.exists():
-                    with open(mlir_path, "rb") as f:
-                        bytecode = f.read()
-                else:
-                    # Downloading MLIR from shark_tank
-                    download_public_file(
-                        "gs://shark_tank/langchain/"
-                        + model_name
-                        + "_"
-                        + args.precision
-                        + ".mlir",
-                        mlir_path.absolute(),
-                        single_file=True,
-                    )
-                    if mlir_path.exists():
-                        with open(mlir_path, "rb") as f:
-                            bytecode = f.read()
-                    else:
-                        raise ValueError(
-                            f"MLIR not found at {mlir_path.absolute()}"
-                            " after downloading! Please check path and try again"
-                        )
-                shark_module = SharkInference(
-                    mlir_module=bytecode,
-                    device=args.device,
-                    mlir_dialect="linalg",
-                )
-                print(f"[DEBUG] generating vmfb.")
-                shark_module = _compile_module(shark_module, vmfb_path, [])
-                print("Saved newly generated vmfb.")
+                # Generating the mlir
+                bytecode = self.get_bytecode(tensor_device, args.precision)
+
+            shark_module = SharkInference(
+                mlir_module=bytecode,
+                device=args.device,
+                mlir_dialect="linalg",
+            )
+            print(f"[DEBUG] generating vmfb.")
+            shark_module = _compile_module(
+                shark_module, extended_model_name, []
+            )
+            print("Saved newly generated vmfb.")
 
         if shark_module is None:
             if vmfb_path.exists():
                 print("Compiled vmfb found. Loading it from: ", vmfb_path)
                 shark_module = SharkInference(
-                    None, device=global_device, mlir_dialect="linalg"
+                    None, device=args.device, mlir_dialect="linalg"
                 )
-                shark_module.load_module(vmfb_path)
+                shark_module.load_module(str(vmfb_path))
                 print("Compiled vmfb loaded successfully.")
             else:
                 raise ValueError("Unable to download/generate a vmfb.")
 
         self.model = shark_module
 
+    def get_bytecode(self, device, precision):
+        h2ogpt_model = H2OGPTModel(device, precision)
+
+        compilation_input_ids = torch.randint(
+            low=1, high=10000, size=(1, 400)
+        ).to(device=device)
+        compilation_attention_mask = torch.ones(1, 400, dtype=torch.int64).to(
+            device=device
+        )
+
+        h2ogptCompileInput = (
+            compilation_input_ids,
+            compilation_attention_mask,
+        )
+
+        print(f"[DEBUG] generating torchscript graph")
+        ts_graph = import_with_fx(
+            h2ogpt_model,
+            h2ogptCompileInput,
+            is_f16=False,
+            precision=precision,
+            f16_input_mask=[False, False],
+            mlir_type="torchscript",
+        )
+        del h2ogpt_model
+        del self.src_model
+
+        print(f"[DEBUG] generating torch mlir")
+        if precision in ["int4", "int8"]:
+            from torch_mlir.compiler_utils import (
+                run_pipeline_with_repro_report,
+            )
+
+            module = torch_mlir.compile(
+                ts_graph,
+                [*h2ogptCompileInput],
+                output_type=torch_mlir.OutputType.TORCH,
+                backend_legal_ops=["quant.matmul_rhs_group_quant"],
+                extra_library=brevitas_matmul_rhs_group_quant_library,
+                use_tracing=False,
+                verbose=False,
+            )
+            print(f"[DEBUG] converting torch to linalg")
+            run_pipeline_with_repro_report(
+                module,
+                "builtin.module(func.func(torch-unpack-torch-tensor),torch-backend-to-linalg-on-tensors-backend-pipeline)",
+                description="Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR",
+            )
+        else:
+            module = torch_mlir.compile(
+                ts_graph,
+                [*h2ogptCompileInput],
+                torch_mlir.OutputType.LINALG_ON_TENSORS,
+                use_tracing=False,
+                verbose=False,
+            )
+        del ts_graph
+
+        print(f"[DEBUG] converting to bytecode")
+        bytecode_stream = BytesIO()
+        module.operation.write_bytecode(bytecode_stream)
+        bytecode = bytecode_stream.getvalue()
+        del module
+
+        return bytecode
+
     def forward(self, input_ids, attention_mask):
         result = torch.from_numpy(
             self.model(
                 "forward",
                 (input_ids.to(device="cpu"), attention_mask.to(device="cpu")),
             )
-        ).to(device=global_device)
+        ).to(device=tensor_device)
         return result
 
 
-h2ogpt_model = H2OGPTSHARKModel()
+def decode_tokens(tokenizer, res_tokens):
+    for i in range(len(res_tokens)):
+        if type(res_tokens[i]) != int:
+            res_tokens[i] = int(res_tokens[i][0])
+
+    res_str = tokenizer.decode(res_tokens, skip_special_tokens=True)
+    return res_str
+
+
+def generate_token(h2ogpt_shark_model, model, tokenizer, **generate_kwargs):
+    del generate_kwargs["max_time"]
+    generate_kwargs["input_ids"] = generate_kwargs["input_ids"].to(
+        device=tensor_device
+    )
+    generate_kwargs["attention_mask"] = generate_kwargs["attention_mask"].to(
+        device=tensor_device
+    )
+    truncated_input_ids = []
+    stopping_criteria = generate_kwargs["stopping_criteria"]
+
+    generation_config_ = GenerationConfig.from_model_config(model.config)
+    generation_config = copy.deepcopy(generation_config_)
+    model_kwargs = generation_config.update(**generate_kwargs)
+
+    logits_processor = LogitsProcessorList()
+    stopping_criteria = (
+        stopping_criteria
+        if stopping_criteria is not None
+        else StoppingCriteriaList()
+    )
+
+    eos_token_id = generation_config.eos_token_id
+    generation_config.pad_token_id = eos_token_id
+
+    (
+        inputs_tensor,
+        model_input_name,
+        model_kwargs,
+    ) = model._prepare_model_inputs(
+        None, generation_config.bos_token_id, model_kwargs
+    )
+
+    model_kwargs["output_attentions"] = generation_config.output_attentions
+    model_kwargs[
+        "output_hidden_states"
+    ] = generation_config.output_hidden_states
+    model_kwargs["use_cache"] = generation_config.use_cache
+
+    input_ids = (
+        inputs_tensor
+        if model_input_name == "input_ids"
+        else model_kwargs.pop("input_ids")
+    )
+
+    input_ids_seq_length = input_ids.shape[-1]
+
+    generation_config.max_length = (
+        generation_config.max_new_tokens + input_ids_seq_length
+    )
+
+    logits_processor = model._get_logits_processor(
+        generation_config=generation_config,
+        input_ids_seq_length=input_ids_seq_length,
+        encoder_input_ids=inputs_tensor,
+        prefix_allowed_tokens_fn=None,
+        logits_processor=logits_processor,
+    )
+
+    stopping_criteria = model._get_stopping_criteria(
+        generation_config=generation_config,
+        stopping_criteria=stopping_criteria,
+    )
+
+    logits_warper = model._get_logits_warper(generation_config)
+
+    (
+        input_ids,
+        model_kwargs,
+    ) = model._expand_inputs_for_generation(
+        input_ids=input_ids,
+        expand_size=generation_config.num_return_sequences,  # 1
+        is_encoder_decoder=model.config.is_encoder_decoder,  # False
+        **model_kwargs,
+    )
+
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+    eos_token_id_tensor = (
+        torch.tensor(eos_token_id).to(device=tensor_device)
+        if eos_token_id is not None
+        else None
+    )
+
+    pad_token_id = generation_config.pad_token_id
+    eos_token_id = eos_token_id
+
+    output_scores = generation_config.output_scores  # False
+    return_dict_in_generate = (
+        generation_config.return_dict_in_generate  # False
+    )
+
+    # init attention / hidden states / scores tuples
+    scores = () if (return_dict_in_generate and output_scores) else None
+
+    # keep track of which sequences are already finished
+    unfinished_sequences = torch.ones(
+        input_ids.shape[0],
+        dtype=torch.long,
+        device=input_ids.device,
+    )
+
+    timesRan = 0
+    import time
+
+    start = time.time()
+    print("\n")
+
+    res_tokens = []
+    while True:
+        model_inputs = model.prepare_inputs_for_generation(
+            input_ids, **model_kwargs
+        )
+
+        outputs = h2ogpt_shark_model.forward(
+            model_inputs["input_ids"], model_inputs["attention_mask"]
+        )
+
+        if args.precision == "fp16":
+            outputs = outputs.to(dtype=torch.float32)
+        next_token_logits = outputs
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # sample
+        probs = torch.nn.functional.softmax(next_token_scores, dim=-1)
+
+        next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+        # finished sentences should have their next token be a padding token
+        if eos_token_id is not None:
+            if pad_token_id is None:
+                raise ValueError(
+                    "If `eos_token_id` is defined, make sure that `pad_token_id` is defined."
+                )
+            next_token = next_token * unfinished_sequences + pad_token_id * (
+                1 - unfinished_sequences
+            )
+
+        input_ids = torch.cat([input_ids, next_token[:, None]], dim=-1)
+
+        model_kwargs["past_key_values"] = None
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [
+                    attention_mask,
+                    attention_mask.new_ones((attention_mask.shape[0], 1)),
+                ],
+                dim=-1,
+            )
+
+        truncated_input_ids.append(input_ids[:, 0])
+        input_ids = input_ids[:, 1:]
+        model_kwargs["attention_mask"] = model_kwargs["attention_mask"][:, 1:]
+
+        new_word = tokenizer.decode(
+            next_token.cpu().numpy(),
+            add_special_tokens=False,
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True,
+        )
+
+        res_tokens.append(next_token)
+        if new_word == "<0x0A>":
+            print("\n", end="", flush=True)
+        else:
+            print(f"{new_word}", end=" ", flush=True)
+
+        part_str = decode_tokens(tokenizer, res_tokens)
+        yield part_str
+
+        # if eos_token was found in one sentence, set sentence to finished
+        if eos_token_id_tensor is not None:
+            unfinished_sequences = unfinished_sequences.mul(
+                next_token.tile(eos_token_id_tensor.shape[0], 1)
+                .ne(eos_token_id_tensor.unsqueeze(1))
+                .prod(dim=0)
+            )
+            # stop when each sentence is finished
+            if unfinished_sequences.max() == 0 or stopping_criteria(
+                input_ids, scores
+            ):
+                break
+        timesRan = timesRan + 1
+
+    end = time.time()
+    print(
+        "\n\nTime taken is {:.2f} seconds/token\n".format(
+            (end - start) / timesRan
+        )
+    )
+
+    torch.cuda.empty_cache()
+    gc.collect()
+
+    res_str = decode_tokens(tokenizer, res_tokens)
+    yield res_str
 
 
 def pad_or_truncate_inputs(
@@ -118,14 +488,14 @@ def pad_or_truncate_inputs(
         num_add_token = max_padding_length - inp_shape[1]
         padded_input_ids = torch.cat(
             [
-                torch.tensor([[11] * num_add_token]).to(device=global_device),
+                torch.tensor([[11] * num_add_token]).to(device=tensor_device),
                 input_ids,
             ],
             dim=1,
         )
         padded_attention_mask = torch.cat(
             [
-                torch.tensor([[0] * num_add_token]).to(device=global_device),
+                torch.tensor([[0] * num_add_token]).to(device=tensor_device),
                 attention_mask,
             ],
             dim=1,
@@ -319,232 +689,6 @@ class H2OTextGenerationPipeline(TextGenerationPipeline):
             )
         return records
 
-    def generate_new_token(self):
-        model_inputs = self.model.prepare_inputs_for_generation(
-            self.input_ids, **self.model_kwargs
-        )
-
-        outputs = h2ogpt_model.forward(
-            model_inputs["input_ids"], model_inputs["attention_mask"]
-        )
-
-        if global_precision == "fp16":
-            outputs = outputs.to(dtype=torch.float32)
-        next_token_logits = outputs
-
-        # pre-process distribution
-        next_token_scores = self.logits_processor(
-            self.input_ids, next_token_logits
-        )
-        next_token_scores = self.logits_warper(
-            self.input_ids, next_token_scores
-        )
-
-        # sample
-        probs = torch.nn.functional.softmax(next_token_scores, dim=-1)
-
-        next_token = torch.multinomial(probs, num_samples=1).squeeze(1)
-
-        # finished sentences should have their next token be a padding token
-        if self.eos_token_id is not None:
-            if self.pad_token_id is None:
-                raise ValueError(
-                    "If `eos_token_id` is defined, make sure that `pad_token_id` is defined."
-                )
-            next_token = (
-                next_token * self.unfinished_sequences
-                + self.pad_token_id * (1 - self.unfinished_sequences)
-            )
-
-        self.input_ids = torch.cat(
-            [self.input_ids, next_token[:, None]], dim=-1
-        )
-
-        self.model_kwargs["past_key_values"] = None
-        if "attention_mask" in self.model_kwargs:
-            attention_mask = self.model_kwargs["attention_mask"]
-            self.model_kwargs["attention_mask"] = torch.cat(
-                [
-                    attention_mask,
-                    attention_mask.new_ones((attention_mask.shape[0], 1)),
-                ],
-                dim=-1,
-            )
-
-        self.truncated_input_ids.append(self.input_ids[:, 0])
-        self.input_ids = self.input_ids[:, 1:]
-        self.model_kwargs["attention_mask"] = self.model_kwargs[
-            "attention_mask"
-        ][:, 1:]
-
-        return next_token
-
-    def generate_token(self, **generate_kwargs):
-        self.truncated_input_ids = []
-
-        generation_config_ = GenerationConfig.from_model_config(
-            self.model.config
-        )
-        generation_config = copy.deepcopy(generation_config_)
-        self.model_kwargs = generation_config.update(**generate_kwargs)
-
-        logits_processor = LogitsProcessorList()
-        self.stopping_criteria = (
-            self.stopping_criteria
-            if self.stopping_criteria is not None
-            else StoppingCriteriaList()
-        )
-
-        eos_token_id = generation_config.eos_token_id
-        generation_config.pad_token_id = eos_token_id
-
-        (
-            inputs_tensor,
-            model_input_name,
-            self.model_kwargs,
-        ) = self.model._prepare_model_inputs(
-            None, generation_config.bos_token_id, self.model_kwargs
-        )
-        batch_size = inputs_tensor.shape[0]
-
-        self.model_kwargs[
-            "output_attentions"
-        ] = generation_config.output_attentions
-        self.model_kwargs[
-            "output_hidden_states"
-        ] = generation_config.output_hidden_states
-        self.model_kwargs["use_cache"] = generation_config.use_cache
-
-        self.input_ids = (
-            inputs_tensor
-            if model_input_name == "input_ids"
-            else self.model_kwargs.pop("input_ids")
-        )
-
-        input_ids_seq_length = self.input_ids.shape[-1]
-
-        generation_config.max_length = (
-            generation_config.max_new_tokens + input_ids_seq_length
-        )
-
-        self.logits_processor = self.model._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
-            encoder_input_ids=inputs_tensor,
-            prefix_allowed_tokens_fn=None,
-            logits_processor=logits_processor,
-        )
-
-        self.stopping_criteria = self.model._get_stopping_criteria(
-            generation_config=generation_config,
-            stopping_criteria=self.stopping_criteria,
-        )
-
-        self.logits_warper = self.model._get_logits_warper(generation_config)
-
-        (
-            self.input_ids,
-            self.model_kwargs,
-        ) = self.model._expand_inputs_for_generation(
-            input_ids=self.input_ids,
-            expand_size=generation_config.num_return_sequences,  # 1
-            is_encoder_decoder=self.model.config.is_encoder_decoder,  # False
-            **self.model_kwargs,
-        )
-
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-        self.eos_token_id_tensor = (
-            torch.tensor(eos_token_id).to(device=global_device)
-            if eos_token_id is not None
-            else None
-        )
-
-        self.pad_token_id = generation_config.pad_token_id
-        self.eos_token_id = eos_token_id
-
-        output_scores = generation_config.output_scores  # False
-        output_attentions = generation_config.output_attentions  # False
-        output_hidden_states = generation_config.output_hidden_states  # False
-        return_dict_in_generate = (
-            generation_config.return_dict_in_generate  # False
-        )
-
-        # init attention / hidden states / scores tuples
-        self.scores = (
-            () if (return_dict_in_generate and output_scores) else None
-        )
-        decoder_attentions = (
-            () if (return_dict_in_generate and output_attentions) else None
-        )
-        cross_attentions = (
-            () if (return_dict_in_generate and output_attentions) else None
-        )
-        decoder_hidden_states = (
-            () if (return_dict_in_generate and output_hidden_states) else None
-        )
-
-        # keep track of which sequences are already finished
-        self.unfinished_sequences = torch.ones(
-            self.input_ids.shape[0],
-            dtype=torch.long,
-            device=self.input_ids.device,
-        )
-
-        timesRan = 0
-        import time
-
-        start = time.time()
-        print("\n")
-
-        while True:
-            next_token = self.generate_new_token()
-            new_word = self.tokenizer.decode(
-                next_token.cpu().numpy(),
-                add_special_tokens=False,
-                skip_special_tokens=True,
-                clean_up_tokenization_spaces=True,
-            )
-
-            print(f"{new_word}", end="", flush=True)
-
-            # if eos_token was found in one sentence, set sentence to finished
-            if self.eos_token_id_tensor is not None:
-                self.unfinished_sequences = self.unfinished_sequences.mul(
-                    next_token.tile(self.eos_token_id_tensor.shape[0], 1)
-                    .ne(self.eos_token_id_tensor.unsqueeze(1))
-                    .prod(dim=0)
-                )
-                # stop when each sentence is finished
-                if (
-                    self.unfinished_sequences.max() == 0
-                    or self.stopping_criteria(self.input_ids, self.scores)
-                ):
-                    break
-            timesRan = timesRan + 1
-
-        end = time.time()
-        print(
-            "\n\nTime taken is {:.2f} seconds/token\n".format(
-                (end - start) / timesRan
-            )
-        )
-
-        self.input_ids = torch.cat(
-            [
-                torch.tensor(self.truncated_input_ids)
-                .to(device=global_device)
-                .unsqueeze(dim=0),
-                self.input_ids,
-            ],
-            dim=-1,
-        )
-
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        return self.input_ids
-
     def _forward(self, model_inputs, **generate_kwargs):
         if self.can_stop:
             stopping_criteria = get_stopping(
@@ -604,32 +748,13 @@ class H2OTextGenerationPipeline(TextGenerationPipeline):
         input_ids, attention_mask = pad_or_truncate_inputs(
             input_ids, attention_mask, max_padding_length=max_padding_length
         )
-        self.stopping_criteria = generate_kwargs["stopping_criteria"]
 
-        generated_sequence = self.generate_token(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            **generate_kwargs,
-        )
-        out_b = generated_sequence.shape[0]
-        if self.framework == "pt":
-            generated_sequence = generated_sequence.reshape(
-                in_b, out_b // in_b, *generated_sequence.shape[1:]
-            )
-        elif self.framework == "tf":
-            from transformers import is_tf_available
-
-            if is_tf_available():
-                import tensorflow as tf
-
-                generated_sequence = tf.reshape(
-                    generated_sequence,
-                    (in_b, out_b // in_b, *generated_sequence.shape[1:]),
-                )
-            else:
-                raise ValueError("TF not avaialble.")
-        return {
-            "generated_sequence": generated_sequence,
+        return_dict = {
+            "model": self.model,
+            "tokenizer": self.tokenizer,
             "input_ids": input_ids,
-            "prompt_text": prompt_text,
+            "attention_mask": attention_mask,
+            "attention_mask": attention_mask,
         }
+        return_dict = {**return_dict, **generate_kwargs}
+        return return_dict

apps/language_models/langchain/langchain_requirements.txt

@@ -1,12 +1,10 @@
 # for generate (gradio server) and finetune
 datasets==2.13.0
 sentencepiece==0.1.99
-gradio==3.35.2
-huggingface_hub==0.15.1
+huggingface_hub==0.16.4
 appdirs==1.4.4
 fire==0.5.0
 docutils==0.20.1
-torch==2.0.1
 evaluate==0.4.0
 rouge_score==0.1.2
 sacrebleu==2.3.1
@@ -19,7 +17,8 @@ matplotlib==3.7.1
 loralib==0.1.1
 bitsandbytes==0.39.0
 accelerate==0.20.3
-git+https://github.com/huggingface/peft.git@0b62b4378b4ce9367932c73540349da9a41bdea8
+peft==0.4.0
+# 4.31.0+ breaks load_in_8bit=True (https://github.com/huggingface/transformers/issues/25026)
 transformers==4.30.2
 tokenizers==0.13.3
 APScheduler==3.10.1
@@ -35,7 +34,7 @@ tensorboard==2.13.0
 neptune==1.2.0
 
