From 7aa90522a3cbd1d90b4bd4199554e6cc8298e17f Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 09:07:26 +0100 Subject: [PATCH 01/20] Add gemma model and change llm_model to list --- prompting/llms/model_zoo.py | 41 +++++++++++++++++++++++-------------- shared/settings.py | 2 +- 2 files changed, 27 insertions(+), 16 deletions(-) diff --git a/prompting/llms/model_zoo.py b/prompting/llms/model_zoo.py index d078a038..d39ac9d2 100644 --- a/prompting/llms/model_zoo.py +++ b/prompting/llms/model_zoo.py @@ -1,4 +1,4 @@ -from typing import ClassVar +from typing import ClassVar, List, Union import numpy as np from loguru import logger @@ -18,20 +18,31 @@ def __hash__(self): class ModelZoo: - # Currently, we are only using one single model - the one the validator is running - models_configs: ClassVar[list[ModelConfig]] = [ - ModelConfig( - llm_model_id=settings.shared_settings.LLM_MODEL, - reward=1, - min_ram=settings.shared_settings.MAX_ALLOWED_VRAM_GB, - ), - ] - - # Code below can be uncommended for testing purposes and demonstrates how we rotate multiple LLMs in the future - # models_configs: ClassVar[list[ModelConfig]] = [ - # ModelConfig(model_id="casperhansen/mistral-nemo-instruct-2407-awq", reward=0.1, min_ram=24), - # ModelConfig(model_id="casperhansen/qwen2-0.5b-instruct-awq", reward=0.1, min_ram=10), - # ] + # Dynamically create model configs from the list of models in settings + models_configs: ClassVar[list[ModelConfig]] = [] + + @classmethod + def _initialize_models(cls): + # Clear existing models + cls.models_configs = [] + + # Handle both string and list configurations + models = settings.shared_settings.LLM_MODEL + if isinstance(models, str): + models = [models] + + # Add each model from settings to the configs + for model in models: + cls.models_configs.append( + ModelConfig( + llm_model_id=model, + reward=1/len(models), + min_ram=settings.shared_settings.MAX_ALLOWED_VRAM_GB, + ) + ) + + # Initialize models when module is loaded + _initialize_models() @classmethod def get_all_models(cls) -> list[str]: diff --git a/shared/settings.py b/shared/settings.py index 81aeb525..a4de5479 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -128,7 +128,7 @@ class SharedSettings(BaseSettings): MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") LLM_MAX_MODEL_LEN: int = Field(4096, env="LLM_MAX_MODEL_LEN") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: str = Field("hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", env="LLM_MODEL") + LLM_MODEL: list[str] = Field(["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"], env="LLM_MODEL") SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From b992a279f9375a53c5ae0cc87e278fc8fb68705c Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 09:08:04 +0100 Subject: [PATCH 02/20] Linting --- prompting/llms/model_zoo.py | 12 ++++++------ shared/settings.py | 4 +++- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/prompting/llms/model_zoo.py b/prompting/llms/model_zoo.py index d39ac9d2..edba9d0b 100644 --- a/prompting/llms/model_zoo.py +++ b/prompting/llms/model_zoo.py @@ -1,4 +1,4 @@ -from typing import ClassVar, List, Union +from typing import ClassVar import numpy as np from loguru import logger @@ -20,27 +20,27 @@ def __hash__(self): class ModelZoo: # Dynamically create model configs from the list of models in settings models_configs: ClassVar[list[ModelConfig]] = [] - + @classmethod def _initialize_models(cls): # Clear existing models cls.models_configs = [] - + # Handle both string and list configurations models = settings.shared_settings.LLM_MODEL if isinstance(models, str): models = [models] - + # Add each model from settings to the configs for model in models: cls.models_configs.append( ModelConfig( llm_model_id=model, - reward=1/len(models), + reward=1 / len(models), min_ram=settings.shared_settings.MAX_ALLOWED_VRAM_GB, ) ) - + # Initialize models when module is loaded _initialize_models() diff --git a/shared/settings.py b/shared/settings.py index a4de5479..ed8f15f6 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -128,7 +128,9 @@ class SharedSettings(BaseSettings): MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") LLM_MAX_MODEL_LEN: int = Field(4096, env="LLM_MAX_MODEL_LEN") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: list[str] = Field(["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"], env="LLM_MODEL") + LLM_MODEL: list[str] = Field( + ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"], env="LLM_MODEL" + ) SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From 44001c23a2ebfc914e05711255dabb811d8fc5a8 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 09:17:22 +0100 Subject: [PATCH 03/20] Remove class method --- prompting/llms/model_zoo.py | 35 ++++++++++++++--------------------- 1 file changed, 14 insertions(+), 21 deletions(-) diff --git a/prompting/llms/model_zoo.py b/prompting/llms/model_zoo.py index edba9d0b..05034284 100644 --- a/prompting/llms/model_zoo.py +++ b/prompting/llms/model_zoo.py @@ -21,28 +21,21 @@ class ModelZoo: # Dynamically create model configs from the list of models in settings models_configs: ClassVar[list[ModelConfig]] = [] - @classmethod - def _initialize_models(cls): - # Clear existing models - cls.models_configs = [] - - # Handle both string and list configurations - models = settings.shared_settings.LLM_MODEL - if isinstance(models, str): - models = [models] - - # Add each model from settings to the configs - for model in models: - cls.models_configs.append( - ModelConfig( - llm_model_id=model, - reward=1 / len(models), - min_ram=settings.shared_settings.MAX_ALLOWED_VRAM_GB, - ) + # Initialize models directly in the class + # Handle both string and list configurations + models = settings.shared_settings.LLM_MODEL + if isinstance(models, str): + models = [models] + + # Add each model from settings to the configs + for model in models: + models_configs.append( + ModelConfig( + llm_model_id=model, + reward=1 / len(models), + min_ram=settings.shared_settings.MAX_ALLOWED_VRAM_GB, ) - - # Initialize models when module is loaded - _initialize_models() + ) @classmethod def get_all_models(cls) -> list[str]: From 071e72587e0bd67beef2f305be6cd18b1c7808ec Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 11:32:11 +0100 Subject: [PATCH 04/20] Adjust for LLM_MODEL being a list --- prompting/tasks/base_task.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/prompting/tasks/base_task.py b/prompting/tasks/base_task.py index ecedbf09..637bde9d 100644 --- a/prompting/tasks/base_task.py +++ b/prompting/tasks/base_task.py @@ -80,7 +80,7 @@ async def make_reference(self, dataset_entry: DatasetEntry) -> str: def generate_reference(self, messages: list[str]) -> str: """Generates a reference answer to be used for scoring miner completions""" - self.reference = model_manager.get_model(settings.shared_settings.LLM_MODEL).generate( + self.reference = model_manager.get_model(settings.shared_settings.LLM_MODEL[0]).generate( messages=messages ) # This should be a list of dict if self.reference is None: From 4fa07ce274e3c03975763abdd915987c8f0e5480 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 12:21:54 +0100 Subject: [PATCH 05/20] Add logic for deleting hf models --- prompting/llms/model_manager.py | 46 ++++++++++++++++++++++++++++++--- 1 file changed, 43 insertions(+), 3 deletions(-) diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index 0e2e10b0..39958a08 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -83,13 +83,53 @@ def unload_model(self, model_config: ModelConfig): return try: - del self.active_models[model_config].llm.llm_engine.model_executor.driver_worker + # Get the model instance + model_instance = self.active_models[model_config] + + # Different model implementations have different structures + # Handle vLLM-based models + # if hasattr(model_instance, 'llm') and hasattr(model_instance.llm, 'llm_engine'): + # if hasattr(model_instance.llm.llm_engine, 'model_executor') and hasattr(model_instance.llm.llm_engine.model_executor, 'driver_worker'): + # del model_instance.llm.llm_engine.model_executor.driver_worker + + # Handle pipeline-based models - directly delete components + if hasattr(model_instance, 'llm'): + # Directly delete model and tokenizer references + if hasattr(model_instance.llm, 'model'): + del model_instance.llm.model + + if hasattr(model_instance.llm, 'tokenizer'): + del model_instance.llm.tokenizer + + # If the llm itself is a pipeline, delete it entirely + del model_instance.llm + + # Remove the model from active models del self.active_models[model_config] + + # Force Python garbage collection + gc.collect() + + # Clear CUDA cache + torch.cuda.empty_cache() + + # Additional memory cleanup for PyTorch + if torch.cuda.is_available(): + # Reset peak memory stats + torch.cuda.reset_peak_memory_stats() + # Synchronize CUDA to ensure operations are complete + torch.cuda.synchronize() + + logger.info(f"Successfully unloaded model {model_config.llm_model_id}") + except Exception as ex: logger.error(f"Failed to unload model {model_config.llm_model_id}. Error: {str(ex)}") - gc.collect() + + # Update