From 89bae08261beb2da83b4d135755188362fe58172 Mon Sep 17 00:00:00 2001 From: endomorphosis Date: Sat, 29 Jun 2024 20:30:16 -0700 Subject: [PATCH] chore: Update IPFS-related code to use ES6 import/export syntax --- .../checkpoint_diffusion_convert.js | 2 - .../checkpoint_gguf_convert.js | 30 - huggingface_scraper/convert.js | 400 - huggingface_scraper/generate_manifest.js | 201 - huggingface_scraper/generator.js | 289 - .../huggingface/convert_ckpt_hf.py | 9 - .../huggingface/convert_ckpt_safetensors.py | 1 - .../convert_diffusers_to_original_sdxl.py | 340 - ..._diffusers_to_original_stable_diffusion.py | 333 - .../huggingface/convert_diffusion.py | 159 - ..._original_stable_diffusion_to_diffusers.py | 188 - .../huggingface/convert_safetensor_hf.py | 14 - .../huggingface/hf_convert_diffusion.py | 159 - .../huggingface/huggingface_hub_upload.py | 118 - .../huggingface/requirements.txt | 4 - huggingface_scraper/ipfs.js | 190 - huggingface_scraper/ipfs_kit.js | 700 - huggingface_scraper/ipfs_kit_lib/cluster.lock | 0 huggingface_scraper/ipfs_kit_lib/config | 147 - .../ipfs_kit_lib/datastore_spec | 1 - .../ipfs_kit_lib/identity.json | 4 - .../ipfs_kit_lib/install_ipfs.js | 1184 - .../ipfs_kit_lib/ipfs-cluster-follow.service | 12 - .../ipfs_kit_lib/ipfs-cluster-service.service | 12 - huggingface_scraper/ipfs_kit_lib/ipfs.js | 685 - huggingface_scraper/ipfs_kit_lib/ipfs.service | 11 - .../ipfs_kit_lib/ipfs_cluster_ctl.js | 257 - .../ipfs_kit_lib/ipfs_cluster_follow.js | 251 - .../ipfs_kit_lib/ipfs_cluster_service.js | 53 - huggingface_scraper/ipfs_kit_lib/ipget.js | 94 - huggingface_scraper/ipfs_kit_lib/peerstore | 28 - huggingface_scraper/ipfs_kit_lib/service.json | 261 - .../ipfs_kit_lib/service_follower.json | 261 - huggingface_scraper/ipfs_kit_lib/test_fio.js | 140 - huggingface_scraper/ipfs_test.js | 22 - .../libllama/avx2/convert-ggml-gguf.py | 1225 - .../avx2/convert-llama-ggmlv3-to-gguf.py | 353 - .../libllama/avx2/convert_models.py | 35 - huggingface_scraper/libllama/avx2/libllama.so | Bin 5039960 -> 0 bytes huggingface_scraper/libllama/avx2/quantize | Bin 5050688 -> 0 bytes .../libllama/avx2/requirements.txt | 1 - huggingface_scraper/libllama/blas/libllama.so | Bin 5039960 -> 0 bytes huggingface_scraper/libllama/blas/quantize | Bin 1714552 -> 0 bytes .../libllama/convert-hf-to-gguf.py | 900 - huggingface_scraper/libllama/convert.py | 1228 - huggingface_scraper/libllama/quantize | Bin 1714552 -> 0 bytes huggingface_scraper/libllama/requirements.txt | 3 - huggingface_scraper/main.js | 103 - huggingface_scraper/manifest.js | 337 - huggingface_scraper/manifests/manifest_api.js | 90 - .../manifests/manifest_dataset.js | 111 - .../manifests/manifest_diffusion.js | 293 - .../manifests/manifest_hf_embed.js | 223 - .../manifests/manifest_hf_faster_whisper.js | 197 - .../manifests/manifest_hf_lm.js | 270 - .../manifests/manifest_hf_t5.js | 243 - .../manifests/manifest_hf_transformers.js | 230 - huggingface_scraper/manifests/manifest_knn.js | 209 - .../manifests/manifest_llama_cpp.js | 874 - huggingface_scraper/model_manager.js | 1793 - .../modeldata/generate_api.json | 1 - .../modeldata/generate_dataset.json | 1 - .../modeldata/generate_diffusion.json | 1 - .../modeldata/generate_hf_embed.json | 111 - .../modeldata/generate_hf_faster_whisper.json | 56 - .../modeldata/generate_hf_lm.json | 1 - .../modeldata/generate_hf_t5.json | 1 - .../modeldata/generate_hf_transformers.json | 82 - .../modeldata/generate_knn.json | 1 - .../modeldata/generate_llama_cpp.json | 171 - .../orbitdb_kit_lib/import-collection.js | 29 - .../orbitdb_kit_lib/import_hf_dataset.js | 1 - .../orbitdb_kit_lib/orbitv3-master.js | 184 - .../orbitdb_kit_lib/package.json | 36 - .../orbitdb_kit_lib/websocket_test_master.js | 19 - huggingface_scraper/orbitdb_kit_lib/yarn.lock | 6371 --- huggingface_scraper/process_manifest.js | 1624 - huggingface_scraper/process_manifest_test.js | 115 - huggingface_scraper/quantize_gguf.js | 1 - huggingface_scraper/readme-generate.js | 55 - huggingface_scraper/s3.js | 37 - huggingface_scraper/s3_kit.js | 706 - huggingface_scraper/s3_kit/s3_kit.js | 894 - huggingface_scraper/test_fio.js | 122 - huggingface_scraper/tmp_file.js | 36 - huggingface_scraper/utils.js | 646 - model_collection_viewer/collection.json | 1 - model_collection_viewer/dev.js | 57 - model_collection_viewer/dist/app.css | 363 - model_collection_viewer/dist/app.js | 2078 - model_collection_viewer/index.html | 12 - model_collection_viewer/models.json | 32683 ---------------- model_collection_viewer/package.json | 18 - model_collection_viewer/src/app.js | 3 - model_collection_viewer/src/app.jsx | 115 - model_collection_viewer/src/style.scss | 336 - model_collection_viewer/style.css | 5 - scraper.js | 61 - 98 files changed, 62312 deletions(-) delete mode 100644 huggingface_scraper/checkpoint_diffusion_convert.js delete mode 100644 huggingface_scraper/checkpoint_gguf_convert.js delete mode 100644 huggingface_scraper/convert.js delete mode 100644 huggingface_scraper/generate_manifest.js delete mode 100644 huggingface_scraper/generator.js delete mode 100644 huggingface_scraper/huggingface/convert_ckpt_hf.py delete mode 100644 huggingface_scraper/huggingface/convert_ckpt_safetensors.py delete mode 100644 huggingface_scraper/huggingface/convert_diffusers_to_original_sdxl.py delete mode 100644 huggingface_scraper/huggingface/convert_diffusers_to_original_stable_diffusion.py delete mode 100644 huggingface_scraper/huggingface/convert_diffusion.py delete mode 100644 huggingface_scraper/huggingface/convert_original_stable_diffusion_to_diffusers.py delete mode 100644 huggingface_scraper/huggingface/convert_safetensor_hf.py delete mode 100644 huggingface_scraper/huggingface/hf_convert_diffusion.py delete mode 100644 huggingface_scraper/huggingface/huggingface_hub_upload.py delete mode 100644 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model_collection_viewer/index.html delete mode 100644 model_collection_viewer/models.json delete mode 100644 model_collection_viewer/package.json delete mode 100644 model_collection_viewer/src/app.js delete mode 100644 model_collection_viewer/src/app.jsx delete mode 100644 model_collection_viewer/src/style.scss delete mode 100644 model_collection_viewer/style.css delete mode 100644 scraper.js diff --git a/huggingface_scraper/checkpoint_diffusion_convert.js b/huggingface_scraper/checkpoint_diffusion_convert.js deleted file mode 100644 index e75518b..0000000 --- a/huggingface_scraper/checkpoint_diffusion_convert.js +++ /dev/null @@ -1,2 +0,0 @@ -import fs from 'fs' -import path from 'path' \ No newline at end of file diff --git a/huggingface_scraper/checkpoint_gguf_convert.js b/huggingface_scraper/checkpoint_gguf_convert.js deleted file mode 100644 index f7b5e28..0000000 --- a/huggingface_scraper/checkpoint_gguf_convert.js +++ /dev/null @@ -1,30 +0,0 @@ -import fs from 'fs' -import path from 'path' -import os from 'os' - -export function convert_fp32_to_gguf(path){ - let this_file = __filename - let this_dir = path.dirname(this_file) - dest_path = path.replace(".ggml", ".gguf") - let command = "python3 " + this_dir + "/libllama/avx2/convert-ggml-gguf.py " + path + " " + dest_path - console.log("command is: " + command) - let results = os.system(command) -} - -export function convert_fp16_to_gguf(path){ - let this_file = __filename - let this_dir = path.dirname(this_file) - dest_path = path.replace(".ggml", ".gguf") - let command = "python3 "+ this_dir + "/libllama/avx2/convert-ggml-gguf.py " + path + " " + dest_path - console.log("command is: " + command) - let results = os.system(command) -} - -export function convert_ggml_to_gguf(path){ - let this_file = __filename - let this_dir = path.dirname(this_file) - dest_path = path.replace(".ggml", ".gguf") - let command = "python3 "+ this_dir + "/libllama/avx2/convert-ggml-gguf.py " + path + " " + dest_path - console.log("command is: " + command) - let results = os.system(command) -} diff --git a/huggingface_scraper/convert.js b/huggingface_scraper/convert.js deleted file mode 100644 index 3e6d65f..0000000 --- a/huggingface_scraper/convert.js +++ /dev/null @@ -1,400 +0,0 @@ -import * as child_process from 'child_process'; -import * as fs from 'fs'; -import path from 'path' -import { generate_md5, folder_data } from './utils.js'; -const cwd = path.dirname(new URL(import.meta.url).pathname) - -export function convert_gguf(generate, manifest){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "convert.py") - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - generate.format = "gguf" - return new_files -} - -export function convert_ggml(generate, manifest){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "convert.py") - let files - if (Object.keys(generate).includes("build_path")){ - let files = fs.readdirSync(generate.build_path) - } - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - generate.format = "gguf" - return new_files -} - -export function convert_llama_fp16_bak(generate, manifest, this_path){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "convert.py") - let quantization = generate.quantization - let ctx_length = generate.ctx_length - let source_dir = path.dirname(this_path) - let model_name = this_path.replace(source_dir,"").replace("/","").replace("/","") - let f16_model_name = model_name + "-f16" - let dest_path = path.join(source_dir, model_name + "-" + quantization + "/" + model_name + "-" + quantization + ".gguf") - let f16_dest_path = path.join(source_dir, model_name + "-" + quantization + "/" + f16_model_name + ".gguf") - if(!fs.existsSync(path.join(source_dir, model_name))){ - fs.mkdirSync(path.join(source_dir, model_name)) - } - let command - if (ctx_length != undefined){ - command = "python3 " + executable_path + " --outfile " + f16_dest_path + " --ctx + " + ctx_length + " " + this_path + quantization + "/" - } - else{ - command = "python3 " + executable_path + " --outfile " + f16_dest_path + " " + this_path +"/" - } - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - if (quantization != "f16"){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "quantize") - let command2 = executable_path + " " + f16_dest_path + " " + dest_path + " " + quantization - console.log("command2: " + command2) - //let results2 = child_process.execSync(command2, {stdio: 'ignore'}) - let command3 = "rm -rf " + f16_dest_path - console.log("command3: " + command3) - let results3 = child_process.execSync(command3, {stdio: 'ignore'}) - } - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - generate.format = "gguf" - return new_files -} - - - -export function convert_llama_fp16(generate, manifest, this_path){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "convert.py") - let quantization = generate.quantization - let ctx_length = generate.ctx_length - let source_dir = path.dirname(this_path) - let model_name = generate.modelName - let f16_model_name = model_name + "-f16" - let dest_path = path.join(source_dir, model_name + "-" + quantization + "@gguf") - dest_path = path.join(dest_path, model_name + "-" + quantization + ".gguf") - let f16_dest_path = path.join(source_dir,generate.id + "@gguf") - f16_dest_path = path.join(f16_dest_path, f16_model_name + ".gguf") - //if(!fs.existsSync(path.join(source_dir, model_name + "-" + quantization))){ - //fs.mkdirSync(path.join(source_dir, model_name + "-" + quantization)) - //} - let command - if (ctx_length != undefined){ - command = "python3 " + executable_path + " --outfile " + f16_dest_path + " --ctx + " + ctx_length + " " + this_path - } - else{ - command = "python3 " + executable_path + " --outfile " + f16_dest_path + " " + this_path - } - console.log("command: " + command) - if(!fs.existsSync(path.dirname(f16_dest_path))){ - fs.mkdirSync(path.dirname(f16_dest_path)) - } - let results = child_process.execSync(command, {stdio: 'ignore'}) - let new_path = path.dirname(f16_dest_path) - let command_1 = "rm -rf " + this_path - console.log("command_1: " + command_1) - if (quantization != "f32"){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "quantize") - let command2 = executable_path + " " + f16_dest_path + " " + dest_path + " " + quantization - console.log("command2: " + command2) - let results2 = child_process.execSync(command2, {stdio: 'ignore'}) - let command3 = "rm -rf " + this_path - console.log("command3: " + command3) - let results3 = child_process.execSync(command3, {stdio: 'ignore'}) - let command4 = "rm " + f16_dest_path - console.log("command4: " + command4) - let results4 = child_process.execSync(command4, {stdio: 'ignore'}) - } - try { - const stats = fs.statSync(dest_path); - - if (stats.isDirectory()) { - console.log(`${dest_path} is a directory.`); - } - else if (stats.isFile()) { - console.log(`${dest_path} is a file.