diff --git a/onnxruntime/python/tools/transformers/models/llama/README.md b/onnxruntime/python/tools/transformers/models/llama/README.md index f9552e02d74b..2e8cd3e1ac7f 100644 --- a/onnxruntime/python/tools/transformers/models/llama/README.md +++ b/onnxruntime/python/tools/transformers/models/llama/README.md @@ -1,7 +1,14 @@ # Contents - [LLaMA-2](#llama-2) + - [Prerequisites](#prerequisites) - [Exporting LLaMA-2](#exporting-llama-2) + - [Examples of Exporting LLaMA-2](#examples-of-exporting-llama-2) + - [Parity Checking LLaMA-2](#parity-checking-llama-2) - [Benchmarking LLaMA-2](#benchmark-llama-2) + - [Variants](#variants) + - [Benchmark All](#benchmark-all) + - [Benchmark E2E](#benchmark-e2e) + - [E2E Inference with LLaMA-2](#e2e-inference-with-llama-2) - [Mistral](#mistral) - [Exporting Mistral](#exporting-mistral) - [Optimizing and Quantizing Mistral](#optimizing-and-quantizing-mistral) @@ -229,6 +236,55 @@ $ ./build.sh --config Release --use_cuda --cuda_home /usr/local/cuda-12.2 --cudn $ CUDA_VISIBLE_DEVICES=0,1,2,3 bash convert_70b_model.sh 4 -m meta-llama/Llama-2-70b-hf --output llama2-70b-distributed --precision fp16 --execution_provider cuda --use_gqa ``` +## Parity Checking LLaMA-2 + +Here are some examples of how you can use the parity checker to verify your LLaMA-2 ONNX model. + +1. Merged ONNX model, FP32 CPU +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \ + --model_name meta-llama/Llama-2-7b-hf \ + --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --merged \ + --execution_provider cpu \ + --precision fp32 \ + --cache_dir ./model_cache \ +``` + +2. Merged ONNX model, FP32 CUDA +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \ + --model_name meta-llama/Llama-2-7b-hf \ + --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --merged \ + --execution_provider cuda \ + --precision fp32 \ + --cache_dir ./model_cache \ +``` + +3. Merged ONNX model, FP16 CUDA +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \ + --model_name meta-llama/Llama-2-7b-hf \ + --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --merged \ + --execution_provider cuda \ + --precision fp16 \ + --cache_dir ./model_cache \ +``` + +4. Merged ONNX model, FP16 CUDA with GroupQueryAttention +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.llama_parity \ + --model_name meta-llama/Llama-2-7b-hf \ + --onnx_model_path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --merged \ + --use_gqa \ + --execution_provider cuda \ + --precision fp16 \ + --cache_dir ./model_cache \ +``` + ## Benchmark LLaMA-2 Here are some examples of how you can benchmark LLaMA-2. @@ -240,6 +296,7 @@ Here are some examples of how you can benchmark LLaMA-2. CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type hf-pt-eager \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp32 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -252,6 +309,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type hf-pt-compile \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -265,6 +323,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type hf-ort \ --hf-ort-dir-path ./Llama-2-7b-hf-onnx/ \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp32 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -278,6 +337,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type hf-ort \ --hf-ort-dir-path ./Llama-2-7b-hf-onnx/ \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -291,6 +351,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type ort-msft \ --ort-model-path ./llama-2-onnx/7B_float32/ONNX/LlamaV2_7B_float32.onnx \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp32 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -303,6 +364,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark \ --benchmark-type ort-msft \ --ort-model-path ./llama-2-onnx/7B_float16/ONNX/LlamaV2_7B_float16.onnx \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -315,6 +377,7 @@ CUDA_VISIBLE_DEVICES=1 python3 -m models.llama.benchmark \ --benchmark-type ort-convert-to-onnx \ --ort-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp32 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -327,6 +390,7 @@ CUDA_VISIBLE_DEVICES=4 python3 -m models.llama.benchmark \ --benchmark-type ort-convert-to-onnx \ --ort-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp16.onnx \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -339,6 +403,7 @@ CUDA_VISIBLE_DEVICES=4,5,6,7 bash benchmark_70b_model.sh 4 \ --benchmark-type ort-convert-to-onnx \ --ort-model-path ./llama2-70b-dis/rank_{}_Llama-2-70b-hf_decoder_merged_model_fp16.onnx \ --model-name meta-llama/Llama-2-70b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --device cuda \ --warmup-runs 5 \ @@ -357,6 +422,7 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_all \ --ort-convert-to-onnx-model-path ./llama2-7b-fp16/Llama-2-7b-hf_decoder_merged_model_fp16.onnx \ --ort-msft-model-path ./llama-2-onnx/7B_float16/ONNX/LlamaV2_7B_float16.onnx \ --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ --precision fp16 \ --batch-sizes "1 2" \ --sequence-lengths "8 16" \ @@ -366,6 +432,72 @@ CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_all \ --timeout 60 # number of minutes before moving to the next benchmark ``` +### Benchmark E2E +You can use `benchmark_e2e.py` to benchmark the full end-to-end scenario and automatically store the results in a CSV file. This tool uses `argmax` for sampling to standardize the benchmarking process. + +1. PyTorch without `torch.compile`, FP32 +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \ + --benchmark-type pt-eager \ + --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ + --prompts-file ./models/llama/prompts.json \ + --precision fp32 \ + --batch-sizes "1 2" \ + --prompt-lengths "16 64" \ + --device cpu \ + --auth +``` + +2. PyTorch with `torch.compile`, FP16 +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \ + --benchmark-type pt-compile \ + --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ + --prompts-file ./models/llama/prompts.json \ + --precision fp16 \ + --batch-sizes "1 2" \ + --prompt-lengths "16 64" \ + --device cuda \ + --auth +``` + +3. ONNX Runtime with `convert_to_onnx`, FP32 +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \ + --benchmark-type ort \ + --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ + --onnx-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --prompts-file ./models/llama/prompts.json \ + --precision fp32 \ + --batch-sizes "1 2" \ + --prompt-lengths "16 64" \ + --device cpu \ + --auth +``` + +4. ONNX Runtime with `convert_to_onnx`, FP16 +``` +CUDA_VISIBLE_DEVICES=0 python3 -m models.llama.benchmark_e2e \ + --benchmark-type ort \ + --model-name meta-llama/Llama-2-7b-hf \ + --cache-dir ./model_cache \ + --onnx-model-path ./llama2-7b/rank_0_Llama-2-7b-hf_decoder_merged_model_fp32.onnx \ + --prompts-file ./models/llama/prompts.json \ + --precision fp16 \ + --batch-sizes "1 2" \ + --prompt-lengths "16 64" \ + --device cuda \ + --use_buffer_share \ + --auth +``` + +## E2E Inference with LLaMA-2 + +For end-to-end inference, please visit the [ONNX Runtime Inference Examples folder](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/python/models/llama) for a step-by-step walkthrough, code examples, and performance metrics. + # Mistral ## Introduction diff --git a/onnxruntime/python/tools/transformers/models/llama/benchmark.py b/onnxruntime/python/tools/transformers/models/llama/benchmark.py index bfe108d21a59..6184298c471a 100644 --- a/onnxruntime/python/tools/transformers/models/llama/benchmark.py +++ b/onnxruntime/python/tools/transformers/models/llama/benchmark.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- import argparse import datetime import gc @@ -14,11 +19,12 @@ from benchmark_helper import measure_memory, setup_logger from dist_settings import get_rank, get_size from llama_inputs import ( - add_io_bindings, + add_io_bindings_as_ortvalues, get_merged_sample_with_past_kv_inputs, get_msft_sample_inputs, get_sample_inputs, get_sample_with_past_kv_inputs, + verify_ort_inputs, ) from optimum.onnxruntime import ORTModelForCausalLM from torch.profiler import ProfilerActivity, profile, record_function @@ -199,6 +205,7 @@ def get_model(args: argparse.Namespace): torch_dtype=torch.float16 if args.use_fp16 else torch.float32, use_auth_token=args.auth, use_cache=True, + cache_dir=args.cache_dir, ).to(args.target_device) end_time = time.time() @@ -444,24 +451,12 @@ def get_logits(inputs): def run_ort_inference(args, init_inputs, iter_inputs, model): def prepare_ort_inputs(inputs, kv_cache_ortvalues): - # Check that all model inputs will be provided - model_inputs = set(map(lambda model_input: model_input.name, model.get_inputs())) - user_inputs = set(inputs.keys()) - missing_inputs = model_inputs - user_inputs - if len(missing_inputs): - logger.error(f"The following model inputs are missing: {missing_inputs}") - raise Exception("There are missing inputs to the model. Please add them and try again.") - - # Remove unnecessary inputs from model inputs - unnecessary_inputs = user_inputs - model_inputs - if len(unnecessary_inputs): - for unnecessary_input in unnecessary_inputs: - logger.info(f"Removing unnecessary input '{unnecessary_input}' from user provided inputs") - del inputs[unnecessary_input] + # Verify model inputs + inputs = verify_ort_inputs(model, inputs) # Add IO bindings for non-CPU execution providers if args.device != "cpu": - io_binding, kv_cache_ortvalues = add_io_bindings( + io_binding, kv_cache_ortvalues = add_io_bindings_as_ortvalues( model, inputs, args.device, int(args.rank), args.use_gqa, kv_cache_ortvalues ) setattr(args, "io_binding", io_binding) # noqa: B010 @@ -612,6 +607,13 @@ def get_args(rank=0): parser.add_argument("--pt-num-rows", type=int, default=1000, help="Number of rows for PyTorch profiler to display") parser.add_argument("--verbose", default=False, action="store_true") parser.add_argument("--log-folder", type=str, default=os.path.join("."), help="Folder to cache log files") + parser.add_argument( + "--cache-dir", + type=str, + required=True, + default="./model_cache", + help="Cache dir where Hugging Face files are stored", + ) args = parser.parse_args() @@ -662,8 +664,8 @@ def main(): args.rank = rank args.world_size = world_size - tokenizer = AutoTokenizer.from_pretrained(args.model_name) - config = AutoConfig.from_pretrained(args.model_name) + tokenizer = AutoTokenizer.from_pretrained(args.model_name, cache_dir=args.cache_dir) + config = AutoConfig.from_pretrained(args.model_name, cache_dir=args.cache_dir) target_device = f"cuda:{args.rank}" if args.device != "cpu" else args.device use_fp16 = args.precision == "fp16" diff --git a/onnxruntime/python/tools/transformers/models/llama/benchmark_all.py b/onnxruntime/python/tools/transformers/models/llama/benchmark_all.py index c6d550d47cf4..2433ae3d9b5e 100644 --- a/onnxruntime/python/tools/transformers/models/llama/benchmark_all.py +++ b/onnxruntime/python/tools/transformers/models/llama/benchmark_all.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- import argparse import datetime import json @@ -78,6 +83,13 @@ def get_args(): help="Path to ONNX model from convert_to_onnx", ) + parser.add_argument( + "--cache-dir", + type=str, + default="./model_cache", + help="Cache dir where Hugging Face files are stored", + ) + parser.add_argument( "--model-name", type=str, @@ -332,6 +344,8 @@ def main(): str(args.num_runs), "--log-folder", args.log_folder, + "--cache-dir", + args.cache_dir, "--auth", ] logger.info("Benchmark PyTorch without torch.compile") @@ -362,6 +376,8 @@ def main(): str(args.num_runs), "--log-folder", args.log_folder, + "--cache-dir", + args.cache_dir, "--auth", ] logger.info("Benchmark PyTorch with torch.compile") @@ -394,6 +410,8 @@ def main(): str(args.num_runs), "--log-folder", args.log_folder, + "--cache-dir", + args.cache_dir, "--auth", ] logger.info("Benchmark Optimum + ONNX Runtime") @@ -426,6 +444,8 @@ def main(): str(args.num_runs), "--log-folder", args.log_folder, + "--cache-dir", + args.cache_dir, ] logger.info("Benchmark Microsoft model in ONNX Runtime") results = benchmark(args, benchmark_cmd, "ort-msft") @@ -457,6 +477,8 @@ def main(): str(args.num_runs), "--log-folder", args.log_folder, + "--cache-dir", + args.cache_dir, ] logger.info("Benchmark convert_to_onnx model in ONNX Runtime") results = benchmark(args, benchmark_cmd, "onnxruntime") diff --git a/onnxruntime/python/tools/transformers/models/llama/benchmark_e2e.py b/onnxruntime/python/tools/transformers/models/llama/benchmark_e2e.py new file mode 100644 index 000000000000..4d0d2e68e898 --- /dev/null +++ b/onnxruntime/python/tools/transformers/models/llama/benchmark_e2e.py @@ -0,0 +1,554 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- + +# This is an end-to-end benchmarking script for the Hugging Face LLaMA-2 model. +# +# Prerequisites: +# 1) Install `huggingface-cli`: +# +# $ pip install huggingface_hub +# +# 2) Authenticate with Hugging Face's CLI: +# +# $ huggingface-cli login +# +# 3) Accept Meta's license in Hugging Face to access the models at https://huggingface.co/meta-llama/ +# +# 4) Install the latest ONNX Runtime version +# +# $ pip install onnxruntime-gpu + +from __future__ import annotations + +import argparse +import datetime +import gc +import itertools +import json +import logging +import os +import textwrap +import time + +import numpy as np +import pandas as pd +import torch +from benchmark_helper import setup_logger +from llama_inputs import add_io_bindings_as_tensors, get_initial_inputs_and_outputs +from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer + +import onnxruntime as ort + +logger = logging.getLogger(__name__) + + +def get_model(args): + if args.benchmark_type in {"pt-eager", "pt-compile"}: + model = AutoModelForCausalLM.from_pretrained( + args.hf_dir_path if args.hf_dir_path != "" else args.model_name, + cache_dir=args.cache_dir, + torch_dtype=args.torch_dtype, + use_auth_token=args.auth, + use_cache=True, + ).to(args.target_device) + model.eval() + + if args.benchmark_type == "pt-compile": + model = torch.compile(model) + + else: + sess_options = ort.SessionOptions() + ep = ( + ("CUDAExecutionProvider", {"device_id": args.device_id}) + if args.device == "cuda" + else "CPUExecutionProvider" + ) + model = ort.InferenceSession(args.onnx_model_path, sess_options=sess_options, providers=[ep]) + + return model + + +def run_inference(args, model, runs, inputs, outputs): + if args.benchmark_type == "pt-compile": + with torch.no_grad(): + outputs = model(**inputs) + + # Synchronize inputs + io_binding = None + if args.benchmark_type in {"pt-eager", "pt-compile"}: + if args.device != "cpu": + torch.cuda.synchronize(args.target_device) + else: + io_binding = add_io_bindings_as_tensors(model, inputs, outputs, args.use_fp16, args.use_buffer_share) + io_binding.synchronize_inputs() + + # Run inference + start = time.perf_counter() + for _ in range(runs): + if args.benchmark_type in {"pt-eager", "pt-compile"}: + with torch.no_grad(): + outputs = model(**inputs) + if args.device != "cpu": + torch.cuda.synchronize(args.target_device) + else: + model.run_with_iobinding(io_binding) + io_binding.synchronize_outputs() + + end = time.perf_counter() + avg = (end - start) / runs + return avg, outputs + + +def prepare_model_for_inference(args, model, config, tokenizer, prompt_length, prompt): + clear_cache() + inputs, outputs = get_initial_inputs_and_outputs( + config, tokenizer, prompt_length, prompt, args.target_device, args.use_fp16, args.use_buffer_share, args.engine + ) + _, outputs = run_inference(args, model, args.warmup_runs, inputs, outputs) + return inputs, outputs + + +def clear_cache(): + gc.collect() + torch.cuda.empty_cache() + + +def save_results(results, filename, gen_length): + df = pd.DataFrame( + results, + columns=[ + "Batch Size", + "Prompt Length", + "Prompt Processing Latency (ms)", + "Prompt Processing Throughput (tps)", + "Sampling Latency (ms)", + "Sampling Throughput (tps)", + "First Token Generated Latency (ms)", + "First Token Generated Throughput (tps)", + f"Average Latency of First {gen_length // 2} Tokens Generated (ms)", + f"Average Throughput of First {gen_length // 2} Tokens Generated (tps)", + f"Average Latency of First {gen_length} Tokens Generated (ms)", + f"Average Throughput of First {gen_length} Tokens Generated (tps)", + "Wall-Clock Latency (s)", + "Wall-Clock Throughput (tps)", + ], + ) + + df.to_csv(filename, index=False) + logger.info(f"Results saved in {filename}!") + + +def get_args(): + parser = argparse.ArgumentParser() + + parser.add_argument( + "-bt", + "--benchmark-type", + type=str, + required=True, + choices=["pt-eager", "pt-compile", "ort"], + ) + + parser.add_argument( + "-m", + "--model-name", + type=str, + required=False, + help="Hugging Face name of model (e.g. 'meta-llama/Llama-2-7b-hf')", + ) + + parser.add_argument( + "-a", + "--auth", + default=False, + action="store_true", + help="Use Hugging Face authentication token to access model", + ) + + parser.add_argument( + "-c", + "--cache-dir", + type=str, + default=os.path.join(".", "model_cache"), + help="Path to directory containing all Hugging Face files (e.g. config, tokenizer, PyTorch model). Use when loading model as `AutoModel.from_pretrained(model_name, cache_dir=cache_dir)`.", + ) + + parser.add_argument( + "--hf-dir-path", + type=str, + default="", + help="Path to directory containing all Hugging Face files (e.g. config, tokenizer, PyTorch model). Use when loading model as `AutoModel.from_pretrained(folder_path)`.", + ) + + parser.add_argument( + "-o", + "--onnx-model-path", + required=False, + help="Path to ONNX model", + ) + + parser.add_argument( + "-f", + "--prompts-file", + required=True, + default=os.path.join(".", "models", "llama", "prompts.json"), + help="JSON file containing entries in the format 'prompt length: prompt' where prompt length = tokenized length of prompt", + ) + + parser.add_argument( + "--use_buffer_share", + default=False, + action="store_true", + help="Use when GroupQueryAttention (GQA) is in ONNX model", + ) + + parser.add_argument( + "--anomaly-filtering", + default=False, + action="store_true", + help="Use this flag to filter anomaly accelerator times for tokens generated. \ + This may give more accurate latency and throughput metrics for tokens generated. \ + Wall-clock metrics are still reported with anomaly times though.", + ), + + parser.add_argument( + "-b", + "--batch-sizes", + default="1 2", + ) + + parser.add_argument( + "-s", + "--prompt-lengths", + default="32 64 128 256 512", + ) + + parser.add_argument( + "-p", + "--precision", + required=True, + type=str, + default="fp32", + choices=["int4", "int8", "fp16", "fp32"], + help="Precision for model. For ONNX models, the model's precision should be set before running this script.", + ) + + parser.add_argument( + "-g", + "--generation-length", + type=int, + default=256, + help="Number of new tokens to generate", + ) + + parser.add_argument( + "-d", + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + choices=["cpu", "cuda"], + ) + + parser.add_argument("-id", "--device-id", type=int, default=0) + parser.add_argument("-w", "--warmup-runs", type=int, default=5) + parser.add_argument("-n", "--num-runs", type=int, default=100) + parser.add_argument("--seed", type=int, default=2) + + args = parser.parse_args() + + # Set seed properties + np.random.seed(args.seed) + torch.manual_seed(args.seed) + + # Set runtime properties + if "ort" in args.benchmark_type: + setattr(args, "execution_provider", f"{args.device.upper()}ExecutionProvider") # noqa: B010 + if args.execution_provider == "CUDAExecutionProvider": + args.execution_provider = (args.execution_provider, {"device_id": args.device_id}) + + # Check that paths have been specified for any benchmarking with ORT + if args.benchmark_type == "ort": + assert args.onnx_model_path, "Please specify a path to `--onnx-model-path`" + + args.batch_sizes = args.batch_sizes.split(" ") + args.prompt_lengths = args.prompt_lengths.split(" ") + + # Use FP32 precision for FP32, INT8, INT4 CPU models, use FP16 precision for FP16 and INT4 GPU models + args.precision = ( + "fp32" if args.precision in {"int8", "fp32"} or (args.precision == "int4" and args.device == "cpu") else "fp16" + ) + + target_device = f"cuda:{args.device_id}" if args.device != "cpu" else args.device + torch_dtype = torch.float16 if args.precision == "fp16" else torch.float32 + engine = "ort" if args.benchmark_type == "ort" else "pt" + setattr(args, "target_device", target_device) # noqa: B010 + setattr(args, "torch_dtype", torch_dtype) # noqa: B010 + setattr(args, "engine", engine) # noqa: B010 + setattr(args, "use_fp16", args.precision == "fp16") # noqa: B010 + + return args + + +def main(): + args = get_args() + setup_logger(False) + logger.info(args.