 # for gradio client
-gradio_client==0.2.7
+gradio_client==0.2.10
 beautifulsoup4==4.12.2
 markdown==3.4.3
 
@@ -45,8 +44,9 @@ pytest-xdist==3.2.1
 nltk==3.8.1
 textstat==0.7.3
 # pandoc==2.3
-#pypandoc==1.11
-pypandoc_binary==1.11
+pypandoc==1.11; sys_platform == "darwin" and platform_machine == "arm64"
+pypandoc_binary==1.11; platform_machine == "x86_64"
+pypandoc_binary==1.11; sys_platform == "win32"
 openpyxl==3.1.2
 lm_dataformat==0.0.20
 bioc==2.0
@@ -63,3 +63,58 @@ text-generation==0.6.0
 tiktoken==0.4.0
 # optional: for OpenAI endpoint or embeddings (requires key)
 openai==0.27.8
+
+# optional for chat with PDF
+langchain==0.0.202
+pypdf==3.12.2
+# avoid textract, requires old six
+#textract==1.6.5
+
+# for HF embeddings
+sentence_transformers==2.2.2
+
+# local vector db
+chromadb==0.3.25
+# server vector db
+#pymilvus==2.2.8
+
+# weak url support, if can't install opencv etc. If comment-in this one, then comment-out unstructured[local-inference]==0.6.6
+# unstructured==0.8.1
+
+# strong support for images
+# Requires on Ubuntu: sudo apt-get install libmagic-dev poppler-utils tesseract-ocr libtesseract-dev libreoffice
+unstructured[local-inference]==0.7.4
+#pdf2image==1.16.3
+#pytesseract==0.3.10
+pillow
+
+pdfminer.six==20221105
+urllib3
+requests_file
+
+#pdf2image==1.16.3
+#pytesseract==0.3.10
+tabulate==0.9.0
+# FYI pandoc already part of requirements.txt
+
+# JSONLoader, but makes some trouble for some users
+# jq==1.4.1
+
+# to check licenses
+# Run: pip-licenses|grep -v 'BSD\|Apache\|MIT'
+pip-licenses==4.3.0
+
+# weaviate vector db
+weaviate-client==3.22.1
+
+gpt4all==1.0.5
+llama-cpp-python==0.1.73
+
+arxiv==1.4.8
+pymupdf==1.22.5 # AGPL license
+# extract-msg==0.41.1  # GPL3
+
+# sometimes unstructured fails, these work in those cases.  See https://github.com/h2oai/h2ogpt/issues/320
+playwright==1.36.0
+# requires Chrome binary to be in path
+selenium==4.10.0

apps/language_models/langchain/make_db.py

@@ -1,5 +1,4 @@
 import os
-import fire
 
 from gpt_langchain import (
     path_to_docs,
@@ -202,7 +201,3 @@ def make_db_main(
     if verbose:
         print("DONE", flush=True)
     return db, collection_name
-
-
-if __name__ == "__main__":
-    fire.Fire(make_db_main)

apps/language_models/src/model_wrappers/minigpt4.py

@@ -0,0 +1,503 @@
+import torch
+import dataclasses
+from enum import auto, Enum
+from typing import List, Any
+from transformers import StoppingCriteria
+
+
+from brevitas_examples.llm.llm_quant.quantize import quantize_model
+from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
+
+
+class LayerNorm(torch.nn.LayerNorm):
+    """Subclass torch's LayerNorm to handle fp16."""
+
+    def forward(self, x: torch.Tensor):
+        orig_type = x.dtype
+        ret = super().forward(x.type(torch.float32))
+        return ret.type(orig_type)
+
+
+class VisionModel(torch.nn.Module):
+    def __init__(
+        self,
+        ln_vision,
+        visual_encoder,
+        precision="fp32",
+        weight_group_size=128,
+    ):
+        super().__init__()
+        self.ln_vision = ln_vision
+        self.visual_encoder = visual_encoder
+        if precision in ["int4", "int8"]:
+            print("Vision Model applying weight quantization to ln_vision")
+            weight_bit_width = 4 if precision == "int4" else 8
+            quantize_model(
+                self.ln_vision,
+                dtype=torch.float32,
+                weight_bit_width=weight_bit_width,
+                weight_param_method="stats",
+                weight_scale_precision="float",
+                weight_quant_type="asym",
+                weight_quant_granularity="per_group",
+                weight_group_size=weight_group_size,
+                quantize_weight_zero_point=False,
+            )
+            print("Weight quantization applied.")
+            print(
+                "Vision Model applying weight quantization to visual_encoder"
+            )
+            quantize_model(
+                self.visual_encoder,
+                dtype=torch.float32,
+                weight_bit_width=weight_bit_width,
+                weight_param_method="stats",
+                weight_scale_precision="float",
+                weight_quant_type="asym",
+                weight_quant_granularity="per_group",
+                weight_group_size=weight_group_size,
+                quantize_weight_zero_point=False,
+            )
+            print("Weight quantization applied.")
+
+    def forward(self, image):
+        image_embeds = self.ln_vision(self.visual_encoder(image))
+        return image_embeds
+
+
+class QformerBertModel(torch.nn.Module):
+    def __init__(self, qformer_bert):
+        super().__init__()
+        self.qformer_bert = qformer_bert
+
+    def forward(self, query_tokens, image_embeds, image_atts):
+        query_output = self.qformer_bert(
+            query_embeds=query_tokens,
+            encoder_hidden_states=image_embeds,
+            encoder_attention_mask=image_atts,
+            return_dict=True,
+        )
+        return query_output.last_hidden_state
+
+
+class FirstLlamaModel(torch.nn.Module):
+    def __init__(self, model, precision="fp32", weight_group_size=128):
+        super().__init__()
+        self.model = model
+        print("SHARK: Loading LLAMA Done")
+        if precision in ["int4", "int8"]:
+            print("First Llama applying weight quantization")
+            weight_bit_width = 4 if precision == "int4" else 8
+            quantize_model(
+                self.model,
+                dtype=torch.float32,
+                weight_bit_width=weight_bit_width,
+                weight_param_method="stats",
+                weight_scale_precision="float",
+                weight_quant_type="asym",
+                weight_quant_granularity="per_group",
+                weight_group_size=weight_group_size,
+                quantize_weight_zero_point=False,
+            )
+            print("Weight quantization applied.")
+
+    def forward(self, inputs_embeds, position_ids, attention_mask):
+        print("************************************")
+        print(
+            "inputs_embeds: ",
+            inputs_embeds.shape,
+            " dtype: ",
+            inputs_embeds.dtype,
+        )
+        print(
+            "position_ids: ",
+            position_ids.shape,
+            " dtype: ",
+            position_ids.dtype,
+        )
+        print(
+            "attention_mask: ",
+            attention_mask.shape,
+            " dtype: ",
+            attention_mask.dtype,
+        )
+        print("************************************")
+        config = {
+            "inputs_embeds": inputs_embeds,
+            "position_ids": position_ids,
+            "past_key_values": None,
+            "use_cache": True,
+            "attention_mask": attention_mask,
+        }
+        output = self.model(
+            **config,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+        return_vals = []
+        return_vals.append(output.logits)
+        temp_past_key_values = output.past_key_values
+        for item in temp_past_key_values:
+            return_vals.append(item[0])
+            return_vals.append(item[1])
+        return tuple(return_vals)
+
+
+class SecondLlamaModel(torch.nn.Module):
+    def __init__(self, model, precision="fp32", weight_group_size=128):
+        super().__init__()
+        self.model = model
+        print("SHARK: Loading LLAMA Done")
+        if precision in ["int4", "int8"]:
+            print("Second Llama applying weight quantization")
+            weight_bit_width = 4 if precision == "int4" else 8
+            quantize_model(
+                self.model,
+                dtype=torch.float32,
+                weight_bit_width=weight_bit_width,
+                weight_param_method="stats",
+                weight_scale_precision="float",
+                weight_quant_type="asym",
+                weight_quant_granularity="per_group",
+                weight_group_size=weight_group_size,
+                quantize_weight_zero_point=False,
+            )
+            print("Weight quantization applied.")
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        attention_mask,
+        i1,
+        i2,
+        i3,
+        i4,
+        i5,
+        i6,
+        i7,
+        i8,
+        i9,
+        i10,
+        i11,
+        i12,
+        i13,
+        i14,
+        i15,
+        i16,
+        i17,
+        i18,
+        i19,
+        i20,
+        i21,
+        i22,
+        i23,
+        i24,
+        i25,
+        i26,
+        i27,
+        i28,
+        i29,
+        i30,
+        i31,
+        i32,
+        i33,
+        i34,
+        i35,
+        i36,
+        i37,
+        i38,
+        i39,
+        i40,
+        i41,
+        i42,
+        i43,
+        i44,
+        i45,
+        i46,
+        i47,
+        i48,
+        i49,
+        i50,
+        i51,
+        i52,
+        i53,
+        i54,
+        i55,
+        i56,
+        i57,
+        i58,
+        i59,
+        i60,
+        i61,
+        i62,
+        i63,
+        i64,
+    ):
+        print("************************************")
+        print("input_ids: ", input_ids.shape, " dtype: ", input_ids.dtype)
+        print(
+            "position_ids: ",
+            position_ids.shape,
+            " dtype: ",
+            position_ids.dtype,
+        )
+        print(
+            "attention_mask: ",
+            attention_mask.shape,
+            " dtype: ",
+            attention_mask.dtype,
+        )
+        print("past_key_values: ", i1.shape, i2.shape, i63.shape, i64.shape)
+        print("past_key_values dtype: ", i1.dtype)
+        print("************************************")
+        config = {
+            "input_ids": input_ids,
+            "position_ids": position_ids,
+            "past_key_values": (
+                (i1, i2),
+                (
+                    i3,
+                    i4,
+                ),
+                (
+                    i5,
+                    i6,
+                ),
+                (
+                    i7,
+                    i8,
+                ),
+                (
+                    i9,
+                    i10,
+                ),
+                (
+                    i11,
+                    i12,
+                ),
+                (
+                    i13,
+                    i14,
+                ),
+                (
+                    i15,
+                    i16,
+                ),
+                (
+                    i17,
+                    i18,
+                ),
+                (
+                    i19,
+                    i20,
+                ),
+                (
+                    i21,
+                    i22,
+                ),
+                (
+                    i23,
+                    i24,
+                ),
+                (
+                    i25,
+                    i26,
+                ),
+                (
+                    i27,
+                    i28,
+                ),
+                (
+                    i29,
+                    i30,
+                ),
+                (
+                    i31,
+                    i32,
+                ),
+                (
+                    i33,
+                    i34,
+                ),
+                (
+                    i35,
+                    i36,
+                ),
+                (
+                    i37,
+                    i38,
+                ),
+                (
+                    i39,
+                    i40,
+                ),
+                (
+                    i41,
+                    i42,
+                ),
+                (
+                    i43,
+                    i44,
+                ),
+                (
+                    i45,
+                    i46,
+                ),
+                (
+                    i47,
+                    i48,
+                ),
+                (
+                    i49,
+                    i50,
+                ),
+                (
+                    i51,
+                    i52,
+                ),
+                (
+                    i53,
+                    i54,
+                ),
+                (
+                    i55,
+                    i56,
+                ),
+                (
+                    i57,
+                    i58,
+                ),
+                (
+                    i59,
+                    i60,
+                ),
+                (
+                    i61,
+                    i62,
+                ),
+                (
+                    i63,
+                    i64,
+                ),
+            ),
+            "use_cache": True,
+            "attention_mask": attention_mask,
+        }
+        output = self.model(
+            **config,
+            return_dict=True,
+            output_attentions=False,
+            output_hidden_states=False,
+        )
+        return_vals = []
+        return_vals.append(output.logits)
+        temp_past_key_values = output.past_key_values
+        for item in temp_past_key_values:
+            return_vals.append(item[0])
+            return_vals.append(item[1])
+        return tuple(return_vals)
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+
+    SINGLE = auto()
+    TWO = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+
+    system: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+
+    skip_next: bool = False
+    conv_id: Any = None
+
+    def get_prompt(self):
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ": " + message + self.sep
+                else:
+                    ret += role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset :]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            conv_id=self.conv_id,
+        )
+
+    def dict(self):
+        return {
+            "system": self.system,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+            "conv_id": self.conv_id,
+        }
+
+
+class StoppingCriteriaSub(StoppingCriteria):
+    def __init__(self, stops=[], encounters=1):
+        super().__init__()
+        self.stops = stops
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
+        for stop in self.stops:
+            if torch.all((stop == input_ids[0][-len(stop) :])).item():
+                return True
+
+        return False
+
+
+CONV_VISION = Conversation(
+    system="Give the following image: <Img>ImageContent</Img>. "
+    "You will be able to see the image once I provide it to you. Please answer my questions.",
+    roles=("Human", "Assistant"),
+    messages=[],
+    offset=2,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)

apps/language_models/src/model_wrappers/vicuna4.py

@@ -0,0 +1,879 @@
+import argparse
+import json
+import re
+from io import BytesIO
+from pathlib import Path
+from tqdm import tqdm
+from typing import List, Optional, Tuple, Union
+import numpy as np
+import iree.runtime
+import itertools
+import subprocess
+
+import torch
+import torch_mlir
+from torch_mlir import TensorPlaceholder
+from torch_mlir.compiler_utils import run_pipeline_with_repro_report
+from transformers import (
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    LlamaPreTrainedModel,
+)
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+
+from apps.language_models.src.pipelines.SharkLLMBase import SharkLLMBase
+from apps.language_models.src.model_wrappers.vicuna_sharded_model import (
+    FirstVicunaLayer,
+    SecondVicunaLayer,
+    CompiledVicunaLayer,
+    ShardedVicunaModel,
+    LMHead,
+    LMHeadCompiled,
+    VicunaEmbedding,
+    VicunaEmbeddingCompiled,
+    VicunaNorm,
+    VicunaNormCompiled,
+)
+from apps.language_models.src.model_wrappers.vicuna_model import (
+    FirstVicuna,
+    SecondVicuna,
+)
+from apps.language_models.utils import (
+    get_vmfb_from_path,
+)
+from shark.shark_downloader import download_public_file
+from shark.shark_importer import get_f16_inputs
+from shark.shark_importer import import_with_fx
+from shark.shark_inference import SharkInference
+
+from brevitas_examples.llm.llm_quant.quantize import quantize_model
+from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
+from transformers.models.llama.configuration_llama import LlamaConfig
+from transformers.models.llama.modeling_llama import (
+    LlamaDecoderLayer,
+    LlamaRMSNorm,
+    _make_causal_mask,
+    _expand_mask,
+)
+from torch import nn
+from time import time
+
+
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [
+                LlamaDecoderLayer(config)
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask,
+        input_shape,
+        inputs_embeds,
+        past_key_values_length,
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        t1 = time()
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = (
+            use_cache if use_cache is not None else self.config.use_cache
+        )
+
+        return_dict = (
+            return_dict
+            if return_dict is not None
+            else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = (
+                seq_length_with_past + past_key_values_length
+            )
+
+        if position_ids is None:
+            device = (
+                input_ids.device
+                if input_ids is not None
+                else inputs_embeds.device
+            )
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.compressedlayers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[8 * idx : 8 * (idx + 1)]
+                if past_key_values is not None
+                else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer.forward(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[1:],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        try:
+            hidden_states = np.asarray(hidden_states, hidden_states.dtype)
+        except:
+            _ = 10
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        next_cache = tuple(itertools.chain.from_iterable(next_cache))
+        print(f"Token generated in {time() - t1} seconds")
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_cache,
+                    all_hidden_states,
+                    all_self_attns,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class EightLayerLayerSV(torch.nn.Module):
+    def __init__(self, layers):
+        super().__init__()
+        assert len(layers) == 8
+        self.layers = layers
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        pkv00,
+        pkv01,
+        pkv10,
+        pkv11,
+        pkv20,
+        pkv21,
+        pkv30,
+        pkv31,
+        pkv40,
+        pkv41,
+        pkv50,
+        pkv51,
+        pkv60,
+        pkv61,
+        pkv70,
+        pkv71,
+    ):
+        pkvs = [
+            (pkv00, pkv01),
+            (pkv10, pkv11),
+            (pkv20, pkv21),
+            (pkv30, pkv31),
+            (pkv40, pkv41),
+            (pkv50, pkv51),
+            (pkv60, pkv61),
+            (pkv70, pkv71),
+        ]
+        new_pkvs = []
+        for layer, pkv in zip(self.layers, pkvs):
+            outputs = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=(
+                    pkv[0],
+                    pkv[1],
+                ),
+                use_cache=True,
+            )
+
+            hidden_states = outputs[0]
+            new_pkvs.append(
+                (
+                    outputs[-1][0],
+                    outputs[-1][1],
+                )
+            )
+        (
+            (new_pkv00, new_pkv01),
+            (new_pkv10, new_pkv11),
+            (new_pkv20, new_pkv21),
+            (new_pkv30, new_pkv31),
+            (new_pkv40, new_pkv41),
+            (new_pkv50, new_pkv51),
+            (new_pkv60, new_pkv61),
+            (new_pkv70, new_pkv71),
+        ) = new_pkvs
+        return (
+            hidden_states,
+            new_pkv00,
+            new_pkv01,
+            new_pkv10,
+            new_pkv11,
+            new_pkv20,
+            new_pkv21,
+            new_pkv30,
+            new_pkv31,
+            new_pkv40,
+            new_pkv41,
+            new_pkv50,
+            new_pkv51,
+            new_pkv60,
+            new_pkv61,
+            new_pkv70,
+            new_pkv71,
+        )
+
+
+class EightLayerLayerFV(torch.nn.Module):
+    def __init__(self, layers):
+        super().__init__()
+        assert len(layers) == 8
+        self.layers = layers
+
+    def forward(self, hidden_states, attention_mask, position_ids):
+        new_pkvs = []
+        for layer in self.layers:
+            outputs = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=None,
+                use_cache=True,
+            )
+
+            hidden_states = outputs[0]
+            new_pkvs.append(
+                (
+                    outputs[-1][0],
+                    outputs[-1][1],
+                )
+            )
+        (
+            (new_pkv00, new_pkv01),
+            (new_pkv10, new_pkv11),
+            (new_pkv20, new_pkv21),
+            (new_pkv30, new_pkv31),
+            (new_pkv40, new_pkv41),
+            (new_pkv50, new_pkv51),
+            (new_pkv60, new_pkv61),
+            (new_pkv70, new_pkv71),
+        ) = new_pkvs
+        return (
+            hidden_states,
+            new_pkv00,
+            new_pkv01,
+            new_pkv10,
+            new_pkv11,
+            new_pkv20,
+            new_pkv21,
+            new_pkv30,
+            new_pkv31,
+            new_pkv40,
+            new_pkv41,
+            new_pkv50,
+            new_pkv51,
+            new_pkv60,
+            new_pkv61,
+            new_pkv70,
+            new_pkv71,
+        )
+
+
+class CompiledEightLayerLayerSV(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value,
+        output_attentions=False,
+        use_cache=True,
+    ):
+        hidden_states = hidden_states.detach()
+        attention_mask = attention_mask.detach()
+        position_ids = position_ids.detach()
+        (
+            (pkv00, pkv01),
+            (pkv10, pkv11),
+            (pkv20, pkv21),
+            (pkv30, pkv31),
+            (pkv40, pkv41),
+            (pkv50, pkv51),
+            (pkv60, pkv61),
+            (pkv70, pkv71),
+        ) = past_key_value
+        pkv00 = pkv00.detatch()
+        pkv01 = pkv01.detatch()
+        pkv10 = pkv10.detatch()
+        pkv11 = pkv11.detatch()
+        pkv20 = pkv20.detatch()
+        pkv21 = pkv21.detatch()
+        pkv30 = pkv30.detatch()
+        pkv31 = pkv31.detatch()
+        pkv40 = pkv40.detatch()
+        pkv41 = pkv41.detatch()
+        pkv50 = pkv50.detatch()
+        pkv51 = pkv51.detatch()
+        pkv60 = pkv60.detatch()
+        pkv61 = pkv61.detatch()
+        pkv70 = pkv70.detatch()
+        pkv71 = pkv71.detatch()
+
+        output = self.model(
+            "forward",
+            (
+                hidden_states,
+                attention_mask,
+                position_ids,
+                pkv00,
+                pkv01,
+                pkv10,
+                pkv11,
+                pkv20,
+                pkv21,
+                pkv30,
+                pkv31,
+                pkv40,
+                pkv41,
+                pkv50,
+                pkv51,
+                pkv60,
+                pkv61,
+                pkv70,
+                pkv71,
+            ),
+            send_to_host=False,
+        )
+        return (
+            output[0],
+            (output[1][0], output[1][1]),
+            (output[2][0], output[2][1]),
+            (output[3][0], output[3][1]),
+            (output[4][0], output[4][1]),
+            (output[5][0], output[5][1]),
+            (output[6][0], output[6][1]),
+            (output[7][0], output[7][1]),
+            (output[8][0], output[8][1]),
+        )
+
+
+def forward_compressed(
+    self,
+    input_ids: torch.LongTensor = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_values: Optional[List[torch.FloatTensor]] = None,
+    inputs_embeds: Optional[torch.FloatTensor] = None,
+    use_cache: Optional[bool] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+):
+    output_attentions = (
+        output_attentions
+        if output_attentions is not None
+        else self.config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states
+        if output_hidden_states is not None
+        else self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+    return_dict = (
+        return_dict if return_dict is not None else self.config.use_return_dict
+    )
+
+    # retrieve input_ids and inputs_embeds
+    if input_ids is not None and inputs_embeds is not None:
+        raise ValueError(
+            "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+        )
+    elif input_ids is not None:
+        batch_size, seq_length = input_ids.shape
+    elif inputs_embeds is not None:
+        batch_size, seq_length, _ = inputs_embeds.shape
+    else:
+        raise ValueError(
+            "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+        )
+
+    seq_length_with_past = seq_length
+    past_key_values_length = 0
+
+    if past_key_values is not None:
+        past_key_values_length = past_key_values[0][0].shape[2]
+        seq_length_with_past = seq_length_with_past + past_key_values_length
+
+    if position_ids is None:
+        device = (
+            input_ids.device if input_ids is not None else inputs_embeds.device
+        )
+        position_ids = torch.arange(
+            past_key_values_length,
+            seq_length + past_key_values_length,
+            dtype=torch.long,
+            device=device,
+        )
+        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+    else:
+        position_ids = position_ids.view(-1, seq_length).long()
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embed_tokens(input_ids)
+    # embed positions
+    if attention_mask is None:
+        attention_mask = torch.ones(
+            (batch_size, seq_length_with_past),
+            dtype=torch.bool,
+            device=inputs_embeds.device,
+        )
+    attention_mask = self._prepare_decoder_attention_mask(
+        attention_mask,
+        (batch_size, seq_length),
+        inputs_embeds,
+        past_key_values_length,
+    )
+
+    hidden_states = inputs_embeds
+
+    # decoder layers
+    all_hidden_states = () if output_hidden_states else None
+    all_self_attns = () if output_attentions else None
+    next_decoder_cache = () if use_cache else None
+
+    for idx, decoder_layer in enumerate(self.compressedlayers):
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        past_key_value = (
+            past_key_values[8 * idx : 8 * (idx + 1)]
+            if past_key_values is not None
+            else None
+        )
+
+        if self.gradient_checkpointing and self.training:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    # None for past_key_value
+                    return module(*inputs, output_attentions, None)
+
+                return custom_forward
+
+            layer_outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(decoder_layer),
+                hidden_states,
+                attention_mask,
+                position_ids,
+                None,
+            )
+        else:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        hidden_states = layer_outputs[0]
+
+        if use_cache:
+            next_decoder_cache += (
+                layer_outputs[2 if output_attentions else 1],
+            )
+
+        if output_attentions:
+            all_self_attns += (layer_outputs[1],)
+
+    hidden_states = self.norm(hidden_states)
+
+    # add hidden states from the last decoder layer
+    if output_hidden_states:
+        all_hidden_states += (hidden_states,)
+
+    next_cache = next_decoder_cache if use_cache else None
+    if not return_dict:
+        return tuple(
+            v
+            for v in [
+                hidden_states,
+                next_cache,
+                all_hidden_states,
+                all_self_attns,
+            ]
+            if v is not None
+        )
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=next_cache,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attns,
+    )
+
+
+class CompiledEightLayerLayer(torch.nn.Module):
+    def __init__(self, model):
+        super().__init__()
+        self.model = model
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        position_ids,
+        past_key_value=None,
+        output_attentions=False,
+        use_cache=True,
+    ):
+        t2 = time()
+        if past_key_value is None:
+            try:
+                hidden_states = np.asarray(hidden_states, hidden_states.dtype)
+            except:
+                pass
+            attention_mask = attention_mask.detach()
+            position_ids = position_ids.detach()
+            t1 = time()
+
+            output = self.model(
+                "first_vicuna_forward",
+                (hidden_states, attention_mask, position_ids),
+                send_to_host=False,
+            )
+            output2 = (
+                output[0],
+                (
+                    output[1],
+                    output[2],
+                ),
+                (
+                    output[3],
+                    output[4],
+                ),
+                (
+                    output[5],
+                    output[6],
+                ),
+                (
+                    output[7],
+                    output[8],
+                ),
+                (
+                    output[9],
+                    output[10],
+                ),
+                (
+                    output[11],
+                    output[12],
+                ),
+                (
+                    output[13],
+                    output[14],
+                ),
+                (
+                    output[15],
+                    output[16],
+                ),
+            )
+            return output2
+        else:
+            (
+                (pkv00, pkv01),
+                (pkv10, pkv11),
+                (pkv20, pkv21),
+                (pkv30, pkv31),
+                (pkv40, pkv41),
+                (pkv50, pkv51),
+                (pkv60, pkv61),
+                (pkv70, pkv71),
+            ) = past_key_value
+
+            try:
+                hidden_states = hidden_states.detach()
+                attention_mask = attention_mask.detach()
+                position_ids = position_ids.detach()
+                pkv00 = pkv00.detach()
+                pkv01 = pkv01.detach()
+                pkv10 = pkv10.detach()
+                pkv11 = pkv11.detach()
+                pkv20 = pkv20.detach()
+                pkv21 = pkv21.detach()
+                pkv30 = pkv30.detach()
+                pkv31 = pkv31.detach()
+                pkv40 = pkv40.detach()
+                pkv41 = pkv41.detach()
+                pkv50 = pkv50.detach()
+                pkv51 = pkv51.detach()
+                pkv60 = pkv60.detach()
+                pkv61 = pkv61.detach()
+                pkv70 = pkv70.detach()
+                pkv71 = pkv71.detach()
+            except:
+                x = 10
+
+            t1 = time()
+            if type(hidden_states) == iree.runtime.array_interop.DeviceArray:
+                hidden_states = np.array(hidden_states, hidden_states.dtype)
+                hidden_states = torch.tensor(hidden_states)
+                hidden_states = hidden_states.detach()
+
+            output = self.model(
+                "second_vicuna_forward",
+                (
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    pkv00,
+                    pkv01,
+                    pkv10,
+                    pkv11,
+                    pkv20,
+                    pkv21,
+                    pkv30,
+                    pkv31,
+                    pkv40,
+                    pkv41,
+                    pkv50,
+                    pkv51,
+                    pkv60,
+                    pkv61,
+                    pkv70,
+                    pkv71,
+                ),
+                send_to_host=False,
+            )
+            print(f"{time() - t1}")
+            del pkv00
+            del pkv01
+            del pkv10
+            del pkv11
+            del pkv20
+            del pkv21
+            del pkv30
+            del pkv31
+            del pkv40
+            del pkv41
+            del pkv50
+            del pkv51
+            del pkv60
+            del pkv61
+            del pkv70
+            del pkv71
+            output2 = (
+                output[0],
+                (
+                    output[1],
+                    output[2],
+                ),
+                (
+                    output[3],
+                    output[4],
+                ),
+                (
+                    output[5],
+                    output[6],
+                ),
+                (
+                    output[7],
+                    output[8],
+                ),
+                (
+                    output[9],
+                    output[10],
+                ),
+                (
+                    output[11],
+                    output[12],
+                ),
+                (
+                    output[13],
+                    output[14],
+                ),
+                (
+                    output[15],
+                    output[16],
+                ),
+            )
+            return output2