used RAM tracking self.used_ram -= model_config.min_ram - torch.cuda.empty_cache() + + # Log current memory state + GPUInfo.log_gpu_info() def get_or_load_model(self, llm_model_id: str) -> ReproducibleHF: model_config = ModelZoo.get_model_by_id(llm_model_id) From 1904ff7fb03f081bfa7d36c13606f81b6680a24f Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Fri, 14 Mar 2025 13:11:19 +0100 Subject: [PATCH 06/20] Move to cpu then delete --- prompting/llms/model_manager.py | 30 ++++++++++++++++++++++-------- 1 file changed, 22 insertions(+), 8 deletions(-) diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index 39958a08..756fbd9f 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -88,26 +88,40 @@ def unload_model(self, model_config: ModelConfig): # Different model implementations have different structures # Handle vLLM-based models - # if hasattr(model_instance, 'llm') and hasattr(model_instance.llm, 'llm_engine'): - # if hasattr(model_instance.llm.llm_engine, 'model_executor') and hasattr(model_instance.llm.llm_engine.model_executor, 'driver_worker'): - # del model_instance.llm.llm_engine.model_executor.driver_worker + if hasattr(model_instance, 'llm') and hasattr(model_instance.llm, 'llm_engine'): + if hasattr(model_instance.llm.llm_engine, 'model_executor') and hasattr(model_instance.llm.llm_engine.model_executor, 'driver_worker'): + del model_instance.llm.llm_engine.model_executor.driver_worker - # Handle pipeline-based models - directly delete components + # Handle pipeline-based models with a hybrid approach if hasattr(model_instance, 'llm'): - # Directly delete model and tokenizer references + # Try to move model to CPU first if it's a PyTorch model if hasattr(model_instance.llm, 'model'): + try: + # Check if it's a PyTorch model with a 'to' method + if hasattr(model_instance.llm.model, 'to'): + logger.debug(f"Moving model {model_config.llm_model_id} to CPU before deletion") + model_instance.llm.model.to('cpu') + # Small delay to allow memory transfer + import time + time.sleep(0.1) + except Exception as e: + logger.debug(f"Could not move model to CPU: {str(e)}, proceeding with direct deletion") + + # Delete the model reference del model_instance.llm.model + # Handle tokenizer if hasattr(model_instance.llm, 'tokenizer'): del model_instance.llm.tokenizer - # If the llm itself is a pipeline, delete it entirely + # Delete the llm object itself del model_instance.llm - # Remove the model from active models + # Remove the model from active models dictionary del self.active_models[model_config] - # Force Python garbage collection + # Force Python garbage collection multiple times to ensure cleanup + gc.collect() gc.collect() # Clear CUDA cache From 10f5897881c1a6df393edcb99e603d7160e29e21 Mon Sep 17 00:00:00 2001 From: Dmytro Bobrenko <17252809+dbobrenko@users.noreply.github.com> Date: Mon, 17 Mar 2025 07:43:27 +0000 Subject: [PATCH 07/20] Fix multi-choice --- prompting/rewards/multi_choice.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/prompting/rewards/multi_choice.py b/prompting/rewards/multi_choice.py index e2cdc1c2..04c927dd 100644 --- a/prompting/rewards/multi_choice.py +++ b/prompting/rewards/multi_choice.py @@ -40,6 +40,9 @@ def process_predictions(self, predictions: dict[str, float]) -> dict[str, float] self.choice_map[k.lower()]: float(v) for k, v in predictions.items() if k.lower() in self.choice_map } + if any(v < 0 for v in valid_choices.values()): + raise ValueError(f"Negative values are not allowed: {valid_choices}") + total = sum(valid_choices.values()) if np.isclose(total, 0.0): raise ValueError(f"Values sum up to 0, total={total}") From a16402149266f4db58f21bea8d8b4840c2b4a00e Mon Sep 17 00:00:00 2001 From: Dmytro Bobrenko <17252809+dbobrenko@users.noreply.github.com> Date: Mon, 17 Mar 2025 07:44:56 +0000 Subject: [PATCH 08/20] Update version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4f92dca9..06b81475 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "prompting" -version = "2.17.5" +version = "2.17.6" description = "Subnetwork 1 runs on Bittensor and is maintained by Macrocosmos. It's an effort to create decentralised AI" authors = ["Kalei Brady, Dmytro Bobrenko, Felix Quinque, Steffen Cruz, Richard Wardle"] readme = "README.md" From 61423665104b8e959d6e01c9a5c11a9adc98c252 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 14:23:53 +0100 Subject: [PATCH 09/20] Remove unused pipeline --- prompting/llms/hf_llm.py | 1 - 1 file changed, 1 deletion(-) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index a24b9fb8..d3a12682 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -26,7 +26,6 @@ def __init__( self.valid_generation_params = set( AutoModelForCausalLM.from_pretrained(model_id).generation_config.to_dict().keys() ) - self.llm = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer) @torch.inference_mode() def generate( From 2280b6f79e3e214e60579ec9bd4c0f02554c5141 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 14:50:39 +0100 Subject: [PATCH 10/20] Load model based on settings config --- prompting/llms/hf_llm.py | 41 ++++++++++++++++++++++++++-------------- shared/settings.py | 4 ++++ 2 files changed, 31 insertions(+), 14 deletions(-) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index d3a12682..6d5cebb2 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -2,8 +2,15 @@ import numpy as np import torch -from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel, pipeline +from transformers import ( + AutoModelForCausalLM, + AutoModelForImageTextToText, + AutoProcessor, + AutoTokenizer, + PreTrainedModel, +) +from shared import settings class ReproducibleHF: def __init__( @@ -15,16 +22,27 @@ def __init__( """Deterministic HuggingFace model.""" self._device = device self.sampling_params = {} if sampling_params is None else sampling_params - self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( - model_id, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - device_map=self._device, - ) + if settings.shared_settings.LLM_TYPE.get(model_id) == "llama": + self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + device_map=self._device, + ) + self.tokenizer = AutoTokenizer.from_pretrained(model_id) + elif settings.shared_settings.LLM_TYPE.get(model_id) == "image-text-to-text": + self.model: AutoModelForImageTextToText = AutoModelForImageTextToText.from_pretrained( + model_id, + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + device_map=self._device, + ) + self.tokenizer = AutoProcessor.from_pretrained(model_id) + else: + raise ValueError(f"Model type {settings.shared_settings.LLM_TYPE.get(model_id)} not supported") - self.tokenizer = AutoTokenizer.from_pretrained(model_id) self.valid_generation_params = set( - AutoModelForCausalLM.from_pretrained(model_id).generation_config.to_dict().keys() + self.model.generation_config.to_dict().keys() ) @torch.inference_mode() @@ -71,8 +89,3 @@ def set_random_seeds(self, seed: int | None = 42): torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False - - -# if __name__ == "__main__": -# llm = ReproducibleHF(model="Qwen/Qwen2-0.5B", tensor_parallel_size=1, seed=42) -# llm.generate({"role": "user", "content": "Hello, world!"}) diff --git a/shared/settings.py b/shared/settings.py index ed8f15f6..a729854f 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -131,6 +131,10 @@ class SharedSettings(BaseSettings): LLM_MODEL: list[str] = Field( ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"], env="LLM_MODEL" ) + LLM_TYPE: dict[str, str] = { + "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4": "text-generation", + "google/gemma-3-27b-it": "image-text-to-text", + } SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From 7eacb91d3bdc84fd13cfc0255a968802dc9b8401 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 15:02:12 +0100 Subject: [PATCH 11/20] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 06b81475..c3a7977c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -147,7 +147,7 @@ pillow = "^11.0.0" torch = { version = "2.5.1", optional = true } # TODO: Switch to original repo when this PR to fix setup gets merged: https://github.com/casper-hansen/AutoAWQ/pull/715 autoawq = { git = "https://github.com/jiqing-feng/AutoAWQ.git", rev = "ae782a99df2f72a2c28764452844cb2d65bd8ffc", optional = true } -transformers = { version = "<=4.47.1", optional = true } +transformers = { git = "https://github.com/huggingface/transformers.git", rev = "v4.49.0-Gemma-3", optional = true } torchvision = { version = ">=0.20.1", optional = true } accelerate = { version = ">=1.1.1", optional = true } angle-emb = { version = "0.4.3", optional = true } From e8b18aaed6385ddb22df9640eacfff14aa6c78c1 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 15:15:02 +0100 Subject: [PATCH 12/20] Rename llama key --- prompting/llms/hf_llm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index 6d5cebb2..36c19d5b 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -22,7 +22,7 @@ def __init__( """Deterministic HuggingFace model.""" self._device = device self.sampling_params = {} if sampling_params is None else sampling_params - if