__dict__) + + # Get prompts and prompt sizes + size_to_prompt = None + with open(args.prompts_file) as f: + size_to_prompt = json.load(f, object_hook=lambda d: {int(k): v for k, v in d.items()}) + + # Get config, tokenizer, and model + config = AutoConfig.from_pretrained( + args.hf_dir_path if args.hf_dir_path != "" else args.model_name, + cache_dir=args.cache_dir, + use_auth_token=args.auth, + ) + tokenizer = AutoTokenizer.from_pretrained( + args.hf_dir_path if args.hf_dir_path != "" else args.model_name, + cache_dir=args.cache_dir, + use_auth_token=args.auth, + ) + model = get_model(args) + + all_csv_metrics = [] + for batch_size, prompt_length in itertools.product(args.batch_sizes, args.prompt_lengths): + batch_size, prompt_length = int(batch_size), int(prompt_length) # noqa: PLW2901 + logger.info(f"Running batch size = {batch_size}, prompt length = {prompt_length}") + clear_cache() + max_length = prompt_length + args.generation_length + + if prompt_length not in size_to_prompt: + raise NotImplementedError( + textwrap.dedent( + f""" + A prompt of size {prompt_length} was not found in '{args.prompts_file}'. There are a couple of solutions to fix this. + 1) You can change one of the keys in '{args.prompts_file}' to be {prompt_length}. + If {prompt_length} < actual prompt's length, the benchmark E2E tool will repeat the first word in the prompt until {prompt_length} = actual prompt's length. + If {prompt_length} > actual prompt's length, the benchmark E2E tool will automatically trim the actual prompt's length so that {prompt_length} = actual prompt's length. + 2) You can add a new key-value entry in '{args.prompts_file}' of the form '{prompt_length}': 'your prompt goes here'. + """ + ) + ) + prompt = [size_to_prompt[prompt_length]] * batch_size + csv_metrics = [batch_size, prompt_length] + + try: + # Measure prompt processing + logger.info("Measuring prompt processing...") + inputs, outputs = prepare_model_for_inference(args, model, config, tokenizer, prompt_length, prompt) + accelerator_prompt_latency_s, outputs = run_inference(args, model, args.num_runs, inputs, outputs) + + # Calculate prompt metrics + accelerator_prompt_latency_ms = accelerator_prompt_latency_s * 1000 + accelerator_prompt_thrpt = batch_size * (prompt_length / accelerator_prompt_latency_s) + logger.info(f"Average Latency of Prompt Processing: {accelerator_prompt_latency_ms} ms") + logger.info( + f"Average Throughput of Prompt Processing: {batch_size * (prompt_length / accelerator_prompt_latency_s)} tps" + ) + csv_metrics.extend([accelerator_prompt_latency_ms, accelerator_prompt_thrpt]) + + # Measure token generation + logger.info("Measuring token generation...") + clear_cache() + inputs, outputs = prepare_model_for_inference(args, model, config, tokenizer, prompt_length, prompt) + + all_token_ids = inputs["input_ids"].clone() + current_length = all_token_ids.shape[-1] + num_heads = config.num_key_value_heads + head_size = ( + config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads + ) + + has_eos = torch.zeros(batch_size, device=args.target_device, dtype=torch.bool) + + # 0th entry will have prompt accelerator time, 1st entry onwards will have token generation accelerator time + accelerator_times = [] + sampling_times = [] # cost to sample after each model run + + wall_clock_start_time = time.perf_counter() + while current_length <= max_length: + # Run inference + accelerator_time_latency_s, outputs = run_inference(args, model, 1, inputs, outputs) + accelerator_times.append(accelerator_time_latency_s) + + # Sample with argmax (greedy search) + sampling_start_time = time.perf_counter() + if outputs["logits"].shape[1] > 1: + prompt_end_indices = inputs["attention_mask"].sum(1) - 1 + idxs = ( + prompt_end_indices.unsqueeze(dim=1) + .repeat(1, config.vocab_size) + .view(batch_size, 1, config.vocab_size) + ) + next_token_logits = torch.gather(outputs["logits"], 1, idxs).squeeze() + else: + next_token_logits = outputs["logits"][:, -1, :] + next_tokens = torch.argmax(next_token_logits, dim=-1) + + # Check if we previously reached EOS token id or if generated token id is EOS token id + has_eos = has_eos | next_tokens == tokenizer.eos_token_id + + # Determine which new tokens to add to list of all token ids + # Add EOS token ids for batch entries that ended early (ragged batching scenario where some batch entries ended early and some haven't) + tokens_to_add = next_tokens.masked_fill(has_eos, tokenizer.eos_token_id).reshape([batch_size, 1]) + sampling_end_time = time.perf_counter() + sampling_times.append(sampling_end_time - sampling_start_time) + + all_token_ids = torch.cat([all_token_ids, tokens_to_add], dim=-1) + + # Return early if all batch entries have reached EOS token id + current_length += 1 + if torch.all(has_eos) or current_length > max_length: + break + + # Update inputs for next inference run + inputs["input_ids"] = tokens_to_add + inputs["attention_mask"] = torch.cat( + [inputs["attention_mask"], (~has_eos).to(torch.int64).reshape(batch_size, 1)], 1 + ) + inputs["position_ids"] = ( + None + if "position_ids" not in inputs + else torch.max(inputs["position_ids"], dim=1)[0].reshape(batch_size, 1) + 1 + ) + + # Set logits to zeros for next inference run and re-use memory buffer + if outputs["logits"].shape[1] != 1: + outputs["logits"] = outputs["logits"][:, :1, :].contiguous() + outputs["logits"].zero_() + + # Update KV caches for next inference run + if args.engine == "pt": + # Update KV caches for PyTorch + inputs["past_key_values"] = outputs["past_key_values"] + elif not args.use_buffer_share: + # Update KV caches for ONNX Runtime if buffer sharing is not used + for i in range(config.num_hidden_layers): + inputs[f"past_key_values.{i}.key"] = outputs[f"present.{i}.key"] + inputs[f"past_key_values.{i}.value"] = outputs[f"present.{i}.value"] + + new_sequence_length = inputs["attention_mask"].shape[1] + for i in range(config.num_hidden_layers): + present_key = torch.zeros( + batch_size, + num_heads, + new_sequence_length, + head_size, + device=args.target_device, + dtype=args.torch_dtype, + ) + present_value = torch.zeros( + batch_size, + num_heads, + new_sequence_length, + head_size, + device=args.target_device, + dtype=args.torch_dtype, + ) + outputs.update( + { + f"present.{i}.key": present_key.contiguous(), + f"present.{i}.value": present_value.contiguous(), + } + ) + + wall_clock_end_time = time.perf_counter() + + # Filter out any anomaly accelerator times (e.g. for `torch.compile`) + accelerator_times.pop(0) # Remove prompt processing time + if args.anomaly_filtering: + anomaly_threshold_factor = 10 + min_time_s = min(accelerator_times) + orig_size = len(accelerator_times) + accelerator_times = list( + filter(lambda acc_time: acc_time < anomaly_threshold_factor * min_time_s, accelerator_times) + ) + new_size = len(accelerator_times) + logger.info( + f"Filtered out {orig_size - new_size} anomaly accelerator times that are {anomaly_threshold_factor}x greater than {min_time_s * 1000} ms..." + ) + + ####################################################### + # Calculate sampling and first token generated metrics + ####################################################### + + # Calculate sampling metrics + avg_sampling_latency_s = sum(sampling_times) / len(sampling_times) + avg_sampling_latency_ms = avg_sampling_latency_s * 1000 + avg_sampling_thrpt = batch_size * (1 / avg_sampling_latency_s) + logger.info(f"Average Latency of Sampling: {avg_sampling_latency_ms} ms") + logger.info(f"Average Throughput of Sampling: {avg_sampling_thrpt} tps") + + # Calculate first token generated metrics + first_token_latency_s = accelerator_times[0] + first_token_latency_ms = first_token_latency_s * 1000 + first_token_thrpt = batch_size * (1 / first_token_latency_s) + logger.info(f"Latency of First Token Generated: {first_token_latency_ms} ms") + logger.info(f"Throughput of First Token Generated: {first_token_thrpt} tps") + + #################################################### + # Calculate first `halfway` token generated metrics + #################################################### + + halfway = args.generation_length // 2 + halfway_token_latency_s = sum(accelerator_times[:halfway]) / len(accelerator_times[:halfway]) + halfway_token_latency_ms = halfway_token_latency_s * 1000 + halfway_token_thrpt = batch_size * (1 / halfway_token_latency_s) + logger.info(f"Average Latency of First {halfway} Tokens Generated: {halfway_token_latency_ms} ms") + logger.info(f"Average Throughput of First {halfway} Tokens Generated: {halfway_token_thrpt} tps") + + ######################################### + # Calculate all tokens generated metrics + ######################################### + + all_token_latency_s = sum(accelerator_times) / len(accelerator_times) + all_token_latency_ms = all_token_latency_s * 1000 + all_token_thrpt = batch_size * (1 / all_token_latency_s) + logger.info( + f"Average Latency of First {args.generation_length} Tokens Generated: {all_token_latency_ms} ms" + ) + logger.info(f"Average Throughput of First {args.generation_length} Tokens Generated: {all_token_thrpt} tps") + + ############################### + # Calculate wall clock metrics + ############################### + + wall_clock_latency_s = wall_clock_end_time - wall_clock_start_time + wall_clock_thrpt = batch_size * ((prompt_length + args.generation_length) / wall_clock_latency_s) + logger.info(f"Wall-Clock Latency: {wall_clock_latency_s} s") + logger.info( + f"Wall-Clock Throughput: {batch_size * ((prompt_length + args.generation_length) / wall_clock_latency_s)} tps" + ) + + # Add metrics to CSV + logger.info("Adding results to CSV") + csv_metrics.extend( + [ + avg_sampling_latency_ms, + avg_sampling_thrpt, + first_token_latency_ms, + first_token_thrpt, + halfway_token_latency_ms, + halfway_token_thrpt, + all_token_latency_ms, + all_token_thrpt, + wall_clock_latency_s, + wall_clock_thrpt, + ] + ) + all_csv_metrics.append(csv_metrics) + + except: # noqa: E722 + logger.info(f"Could not benchmark at batch size = {batch_size}, prompt length = {prompt_length}") + + filename = f"benchmark_{args.engine}_e2e_{datetime.datetime.now():%Y-%m-%d_%H:%M:%S}.csv" + save_results(all_csv_metrics, filename, args.generation_length) + + +if __name__ == "__main__": + main() diff --git a/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py b/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py index 1ad58327b7fc..b649f7ab6504 100644 --- a/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py +++ b/onnxruntime/python/tools/transformers/models/llama/convert_to_onnx.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- from __future__ import annotations import argparse diff --git a/onnxruntime/python/tools/transformers/models/llama/dist_settings.py b/onnxruntime/python/tools/transformers/models/llama/dist_settings.py index 72192ce8d8c6..3b53f60758b2 100644 --- a/onnxruntime/python/tools/transformers/models/llama/dist_settings.py +++ b/onnxruntime/python/tools/transformers/models/llama/dist_settings.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- import os import torch.distributed as dist diff --git a/onnxruntime/python/tools/transformers/models/llama/llama_inputs.py b/onnxruntime/python/tools/transformers/models/llama/llama_inputs.py index 18202f4b81c0..5aed55c12f38 100644 --- a/onnxruntime/python/tools/transformers/models/llama/llama_inputs.py +++ b/onnxruntime/python/tools/transformers/models/llama/llama_inputs.py @@ -1,8 +1,13 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- from __future__ import annotations import numpy as np import torch -from transformers import AutoConfig +from transformers import AutoConfig, AutoTokenizer from onnxruntime import InferenceSession, OrtValue @@ -269,6 +274,8 @@ def convert_inputs_for_ort( return ort_inputs +# Re-allocate KV caches from (batch_size, num_heads, past_sequence_length, head_size) to +# (batch_size, num_heads, max_sequence_length, head_size) for past-present buffer sharing def enable_past_present_share_buffer(ort_inputs: dict, past_seq_len: int, max_seq_len: int): for k, v in ort_inputs.items(): # Allocate new buffers with max_sequence_length for GQA @@ -281,8 +288,29 @@ def enable_past_present_share_buffer(ort_inputs: dict, past_seq_len: int, max_se return ort_inputs -# Add IO bindings for execution providers -def add_io_bindings( +# Verify ONNX Runtime inputs with model +def verify_ort_inputs(model: InferenceSession, ort_inputs: dict): + # Check that all model inputs will be provided + model_inputs = set(map(lambda model_input: model_input.name, model.get_inputs())) + user_inputs = set(ort_inputs.keys()) + missing_inputs = model_inputs - user_inputs + if len(missing_inputs): + print(f"The following model inputs are missing: {missing_inputs}") + raise Exception("There are missing inputs to the model. Please add them and try again.") + + # Remove unnecessary inputs from model inputs + unnecessary_inputs = user_inputs - model_inputs + if len(unnecessary_inputs): + for unnecessary_input in unnecessary_inputs: + print(f"Removing unnecessary input '{unnecessary_input}' from user provided inputs") + del ort_inputs[unnecessary_input] + + return ort_inputs + + +# Add IO bindings for execution providers using OrtValue +# Use when you need to run inference once or twice to save memory +def add_io_bindings_as_ortvalues( model: InferenceSession, ort_inputs: dict, device: str, device_id: int, use_gqa: bool, kv_cache_ortvalues: dict ): io_binding = model.io_binding() @@ -318,3 +346,163 @@ def add_io_bindings( io_binding.bind_output(name, device_type=device, device_id=device_id) return io_binding, kv_cache_ortvalues + + +# Add IO bindings for execution providers using PyTorch tensors +# Use when you need to run inference many times +def add_io_bindings_as_tensors( + model: InferenceSession, inputs: dict, outputs: dict, use_fp16: bool, use_buffer_share: bool +): + # Verify model inputs + inputs = verify_ort_inputs(model, inputs) + + device = None + pt_to_np = { + "torch.int32": np.int32, + "torch.int64": np.int64, + "torch.float16": np.float16, + "torch.float32": np.float32, + } + + # Bind inputs/outputs to IO binding + io_binding = model.io_binding() + for k, v in inputs.items(): + io_binding.bind_input( + name=k, + device_type=v.device.type, + device_id=0 if v.device.type == "cpu" else v.device.index, + element_type=pt_to_np[repr(v.dtype)], + shape=tuple(v.shape), + buffer_ptr=v.data_ptr(), + ) + device = v.device + + for output in model.get_outputs(): + name = output.name + if use_buffer_share and "present" in name: + # Bind KV cache outputs to KV cache inputs + v = inputs[name.replace("present", "past_key_values")] + io_binding.bind_output( + name=name, + device_type=v.device.type, + device_id=v.device.index, + element_type=np.float16, + shape=tuple(v.shape), + buffer_ptr=v.data_ptr(), + ) + else: + v = outputs[name] + io_binding.bind_output( + name=name, + device_type=device.type, + device_id=0 if device.type == "cpu" else device.index, + element_type=(np.float16 if use_fp16 else np.float32), + shape=tuple(v.shape), + buffer_ptr=v.data_ptr(), + ) + + return io_binding + + +# Get actual inputs when using real data (instead of sample data) and initialize outputs +def get_initial_inputs_and_outputs( + config: AutoConfig, + tokenizer: AutoTokenizer, + requested_length: int, + prompt: list[str], + device: torch.device, + use_fp16: bool, + use_buffer_share: bool, + engine: str, +): + tokenizer.pad_token = "[PAD]" + encodings_dict = tokenizer.batch_encode_plus(prompt, padding=True) + torch_dtype = torch.float16 if use_fp16 else torch.float32 + + # input_ids: pad token id is 0 + # attention_mask: pad token id is 0 + # position_ids: pad token id is 1 + input_ids = torch.tensor(encodings_dict["input_ids"], device=device, dtype=torch.int64) + attention_mask = torch.tensor(encodings_dict["attention_mask"], device=device, dtype=torch.int64) + position_ids = get_position_ids(attention_mask, use_past_kv=False) + + # Check if tokenized prompt length matches the requested prompt length + tokenized_length = input_ids.shape[-1] + if tokenized_length > requested_length: + # Shorten the inputs from (batch_size, tokenized_length) to (batch_size, requested_length) + input_ids = input_ids[:, :requested_length] + attention_mask = attention_mask[:, :requested_length] + position_ids = get_position_ids(attention_mask, use_past_kv=False) + elif tokenized_length < requested_length: + # Lengthen the inputs from (batch_size, tokenized_length) to (batch_size, requested_length) + input_ids_first_col = input_ids[:, 0].unsqueeze(0).T + attention_mask_first_col = attention_mask[:, 0].unsqueeze(0).T + for _ in range(requested_length - tokenized_length): + input_ids = torch.hstack((input_ids_first_col, input_ids)) + attention_mask = torch.hstack((attention_mask_first_col, attention_mask)) + position_ids = get_position_ids(attention_mask, use_past_kv=False) + + tokenized_length = input_ids.shape[-1] + assert tokenized_length == requested_length + + # Create inputs + inputs = { + "input_ids": input_ids.contiguous() if engine == "ort" else input_ids, + "attention_mask": attention_mask.contiguous() if engine == "ort" else attention_mask, + "position_ids": position_ids.contiguous() if engine == "ort" else position_ids, + } + if engine != "ort": + inputs["past_key_values"] = [] + + # Get shape of KV cache inputs + batch_size, sequence_length = input_ids.shape + max_sequence_length = config.max_position_embeddings + num_heads = config.num_key_value_heads + head_size = config.head_dim if hasattr(config, "head_dim") else config.hidden_size // config.num_attention_heads + + # Create KV cache inputs + for i in range(config.num_hidden_layers): + past_key = torch.zeros( + batch_size, + num_heads, + max_sequence_length if use_buffer_share else 0, + head_size, + device=device, + dtype=torch_dtype, + ) + past_value = torch.zeros( + batch_size, + num_heads, + max_sequence_length if use_buffer_share else 0, + head_size, + device=device, + dtype=torch_dtype, + ) + if engine == "ort": + inputs.update( + { + f"past_key_values.