apps/language_models/src/model_wrappers/vicuna_model.py

@@ -12,11 +12,12 @@ class FirstVicuna(torch.nn.Module):
         precision="fp32",
         weight_group_size=128,
         model_name="vicuna",
+        hf_auth_token: str = None,
     ):
         super().__init__()
         kwargs = {"torch_dtype": torch.float32}
-        if model_name == "llama2":
-            kwargs["use_auth_token"] = "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"
+        if "llama2" in model_name:
+            kwargs["use_auth_token"] = hf_auth_token
         self.model = AutoModelForCausalLM.from_pretrained(
             model_path, low_cpu_mem_usage=True, **kwargs
         )
@@ -26,20 +27,13 @@ class FirstVicuna(torch.nn.Module):
             quantize_model(
                 get_model_impl(self.model).layers,
                 dtype=torch.float32,
-                weight_quant_type="asym",
                 weight_bit_width=weight_bit_width,
                 weight_param_method="stats",
                 weight_scale_precision="float",
+                weight_quant_type="asym",
                 weight_quant_granularity="per_group",
                 weight_group_size=weight_group_size,
                 quantize_weight_zero_point=False,
-                input_bit_width=None,
-                input_scale_type="float",
-                input_param_method="stats",
-                input_quant_type="asym",
-                input_quant_granularity="per_tensor",
-                quantize_input_zero_point=False,
-                seqlen=2048,
             )
             print("Weight quantization applied.")
 
@@ -61,11 +55,12 @@ class SecondVicuna(torch.nn.Module):
         precision="fp32",
         weight_group_size=128,
         model_name="vicuna",
+        hf_auth_token: str = None,
     ):
         super().__init__()
         kwargs = {"torch_dtype": torch.float32}
-        if model_name == "llama2":
-            kwargs["use_auth_token"] = "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"
+        if "llama2" in model_name:
+            kwargs["use_auth_token"] = hf_auth_token
         self.model = AutoModelForCausalLM.from_pretrained(
             model_path, low_cpu_mem_usage=True, **kwargs
         )
@@ -75,20 +70,13 @@ class SecondVicuna(torch.nn.Module):
             quantize_model(
                 get_model_impl(self.model).layers,
                 dtype=torch.float32,
-                weight_quant_type="asym",
                 weight_bit_width=weight_bit_width,
                 weight_param_method="stats",
                 weight_scale_precision="float",
+                weight_quant_type="asym",
                 weight_quant_granularity="per_group",
                 weight_group_size=weight_group_size,
                 quantize_weight_zero_point=False,
-                input_bit_width=None,
-                input_scale_type="float",
-                input_param_method="stats",
-                input_quant_type="asym",
-                input_quant_granularity="per_tensor",
-                quantize_input_zero_point=False,
-                seqlen=2048,
             )
             print("Weight quantization applied.")
 
@@ -313,12 +301,13 @@ class CombinedModel(torch.nn.Module):
         self.second_vicuna = SecondVicuna(second_vicuna_model_path)
 
     def forward(self, input_ids):
-        first_output = self.first_vicuna(input_ids=input_ids, use_cache=True)
-        logits = first_output[0]
-        pkv = first_output[1:]
-
-        token = torch.argmax(torch.tensor(logits)[:, -1, :], dim=1)
-        token = token.to(torch.int64).reshape([1, 1])
-        secondVicunaInput = (token,) + tuple(pkv)
-        second_output = self.second_vicuna(secondVicunaInput)
+        first_output = self.first_vicuna(input_ids=input_ids)
+        # generate second vicuna
+        compilation_input_ids = torch.zeros([1, 1], dtype=torch.int64)
+        pkv = tuple(
+            (torch.zeros([1, 32, 19, 128], dtype=torch.float32))
+            for _ in range(64)
+        )
+        secondVicunaCompileInput = (compilation_input_ids,) + pkv
+        second_output = self.second_vicuna(*secondVicunaCompileInput)
         return second_output

apps/language_models/src/model_wrappers/vicuna_sharded_model.py

@@ -66,7 +66,7 @@ class ShardedVicunaModel(torch.nn.Module):
     def __init__(self, model, layers, lmhead, embedding, norm):
         super().__init__()
         self.model = model
-        assert len(layers) == len(model.model.layers)
+        # assert len(layers) == len(model.model.layers)
         self.model.model.config.use_cache = True
         self.model.model.config.output_attentions = False
         self.layers = layers
@@ -132,7 +132,10 @@ class VicunaNormCompiled(torch.nn.Module):
         self.model = shark_module
 
     def forward(self, hidden_states):
-        hidden_states.detach()
+        try:
+            hidden_states.detach()
+        except:
+            pass
         output = self.model("forward", (hidden_states,))
         output = torch.tensor(output)
         return output

apps/language_models/src/pipelines/minigpt4_utils/blip_processors.py

@@ -0,0 +1,68 @@
+"""
+ Copyright (c) 2022, salesforce.com, inc.
+ All rights reserved.
+ SPDX-License-Identifier: BSD-3-Clause
+ For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
+"""
+from omegaconf import OmegaConf
+from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
+
+
+class BaseProcessor:
+    def __init__(self):
+        self.transform = lambda x: x
+        return
+
+    def __call__(self, item):
+        return self.transform(item)
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        return cls()
+
+    def build(self, **kwargs):
+        cfg = OmegaConf.create(kwargs)
+
+        return self.from_config(cfg)
+
+
+class BlipImageBaseProcessor(BaseProcessor):
+    def __init__(self, mean=None, std=None):
+        if mean is None:
+            mean = (0.48145466, 0.4578275, 0.40821073)
+        if std is None:
+            std = (0.26862954, 0.26130258, 0.27577711)
+
+        self.normalize = transforms.Normalize(mean, std)
+
+
+class Blip2ImageEvalProcessor(BlipImageBaseProcessor):
+    def __init__(self, image_size=224, mean=None, std=None):
+        super().__init__(mean=mean, std=std)
+
+        self.transform = transforms.Compose(
+            [
+                transforms.Resize(
+                    (image_size, image_size),
+                    interpolation=InterpolationMode.BICUBIC,
+                ),
+                transforms.ToTensor(),
+                self.normalize,
+            ]
+        )
+
+    def __call__(self, item):
+        return self.transform(item)
+
+    @classmethod
+    def from_config(cls, cfg=None):
+        if cfg is None:
+            cfg = OmegaConf.create()
+
+        image_size = cfg.get("image_size", 224)
+
+        mean = cfg.get("mean", None)
+        std = cfg.get("std", None)
+
+        return cls(image_size=image_size, mean=mean, std=std)

apps/language_models/src/pipelines/minigpt4_utils/configs/cc_sbu_align.yaml

@@ -0,0 +1,5 @@
+datasets:
+  cc_sbu_align:
+    data_type: images
+    build_info:
+      storage: /path/to/cc_sbu_align/

apps/language_models/src/pipelines/minigpt4_utils/configs/minigpt4.yaml

@@ -0,0 +1,33 @@
+model:
+  arch: mini_gpt4
+
+  # vit encoder
+  image_size: 224
+  drop_path_rate: 0
+  use_grad_checkpoint: False
+  vit_precision: "fp16"
+  freeze_vit: True
+  freeze_qformer: True
+
+  # Q-Former
+  num_query_token: 32
+
+  # Vicuna
+  llama_model: "lmsys/vicuna-7b-v1.3"
+
+  # generation configs
+  prompt: ""
+
+preprocess:
+    vis_processor:
+        train:
+          name: "blip2_image_train"
+          image_size: 224
+        eval:
+          name: "blip2_image_eval"
+          image_size: 224
+    text_processor:
+        train:
+          name: "blip_caption"
+        eval:
+          name: "blip_caption"

apps/language_models/src/pipelines/minigpt4_utils/configs/minigpt4_eval.yaml

@@ -0,0 +1,25 @@
+model:
+  arch: mini_gpt4
+  model_type: pretrain_vicuna
+  freeze_vit: True
+  freeze_qformer: True
+  max_txt_len: 160
+  end_sym: "###"
+  low_resource: False
+  prompt_path: "apps/language_models/src/pipelines/minigpt4_utils/prompts/alignment.txt"
+  prompt_template: '###Human: {} ###Assistant: '
+  ckpt: 'prerained_minigpt4_7b.pth'
+
+
+datasets:
+  cc_sbu_align:
+    vis_processor:
+      train:
+        name: "blip2_image_eval"
+        image_size: 224
+    text_processor:
+      train:
+        name: "blip_caption"
+
+run:
+  task: image_text_pretrain

apps/language_models/src/pipelines/minigpt4_utils/eva_vit.py

@@ -0,0 +1,629 @@
+# Based on EVA, BEIT, timm and DeiT code bases
+# https://github.com/baaivision/EVA
+# https://github.com/rwightman/pytorch-image-models/tree/master/timm
+# https://github.com/microsoft/unilm/tree/master/beit
+# https://github.com/facebookresearch/deit/
+# https://github.com/facebookresearch/dino
+# --------------------------------------------------------'
+import math
+import requests
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import drop_path, to_2tuple, trunc_normal_
+
+
+def _cfg(url="", **kwargs):
+    return {
+        "url": url,
+        "num_classes": 1000,
+        "input_size": (3, 224, 224),
+        "pool_size": None,
+        "crop_pct": 0.9,
+        "interpolation": "bicubic",
+        "mean": (0.5, 0.5, 0.5),
+        "std": (0.5, 0.5, 0.5),
+        **kwargs,
+    }
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+    def extra_repr(self) -> str:
+        return "p={}".format(self.drop_prob)
+
+
+class Mlp(nn.Module):
+    def __init__(
+        self,
+        in_features,
+        hidden_features=None,
+        out_features=None,
+        act_layer=nn.GELU,
+        drop=0.0,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        # x = self.drop(x)
+        # commit this for the orignal BERT implement
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads=8,
+        qkv_bias=False,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+        window_size=None,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        if attn_head_dim is not None:
+            head_dim = attn_head_dim
+        all_head_dim = head_dim * self.num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False)
+        if qkv_bias:
+            self.q_bias = nn.Parameter(torch.zeros(all_head_dim))
+            self.v_bias = nn.Parameter(torch.zeros(all_head_dim))
+        else:
+            self.q_bias = None
+            self.v_bias = None
+
+        if window_size:
+            self.window_size = window_size
+            self.num_relative_distance = (2 * window_size[0] - 1) * (
+                2 * window_size[1] - 1
+            ) + 3
+            self.relative_position_bias_table = nn.Parameter(
+                torch.zeros(self.num_relative_distance, num_heads)
+            )  # 2*Wh-1 * 2*Ww-1, nH
+            # cls to token & token 2 cls & cls to cls
+
+            # get pair-wise relative position index for each token inside the window
+            coords_h = torch.arange(window_size[0])
+            coords_w = torch.arange(window_size[1])
+            coords = torch.stack(
+                torch.meshgrid([coords_h, coords_w])
+            )  # 2, Wh, Ww
+            coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+            relative_coords = (
+                coords_flatten[:, :, None] - coords_flatten[:, None, :]
+            )  # 2, Wh*Ww, Wh*Ww
+            relative_coords = relative_coords.permute(
+                1, 2, 0
+            ).contiguous()  # Wh*Ww, Wh*Ww, 2
+            relative_coords[:, :, 0] += (
+                window_size[0] - 1
+            )  # shift to start from 0
+            relative_coords[:, :, 1] += window_size[1] - 1
+            relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+            relative_position_index = torch.zeros(
+                size=(window_size[0] * window_size[1] + 1,) * 2,
+                dtype=relative_coords.dtype,
+            )
+            relative_position_index[1:, 1:] = relative_coords.sum(
+                -1
+            )  # Wh*Ww, Wh*Ww
+            relative_position_index[0, 0:] = self.num_relative_distance - 3
+            relative_position_index[0:, 0] = self.num_relative_distance - 2
+            relative_position_index[0, 0] = self.num_relative_distance - 1
+
+            self.register_buffer(
+                "relative_position_index", relative_position_index
+            )
+        else:
+            self.window_size = None
+            self.relative_position_bias_table = None
+            self.relative_position_index = None
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(all_head_dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, rel_pos_bias=None):
+        B, N, C = x.shape
+        qkv_bias = None
+        if self.q_bias is not None:
+            qkv_bias = torch.cat(
+                (
+                    self.q_bias,
+                    torch.zeros_like(self.v_bias, requires_grad=False),
+                    self.v_bias,
+                )
+            )
+        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
+        qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
+        q, k, v = (
+            qkv[0],
+            qkv[1],
+            qkv[2],
+        )  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        if self.relative_position_bias_table is not None:
+            relative_position_bias = self.relative_position_bias_table[
+                self.relative_position_index.view(-1)
+            ].view(
+                self.window_size[0] * self.window_size[1] + 1,
+                self.window_size[0] * self.window_size[1] + 1,
+                -1,
+            )  # Wh*Ww,Wh*Ww,nH
+            relative_position_bias = relative_position_bias.permute(
+                2, 0, 1
+            ).contiguous()  # nH, Wh*Ww, Wh*Ww
+            attn = attn + relative_position_bias.unsqueeze(0)
+
+        if rel_pos_bias is not None:
+            attn = attn + rel_pos_bias
+
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class Block(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        mlp_ratio=4.0,
+        qkv_bias=False,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        init_values=None,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+        window_size=None,
+        attn_head_dim=None,
+    ):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = Attention(
+            dim,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+            window_size=window_size,
+            attn_head_dim=attn_head_dim,
+        )
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = (
+            DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        )
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim,
+            hidden_features=mlp_hidden_dim,
+            act_layer=act_layer,
+            drop=drop,
+        )
+
+        if init_values is not None and init_values > 0:
+            self.gamma_1 = nn.Parameter(
+                init_values * torch.ones((dim)), requires_grad=True
+            )
+            self.gamma_2 = nn.Parameter(
+                init_values * torch.ones((dim)), requires_grad=True
+            )
+        else:
+            self.gamma_1, self.gamma_2 = None, None
+
+    def forward(self, x, rel_pos_bias=None):
+        if self.gamma_1 is None:
+            x = x + self.drop_path(
+                self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)
+            )
+            x = x + self.drop_path(self.mlp(self.norm2(x)))
+        else:
+            x = x + self.drop_path(
+                self.gamma_1
+                * self.attn(self.norm1(x), rel_pos_bias=rel_pos_bias)
+            )
+            x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding"""
+
+    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
+        super().__init__()
+        img_size = to_2tuple(img_size)
+        patch_size = to_2tuple(patch_size)
+        num_patches = (img_size[1] // patch_size[1]) * (
+            img_size[0] // patch_size[0]
+        )
+        self.patch_shape = (
+            img_size[0] // patch_size[0],
+            img_size[1] // patch_size[1],
+        )
+        self.img_size = img_size
+        self.patch_size = patch_size
+        self.num_patches = num_patches
+
+        self.proj = nn.Conv2d(
+            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
+        )
+
+    def forward(self, x, **kwargs):
+        B, C, H, W = x.shape
+        # FIXME look at relaxing size constraints
+        assert (
+            H == self.img_size[0] and W == self.img_size[1]
+        ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
+        x = self.proj(x).flatten(2).transpose(1, 2)
+        return x
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, window_size, num_heads):
+        super().__init__()
+        self.window_size = window_size
+        self.num_relative_distance = (2 * window_size[0] - 1) * (
+            2 * window_size[1] - 1
+        ) + 3
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros(self.num_relative_distance, num_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+        # cls to token & token 2 cls & cls to cls
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(window_size[0])
+        coords_w = torch.arange(window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = (
+            coords_flatten[:, :, None] - coords_flatten[:, None, :]
+        )  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(
+            1, 2, 0
+        ).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * window_size[1] - 1
+        relative_position_index = torch.zeros(
+            size=(window_size[0] * window_size[1] + 1,) * 2,
+            dtype=relative_coords.dtype,
+        )
+        relative_position_index[1:, 1:] = relative_coords.sum(
+            -1
+        )  # Wh*Ww, Wh*Ww
+        relative_position_index[0, 0:] = self.num_relative_distance - 3
+        relative_position_index[0:, 0] = self.num_relative_distance - 2
+        relative_position_index[0, 0] = self.num_relative_distance - 1
+
+        self.register_buffer(
+            "relative_position_index", relative_position_index
+        )
+
+        # trunc_normal_(self.relative_position_bias_table, std=.02)
+
+    def forward(self):
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1] + 1,
+            self.window_size[0] * self.window_size[1] + 1,
+            -1,
+        )  # Wh*Ww,Wh*Ww,nH
+        return relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+
+
+class VisionTransformer(nn.Module):
+    """Vision Transformer with support for patch or hybrid CNN input stage"""
+
+    def __init__(
+        self,
+        img_size=224,
+        patch_size=16,
+        in_chans=3,
+        num_classes=1000,
+        embed_dim=768,
+        depth=12,
+        num_heads=12,
+        mlp_ratio=4.0,
+        qkv_bias=False,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.0,
+        norm_layer=nn.LayerNorm,
+        init_values=None,
+        use_abs_pos_emb=True,
+        use_rel_pos_bias=False,
+        use_shared_rel_pos_bias=False,
+        use_mean_pooling=True,
+        init_scale=0.001,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.image_size = img_size
+        self.num_classes = num_classes
+        self.num_features = (
+            self.embed_dim
+        ) = embed_dim  # num_features for consistency with other models
+
+        self.patch_embed = PatchEmbed(
+            img_size=img_size,
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+        )
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        if use_abs_pos_emb:
+            self.pos_embed = nn.Parameter(
+                torch.zeros(1, num_patches + 1, embed_dim)
+            )
+        else:
+            self.pos_embed = None
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        if use_shared_rel_pos_bias:
+            self.rel_pos_bias = RelativePositionBias(
+                window_size=self.patch_embed.patch_shape, num_heads=num_heads
+            )
+        else:
+            self.rel_pos_bias = None
+        self.use_checkpoint = use_checkpoint
+
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, depth)
+        ]  # stochastic depth decay rule
+        self.use_rel_pos_bias = use_rel_pos_bias
+        self.blocks = nn.ModuleList(
+            [
+                Block(
+                    dim=embed_dim,
+                    num_heads=num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop_rate,
+                    attn_drop=attn_drop_rate,
+                    drop_path=dpr[i],
+                    norm_layer=norm_layer,
+                    init_values=init_values,
+                    window_size=self.patch_embed.patch_shape
+                    if use_rel_pos_bias
+                    else None,
+                )
+                for i in range(depth)
+            ]
+        )
+        #         self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim)
+        #         self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None
+        #         self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        if self.pos_embed is not None:
+            trunc_normal_(self.pos_embed, std=0.02)
+        trunc_normal_(self.cls_token, std=0.02)
+        # trunc_normal_(self.mask_token, std=.02)
+        #         if isinstance(self.head, nn.Linear):
+        #             trunc_normal_(self.head.weight, std=.02)
+        self.apply(self._init_weights)
+        self.fix_init_weight()
+
+    #         if isinstance(self.head, nn.Linear):
+    #             self.head.weight.data.mul_(init_scale)
+    #             self.head.bias.data.mul_(init_scale)
+
+    def fix_init_weight(self):
+        def rescale(param, layer_id):
+            param.div_(math.sqrt(2.0 * layer_id))
+
+        for layer_id, layer in enumerate(self.blocks):
+            rescale(layer.attn.proj.weight.data, layer_id + 1)
+            rescale(layer.mlp.fc2.weight.data, layer_id + 1)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=0.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, num_classes, global_pool=""):
+        self.num_classes = num_classes
+        self.head = (
+            nn.Linear(self.embed_dim, num_classes)
+            if num_classes > 0
+            else nn.Identity()
+        )
+
+    def forward_features(self, x):
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        cls_tokens = self.cls_token.expand(
+            batch_size, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        rel_pos_bias = (
+            self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        )
+        for blk in self.blocks:
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, rel_pos_bias)
+            else:
+                x = blk(x, rel_pos_bias)
+        return x
+
+    #         x = self.norm(x)
+
+    #         if self.fc_norm is not None:
+    #             t = x[:, 1:, :]
+    #             return self.fc_norm(t.mean(1))
+    #         else:
+    #             return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        #         x = self.head(x)
+        return x
+
+    def get_intermediate_layers(self, x):
+        x = self.patch_embed(x)
+        batch_size, seq_len, _ = x.size()
+
+        cls_tokens = self.cls_token.expand(
+            batch_size, -1, -1
+        )  # stole cls_tokens impl from Phil Wang, thanks
+        x = torch.cat((cls_tokens, x), dim=1)
+        if self.pos_embed is not None:
+            x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        features = []
+        rel_pos_bias = (
+            self.rel_pos_bias() if self.rel_pos_bias is not None else None
+        )
+        for blk in self.blocks:
+            x = blk(x, rel_pos_bias)
+            features.append(x)
+
+        return features
+
+
+def interpolate_pos_embed(model, checkpoint_model):
+    if "pos_embed" in checkpoint_model:
+        pos_embed_checkpoint = checkpoint_model["pos_embed"].float()
+        embedding_size = pos_embed_checkpoint.shape[-1]
+        num_patches = model.patch_embed.num_patches
+        num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+        # height (== width) for the checkpoint position embedding
+        orig_size = int(
+            (pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5
+        )
+        # height (== width) for the new position embedding
+        new_size = int(num_patches**0.5)
+        # class_token and dist_token are kept unchanged
+        if orig_size != new_size:
+            print(
+                "Position interpolate from %dx%d to %dx%d"
+                % (orig_size, orig_size, new_size, new_size)
+            )
+            extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+            # only the position tokens are interpolated
+            pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+            pos_tokens = pos_tokens.reshape(
+                -1, orig_size, orig_size, embedding_size
+            ).permute(0, 3, 1, 2)
+            pos_tokens = torch.nn.functional.interpolate(
+                pos_tokens,
+                size=(new_size, new_size),
+                mode="bicubic",
+                align_corners=False,
+            )
+            pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+            new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+            checkpoint_model["pos_embed"] = new_pos_embed
+
+
+def convert_weights_to_fp16(model: nn.Module):
+    """Convert applicable model parameters to fp16"""
+
+    def _convert_weights_to_fp16(l):
+        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
+            # l.weight.data = l.weight.data.half()
+            l.weight.data = l.weight.data
+            if l.bias is not None:
+                # l.bias.data = l.bias.data.half()
+                l.bias.data = l.bias.data
+
+    #         if isinstance(l, (nn.MultiheadAttention, Attention)):
+    #             for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
+    #                 tensor = getattr(l, attr)
+    #                 if tensor is not None:
+    #                     tensor.data = tensor.data.half()
+
+    model.apply(_convert_weights_to_fp16)
+
+
+def create_eva_vit_g(
+    img_size=224, drop_path_rate=0.4, use_checkpoint=False, precision="fp16"
+):
+    model = VisionTransformer(
+        img_size=img_size,
+        patch_size=14,
+        use_mean_pooling=False,
+        embed_dim=1408,
+        depth=39,
+        num_heads=1408 // 88,
+        mlp_ratio=4.3637,
+        qkv_bias=True,
+        drop_path_rate=drop_path_rate,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6),
+        use_checkpoint=use_checkpoint,
+    )
+    url = "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/eva_vit_g.pth"
+
+    local_filename = "eva_vit_g.pth"
+    response = requests.get(url)
+    if response.status_code == 200:
+        with open(local_filename, "wb") as f:
+            f.write(response.content)
+        print("File downloaded successfully.")
+    state_dict = torch.load(local_filename, map_location="cpu")
+    interpolate_pos_embed(model, state_dict)
+
+    incompatible_keys = model.load_state_dict(state_dict, strict=False)
+
+    if precision == "fp16":
+        #         model.to("cuda")
+        convert_weights_to_fp16(model)
+    return model