settings.shared_settings.LLM_TYPE.get(model_id) == "llama": + if settings.shared_settings.LLM_TYPE.get(model_id) == "text-generation": self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, From 4be892e7ea8865766e38a9f6779cce059818004f Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 15:40:57 +0100 Subject: [PATCH 13/20] Update Lock File --- poetry.lock | 3353 +++++++++++++++++++++++++-------------------------- 1 file changed, 1652 insertions(+), 1701 deletions(-) diff --git a/poetry.lock b/poetry.lock index 119a6005..58bd24e6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,16 +1,14 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. [[package]] name = "accelerate" -version = "1.3.0" +version = "1.5.2" description = "Accelerate" optional = true python-versions = ">=3.9.0" -groups = ["main"] -markers = "extra == \"validator\"" files = [ - {file = "accelerate-1.3.0-py3-none-any.whl", hash = "sha256:5788d9e6a7a9f80fed665cf09681c4dddd9dc056bea656db4140ffc285ce423e"}, - {file = "accelerate-1.3.0.tar.gz", hash = "sha256:518631c0adb80bd3d42fb29e7e2dc2256bcd7c786b0ba9119bbaa08611b36d9c"}, + {file = "accelerate-1.5.2-py3-none-any.whl", hash = "sha256:68a3b272f6a6ffebb457bdc138581a2bf52efad6a5e0214dc46675f3edd98792"}, + {file = "accelerate-1.5.2.tar.gz", hash = "sha256:a1cf39473edc0e42772a9d9a18c9eb1ce8ffd9e1719dc0ab80670f5c1fd4dc43"}, ] [package.dependencies] @@ -24,25 +22,24 @@ torch = ">=2.0.0" [package.extras] deepspeed = ["deepspeed"] -dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"] rich = ["rich"] sagemaker = ["sagemaker"] test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] -test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"] +test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist"] test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"] -testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] +testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-order", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"] [[package]] name = "aiohappyeyeballs" -version = "2.4.4" +version = "2.6.1" description = "Happy Eyeballs for asyncio" optional = false -python-versions = ">=3.8" -groups = ["main"] +python-versions = ">=3.9" files = [ - {file = "aiohappyeyeballs-2.4.4-py3-none-any.whl", hash = "sha256:a980909d50efcd44795c4afeca523296716d50cd756ddca6af8c65b996e27de8"}, - {file = "aiohappyeyeballs-2.4.4.tar.gz", hash = "sha256:5fdd7d87889c63183afc18ce9271f9b0a7d32c2303e394468dd45d514a757745"}, + {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"}, + {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"}, ] [[package]] @@ -51,7 +48,6 @@ version = "3.10.11" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" -groups = ["main"] files = [ {file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:5077b1a5f40ffa3ba1f40d537d3bec4383988ee51fbba6b74aa8fb1bc466599e"}, {file = "aiohttp-3.10.11-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:8d6a14a4d93b5b3c2891fca94fa9d41b2322a68194422bef0dd5ec1e57d7d298"}, @@ -164,7 +160,6 @@ version = "1.3.2" description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.9" -groups = ["main"] files = [ {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = "sha256:45cde58e409a301715980c2b01d0c28bdde3770d8290b5eb2173759d9acb31a5"}, {file = "aiosignal-1.3.2.tar.gz", hash = "sha256:a8c255c66fafb1e499c9351d0bf32ff2d8a0321595ebac3b93713656d2436f54"}, @@ -179,8 +174,6 @@ version = "0.4.3" description = "AnglE-optimize Text Embeddings" optional = true python-versions = "*" -groups = ["main"] -markers = "extra == \"validator\"" files = [ {file = "angle_emb-0.4.3-py3-none-any.whl", hash = "sha256:65101325dae428d0294322283a02dfc8fb645320349091905a95e50fb03b4ad2"}, {file = "angle_emb-0.4.3.tar.gz", hash = "sha256:4d3d47f978813e16dbdd4d1054df27cb8ffd740e697a6ddc8755af030b2bcf7c"}, @@ -203,7 +196,6 @@ version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" optional = false python-versions = ">=3.8" -groups = ["main"] files = [ {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, @@ -211,14 +203,13 @@ files = [ [[package]] name = "anyio" -version = "4.8.0" +version = "4.9.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" optional = false python-versions = ">=3.9" -groups = ["main"] files = [ - {file = "anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a"}, - {file = "anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a"}, + {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"}, + {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"}, ] [package.dependencies] @@ -228,8 +219,8 @@ sniffio = ">=1.1" typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} [package.extras] -doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] -test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] +doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] +test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"] trio = ["trio (>=0.26.1)"] [[package]] @@ -238,8 +229,6 @@ version = "0.1.4" description = "Disable App Nap on macOS >= 10.9" optional = false python-versions = ">=3.6" -groups = ["dev"] -markers = "platform_system == \"Darwin\"" files = [ {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"}, {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"}, @@ -251,7 +240,6 @@ version = "3.0.0" description = "Annotate AST trees with source code positions" optional = false python-versions = ">=3.8" -groups = ["dev"] files = [ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, @@ -267,7 +255,6 @@ version = "0.2.2" description = "Python decorator for async properties." optional = false python-versions = "*" -groups = ["main"] files = [ {file = "async_property-0.2.2-py2.py3-none-any.whl", hash = "sha256:8924d792b5843994537f8ed411165700b27b2bd966cefc4daeefc1253442a9d7"}, {file = "async_property-0.2.2.tar.gz", hash = "sha256:17d9bd6ca67e27915a75d92549df64b5c7174e9dc806b30a3934dc4ff0506380"}, @@ -275,27 +262,26 @@ files = [ [[package]] name = "async-substrate-interface" -version = "1.0.0" +version = "1.0.7" description = "Asyncio library for interacting with substrate. Mostly API-compatible with py-substrate-interface" optional = false python-versions = "<3.13,>=3.9" -groups = ["main"] files = [ - {file = "async_substrate_interface-1.0.0-py3-none-any.whl", hash = "sha256:1df2107e221559c5127e4673c55c42db310bf72a344a7da0aaaa4cde400d28c0"}, - {file = "async_substrate_interface-1.0.0.tar.gz", hash = "sha256:1b045fa9fb4077bd20dbe5e670efe1a7346a50db1a126380c9fa4e32b03cb5de"}, + {file = "async_substrate_interface-1.0.7-py3-none-any.whl", hash = "sha256:0f8dfbee564400d059340dd047896ea5202034f3fd988724437cf315eb2694c2"}, + {file = "async_substrate_interface-1.0.7.tar.gz", hash = "sha256:05e8b5422d2e77f9ba3775c6c523276ed671437cbb2ec0ede66bf14fac6e174f"}, ] [package.dependencies] asyncstdlib = ">=3.13.0,<3.14.0" bittensor-wallet = ">=2.1.3" -bt-decode = "v0.5.0-a2" +bt-decode = "v0.5.0" scalecodec = ">=1.2.11,<1.3.0" websockets = ">=14.1" wheel = "*" xxhash = "*" [package.extras] -dev = ["pytest"] +dev = ["bittensor", "pytest", "pytest-asyncio"] [[package]] name = "async-timeout" @@ -303,7 +289,6 @@ version = "5.0.1" description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.8" -groups = ["main"] files = [ {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"}, {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"}, @@ -311,14 +296,13 @@ files = [ [[package]] name = "asyncstdlib" -version = "3.13.0" +version = "3.13.1" description = "The missing async toolbox" optional = false python-versions = "~=3.8" -groups = ["main"] files = [ - {file = "asyncstdlib-3.13.0-py3-none-any.whl", hash = "sha256:60e097c19e815f3c419a77426cf6c3653aebcb766544d631d5ce6128d0851ae8"}, - {file = "asyncstdlib-3.13.0.tar.gz", hash = "sha256:f2a6ffb44f118233bb99bef50861d6f64c432decbdcc4c2cb93b3fff40d1b533"}, + {file = "asyncstdlib-3.13.1-py3-none-any.whl", hash = "sha256:a64da68176af1da8c699026cad98f70b184f82b4cb39739e0b9701a2a7541cf9"}, + {file = "asyncstdlib-3.13.1.tar.gz", hash = "sha256:f47564b9a3566f8f9172631d88c75fe074b0ce2127963b7265d310df9aeed03a"}, ] [package.extras] @@ -328,21 +312,20 @@ typetest = ["mypy", "pyright", "typing-extensions"] [[package]] name = "attrs" -version = "25.1.0" +version = "25.3.