{i}.key": past_key.contiguous(), + f"past_key_values.{i}.value": past_value.contiguous(), + } + ) + else: + inputs["past_key_values"].append((past_key, past_value)) + + outputs = None + if engine == "ort": + # Create outputs + logits = torch.zeros(batch_size, sequence_length, config.vocab_size, device=device, dtype=torch_dtype) + outputs = {"logits": logits.contiguous()} + if not use_buffer_share: + for i in range(config.num_hidden_layers): + present_key = torch.zeros( + batch_size, num_heads, sequence_length, head_size, device=device, dtype=torch_dtype + ) + present_value = torch.zeros( + batch_size, num_heads, sequence_length, head_size, device=device, dtype=torch_dtype + ) + outputs.update( + {f"present.{i}.key": present_key.contiguous(), f"present.{i}.value": present_value.contiguous()} + ) + + return inputs, outputs diff --git a/onnxruntime/python/tools/transformers/models/llama/llama_parity.py b/onnxruntime/python/tools/transformers/models/llama/llama_parity.py index f41a90208c51..9cbc9af7fe9b 100644 --- a/onnxruntime/python/tools/transformers/models/llama/llama_parity.py +++ b/onnxruntime/python/tools/transformers/models/llama/llama_parity.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- from __future__ import annotations import argparse @@ -10,7 +15,7 @@ from benchmark_helper import setup_logger from dist_settings import get_rank, get_size from llama_inputs import ( - add_io_bindings, + add_io_bindings_as_ortvalues, convert_inputs_for_ort, get_merged_sample_with_past_kv_inputs, get_sample_inputs, @@ -123,7 +128,7 @@ def verify_parity( # Add IO bindings for non-CPU execution providers if args.execution_provider != "cpu": - io_binding, kv_cache_ortvalues = add_io_bindings( + io_binding, kv_cache_ortvalues = add_io_bindings_as_ortvalues( ort_model, inputs, args.execution_provider, diff --git a/onnxruntime/python/tools/transformers/models/llama/llama_torch.py b/onnxruntime/python/tools/transformers/models/llama/llama_torch.py index 89b459c80bee..d570e2d7ee08 100644 --- a/onnxruntime/python/tools/transformers/models/llama/llama_torch.py +++ b/onnxruntime/python/tools/transformers/models/llama/llama_torch.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- import logging import os diff --git a/onnxruntime/python/tools/transformers/models/llama/prompts.json b/onnxruntime/python/tools/transformers/models/llama/prompts.json new file mode 100644 index 000000000000..5d8fae99dbc7 --- /dev/null +++ b/onnxruntime/python/tools/transformers/models/llama/prompts.json @@ -0,0 +1,11 @@ +{ + "16": "How are astronauts launched into space quickly on those rockets? ", + "64": "Today, we will learn how to bake a chocolate cake. First, you need to have all of the ingredients to bake. Otherwise, the chocolate cake won't be tasty. You will also need a large baking pan to hold the batter. ", + "256": "Risk Management and Insurance (RMI) is a field that focuses on the identification, assessment and financial mitigation of risk. It's about insurance but also more than that. For example, insurance companies look at risk factors such as age, gender and medical history to determine how much they will charge for life insurance coverage. However, RMI is not just about buying insurance (although it is a big part of this). It is also about taking steps to reduce the likelihood that something bad happens in the first place. For example, you may think twice before crossing a busy road if there's a high risk of being hit by a car or getting injured. In addition to insurance companies and financial services firms, RMI professionals work with individuals (customers), businesses and other entities (clients). Their job is to identify potential risks and help mitigate them before they become problems for their clients. This can include helping people prepare financially for unexpected events like losing a job or being injured in an accident, as well as assisting businesses with managing risk exposure from things like natural disasters or cyber attacks. Insurance companies use RMI to ", + "1024": "Risk Management and Insurance (RMI) is a field that focuses on the identification, assessment and financial mitigation of risk. It's about insurance but also more than that. For example, insurance companies look at risk factors such as age, gender and medical history to determine how much they will charge for life insurance coverage. However, RMI is not just about buying insurance (although it is a big part of this). It is also about taking steps to reduce the likelihood that something bad happens in the first place. For example, you may think twice before crossing a busy road if there's a high risk of being hit by a car or getting injured. In addition to insurance companies and financial services firms, RMI professionals work with individuals (customers), businesses and other entities (clients). Their job is to identify potential risks and help mitigate them before they become problems for their clients. This can include helping people prepare financially for unexpected events like losing a job or being injured in an accident, as well as assisting businesses with managing risk exposure from things like natural disasters or cyber attacks. Insurance companies use RMI to assess the level of risk associated with potential customers and determine how much they should charge them for coverage. For example, if you are a healthy 25-year old male who doesn't smoke and has never been in an accident, your insurance premiums will likely be lower than those of someone else who fits into one or more of these categories (or all three). Risk Management & Insurance is the process by which you can protect yourself from financial loss. It's about taking control of your money and making sure that it's safe, secure and accessible to you when you need it most. The first step in risk management is understanding what risks are important to you as an individual or a family member who may depend on the income generated by these investments for their livelihood. Once you have identified these key risk factors, then we can help identify how best to manage them through various strategies such as setting up automatic payments into savings accounts so that money is always available when needed most; setting aside emergency funds in case something unexpected happens (e.g., illness); investing wisely so that returns outpace inflation over time; diversifying portfolios by adding stocks and bonds which will help reduce volatility while still providing growth potential through dividends/interest payments over longer periods of time than if invested solely into one type of asset class alone etc. The field of risk management and insurance is growing rapidly, as more people become aware of the potential dangers that can arise from an unforeseen event or accident. As a result, there are many different careers within this field that you may want to consider if you're interested in working with risks and helping others protect themselves from them.One common career path in risk management is as an insurance agent/broker. This person would work for an insurance company or brokerage firm, selling policies to clients who need coverage against things like car accidents or home damage caused by natural disasters such as fires or floods. Insurance agents typically work on commission (i.e., they receive a percentage of every sale). This is important because it means that the more successful an agent is at selling policies, the higher his/her income will be. Another career option within risk management is working for an insurance company itself rather than as an external broker or salesperson. In this case, you'd help manage claims made by policyholders who have been injured through no fault of their own (for example after being hit by another driver). You can also work in risk analysis, a field that involves analyzing the potential risks associated with various investments and projects. This is done to determine whether or not an opportunity has enough upside to justify taking on any related risks. In addition, you might also be responsible for developing strategies to minimize those risks so they don't result in big losses if something goes wrong down the road. If your goal is to work as a broker or agent, then there are some prerequisites that will need to be met before beginning this career path: You must have an associate's degree from an accredited college; pass an exam administered by state regulators (the Series 6) and/or complete additional training offered by professional organizations such as NAFA, which stands for National Association of Financial Advisors. After meeting these requirements, you'll then need to find employment at one or more insurance companies where they offer positions that allow new hires some flexibility when starting out their careers.Risk management and insurance is a broad field that includes many different types of jobs. ", + "2048": "Artificial Intelligence (AI) is a transformative technology that has the potential to revolutionize society in many ways. AI can be used to enhance the accuracy and efficiency of decision-making, improve lives through new apps and services, and solve some of the thorny policy problems of climate change, infrastructure, and healthcare. In this essay, I will discuss some of the ways AI can benefit society. One of the most significant benefits of AI is its ability to improve healthcare. AI can assist doctors, nurses, and other healthcare professionals in making better diagnoses and faster decisions on a course of treatment, based on the large amount of data that currently exists. AI allows doctors to pinpoint effective drugs that may have otherwise been overlooked and can identify higher-risk individuals before any human can. AI can also help relieve the burden on healthcare professionals by taking care of routine data collection and filing, freeing up time for other higher-value activities. Another area where AI can benefit society is in the fight against climate change. AI can be used to analyze vast amounts of data, identify patterns, and provide accurate predictions. It can help us forecast what further spread of pandemics is going to look like, and track their development around the world. AI can also help us predict the impact of climate change on our planet and develop strategies to mitigate its effects. For example, AI can be used to optimize energy consumption, reduce waste, and improve the efficiency of transportation systems. AI can also benefit society by improving education. AI-powered educational tools can help students learn more effectively by providing personalized learning experiences tailored to their individual needs. AI can also help teachers by automating routine tasks such as grading and providing feedback on student work. This can free up time for teachers to focus on more important tasks such as lesson planning and student engagement. AI can also benefit society by improving public safety. AI-powered surveillance systems can help law enforcement agencies detect and prevent crime more effectively. AI can also be used to analyze social media data to identify potential threats and prevent them before they occur. For example, AI can be used to detect hate speech and other forms of online harassment, which can help prevent cyberbullying and other forms of online abuse. Finally, AI can benefit society by improving the economy. AI can help businesses become more efficient by automating routine tasks and providing insights into customer behavior. This can help businesses make better decisions and improve their bottom line. AI can also help create new jobs by enabling the development of new products and services that were previously impossible. In conclusion, AI has the potential to benefit society in many ways. From improving healthcare and education to fighting climate change and improving public safety, AI can help us solve some of the most pressing problems facing our world today. As we continue to develop and refine this transformative technology, it is important that we do so in an ethical and responsible manner, ensuring that the benefits of AI are shared by all members of society. AI has been a topic of discussion for many years, and while it has brought many benefits to society, there are also concerns about its impact. In this essay, I will discuss some of the reasons why AI may not help society. Firstly, AI can be biased. AI systems are designed by humans, and they can be infused with the biases of their creators. This can lead to discrimination against certain groups of people and can perpetuate existing inequalities in society. Additionally, AI can lack transparency, making it difficult to understand how decisions are being made. This can lead to mistrust of AI systems and can hinder their adoption. Secondly, AI can be used to automate jobs, which can lead to unemployment. While AI can increase productivity and efficiency, it can also lead to job displacement, particularly in industries that rely heavily on manual labor. This can have a negative impact on individuals and communities, particularly those that are already marginalized. Thirdly, AI can be used to create fake content, such as deepfakes, which can be used to spread misinformation and propaganda. This can have serious consequences for democracy and can undermine trust in institutions. Fourthly, AI can be used to create autonomous weapons, which can have devastating consequences. These weapons can make decisions without human intervention, which can lead to unintended consequences and can be difficult to control. Fifthly, AI can be used to create surveillance systems that infringe on privacy rights. These systems can be used to monitor individuals without their knowledge or consent, which can have serious consequences for civil liberties. In conclusion, while AI has many potential benefits, there are also concerns about its impact on society. It is important to consider these concerns and to ensure that AI is developed and used in a responsible and ethical manner. Within AI, there are also many subfields. Reinforcement learning is a type of machine learning algorithm that focuses on training models to make decisions in an environment in order to maximize a reward. This is typically done through trial and error, as the algorithm receives feedback in the form of rewards or punishments for its actions. Reinforcement learning has many potential benefits for society, some of which are discussed below. Firstly, reinforcement learning can be used to improve industrial automation and robotics. By training robots to learn from their own experiences, they can gain the skills necessary to perform complex tasks without human intervention. This can lead to increased efficiency and productivity in industries such as manufacturing and logistics. Secondly, reinforcement learning can be used to optimize traffic control systems. By training models to make real-time decisions based on traffic patterns and other data, traffic flow can be improved, reducing congestion and travel times. Thirdly, reinforcement learning can be used to improve healthcare. By training models to make decisions based on patient data, doctors can make more accurate diagnoses and develop more effective treatment plans. This can lead to better health outcomes for patients and can reduce healthcare costs. Fourthly, reinforcement learning can be used to improve education. By training models to adapt to individual student needs, personalized learning experiences can be created that are tailored to each student\u2019s strengths and weaknesses. This can lead to improved academic performance and can help to close the achievement gap. Finally, reinforcement learning can be used to improve environmental sustainability. By training models to make decisions based on environmental data, such as weather patterns and pollution levels, more effective policies can be developed to reduce carbon emissions and protect natural resources. In conclusion, reinforcement learning has many potential benefits for society. By training models to make decisions based on feedback from their environment, we can create more efficient and effective systems in a wide range of fields. However, it is important to consider the ethical implications of these technologies and to ensure that they are developed and used in a responsible and ethical manner. Multi-modal models are another type of machine learning that can process and find relationships between different types of data, such as images, video, audio, and text. They have the potential to revolutionize many aspects of our lives, from healthcare to transportation to education. In this essay, I will discuss how multi-modal models can help society in various ways. One of the most significant benefits of multi-modal models is their ability to transform unstructured data into structured data that can be analyzed. For example, a company could use a multi-modal model to extract data from images or PDFs of invoices or receipts. This would enable them to analyze the data more efficiently and make better-informed decisions. Another benefit of multi-modal models is their ability to cater to various learning styles. Blended and multi-modal learning can reach people who benefit from different learning styles. By understanding their individual learning styles, employees can leverage resources that are compatible with how they process information most effectively. Multi-modal models can also help improve healthcare. For example, they can be used to analyze medical images and identify patterns that might be difficult for human doctors to detect. This can lead to earlier diagnoses and more effective treatments. In addition, multi-modal models can help improve transportation. For example, they can be used to analyze traffic patterns and optimize traffic flow. This can help reduce congestion and improve safety on the roads. Finally, multi-modal models can help improve education. For example, they can be used to create personalized learning experiences for students based on their individual learning styles. This can help students learn more effectively and efficiently. In conclusion, multi-modal models have the potential to help society in many ways. They can transform unstructured data into structured data, cater to various learning styles, improve healthcare, transportation, and education. However, like any new technology, it is important to approach it with caution and consider the potential risks and benefits. I hope this essay has provided some insight into the potential benefits of multi-modal models. Throughout this essay, I have demonstrated the numerous benefits that artificial intelligence will bring to our society. I have also shown some examples of various categories within artificial intelligence that have varying purposes. It is important to consider that each category has its own purpose and has its own pros and cons to it. In conclusion, we must use AI responsibly. ", + "3840": "Artificial Intelligence (AI) is a transformative technology that has the potential to revolutionize society in many ways. AI can be used to enhance the accuracy and efficiency of decision-making, improve lives through new apps and services, and solve some of the thorny policy problems of climate