apps/language_models/src/pipelines/minigpt4_utils/prompts/alignment.txt

@@ -0,0 +1,4 @@
+<Img><ImageHere></Img> Describe this image in detail.
+<Img><ImageHere></Img> Take a look at this image and describe what you notice.
+<Img><ImageHere></Img> Please provide a detailed description of the picture.
+<Img><ImageHere></Img> Could you describe the contents of this image for me?

apps/language_models/utils.py

@@ -3,6 +3,7 @@ from torch.fx.experimental.proxy_tensor import make_fx
 from torch._decomp import get_decompositions
 from typing import List
 from pathlib import Path
+from shark.shark_downloader import download_public_file
 
 
 # expects a Path / str as arg
@@ -17,9 +18,23 @@ def get_vmfb_from_path(vmfb_path, device, mlir_dialect):
         return None
 
     print("Loading vmfb from: ", vmfb_path)
+    print("Device from get_vmfb_from_path - ", device)
     shark_module = SharkInference(
         None, device=device, mlir_dialect=mlir_dialect
     )
     shark_module.load_module(vmfb_path)
     print("Successfully loaded vmfb")
     return shark_module
+
+
+def get_vmfb_from_config(
+    shark_container, model, precision, device, vmfb_path, padding=None
+):
+    vmfb_url = (
+        f"gs://shark_tank/{shark_container}/{model}_{precision}_{device}"
+    )
+    if padding:
+        vmfb_url = vmfb_url + f"_{padding}"
+    vmfb_url = vmfb_url + ".vmfb"
+    download_public_file(vmfb_url, vmfb_path.absolute(), single_file=True)
+    return get_vmfb_from_path(vmfb_path, device, "tm_tensor")

apps/stable_diffusion/shark_studio_imports.py

@@ -7,7 +7,11 @@ import sys
 sys.setrecursionlimit(sys.getrecursionlimit() * 5)
 
 # python path for pyinstaller
-pathex = [".", "./apps/language_models/langchain"]
+pathex = [
+    ".",
+    "./apps/language_models/langchain",
+    "./apps/language_models/src/pipelines/minigpt4_utils",
+]
 
 # datafiles for pyinstaller
 datas = []
@@ -25,6 +29,8 @@ datas += copy_metadata("omegaconf")
 datas += copy_metadata("safetensors")
 datas += copy_metadata("Pillow")
 datas += copy_metadata("sentencepiece")
+datas += copy_metadata("pyyaml")
+datas += copy_metadata("huggingface-hub")
 datas += collect_data_files("tokenizers")
 datas += collect_data_files("tiktoken")
 datas += collect_data_files("accelerate")
@@ -37,13 +43,15 @@ datas += collect_data_files("gradio")
 datas += collect_data_files("gradio_client")
 datas += collect_data_files("iree")
 datas += collect_data_files("google_cloud_storage")
-datas += collect_data_files("shark")
+datas += collect_data_files("shark", include_py_files=True)
+datas += collect_data_files("timm", include_py_files=True)
 datas += collect_data_files("tkinter")
 datas += collect_data_files("webview")
 datas += collect_data_files("sentencepiece")
 datas += collect_data_files("jsonschema")
 datas += collect_data_files("jsonschema_specifications")
 datas += collect_data_files("cpuinfo")
+datas += collect_data_files("langchain")
 datas += [
     ("src/utils/resources/prompts.json", "resources"),
     ("src/utils/resources/model_db.json", "resources"),
@@ -51,6 +59,14 @@ datas += [
     ("src/utils/resources/base_model.json", "resources"),
     ("web/ui/css/*", "ui/css"),
     ("web/ui/logos/*", "logos"),
+    (
+        "../language_models/src/pipelines/minigpt4_utils/configs/*",
+        "minigpt4_utils/configs",
+    ),
+    (
+        "../language_models/src/pipelines/minigpt4_utils/prompts/*",
+        "minigpt4_utils/prompts",
+    ),
 ]
 
 
@@ -61,3 +77,4 @@ hiddenimports += [
     x for x in collect_submodules("transformers") if "tests" not in x
 ]
 hiddenimports += [x for x in collect_submodules("iree") if "tests" not in x]
+hiddenimports += ["iree._runtime", "iree._runtime_libs"]

apps/stable_diffusion/src/utils/__init__.py

@@ -28,6 +28,7 @@ from apps.stable_diffusion.src.utils.utils import (
     fetch_and_update_base_model_id,
     get_path_to_diffusers_checkpoint,
     sanitize_seed,
+    parse_seed_input,
     batch_seeds,
     get_path_stem,
     get_extended_name,

apps/stable_diffusion/src/utils/profiler.py

@@ -3,7 +3,9 @@ from apps.stable_diffusion.src.utils.stable_args import args
 
 # Helper function to profile the vulkan device.
 def start_profiling(file_path="foo.rdc", profiling_mode="queue"):
-    if args.vulkan_debug_utils and "vulkan" in args.device:
+    from shark.parser import shark_args
+
+    if shark_args.vulkan_debug_utils and "vulkan" in args.device:
         import iree
 
         print(f"Profiling and saving to {file_path}.")

apps/stable_diffusion/src/utils/sd_annotation.py

@@ -109,7 +109,7 @@ def load_lower_configs(base_model_id=None):
             spec = spec.split("-")[0]
 
     if args.annotation_model == "vae":
-        if not spec or spec in ["rdna3", "sm_80"]:
+        if not spec or spec in ["sm_80"]:
             config_name = (
                 f"{args.annotation_model}_{args.precision}_{device}.json"
             )
@@ -281,9 +281,13 @@ def sd_model_annotation(mlir_model, model_name, base_model_id=None):
         if "rdna2" not in args.iree_vulkan_target_triple.split("-")[0]:
             use_winograd = True
             winograd_config_dir = load_winograd_configs()
-            tuned_model = annotate_with_winograd(
+            winograd_model = annotate_with_winograd(
                 mlir_model, winograd_config_dir, model_name
             )
+            lowering_config_dir = load_lower_configs(base_model_id)
+            tuned_model = annotate_with_lower_configs(
+                winograd_model, lowering_config_dir, model_name, use_winograd
+            )
         else:
             tuned_model = mlir_model
     else:

apps/stable_diffusion/src/utils/stable_args.py

@@ -66,9 +66,9 @@ p.add_argument(
 
 p.add_argument(
     "--seed",
-    type=int,
+    type=str,
     default=-1,
-    help="The seed to use. -1 for a random one.",
+    help="The seed or list of seeds to use. -1 for a random one.",
 )
 
 p.add_argument(
@@ -400,6 +400,13 @@ p.add_argument(
     help="Load and unload models for low VRAM.",
 )
 
+p.add_argument(
+    "--hf_auth_token",
+    type=str,
+    default=None,
+    help="Specify your own huggingface authentication tokens for models like Llama2.",
+)
+
 ##############################################################################
 # IREE - Vulkan supported flags
 ##############################################################################
@@ -418,27 +425,6 @@ p.add_argument(
     help="Specify target triple for metal.",
 )
 
-p.add_argument(
-    "--vulkan_debug_utils",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Profiles vulkan device and collects the .rdc info.",
-)
-
-p.add_argument(
-    "--vulkan_large_heap_block_size",
-    default="2073741824",
-    help="Flag for setting VMA preferredLargeHeapBlockSize for "
-    "vulkan device, default is 4G.",
-)
-
-p.add_argument(
-    "--vulkan_validation_layers",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="Flag for disabling vulkan validation layers when benchmarking.",
-)
-
 ##############################################################################
 # Misc. Debug and Optimization flags
 ##############################################################################
@@ -533,6 +519,12 @@ p.add_argument(
     "in shark importer. Does nothing if import_mlir is false (the default).",
 )
 
+p.add_argument(
+    "--iree_constant_folding",
+    default=True,
+    action=argparse.BooleanOptionalAction,
+    help="Controls constant folding in iree-compile for all SD models.",
+)
 
 ##############################################################################
 # Web UI flags

apps/stable_diffusion/src/utils/utils.py

@@ -22,6 +22,7 @@ from shark.shark_importer import import_with_fx
 from shark.iree_utils.vulkan_utils import (
     set_iree_vulkan_runtime_flags,
     get_vulkan_target_triple,
+    get_iree_vulkan_runtime_flags,
 )
 from shark.iree_utils.metal_utils import get_metal_target_triple
 from shark.iree_utils.gpu_utils import get_cuda_sm_cc
@@ -183,10 +184,7 @@ def compile_through_fx(
 
 
 def set_iree_runtime_flags():
-    vulkan_runtime_flags = [
-        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
-        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
-    ]
+    vulkan_runtime_flags = get_iree_vulkan_runtime_flags()
     if args.enable_rgp:
         vulkan_runtime_flags += [
             f"--enable_rgp=true",
@@ -461,7 +459,12 @@ def get_available_devices():
                 device_name = (
                     cpu_name if device["name"] == "default" else device["name"]
                 )
-                device_list.append(f"{device_name} => {driver_name}://{i}")
+                if "local" in driver_name:
+                    device_list.append(
+                        f"{device_name} => {driver_name.replace('local', 'cpu')}"
+                    )
+                else:
+                    device_list.append(f"{device_name} => {driver_name}://{i}")
         return device_list
 
     set_iree_runtime_flags()
@@ -497,6 +500,12 @@ def get_opt_flags(model, precision="fp16"):
             f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
         )
 
+    if args.iree_constant_folding == False:
+        iree_flags.append("--iree-opt-const-expr-hoisting=False")
+        iree_flags.append(
+            "--iree-codegen-linalg-max-constant-fold-elements=9223372036854775807"
+        )
+
     # Disable bindings fusion to work with moltenVK.
     if sys.platform == "darwin":
         iree_flags.append("-iree-stream-fuse-binding=false")
@@ -727,7 +736,8 @@ def fetch_and_update_base_model_id(model_to_run, base_model=""):
 
 # Generate and return a new seed if the provided one is not in the
 # supported range (including -1)
-def sanitize_seed(seed):
+def sanitize_seed(seed: int | str):
+    seed = int(seed)
     uint32_info = np.iinfo(np.uint32)
     uint32_min, uint32_max = uint32_info.min, uint32_info.max
     if seed < uint32_min or seed >= uint32_max:
@@ -735,20 +745,48 @@ def sanitize_seed(seed):
     return seed
 
 
-# Generate a set of seeds, using as the first seed of the set,
-# optionally using it as the rng seed for subsequent seeds in the set
-def batch_seeds(seed, batch_count, repeatable=False):
-    # use the passed seed as the initial seed of the batch
-    seeds = [sanitize_seed(seed)]
+# take a seed expression in an input format and convert it to
+# a list of integers, where possible
+def parse_seed_input(seed_input: str | list | int):
+    if isinstance(seed_input, str):
+        try:
+            seed_input = json.loads(seed_input)
+        except (ValueError, TypeError):
+            seed_input = None
+
+    if isinstance(seed_input, int):
+        return [seed_input]
+
+    if isinstance(seed_input, list) and all(
+        type(seed) is int for seed in seed_input
+    ):
+        return seed_input
+
+    raise TypeError(
+        "Seed input must be an integer or an array of integers in JSON format"
+    )
+
+
+# Generate a set of seeds from an input expression for batch_count batches,
+# optionally using that input as the rng seed for any randomly generated seeds.
+def batch_seeds(
+    seed_input: str | list | int, batch_count: int, repeatable=False
+):
+    # turn the input into a list if possible
+    seeds = parse_seed_input(seed_input)
+
+    # slice or pad the list to be of batch_count length
+    seeds = seeds[:batch_count] + [-1] * (batch_count - len(seeds))
 
     if repeatable:
-        # use the initial seed as the rng generator seed
+        # set seed for the rng based on what we have so far
         saved_random_state = random_getstate()
-        seed_random(seed)
+        if all(seed < 0 for seed in seeds):
+            seeds[0] = sanitize_seed(seeds[0])
+        seed_random(str(seeds))
 
-    # generate the additional seeds
-    for i in range(1, batch_count):
-        seeds.append(sanitize_seed(-1))
+    # generate any seeds that are unspecified
+    seeds = [sanitize_seed(seed) for seed in seeds]
 
     if repeatable:
         # reset the rng back to normal

apps/stable_diffusion/web/index.py

@@ -37,7 +37,7 @@ def launch_app(address):
         height=height,
         text_select=True,
     )
-    webview.start(private_mode=False)
+    webview.start(private_mode=False, storage_path=os.getcwd())
 
 
 if __name__ == "__main__":
@@ -115,7 +115,8 @@ if __name__ == "__main__":
         txt2img_sendto_inpaint,
         txt2img_sendto_outpaint,
         txt2img_sendto_upscaler,
-        h2ogpt_web,
+        # h2ogpt_upload,
+        # h2ogpt_web,
         img2img_web,
         img2img_custom_model,
         img2img_hf_model_id,
@@ -154,6 +155,7 @@ if __name__ == "__main__":
         upscaler_sendto_outpaint,
         lora_train_web,
         model_web,
+        model_config_web,
         hf_models,
         modelmanager_sendto_txt2img,
         modelmanager_sendto_img2img,
@@ -161,6 +163,7 @@ if __name__ == "__main__":
         modelmanager_sendto_outpaint,
         modelmanager_sendto_upscaler,
         stablelm_chat,
+        minigpt4_web,
         outputgallery_web,
         outputgallery_tab_select,
         outputgallery_watch,
@@ -210,6 +213,15 @@ if __name__ == "__main__":
         css=dark_theme, analytics_enabled=False, title="Stable Diffusion"
     ) as sd_web:
         with gr.Tabs() as tabs:
+            # NOTE: If adding, removing, or re-ordering tabs, make sure that they
+            # have a unique id that doesn't clash with any of the other tabs,
+            # and that the order in the code here is the order they should
+            # appear in the ui, as the id value doesn't determine the order.
+
+            # Where possible, avoid changing the id of any tab that is the
+            # destination of one of the 'send to' buttons. If you do have to change
+            # that id, make sure you update the relevant register_button_click calls
+            # further down with the new id.
             with gr.TabItem(label="Text-to-Image", id=0):
                 txt2img_web.render()
             with gr.TabItem(label="Image-to-Image", id=1):
@@ -220,14 +232,8 @@ if __name__ == "__main__":
                 outpaint_web.render()
             with gr.TabItem(label="Upscaler", id=4):
                 upscaler_web.render()
-            with gr.TabItem(label="Model Manager", id=5):
-                model_web.render()
-            with gr.TabItem(label="Chat Bot(Experimental)", id=6):
-                stablelm_chat.render()
-            with gr.TabItem(label="LoRA Training(Experimental)", id=7):
-                lora_train_web.render()
             if args.output_gallery:
-                with gr.TabItem(label="Output Gallery", id=8) as og_tab:
+                with gr.TabItem(label="Output Gallery", id=5) as og_tab:
                     outputgallery_web.render()
 
                 # extra output gallery configuration
@@ -241,8 +247,22 @@ if __name__ == "__main__":
                         upscaler_status,
                     ]
                 )
-            with gr.TabItem(label="DocuChat(Experimental)", id=9):
-                h2ogpt_web.render()
+            with gr.TabItem(label="Model Manager", id=6):
+                model_web.render()
+            with gr.TabItem(label="LoRA Training (Experimental)", id=7):
+                lora_train_web.render()
+            with gr.TabItem(label="Chat Bot (Experimental)", id=8):
+                stablelm_chat.render()
+            with gr.TabItem(
+                label="Generate Sharding Config (Experimental)", id=9
+            ):
+                model_config_web.render()
+            with gr.TabItem(label="MultiModal (Experimental)", id=10):
+                minigpt4_web.render()
+            # with gr.TabItem(label="DocuChat Upload", id=11):
+            #     h2ogpt_upload.render()
+            # with gr.TabItem(label="DocuChat(Experimental)", id=12):
+            #     h2ogpt_web.render()
 
         # send to buttons
         register_button_click(

apps/stable_diffusion/web/ui/__init__.py

@@ -78,7 +78,8 @@ from apps.stable_diffusion.web.ui.stablelm_ui import (
     stablelm_chat,
     llm_chat_api,
 )
-from apps.stable_diffusion.web.ui.h2ogpt import h2ogpt_web
+from apps.stable_diffusion.web.ui.generate_config import model_config_web
+from apps.stable_diffusion.web.ui.minigpt4_ui import minigpt4_web
 from apps.stable_diffusion.web.ui.outputgallery_ui import (
     outputgallery_web,
     outputgallery_tab_select,

apps/stable_diffusion/web/ui/css/sd_dark_theme.css

@@ -117,16 +117,12 @@ body {
     padding: 0 var(--size-4) !important;
 }
 
-.container {
-    background-color: black !important;
-    padding-top: var(--size-5) !important;
-}
-
 #ui_title {
     padding: var(--size-2) 0 0 var(--size-1);
 }
 
 #top_logo {
+    color: transparent;
     background-color: transparent;
     border-radius: 0 !important;
     border: 0;

apps/stable_diffusion/web/ui/generate_config.py

@@ -0,0 +1,41 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer
+from apps.language_models.src.model_wrappers.vicuna_model import CombinedModel
+from shark.shark_generate_model_config import GenerateConfigFile
+
+
+def get_model_config():
+    hf_model_path = "TheBloke/vicuna-7B-1.1-HF"
+    tokenizer = AutoTokenizer.from_pretrained(hf_model_path, use_fast=False)
+    compilation_prompt = "".join(["0" for _ in range(17)])
+    compilation_input_ids = tokenizer(
+        compilation_prompt,
+        return_tensors="pt",
+    ).input_ids
+    compilation_input_ids = torch.tensor(compilation_input_ids).reshape(
+        [1, 19]
+    )
+    firstVicunaCompileInput = (compilation_input_ids,)
+
+    model = CombinedModel()
+    c = GenerateConfigFile(model, 1, ["gpu_id"], firstVicunaCompileInput)
+    return c.split_into_layers()
+
+
+with gr.Blocks() as model_config_web:
+    with gr.Row():
+        hf_models = gr.Dropdown(
+            label="Model List",
+            choices=["Vicuna"],
+            value="Vicuna",
+            visible=True,
+        )
+        get_model_config_btn = gr.Button(value="Get Model Config")
+    json_view = gr.JSON()
+
+    get_model_config_btn.click(
+        fn=get_model_config,
+        inputs=[],
+        outputs=[json_view],
+    )

apps/stable_diffusion/web/ui/h2ogpt.py

@@ -12,6 +12,10 @@ from apps.language_models.langchain.enums import (
     LangChainAction,
 )
 import apps.language_models.langchain.gen as gen
+from gpt_langchain import (
+    path_to_docs,
+    create_or_update_db,
+)
 from apps.stable_diffusion.src import args
 
 
@@ -21,129 +25,148 @@ def user(message, history):
 
 
 sharkModel = 0
-sharded_model = 0
 h2ogpt_model = 0
 
-past_key_values = None
-
-model_map = {
-    "codegen": "Salesforce/codegen25-7b-multi",
-    "vicuna1p3": "lmsys/vicuna-7b-v1.3",
-    "vicuna": "TheBloke/vicuna-7B-1.1-HF",
-    "StableLM": "stabilityai/stablelm-tuned-alpha-3b",
-}
 
 # NOTE: Each `model_name` should have its own start message
-start_message = {
-    "StableLM": (
-        "<|SYSTEM|># StableLM Tuned (Alpha version)"
-        "\n- StableLM is a helpful and harmless open-source AI language model "
-        "developed by StabilityAI."
-        "\n- StableLM is excited to be able to help the user, but will refuse "
-        "to do anything that could be considered harmful to the user."
-        "\n- StableLM is more than just an information source, StableLM is also "
-        "able to write poetry, short stories, and make jokes."
-        "\n- StableLM will refuse to participate in anything that "
-        "could harm a human."
-    ),
-    "vicuna": (
-        "A chat between a curious user and an artificial intelligence assistant. "
-        "The assistant gives helpful, detailed, and polite answers to the user's "
-        "questions.\n"
-    ),
-    "vicuna1p3": (
-        "A chat between a curious user and an artificial intelligence assistant. "
-        "The assistant gives helpful, detailed, and polite answers to the user's "
-        "questions.\n"
-    ),
-    "codegen": "",
-}
+start_message = """
+    SHARK DocuChat
+    Chat with an AI, contextualized with provided files.
+"""
 