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.8" -groups = ["main"] files = [ - {file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"}, - {file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"}, + {file = "attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3"}, + {file = "attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b"}, ] [package.extras] benchmark = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-codspeed", "pytest-mypy-plugins", "pytest-xdist[psutil]"] cov = ["cloudpickle", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] dev = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] -docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier (<24.7)"] +docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-xdist[psutil]"] tests-mypy = ["mypy (>=1.11.1)", "pytest-mypy-plugins"] @@ -352,8 +335,6 @@ version = "0.2.8" description = "AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference." optional = true python-versions = ">=3.8.0" -groups = ["main"] -markers = "extra == \"validator\"" files = [] develop = false @@ -382,19 +363,17 @@ resolved_reference = "ae782a99df2f72a2c28764452844cb2d65bd8ffc" [[package]] name = "babel" -version = "2.16.0" +version = "2.17.0" description = "Internationalization utilities" optional = true python-versions = ">=3.8" -groups = ["main"] -markers = "extra == \"validator\"" files = [ - {file = "babel-2.16.0-py3-none-any.whl", hash = "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b"}, - {file = "babel-2.16.0.tar.gz", hash = "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"}, + {file = "babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2"}, + {file = "babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d"}, ] [package.extras] -dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"] +dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"] [[package]] name = "backoff" @@ -402,7 +381,6 @@ version = "2.2.1" description = "Function decoration for backoff and retry" optional = false python-versions = ">=3.7,<4.0" -groups = ["main"] files = [ {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"}, {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"}, @@ -414,7 +392,6 @@ version = "2.1.1" description = "Base58 and Base58Check implementation." optional = false python-versions = ">=3.5" -groups = ["main"] files = [ {file = "base58-2.1.1-py3-none-any.whl", hash = "sha256:11a36f4d3ce51dfc1043f3218591ac4eb1ceb172919cebe05b52a5bcc8d245c2"}, {file = "base58-2.1.1.tar.gz", hash = "sha256:c5d0cb3f5b6e81e8e35da5754388ddcc6d0d14b6c6a132cb93d69ed580a7278c"}, @@ -425,19 +402,18 @@ tests = ["PyHamcrest (>=2.0.2)", "mypy", "pytest (>=4.6)", "pytest-benchmark", " [[package]] name = "beautifulsoup4" -version = "4.12.3" +version = "4.13.3" description = "Screen-scraping library" optional = true -python-versions = ">=3.6.0" -groups = ["main"] -markers = "extra == \"validator\"" +python-versions = ">=3.7.0" files = [ - {file = "beautifulsoup4-4.12.3-py3-none-any.whl", hash = "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"}, - {file = "beautifulsoup4-4.12.3.tar.gz", hash = "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051"}, + {file = "beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"}, + {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"}, ] [package.dependencies] soupsieve = ">1.2" 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library to download and publish models, datasets and other repos on the huggingface.co hub" optional = true python-versions = ">=3.8.0" -groups = ["main"] -markers = "extra == \"validator\"" files = [ - {file = "huggingface_hub-0.27.1-py3-none-any.whl", hash = "sha256:1c5155ca7d60b60c2e2fc38cbb3ffb7f7c3adf48f824015b219af9061771daec"}, - {file = "huggingface_hub-0.27.1.tar.gz", hash = "sha256:c004463ca870283909d715d20f066ebd6968c2207dae9393fdffb3c1d4d8f98b"}, + {file = "huggingface_hub-0.29.3-py3-none-any.whl", hash = "sha256:0b25710932ac649c08cdbefa6c6ccb8e88eef82927cacdb048efb726429453aa"}, + {file = "huggingface_hub-0.29.3.tar.gz", hash = "sha256:64519a25716e0ba382ba2d3fb3ca082e7c7eb4a2fc634d200e8380006e0760e5"}, ] [package.dependencies] @@ -1967,29 +2080,39 @@ tqdm = ">=4.42.1" typing-extensions = ">=3.7.4.3" [package.extras] -all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio (>=4.0.0)", "jedi", "libcst (==1.4.0)", "mypy (==1.5.1)", "numpy", "pytest 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\"Linux\" and platform_machine == \"x86_64\" and extra == \"validator\"" files = [ {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"}, {file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a178759ebb095827bd30ef56598ec182b85547f1508941a3d560eb7ea1fbf338"}, @@ -3237,8 +3319,6 @@ version = "12.4.127" description = "CUDA Runtime native Libraries" optional = true python-versions = ">=3" -groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"validator\"" files = [ {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"}, {file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:64403288fa2136ee8e467cdc9c9427e0434110899d07c779f25b5c068934faa5"}, @@ -3251,8 +3331,6 @@ version = "9.1.0.70" description = "cuDNN runtime libraries" optional = true python-versions = ">=3" -groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"validator\"" files = [ {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"}, {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"}, @@ -3267,8 +3345,6 @@ version = "11.2.1.3" description = "CUFFT native runtime libraries" optional = true python-versions = ">=3" -groups = ["main"] -markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and extra == \"validator\"" files = [ {file = "nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:5dad8008fc7f92f5ddfa2101430917ce2ffacd86824914c82e28990ad7f00399"}, {file = 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"sha256:fc9ca1c9718cb3b06634c7c8dec57d24e9438b2aa9a0f02b8bb36bf478538880"}, @@ -6921,6 +6872,6 @@ cffi = ["cffi (>=1.11)"] validator = ["accelerate", "angle-emb", "autoawq", "bs4", "datasets", "duckduckgo-search", "huggingface-hub", "nltk", "numpy", "pandas", "rouge", "thefuzz", "torch", "torchvision", "trafilatura", "transformers", "wandb", "wikipedia"] [metadata] -lock-version = "2.1" +lock-version = "2.0" python-versions = ">=3.10 <3.11" -content-hash = "5010601e1a0caa162dfd6fb96f143f5e49de524fcbaf1ca15c4073190bad14fa" +content-hash = "689da73ab97c96783575721d87e8f62eabe6dcfa259d87e5e24022133879c425" From b3151bd4560abe78134782b9bec038f14cbb4506 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 15:40:03 +0000 Subject: [PATCH 14/20] Format gemma messages before inferencing --- prompting/llms/hf_llm.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index 36c19d5b..a22493ff 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -30,6 +30,7 @@ def __init__( device_map=self._device, ) self.tokenizer = AutoTokenizer.from_pretrained(model_id) + self.message_formater = lambda messages: messages elif settings.shared_settings.LLM_TYPE.get(model_id) == "image-text-to-text": self.model: AutoModelForImageTextToText = AutoModelForImageTextToText.from_pretrained( model_id, @@ -38,6 +39,7 @@ def __init__( device_map=self._device, ) self.tokenizer = AutoProcessor.from_pretrained(model_id) + self.message_formater = self.gemma_format else: raise ValueError(f"Model type {settings.shared_settings.LLM_TYPE.get(model_id)} not supported") @@ -45,6 +47,31 @@ def __init__( self.model.generation_config.to_dict().keys() ) + def gemma_format(self, messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: + """Format the messages for the gemma model. + + Converts message content strings to dictionaries with type and text fields. + Example: + Input: [{"role": "user", "content": "Hello"}] + Output: [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] + """ + formatted_messages = [] + for message in messages: + if isinstance(message, dict) and "content" in message: + # If content is a string, convert it to a list with a dictionary + if isinstance(message["content"], str): + formatted_message = message.copy() + formatted_message["content"] = [{"type": "text", "text": message["content"]}] + formatted_messages.append(formatted_message) + else: + # If content is already in the correct format, keep it as is + formatted_messages.append(message) + else: + # Handle other message formats if needed + formatted_messages.append(message) + + return formatted_messages + @torch.inference_mode() def generate( self, @@ -56,7 +83,7 @@ def generate( self.set_random_seeds(seed) inputs = self.tokenizer.apply_chat_template( - messages, + self.message_formater(messages), tokenize=True, add_generation_prompt=True, return_tensors="pt", From 4610cd422ada2ddf4f4baccca830f26c75a3ac22 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 17:51:08 +0000 Subject: [PATCH 15/20] Create 3 classes --- prompting/llms/hf_llm.py | 124 ++++++++++---------------------- prompting/llms/hf_text.py | 28 ++++++++ prompting/llms/hf_text_image.py | 53 ++++++++++++++ prompting/llms/model_manager.py | 48 +++++++------ prompting/llms/model_zoo.py | 1 - prompting/llms/utils.py | 10 +++ shared/epistula.py | 2 + 7 files changed, 157 insertions(+), 109 deletions(-) create mode 100644 prompting/llms/hf_text.py create mode 100644 prompting/llms/hf_text_image.py diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index a22493ff..098ca92b 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -1,78 +1,28 @@ import random +from abc import abstractmethod import numpy as np -import torch -from transformers import ( - AutoModelForCausalLM, - AutoModelForImageTextToText, - AutoProcessor, - AutoTokenizer, - PreTrainedModel, -) +from loguru import logger +from pydantic import BaseModel + +try: + import torch +except ImportError: + logger.warning("torch is not installed. This module will not be available.") -from shared import settings class ReproducibleHF: - def __init__( - self, - model_id: str = "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", - device: str = "cuda:0", - sampling_params: dict[str, str | float | int | bool] | None = None, - ): - """Deterministic HuggingFace model.""" - self._device = device - self.sampling_params = {} if sampling_params is None else sampling_params - if settings.shared_settings.LLM_TYPE.get(model_id) == "text-generation": - self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( - model_id, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - device_map=self._device, - ) - self.tokenizer = AutoTokenizer.from_pretrained(model_id) - self.message_formater = lambda messages: messages - elif settings.shared_settings.LLM_TYPE.get(model_id) == "image-text-to-text": - self.model: AutoModelForImageTextToText = AutoModelForImageTextToText.from_pretrained( - model_id, - torch_dtype=torch.float16, - low_cpu_mem_usage=True, - device_map=self._device, - ) - self.tokenizer = AutoProcessor.from_pretrained(model_id) - self.message_formater = self.gemma_format - else: - raise ValueError(f"Model type {settings.shared_settings.LLM_TYPE.get(model_id)} not supported") - self.valid_generation_params = set( - self.model.generation_config.to_dict().keys() - ) + def __init__(self, model_id: str, device: str, sampling_params: dict[str, str | float | int | bool] | None = None): + self.model_id = model_id + self._device = device + self.sampling_params = sampling_params if sampling_params else {} - def gemma_format(self, messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: - """Format the messages for the gemma model. - - Converts message content strings to dictionaries with type and text fields. - Example: - Input: [{"role": "user", "content": "Hello"}] - Output: [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] - """ - formatted_messages = [] - for message in messages: - if isinstance(message, dict) and "content" in message: - # If content is a string, convert it to a list with a dictionary - if isinstance(message["content"], str): - formatted_message = message.copy() - formatted_message["content"] = [{"type": "text", "text": message["content"]}] - formatted_messages.append(formatted_message) - else: - # If content is already in the correct format, keep it as is - formatted_messages.append(message) - else: - # Handle other message formats if needed - formatted_messages.append(message) - - return formatted_messages + @staticmethod + @abstractmethod + def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: + raise NotImplementedError("This method must be implemented by the subclass") - @torch.inference_mode() def generate( self, messages: list[str] | list[dict[str, str]], @@ -80,31 +30,31 @@ def generate( seed: int | None = None, ) -> str: """Generate text with optimized performance.""" - self.set_random_seeds(seed) + with torch.inference_mode(): + self.set_random_seeds(seed) - inputs = self.tokenizer.apply_chat_template( - self.message_formater(messages), - tokenize=True, - add_generation_prompt=True, - return_tensors="pt", - return_dict=True, - ).to(self._device) + inputs = self.tokenizer.apply_chat_template( + self.message_formater(messages), + tokenize=True, + add_generation_prompt=True, + return_tensors="pt", + return_dict=True, + ).to(self._device) - params = sampling_params if sampling_params else self.sampling_params - filtered_params = {k: v for k, v in params.items() if k in self.valid_generation_params} + params = sampling_params if sampling_params else self.sampling_params + filtered_params = {k: v for k, v in params.items() if k in self.valid_generation_params} - outputs = self.model.generate( - **inputs, - **filtered_params, - eos_token_id=self.tokenizer.eos_token_id, - ) + outputs = self.model.generate( + **inputs, + **filtered_params, + ) - results = self.tokenizer.batch_decode( - outputs[:, inputs["input_ids"].shape[1] :], - skip_special_tokens=True, - )[0] + results = self.tokenizer.batch_decode( + outputs[:, inputs["input_ids"].shape[1] :], + skip_special_tokens=True, + )[0] - return results if len(results) > 1 else results[0] + return results if len(results) > 1 else results[0] def set_random_seeds(self, seed: int | None = 42): """Set random seeds for reproducibility across all relevant libraries.""" @@ -116,3 +66,5 @@ def set_random_seeds(self, seed: int | None = 42): torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False + + diff --git a/prompting/llms/hf_text.py b/prompting/llms/hf_text.py new file mode 100644 index 00000000..4a388e8b --- /dev/null +++ b/prompting/llms/hf_text.py @@ -0,0 +1,28 @@ +from loguru import logger + +try: + from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel + import torch +except ImportError: + logger.warning("Transformers or torch is not installed. This module will not be available.") + +from .hf_llm import ReproducibleHF + +class HFTextGeneration(ReproducibleHF): + def __init__(self, model_id: str = "meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", device: str = "cuda:0", sampling_params: dict[str, str | float | int | bool] | None = None): + super().__init__(model_id, device, sampling_params) + self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + low_cpu_mem_usage=True, + device_map=self._device, + ) + self.tokenizer = AutoTokenizer.from_pretrained(model_id) + self.valid_generation_params = set( + self.model.generation_config.to_dict().keys() + ) + self.message_formater = self.format_messages + + @staticmethod + def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: + return messages diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py new file mode 100644 index 00000000..a3499163 --- /dev/null +++ b/prompting/llms/hf_text_image.py @@ -0,0 +1,53 @@ +from loguru import logger + +try: + from transformers import AutoProcessor, Gemma3ForConditionalGeneration + import torch +except ImportError: + logger.warning("Transformers or torch is not installed. This module will not be available.") + +from prompting.llms.hf_llm import ReproducibleHF + +class HFTextImageToText(ReproducibleHF): + def __init__(self, model_id: str = "google/gemma-3-27b-it", device: str = "cuda:0", sampling_params: dict[str, str | float | int | bool] | None = None): + super().__init__(model_id, device, sampling_params) + self.model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained( + model_id, + torch_dtype=torch.bfloat16, + device_map=self._device, + ) + self.tokenizer = AutoProcessor.from_pretrained(model_id) + self.valid_generation_params = set( + self.model.generation_config.to_dict().keys() + ) + self.message_formater = self.format_messages + + @staticmethod + def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: + """Format the messages for the gemma model. + + Converts message content strings to dictionaries with type and text fields. + Example: + Input: [{"role": "user", "content": "Hello"}] + Output: [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] + """ + formatted_messages = [] + for message in messages: + if isinstance(message, dict) and "content" in message: + # If content is a string, convert it to a list with a dictionary + if isinstance(message["content"], str): + formatted_message = message.copy() + formatted_message["content"] = [{"type": "text", "text": message["content"]}] + formatted_messages.append(formatted_message) + else: + # If content is already in the correct format, keep it as is + formatted_messages.append(message) + else: + # Handle other message formats if needed + formatted_messages.append(message) + + return formatted_messages + +if __name__ == "__main__": + model = HFTextImageToText(model_id="google/gemma-3-27b-it", device="cuda:0") + print(model.generate([{"role": "user", "content": "What's ur name?"}])) \ No newline at end of file diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index 756fbd9f..25e356d1 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -8,10 +8,11 @@ from prompting.llms.hf_llm import ReproducibleHF from prompting.llms.model_zoo import ModelConfig, ModelZoo -from prompting.llms.utils import GPUInfo +from prompting.llms.utils import GPUInfo, model_factory from shared import settings from shared.loop_runner import AsyncLoopRunner + # This maintains a list of tasks for which we need to generate references. Since # we can only generate the references, when the correct model is loaded, we work # through the tasks based on the currently loaded model. @@ -64,7 +65,7 @@ def load_model(self, model_config: ModelConfig, force: bool = True): try: GPUInfo.log_gpu_info() - model = ReproducibleHF( + model = model_factory(model_config.llm_model_id)( model_id=model_config.llm_model_id, device=settings.shared_settings.NEURON_DEVICE, sampling_params=settings.shared_settings.SAMPLING_PARAMS, @@ -85,63 +86,66 @@ def unload_model(self, model_config: ModelConfig): try: # Get the model instance model_instance = self.active_models[model_config] - + # Different model implementations have different structures # Handle vLLM-based models - if hasattr(model_instance, 'llm') and hasattr(model_instance.llm, 'llm_engine'): - if hasattr(model_instance.llm.llm_engine, 'model_executor') and hasattr(model_instance.llm.llm_engine.model_executor, 'driver_worker'): + if