change, infrastructure, and healthcare. In this essay, I will discuss some of the ways AI can benefit society. One of the most significant benefits of AI is its ability to improve healthcare. AI can assist doctors, nurses, and other healthcare professionals in making better diagnoses and faster decisions on a course of treatment, based on the large amount of data that currently exists. AI allows doctors to pinpoint effective drugs that may have otherwise been overlooked and can identify higher-risk individuals before any human can. AI can also help relieve the burden on healthcare professionals by taking care of routine data collection and filing, freeing up time for other higher-value activities. Another area where AI can benefit society is in the fight against climate change. AI can be used to analyze vast amounts of data, identify patterns, and provide accurate predictions. It can help us forecast what further spread of pandemics is going to look like, and track their development around the world. AI can also help us predict the impact of climate change on our planet and develop strategies to mitigate its effects. For example, AI can be used to optimize energy consumption, reduce waste, and improve the efficiency of transportation systems. AI can also benefit society by improving education. AI-powered educational tools can help students learn more effectively by providing personalized learning experiences tailored to their individual needs. AI can also help teachers by automating routine tasks such as grading and providing feedback on student work. This can free up time for teachers to focus on more important tasks such as lesson planning and student engagement. AI can also benefit society by improving public safety. AI-powered surveillance systems can help law enforcement agencies detect and prevent crime more effectively. AI can also be used to analyze social media data to identify potential threats and prevent them before they occur. For example, AI can be used to detect hate speech and other forms of online harassment, which can help prevent cyberbullying and other forms of online abuse. Finally, AI can benefit society by improving the economy. AI can help businesses become more efficient by automating routine tasks and providing insights into customer behavior. This can help businesses make better decisions and improve their bottom line. AI can also help create new jobs by enabling the development of new products and services that were previously impossible. In conclusion, AI has the potential to benefit society in many ways. From improving healthcare and education to fighting climate change and improving public safety, AI can help us solve some of the most pressing problems facing our world today. As we continue to develop and refine this transformative technology, it is important that we do so in an ethical and responsible manner, ensuring that the benefits of AI are shared by all members of society. AI has been a topic of discussion for many years, and while it has brought many benefits to society, there are also concerns about its impact. In this essay, I will discuss some of the reasons why AI may not help society. Firstly, AI can be biased. AI systems are designed by humans, and they can be infused with the biases of their creators. This can lead to discrimination against certain groups of people and can perpetuate existing inequalities in society. Additionally, AI can lack transparency, making it difficult to understand how decisions are being made. This can lead to mistrust of AI systems and can hinder their adoption. Secondly, AI can be used to automate jobs, which can lead to unemployment. While AI can increase productivity and efficiency, it can also lead to job displacement, particularly in industries that rely heavily on manual labor. This can have a negative impact on individuals and communities, particularly those that are already marginalized. Thirdly, AI can be used to create fake content, such as deepfakes, which can be used to spread misinformation and propaganda. This can have serious consequences for democracy and can undermine trust in institutions. Fourthly, AI can be used to create autonomous weapons, which can have devastating consequences. These weapons can make decisions without human intervention, which can lead to unintended consequences and can be difficult to control. Fifthly, AI can be used to create surveillance systems that infringe on privacy rights. These systems can be used to monitor individuals without their knowledge or consent, which can have serious consequences for civil liberties. In conclusion, while AI has many potential benefits, there are also concerns about its impact on society. It is important to consider these concerns and to ensure that AI is developed and used in a responsible and ethical manner. Within AI, there are also many subfields. Reinforcement learning is a type of machine learning algorithm that focuses on training models to make decisions in an environment in order to maximize a reward. This is typically done through trial and error, as the algorithm receives feedback in the form of rewards or punishments for its actions. Reinforcement learning has many potential benefits for society, some of which are discussed below. Firstly, reinforcement learning can be used to improve industrial automation and robotics. By training robots to learn from their own experiences, they can gain the skills necessary to perform complex tasks without human intervention. This can lead to increased efficiency and productivity in industries such as manufacturing and logistics. Secondly, reinforcement learning can be used to optimize traffic control systems. By training models to make real-time decisions based on traffic patterns and other data, traffic flow can be improved, reducing congestion and travel times. Thirdly, reinforcement learning can be used to improve healthcare. By training models to make decisions based on patient data, doctors can make more accurate diagnoses and develop more effective treatment plans. This can lead to better health outcomes for patients and can reduce healthcare costs. Fourthly, reinforcement learning can be used to improve education. By training models to adapt to individual student needs, personalized learning experiences can be created that are tailored to each student\u2019s strengths and weaknesses. This can lead to improved academic performance and can help to close the achievement gap. Finally, reinforcement learning can be used to improve environmental sustainability. By training models to make decisions based on environmental data, such as weather patterns and pollution levels, more effective policies can be developed to reduce carbon emissions and protect natural resources. In conclusion, reinforcement learning has many potential benefits for society. By training models to make decisions based on feedback from their environment, we can create more efficient and effective systems in a wide range of fields. However, it is important to consider the ethical implications of these technologies and to ensure that they are developed and used in a responsible and ethical manner. Multi-modal models are another type of machine learning that can process and find relationships between different types of data, such as images, video, audio, and text. They have the potential to revolutionize many aspects of our lives, from healthcare to transportation to education. In this essay, I will discuss how multi-modal models can help society in various ways. One of the most significant benefits of multi-modal models is their ability to transform unstructured data into structured data that can be analyzed. For example, a company could use a multi-modal model to extract data from images or PDFs of invoices or receipts. This would enable them to analyze the data more efficiently and make better-informed decisions. Another benefit of multi-modal models is their ability to cater to various learning styles. Blended and multi-modal learning can reach people who benefit from different learning styles. By understanding their individual learning styles, employees can leverage resources that are compatible with how they process information most effectively. Multi-modal models can also help improve healthcare. For example, they can be used to analyze medical images and identify patterns that might be difficult for human doctors to detect. This can lead to earlier diagnoses and more effective treatments. In addition, multi-modal models can help improve transportation. For example, they can be used to analyze traffic patterns and optimize traffic flow. This can help reduce congestion and improve safety on the roads. Finally, multi-modal models can help improve education. For example, they can be used to create personalized learning experiences for students based on their individual learning styles. This can help students learn more effectively and efficiently. In conclusion, multi-modal models have the potential to help society in many ways. They can transform unstructured data into structured data, cater to various learning styles, improve healthcare, transportation, and education. However, like any new technology, it is important to approach it with caution and consider the potential risks and benefits. I hope this essay has provided some insight into the potential benefits of multi-modal models. Semi-supervised learning is a type of machine learning that falls in between supervised and unsupervised learning. It is a method that uses a small amount of labeled data and a large amount of unlabeled data to train a model. The goal of semi-supervised learning is to learn a function that can accurately predict the output variable based on the input variables, similar to supervised learning. However, unlike supervised learning, the algorithm is trained on a dataset that contains both labeled and unlabeled data. Semi-supervised learning is particularly useful when there is a large amount of unlabeled data available, but it\u2019s too expensive or difficult to label all of it. The primary advantage of semi-supervised learning is that it can reduce the amount of annotated data used. This is particularly useful when labeled data is scarce or expensive to obtain. By using a small amount of labeled data and a large amount of unlabeled data, semi-supervised learning algorithms can learn from both types of data and improve their accuracy. Semi-supervised learning algorithms are also capable of consolidating overfitting tendencies, which is a common problem in supervised learning. Another advantage of semi-supervised learning is that it is versatile. It can be applied in various situations, from image recognition to crawlers. For example, in text classification, the goal is to classify a given text into one or more predefined categories. Semi-supervised learning can be used to train a text classification model using a small amount of labeled data and a large amount of unlabeled text data. In image classification, the goal is to classify a given image into one or more predefined categories. Semi-supervised learning can be used to train an image classification model using a small amount of labeled data and a large amount of unlabeled image data. In anomaly detection, the goal is to detect patterns or observations that are unusual or different from the norm. Semi-supervised learning can be used to detect anomalies using a small amount of labeled data and a large amount of unlabeled data. Semi-supervised learning algorithms are also stable and simple. They have high efficiency and can be used to improve the performance and generalization of models. However, semi-supervised learning algorithms also have some disadvantages. One of the main disadvantages is that they require a large amount of unlabeled data to be effective. If there is not enough unlabeled data available, the algorithm may not be able to learn effectively. Additionally, semi-supervised learning algorithms can be sensitive to the quality of the labeled data. If the labeled data is noisy or incorrect, the algorithm may not be able to learn effectively. In conclusion, semi-supervised learning is a powerful tool that can be used to improve the accuracy and generalization of machine learning models. It is particularly useful when labeled data is scarce or expensive to obtain. Semi-supervised learning algorithms can learn from both labeled and unlabeled data, which makes them versatile and capable of consolidating overfitting tendencies. However, semi-supervised learning algorithms also have some disadvantages, such as requiring a large amount of unlabeled data to be effective and being sensitive to the quality of the labeled data. Despite these disadvantages, semi-supervised learning is a valuable technique that can be used to improve the performance of machine learning models. Supervised learning is a type of machine learning that involves training a model on labeled data. The goal of supervised learning is to learn a function that can accurately predict the output variable based on the input variables. Supervised learning is widely used in various fields, including image recognition, speech recognition, natural language processing, and more. One of the primary advantages of supervised learning is that it allows for accurate predictions. Supervised learning models can provide highly accurate predictions or classifications when trained on a diverse and representative dataset. This makes supervised learning particularly useful in situations where accuracy is critical, such as in medical diagnosis or fraud detection. Another advantage of supervised learning is that it is easy to understand and implement. Supervised learning algorithms are relatively simple and can be implemented using a variety of programming languages and libraries. This makes it accessible to a wide range of developers and data scientists. Supervised learning is also versatile. It can be applied to a wide range of problem domains, making it a flexible approach for various industries and applications. For example, in image classification, the goal is to classify a given image into one or more predefined categories. Supervised learning can be used to train an image classification model using a labeled dataset of images and their corresponding categories. In speech recognition, the goal is to transcribe spoken words into text. Supervised learning can be used to train a speech recognition model using a labeled dataset of audio recordings and their corresponding transcriptions. Supervised learning algorithms are also capable of handling missing data. If there is missing data in the labeled dataset, supervised learning algorithms can still learn from the available data and make accurate predictions. This is particularly useful in situations where data is incomplete or noisy. However, supervised learning algorithms also have some disadvantages. One of the main disadvantages is that they require a large amount of labeled data to be effective. If there is not enough labeled data available, the algorithm may not be able to learn effectively. Additionally, supervised learning algorithms can be sensitive to the quality of the labeled data. If the labeled data is noisy or incorrect, the algorithm may not be able to learn effectively. In conclusion, supervised learning is a powerful tool that can be used to make accurate predictions and classifications. It is easy to understand and implement, and it is versatile enough to be applied to a wide range of problem domains. However, supervised learning algorithms also have some disadvantages, such as requiring a large amount of labeled data to be effective and being sensitive to the quality of the labeled data. Despite these disadvantages, supervised learning is a valuable technique that can be used to improve the performance of machine learning models. Unsupervised learning is a type of machine learning that involves training a model on unlabeled data. The goal of unsupervised learning is to learn the underlying structure of the data, without any prior knowledge of the output variable. Unsupervised learning is widely used in various fields, including image recognition, natural language processing, and more. One of the primary advantages of unsupervised learning is that it can handle large amounts of unlabeled and unstructured data. This makes unsupervised learning particularly useful in situations where labeled data is scarce or expensive to obtain. By using unsupervised learning algorithms, we can learn from the available data and make accurate predictions. Another advantage of unsupervised learning is that it can identify previously undetected patterns in data. Unsupervised learning algorithms can be used to cluster data points into groups based on their similarities. This can be useful in various applications, such as customer segmentation, anomaly detection, and more. Unsupervised learning algorithms are also capable of dimensionality reduction. This is particularly useful when dealing with high-dimensional data, such as images or text. By reducing the dimensionality of the data, unsupervised learning algorithms can improve the efficiency and accuracy of the model. Unsupervised learning algorithms are also capable of feature learning. Feature learning is the process of automatically learning features from the input data. This can be useful in various applications, such as image recognition, where the algorithm can learn features such as edges, corners, and more. However, unsupervised learning algorithms also have some disadvantages. One of the main disadvantages is that they require a large amount of unlabeled data to be effective. If there is not enough unlabeled data available, the algorithm may not be able to learn effectively. Additionally, unsupervised learning algorithms can be sensitive to the quality of the data. If the data is noisy or incorrect, the algorithm may not be able to learn effectively. As you can see, artificial intelligence (AI) is a wide-ranging field that encompasses various sub-fields. Some of the sub-fields that we have previously discussed include reinforcement learning, multi-modal learning, semi-supervised learning, supervised learning, unsupervised learning, and much more. There are also many application domains for artificial intelligence (AI) that can utilize it. Throughout this essay, I have demonstrated the numerous benefits that artificial intelligence (AI) will bring to our society. I have also shown some examples of various categories within artificial intelligence that have varying purposes. It is important to consider that each category has its own purpose and has its own pros and cons to it. What do you think artificial intelligence will bring to our society? Will it be used in a responsible manner? ", + "4096": "In the heart of Eldoria, where ancient forests whispered secrets and rivers sang forgotten melodies, lay the Enchanted Labyrinth. Its walls, adorned with shimmering runes, concealed a portal to realms unknown. Few dared to venture inside, for the labyrinth was said to twist time and reality. Evelyn, a curious young mage, stood before the labyrinth's entrance. Her emerald eyes sparkled with determination. She clutched a cracked map, its ink fading like memories lost to the wind. Legends spoke of a treasure hidden deep within - a relic capable of granting any wish. As Evelyn stepped across the threshold, the air thickened. The walls shifted, rearranging themselves. She followed the faint glow of her lantern, each step echoing through eternity. Shadows danced, whispering forgotten names. Was this a dream or a nightmare? Deeper into the labyrinth, Evelyn encountered Aelar, the Guardian of Time. His silver hair flowed like moonlight, and his eyes held the weight of centuries. Aelar barred her path, his staff crackling with energy. 