 
-def create_prompt(model_name, history):
-    system_message = start_message[model_name]
+def create_prompt(history):
+    system_message = start_message
+    for item in history:
+        print("His item: ", item)
 
-    if model_name in ["StableLM", "vicuna", "vicuna1p3"]:
-        conversation = "".join(
-            [
-                "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
-                for item in history
-            ]
-        )
-    else:
-        conversation = "".join(
-            ["".join([item[0], item[1]]) for item in history]
-        )
+    conversation = "<|endoftext|>".join(
+        [
+            "<|endoftext|><|answer|>".join([item[0], item[1]])
+            for item in history
+        ]
+    )
 
     msg = system_message + conversation
     msg = msg.strip()
     return msg
 
 
-def chat(curr_system_message, history, model, device, precision):
+def chat(curr_system_message, history, device, precision):
     args.run_docuchat_web = True
-    global sharded_model
-    global past_key_values
     global h2ogpt_model
+    global sharkModel
+    global h2ogpt_tokenizer
+    global model_state
+    global langchain
+    global userpath_selector
+    from apps.language_models.langchain.h2oai_pipeline import generate_token
 
-    model_name, model_path = list(map(str.strip, model.split("=>")))
-    print(f"In chat for {model_name}")
+    if h2ogpt_model == 0:
+        if "cuda" in device:
+            shark_device = "cuda"
+        elif "sync" in device:
+            shark_device = "cpu"
+        elif "task" in device:
+            shark_device = "cpu"
+        elif "vulkan" in device:
+            shark_device = "vulkan"
+        else:
+            print("unrecognized device")
 
-    # if h2ogpt_model == 0:
-    #     if "cuda" in device:
-    #         device = "cuda"
-    #     elif "sync" in device:
-    #         device = "cpu-sync"
-    #     elif "task" in device:
-    #         device = "cpu-task"
-    #     elif "vulkan" in device:
-    #         device = "vulkan"
-    #     else:
-    #         print("unrecognized device")
+        device = "cpu" if shark_device == "cpu" else "cuda"
 
-    #     max_toks = 128 if model_name == "codegen" else 512
-    #     h2ogpt_model = UnshardedVicuna(
-    #         model_name,
-    #         hf_model_path=model_path,
-    #         device=device,
-    #         precision=precision,
-    #         max_num_tokens=max_toks,
-    #     )
-    # prompt = create_prompt(model_name, history)
-    # print("prompt = ", prompt)
+        args.device = shark_device
+        args.precision = precision
 
-    # for partial_text in h2ogpt_model.generate(prompt):
-    #     history[-1][1] = partial_text
-    #     yield history
-    output = gen.evaluate(
-        None,  # model_state
-        None,  # my_db_state
-        None,  # instruction
-        None,  # iinput
-        history,  # context
-        False,  # stream_output
-        None,  # prompt_type
-        None,  # prompt_dict
-        None,  # temperature
-        None,  # top_p
-        None,  # top_k
-        None,  # num_beams
-        None,  # max_new_tokens
-        None,  # min_new_tokens
-        None,  # early_stopping
-        None,  # max_time
-        None,  # repetition_penalty
-        None,  # num_return_sequences
-        False,  # do_sample
-        False,  # chat
-        None,  # instruction_nochat
-        curr_system_message,  # iinput_nochat
-        "Disabled",  # langchain_mode
-        LangChainAction.QUERY.value,  # langchain_action
-        3,  # top_k_docs
-        True,  # chunk
-        512,  # chunk_size
-        [DocumentChoices.All_Relevant.name],  # document_choice
+        from apps.language_models.langchain.gen import Langchain
+
+        langchain = Langchain(device, precision)
+        h2ogpt_model, h2ogpt_tokenizer, _ = langchain.get_model(
+            load_4bit=True
+            if device == "cuda"
+            else False,  # load model in 4bit if device is cuda to save memory
+            load_gptq="",
+            use_safetensors=False,
+            infer_devices=True,
+            device=device,
+            base_model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            inference_server="",
+            tokenizer_base_model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            lora_weights="",
+            gpu_id=0,
+            reward_type=None,
+            local_files_only=False,
+            resume_download=True,
+            use_auth_token=False,
+            trust_remote_code=True,
+            offload_folder=None,
+            compile_model=False,
+            verbose=False,
+        )
+        model_state = dict(
+            model=h2ogpt_model,
+            tokenizer=h2ogpt_tokenizer,
+            device=device,
+            base_model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            tokenizer_base_model="h2oai/h2ogpt-gm-oasst1-en-2048-falcon-7b-v3",
+            lora_weights="",
+            inference_server="",
+            prompt_type=None,
+            prompt_dict=None,
+        )
+        from apps.language_models.langchain.h2oai_pipeline import (
+            H2OGPTSHARKModel,
+        )
+
+        sharkModel = H2OGPTSHARKModel()
+
+    prompt = create_prompt(history)
+    output_dict = langchain.evaluate(
+        model_state=model_state,
+        my_db_state=None,
+        instruction=prompt,
+        iinput="",
+        context="",
+        stream_output=True,
+        prompt_type="prompt_answer",
+        prompt_dict={
+            "promptA": "",
+            "promptB": "",
+            "PreInstruct": "<|prompt|>",
+            "PreInput": None,
+            "PreResponse": "<|answer|>",
+            "terminate_response": [
+                "<|prompt|>",
+                "<|answer|>",
+                "<|endoftext|>",
+            ],
+            "chat_sep": "<|endoftext|>",
+            "chat_turn_sep": "<|endoftext|>",
+            "humanstr": "<|prompt|>",
+            "botstr": "<|answer|>",
+            "generates_leading_space": False,
+        },
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=1,
+        max_new_tokens=256,
+        min_new_tokens=0,
+        early_stopping=False,
+        max_time=180,
+        repetition_penalty=1.07,
+        num_return_sequences=1,
+        do_sample=False,
+        chat=True,
+        instruction_nochat=prompt,
+        iinput_nochat="",
+        langchain_mode="UserData",
+        langchain_action=LangChainAction.QUERY.value,
+        top_k_docs=3,
+        chunk=True,
+        chunk_size=512,
+        document_choice=[DocumentChoices.All_Relevant.name],
         concurrency_count=1,
         memory_restriction_level=2,
         raise_generate_gpu_exceptions=False,
@@ -154,24 +177,28 @@ def chat(curr_system_message, history, model, device, precision):
         db_type="chroma",
         n_jobs=-1,
         first_para=False,
+        max_max_time=60 * 2,
+        model_state0=model_state,
+        model_lock=True,
+        user_path=userpath_selector.value,
     )
+
+    output = generate_token(sharkModel, **output_dict)
     for partial_text in output:
         history[-1][1] = partial_text
         yield history
-
     return history
 
 
-with gr.Blocks(title="H2OGPT") as h2ogpt_web:
+userpath_selector = gr.Textbox(
+    label="Document Directory",
+    value=str(os.path.abspath("apps/language_models/langchain/user_path/")),
+    interactive=True,
+    container=True,
+)
+
+with gr.Blocks(title="DocuChat") as h2ogpt_web:
     with gr.Row():
-        model_choices = list(
-            map(lambda x: f"{x[0]: <10} => {x[1]}", model_map.items())
-        )
-        model = gr.Dropdown(
-            label="Select Model",
-            value=model_choices[0],
-            choices=model_choices,
-        )
         supported_devices = available_devices
         enabled = len(supported_devices) > 0
         # show cpu-task device first in list for chatbot
@@ -220,7 +247,7 @@ with gr.Blocks(title="H2OGPT") as h2ogpt_web:
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model, device, precision],
+        inputs=[system_msg, chatbot, device, precision],
         outputs=[chatbot],
         queue=True,
     )
@@ -228,7 +255,7 @@ with gr.Blocks(title="H2OGPT") as h2ogpt_web:
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model, device, precision],
+        inputs=[system_msg, chatbot, device, precision],
         outputs=[chatbot],
         queue=True,
     )
@@ -240,3 +267,100 @@ with gr.Blocks(title="H2OGPT") as h2ogpt_web:
         queue=False,
     )
     clear.click(lambda: None, None, [chatbot], queue=False)
+
+
+with gr.Blocks(title="DocuChat Upload") as h2ogpt_upload:
+    import pathlib
+
+    upload_path = None
+    database = None
+    database_directory = os.path.abspath(
+        "apps/language_models/langchain/db_path/"
+    )
+
+    def read_path():
+        global upload_path
+        filenames = [
+            [f]
+            for f in os.listdir(upload_path)
+            if os.path.isfile(os.path.join(upload_path, f))
+        ]
+        filenames.sort()
+        return filenames
+
+    def upload_file(f):
+        names = []
+        for tmpfile in f:
+            name = tmpfile.name.split("/")[-1]
+            basename = os.path.join(upload_path, name)
+            with open(basename, "wb") as w:
+                with open(tmpfile.name, "rb") as r:
+                    w.write(r.read())
+        update_or_create_db()
+        return read_path()
+
+    def update_userpath(newpath):
+        global upload_path
+        upload_path = newpath
+        pathlib.Path(upload_path).mkdir(parents=True, exist_ok=True)
+        return read_path()
+
+    def update_or_create_db():
+        global database
+        global upload_path
+
+        sources = path_to_docs(
+            upload_path,
+            verbose=True,
+            fail_any_exception=False,
+            n_jobs=-1,
+            chunk=True,
+            chunk_size=512,
+            url=None,
+            enable_captions=False,
+            captions_model=None,
+            caption_loader=None,
+            enable_ocr=False,
+        )
+
+        pathlib.Path(database_directory).mkdir(parents=True, exist_ok=True)
+
+        database = create_or_update_db(
+            "chroma",
+            database_directory,
+            "UserData",
+            sources,
+            False,
+            True,
+            True,
+            "sentence-transformers/all-MiniLM-L6-v2",
+        )
+
+    def first_run():
+        global database
+        if database is None:
+            update_or_create_db()
+
+    update_userpath(
+        os.path.abspath("apps/language_models/langchain/user_path/")
+    )
+    h2ogpt_upload.load(fn=first_run)
+    h2ogpt_web.load(fn=first_run)
+
+    with gr.Column():
+        text = gr.DataFrame(
+            col_count=(1, "fixed"),
+            type="array",
+            label="Documents",
+            value=read_path(),
+        )
+        with gr.Row():
+            upload = gr.UploadButton(
+                label="Upload documents",
+                file_count="multiple",
+            )
+            upload.upload(fn=upload_file, inputs=upload, outputs=text)
+            userpath_selector.render()
+            userpath_selector.input(
+                fn=update_userpath, inputs=userpath_selector, outputs=text
+            ).then(fn=update_or_create_db)

apps/stable_diffusion/web/ui/img2img_ui.py

@@ -50,7 +50,7 @@ def img2img_inf(
     steps: int,
     strength: float,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -230,10 +230,12 @@ def img2img_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     extra_info = {"STRENGTH": strength}
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -617,8 +619,10 @@ with gr.Blocks(title="Image-to-Image") as img2img_web:
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/inpaint_ui.py

@@ -49,7 +49,7 @@ def inpaint_inf(
     inpaint_full_res_padding: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -181,10 +181,13 @@ def inpaint_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     image = image_dict["image"]
     mask_image = image_dict["mask"]
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -514,8 +517,10 @@ with gr.Blocks(title="Inpainting") as inpaint_web:
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/lora_train_ui.py

@@ -3,7 +3,7 @@ import os
 import gradio as gr
 from PIL import Image
 from apps.stable_diffusion.scripts import lora_train
-from apps.stable_diffusion.src import prompt_examples, args
+from apps.stable_diffusion.src import prompt_examples, args, utils
 from apps.stable_diffusion.web.ui.utils import (
     available_devices,
     nodlogo_loc,
@@ -168,7 +168,9 @@ with gr.Blocks(title="Lora Training") as lora_train_web:
                         stop_batch = gr.Button("Stop Batch")
                 with gr.Row():
                     seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                        value=utils.parse_seed_input(args.seed)[0],
+                        precision=0,
+                        label="Seed",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/minigpt4_ui.py

@@ -0,0 +1,193 @@
+# ========================================
+#             Gradio Setting
+# ========================================
+import gradio as gr
+
+# from apps.language_models.src.pipelines.minigpt4_pipeline import (
+#     # MiniGPT4,
+#     CONV_VISION,
+# )
+from pathlib import Path
+
+chat = None
+
+
+def gradio_reset(chat_state, img_list):
+    if chat_state is not None:
+        chat_state.messages = []
+    if img_list is not None:
+        img_list = []
+    return (
+        None,
+        gr.update(value=None, interactive=True),
+        gr.update(
+            placeholder="Please upload your image first", interactive=False
+        ),
+        gr.update(value="Upload & Start Chat", interactive=True),
+        chat_state,
+        img_list,
+    )
+
+
+def upload_img(gr_img, text_input, chat_state, device, precision, _compile):
+    global chat
+    if chat is None:
+        from apps.language_models.src.pipelines.minigpt4_pipeline import (
+            MiniGPT4,
+            CONV_VISION,
+        )
+
+        vision_model_precision = precision
+        if precision in ["int4", "int8"]:
+            vision_model_precision = "fp16"
+        vision_model_vmfb_path = Path(
+            f"vision_model_{vision_model_precision}_{device}.vmfb"
+        )
+        qformer_vmfb_path = Path(f"qformer_fp32_{device}.vmfb")
+        chat = MiniGPT4(
+            model_name="MiniGPT4",
+            hf_model_path=None,
+            max_new_tokens=30,
+            device=device,
+            precision=precision,
+            _compile=_compile,
+            vision_model_vmfb_path=vision_model_vmfb_path,
+            qformer_vmfb_path=qformer_vmfb_path,
+        )
+    if gr_img is None:
+        return None, None, gr.update(interactive=True), chat_state, None
+    chat_state = CONV_VISION.copy()
+    img_list = []
+    llm_message = chat.upload_img(gr_img, chat_state, img_list)
+    return (
+        gr.update(interactive=False),
+        gr.update(interactive=True, placeholder="Type and press Enter"),
+        gr.update(value="Start Chatting", interactive=False),
+        chat_state,
+        img_list,
+    )
+
+
+def gradio_ask(user_message, chatbot, chat_state):
+    if len(user_message) == 0:
+        return (
+            gr.update(
+                interactive=True, placeholder="Input should not be empty!"
+            ),
+            chatbot,
+            chat_state,
+        )
+    chat.ask(user_message, chat_state)
+    chatbot = chatbot + [[user_message, None]]
+    return "", chatbot, chat_state
+
+
+def gradio_answer(chatbot, chat_state, img_list, num_beams, temperature):
+    llm_message = chat.answer(
+        conv=chat_state,
+        img_list=img_list,
+        num_beams=num_beams,
+        temperature=temperature,
+        max_new_tokens=300,
+        max_length=2000,
+    )[0]
+    print(llm_message)
+    print("************")
+    chatbot[-1][1] = llm_message
+    return chatbot, chat_state, img_list
+
+
+title = """<h1 align="center">MultiModal SHARK (experimental)</h1>"""
+description = """<h3>Upload your images and start chatting!</h3>"""
+article = """<p><a href='https://minigpt-4.github.io'><img src='https://img.shields.io/badge/Project-Page-Green'></a></p><p><a href='https://github.com/Vision-CAIR/MiniGPT-4'><img src='https://img.shields.io/badge/Github-Code-blue'></a></p><p><a href='https://raw.githubusercontent.com/Vision-CAIR/MiniGPT-4/main/MiniGPT_4.pdf'><img src='https://img.shields.io/badge/Paper-PDF-red'></a></p>
+"""
+
+# TODO show examples below
+
+with gr.Blocks() as minigpt4_web:
+    gr.Markdown(title)
+    gr.Markdown(description)
+
+    with gr.Row():
+        with gr.Column(scale=0.5):
+            image = gr.Image(type="pil")
+            upload_button = gr.Button(
+                value="Upload & Start Chat",
+                interactive=True,
+                variant="primary",
+            )
+            clear = gr.Button("Restart")
+
+            num_beams = gr.Slider(
+                minimum=1,
+                maximum=10,
+                value=1,
+                step=1,
+                interactive=True,
+                label="beam search numbers)",
+            )
+
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=2.0,
+                value=1.0,
+                step=0.1,
+                interactive=True,
+                label="Temperature",
+            )
+
+            device = gr.Dropdown(
+                label="Device",
+                value="cuda",
+                # if enabled
+                # else "Only CUDA Supported for now",
+                choices=["cuda"],
+                interactive=False,
+            )
+
+        with gr.Column():
+            chat_state = gr.State()
+            img_list = gr.State()
+            chatbot = gr.Chatbot(label="MiniGPT-4")
+            text_input = gr.Textbox(
+                label="User",
+                placeholder="Please upload your image first",
+                interactive=False,
+            )
+            precision = gr.Radio(
+                label="Precision",
+                value="int8",
+                choices=[
+                    "int8",
+                    "fp16",
+                    "fp32",
+                ],
+                visible=True,
+            )
+            _compile = gr.Checkbox(
+                value=False,
+                label="Compile",
+                interactive=True,
+            )
+
+    upload_button.click(
+        upload_img,
+        [image, text_input, chat_state, device, precision, _compile],
+        [image, text_input, upload_button, chat_state, img_list],
+    )
+
+    text_input.submit(
+        gradio_ask,
+        [text_input, chatbot, chat_state],
+        [text_input, chatbot, chat_state],
+    ).then(
+        gradio_answer,
+        [chatbot, chat_state, img_list, num_beams, temperature],
+        [chatbot, chat_state, img_list],
+    )
+    clear.click(
+        gradio_reset,
+        [chat_state, img_list],
+        [chatbot, image, text_input, upload_button, chat_state, img_list],
+        queue=False,
+    )

apps/stable_diffusion/web/ui/outpaint_ui.py

@@ -49,7 +49,7 @@ def outpaint_inf(
     width: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -178,7 +178,10 @@ def outpaint_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     left = True if "left" in directions else False
     right = True if "right" in directions else False
@@ -542,8 +545,10 @@ with gr.Blocks(title="Outpainting") as outpaint_web:
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/stablelm_ui.py

@@ -7,6 +7,8 @@ from transformers import (
 )
 from apps.stable_diffusion.web.ui.utils import available_devices
 from datetime import datetime as dt
+import json
+import time
 
 
 def user(message, history):
@@ -26,12 +28,22 @@ model_map = {
     "codegen": "Salesforce/codegen25-7b-multi",
     "vicuna1p3": "lmsys/vicuna-7b-v1.3",
     "vicuna": "TheBloke/vicuna-7B-1.1-HF",
+    "vicuna4": "TheBloke/vicuna-7B-1.1-HF",
     "StableLM": "stabilityai/stablelm-tuned-alpha-3b",
 }
 
 # NOTE: Each `model_name` should have its own start message
 start_message = {
-    "llama2": (
+    "llama2_7b": (
+        "System: You are a helpful, respectful and honest assistant. Always answer "
+        "as helpfully as possible, while being safe.  Your answers should not "
+        "include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal "
+        "content. Please ensure that your responses are socially unbiased and positive "
+        "in nature. If a question does not make any sense, or is not factually coherent, "
+        "explain why instead of answering something not correct. If you don't know the "
+        "answer to a question, please don't share false information."
+    ),
+    "llama2_70b": (
         "System: You are a helpful, respectful and honest assistant. Always answer "
         "as helpfully as possible, while being safe.  Your answers should not "
         "include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal "
@@ -56,6 +68,11 @@ start_message = {
         "The assistant gives helpful, detailed, and polite answers to the user's "
         "questions.\n"
     ),
+    "vicuna4": (
+        "A chat between a curious user and an artificial intelligence assistant. "
+        "The assistant gives helpful, detailed, and polite answers to the user's "
+        "questions.\n"
+    ),
     "vicuna1p3": (
         "A chat between a curious user and an artificial intelligence assistant. "
         "The assistant gives helpful, detailed, and polite answers to the user's "
@@ -68,7 +85,14 @@ start_message = {
 def create_prompt(model_name, history):
     system_message = start_message[model_name]
 
-    if model_name in ["StableLM", "vicuna", "vicuna1p3", "llama2"]:
+    if model_name in [
+        "StableLM",
+        "vicuna",
+        "vicuna4",
+        "vicuna1p3",
+        "llama2_7b",
+        "llama2_70b",
+    ]:
         conversation = "".join(
             [
                 "".join(["<|USER|>" + item[0], "<|ASSISTANT|>" + item[1]])
@@ -90,45 +114,144 @@ def set_vicuna_model(model):
     vicuna_model = model
 
 
+def get_default_config():
+    import torch
+    from transformers import AutoTokenizer
+
+    hf_model_path = "TheBloke/vicuna-7B-1.1-HF"
+    tokenizer = AutoTokenizer.from_pretrained(hf_model_path, use_fast=False)
+    compilation_prompt = "".join(["0" for _ in range(17)])
+    compilation_input_ids = tokenizer(
+        compilation_prompt,
+        return_tensors="pt",
+    ).input_ids
+    compilation_input_ids = torch.tensor(compilation_input_ids).reshape(
+        [1, 19]
+    )
+    firstVicunaCompileInput = (compilation_input_ids,)
+    from apps.language_models.src.model_wrappers.vicuna_model import (
+        CombinedModel,
+    )
+    from shark.shark_generate_model_config import GenerateConfigFile
+
+    model = CombinedModel()
+    c = GenerateConfigFile(model, 1, ["gpu_id"], firstVicunaCompileInput)
+    c.split_into_layers()
+
+
+model_vmfb_key = ""
+
+
 # TODO: Make chat reusable for UI and API
-def chat(curr_system_message, history, model, device, precision, cli=True):
+def chat(
+    curr_system_message,
+    history,
+    model,
+    device,
+    precision,
+    config_file,
+    cli=False,
+    progress=gr.Progress(),
+):
     global past_key_values
+    global model_vmfb_key
 
     global vicuna_model
     model_name, model_path = list(map(str.strip, model.split("=>")))
+    if "cuda" in device:
+        device = "cuda"
+    elif "sync" in device:
+        device = "cpu-sync"
+    elif "task" in device:
+        device = "cpu-task"
+    elif "vulkan" in device:
+        device = "vulkan"
+    else:
+        print("unrecognized device")
 
-    if model_name in ["vicuna", "vicuna1p3", "codegen", "llama2"]:
-        from apps.language_models.scripts.vicuna import (
-            UnshardedVicuna,
-        )
-
-        if vicuna_model == 0:
-            if "cuda" in device:
-                device = "cuda"
-            elif "sync" in device:
-                device = "cpu-sync"
-            elif "task" in device:
-                device = "cpu-task"
-            elif "vulkan" in device:
-                device = "vulkan"
-            else:
-                print("unrecognized device")
+    new_model_vmfb_key = f"{model_name}#{model_path}#{device}#{precision}"
+    if model_name in [
+        "vicuna",
+        "vicuna4",
+        "vicuna1p3",
+        "codegen",
+        "llama2_7b",
+        "llama2_70b",
+    ]:
+        from apps.language_models.scripts.vicuna import ShardedVicuna
+        from apps.language_models.scripts.vicuna import UnshardedVicuna
+        from apps.stable_diffusion.src import args
 