hasattr(model_instance, "llm") and hasattr(model_instance.llm, "llm_engine"): + if hasattr(model_instance.llm.llm_engine, "model_executor") and hasattr( + model_instance.llm.llm_engine.model_executor, "driver_worker" + ): del model_instance.llm.llm_engine.model_executor.driver_worker - + # Handle pipeline-based models with a hybrid approach - if hasattr(model_instance, 'llm'): + if hasattr(model_instance, "llm"): # Try to move model to CPU first if it's a PyTorch model - if hasattr(model_instance.llm, 'model'): + if hasattr(model_instance.llm, "model"): try: # Check if it's a PyTorch model with a 'to' method - if hasattr(model_instance.llm.model, 'to'): + if hasattr(model_instance.llm.model, "to"): logger.debug(f"Moving model {model_config.llm_model_id} to CPU before deletion") - model_instance.llm.model.to('cpu') + model_instance.llm.model.to("cpu") # Small delay to allow memory transfer import time + time.sleep(0.1) except Exception as e: logger.debug(f"Could not move model to CPU: {str(e)}, proceeding with direct deletion") - + # Delete the model reference del model_instance.llm.model - + # Handle tokenizer - if hasattr(model_instance.llm, 'tokenizer'): + if hasattr(model_instance.llm, "tokenizer"): del model_instance.llm.tokenizer - + # Delete the llm object itself del model_instance.llm - + # Remove the model from active models dictionary del self.active_models[model_config] - + # Force Python garbage collection multiple times to ensure cleanup gc.collect() gc.collect() - + # Clear CUDA cache torch.cuda.empty_cache() - + # Additional memory cleanup for PyTorch if torch.cuda.is_available(): # Reset peak memory stats torch.cuda.reset_peak_memory_stats() # Synchronize CUDA to ensure operations are complete torch.cuda.synchronize() - + logger.info(f"Successfully unloaded model {model_config.llm_model_id}") - + except Exception as ex: logger.error(f"Failed to unload model {model_config.llm_model_id}. Error: {str(ex)}") - + # Update used RAM tracking self.used_ram -= model_config.min_ram - + # Log current memory state GPUInfo.log_gpu_info() diff --git a/prompting/llms/model_zoo.py b/prompting/llms/model_zoo.py index 05034284..9069fb5d 100644 --- a/prompting/llms/model_zoo.py +++ b/prompting/llms/model_zoo.py @@ -6,7 +6,6 @@ from shared import settings - class ModelConfig(BaseModel): llm_model_id: str reward: float diff --git a/prompting/llms/utils.py b/prompting/llms/utils.py index 0b4339d0..79fa6500 100644 --- a/prompting/llms/utils.py +++ b/prompting/llms/utils.py @@ -4,6 +4,9 @@ import torch from loguru import logger +from prompting.llms.hf_llm import ReproducibleHF +from prompting.llms.hf_text import HFTextGeneration +from prompting.llms.hf_text_image import HFTextImageToText from shared.misc import classproperty @@ -112,3 +115,10 @@ def n_gpus(cls): @classproperty def gpu_utilization(cls): return cls.used_memory / cls.total_memory + + +def model_factory(model_name: str) -> type[ReproducibleHF]: + if model_name == "google/gemma-3-27b-it": + return HFTextImageToText + else: + return HFTextGeneration diff --git a/shared/epistula.py b/shared/epistula.py index b2d43417..e71f36b0 100644 --- a/shared/epistula.py +++ b/shared/epistula.py @@ -17,6 +17,7 @@ from openai.types.chat.chat_completion_message import ChatCompletionMessage from substrateinterface import Keypair +from prompting.llms.utils import model_factory from shared import settings from shared.dendrite import SynapseStreamResult @@ -224,6 +225,7 @@ async def make_openai_query( ), ) extra_body = {k: v for k, v in body.items() if k not in ["messages", "model"]} + body["messages"] = model_factory(body.get("model")).format_messages(body["messages"]) start_time = time.perf_counter() chat = await miner.chat.completions.create( # model=None, From 4ca49f71969981f6bbb9740b59097603fb74f89c Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Mon, 17 Mar 2025 20:25:45 +0000 Subject: [PATCH 16/20] Alternate Assistant and User --- prompting/datasets/sn13.py | 7 +++++-- prompting/llms/hf_text_image.py | 5 ++++- shared/settings.py | 9 +-------- 3 files changed, 10 insertions(+), 11 deletions(-) diff --git a/prompting/datasets/sn13.py b/prompting/datasets/sn13.py index 0615f8c4..6283f191 100644 --- a/prompting/datasets/sn13.py +++ b/prompting/datasets/sn13.py @@ -38,9 +38,12 @@ def sample(self) -> ChatEntry: raise self.exception # Randomly select a sample from the dataset. messages = [] - for _ in range(4): + for i in range(4): sample_idx = random.randint(0, len(self.dataset) - 1) if message := self.dataset[sample_idx]["text"]: - messages.append({"role": random.choice(["user", "assistant"]), "content": message}) + if i % 2 == 0: + messages.append({"role": "user", "content": message}) + else: + messages.append({"role": "assistant", "content": message}) return ChatEntry(messages=messages, organic=False, source=self._url) diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py index a3499163..7c4ade57 100644 --- a/prompting/llms/hf_text_image.py +++ b/prompting/llms/hf_text_image.py @@ -20,7 +20,7 @@ def __init__(self, model_id: str = "google/gemma-3-27b-it", device: str = "cuda: self.valid_generation_params = set( self.model.generation_config.to_dict().keys() ) - self.message_formater = self.format_messages + self.message_formater = HFTextImageToText.format_messages @staticmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: @@ -32,6 +32,9 @@ def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str Output: [{"role": "user", "content": [{"type": "text", "text": "Hello"}]}] """ formatted_messages = [] + # Check if the message is a list of only one element and that element is a list + if isinstance(messages, list) and len(messages) == 1 and isinstance(messages[0], list): + messages = messages[0] for message in messages: if isinstance(message, dict) and "content" in message: # If content is a string, convert it to a list with a dictionary diff --git a/shared/settings.py b/shared/settings.py index a729854f..dc0f682b 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -126,15 +126,8 @@ class SharedSettings(BaseSettings): TEST_MINER_IDS: list[int] = Field([], env="TEST_MINER_IDS") SUBTENSOR_NETWORK: Optional[str] = Field(None, env="SUBTENSOR_NETWORK") MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") - LLM_MAX_MODEL_LEN: int = Field(4096, env="LLM_MAX_MODEL_LEN") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: list[str] = Field( - ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"], env="LLM_MODEL" - ) - LLM_TYPE: dict[str, str] = { - "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4": "text-generation", - "google/gemma-3-27b-it": "image-text-to-text", - } + LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4","google/gemma-3-27b-it"] SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From 03d16338b30acb045074c87ea96efa6dfc7085e1 Mon Sep 17 00:00:00 2001 From: richwardle Date: Tue, 18 Mar 2025 10:52:51 +0000 Subject: [PATCH 17/20] Precommit Fix --- prompting/llms/hf_llm.py | 4 ---- prompting/llms/hf_text.py | 16 ++++++++++------ prompting/llms/hf_text_image.py | 23 ++++++++++++++--------- prompting/llms/model_manager.py | 1 - prompting/llms/model_zoo.py | 1 + shared/settings.py | 2 +- 6 files changed, 26 insertions(+), 21 deletions(-) diff --git a/prompting/llms/hf_llm.py b/prompting/llms/hf_llm.py index 098ca92b..540bee85 100644 --- a/prompting/llms/hf_llm.py +++ b/prompting/llms/hf_llm.py @@ -3,7 +3,6 @@ import numpy as np from loguru import logger -from pydantic import BaseModel try: import torch @@ -12,7 +11,6 @@ class ReproducibleHF: - def __init__(self, model_id: str, device: str, sampling_params: dict[str, str | float | int | bool] | None = None): self.model_id = model_id self._device = device @@ -66,5 +64,3 @@ def set_random_seeds(self, seed: int | None = 42): torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False - - diff --git a/prompting/llms/hf_text.py b/prompting/llms/hf_text.py index 4a388e8b..fcce7178 100644 --- a/prompting/llms/hf_text.py +++ b/prompting/llms/hf_text.py @@ -1,15 +1,21 @@ from loguru import logger try: - from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel import torch + from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedModel except ImportError: logger.warning("Transformers or torch is not installed. This module will not be available.") from .hf_llm import ReproducibleHF + class HFTextGeneration(ReproducibleHF): - def __init__(self, model_id: str = "meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", device: str = "cuda:0", sampling_params: dict[str, str | float | int | bool] | None = None): + def __init__( + self, + model_id: str = "meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", + device: str = "cuda:0", + sampling_params: dict[str, str | float | int | bool] | None = None, + ): super().