'Seeker,' he intoned, 'answer my riddle, and the way shall open.' Evelyn's heart raced. 'Ask, Guardian.' 'What has roots as old as time, yet dances with the wind?' She pondered, memories of her grandmother's tales flooding her mind. 'A tree,' she replied. Aelar smiled, and the walls shifted once more. 'Proceed, Seeker.' The labyrinth twisted, revealing a moonlit grove. Trees hummed ancient lullabies, and fireflies wove constellations in the air. At the center stood a weeping willow, its branches brushing the ground like a grieving widow's veil. Evelyn approached, her fingers tracing the bark. 'Why do you weep?' The willow's voice, soft as falling petals, answered, 'I guard the Tear of Eternity.' Evelyn's breath caught. The Tear - a gem said to hold memories of lost civilizations. She plucked it from a low branch, its facets reflecting forgotten faces. As Evelyn pressed onward, the labyrinth tightened its grip. She faced illusions - lovers lost, friends betrayed. Doubt gnawed at her resolve. Was the treasure worth the cost? At the labyrinth's heart, she found a mirror. Her reflection wavered, revealing her deepest desire: her sister, Lysandra, who vanished years ago. Tears blurred the glass. 'Speak your wish,' the mirror whispered. Evelyn's voice trembled. 'Bring Lysandra back.' The mirror shattered, and reality fractured. Lysandra stepped through, eyes wide with wonder. 'Evelyn?' Lysandra's return came at a cost - the labyrinth demanded balance. For every wish granted, a memory faded. Evelyn watched as her childhood laughter dissolved like mist. Together, they exited the labyrinth, the Tear pulsing in Evelyn's palm. She gazed at her sister, both joy and sorrow in her eyes. 'Was it worth it?' Lysandra asked. Evelyn smiled. 'In Eldoria, every choice we make becomes a story. And ours, dear sister, is woven in stardust and sacrifice.' And so, the Enchanted Labyrinth whispered its final secret: Wishes are threads, and memories their loom. In the land of Aetherfall, where mist-clad mountains touched the heavens and rivers whispered forgotten spells, a prophecy echoed through time. It spoke of the Starstone, a gem said to hold the universe's secrets - the key to creation and destruction. Eldric, a humble blacksmith with eyes like storm clouds, stumbled upon an ancient map. Its ink had faded, but the constellations remained. Guided by fate, he set forth, leaving his forge behind. Eldric's journey led him to the Whispering Forest, where trees conversed in hushed tones. Their leaves whispered of hidden paths and treacherous guardians. Eldric's heart pounded as he stepped into the shadows. There, he met Lyria, a forest nymph with silver hair and eyes like moonlit pools. She guarded the first clue - a riddle etched into a petal: 'In the heart of the forest, where time bends, seek the Wellspring of Echoes. There, the Starstone awaits.' Eldric followed Lyria's guidance. The Wellspring lay within a moon-kissed glade. Its waters shimmered, reflecting memories of lost lovers, ancient battles, and forgotten oaths. Eldric dipped his hand, and the riddle unfolded: 'To find the Starstone, seek the Three Keys: the tear of a fallen star, the breath of a dragon, and the song of a forgotten bard.' Eldric climbed the Stardust Peaks, where fallen stars lay embedded in the rock. Each tear held a fragment of cosmic sorrow. He found one - a sapphire gem pulsing with celestial fire. But it was guarded by Drakor, the last of the star dragons. Drakor's scales shimmered like galaxies. His eyes held eons of wisdom. 'Why seek the Tear, mortal?' 'To save Aetherfall,' Eldric replied. 'To restore balance.' Drakor nodded, and with a breath, he shattered the gem. Eldric caught the falling tear - a shard of eternity. Next, Eldric sailed to the Isle of Shadows, where the void whispered secrets. There, he faced Nyxia, the ancient shadow dragon. Her wings spanned continents, and her breath could devour stars. 'Why seek my breath?' Nyxia hissed. 'To awaken the Starstone,' Eldric said. 'To mend the rifts.' Nyxia's eyes glowed. She exhaled - a stream of darkness. Eldric captured it in a crystal vial - the Breath of the Void. The final key lay in the Bard's Hollow, where echoes of lost melodies lingered. Eldric met Silvan, a ghostly minstrel who strummed a lute of moonwood. 'Sing,' Silvan urged. 'The Song of the Forgotten.' Eldric sang of battles, love, and sacrifice. The hollow trembled, and from the mist, a spectral harp appeared. Its strings hummed - the Song of Ages. Eldric plucked the notes, and they merged into a silver key - the Song of the Forgotten. At the Nexus of Worlds, Eldric assembled the keys - the Tear, the Breath, and the Song. The ground quaked, and the Starstone emerged - a gem of cosmic hues. Its light wove reality, mending fractures in Aetherfall. But the prophecy held a twist: the Starstone demanded a choice. Eldric could use it to reshape the world or sacrifice it to heal the void. He gazed at Lyria, Drakor, Nyxia, and Silvan - their fates intertwined. With a heavy heart, he whispered, 'Balance.' And so, the Starstone shattered, its fragments seeding new constellations. Eldric returned to his forge, but his hammer now shaped more than iron - it forged destiny. Lyria, the Forest Nymph Lyria, with her silver hair and eyes like moonlit pools, remained in the Whispering Forest. She became its guardian, weaving spells to protect the ancient trees. Her laughter echoed through the glades, and travelers whispered of a nymph who danced with moonbeams. Lyria's heart held a secret - the memory of Eldric's touch, the warmth of their shared quest. She tended to the Wellspring of Echoes, ensuring its waters flowed through time, carrying whispers of forgotten tales. Drakor, the Last Star Dragon Drakor, the last of the star dragons, retreated to the highest peak of the Stardust Peaks. There, he curled his immense form around the shattered Tear of the Fallen. His scales absorbed its cosmic fire, and he became a living constellation - a beacon for lost souls. Drakor's breath no longer consumed stars; instead, it birthed new constellations. Travelers gazed at the night sky, seeking guidance in his patterns. Drakor's eyes held both sorrow and hope, for he knew that balance required sacrifice. Nyxia, the Ancient Shadow Dragon Nyxia, with wings spanning continents, chose a different path. She descended to the Isle of Shadows, where the void whispered secrets. There, she guarded the Abyss of Remembrance - a rift between worlds. Nyxia's breath no longer devoured stars; it sealed the rifts. She became a bridge, allowing souls to traverse realms. Those who sought lost loved ones or glimpses of forgotten memories found solace in her shadowed embrace. Nyxia's eyes held the weight of choices made and unmade, and she vowed to keep the balance intact. Silvan, the Ghostly Minstrel Silvan, the spectral minstrel, wandered the Bard's Hollow. His lute of moonwood sang melodies of love, loss, and courage. Silvan's song echoed through time, touching hearts across Aetherfall. He became the keeper of memories - the forgotten bard who whispered forgotten names. When travelers stumbled upon the hollow, Silvan strummed his lute, and their own stories surfaced. He wove their experiences into the Song of Ages, ensuring that no tale would fade into oblivion. Silvan's translucent form danced in moonlight, a bridge between the living and the departed. Eldric, the Blacksmith As for Eldric, the humble blacksmith, he returned to his forge in the village of Hearthstone. His hammer now shaped more than iron - it forged destiny. Eldric crafted talismans from the Tear of the Fallen, the Breath of the Void, and the Song of the Forgotten. These talismans healed rifts, mended broken hearts, and ignited hope. Eldric's eyes held the wisdom of realms explored, and he knew that Aetherfall's balance rested on the choices of ordinary souls. He continued to tell the tale of the Starstone, passing it down through generations, ensuring that the magic endured. And so, dear reader, the threads of fate intertwined - a forest nymph, a star dragon, a shadow, and a minstrel - all bound by the echoes of a forgotten song. The Chronicles of the Celestial Weaver In the forgotten village of Astralis, where the night sky wept silver tears, lived a young girl named Elara. Her eyes held the secrets of constellations, and her fingers danced like stardust. But Astralis suffered - a curse had befallen the heavens. The stars dimmed, their brilliance fading. Elara's grandmother, Lyris, whispered of an ancient prophecy: 'When the stars falter, seek the Celestial Weaver.' Elara vowed to unravel the mystery and save her village. Guided by Lyris's map, Elara ventured into the Veiled Forest, where moonlight wove through ancient oaks. There, she met Silas, the enigmatic weaver. His loom hummed with cosmic threads - the Loom of Eternity. 'Seek the lost constellations,' Silas said. 'Weave them anew.' Elara's heart raced. She plucked a silver thread - the remnants of Orion - and began to weave. The loom responded, stars rekindling. But the cost was memory - Elara forgot her childhood laughter. Elara's journey spanned realms: The Nebula Caves: She retrieved the Pleiades, their sisterhood echoing through time. The Comet's Trail: She chased Halley's Comet, capturing its fiery tail. The Abyss of Lyra: There, Vega's song echoed - a melody of love and longing. Each constellation restored, Elara's memories faded. She forgot her first kiss, her mother's lullabies. Yet Astralis glimmered - the stars brightened. In the Celestial Citadel, Elara faced Draco, the fallen dragon. His scales bore scars - the price of rebellion. He guarded the final constellation - the Serpent. 'Why weave the stars?' Draco hissed. 'They betrayed me.' Elara's fingers trembled. 'To save my village.' Draco's eyes softened. 'We were once kin. We'll share this memory.' As Elara wove the Serpent, she glimpsed Draco's love for Lyris - their forbidden bond. The constellation blazed, and Elara remembered both love and sacrifice. Back in Astralis, the stars blazed anew. Villagers rejoiced, but Elara's memories were fragile threads. Lyris embraced her. 'You've woven fate,' Lyris said. 'But the Loom demands balance.' Elara faced Silas. 'What price?' He smiled - a constellation of wrinkles. 'Your memories or the stars.' Elara hesitated. She remembered her grandmother's stories, her stolen kisses. She chose the stars. Elara became the new Celestial Weaver. Her memories - her life - wove into the cosmos. Astralis thrived, but Elara forgot her name, her laughter, her love. Lyris whispered, 'Weavers are forgotten, but their constellations endure.' And so, Elara wove - the forgotten girl who stitched eternity. Elara, now the Celestial Weaver, wove constellations with threads of memory. Astralis thrived - the villagers danced under starlit skies, unaware of their forgotten histories. Lyris watched her granddaughter, her eyes both proud and sorrowful. 'Elara,' Lyris whispered, 'the Loom demands more than memories.' Elara's fingers trembled. She glimpsed her own reflection in the cosmic threads - the girl who once dreamed of love and laughter. But now, her past was a constellation of faded stars. Silas, the former weaver, lingered in the shadows. His form blurred - a specter between realms. He spoke of the Whispering Veil, a boundary separating memory from oblivion. Beyond it lay forgotten worlds, lost loves, and forbidden truths. 'Cross the Veil,' Silas urged. 'Retrieve what was sacrificed.' Elara hesitated. She yearned for her stolen memories - the taste of strawberries, the warmth of a lover's touch. But the Veil was treacherous - a labyrinth of half-remembered echoes. Elara stepped into the Veil. Its mist clung to her skin, whispering secrets. She glimpsed fragments of her past - a stolen kiss, a tear shed for a fallen friend. The path forked: The Garden of Remembrance: Blooming with forgotten faces, this garden promised reunion. Elara could reclaim her lost memories, but at a cost - the stars would dim once more. The Abyss of Oblivion: A chasm of emptiness. Here, Elara could sever her ties to Astralis, becoming a true Celestial Weaver. The stars would blaze forever, but her existence would be a threadless void. Elara hesitated. She remembered Lyris's lullabies, Silas's enigmatic smile, and Draco's love for her grandmother. She yearned for her stolen laughter - the taste of strawberries, the warmth of a lover's touch. But the stars - Astralis - called to her. The village thrived, its people dancing under constellations she had rekindled. Elara's choice would echo across eternity. She faced the Veil's center - a mirror reflecting her fragmented self. Her fingers trembled. 'Balance,' she whispered. And so, Elara wove anew. She plucked threads from the Garden of Remembrance, reclaiming stolen moments. The stars dimmed, but Astralis glowed with forgotten love. Silas nodded. 'You've chosen well, Weaver.' Elara's memories returned - the taste of strawberries, the warmth of a lover's touch. She kissed Lyris's forehead, whispered Draco's name, and stepped back into Astralis. The stars blazed - the legacy of a girl who stitched eternity. Short stories like these are great to listen and read because they allow us to explore our creative minds and broaden our imaginations. They also inspire us to learn from others and can become culturally impactful. The themes of these stories can also dive deep into philosophical questions and raise awareness for important issues. The plots for these stories are sometimes based on real life events as well and can have deep emotional impact.", + "7936": "The Effects of Airplanes: A Closer Look Airplanes have revolutionized the way we travel, connect, and explore the world. From short domestic flights to transcontinental journeys, these metal birds have become an integral part of our lives. However, their impact extends beyond convenience and adventure. Let's delve into the effects of airplanes from various angles. Environmental Impact Fuel Consumption and Emissions Airplanes consume vast amounts of fuel during flight. For instance, a Boeing 747, with a gas tank capacity of 63,500 gallons, burns approximately five gallons of jet fuel per mile traveled. On a 4,000-mile flight, this translates to 20,000 gallons of fuel. However, when we consider the number of passengers (around 400), the fuel efficiency per traveler is surprisingly better than that of cars. A Honda Civic, which gets 30 miles per gallon, would need 133 gallons of fuel for the same distance. Even an RV, which moves just seven miles on a gallon of gasoline, would require about 285 gallons per traveler. Greenhouse Gas Emissions Airplanes emit greenhouse gases directly into the upper atmosphere, where they can linger longer and cause more damage than the same gases at lower altitudes. While air travel contributes to climate change, it's essential to recognize that other forms of transportation, such as cars and ships, also emit greenhouse gases. The challenge lies in finding ways to reduce aviation emissions without compromising connectivity and mobility. Ozone Depletion and Contrails Planes affect the concentration of other gases and pollutants in the atmosphere. They lead to a short-term increase in ozone (O3) but a long-term decrease. Contrails - those white streaks left behind by planes - can contribute to cloud formation and impact local weather patterns. Balancing the benefits of air travel with environmental concerns remains a critical challenge. Human Health Implications Jet Lag and Sleep Disruption Frequent flyers are no strangers to jet lag. Crossing time zones disrupts our circadian rhythms, affecting sleep patterns, mood, and overall well-being. Pilots, flight attendants, and passengers alike experience the effects of rapid travel across time zones. Dehydration and Blood Pressure Changes The low humidity in airplane cabins can lead to dehydration. Additionally, changes in cabin pressure affect blood pressure, especially during takeoff and landing. Staying hydrated and moving around during long flights can mitigate these effects. Risk of Contagious Diseases Airplanes put passengers in close proximity to one another. Recirculated air, shared surfaces, and confined spaces create an environment conducive to the spread of infections. While airlines take precautions, travelers should remain vigilant, especially during flu seasons. The Perspective Shift: Seeing Earth from Above Beyond the environmental and health impacts, airplanes have transformed our worldview. Before the Wright brothers' epochal breakthrough, humans were grounded, limited to terrestrial views. The advent of flight not only boosted our power of movement but also enhanced our vision. From above, we witness the curvature of the Earth, the vastness of oceans, and the intricate patterns of landscapes. Airplanes have made us global citizens, connecting us to distant lands and cultures. In conclusion, airplanes are a double-edged sword. They offer unparalleled mobility and exploration but come with environmental consequences and health considerations. As we continue to innovate and improve aviation technology, let's strive for a balance - a world where we soar through the skies while safeguarding our planet and well-being. Economic Impact Air Travel Industry The aviation industry is a significant contributor to the global economy. Airlines, airports, manufacturers, and associated services generate substantial revenue and employment. Air travel facilitates international trade, tourism, and business interactions. However, it also faces challenges such as fuel price fluctuations, competition, and regulatory complexities. Supply Chain and Cargo Transport Airplanes play a crucial role in transporting goods across continents. High-value and time-sensitive cargo, including perishable items, pharmaceuticals, and electronics, rely on air freight. The efficiency of supply chains owes much to the speed and reach of airplanes. Tourism and Local Economies Tourism heavily depends on air travel. Popular destinations thrive due to the influx of visitors arriving by plane. Local economies benefit from tourism-related activities, including hospitality, restaurants, and souvenir shops. Conversely, overreliance on tourism can strain natural resources and cultural heritage. Technological Advancements Aerospace Engineering The development of airplanes has driven advancements in aerospace engineering. Innovations in materials, aerodynamics, and propulsion systems have led to more efficient and safer aircraft. Research in areas like supersonic flight, electric planes, and autonomous drones continues to shape the industry. Navigation and Communication Airplanes rely on sophisticated navigation systems, including GPS, radar, and inertial guidance. These technologies enhance safety, accuracy, and