+        if new_model_vmfb_key != model_vmfb_key:
+            model_vmfb_key = new_model_vmfb_key
             max_toks = 128 if model_name == "codegen" else 512
-            vicuna_model = UnshardedVicuna(
-                model_name,
-                hf_model_path=model_path,
-                device=device,
-                precision=precision,
-                max_num_tokens=max_toks,
-            )
+
+            # get iree flags that need to be overridden, from commandline args
+            _extra_args = []
+            # vulkan target triple
+            if args.iree_vulkan_target_triple != "":
+                _extra_args.append(
+                    f"-iree-vulkan-target-triple={args.iree_vulkan_target_triple}"
+                )
+
+            if model_name == "vicuna4":
+                vicuna_model = ShardedVicuna(
+                    model_name,
+                    hf_model_path=model_path,
+                    device=device,
+                    precision=precision,
+                    max_num_tokens=max_toks,
+                    compressed=True,
+                    extra_args_cmd=_extra_args,
+                )
+            else:
+                #  if config_file is None:
+                vicuna_model = UnshardedVicuna(
+                    model_name,
+                    hf_model_path=model_path,
+                    hf_auth_token=args.hf_auth_token,
+                    device=device,
+                    precision=precision,
+                    max_num_tokens=max_toks,
+                    extra_args_cmd=_extra_args,
+                )
+                #  else:
+                #      if config_file is not None:
+                #          config_file = open(config_file)
+                #          config_json = json.load(config_file)
+                #          config_file.close()
+                #      else:
+                #          config_json = get_default_config()
+                #      vicuna_model = ShardedVicuna(
+                #          model_name,
+                #          device=device,
+                #          precision=precision,
+                #          config_json=config_json,
+                #      )
+
         prompt = create_prompt(model_name, history)
 
-        for partial_text in vicuna_model.generate(prompt, cli=cli):
-            history[-1][1] = partial_text
-            yield history
+        partial_text = ""
+        count = 0
+        start_time = time.time()
+        for text, msg in progress.tqdm(
+            vicuna_model.generate(prompt, cli=cli),
+            desc="generating response",
+        ):
+            count += 1
+            if "formatted" in msg:
+                history[-1][1] = text
+                end_time = time.time()
+                tokens_per_sec = count / (end_time - start_time)
+                yield history, str(
+                    format(tokens_per_sec, ".2f")
+                ) + " tokens/sec"
+            else:
+                partial_text += text + " "
+                history[-1][1] = partial_text
+                yield history, ""
 
-        return history
+        return history, ""
 
     # else Model is StableLM
     global sharkModel
@@ -136,7 +259,8 @@ def chat(curr_system_message, history, model, device, precision, cli=True):
         SharkStableLM,
     )
 
-    if sharkModel == 0:
+    if new_model_vmfb_key != model_vmfb_key:
+        model_vmfb_key = new_model_vmfb_key
         # max_new_tokens=512
         shark_slm = SharkStableLM(
             model_name
@@ -153,7 +277,6 @@ def chat(curr_system_message, history, model, device, precision, cli=True):
 
     partial_text = ""
     for new_text in words_list:
-        print(new_text)
         partial_text += new_text
         history[-1][1] = partial_text
         # Yield an empty string to clean up the message textbox and the updated
@@ -261,6 +384,13 @@ def llm_chat_api(InputData: dict):
     }
 
 
+def view_json_file(file_obj):
+    content = ""
+    with open(file_obj.name, "r") as fopen:
+        content = fopen.read()
+    return content
+
+
 with gr.Blocks(title="Chatbot") as stablelm_chat:
     with gr.Row():
         model_choices = list(
@@ -268,7 +398,7 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
         )
         model = gr.Dropdown(
             label="Select Model",
-            value=model_choices[0],
+            value=model_choices[4],
             choices=model_choices,
         )
         supported_devices = available_devices
@@ -276,26 +406,38 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
         # show cpu-task device first in list for chatbot
         supported_devices = supported_devices[-1:] + supported_devices[:-1]
         supported_devices = [x for x in supported_devices if "sync" not in x]
-        print(supported_devices)
-        device = gr.Dropdown(
+        #  print(supported_devices)
+        devices = gr.Dropdown(
             label="Device",
             value=supported_devices[0]
             if enabled
             else "Only CUDA Supported for now",
             choices=supported_devices,
             interactive=enabled,
+            #  multiselect=True,
         )
         precision = gr.Radio(
             label="Precision",
-            value="fp16",
+            value="int8",
             choices=[
                 "int4",
                 "int8",
                 "fp16",
-                "fp32",
             ],
             visible=True,
         )
+        tokens_time = gr.Textbox(label="Tokens generated per second")
+
+    with gr.Row(visible=False):
+        with gr.Group():
+            config_file = gr.File(
+                label="Upload sharding configuration", visible=False
+            )
+            json_view_button = gr.Button(label="View as JSON", visible=False)
+        json_view = gr.JSON(interactive=True, visible=False)
+        json_view_button.click(
+            fn=view_json_file, inputs=[config_file], outputs=[json_view]
+        )
     chatbot = gr.Chatbot(height=500)
     with gr.Row():
         with gr.Column():
@@ -319,16 +461,16 @@ with gr.Blocks(title="Chatbot") as stablelm_chat:
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model, device, precision],
-        outputs=[chatbot],
+        inputs=[system_msg, chatbot, model, devices, precision, config_file],
+        outputs=[chatbot, tokens_time],
         queue=True,
     )
     submit_click_event = submit.click(
         fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False
     ).then(
         fn=chat,
-        inputs=[system_msg, chatbot, model, device, precision],
-        outputs=[chatbot],
+        inputs=[system_msg, chatbot, model, devices, precision, config_file],
+        outputs=[chatbot, tokens_time],
         queue=True,
     )
     stop.click(

apps/stable_diffusion/web/ui/txt2img_ui.py

@@ -46,7 +46,7 @@ def txt2img_inf(
     width: int,
     steps: int,
     guidance_scale: float,
-    seed: int,
+    seed: str | int,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -178,8 +178,11 @@ def txt2img_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     text_output = ""
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         out_imgs = global_obj.get_sd_obj().generate_images(
@@ -395,7 +398,7 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                             value=args.scheduler,
                             choices=scheduler_list,
                         )
-                        with gr.Group():
+                        with gr.Column():
                             save_metadata_to_png = gr.Checkbox(
                                 label="Save prompt information to PNG",
                                 value=args.write_metadata_to_png,
@@ -481,8 +484,10 @@ with gr.Blocks(title="Text-to-Image") as txt2img_web:
                             label="Repeatable Seeds",
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

apps/stable_diffusion/web/ui/upscaler_ui.py

@@ -42,7 +42,7 @@ def upscaler_inf(
     steps: int,
     noise_level: int,
     guidance_scale: float,
-    seed: int,
+    seed: str,
     batch_count: int,
     batch_size: int,
     scheduler: str,
@@ -177,8 +177,11 @@ def upscaler_inf(
     start_time = time.time()
     global_obj.get_sd_obj().log = ""
     generated_imgs = []
-    seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
     extra_info = {"NOISE LEVEL": noise_level}
+    try:
+        seeds = utils.batch_seeds(seed, batch_count, repeatable_seeds)
+    except TypeError as error:
+        raise gr.Error(str(error)) from None
 
     for current_batch in range(batch_count):
         low_res_img = image
@@ -534,8 +537,10 @@ with gr.Blocks(title="Upscaler") as upscaler_web:
                             visible=False,
                         )
                 with gr.Row():
-                    seed = gr.Number(
-                        value=args.seed, precision=0, label="Seed"
+                    seed = gr.Textbox(
+                        value=args.seed,
+                        label="Seed",
+                        info="An integer or a JSON list of integers, -1 for random",
                     )
                     device = gr.Dropdown(
                         elem_id="device",

build_tools/image_comparison.py

@@ -24,13 +24,13 @@ def get_image(url, local_filename):
             shutil.copyfileobj(res.raw, f)
 
 
-def compare_images(new_filename, golden_filename):
+def compare_images(new_filename, golden_filename, upload=False):
     new = np.array(Image.open(new_filename)) / 255.0
     golden = np.array(Image.open(golden_filename)) / 255.0
     diff = np.abs(new - golden)
     mean = np.mean(diff)
     if mean > 0.1:
-        if os.name != "nt":
+        if os.name != "nt" and upload == True:
             subprocess.run(
                 [
                     "gsutil",
@@ -39,7 +39,7 @@ def compare_images(new_filename, golden_filename):
                     "gs://shark_tank/testdata/builder/",
                 ]
             )
-        raise SystemExit("new and golden not close")
+        raise AssertionError("new and golden not close")
     else:
         print("SUCCESS")
 

build_tools/populate_sharktank_ci.sh

@@ -1,5 +1,6 @@
 #!/bin/bash
 
-IMPORTER=1 BENCHMARK=1 ./setup_venv.sh
+IMPORTER=1 BENCHMARK=1 NO_BREVITAS=1 ./setup_venv.sh
 source $GITHUB_WORKSPACE/shark.venv/bin/activate
+python build_tools/stable_diffusion_testing.py --gen
 python tank/generate_sharktank.py

build_tools/stable_diffusion_testing.py

@@ -63,7 +63,14 @@ def get_inpaint_inputs():
     open("./test_images/inputs/mask.png", "wb").write(mask.content)
 
 
-def test_loop(device="vulkan", beta=False, extra_flags=[]):
+def test_loop(
+    device="vulkan",
+    beta=False,
+    extra_flags=[],
+    upload_bool=True,
+    exit_on_fail=True,
+    do_gen=False,
+):
     # Get golden values from tank
     shutil.rmtree("./test_images", ignore_errors=True)
     model_metrics = []
@@ -81,6 +88,8 @@ def test_loop(device="vulkan", beta=False, extra_flags=[]):
     if beta:
         extra_flags.append("--beta_models=True")
     extra_flags.append("--no-progress_bar")
+    if do_gen:
+        extra_flags.append("--import_debug")
     to_skip = [
         "Linaqruf/anything-v3.0",
         "prompthero/openjourney",
@@ -181,7 +190,14 @@ def test_loop(device="vulkan", beta=False, extra_flags=[]):
                         "./test_images/golden/" + model_name + "/*.png"
                     )
                     golden_file = glob(golden_path)[0]
-                    compare_images(test_file, golden_file)
+                    try:
+                        compare_images(
+                            test_file, golden_file, upload=upload_bool
+                        )
+                    except AssertionError as e:
+                        print(e)
+                        if exit_on_fail == True:
+                            raise
                 else:
                     print(command)
                     print("failed to generate image for this configuration")
@@ -200,6 +216,9 @@ def test_loop(device="vulkan", beta=False, extra_flags=[]):
                             extra_flags.remove(
                                 "--iree_vulkan_target_triple=rdna2-unknown-windows"
                             )
+            if do_gen:
+                prepare_artifacts()
+
     with open(os.path.join(os.getcwd(), "sd_testing_metrics.csv"), "w+") as f:
         header = "model_name;device;use_tune;import_opt;Clip Inference time(ms);Average Step (ms/it);VAE Inference time(ms);total image generation(s);command\n"
         f.write(header)
@@ -218,15 +237,49 @@ def test_loop(device="vulkan", beta=False, extra_flags=[]):
             f.write(";".join(output) + "\n")
 
 
+def prepare_artifacts():
+    gen_path = os.path.join(os.getcwd(), "gen_shark_tank")
+    if not os.path.isdir(gen_path):
+        os.mkdir(gen_path)
+    for dirname in os.listdir(os.getcwd()):
+        for modelname in ["clip", "unet", "vae"]:
+            if modelname in dirname and "vmfb" not in dirname:
+                if not os.path.isdir(os.path.join(gen_path, dirname)):
+                    shutil.move(os.path.join(os.getcwd(), dirname), gen_path)
+                    print(f"Moved dir: {dirname} to {gen_path}.")
+
+
 parser = argparse.ArgumentParser()
 
 parser.add_argument("-d", "--device", default="vulkan")
 parser.add_argument(
     "-b", "--beta", action=argparse.BooleanOptionalAction, default=False
 )
-
+parser.add_argument("-e", "--extra_args", type=str, default=None)
+parser.add_argument(
+    "-u", "--upload", action=argparse.BooleanOptionalAction, default=True
+)
+parser.add_argument(
+    "-x", "--exit_on_fail", action=argparse.BooleanOptionalAction, default=True
+)
+parser.add_argument(
+    "-g", "--gen", action=argparse.BooleanOptionalAction, default=False
+)
 
 if __name__ == "__main__":
     args = parser.parse_args()
     print(args)
-    test_loop(args.device, args.beta, [])
+    extra_args = []
+    if args.extra_args:
+        for arg in args.extra_args.split(","):
+            extra_args.append(arg)
+    test_loop(
+        args.device,
+        args.beta,
+        extra_args,
+        args.upload,
+        args.exit_on_fail,
+        args.gen,
+    )
+    if args.gen:
+        prepare_artifacts()

build_tools/vicuna_testing.py

@@ -0,0 +1,14 @@
+import os
+from sys import executable
+import subprocess
+from apps.language_models.scripts import vicuna
+
+
+def test_loop():
+    precisions = ["fp16", "int8", "int4"]
+    devices = ["cpu"]
+    for precision in precisions:
+        for device in devices:
+            model = vicuna.UnshardedVicuna(device=device, precision=precision)
+            model.compile()
+            del model

cpp/CMakeLists.txt

@@ -27,7 +27,7 @@ include(FetchContent)
 
 FetchContent_Declare(
   iree
-  GIT_REPOSITORY https://github.com/nod-ai/shark-runtime.git
+  GIT_REPOSITORY https://github.com/nod-ai/srt.git
   GIT_TAG shark 
   GIT_SUBMODULES_RECURSE OFF
   GIT_SHALLOW OFF

docs/shark_iree_profiling.md

@@ -63,8 +63,8 @@ Where `${NUM}` is the dispatch number that you want to benchmark/profile in isol
 
 ### Enabling Tracy for Vulkan profiling
 
-To begin profiling with Tracy, a build of IREE runtime with tracing enabled is needed. SHARK-Runtime builds an
-instrumented version alongside the normal version nightly (.whls typically found [here](https://github.com/nod-ai/SHARK-Runtime/releases)), however this is only available for Linux. For Windows, tracing can be enabled by enabling a CMake flag.
+To begin profiling with Tracy, a build of IREE runtime with tracing enabled is needed. SHARK-Runtime (SRT) builds an
+instrumented version alongside the normal version nightly (.whls typically found [here](https://github.com/nod-ai/SRT/releases)), however this is only available for Linux. For Windows, tracing can be enabled by enabling a CMake flag.
 ```
 $env:IREE_ENABLE_RUNTIME_TRACING="ON"
 ```

inference/CMakeLists.txt

@@ -95,7 +95,7 @@ target_include_directories(
 
 list(APPEND CMAKE_MODULE_PATH "${PROJECT_BINARY_DIR}/lib/cmake/mlir")
 
-add_subdirectory(thirdparty/shark-runtime EXCLUDE_FROM_ALL)
+add_subdirectory(thirdparty/srt EXCLUDE_FROM_ALL)
 
 target_link_libraries(triton-dshark-backend PRIVATE iree_base_base
   iree_hal_hal

inference/README.md

@@ -22,7 +22,7 @@ git submodule update --init
 update the submodules of iree
 
 ```
-cd thirdparty/shark-runtime
+cd thirdparty/srt
 git submodule update --init
 ```
 

process_skipfiles.py

@@ -56,3 +56,14 @@ for line in fileinput.input(path_to_lazy_loader, inplace=True):
         )
     else:
         print(line, end="")
+
+# For getting around timm's packaging.
+# Refer: https://github.com/pyinstaller/pyinstaller/issues/5673#issuecomment-808731505
+path_to_timm_activations = Path(
+    get_python_lib() + "/timm/layers/activations_jit.py"
+)
+for line in fileinput.input(path_to_timm_activations, inplace=True):
+    if "@torch.jit.script" in line:
+        print("@torch.jit._script_if_tracing", end="\n")
+    else:
+        print(line, end="")

pyproject.toml

@@ -5,7 +5,7 @@ requires = [
     "packaging",
 
     "numpy>=1.22.4",
-    "torch-mlir>=20221021.633",
+    "torch-mlir>=20230620.875",
     "iree-compiler>=20221022.190",
     "iree-runtime>=20221022.190",
 ]
@@ -15,3 +15,4 @@ build-backend = "setuptools.build_meta"
 line-length = 79
 include = '\.pyi?$'
 exclude = "apps/language_models/scripts/vicuna.py"
+extend-exclude = "apps/language_models/src/pipelines/minigpt4_pipeline.py"

requirements-importer.txt

@@ -3,7 +3,7 @@
 
 numpy>1.22.4
 pytorch-triton
-torchvision==0.16.0.dev20230322 
+torchvision 
 tabulate
 
 tqdm
@@ -15,7 +15,7 @@ iree-tools-tf
 
 # TensorFlow and JAX.
 gin-config
-tensorflow>2.11
+tf-nightly
 keras
 #tf-models-nightly
 #tensorflow-text-nightly

requirements.txt

@@ -1,3 +1,6 @@
+-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
+--pre
+
 setuptools
 wheel
 
@@ -16,7 +19,7 @@ parameterized
 
 # Add transformers, diffusers and scipy since it most commonly used
 transformers
-diffusers
+diffusers==0.19.3
 #accelerate is now required for diffusers import from ckpt.
 accelerate
 scipy
@@ -24,7 +27,8 @@ ftfy
 gradio
 altair
 omegaconf
-safetensors
+# 0.3.2 doesn't have binaries for arm64
+safetensors==0.3.1
 opencv-python
 scikit-image
 pytorch_lightning # for runwayml models
@@ -34,10 +38,12 @@ sentencepiece
 py-cpuinfo
 tiktoken # for codegen
 joblib # for langchain
+timm # for MiniGPT4
+langchain
 
 # Keep PyInstaller at the end. Sometimes Windows Defender flags it but most folks can continue even if it errors
 pefile
 pyinstaller
 
-# low precision vicuna
-brevitas @ git+https://github.com/Xilinx/brevitas.git@llm
+# vicuna quantization
+brevitas @ git+https://github.com/Xilinx/brevitas.git@dev

setup_venv.ps1

@@ -90,8 +90,8 @@ python -m pip install --upgrade pip
 pip install wheel
 pip install -r requirements.txt
 pip install --pre torch-mlir torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://llvm.github.io/torch-mlir/package-index/
-pip install --upgrade -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html iree-compiler iree-runtime
+pip install --upgrade -f https://nod-ai.github.io/SRT/pip-release-links.html iree-compiler iree-runtime
 Write-Host "Building SHARK..."
-pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html
+pip install -e . -f https://llvm.github.io/torch-mlir/package-index/ -f https://nod-ai.github.io/SRT/pip-release-links.html
 Write-Host "Build and installation completed successfully"
 Write-Host "Source your venv with ./shark.venv/Scripts/activate"

setup_venv.sh

@@ -103,7 +103,7 @@ else
 fi
 if [[ -z "${USE_IREE}" ]]; then
   rm .use-iree
-  RUNTIME="https://nod-ai.github.io/SHARK-Runtime/pip-release-links.html"
+  RUNTIME="https://nod-ai.github.io/SRT/pip-release-links.html"
 else
   touch ./.use-iree
   RUNTIME="https://openxla.github.io/iree/pip-release-links.html"
@@ -128,7 +128,7 @@ if [[ ! -z "${IMPORTER}" ]]; then
   fi
 fi
 
-$PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f https://download.pytorch.org/whl/nightly/torch/
+$PYTHON -m pip install --no-warn-conflicts -e . -f https://llvm.github.io/torch-mlir/package-index/ -f ${RUNTIME} -f https://download.pytorch.org/whl/nightly/cpu/
 
 if [[ $(uname -s) = 'Linux' && ! -z "${BENCHMARK}" ]]; then
   T_VER=$($PYTHON -m pip show torch | grep Version)
@@ -145,19 +145,11 @@ if [[ $(uname -s) = 'Linux' && ! -z "${BENCHMARK}" ]]; then
   fi
 fi
 
-if [[ ! -z "${ONNX}" ]]; then
-  echo "${Yellow}Installing ONNX and onnxruntime for benchmarks..."
-  $PYTHON -m pip install onnx onnxruntime psutil
-  if [ $? -eq 0 ];then
-    echo "Successfully installed ONNX and ONNX runtime."
-  else
-    echo "Could not install ONNX." >&2
-  fi
+if [[ -z "${NO_BREVITAS}" ]]; then
+  $PYTHON -m pip install git+https://github.com/Xilinx/brevitas.git@llm
 fi
 
 if [[ -z "${CONDA_PREFIX}" && "$SKIP_VENV" != "1" ]]; then
   echo "${Green}Before running examples activate venv with:"
   echo "  ${Green}source $VENV_DIR/bin/activate"
 fi
-
-$PYTHON -m pip install git+https://github.com/Xilinx/brevitas.git@llm

shark/examples/shark_inference/albert_maskfill_pt.py

@@ -43,9 +43,7 @@ if __name__ == "__main__":
     minilm_mlir, func_name = mlir_importer.import_mlir(
         is_dynamic=False, tracing_required=True
     )
-    shark_module = SharkInference(
-        minilm_mlir, func_name, mlir_dialect="linalg"
-    )
+    shark_module = SharkInference(minilm_mlir)
     shark_module.compile()
     token_logits = torch.tensor(shark_module.forward(inputs))
     mask_id = torch.where(

shark/examples/shark_inference/upscaler/upscaler_args.py

@@ -94,18 +94,5 @@ p.add_argument(
     help="Profiles vulkan device and collects the .rdc info",
 )
 
-p.add_argument(
-    "--vulkan_large_heap_block_size",
-    default="4147483648",
-    help="flag for setting VMA preferredLargeHeapBlockSize for vulkan device, default is 4G",
-)
-
-p.add_argument(
-    "--vulkan_validation_layers",
-    default=False,
-    action=argparse.BooleanOptionalAction,
-    help="flag for disabling vulkan validation layers when benchmarking",
-)
-
 
 args = p.parse_args()

shark/examples/shark_inference/upscaler/utils.py

@@ -6,6 +6,7 @@ from shark.shark_importer import import_with_fx
 from shark.iree_utils.vulkan_utils import (
     set_iree_vulkan_runtime_flags,
     get_vulkan_target_triple,
+    get_iree_vulkan_runtime_flags,
 )
 
 
@@ -75,10 +76,7 @@ def compile_through_fx(
 
 
 def set_iree_runtime_flags():
-    vulkan_runtime_flags = [
-        f"--vulkan_large_heap_block_size={args.vulkan_large_heap_block_size}",
-        f"--vulkan_validation_layers={'true' if args.vulkan_validation_layers else 'false'}",
-    ]
+    vulkan_runtime_flags = get_iree_vulkan_runtime_flags()
     if args.enable_rgp:
         vulkan_runtime_flags += [
             f"--enable_rgp=true",

shark/iree_utils/_common.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 ## Common utilities to be shared by iree utilities.
-
+import functools
 import os
 import sys
 import subprocess
@@ -93,6 +93,7 @@ _IREE_TARGET_MAP = {
 