__init__(model_id, device, sampling_params) self.model: PreTrainedModel = AutoModelForCausalLM.from_pretrained( model_id, @@ -18,11 +24,9 @@ def __init__(self, model_id: str = "meta-llama/Meta-Llama-3.1-70B-Instruct-AWQ-I device_map=self._device, ) self.tokenizer = AutoTokenizer.from_pretrained(model_id) - self.valid_generation_params = set( - self.model.generation_config.to_dict().keys() - ) + self.valid_generation_params = set(self.model.generation_config.to_dict().keys()) self.message_formater = self.format_messages - + @staticmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: return messages diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py index 7c4ade57..7d8f2216 100644 --- a/prompting/llms/hf_text_image.py +++ b/prompting/llms/hf_text_image.py @@ -1,15 +1,21 @@ from loguru import logger try: - from transformers import AutoProcessor, Gemma3ForConditionalGeneration import torch + from transformers import AutoProcessor, Gemma3ForConditionalGeneration except ImportError: logger.warning("Transformers or torch is not installed. This module will not be available.") from prompting.llms.hf_llm import ReproducibleHF + class HFTextImageToText(ReproducibleHF): - def __init__(self, model_id: str = "google/gemma-3-27b-it", device: str = "cuda:0", sampling_params: dict[str, str | float | int | bool] | None = None): + def __init__( + self, + model_id: str = "google/gemma-3-27b-it", + device: str = "cuda:0", + sampling_params: dict[str, str | float | int | bool] | None = None, + ): super().__init__(model_id, device, sampling_params) self.model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained( model_id, @@ -17,15 +23,13 @@ def __init__(self, model_id: str = "google/gemma-3-27b-it", device: str = "cuda: device_map=self._device, ) self.tokenizer = AutoProcessor.from_pretrained(model_id) - self.valid_generation_params = set( - self.model.generation_config.to_dict().keys() - ) + self.valid_generation_params = set(self.model.generation_config.to_dict().keys()) self.message_formater = HFTextImageToText.format_messages @staticmethod def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str, str | list[dict[str, str]]]]: """Format the messages for the gemma model. - + Converts message content strings to dictionaries with type and text fields. Example: Input: [{"role": "user", "content": "Hello"}] @@ -48,9 +52,10 @@ def format_messages(messages: list[str] | list[dict[str, str]]) -> list[dict[str else: # Handle other message formats if needed formatted_messages.append(message) - + return formatted_messages - + + if __name__ == "__main__": model = HFTextImageToText(model_id="google/gemma-3-27b-it", device="cuda:0") - print(model.generate([{"role": "user", "content": "What's ur name?"}])) \ No newline at end of file + print(model.generate([{"role": "user", "content": "What's ur name?"}])) diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index 25e356d1..f1647be5 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -12,7 +12,6 @@ from shared import settings from shared.loop_runner import AsyncLoopRunner - # This maintains a list of tasks for which we need to generate references. Since # we can only generate the references, when the correct model is loaded, we work # through the tasks based on the currently loaded model. diff --git a/prompting/llms/model_zoo.py b/prompting/llms/model_zoo.py index 9069fb5d..05034284 100644 --- a/prompting/llms/model_zoo.py +++ b/prompting/llms/model_zoo.py @@ -6,6 +6,7 @@ from shared import settings + class ModelConfig(BaseModel): llm_model_id: str reward: float diff --git a/shared/settings.py b/shared/settings.py index dc0f682b..3bfa8517 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -127,7 +127,7 @@ class SharedSettings(BaseSettings): SUBTENSOR_NETWORK: Optional[str] = Field(None, env="SUBTENSOR_NETWORK") MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4","google/gemma-3-27b-it"] + LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"] SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From f7b8b454b2e0256e4f57d9dfcc780785cbf7c547 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Tue, 18 Mar 2025 12:47:18 +0000 Subject: [PATCH 18/20] Update Gemma Syntax --- prompting/llms/hf_text_image.py | 4 +- prompting/llms/model_manager.py | 92 ++++++++++++++++++++++++++++++--- prompting/llms/utils.py | 2 + shared/epistula.py | 4 +- 4 files changed, 92 insertions(+), 10 deletions(-) diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py index 7d8f2216..58d6699d 100644 --- a/prompting/llms/hf_text_image.py +++ b/prompting/llms/hf_text_image.py @@ -2,7 +2,7 @@ try: import torch - from transformers import AutoProcessor, Gemma3ForConditionalGeneration + from transformers import AutoProcessor, AutoModelForImageTextToText except ImportError: logger.warning("Transformers or torch is not installed. This module will not be available.") @@ -17,7 +17,7 @@ def __init__( sampling_params: dict[str, str | float | int | bool] | None = None, ): super().__init__(model_id, device, sampling_params) - self.model: Gemma3ForConditionalGeneration = Gemma3ForConditionalGeneration.from_pretrained( + self.model: AutoModelForImageTextToText = AutoModelForImageTextToText.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map=self._device, diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index f1647be5..9ce564e4 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -49,6 +49,12 @@ def load_model(self, model_config: ModelConfig, force: bool = True): logger.debug(f"Enough RAM for model {model_config.llm_model_id} free") GPUInfo.log_gpu_info() break + + # If no active models remain but GPU is still showing significant usage, + # perform emergency cleanup + if len(self.active_models) == 0 and GPUInfo.gpu_utilization > 0.25: # More than 25% still in use + logger.warning(f"GPU still showing high utilization after unloading all models. Performing emergency cleanup.") + self._emergency_gpu_cleanup() if self.used_ram + model_config.min_ram > self.total_ram or GPUInfo.free_memory < model_config.min_ram: if not force: @@ -86,6 +92,10 @@ def unload_model(self, model_config: ModelConfig): # Get the model instance model_instance = self.active_models[model_config] + # Record initial memory state for debugging + initial_free_memory = GPUInfo.free_memory + logger.debug(f"Initial free GPU memory before unloading: {initial_free_memory} GB") + # Different model implementations have different structures # Handle vLLM-based models if hasattr(model_instance, "llm") and hasattr(model_instance.llm, "llm_engine"): @@ -105,12 +115,27 @@ def unload_model(self, model_config: ModelConfig): model_instance.llm.model.to("cpu") # Small delay to allow memory transfer import time - time.sleep(0.1) + + # Explicitly set requires_grad to False for all parameters if possible + if hasattr(model_instance.llm.model, "parameters"): + for param in model_instance.llm.model.parameters(): + if hasattr(param, "requires_grad"): + param.requires_grad = False + except Exception as e: logger.debug(f"Could not move model to CPU: {str(e)}, proceeding with direct deletion") - # Delete the model reference + # Delete the model reference and any cached states + if hasattr(model_instance.llm.model, "_clear_cache"): + model_instance.llm.model._clear_cache() + + # Explicitly delete model components if available + if hasattr(model_instance.llm.model, "modules"): + for module in list(model_instance.llm.model.modules()): + del module + + # Final deletion of model del model_instance.llm.model # Handle tokenizer @@ -125,10 +150,8 @@ def unload_model(self, model_config: ModelConfig): # Force Python garbage collection multiple times to ensure cleanup gc.collect() - gc.collect() - - # Clear CUDA cache torch.cuda.empty_cache() + gc.collect() # Additional memory cleanup for PyTorch if torch.cuda.is_available(): @@ -136,8 +159,24 @@ def unload_model(self, model_config: ModelConfig): torch.cuda.reset_peak_memory_stats() # Synchronize CUDA to ensure operations are complete torch.cuda.synchronize() + + # Force additional cleanup with multiple empty_cache calls + torch.cuda.empty_cache() + + # Wait a bit longer to ensure memory is released back to the system + import time + time.sleep(0.5) + torch.cuda.empty_cache() + + # Additional synchronization point + torch.cuda.synchronize() + + # One final garbage collection + gc.collect() - logger.info(f"Successfully unloaded model {model_config.llm_model_id}") + # Report memory change + memory_freed = GPUInfo.free_memory - initial_free_memory + logger.info(f"Successfully unloaded model {model_config.llm_model_id}. Memory freed: {memory_freed:.2f} GB") except Exception as ex: logger.error(f"Failed to unload model {model_config.llm_model_id}. Error: {str(ex)}") @@ -209,6 +248,47 @@ def generate( return responses + def _emergency_gpu_cleanup(self): + """ + Perform an emergency cleanup of GPU memory when standard unloading + doesn't free up expected memory. + """ + logger.info("Performing emergency GPU cleanup") + + # Reset model tracking state + self.active_models = {} + self.used_ram = 0.0 + + # Run aggressive cleanup sequence + import time + + # Multiple rounds of garbage collection + for _ in range(3): + gc.collect() + torch.cuda.empty_cache() + time.sleep(0.1) + + # Force CUDA synchronization + torch.cuda.synchronize() + + # Reset all CUDA cached memory + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + torch.cuda.reset_accumulated_memory_stats() + + # Try