efficiency. Communication networks allow pilots to stay connected with air traffic control, other planes, and ground stations. Social and Cultural Effects Global Connectivity Airplanes have transformed our perception of distance. What once took weeks by ship or months by land can now be accomplished in hours. Families separated by oceans reunite, students study abroad, and cultural exchange flourishes. The world feels smaller, and our interconnectedness grows. Iconic Symbols Airplanes evoke a sense of wonder and adventure. The iconic silhouettes of jumbo jets, fighter planes, and vintage biplanes symbolize human achievement and exploration. Airshows, aviation museums, and historical flights celebrate this legacy. Challenges and Future Prospects Sustainability The aviation industry faces the challenge of reducing its environmental impact. Researchers explore alternative fuels, electric propulsion, and lightweight materials. Balancing growth with sustainability remains critical. Airspace Congestion As air travel becomes more accessible, airspace congestion intensifies. Efficient air traffic management, improved routes, and next-generation air traffic control systems are essential to prevent gridlock. Security and Safety Ensuring the safety of passengers, crew, and cargo remains paramount. Rigorous security protocols, maintenance standards, and emergency preparedness are vital. In conclusion, airplanes are more than mere vessels of transportation. They shape economies, connect cultures, and inspire innovation. As we soar into the future, let's navigate the skies responsibly, appreciating both the marvels and challenges of flight. The Effects of Space Travel on the Human Body Space travel, with its awe-inspiring vistas and boundless possibilities, has captivated humanity for decades. However, venturing beyond our home planet comes with a price - a price paid not only in technological challenges but also in the toll it takes on the human body. Let us explore the effects of space travel, from radiation exposure to altered gravity, and how astronauts adapt to these extreme conditions. Space Radiation: A Silent Threat Radiation Exposure On Earth, our protective magnetic field and atmosphere shield us from the majority of space radiation. However, in space, astronauts face direct exposure to cosmic rays and solar particles. These high-energy particles can penetrate the body, damaging cells and DNA. Increased risk of cancer and degenerative diseases, such as heart disease and cataracts, have been observed in human populations exposed to radiation on Earth. In space, health risks from radiation are mainly driven by long-term impacts. Altered Gravity: A Weighty Matter Microgravity and Muscle Atrophy Astronauts aboard the International Space Station (ISS) experience microgravity, where their bodies float freely. While this weightlessness allows for breathtaking experiments and observations, it wreaks havoc on muscles and bones. Without the constant pull of gravity, muscles weaken, and bones lose density. Astronauts must engage in rigorous exercise routines to counteract muscle atrophy and maintain bone health. Fluid Redistribution and Swollen Faces In microgravity, bodily fluids shift upward, causing facial puffiness and fluid retention. Astronauts often joke about their 'moon faces.' This fluid redistribution can also affect vision, leading to a condition known as spaceflight-associated neuro-ocular syndrome (SANS). Isolation and Confinement: The Mental Strain Psychological Challenges Space missions involve prolonged isolation and confinement. Astronauts live in tight quarters, cut off from the natural world. The absence of familiar sights, sounds, and smells can lead to feelings of loneliness and anxiety. Coping mechanisms, communication with loved ones, and psychological support are crucial to maintaining mental well-being. Distance from Earth: A Cosmic Solitude Emotional Impact The vastness of space can evoke existential thoughts. Astronauts gaze back at Earth - a tiny blue dot suspended in the cosmic void - and grapple with their insignificance. The emotional weight of being far from home, family, and friends can be profound. Hostile and Closed Environments: Surviving in the Void Spacecraft Living Conditions Spacecraft are marvels of engineering, but they are also confined capsules. Astronauts adapt to tight spaces, recycled air, and limited privacy. The constant hum of machinery and the absence of natural light can wear on their senses. Risk of Infection In closed environments, microbes thrive. Astronauts must maintain strict hygiene to prevent infections. The immune system faces unique challenges, especially during extended missions. The Resilience of Astronauts Adaptation and Innovation Astronauts are remarkable in their ability to adapt. They learn to navigate microgravity, perform complex tasks, and troubleshoot technical glitches. Their resilience drives innovation, leading to better spacecraft design and life support systems. The Twin Study: Scott and Mark Kelly Scott Kelly and his identical twin brother, Mark Kelly, participated in the unique Twins Study. Scott spent nearly a year aboard the ISS, while Mark remained on Earth. By comparing their physiological and psychological changes, researchers gained valuable insights into the effects of space travel. Looking Ahead: Mars and Beyond Challenges for Deep Space Missions As we plan for Mars missions and beyond, we face the RIDGE of space travel: Space Radiation: Shielding astronauts from cosmic rays. Isolation and Confinement: Maintaining mental health during long journeys. Distance from Earth: Coping with cosmic solitude. Gravity Fields: Addressing muscle and bone health. Hostile/Closed Environments: Ensuring safety and hygiene. In conclusion, space travel is a delicate balance between exploration and preservation. As we venture farther into the cosmos, we must safeguard both our scientific curiosity and the well-being of those who dare to explore the final frontier. The Environmental Impact of Airplanes and Spaceships Airplanes and spaceships have transformed the way we explore our planet and beyond. However, their operations come with significant environmental consequences. Let's delve into the effects of these flying machines on our delicate ecosystem. Climate Change Air travel is a major contributor to climate change due to greenhouse gas emissions. Jet engines burn fossil fuels (mostly aviation gasoline or jet fuel), releasing carbon dioxide (CO2), nitrogen oxides (NOx), and water vapor into the atmosphere. These emissions trap heat, leading to global warming. Although aviation accounts for about 3.5 percent of human-induced climate change, its impact is disproportionately high due to emissions at high altitudes. Air Quality Airplanes emit pollutants such as sulfur dioxide (SO2), particulate matter (PM), and volatile organic compounds (VOCs). These pollutants degrade air quality near airports and along flight paths. Ground-level ozone formation, which harms human health and ecosystems, is also influenced by aviation emissions. Noise Pollution The roar of jet engines disrupts communities around airports. Noise pollution affects sleep patterns, stress levels, and overall well-being. Efforts to reduce noise include quieter engine designs and flight path adjustments. Spaceships: Earth's Atmospheric Guardians Rocket Launches and Pollution Rocket launches, essential for space exploration, release pollutants into the atmosphere. The fuel used - such as unsymmetrical dimethylhydrazine (UDMH) - can be highly carcinogenic and ecologically damaging. For instance, the Baikonur Cosmodrome in Kazakhstan, the world's oldest spaceport, has left a large zone of pollution due to toxic rocket fuel seeping into the soil. Carbon Particles and Geo-Engineering Recent research highlights the impact of rocket emissions on the atmosphere. Black carbon (soot) particles from rockets can absorb heat, acting as a form of geo-engineering. As commercial space launches increase, so does the concern about their environmental effects. Balancing Exploration and Preservation Space Tourism The rise of space tourism introduces new challenges. As more people venture beyond Earth, we must consider the cumulative impact of rocket emissions. Balancing our curiosity with environmental stewardship is crucial. Sustainable Practices Efforts are underway to develop cleaner propulsion technologies, use alternative fuels, and minimize space debris. Innovations like reusable rockets and electric propulsion aim to reduce the environmental footprint of space travel. Looking Ahead: A Cosmic Responsibility Mars and Beyond As we dream of Mars colonies and interstellar travel, we must tread carefully. The RIDGE of space exploration - Radiation, Isolation, Distance, Gravity, and Environment - requires sustainable solutions. Let's explore the cosmos while safeguarding our home planet. In conclusion, airplanes and spaceships propel us toward the stars, but their effects ripple through our atmosphere and ecosystems. As stewards of both Earth and space, we must navigate the skies responsibly, seeking harmony between exploration and preservation. From the ground to the sky, dining experiences have transcended traditional restaurant settings. Imagine savoring gourmet meals while suspended high above the earth, with breathtaking views stretching as far as the eye can see. Welcome to the world of aerial dining, where culinary delights meet gravity-defying elegance. Dinner in the Sky: Elevating Gastronomy The Original Concept Dinner in the Sky, born in 2006, is the epitome of dining with a twist. Picture a massive table - more like a platform - hoisted almost 200 feet into the air by a sturdy crane. Guests, chefs, and waitstaff don their white hats as they ascend to the skies. The setting? A floating dinner table, surrounded by nothing but open air and panoramic vistas. The Experience As you settle into your seat, the anticipation builds. The restaurant staff orchestrates a three-course fine dining experience, all while suspended in midair. The menu features carefully crafted dishes, often prepared beforehand and finished in a convection oven right there in the sky. Each bite is accompanied by awe-inspiring views - city skylines, rolling landscapes, or even the vastness of the ocean. Safety First Before you ascend, a safety briefing ensures that you're securely strapped in. The thrill of being airborne mingles with the elegance of haute cuisine. Whether it's a romantic date night or a corporate event, Dinner in the Sky promises an unforgettable meal. Sky-High Restaurants Around the World Dubai Marina: A Feast Above the Waters Situated in Dubai Marina, this dining concept boasts some of the best views of the city skyline, surrounding waters, and the iconic Palm Jumeirah. Imagine floating above the ground while you dine - a one-of-a-kind experience you simply cannot miss. After the safety briefing near Skydive Dubai, you're hoisted 50 meters into the air, suspended over the bustling marina. The fusion of flavors meets the fusion of horizons. Las Vegas: Unparalleled Views of the Strip In the entertainment capital of the world, Dinner in the Sky Las Vegas takes fine dining to new heights - literally. As the sun sets, you ascend, and the glittering lights of the Las Vegas Strip come alive. The most unforgettable dinner you'll ever have awaits, with the cityscape stretching out beneath you. It's a feast for the senses, where culinary artistry meets architectural marvels. The Future of Aerial Gastronomy Sustainability and Innovation As we look ahead, the challenge lies in balancing indulgence with environmental responsibility. How can we minimize the carbon footprint of these lofty dining experiences? Innovations like electric-powered cranes, locally sourced ingredients, and waste reduction strategies are steps toward a more sustainable future. Beyond Earth: Space Tourism and Cosmic Cuisine With the rise of space tourism, could we soon dine among the stars? Imagine a celestial restaurant aboard a spacecraft, overlooking Earth from orbit. Cosmic cuisine - crafted by zero-gravity chefs - might become the ultimate bucket-list experience. As we explore the cosmos, let's ensure that our gastronomic adventures leave no trace behind. In conclusion, dining in the air transcends mere sustenance. It's a celebration of human ingenuity, a fusion of flavors and vistas, and a reminder that our appetite for exploration knows no bounds. So, raise your glass (carefully!) to the skies and savor the magic of dining aloft. Dining in the Sky is a unique and exhilarating culinary experience that elevates traditional dining to new heights - literally. Here are the key aspects of this extraordinary concept: The Setting: Up, Up, and Away! Imagine being seated at a massive table suspended high above the ground, often hundreds of feet in the air. The dining platform is typically hoisted by a sturdy crane or other mechanical means. Guests, chefs, and waitstaff ascend together, creating an unforgettable communal experience. The Experience: A Feast with a View As you settle into your seat, anticipation builds. The thrill of being airborne mingles with the elegance of haute cuisine. The menu features carefully crafted dishes, often prepared beforehand and finished on-site. Whether it's breakfast, lunch, or dinner, each course is served against a backdrop of breathtaking views - city skylines, rolling landscapes, or even the vastness of the ocean. The floating table becomes a stage for culinary artistry, where flavors dance amidst the clouds. Safety First: Buckle Up! Before ascending, guests receive a safety briefing. Straps secure them to their seats, ensuring a worry-free dining experience. The focus shifts from gravity to gastronomy as the platform rises, leaving the ground far below. Locations Around the World: Where the Sky Meets the Plate Dubai Marina: Suspended above the bustling marina, diners enjoy views of the city skyline and the iconic Palm Jumeirah. Las Vegas: As the sun sets, guests ascend over the glittering lights of the Las Vegas Strip, creating an unparalleled dining spectacle. The Future: Sustainability and Cosmic Cuisine Balancing indulgence with environmental responsibility is crucial. Innovations like electric-powered cranes and locally sourced ingredients aim to reduce the carbon footprint. Could cosmic cuisine be next? With the rise of space tourism, imagine dining aboard a spacecraft, overlooking Earth from orbit. Zero-gravity chefs crafting celestial dishes - it's a tantalizing prospect. Introduction The sky, our celestial canvas, is a dynamic theater where cosmic phenomena unfold. From twinkling stars to majestic planets, the sky offers a mesmerizing display that captivates astronomers and dreamers alike. In this essay, we'll explore the various elements of celestial weather, from meteor showers to planetary alignments. Stars and Constellations Stellar Climates Stars, like earthly weather patterns, exhibit their own 'climates.' Some stars burn fiercely, radiating intense heat, while others are cooler and more temperate. The constellations, those celestial neighborhoods, form intricate patterns across the night sky. Imagine them as cosmic weather maps, guiding our eyes to distant realms. Meteor Showers: Celestial Rainfall Meteor showers are cosmic storms, where Earth passes through debris left behind by comets. As these tiny particles collide with our atmosphere, they ignite, creating streaks of light - the meteors. The Perseids in August and the Geminids in December are celestial fireworks, painting the sky with ephemeral beauty. Planets and Their Dance Planetary Weather Systems Our solar system hosts a diverse range of planets, each with its own atmospheric conditions. Venus, shrouded in thick clouds of sulfuric acid, experiences hurricane-force winds. Mars, with its rusty surface, battles dust storms that engulf the entire planet. Jupiter's Great Red Spot - a colossal storm - has raged for centuries. Conjunctions and Oppositions Planets engage in a cosmic ballet. Conjunctions occur when two planets appear close together in the sky, as if sharing a celestial embrace. Oppositions, on the other hand, position a planet directly opposite the Sun, making it visible all night. Witnessing Mars during opposition feels like meeting an old friend. Lunar Weather Phases of the Moon The Moon, Earth's faithful companion, cycles through its phases. New Moon, First Quarter, Full Moon - the lunar weather changes predictably. During a lunar eclipse, our planet casts a shadow on the Moon, turning it coppery red. It's a cosmic reminder of our place in the grand celestial drama. Tides: The Ocean's Cosmic Response The Moon's gravitational pull orchestrates tides on Earth. High tides and low tides ebb and flow, responding to lunar cues. The celestial dance between Earth, Moon, and Sun shapes our oceans, affecting coastlines and marine life. Celestial Events Comets: Cosmic Visitors Comets, celestial vagabonds, journey through our solar system. Their icy cores release gas and dust, forming magnificent tails. Halley's Comet, a recurring visitor, graces our skies once every 76 years. Its return is a cosmic homecoming. Supernovae: Stellar Explosions When massive stars reach the end of their lives, they explode in brilliant supernovae. These cosmic fireworks outshine entire galaxies. Witnessing a supernova - a rare event - is like glimpsing the universe's raw power. Conclusion As we gaze upward, let's remember that the sky is not merely a backdrop but a living, breathing entity. Its weather - both familiar and otherworldly - shapes our cosmic experience. So, next time you look up, consider the celestial forecast: a blend of stardust, wonder, and infinite possibilities. In the words of Carl Sagan, 'The cosmos is within us. We are made of star-stuff.' Cosmic Mysteries Dark Matter and Dark Energy The sky harbors secrets beyond our comprehension. Among them are dark matter and dark energy. Dark matter, invisible and elusive, exerts gravitational influence on galaxies, holding them together. Imagine it as the cosmic glue binding the universe. Dark energy, on the other hand, accelerates the universe's expansion, pushing galaxies apart. These cosmic enigmas remain shrouded in mystery, awaiting discovery. Auroras: Celestial Light Shows When charged particles from the Sun collide with Earth's magnetic field, they create auroras - the ethereal dance of light near the poles. The Northern Lights (Aurora Borealis) and Southern Lights (Aurora Australis) paint the night sky with hues of green, pink, and purple. These celestial ballets remind us of our interconnectedness with the solar system. Celestial Timekeeping Stellar Clocks The sky serves as humanity's oldest timekeeper. Ancient civilizations relied on celestial events for calendars. The sidereal day, based on Earth's rotation relative to distant stars, is approximately 23 hours, 56 minutes, and 4 seconds. Constellations rise and set, marking the passage of time - a cosmic heartbeat. Eclipses: Celestial Alignments Solar and lunar eclipses are cosmic alignments. During a solar eclipse, the Moon obscures the Sun, casting a shadow on Earth. The eerie twilight and the diamond ring effect evoke awe. Lunar eclipses, when Earth's shadow engulfs the Moon, transform it into a