 
 # Finds whether the required drivers are installed for the given device.
+@functools.cache
 def check_device_drivers(device):
     """Checks necessary drivers present for gpu and vulkan devices"""
     if "://" in device:

shark/iree_utils/benchmark_utils.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import iree.runtime.scripts.iree_benchmark_module as benchmark_module
+import iree._runtime.scripts.iree_benchmark_module as benchmark_module
 from shark.iree_utils._common import run_cmd, iree_device_map
 from shark.iree_utils.cpu_utils import get_cpu_count
 import numpy as np
@@ -62,16 +62,12 @@ def build_benchmark_args(
     and whether it is training or not.
     Outputs: string that execute benchmark-module on target model.
     """
-    path = benchmark_module.__path__[0]
+    path = os.path.join(os.environ["VIRTUAL_ENV"], "bin")
     if platform.system() == "Windows":
-        benchmarker_path = os.path.join(
-            path, "..", "..", "iree-benchmark-module.exe"
-        )
+        benchmarker_path = os.path.join(path, "iree-benchmark-module.exe")
         time_extractor = None
     else:
-        benchmarker_path = os.path.join(
-            path, "..", "..", "iree-benchmark-module"
-        )
+        benchmarker_path = os.path.join(path, "iree-benchmark-module")
         time_extractor = "| awk 'END{{print $2 $3}}'"
     benchmark_cl = [benchmarker_path, f"--module={input_file}"]
     # TODO: The function named can be passed as one of the args.

shark/iree_utils/compile_utils.py

@@ -11,18 +11,23 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import iree.runtime as ireert
-import iree.compiler as ireec
-from shark.iree_utils._common import iree_device_map, iree_target_map
-from shark.iree_utils.cpu_utils import get_iree_cpu_rt_args
-from shark.iree_utils.benchmark_utils import *
-from shark.parser import shark_args
+import functools
 import numpy as np
 import os
 import re
 import tempfile
+import time
 from pathlib import Path
 
+import iree.runtime as ireert
+import iree.compiler as ireec
+from shark.parser import shark_args
+
+from .trace import DetailLogger
+from ._common import iree_device_map, iree_target_map
+from .cpu_utils import get_iree_cpu_rt_args
+from .benchmark_utils import *
+
 
 # Get the iree-compile arguments given device.
 def get_iree_device_args(device, extra_args=[]):
@@ -90,6 +95,7 @@ def get_iree_frontend_args(frontend):
 def get_iree_common_args():
     return [
         "--iree-stream-resource-index-bits=64",
+        "--iree-stream-resource-max-allocation-size=4294967295",
         "--iree-vm-target-index-bits=64",
         "--iree-vm-bytecode-module-strip-source-map=true",
         "--iree-util-zero-fill-elided-attrs",
@@ -317,7 +323,6 @@ def get_iree_module(flatbuffer_blob, device, device_idx=None):
         device = iree_device_map(device)
         print("registering device id: ", device_idx)
         haldriver = ireert.get_driver(device)
-
         haldevice = haldriver.create_device(
             haldriver.query_available_devices()[device_idx]["device_id"],
             allocators=shark_args.device_allocator,
@@ -337,58 +342,64 @@ def get_iree_module(flatbuffer_blob, device, device_idx=None):
 def load_vmfb_using_mmap(
     flatbuffer_blob_or_path, device: str, device_idx: int = None
 ):
-    instance = ireert.VmInstance()
-    device = iree_device_map(device)
-    haldriver = ireert.get_driver(device)
-    haldevice = haldriver.create_device_by_uri(
-        device,
-        allocators=[],
-    )
-    # First get configs.
-    if device_idx is not None:
-        device = iree_device_map(device)
-        print("registering device id: ", device_idx)
-        haldriver = ireert.get_driver(device)
+    print(f"Loading module {flatbuffer_blob_or_path}...")
 
-        haldevice = haldriver.create_device(
-            haldriver.query_available_devices()[device_idx]["device_id"],
-            allocators=shark_args.device_allocator,
-        )
-        config = ireert.Config(device=haldevice)
-    else:
-        config = get_iree_runtime_config(device)
-    if "task" in device:
-        print(
-            f"[DEBUG] setting iree runtime flags for cpu:\n{' '.join(get_iree_cpu_rt_args())}"
-        )
-        for flag in get_iree_cpu_rt_args():
-            ireert.flags.parse_flags(flag)
-    # Now load vmfb.
-    # Two scenarios we have here :-
-    #      1. We either have the vmfb already saved and therefore pass the path of it.
-    #         (This would arise if we're invoking `load_module` from a SharkInference obj)
-    #   OR 2. We are compiling on the fly, therefore we have the flatbuffer blob to play with.
-    #         (This would arise if we're invoking `compile` from a SharkInference obj)
-    temp_file_to_unlink = None
-    if isinstance(flatbuffer_blob_or_path, Path):
-        flatbuffer_blob_or_path = flatbuffer_blob_or_path.__str__()
-    if (
-        isinstance(flatbuffer_blob_or_path, str)
-        and ".vmfb" in flatbuffer_blob_or_path
-    ):
-        vmfb_file_path = flatbuffer_blob_or_path
-        mmaped_vmfb = ireert.VmModule.mmap(instance, flatbuffer_blob_or_path)
-        ctx = ireert.SystemContext(config=config)
-        ctx.add_vm_module(mmaped_vmfb)
-        mmaped_vmfb = getattr(ctx.modules, mmaped_vmfb.name)
-    else:
-        with tempfile.NamedTemporaryFile(delete=False) as tf:
-            tf.write(flatbuffer_blob_or_path)
-            tf.flush()
-            vmfb_file_path = tf.name
-        temp_file_to_unlink = vmfb_file_path
-        mmaped_vmfb = ireert.VmModule.mmap(instance, vmfb_file_path)
-    return mmaped_vmfb, config, temp_file_to_unlink
+    with DetailLogger(timeout=2.5) as dl:
+        # First get configs.
+        if device_idx is not None:
+            dl.log(f"Mapping device id: {device_idx}")
+            device = iree_device_map(device)
+            haldriver = ireert.get_driver(device)
+            dl.log(f"ireert.get_driver()")
+
+            haldevice = haldriver.create_device(
+                haldriver.query_available_devices()[device_idx]["device_id"],
+                allocators=shark_args.device_allocator,
+            )
+            dl.log(f"ireert.create_device()")
+            config = ireert.Config(device=haldevice)
+            dl.log(f"ireert.Config()")
+        else:
+            config = get_iree_runtime_config(device)
+            dl.log("get_iree_runtime_config")
+        if "task" in device:
+            print(
+                f"[DEBUG] setting iree runtime flags for cpu:\n{' '.join(get_iree_cpu_rt_args())}"
+            )
+            for flag in get_iree_cpu_rt_args():
+                ireert.flags.parse_flags(flag)
+        # Now load vmfb.
+        # Two scenarios we have here :-
+        #      1. We either have the vmfb already saved and therefore pass the path of it.
+        #         (This would arise if we're invoking `load_module` from a SharkInference obj)
+        #   OR 2. We are compiling on the fly, therefore we have the flatbuffer blob to play with.
+        #         (This would arise if we're invoking `compile` from a SharkInference obj)
+        temp_file_to_unlink = None
+        if isinstance(flatbuffer_blob_or_path, Path):
+            flatbuffer_blob_or_path = flatbuffer_blob_or_path.__str__()
+        if (
+            isinstance(flatbuffer_blob_or_path, str)
+            and ".vmfb" in flatbuffer_blob_or_path
+        ):
+            vmfb_file_path = flatbuffer_blob_or_path
+            mmaped_vmfb = ireert.VmModule.mmap(
+                config.vm_instance, flatbuffer_blob_or_path
+            )
+            dl.log(f"mmap {flatbuffer_blob_or_path}")
+            ctx = ireert.SystemContext(config=config)
+            dl.log(f"ireert.SystemContext created")
+            ctx.add_vm_module(mmaped_vmfb)
+            dl.log(f"module initialized")
+            mmaped_vmfb = getattr(ctx.modules, mmaped_vmfb.name)
+        else:
+            with tempfile.NamedTemporaryFile(delete=False) as tf:
+                tf.write(flatbuffer_blob_or_path)
+                tf.flush()
+                vmfb_file_path = tf.name
+            temp_file_to_unlink = vmfb_file_path
+            mmaped_vmfb = ireert.VmModule.mmap(instance, vmfb_file_path)
+            dl.log(f"mmap temp {vmfb_file_path}")
+        return mmaped_vmfb, config, temp_file_to_unlink
 
 
 def get_iree_compiled_module(
@@ -410,7 +421,6 @@ def get_iree_compiled_module(
     #       we're setting delete=False when creating NamedTemporaryFile. That's why
     #       I'm getting hold of the name of the temporary file in `temp_file_to_unlink`.
     if mmap:
-        print(f"Will load the compiled module as a mmapped temporary file")
         vmfb, config, temp_file_to_unlink = load_vmfb_using_mmap(
             flatbuffer_blob, device, device_idx
         )
@@ -434,7 +444,6 @@ def load_flatbuffer(
 ):
     temp_file_to_unlink = None
     if mmap:
-        print(f"Loading flatbuffer at {flatbuffer_path} as a mmapped file")
         vmfb, config, temp_file_to_unlink = load_vmfb_using_mmap(
             flatbuffer_path, device, device_idx
         )
@@ -498,37 +507,56 @@ def get_results(
     config,
     frontend="torch",
     send_to_host=True,
+    debug_timeout: float = 5.0,
 ):
     """Runs a .vmfb file given inputs and config and returns output."""
-    device_inputs = [ireert.asdevicearray(config.device, a) for a in input]
-    result = compiled_vm[function_name](*device_inputs)
-    result_tensors = []
-    if isinstance(result, tuple):
-        if send_to_host:
-            for val in result:
-                result_tensors.append(np.asarray(val, val.dtype))
+    with DetailLogger(debug_timeout) as dl:
+        device_inputs = []
+        for input_array in input:
+            dl.log(f"Load to device: {input_array.shape}")
+            device_inputs.append(
+                ireert.asdevicearray(config.device, input_array)
+            )
+        dl.log(f"Invoke function: {function_name}")
+        result = compiled_vm[function_name](*device_inputs)
+        dl.log(f"Invoke complete")
+        result_tensors = []
+        if isinstance(result, tuple):
+            if send_to_host:
+                for val in result:
+                    dl.log(f"Result to host: {val.shape}")
+                    result_tensors.append(np.asarray(val, val.dtype))
+            else:
+                for val in result:
+                    result_tensors.append(val)
+            return result_tensors
+        elif isinstance(result, dict):
+            data = list(result.items())
+            if send_to_host:
+                res = np.array(data, dtype=object)
+                return np.copy(res)
+            return data
         else:
-            for val in result:
-                result_tensors.append(val)
-        return result_tensors
-    elif isinstance(result, dict):
-        data = list(result.items())
-        if send_to_host:
-            res = np.array(data, dtype=object)
-            return np.copy(res)
-        return data
-    else:
-        if send_to_host and result is not None:
-            return result.to_host()
-        return result
+            if send_to_host and result is not None:
+                dl.log("Result to host")
+                return result.to_host()
+            return result
+        dl.log("Execution complete")
 
 
+@functools.cache
 def get_iree_runtime_config(device):
     device = iree_device_map(device)
     haldriver = ireert.get_driver(device)
+    if device == "metal" and shark_args.device_allocator == "caching":
+        print(
+            "[WARNING] metal devices can not have a `caching` allocator."
+            "\nUsing default allocator `None`"
+        )
     haldevice = haldriver.create_device_by_uri(
         device,
-        allocators=shark_args.device_allocator,
+        # metal devices have a failure with caching allocators atm. blcking this util it gets fixed upstream.
+        allocators=shark_args.device_allocator if device != "metal" else None,
     )
     config = ireert.Config(device=haldevice)
     return config

shark/iree_utils/cpu_utils.py

@@ -14,6 +14,7 @@
 
 # All the iree_cpu related functionalities go here.
 
+import functools
 import subprocess
 import platform
 from shark.parser import shark_args
@@ -30,6 +31,7 @@ def get_cpu_count():
 
 
 # Get the default cpu args.
+@functools.cache
 def get_iree_cpu_args():
     uname = platform.uname()
     os_name, proc_name = uname.system, uname.machine
@@ -51,6 +53,7 @@ def get_iree_cpu_args():
 
 
 # Get iree runtime flags for cpu
+@functools.cache
 def get_iree_cpu_rt_args():
     default = get_cpu_count()
     default = default if default <= 8 else default - 2

shark/iree_utils/gpu_utils.py

@@ -14,12 +14,14 @@
 
 # All the iree_gpu related functionalities go here.
 
+import functools
 import iree.runtime as ireert
 import ctypes
 from shark.parser import shark_args
 
 
 # Get the default gpu args given the architecture.
+@functools.cache
 def get_iree_gpu_args():
     ireert.flags.FUNCTION_INPUT_VALIDATION = False
     ireert.flags.parse_flags("--cuda_allow_inline_execution")
@@ -37,6 +39,7 @@ def get_iree_gpu_args():
 
 
 # Get the default gpu args given the architecture.
+@functools.cache
 def get_iree_rocm_args():
     ireert.flags.FUNCTION_INPUT_VALIDATION = False
     # get arch from rocminfo.
@@ -65,6 +68,7 @@ CU_DEVICE_ATTRIBUTE_CLOCK_RATE = 13
 CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE = 36
 
 
+@functools.cache
 def get_cuda_sm_cc():
     libnames = ("libcuda.so", "libcuda.dylib", "nvcuda.dll")
     for libname in libnames:

shark/iree_utils/metal_utils.py

@@ -14,12 +14,15 @@
 
 # All the iree_vulkan related functionalities go here.
 
+import functools
+
 from shark.iree_utils._common import run_cmd
 import iree.runtime as ireert
 from sys import platform
 from shark.iree_utils.vulkan_target_env_utils import get_vulkan_target_env_flag
 
 
+@functools.cache
 def get_metal_device_name(device_num=0):
     iree_device_dump = run_cmd("iree-run-module --dump_devices")
     iree_device_dump = iree_device_dump[0].split("\n\n")

shark/iree_utils/trace.py

@@ -0,0 +1,76 @@
+# Copyright 2023 The Nod Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Tuple
+
+import os
+import threading
+import time
+
+
+def _enable_detail_trace() -> bool:
+    return os.getenv("SHARK_DETAIL_TRACE", "0") == "1"
+
+
+class DetailLogger:
+    """Context manager which can accumulate detailed log messages.
+
+    Detailed log is only emitted if the operation takes a long time
+    or errors.
+    """
+
+    def __init__(self, timeout: float):
+        self._timeout = timeout
+        self._messages: List[Tuple[float, str]] = []
+        self._start_time = time.time()
+        self._active = not _enable_detail_trace()
+        self._lock = threading.RLock()
+        self._cond = threading.Condition(self._lock)
+        self._thread = None
+
+    def __enter__(self):
+        self._thread = threading.Thread(target=self._run)
+        self._thread.start()
+        return self
+
+    def __exit__(self, type, value, traceback):
+        with self._lock:
+            self._active = False
+            self._cond.notify()
+        if traceback:
+            self.dump_on_error(f"exception")
+
+    def _run(self):
+        with self._lock:
+            timed_out = not self._cond.wait(self._timeout)
+        if timed_out:
+            self.dump_on_error(f"took longer than {self._timeout}s")
+
+    def log(self, msg):
+        with self._lock:
+            timestamp = time.time()
+            if self._active:
+                self._messages.append((timestamp, msg))
+            else:
+                print(f"  +{(timestamp - self._start_time) * 1000}ms: {msg}")
+
+    def dump_on_error(self, summary: str):
+        with self._lock:
+            if self._active:
+                print(f"::: Detailed report ({summary}):")
+                for timestamp, msg in self._messages:
+                    print(
+                        f"  +{(timestamp - self._start_time) * 1000}ms: {msg}"
+                    )
+            self._active = False

shark/iree_utils/vulkan_target_env_utils.py

@@ -13,8 +13,10 @@
 # limitations under the License.
 
 from collections import OrderedDict
+import functools
 
 
+@functools.cache
 def get_vulkan_target_env(vulkan_target_triple):
     arch, product, os = vulkan_target_triple.split("=")[1].split("-")
     triple = (arch, product, os)
@@ -52,6 +54,7 @@ def get_version(triple):
     return "v1.3"
 
 
+@functools.cache
 def get_extensions(triple):
     def make_ext_list(ext_list):
         res = ""
@@ -122,6 +125,7 @@ def get_extensions(triple):
     return make_ext_list(ext_list=ext)
 
 
+@functools.cache
 def get_vendor(triple):
     arch, product, os = triple
     if arch == "unknown":
@@ -146,6 +150,7 @@ def get_vendor(triple):
     return "Unknown"
 
 
+@functools.cache
 def get_device_type(triple):
     arch, product, _ = triple
     if arch == "unknown":
@@ -166,6 +171,7 @@ def get_device_type(triple):
 
 # get all the capabilities for the device
 # TODO: make a dataclass for capabilites and init using vulkaninfo
+@functools.cache
 def get_vulkan_target_capabilities(triple):
     def get_subgroup_val(l):
         return int(sum([subgroup_feature[sgf] for sgf in l]))

shark/iree_utils/vulkan_utils.py

@@ -14,13 +14,16 @@
 
 # All the iree_vulkan related functionalities go here.
 
+import functools
 from os import linesep
 from shark.iree_utils._common import run_cmd
 import iree.runtime as ireert
 from sys import platform
 from shark.iree_utils.vulkan_target_env_utils import get_vulkan_target_env_flag
+from shark.parser import shark_args
 
 
+@functools.cache
 def get_vulkan_device_name(device_num=0):
     vulkaninfo_dump, _ = run_cmd("vulkaninfo")
     vulkaninfo_dump = vulkaninfo_dump.split(linesep)
@@ -47,6 +50,7 @@ def get_os_name():
         return "linux"
 
 
+@functools.cache
 def get_vulkan_target_triple(device_name):
     """This method provides a target triple str for specified vulkan device.
 
@@ -171,6 +175,16 @@ def get_iree_vulkan_args(device_num=0, extra_args=[]):
     return res_vulkan_flag
 
 
+@functools.cache
+def get_iree_vulkan_runtime_flags():
+    vulkan_runtime_flags = [
+        f"--vulkan_large_heap_block_size={shark_args.vulkan_large_heap_block_size}",
+        f"--vulkan_validation_layers={'true' if shark_args.vulkan_validation_layers else 'false'}",
+        f"--vulkan_vma_allocator={'true' if shark_args.vulkan_vma_allocator else 'false'}",
+    ]
+    return vulkan_runtime_flags
+
+
 def set_iree_vulkan_runtime_flags(flags):
     for flag in flags:
         ireert.flags.parse_flags(flag)

shark/parser.py

@@ -114,7 +114,7 @@ parser.add_argument(
     "--device_allocator",
     type=str,
     nargs="*",
-    default=[],
+    default=["caching"],
     help="Specifies one or more HAL device allocator specs "
     "to augment the base device allocator",
     choices=["debug", "caching"],
@@ -126,4 +126,32 @@ parser.add_argument(
     help="passthrough flag for the iree flag of the same name. If None, defaults to cpu-count",
 )
 
+parser.add_argument(
+    "--vulkan_debug_utils",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Profiles vulkan device and collects the .rdc info.",
+)
+
+parser.add_argument(
+    "--vulkan_large_heap_block_size",
+    default="2073741824",
+    help="Flag for setting VMA preferredLargeHeapBlockSize for "
+    "vulkan device, default is 4G.",
+)
+
+parser.add_argument(
+    "--vulkan_validation_layers",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for disabling vulkan validation layers when benchmarking.",
+)
+
+parser.add_argument(
+    "--vulkan_vma_allocator",
+    default=False,
+    action=argparse.BooleanOptionalAction,
+    help="Flag for enabling / disabling Vulkan VMA Allocator.",
+)
+
 shark_args, unknown = parser.parse_known_args()

shark/shark_benchmark_runner.py

@@ -13,7 +13,11 @@
 # limitations under the License.
 
 from shark.shark_runner import SharkRunner
-from shark.iree_utils.compile_utils import export_iree_module_to_vmfb
+from shark.iree_utils.compile_utils import (
+    export_iree_module_to_vmfb,
+    load_flatbuffer,
+    get_iree_runtime_config,
+)
 from shark.iree_utils.benchmark_utils import (
     build_benchmark_args,
     run_benchmark_module,
@@ -79,22 +83,31 @@ class SharkBenchmarkRunner(SharkRunner):
         self.mlir_dialect = mlir_dialect
         self.extra_args = extra_args
         self.import_args = {}
+        self.temp_file_to_unlink = None
         SharkRunner.__init__(
             self,
             mlir_module,
             device,
             self.mlir_dialect,
             self.extra_args,
-            compile_vmfb=True,
+            compile_vmfb=False,
         )
-        if self.vmfb_file == None:
-            self.vmfb_file = export_iree_module_to_vmfb(
-                mlir_module,
-                device,
-                ".",
-                self.mlir_dialect,
-                extra_args=self.extra_args,
-            )
+        self.vmfb_file = export_iree_module_to_vmfb(
+            mlir_module,
+            device,
+            ".",
+            self.mlir_dialect,
+            extra_args=self.extra_args,
+        )
+        params = load_flatbuffer(
+            self.vmfb_file,
+            device,
+            mmap=True,
+        )
+        self.iree_compilation_module = params["vmfb"]
+        self.iree_config = params["config"]
+        self.temp_file_to_unlink = params["temp_file_to_unlink"]
+        del params
 
     def setup_cl(self, input_tensors):
         self.benchmark_cl = build_benchmark_args(
@@ -111,42 +124,41 @@ class SharkBenchmarkRunner(SharkRunner):
         elif self.mlir_dialect in ["mhlo", "tf"]:
             return self.benchmark_tf(modelname)
 
-    def benchmark_torch(self, modelname):
+    def benchmark_torch(self, modelname, device="cpu"):
         import torch
         from tank.model_utils import get_torch_model
 
-        if self.device == "cuda":
-            torch.set_default_tensor_type(torch.cuda.FloatTensor)
-            if self.enable_tf32:
-                torch.backends.cuda.matmul.allow_tf32 = True
+        # TODO: Pass this as an arg. currently the best way is to setup with BENCHMARK=1 if we want to use torch+cuda, else use cpu.
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if device == "cuda":
+            torch.set_default_device("cuda:0")
+            # if self.enable_tf32:
+            #    torch.backends.cuda.matmul.allow_tf32 = True
         else:
-            torch.set_default_tensor_type(torch.FloatTensor)
-        torch_device = torch.device(
-            "cuda:0" if self.device == "cuda" else "cpu"
-        )
+            torch.set_default_dtype(torch.float32)
+            torch.set_default_device("cpu")
+        torch_device = torch.device("cuda:0" if device == "cuda" else "cpu")
         HFmodel, input = get_torch_model(modelname, self.import_args)[:2]
         frontend_model = HFmodel.model
         frontend_model.to(torch_device)
-        input.to(torch_device)
-
-        # TODO: re-enable as soon as pytorch CUDA context issues are resolved
-        try:
-            frontend_model = torch.compile(
-                frontend_model, mode="max-autotune", backend="inductor"
-            )
-        except RuntimeError:
-            frontend_model = HFmodel.model
+        if device == "cuda":
+            frontend_model.cuda()
+            input.to(torch.device("cuda:0"))
+            print(input)
+        else:
+            frontend_model.cpu()
+            input.cpu()
 
         for i in range(shark_args.num_warmup_iterations):
             frontend_model.forward(input)
 