to release all unreachable objects + gc.collect(generation=2) + + # Delay to allow OS to reclaim memory + time.sleep(1.0) + + # Final cache clear + torch.cuda.empty_cache() + torch.cuda.synchronize() + + logger.info(f"Emergency cleanup complete. Current GPU utilization: {GPUInfo.gpu_utilization * 100:.2f}%") + GPUInfo.log_gpu_info() + class AsyncModelScheduler(AsyncLoopRunner): llm_model_manager: ModelManager diff --git a/prompting/llms/utils.py b/prompting/llms/utils.py index 79fa6500..32312a81 100644 --- a/prompting/llms/utils.py +++ b/prompting/llms/utils.py @@ -120,5 +120,7 @@ def gpu_utilization(cls): def model_factory(model_name: str) -> type[ReproducibleHF]: if model_name == "google/gemma-3-27b-it": return HFTextImageToText + elif model_name == "mistralai/Mistral-Small-3.1-24B-Instruct-2503": + return HFTextImageToText else: return HFTextGeneration diff --git a/shared/epistula.py b/shared/epistula.py index e71f36b0..bd674ffd 100644 --- a/shared/epistula.py +++ b/shared/epistula.py @@ -153,8 +153,8 @@ async def query_miners( logger.error(f"Unknown response type: {response}") results.append(SynapseStreamResult(uid=uid, exception=f"Unknown response type: {response}")) return results - except Exception: - # logger.error(f"Error in query_miners: {e}") + except Exception as e: + logger.error(f"Error in query_miners: {e}") return [] From fbf3611ed0c606b3d8951b8631379e8c8ea6c487 Mon Sep 17 00:00:00 2001 From: bkb2135 Date: Tue, 18 Mar 2025 12:51:24 +0000 Subject: [PATCH 19/20] Replace Gemma with Mistral Model --- shared/settings.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/shared/settings.py b/shared/settings.py index 3bfa8517..b0d5438e 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -127,7 +127,7 @@ class SharedSettings(BaseSettings): SUBTENSOR_NETWORK: Optional[str] = Field(None, env="SUBTENSOR_NETWORK") MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "google/gemma-3-27b-it"] + LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "mrfakename/mistral-small-3.1-24b-instruct-2503-hf"] SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95, From 25b9586c5658e5572b962c9bb6c72c8b775b3436 Mon Sep 17 00:00:00 2001 From: richwardle Date: Tue, 18 Mar 2025 13:21:40 +0000 Subject: [PATCH 20/20] Simplify Unload Model --- prompting/llms/hf_text_image.py | 2 +- prompting/llms/model_manager.py | 96 +++++++++++++++++---------------- shared/settings.py | 5 +- 3 files changed, 56 insertions(+), 47 deletions(-) diff --git a/prompting/llms/hf_text_image.py b/prompting/llms/hf_text_image.py index 58d6699d..2b7709e7 100644 --- a/prompting/llms/hf_text_image.py +++ b/prompting/llms/hf_text_image.py @@ -2,7 +2,7 @@ try: import torch - from transformers import AutoProcessor, AutoModelForImageTextToText + from transformers import AutoModelForImageTextToText, AutoProcessor except ImportError: logger.warning("Transformers or torch is not installed. This module will not be available.") diff --git a/prompting/llms/model_manager.py b/prompting/llms/model_manager.py index 9ce564e4..a12178ff 100644 --- a/prompting/llms/model_manager.py +++ b/prompting/llms/model_manager.py @@ -1,5 +1,6 @@ import asyncio import gc +import time from typing import Dict import torch @@ -49,11 +50,13 @@ def load_model(self, model_config: ModelConfig, force: bool = True): logger.debug(f"Enough RAM for model {model_config.llm_model_id} free") GPUInfo.log_gpu_info() break - + # If no active models remain but GPU is still showing significant usage, # perform emergency cleanup if len(self.active_models) == 0 and GPUInfo.gpu_utilization > 0.25: # More than 25% still in use - logger.warning(f"GPU still showing high utilization after unloading all models. Performing emergency cleanup.") + logger.warning( + "GPU still showing high utilization after unloading all models. Performing emergency cleanup." + ) self._emergency_gpu_cleanup() if self.used_ram + model_config.min_ram > self.total_ram or GPUInfo.free_memory < model_config.min_ram: @@ -83,6 +86,37 @@ def load_model(self, model_config: ModelConfig, force: bool = True): except Exception as e: logger.exception(f"Failed to load model {model_config.llm_model_id}. Error: {str(e)}") + def _cleanup_pytorch_model(self, model_instance, model_config: ModelConfig): + """Handle cleanup specifically for PyTorch-based models.""" + if hasattr(model_instance.llm, "model"): + try: + # Check if it's a PyTorch model with a 'to' method + if hasattr(model_instance.llm.model, "to"): + logger.debug(f"Moving model {model_config.llm_model_id} to CPU before deletion") + model_instance.llm.model.to("cpu") + time.sleep(0.1) + + # Explicitly set requires_grad to False for all parameters if possible + if hasattr(model_instance.llm.model, "parameters"): + for param in model_instance.llm.model.parameters(): + if hasattr(param, "requires_grad"): + param.requires_grad = False + + except Exception as e: + logger.debug(f"Could not move model to CPU: {str(e)}, proceeding with direct deletion") + + # Delete the model reference and any cached states + if hasattr(model_instance.llm.model, "_clear_cache"): + model_instance.llm.model._clear_cache() + + # Explicitly delete model components if available + if hasattr(model_instance.llm.model, "modules"): + for module in list(model_instance.llm.model.modules()): + del module + + # Final deletion of model + del model_instance.llm.model + def unload_model(self, model_config: ModelConfig): if model_config not in self.active_models: logger.warning("Couldn't find model to unload.") @@ -107,36 +141,7 @@ def unload_model(self, model_config: ModelConfig): # Handle pipeline-based models with a hybrid approach if hasattr(model_instance, "llm"): # Try to move model to CPU first if it's a PyTorch model - if hasattr(model_instance.llm, "model"): - try: - # Check if it's a PyTorch model with a 'to' method - if hasattr(model_instance.llm.model, "to"): - logger.debug(f"Moving model {model_config.llm_model_id} to CPU before deletion") - model_instance.llm.model.to("cpu") - # Small delay to allow memory transfer - import time - time.sleep(0.1) - - # Explicitly set requires_grad to False for all parameters if possible - if hasattr(model_instance.llm.model, "parameters"): - for param in model_instance.llm.model.parameters(): - if hasattr(param, "requires_grad"): - param.requires_grad = False - - except Exception as e: - logger.debug(f"Could not move model to CPU: {str(e)}, proceeding with direct deletion") - - # Delete the model reference and any cached states - if hasattr(model_instance.llm.model, "_clear_cache"): - model_instance.llm.model._clear_cache() - - # Explicitly delete model components if available - if hasattr(model_instance.llm.model, "modules"): - for module in list(model_instance.llm.model.modules()): - del module - - # Final deletion of model - del model_instance.llm.model + self._cleanup_pytorch_model(model_instance, model_config) # Handle tokenizer if hasattr(model_instance.llm, "tokenizer"): @@ -159,18 +164,19 @@ def unload_model(self, model_config: ModelConfig): torch.cuda.reset_peak_memory_stats() # Synchronize CUDA to ensure operations are complete torch.cuda.synchronize() - + # Force additional cleanup with multiple empty_cache calls torch.cuda.empty_cache() - + # Wait a bit longer to ensure memory is released back to the system import time + time.sleep(0.5) torch.cuda.empty_cache() - + # Additional synchronization point torch.cuda.synchronize() - + # One final garbage collection gc.collect() @@ -254,38 +260,38 @@ def _emergency_gpu_cleanup(self): doesn't free up expected memory. """ logger.info("Performing emergency GPU cleanup") - + # Reset model tracking state self.active_models = {} self.used_ram = 0.0 - + # Run aggressive cleanup sequence import time - + # Multiple rounds of garbage collection for _ in range(3): gc.collect() torch.cuda.empty_cache() time.sleep(0.1) - + # Force CUDA synchronization torch.cuda.synchronize() - + # Reset all CUDA cached memory torch.cuda.empty_cache() torch.cuda.reset_peak_memory_stats() torch.cuda.reset_accumulated_memory_stats() - + # Try to release all unreachable objects gc.collect(generation=2) - + # Delay to allow OS to reclaim memory time.sleep(1.0) - + # Final cache clear torch.cuda.empty_cache() torch.cuda.synchronize() - + logger.info(f"Emergency cleanup complete. Current GPU utilization: {GPUInfo.gpu_utilization * 100:.2f}%") GPUInfo.log_gpu_info() diff --git a/shared/settings.py b/shared/settings.py index b0d5438e..f2819450 100644 --- a/shared/settings.py +++ b/shared/settings.py @@ -127,7 +127,10 @@ class SharedSettings(BaseSettings): SUBTENSOR_NETWORK: Optional[str] = Field(None, env="SUBTENSOR_NETWORK") MAX_ALLOWED_VRAM_GB: float = Field(62, env="MAX_ALLOWED_VRAM_GB") PROXY_URL: Optional[str] = Field(None, env="PROXY_URL") - LLM_MODEL: list[str] = ["hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", "mrfakename/mistral-small-3.1-24b-instruct-2503-hf"] + LLM_MODEL: list[str] = [ + "hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4", + "mrfakename/mistral-small-3.1-24b-instruct-2503-hf", + ] SAMPLING_PARAMS: dict[str, Any] = { "temperature": 0.7, "top_p": 0.95,