reddish orb - an astronomical spectacle witnessed by civilizations across millennia. Cosmic Harmony Music of the Spheres Ancient philosophers believed in the 'music of the spheres.' They imagined celestial bodies - planets, stars, and moons - emitting harmonious vibrations. Each celestial note contributed to a cosmic symphony. While we no longer hear this celestial music, its metaphorical resonance persists - a reminder that the universe hums with hidden melodies. Galactic Weather Patterns Galaxies, like weather systems, evolve. Spiral galaxies, with their graceful arms, resemble cosmic hurricanes. Elliptical galaxies, shaped like celestial footballs, harbor dormant black holes at their cores. Colliding galaxies create celestial tempests, birthing new stars. The cosmic weather forecast predicts galactic collisions, stellar births, and cosmic winds. Conclusion: Our Cosmic Home As we conclude our cosmic odyssey, remember that the sky is not an abstract canvas - it's our celestial home. Whether you're stargazing from a mountaintop or contemplating the Moon's craters, you participate in the grand cosmic narrative. The sky whispers tales of creation, destruction, and eternity. So, dear reader, look up. Embrace the celestial weather - the storms and serenades. For in the vastness of space, we find wonder, humility, and a shared cosmic kinship. As Carl Sagan eloquently put it, 'We are a way for the cosmos to know itself.' Introduction The universe is a symphony, and planets are its celestial notes. These enigmatic orbs dance around stars, weaving tales of creation, destruction, and cosmic balance. In this essay, we embark on a cosmic journey to explore the eight planets of our solar system and their profound significance. Mercury: The Swift Messenger Mercury, the swiftest planet, orbits closest to the Sun. Its surface is a rugged landscape of craters and cliffs, baked by scorching temperatures during the day and chilled at night. Named after the Roman messenger god, Mercury shuttles between extremes, delivering cosmic messages across the solar system. Venus: Earth's Fiery Twin Venus, Earth's twin sister, hides behind thick clouds of sulfuric acid. Its surface resembles a volcanic inferno, with temperatures hot enough to melt lead. Yet, its beauty lies in its radiant glow - the Morning and Evening Star - illuminating our dawn and dusk. Earth: Our Blue Gem Earth, our precious home, teems with life. Its oceans, forests, and deserts form a delicate biosphere. From the icy poles to the equatorial rainforests, Earth's diverse climates sustain a symphony of ecosystems. We are its guardians, entrusted with its care. Mars: The Red Planet's Mysteries Mars, the Red Planet, beckons explorers. Its rusty surface bears ancient river valleys and polar ice caps. Could Mars harbor hidden reservoirs of life? Robotic rovers traverse its deserts, seeking answers beneath its crimson skies. Jupiter: King of the Gas Giants Jupiter, the colossal gas giant, boasts a mesmerizing tapestry of bands and storms. Its Great Red Spot - a tempest larger than Earth - has raged for centuries. Jupiter's gravitational pull shapes the solar system, protecting inner planets from cosmic debris. Saturn: Jewel of the Rings Saturn, adorned with majestic rings, is a cosmic jewel. These icy hoops, composed of countless particles, create a celestial ballet. Saturn's moons - Titan, Enceladus, and others - beckon us to explore their icy landscapes. Uranus: The Original Ice Giant Uranus, tipped on its side, spins like a cosmic top. Its icy blue hue conceals turbulent storms. Uranus remains a mystery, awaiting further study by future missions. Neptune: The Farthest Wanderer Neptune, shrouded in azure clouds, is the outermost planet. Its winds whip at supersonic speeds, and its icy heart harbors storms that rival Jupiter's. Voyager 2, our interstellar traveler, captured Neptune's beauty as it sailed past. Conclusion: Cosmic Harmony Planets are cosmic harmonizers. Their gravitational dances sculpt orbits, stir tides, and guide comets. They remind us of our place in the grand cosmic orchestra. As we gaze at the night sky, let us cherish these celestial companions - the guardians of harmony. In the words of Carl Sagan, 'We are made of star-stuff.' Our existence echoes the cosmic rhythm, and planets are our celestial partners in this cosmic waltz. Pluto, once considered our ninth planet, now holds the title of a dwarf planet. The International Astronomical Union (IAU) made this reclassification in 2006. Pluto didn't meet one of the three criteria the IAU uses to define a full-sized planet: it has not cleared its neighboring region of other objects. Despite its demotion, Pluto remains a fascinating member of the Kuiper belt, a ring of bodies beyond Neptune's orbit. It is the ninth-largest and tenth-most-massive known object to directly orbit the Sun. Although smaller than Earth's moon, Pluto's icy and rocky composition continues to intrigue astronomers and stargazers alike. NASA's New Horizons mission is a remarkable endeavor that has expanded our understanding of the outer reaches of our solar system. Let's delve into the details of this pioneering spacecraft: Objective: New Horizons was designed to study the dwarf planet Pluto, its moons, and other objects in the Kuiper Belt. Launch Date: On January 19, 2006, New Horizons embarked on its epic journey. Spacecraft Mass: Weighing 1,054 pounds (478 kilograms), it carried a suite of scientific instruments. Mission Design and Management: The mission was led by NASA in collaboration with the Johns Hopkins University Applied Physics Laboratory (APL). Historic Flyby: On July 14, 2015, New Horizons made history by becoming the first spacecraft to explore Pluto up close. It captured stunning images of Pluto's diverse geological features, including its icy plains, rugged mountains, and frozen canyons. Moons of Pluto: During the flyby, New Horizons also studied Pluto's five moons, including the intriguing Charon. Arrokoth Flyby: In early 2019, New Horizons achieved another milestone by flying past Arrokoth (2014 MU69). Arrokoth is a Kuiper Belt Object, making it the most distant object ever explored up close. Kuiper Belt: This region extends from about 30 AU (near Neptune's orbit) to about 50 AU from the Sun. New Horizons ventured into this uncharted territory. New Horizons carried an impressive array of instruments, including: Ralph: A visible and infrared imager/spectrometer. Alice: An ultraviolet imaging spectrometer. Radio-Science Experiment (REX): Studied radio signals. Long-Range Reconnaissance Imager (LORRI): Captured high-resolution images. Solar Wind and Plasma Spectrometer (SWAP): Analyzed solar wind. Pluto Energetic Particle Spectrometer Science Investigation (PEPSSI): Studied particles around Pluto. Student Dust Counter (SDC): Measured dust impacts. New Horizons provided insights into Pluto's atmosphere, surface, and geology. It revealed icy mountains, glaciers, and mysterious dark regions. The spacecraft also observed Jupiter's moons (Io, Europa, and Ganymede) during its long journey. As of 2023, New Horizons continues to explore the outer solar system, contributing to our understanding of distant bodies. In summary, New Horizons has been a trailblazer, revealing the secrets of Pluto and venturing into the cosmic frontier. Its legacy inspires future missions and fuels our curiosity about the cosmos. ", + "8192": "Once upon a time, in a quaint little village nestled amidst rolling hills, there existed an old teapot. But this was no ordinary teapot; it was a magical one. Its handle curved just so, and its spout seemed to whisper secrets to the wind. The villagers called it 'Elara,' and they believed it held the power to grant wishes. Elara sat on the windowsill of Mrs. Abernathy's cozy cottage. Mrs. Abernathy was a kind-hearted woman with twinkling eyes and a penchant for herbal teas. She'd inherited the teapot from her grandmother, who, in turn, had received it from a mysterious traveler. One chilly evening, as the sun dipped below the horizon, Mrs. Abernathy brewed her favorite chamomile tea. She poured the fragrant liquid into Elara, and to her astonishment, the teapot began to glow. The room filled with a soft, golden light, and Mrs. Abernathy felt a tingle in her fingertips. 'Make a wish,' whispered Elara, its spout quivering. Mrs. Abernathy hesitated. She'd heard tales of wishes gone awry - of greedy desires leading to unintended consequences. But her heart yearned for something simple: a garden filled with blooming roses. So, she closed her eyes and wished for just that. The next morning, Mrs. Abernathy stepped outside, and her breath caught. The air smelled of roses - sweet and heady. But when she looked around, she gasped. Her modest garden had transformed into a riot of colors. Roses of every hue - crimson, ivory, apricot - bloomed in profusion. They climbed the walls, twined around the picket fence, and even spilled onto the cobblestone path. Word spread throughout the village, and soon everyone wanted a turn with Elara. The baker wished for the perfect sourdough loaf, and it appeared in his oven. The blacksmith wished for strength, and his arms bulged with newfound muscle. The schoolteacher wished for wisdom, and her lectures became captivating tales. But as wishes multiplied, so did the consequences. The baker's sourdough grew sentient and demanded to be called 'Doughbert.' The blacksmith's strength made him accidentally crush his anvil. And the schoolteacher's wisdom led her to question the very fabric of reality. Mrs. Abernathy watched with a mix of amusement and concern. Elara seemed to thrive on granting wishes, but its porcelain surface bore faint cracks. Was it growing weaker? One day, a young girl named Lily approached Elara. Her eyes sparkled with innocence, and she clutched a dandelion in her hand. 'Teapot,' she said, 'I wish for a friend.' Elara hesitated. It sensed the purity of Lily's heart, but it also knew the weight of loneliness. With a shudder, it granted the wish. And so, Lily's dandelion transformed into a giggling sprite named Petal. They danced through meadows, shared secrets, and became inseparable. Elara's cracks deepened, but it didn't mind. As seasons passed, Mrs. Abernathy sat by the window, watching Elara fade. Yet, she felt no regret. For in granting wishes, the teapot had found purpose. And perhaps, just perhaps, it had one final wish left - to be remembered. And so, when Mrs. Abernathy's time came, she whispered to Elara, 'Thank you.' The teapot glowed one last time, and Mrs. Abernathy drifted away, leaving behind a garden of roses and a village full of stories. And that, my dear reader, is how the enchanted teapot became a legend - a vessel of magic, love, and wishes granted with a fragile heart. As the seasons changed, so did the village. The once-sleepy hamlet now buzzed with visitors from distant lands. They came seeking Elara, the legendary teapot that granted wishes. Some sought riches, others fame, but most yearned for something deeper - a connection to the mystical. Among the newcomers was a weary traveler named Ezra. His cloak was tattered, and his boots bore the marks of countless miles. He'd heard whispers of Elara's magic and hoped it could mend his broken heart. For Ezra had lost his beloved, and grief weighed upon him like an anchor. Mrs. Abernathy, now an old woman with silver hair, welcomed Ezra into her cottage. Elara sat on the windowsill, its porcelain surface etched with memories. Mrs. Abernathy poured chamomile tea into the teapot, and it glowed faintly, as if recognizing an old friend. 'Make a wish,' Mrs. Abernathy said, her voice soft. Ezra hesitated. His wish was simple yet profound: to see his love once more, if only in a dream. He closed his eyes and whispered, 'I wish for a single night with her.' Elara trembled, its spout quivering. It understood the ache of lost love - the longing that transcended time. And so, it granted Ezra's wish. That night, as the moon hung low in the sky, Ezra lay on Mrs. Abernathy's creaky bed. Elara sat beside him, its glow illuminating the room. He drifted into slumber, and there, in the realm between wakefulness and dreams, he found himself in a moonlit garden. His love, Isolde, stood before him. Her eyes were the color of forget-me-nots, and her laughter echoed like wind chimes. They danced beneath a silver canopy, twirling through memories - their first kiss, stolen moments by the river, promises whispered under ancient oaks. But dreams are fragile, and dawn approached. Isolde's form wavered, and Ezra clung to her. 'Stay,' he pleaded. 'Just a little longer.' Isolde smiled, her touch like a butterfly's kiss. 'Time bends here,' she said. 'But you must wake, my love.' As the sun peeked over the horizon, Ezra opened his eyes. Elara sat on the windowsill, its glow fading. Mrs. Abernathy watched him, her gaze knowing. 'Did you see her?' she asked. Ezra nodded, tears glistening. 'She was real, Mrs. Abernathy. I held her again.' The village marveled at Ezra's tale - the man who danced with a ghost. They flocked to Elara, each with their wishes. The blacksmith wished for forgiveness, the baker for inspiration, and the schoolteacher for courage. Elara obliged, its cracks deepening, but it never complained. One day, as winter painted the landscape white, Mrs. Abernathy grew frail. She called Ezra to her bedside. 'Elara's magic wanes,' she whispered. 'But it has one final wish.' Ezra knelt beside her. 'What is it?' 'Take Elara beyond the hills,' Mrs. Abernathy said. 'To the ancient oak where Isolde and I carved our initials. There, bury the teapot. It will become part of the earth, and its magic will seep into the roots.' And so, on a frost-kissed morning, Ezra carried Elara to the oak. He dug a small hole, placed the teapot inside, and covered it with soil. As he patted the ground, he felt a tremor - a farewell. The next spring, the oak bloomed with roses - crimson, ivory, apricot. And in its shade, a dandelion sprouted. Its petals glowed like moonlight, and when the wind whispered, it carried echoes of laughter. Ezra knew then that Elara's wish had come true. It had become part of the land, woven into the fabric of stories. And perhaps, just perhaps, it still listened, granting silent wishes to those who believed. And so, the legend of Elara lived on - a teapot turned earth, a vessel of love, and a bridge between worlds. In the heart of the Whispering Forest, where ancient trees leaned close and their leaves murmured secrets, lived a young girl named Evelyn. She had eyes the color of moss and hair that tangled like wild vines. Evelyn was no ordinary child; she could hear the forest's whispers - the soft rustle of leaves, the creaking of branches, and the laughter of unseen creatures. The villagers feared the Whispering Forest. They said it was cursed - a place where time flowed differently, where shadows danced with mischief, and where lost souls wandered forever. But Evelyn felt drawn to its heart. She believed the forest held answers - about her missing parents, about the world beyond the village. One moonlit night, when the forest beckoned with silver fingers, Evelyn slipped away from her tiny cottage. She wore a cloak spun from spider silk and carried a lantern that glowed like a captured star. The trees leaned in, their bark etched with ancient runes. They whispered her name - Evelyn, Evelyn - as if they knew her purpose. Deeper she ventured, past gnarled roots and dew-kissed ferns. The air smelled of moss and memories. The lantern's light flickered, casting eerie shadows on the forest floor. And then, she heard it - the melody of the Whispering Forest. It was a haunting tune, sung by unseen lips, and it tugged at her heart. 'Who are you?' Evelyn whispered. The forest answered - a chorus of voices, overlapping and harmonizing. 'We are the echoes of forgotten dreams, the guardians of lost paths. Seek what you desire, but beware the price.' Evelyn pressed on. She reached a clearing where moonflowers bloomed - a sea of pale petals that glowed like fallen stars. In their midst stood a stone pedestal, and atop it rested a silver key. It was unlike any key she'd seen - twisted and delicate, with a single emerald set in its bow. The whispers intensified. 'Take the key,' they urged. 'Unlock the door to your destiny.' Evelyn hesitated. What door? What destiny? She thought of her parents - their laughter, their scent of pine and adventure. They'd vanished when she was a baby, leaving only a crumpled map with cryptic symbols. With trembling fingers, she picked up the key. It felt warm, alive. And then, she saw it - a door, half-hidden behind an ancient oak. Its wood was etched with constellations, and its handle bore the same emerald as the key. Evelyn inserted the key into the lock. The door groaned open, revealing a tunnel - a ribbon of darkness that wound deeper into the forest. The whispers grew urgent. 'Step through, Evelyn. Find your truth.' She stepped into the tunnel, and the world shifted. Time blurred, and she glimpsed her parents - laughing, dancing, fading like smoke. The tunnel led to a chamber - a celestial cavern where stars swirled in liquid patterns. And there, on a stone pedestal, lay a crystal vial. The whispers crescendoed. 'Drink,' they urged. 'Remember.' Evelyn uncorked the vial. Memories flooded her - the scent of pine, her parents' laughter, the taste of adventure. Tears blurred her vision. She drank, and the forest embraced her - a cocoon of whispers, of love, of belonging. When Evelyn emerged, the Whispering Forest had changed. It no longer whispered of curses but sang of hope. She carried her parents' memories - their legacy - and vowed to protect the forest's secrets. And so, Evelyn became the new guardian. She tended the moonflowers, listened to the trees, and sang the haunting melody. The villagers no longer feared the forest; they sought its solace, its magic. And every night, as the moon rose, Evelyn stood by the ancient oak. She whispered her parents' names, and the forest whispered back - a lullaby woven from stardust and love. Beyond the Whispering Forest, where the moonflowers bloomed and the stars whispered secrets, lay a forgotten path. It was a narrow trail, overgrown with moss and guarded by ancient stones. Few dared to tread there, for it led to the Compass Grove. Lysander, a young cartographer with ink-stained fingers and a heart full of wanderlust, stumbled upon this path one misty morning. His boots sank into damp earth, and the air smelled of pine and possibility. He carried a tattered map - a relic passed down through generations. Its edges bore cryptic symbols, and its center held a blank space - an uncharted territory. The Compass Grove was said to house a mystical compass - the Wayfinder's Compass - forged by the first explorers. It was no ordinary instrument; it pointed not to north, but to one's true desire. Legends whispered that whoever held the compass could navigate not only the physical world but also the labyrinth of their own heart. Lysander's pulse quickened. He yearned for adventure - to map uncharted lands, to unravel mysteries. His parents had vanished during an expedition, leaving behind a single clue: the blank space on the map. Perhaps the Compass Grove held answers. As he pushed through brambles and ferns, the forest seemed to guide him. Sunlight filtered through leaves, casting dappled patterns on the ground. And then, he saw it - a circle of ancient stones, their surfaces etched with symbols. At the center stood a pedestal, and atop it rested the Wayfinder's Compass. Lysander's breath caught. The compass was unlike any he'd seen. Its needle shimmered like a captured star, and its dial bore not cardinal directions but enigmatic words: Dreams, Regret, Destiny, and Hope. He touched the compass, and it hummed - a vibration that resonated in his bones. The whispers began - the voices of long-lost explorers, of forgotten dreams. 