-        if self.device == "cuda":
+        if device == "cuda":
             torch.cuda.reset_peak_memory_stats()
         begin = time.time()
         for i in range(shark_args.num_iterations):
             out = frontend_model.forward(input)
         end = time.time()
-        if self.device == "cuda":
+        if device == "cuda":
             stats = torch.cuda.memory_stats()
             device_peak_b = stats["allocated_bytes.all.peak"]
             frontend_model.to(torch.device("cpu"))
@@ -158,7 +170,7 @@ class SharkBenchmarkRunner(SharkRunner):
         print(
             f"Torch benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
         )
-        if self.device == "cuda":
+        if device == "cuda":
             # Set device to CPU so we don't run into segfaults exiting pytest subprocesses.
             torch_device = torch.device("cpu")
         return [

shark/shark_compile.py

@@ -2,6 +2,40 @@ import os
 import tempfile
 from shark.shark_inference import SharkInference
 from shark.shark_importer import import_with_fx
+import torch
+import torch_mlir
+from torch_mlir.compiler_utils import run_pipeline_with_repro_report
+from typing import List, Tuple
+from io import BytesIO
+from brevitas_examples.llm.llm_quant.quantize import quantize_model
+from brevitas_examples.llm.llm_quant.run_utils import get_model_impl
+
+
+# fmt: off
+def quant〇matmul_rhs_group_quant〡shape(lhs: List[int], rhs: List[int], rhs_scale: List[int], rhs_zero_point: List[int], rhs_bit_width: int, rhs_group_size: int) -> List[int]:
+    if len(lhs) == 3 and len(rhs) == 2:
+        return [lhs[0], lhs[1], rhs[0]]
+    elif len(lhs) == 2 and len(rhs) == 2:
+        return [lhs[0], rhs[0]]
+    else:
+        raise ValueError("Input shapes not supported.")
+
+
+def quant〇matmul_rhs_group_quant〡dtype(lhs_rank_dtype: Tuple[int, int], rhs_rank_dtype: Tuple[int, int], rhs_scale_rank_dtype: Tuple[int, int], rhs_zero_point_rank_dtype: Tuple[int, int], rhs_bit_width: int, rhs_group_size: int) -> int:
+    # output dtype is the dtype of the lhs float input
+    lhs_rank, lhs_dtype = lhs_rank_dtype
+    return lhs_dtype
+
+
+def quant〇matmul_rhs_group_quant〡has_value_semantics(lhs, rhs, rhs_scale, rhs_zero_point, rhs_bit_width, rhs_group_size) -> None:
+    return
+
+
+brevitas_matmul_rhs_group_quant_library = [
+    quant〇matmul_rhs_group_quant〡shape,
+    quant〇matmul_rhs_group_quant〡dtype,
+    quant〇matmul_rhs_group_quant〡has_value_semantics]
+# fmt: on
 
 
 def load_vmfb(extended_model_name, device, mlir_dialect, extra_args=[]):
@@ -39,11 +73,90 @@ def compile_module(
     return shark_module
 
 
+def compile_int_precision(
+    model, inputs, precision, device, generate_vmfb, extended_model_name
+):
+    weight_bit_width = 4 if precision == "int4" else 8
+    weight_group_size = 128
+    quantize_model(
+        get_model_impl(model),
+        dtype=torch.float32,
+        weight_quant_type="asym",
+        weight_bit_width=weight_bit_width,
+        weight_param_method="stats",
+        weight_scale_precision="float",
+        weight_quant_granularity="per_group",
+        weight_group_size=weight_group_size,
+        quantize_weight_zero_point=False,
+        input_bit_width=None,
+        input_scale_type="float",
+        input_param_method="stats",
+        input_quant_type="asym",
+        input_quant_granularity="per_tensor",
+        quantize_input_zero_point=False,
+        seqlen=2048,
+    )
+    print("Weight quantization applied.")
+    torchscript_module = import_with_fx(
+        model,
+        inputs,
+        precision=precision,
+        mlir_type="torchscript",
+    )
+    mlir_module = torch_mlir.compile(
+        torchscript_module,
+        inputs,
+        output_type="torch",
+        backend_legal_ops=["quant.matmul_rhs_group_quant"],
+        extra_library=brevitas_matmul_rhs_group_quant_library,
+        use_tracing=False,
+        verbose=False,
+    )
+    print(f"[DEBUG] converting torch to linalg")
+    run_pipeline_with_repro_report(
+        mlir_module,
+        "builtin.module(func.func(torch-unpack-torch-tensor),torch-backend-to-linalg-on-tensors-backend-pipeline)",
+        description="Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR",
+    )
+    from contextlib import redirect_stdout
+
+    mlir_file_path = os.path.join(
+        os.getcwd(), f"{extended_model_name}_linalg.mlir"
+    )
+    with open(mlir_file_path, "w") as f:
+        with redirect_stdout(f):
+            print(mlir_module.operation.get_asm())
+    mlir_module = str(mlir_module)
+    mlir_module = mlir_module.encode("UTF-8")
+    mlir_module = BytesIO(mlir_module)
+    bytecode = mlir_module.read()
+    print(f"Elided IR written for {extended_model_name}")
+    return bytecode
+    shark_module = SharkInference(
+        mlir_module=bytecode, device=device, mlir_dialect="tm_tensor"
+    )
+    extra_args = [
+        "--iree-hal-dump-executable-sources-to=ies",
+        "--iree-vm-target-truncate-unsupported-floats",
+        "--iree-codegen-check-ir-before-llvm-conversion=false",
+        "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+    ]
+    return (
+        compile_module(
+            shark_module,
+            extended_model_name=extended_model_name,
+            generate_vmfb=generate_vmfb,
+            extra_args=extra_args,
+        ),
+        bytecode,
+    )
+
+
 def shark_compile_through_fx(
     model,
     inputs,
     extended_model_name,
-    is_f16=False,
+    precision,
     f16_input_mask=None,
     save_dir=tempfile.gettempdir(),
     debug=False,
@@ -52,6 +165,7 @@ def shark_compile_through_fx(
     device=None,
     mlir_dialect="tm_tensor",
 ):
+    is_f16 = precision == "fp16"
     if generate_or_load_vmfb:
         shark_module = load_vmfb(
             extended_model_name=extended_model_name,
@@ -70,18 +184,34 @@ def shark_compile_through_fx(
     if "cuda" in device:
         shark_args.enable_tf32 = True
 
-    (
-        mlir_module,
-        _,
-    ) = import_with_fx(
-        model=model,
-        inputs=inputs,
-        is_f16=is_f16,
-        f16_input_mask=f16_input_mask,
-        debug=debug,
-        model_name=extended_model_name,
-        save_dir=save_dir,
-    )
+    if precision in ["int4", "int8"]:
+        mlir_module = compile_int_precision(
+            model,
+            inputs,
+            precision,
+            device,
+            generate_or_load_vmfb,
+            extended_model_name,
+        )
+        extra_args = [
+            "--iree-hal-dump-executable-sources-to=ies",
+            "--iree-vm-target-truncate-unsupported-floats",
+            "--iree-codegen-check-ir-before-llvm-conversion=false",
+            "--iree-vm-bytecode-module-output-format=flatbuffer-binary",
+        ]
+    else:
+        (
+            mlir_module,
+            _,
+        ) = import_with_fx(
+            model=model,
+            inputs=inputs,
+            is_f16=is_f16,
+            f16_input_mask=f16_input_mask,
+            debug=debug,
+            model_name=extended_model_name,
+            save_dir=save_dir,
+        )
 
     shark_module = SharkInference(
         mlir_module,

shark/shark_downloader.py

@@ -111,22 +111,20 @@ os.makedirs(WORKDIR, exist_ok=True)
 def check_dir_exists(model_name, frontend="torch", dynamic=""):
     model_dir = os.path.join(WORKDIR, model_name)
 
-    # Remove the _tf keyword from end.
-    if frontend in ["tf", "tensorflow"]:
-        model_name = model_name[:-3]
-    elif frontend in ["tflite"]:
-        model_name = model_name[:-7]
-    elif frontend in ["torch", "pytorch"]:
-        model_name = model_name[:-6]
+    # Remove the _tf keyword from end only for non-SD models.
+    if not any(model in model_name for model in ["clip", "unet", "vae"]):
+        if frontend in ["tf", "tensorflow"]:
+            model_name = model_name[:-3]
+        elif frontend in ["tflite"]:
+            model_name = model_name[:-7]
+        elif frontend in ["torch", "pytorch"]:
+            model_name = model_name[:-6]
+
+    model_mlir_file_name = f"{model_name}{dynamic}_{frontend}.mlir"
 
     if os.path.isdir(model_dir):
         if (
-            os.path.isfile(
-                os.path.join(
-                    model_dir,
-                    model_name + dynamic + "_" + str(frontend) + ".mlir",
-                )
-            )
+            os.path.isfile(os.path.join(model_dir, model_mlir_file_name))
             and os.path.isfile(os.path.join(model_dir, "function_name.npy"))
             and os.path.isfile(os.path.join(model_dir, "inputs.npz"))
             and os.path.isfile(os.path.join(model_dir, "golden_out.npz"))

shark/shark_generate_model_config.py

@@ -1,5 +1,7 @@
 import re
 import json
+import numpy as np
+
 import torch_mlir
 from iree.compiler import compile_str
 from shark.shark_importer import import_with_fx, get_f16_inputs
@@ -11,6 +13,7 @@ class GenerateConfigFile:
         model,
         num_sharding_stages: int,
         sharding_stages_id: list[str],
+        units_in_each_stage: list[int],
         model_input=None,
         config_file_path="model_config.json",
     ):
@@ -22,13 +25,16 @@ class GenerateConfigFile:
         ), "Number of sharding stages should be equal to the list of their ID"
         self.model_input = model_input
         self.config_file_path = config_file_path
+        # (Nithin) this is a quick fix - revisit and rewrite
+        self.units_in_each_stage = np.array(units_in_each_stage)
+        self.track_loop = np.zeros(len(self.sharding_stages_id)).astype(int)
 
     def split_into_dispatches(
         self,
         backend,
-        fx_tracing_required=True,
+        fx_tracing_required=False,
         f16_model=False,
-        torch_mlir_tracing=False,
+        torch_mlir_tracing=True,
     ):
         graph_for_compilation = self.model
         if fx_tracing_required:
@@ -95,7 +101,17 @@ class GenerateConfigFile:
             if substring_before_final_period in model_dictionary:
                 del model_dictionary[substring_before_final_period]
 
-            layer_dict = {n: "None" for n in self.sharding_stages_id}
+            # layer_dict = {n: "None" for n in self.sharding_stages_id}
+
+            # By default embed increasing device id's for each layer
+            increasing_wraparound_idx_list = (
+                self.track_loop % self.units_in_each_stage
+            )
+            layer_dict = {
+                n: int(increasing_wraparound_idx_list[idx][0][0])
+                for idx, n in enumerate(self.sharding_stages_id)
+            }
+            self.track_loop += 1
             model_dictionary[name] = layer_dict
 
         self.generate_json(model_dictionary)
@@ -103,3 +119,29 @@ class GenerateConfigFile:
     def generate_json(self, artifacts):
         with open(self.config_file_path, "w") as outfile:
             json.dump(artifacts, outfile)
+
+
+if __name__ == "__main__":
+    import torch
+    from transformers import AutoTokenizer
+
+    hf_model_path = "TheBloke/vicuna-7B-1.1-HF"
+    tokenizer = AutoTokenizer.from_pretrained(hf_model_path, use_fast=False)
+    compilation_prompt = "".join(["0" for _ in range(17)])
+    compilation_input_ids = tokenizer(
+        compilation_prompt,
+        return_tensors="pt",
+    ).input_ids
+    compilation_input_ids = torch.tensor(compilation_input_ids).reshape(
+        [1, 19]
+    )
+    firstVicunaCompileInput = (compilation_input_ids,)
+    from apps.language_models.src.model_wrappers.vicuna_model import (
+        FirstVicuna,
+        SecondVicuna,
+        CombinedModel,
+    )
+
+    model = CombinedModel()
+    c = GenerateConfigFile(model, 1, ["gpu_id"], firstVicunaCompileInput)
+    c.split_into_layers()

shark/shark_importer.py

@@ -488,7 +488,7 @@ def flatten_training_input(inputs):
     return tuple(flattened_input)
 
 
-# TODO: get rid of is_f16 by using precision
+# TODO: Remove is_f16 and fix all calls with using precision instead
 # Applies fx conversion to the model and imports the mlir.
 def import_with_fx(
     model,
@@ -612,7 +612,7 @@ def import_with_fx(
         replace_call_fn_target(
             fx_g,
             src=matmul_rhs_group_quant_placeholder,
-            target=torch.ops.brevitas.matmul_rhs_group_quant,
+            target=torch.ops.quant.matmul_rhs_group_quant,
         )
 
         fx_g.recompile()

shark/shark_inference.py

@@ -141,6 +141,10 @@ class SharkInference:
     def __call__(self, function_name: str, inputs: tuple, send_to_host=True):
         return self.shark_runner.run(function_name, inputs, send_to_host)
 
+    # forward function.
+    def forward(self, inputs: tuple, send_to_host=True):
+        return self.shark_runner.run("forward", inputs, send_to_host)
+
     # Get all function names defined within the compiled module.
     def get_functions_in_module(self):
         return self.shark_runner.get_functions_in_module()

tank/all_models.csv

@@ -13,7 +13,6 @@ google/vit-base-patch16-224,stablehlo,tf,1e-2,1e-3,tf_vit,nhcw-nhwc,False,False,
 microsoft/MiniLM-L12-H384-uncased,stablehlo,tf,1e-2,1e-3,tf_hf,None,True,False,False,"Fails during iree-compile.",""
 microsoft/layoutlm-base-uncased,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"",""
 microsoft/mpnet-base,stablehlo,tf,1e-2,1e-2,default,None,True,True,True,"",""
-albert-base-v2,linalg,torch,1e-2,1e-3,default,None,True,True,True,"issue with aten.tanh in torch-mlir",""
 alexnet,linalg,torch,1e-2,1e-3,default,None,True,True,False,"https://github.com/nod-ai/SHARK/issues/879",""
 bert-base-cased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
 bert-base-uncased,linalg,torch,1e-2,1e-3,default,None,False,True,False,"",""
@@ -30,7 +29,7 @@ nvidia/mit-b0,linalg,torch,1e-2,1e-3,default,None,True,True,True,"https://github
 resnet101,linalg,torch,1e-2,1e-3,default,nhcw-nhwc/img2col,True,False,False,"","macos"
 resnet18,linalg,torch,1e-2,1e-3,default,None,True,True,False,"","macos"
 resnet50,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
-resnet50_fp16,linalg,torch,1e-2,1e-2,default,nhcw-nhwc/img2col,True,False,True,"",""
+resnet50_fp16,linalg,torch,1e-2,1e-2,default,nhcw-nhwc/img2col,True,True,True,"Numerics issues, awaiting cuda-independent fp16 integration",""
 squeezenet1_0,linalg,torch,1e-2,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
 wide_resnet50_2,linalg,torch,1e-2,1e-3,default,nhcw-nhwc/img2col,True,False,False,"","macos"
 efficientnet-v2-s,stablehlo,tf,1e-02,1e-3,default,nhcw-nhwc,False,False,False,"","macos"
@@ -44,4 +43,3 @@ t5-base,linalg,torch,1e-2,1e-3,default,None,True,True,True,"Inputs for seq2seq m
 t5-base,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"","macos"
 t5-large,linalg,torch,1e-2,1e-3,default,None,True,True,True,"Inputs for seq2seq models in torch currently unsupported","macos"
 t5-large,stablehlo,tf,1e-2,1e-3,default,None,False,False,False,"","macos"
-stabilityai/stable-diffusion-2-1-base,linalg,torch,1e-3,1e-3,default,None,True,False,False,"","macos"

tank/generate_sharktank.py

@@ -16,12 +16,6 @@ import subprocess as sp
 import hashlib
 import numpy as np
 from pathlib import Path
-from apps.stable_diffusion.src.models import (
-    model_wrappers as mw,
-)
-from apps.stable_diffusion.src.utils.stable_args import (
-    args,
-)
 
 
 def create_hash(file_name):
@@ -60,31 +54,6 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
             print("generating artifacts for: " + torch_model_name)
             model = None
             input = None
-            if model_type == "stable_diffusion":
-                args.use_tuned = False
-                args.import_mlir = True
-                args.local_tank_cache = local_tank_cache
-
-                precision_values = ["fp16"]
-                seq_lengths = [64, 77]
-                for precision_value in precision_values:
-                    args.precision = precision_value
-                    for length in seq_lengths:
-                        model = mw.SharkifyStableDiffusionModel(
-                            model_id=torch_model_name,
-                            custom_weights="",
-                            precision=precision_value,
-                            max_len=length,
-                            width=512,
-                            height=512,
-                            use_base_vae=False,
-                            custom_vae="",
-                            debug=True,
-                            sharktank_dir=local_tank_cache,
-                            generate_vmfb=False,
-                        )
-                        model()
-                continue
             if model_type == "vision":
                 model, input, _ = get_vision_model(
                     torch_model_name, import_args
@@ -103,10 +72,11 @@ def save_torch_model(torch_model_list, local_tank_cache, import_args):
                 model, input, _ = get_hf_img_cls_model(
                     torch_model_name, import_args
                 )
-            elif model_type == "fp16":
-                model, input, _ = get_fp16_model(torch_model_name, import_args)
             torch_model_name = torch_model_name.replace("/", "_")
-            if import_args["batch_size"] != 1:
+            if import_args["batch_size"] > 1:
+                print(
+                    f"Batch size for this model set to {import_args['batch_size']}"
+                )
                 torch_model_dir = os.path.join(
                     local_tank_cache,
                     str(torch_model_name)
@@ -391,7 +361,7 @@ if __name__ == "__main__":
 
     # old_import_args = parser.parse_import_args()
     import_args = {
-        "batch_size": "1",
+        "batch_size": 1,
     }
     print(import_args)
     home = str(Path.home())
@@ -404,11 +374,6 @@ if __name__ == "__main__":
         os.path.dirname(__file__), "tflite", "tflite_model_list.csv"
     )
 
-    save_torch_model(
-        os.path.join(os.path.dirname(__file__), "torch_sd_list.csv"),
-        WORKDIR,
-        import_args,
-    )
     save_torch_model(torch_model_csv, WORKDIR, import_args)
-    save_tf_model(tf_model_csv, WORKDIR, import_args)
-    save_tflite_model(tflite_model_csv, WORKDIR, import_args)
+    # save_tf_model(tf_model_csv, WORKDIR, import_args)
+    # save_tflite_model(tflite_model_csv, WORKDIR, import_args)

tank/model_utils.py

@@ -278,7 +278,7 @@ def get_vision_model(torch_model, import_args):
         int(import_args["batch_size"]), 3, *input_image_size
     )
     actual_out = model(test_input)
-    if fp16_model is not None:
+    if fp16_model == True:
         test_input_fp16 = test_input.to(
             device=torch.device("cuda"), dtype=torch.half
         )

tank/test_models.py

@@ -145,6 +145,7 @@ class SharkModuleTester:
         shark_args.shark_prefix = self.shark_tank_prefix
         shark_args.local_tank_cache = self.local_tank_cache
         shark_args.dispatch_benchmarks = self.benchmark_dispatches
+        shark_args.enable_tf32 = self.tf32
 
         if self.benchmark_dispatches is not None:
             _m = self.config["model_name"].split("/")
@@ -216,10 +217,12 @@ class SharkModuleTester:
 
         result = shark_module(func_name, inputs)
         golden_out, result = self.postprocess_outputs(golden_out, result)
-        if self.tf32 == "true":
-            print("Validating with relaxed tolerances.")
-            atol = 1e-02
-            rtol = 1e-03
+        if self.tf32 == True:
+            print(
+                "Validating with relaxed tolerances for TensorFloat32 calculations."
+            )
+            self.config["atol"] = 1e-01
+            self.config["rtol"] = 1e-02
         try:
             np.testing.assert_allclose(
                 golden_out,
@@ -254,9 +257,6 @@ class SharkModuleTester:
         model_config = {
             "batch_size": self.batch_size,
         }
-        shark_args.enable_tf32 = self.tf32
-        if shark_args.enable_tf32 == True:
-            shark_module.compile()
 
         shark_args.onnx_bench = self.onnx_bench
         shark_module.shark_runner.benchmark_all_csv(

tank/torch_model_list.csv

@@ -5,7 +5,6 @@ microsoft/MiniLM-L12-H384-uncased,True,hf,True,linalg,False,66M,"nlp;bert-varian
 bert-base-uncased,True,hf,True,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 bert-base-cased,True,hf,True,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 google/mobilebert-uncased,True,hf,True,linalg,False,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
-alexnet,False,vision,True,linalg,False,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
 resnet18,False,vision,True,linalg,False,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"
 resnet50,False,vision,True,linalg,False,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
 resnet101,False,vision,True,linalg,False,29M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
@@ -18,11 +17,9 @@ facebook/deit-small-distilled-patch16-224,True,hf_img_cls,False,linalg,False,22M
 microsoft/beit-base-patch16-224-pt22k-ft22k,True,hf_img_cls,False,linalg,False,86M,"image-classification,transformer-encoder,bert-variant,vision-transformer",N/A
 nvidia/mit-b0,True,hf_img_cls,False,linalg,False,3.7M,"image-classification,transformer-encoder",SegFormer
 mnasnet1_0,False,vision,True,linalg,False,-,"cnn, torchvision, mobile, architecture-search","Outperforms other mobile CNNs on Accuracy vs. Latency"
-resnet50_fp16,False,vision,True,linalg,False,23M,"cnn,image-classification,residuals,resnet-variant","Bottlenecks with only conv2d (1x1 conv -> 3x3 conv -> 1x1 conv blocks)"
-bert-base-uncased_fp16,True,fp16,False,linalg,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 bert-large-uncased,True,hf,True,linalg,False,330M,"nlp;bert-variant;transformer-encoder","24 layers, 1024 hidden units, 16 attention heads"
 bert-base-uncased,True,hf,False,stablehlo,False,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 gpt2,True,hf_causallm,False,stablehlo,True,125M,"nlp;transformer-encoder","-"
 facebook/opt-125m,True,hf,False,stablehlo,True,125M,"nlp;transformer-encoder","-"
 distilgpt2,True,hf,False,stablehlo,True,88M,"nlp;transformer-encoder","-"
-microsoft/deberta-v3-base,True,hf,False,stablehlo,True,88M,"nlp;transformer-encoder","-"
+microsoft/deberta-v3-base,True,hf,False,stablehlo,True,88M,"nlp;transformer-encoder","-"