'Choose,' they urged. 'Choose your path.' Lysander hesitated. Dreams? Regret? Destiny? Hope? Each word held a promise, a peril. He thought of his parents - their laughter, their courage. He thought of the blank space on the map - the uncharted territory that beckoned. And so, he turned the dial to Dreams. The needle quivered, then settled - a path leading deeper into the forest. Lysander followed, lantern in hand, heart pounding. The compass guided him past silver streams and ancient oaks. It led him to a hidden waterfall - a curtain of moonlight that shimmered like stardust. There, he glimpsed a figure - a woman with eyes like forgotten constellations. She wore a cloak spun from spider silk, and her hair flowed like a river. 'Lysander,' she said, her voice a melody. 'You seek dreams.' He nodded. 'I seek answers. About my parents.' The woman touched his forehead, and memories flooded him - the scent of pine, his parents' laughter, the taste of adventure. 'Dreams are maps,' she said. 'They guide us beyond what we see.' Lysander understood. Dreams were compasses of the soul. His parents had followed theirs, and now he would follow his. He stepped through the waterfall, and the world shifted. He found himself on a cliff overlooking a vast sea - a sea of blank parchment. Islands floated in the distance, waiting to be charted. Lysander unrolled his map - the one with the blank space - and dipped his quill. He drew coastlines, marked mountains, and named each land. And as he mapped, the compass glowed - a beacon of dreams fulfilled. Lysander knew then that he was not merely a cartographer; he was a dreamweaver. His parents' legacy flowed through him - their courage, their laughter, their love. And so, Lysander sailed the uncharted seas, guided by the Wayfinder's Compass. He discovered islands of forgotten myths, forests of whispered tales, and cities where stars danced in the streets. He wrote his own story - a cartography of dreams. And in the Compass Grove, the ancient stones whispered his name - Lysander, Lysander - as if they knew he'd found his true north. In the heart of the city, where cobblestone streets wound like forgotten memories, stood an abandoned mansion. Its windows were boarded up, and ivy clung to its crumbling walls. But within those decaying walls lay a secret - a clockwork garden. Evelyn, a curious girl with eyes like rain-kissed petals, discovered the mansion one rainy afternoon. She wore mismatched socks and carried a notebook filled with sketches - a testament to her love for hidden wonders. The mansion's gate creaked open, and Evelyn stepped into a world frozen in time. The clockwork garden was unlike any other. Its flowers were made of gears and springs, their petals unfolding with precise clicks. The roses ticked, the daffodils whirred, and the tulips chimed. And at the center stood a colossal mechanical tree - its branches reaching toward the sky, its leaves spinning like miniature windmills. Evelyn gasped. She'd read about clockwork wonders - the automatons that danced at royal balls, the pocket watches that whispered secrets. But this garden was alive - a symphony of metal and magic. As she explored, she noticed a silver key embedded in the tree's trunk. It gleamed, beckoning her. Evelyn hesitated. What did the key unlock? And why had the clockwork garden been abandoned? The flowers seemed to whisper. 'Unlock the tree,' they urged. 'Discover its heart.' Evelyn turned the key. The tree shuddered, and its branches parted, revealing a hidden chamber. Inside, a mechanical heart pulsed - a delicate contraption of brass and crystal. It hummed, resonating with the rhythm of forgotten time. And then, she heard it - the voice of the tree. 'I am Chronos,' it said. 'Guardian of moments.' Evelyn's heart raced. 'Moments?' 'Every petal, every leaf,' Chronos explained. 'They hold memories - the laughter of lovers, the tears of parting, the whispers of dreams. But time has fractured. The clockwork garden is frozen, and I am fading.' Evelyn understood. The mansion's former owner - a clockmaker named Lysander - had built this garden to capture fleeting moments. But Lysander had vanished, leaving Chronos incomplete. 'I can mend you,' Evelyn said. 'But why was the garden abandoned?' Chronos sighed - a sound like winding gears. 'Lysander sought eternity. He believed that by freezing time, he could preserve love, prevent loss. But he forgot that life thrives in impermanence.' Evelyn touched the mechanical heart. 'Can we fix it?' Chronos nodded. 'You must find Lysander's final creation - the Celestial Gear. It lies beyond the city, where the river meets the stars.' And so, Evelyn embarked on her quest. She followed the river, past moonlit bridges and forgotten docks. The Celestial Gear awaited - a constellation of interlocking wheels, its center a pulsing light. As she placed the gear in Chronos's heart, the clockwork garden stirred. Flowers bloomed, petals unfurling with joy. The mechanical tree's leaves spun faster, and time flowed once more. But Chronos grew weaker. 'I am bound to this place,' it said. 'My purpose fulfilled.' Evelyn wept. 'Can't you come with me?' Chronos smiled - a clockwork smile. 'I am part of the garden now. But you, dear Evelyn, carry its memory.' And so, she returned to the mansion, where the clockwork garden thrived. She sketched its wonders, capturing gears and petals on paper. And when she closed her eyes, she heard the whispers - the laughter of lovers, the tears of parting, the echoes of dreams. Evelyn became the new guardian. She tended the flowers, wound the tree, and listened to Chronos's fading heartbeat. And every night, as the stars wheeled overhead, she whispered her thanks. For in the heart of the clockwork garden, time danced - a fragile waltz of moments, preserved and cherished. In the heart of the Astronomer's Quarter, where cobblestone streets wound like celestial paths, stood an ancient observatory. Its domed roof bore the scars of countless meteor showers, and its telescopes whispered secrets to the night sky. But within those hallowed walls lay a mystery - a forgotten constellation. Aria, a young stargazer with eyes like distant galaxies, discovered the observatory one moonless night. She wore a cloak spun from stardust and carried a pocket-sized atlas - a testament to her love for the heavens. The observatory's door creaked open, and Aria stepped into a world woven with cosmic threads. The forgotten constellation was unlike any other. Its stars were elusive, their positions shifting with each passing century. Astronomers had once mapped it - a celestial tapestry of myth and memory - but over time, its name faded, its stories lost. As Aria explored, she noticed a silver quill resting on an ancient star chart. Its nib gleamed, beckoning her. Aria hesitated. What secrets did the quill hold? And why had the forgotten constellation slipped from memory? The stars seemed to whisper. 'Write,' they urged. 'Illuminate the night.' Aria dipped the quill in ink. The constellations above shifted - a celestial dance awaiting completion. She traced the forgotten lines - the Hunter's Bow, the Weaver's Loom, the Lost Lyre. And then, she saw it - a gap in the sky, a void where a constellation once blazed. The quill hummed - a vibration that resonated in her bones. The whispers intensified. 'Remember,' they urged. 'Remember the story.' And so, Aria wrote - a tale woven from stardust and longing. She penned the forgotten constellation's name: Lyra's Veil. Its stars had once guided lovers across oceans, inspired poets to verses, and cradled dreams in their luminous arms. But Lyra's Veil had vanished - a casualty of time's relentless march. Its stories faded, its purpose lost. Aria vowed to restore it - to stitch the celestial fabric, thread by thread. She climbed to the observatory's rooftop, where telescopes pointed toward infinity. Aria gazed at the sky, her breath mingling with the Milky Way. And there, in the gap, she saw it - the faint glimmer of Lyra's Veil. The quill guided her. She drew the missing lines - the Weaver's Loom reconnected, the Lost Lyre's melody restored. And as she wrote, the stars responded. Lyra's Veil emerged - a constellation reborn. But Aria felt a pull - a cosmic yearning. She touched the quill to her heart, and memories flooded her - the scent of stardust, her grandmother's bedtime stories, the taste of wonder. 'Guard it,' whispered the stars. 'Guard Lyra's Veil.' And so, Aria became the new guardian. She tended the observatory, charted the skies, and whispered the forgotten stories. The astronomers marveled - the gap was gone, and Lyra's Veil blazed once more. But Aria knew her duty. She would write new tales - of love, of courage, of dreams stitched together. And every night, as the constellations wheeled overhead, she whispered her thanks. For in the heart of the forgotten constellation, time danced - a fragile waltz of memory, preserved and cherished. In the heart of the bustling city, where skyscrapers touched the clouds and neon signs flickered like distant stars, lived a forgotten runner named Evelyn. She wasn't famous like the sprinters on billboards or the marathon champions with their gleaming medals. No, Evelyn was an ordinary woman who ran for the sheer joy of it. Every morning, before the sun peeked over the horizon, Evelyn laced up her worn-out sneakers. She followed the same route - a loop around the park, past the fountain where pigeons bathed, and along the riverbank where willow trees whispered secrets. Her pace was steady, her breaths rhythmic. She ran not to win races but to escape the noise of life - to find solace in the rhythm of her footsteps. But the city had forgotten Evelyn. The sports channels didn't broadcast her runs, and the local newspapers didn't write about her achievements. She was a lone figure - a silhouette against the dawn, chasing dreams that no one else cared about. One chilly morning, as Evelyn jogged along the river, she noticed a poster taped to a lamppost. It announced the city's annual marathon - the grand event that drew elite athletes from around the world. Evelyn's heart skipped a beat. She'd never run a marathon, but the idea tugged at her like a distant constellation. She tore off the poster and studied it. The race would wind through the city's streets, past cheering crowds and historic landmarks. The finish line was the grand stadium - the same stadium where she'd watched her heroes cross the tape, their names echoing through the loudspeakers. Evelyn hesitated. She wasn't a professional runner. She didn't have a coach or a team. But something stirred within her - a longing to be part of the marathon, to leave her mark on the city she loved. And so, she trained. She woke earlier, ran farther, and pushed her limits. She practiced pacing, fueled by oatmeal and determination. The other runners didn't notice her - a middle-aged woman with graying hair - but Evelyn didn't mind. She was a comet streaking through the pre-dawn darkness, fueled by her own quiet fire. On marathon day, the city buzzed with excitement. The streets were lined with spectators - families with homemade signs, old couples in folding chairs, children waving tiny flags. The elite runners surged ahead, their strides effortless. But Evelyn was in the middle of the pack - a forgotten runner among thousands. As she crossed each mile marker, Evelyn felt a surge of pride. She wasn't breaking records, but she was breaking barriers - the ones she'd built around herself. The cheers of the crowd fueled her - their encouragement like solar winds pushing her forward. And then, at mile 20, exhaustion hit. Evelyn's legs wobbled, her breaths came in ragged gasps. She glanced at the grand stadium - the finish line shimmering like a distant galaxy. But her body rebelled. She wanted to collapse, to fade into anonymity. And that's when she saw him - a young boy with a crumpled sign. It read, 'Go, Evelyn! You're not forgotten.' Tears blurred her vision. She pushed through the pain, her heartbeat a metronome of determination. As Evelyn crossed the finish line, the crowd erupted. The loudspeakers blared her name - Evelyn, Evelyn - and the forgotten runner became a star. She collapsed into the arms of a volunteer, her legs trembling. But she'd done it. She'd run the marathon - the one that mattered to her. The newspapers wrote about her - the woman who defied odds, who ran not for glory but for love. And the city remembered Evelyn - the forgotten runner who'd become a constellation, lighting the way for others. Lysander stood at the finish line of the marathon, his chest heaving, sweat-soaked shirt clinging to his skin. The stadium roared - a symphony of applause and encouragement. But amidst the cheers, he felt a void - an ache that no medal could fill. He'd run the race - the one that mattered to him. Yet, as he caught his breath, Lysander wondered about the blank space on his map. The uncharted territory - the reason his parents had vanished - still haunted him. A shadow fell across the track. It was Evelyn, the forgotten runner. Her eyes sparkled with determination, and her worn-out sneakers bore the marks of countless miles. She'd finished the marathon too, her name echoing through the loudspeakers. 'Evelyn,' Lysander said, his voice hoarse. 'Why do we run?' She leaned against the railing, gazing at the city beyond. 'For the same reason we map,' she replied. 'To find what's lost.' Lysander nodded. 'The Compass Grove,' he said. 'The Wayfinder's Compass.' Evelyn's eyes widened. 'You know of it?' He traced the blank space on his map - the gap where the forgotten constellation should be. 'My parents sought it,' Lysander confessed. 'They believed it held answers - about time, about destiny.' Evelyn's fingers brushed the silver quill in her pocket. 'And did they find it?' He shook his head. 'They vanished. But I won't stop searching.' Together, they left the stadium - the forgotten runner and the cartographer. They followed the same path - the one that led beyond the city, into the Whispering Forest. The compass guided them - the needle pointing not to north, but to dreams. As they reached the ancient stones of the Compass Grove, Evelyn gasped. 'Look,' she said, her voice hushed. There, etched into the stones, were symbols - the Weaver's Loom, the Lost Lyre, and the Hunter's Bow. And at the center stood the pedestal - the Wayfinder's Compass. Lysander touched it - the needle quivering. 'What do we seek?' he asked. Evelyn's eyes held galaxies. 'Not just answers,' she said. 'But connection - to the forgotten, to each other.' And so, they turned the dial - to Hope. The compass hummed, and the forest whispered. A path opened - a ribbon of moonlight leading deeper. They stepped through, and the world shifted. Stars swirled - a celestial dance. And there, in the gap, they saw it - the forgotten constellation. Lyra's Veil blazed - a tapestry of memories, stitched by stardust. Its stars guided lovers, inspired poets, and cradled dreams. Lysander and Evelyn held hands - the cartographer and the runner. They traced the lines - the Weaver's Loom reconnected, the Lost Lyre's melody restored. And as they gazed at Lyra's Veil, they felt it - a cosmic yearning. Not for fame or medals, but for eternity - the kind woven into forgotten constellations. Together, they whispered their thanks - to the stars, to the forest, to each other. In the small town of Maplewood, basketball was more than a game - it was a way of life. The local high school gym, with its creaky wooden floors and flickering lights, held memories etched into the hearts of generations. Tommy Reynolds, a lanky teenager with dreams as big as the full moon, had grown up shooting hoops in that gym. His father, a former basketball star, had taught him the art of the game - the perfect arc of a jump shot, the rhythm of dribbling, and the magic of teamwork. But Tommy wasn't like his father. He lacked the height and the natural talent. Still, he practiced tirelessly, his sneakers squeaking on the polished floor. He'd stare at the faded championship banners hanging from the rafters - the ones his father had helped win - and imagine his own name there someday. Senior year arrived, and Tommy made the varsity team. He wasn't a star player, but he hustled, diving for loose balls and setting screens. The crowd cheered louder for the flashy slam dunks, but Tommy's heart beat for the fundamentals - the bounce pass, the defensive stance, the pick-and-roll. The state championship game loomed - a David-and-Goliath matchup against the undefeated Oakwood Tigers. They had a towering center, a lightning-fast point guard, and a reputation for crushing opponents. Maplewood was the underdog, the team with heart but not much else. As the final seconds ticked away, the score was tied. Tommy stood at center court, sweat dripping down his face. The gym seemed to hold its breath. He glanced at the banners - the ghosts of champions past urging him on. The ball found its way to Tommy. He dribbled, eyes scanning the court. His father's voice echoed in his mind: 'Trust your instincts, son.' He drove toward the basket, the Tigers' defense closing in. But instead of taking the shot, Tommy passed - the perfect bounce pass to his teammate, Danny. Danny leaped, releasing the ball just as the buzzer sounded. The gym erupted. The ball swirled through the net - a miracle shot that defied physics. Maplewood had won - the underdogs had toppled the giants. Tommy's teammates lifted him on their shoulders. The crowd chanted his name. But as he glanced at the banners, he knew the truth. It wasn't just his shot - it was the culmination of every bounce pass, every defensive stance, every pick-and-roll. His father hugged him - a rare display of emotion. 'You did it, Tommy,' he whispered. 'You made your mark.' And there, in the glow of victory, Tommy realized that sometimes the greatest miracles happen at center court - not in the spotlight, but in the quiet moments of practice, persistence, and heart." +} diff --git a/onnxruntime/python/tools/transformers/models/llama/quant_kv_dataloader.py b/onnxruntime/python/tools/transformers/models/llama/quant_kv_dataloader.py index e8b563261001..33084aec214c 100644 --- a/onnxruntime/python/tools/transformers/models/llama/quant_kv_dataloader.py +++ b/onnxruntime/python/tools/transformers/models/llama/quant_kv_dataloader.py @@ -1,3 +1,8 @@ +# ------------------------------------------------------------------------- +# Copyright (c) Microsoft Corporation. All rights reserved. +# Licensed under the MIT License. See License.txt in the project root for +# license information. +# -------------------------------------------------------------------------- import argparse import numpy as np