`); - dest_path = path.dirname(dest_path) - } - } catch (err) { - console.error(err); - } - - let command4 = "cd " + dest_path + " ; find *" - console.log("command4: " + command4) - let file_structure = child_process.execSync(command4) - file_structure = file_structure.toString().split("\n") - for (var i = 0; i < file_structure.length; i++){ - file_structure[i] = "/" + file_structure[i] - } - console.log("file_structure: " + file_structure) - let files = fs.readdirSync(new_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - generate.format = "gguf" - return new_files -} - -export function convert_llama_fp32(generate, manifest, this_path){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "convert.py") - let quantization = generate.quantization - let ctx_length = generate.ctx_length - let source_dir = path.dirname(this_path) - let model_name = generate.modelName - let f32_model_name = model_name + "-f32" - let dest_path = path.join(source_dir, model_name + "-" + quantization) - dest_path = path.join(dest_path, model_name + "-" + quantization + ".gguf") - let f32_dest_path = path.join(source_dir,generate.id) - f32_dest_path = path.join(f32_dest_path, f32_model_name + ".gguf") - //if(!fs.existsSync(path.join(source_dir, model_name + "-" + quantization))){ - //fs.mkdirSync(path.join(source_dir, model_name + "-" + quantization)) - //} - let command - if (ctx_length != undefined){ - command = "python3 " + executable_path + " --outfile " + f32_dest_path + " --ctx + " + ctx_length + " " + this_path - } - else{ - command = "python3 " + executable_path + " --outfile " + f32_dest_path + " " + this_path - } - console.log("command: " + command) - if(!fs.existsSync(path.dirname(f32_dest_path))){ - fs.mkdirSync(path.dirname(f32_dest_path)) - } - let results = child_process.execSync(command, {stdio: 'ignore'}) - let new_path = path.dirname(f32_dest_path) - let command_1 = "rm -rf " + this_path - console.log("command_1: " + command_1) - if (quantization != "f32"){ - let executable_path = path.join(cwd,"libllama") - executable_path = path.join(executable_path, "quantize") - let command2 = executable_path + " " + f32_dest_path + " " + dest_path + " " + quantization - console.log("command2: " + command2) - let results2 = child_process.execSync(command2, {stdio: 'ignore'}) - let command3 = "rm -rf " + this_path - console.log("command3: " + command3) - let results3 = child_process.execSync(command3, {stdio: 'ignore'}) - let command4 = "rm " + f32_dest_path - console.log("command4: " + command4) - let results4 = child_process.execSync(command4, {stdio: 'ignore'}) - } - try { - const stats = fs.statSync(dest_path); - - if (stats.isDirectory()) { - console.log(`${dest_path} is a directory.`); - } - else if (stats.isFile()) { - console.log(`${dest_path} is a file.`); - dest_path = path.dirname(dest_path) - } - } catch (err) { - console.error(err); - } - - let command4 = "cd " + dest_path + " ; find *" - console.log("command4: " + command4) - let file_structure = child_process.execSync(command4) - file_structure = file_structure.toString().split("\n") - for (var i = 0; i < file_structure.length; i++){ - file_structure[i] = "/" + file_structure[i] - } - console.log("file_structure: " + file_structure) - let files = fs.readdirSync(new_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - generate.format = "gguf" - return new_files -} - -export function convert_safetensor_hf(generate, manifest, this_path){ - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_safetensor_hf.py") - let dest_path = this_path.replace(".safetensor", "/") - let command = "python3 " + executable_path + " " + this_path + " " + dest_path - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_ckpt_hf(generate, manifest, this_path){ - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_ckpt_hf.py") - let dest_path = this_path.replace(".ckpt", "/") - let command = "python3 " + executable_path + " " + this_path + " " + dest_path - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_safetensor_ckpt(generate, manifest, this_path){ - let command - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_original_stable_diffusion_to_diffusers.py") - let source_dir = path.dirname(this_path) - let model_name = this_path.replace(source_dir,"") - let dest_path = source_dir + model_name.replace(".safetensors","") + "/" - command = "python3 " + executable_path + " --checkpoint_path " + this_path + " --dump_path " + dest_path + - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - convert_hf_ckpt(generate, manifest, dest_path) - let command_2 = "rm -rf " + dest_path - results = child_process.execSync(command_2, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_ckpt_safetensor(generate, manifest, this_path){ - let command - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_original_stable_diffusion_to_diffusers.py") - let source_dir = path.dirname(this_path) - let model_name = this_path.replace(source_dir,"") - let dest_path = source_dir + model_name.replace(".ckpt","") + "/" - command = "python3 " + executable_path + " --checkpoint_path " + this_path + " --dump_path " + dest_path - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - convert_hf_safetensor(generate, manifest, dest_path) - let command_2 = "rm -rf " + dest_path - console.log("command_2: " + command_2) - results = child_process.execSync(command_2, {stdio: 'ignore'}) - //let command_3 = "rm -rf " + this_path - //console.log("command_3: " + command_3) - //results = child_process.execSync(command_3, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_hf_ckpt(generate, manifest, source_path){ - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_diffusers_to_original_stable_diffusion.py") - let source_dir = path.dirname(source_path) - let model_name = source_path.replace(source_dir,"").replace("/","") - let dest_path = path.join(source_dir, model_name + ".ckpt") - let command = "python3 " + executable_path + " --model_path " + this_path + " --checkpoint_path " + dest_path - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_hf_safetensor(generate, manifest, source_path){ - let executable_path = path.join(cwd,"huggingface") - executable_path = path.join(executable_path, "convert_diffusers_to_original_stable_diffusion.py") - let source_dir = path.dirname(source_path) - let model_name = source_path.replace(source_dir,"").replace("/","").replace("/","") - let dest_path = path.join(source_dir, model_name + ".safetensors") - let command = "python3 " + executable_path + " --model_path " + source_path + " --checkpoint_path " + dest_path + " --use_safetensors" - console.log("command: " + command) - let results = child_process.execSync(command, {stdio: 'ignore'}) - let files = fs.readdirSync(generate.build_path) - let new_files = [] - for(var file in files){ - let this_file = "/" + files[file] - new_files.push(this_file) - } - return new_files -} - -export function convert_model(generate, manifest, this_path){ - let results = "" - - function convert(generate, manifest, this_path){ - if(generate.skill == "llama_cpp"){ - if (generate.format == 'gguf'){ - return convert_ggml(generate, manifest, this_path) - } - if (generate.format == 'ggml'){ - return convert_ggml(generate, manifest, this_path) - } - if (generate.format == 'llama_fp16'){ - return convert_llama_fp16(generate, manifest, this_path) - } - if (generate.format == 'llama_fp32'){ - return convert_llama_fp32(generate, manifest, this_path) - } - } - if(generate.skill == "diffusion"){ - if (generate.format == 'safetensor'){ - console.log() - } - if (generate.format == 'ckpt'){ - return convert_ckpt_safetensor(generate, manifest, this_path) - } - if (generate.format == 'hf'){ - return convert_hf_safetensor(generate, manifest, this_path) - } - } - } - - function convert_and_move(generate, manifest, this_path){ - let old_format = generate.format - convert(generate, manifest, this_path) - let new_format = generate.format - if (old_format != new_format){ - generate.converted = true - } - - //let build_path_split = generate.build_path.split("@") - //build_path_split[1] = generate.format - //let new_build_path = build_path_split.join("@") - //let command = "rm " + generate.build_path + " " + new_build_path - //let results = child_process.execSync(command, {stdio: 'ignore'}) - - //generate.build_path = new_build_path - manifest.folderData = folder_data(generate, manifest, generate.build_path) - return Object.keys(manifest.folderData) - } - - return convert_and_move(generate, manifest, this_path) - -} - - diff --git a/huggingface_scraper/generate_manifest.js b/huggingface_scraper/generate_manifest.js deleted file mode 100644 index 8df6e95..0000000 --- a/huggingface_scraper/generate_manifest.js +++ /dev/null @@ -1,201 +0,0 @@ -import fs from 'fs' -import path, { parse } from 'path' -import prompt_sync from 'prompt-sync' -import prompt_sync_history from 'prompt-sync-history' -import hf_embed_calc from './manifests/manifest_hf_embed.js' -import hf_embed_add from './manifests/manifest_hf_embed.js' -import llama_cpp_calc from './manifests/manifest_llama_cpp.js' -import llama_cpp_add from './manifests/manifest_llama_cpp.js' -import hf_lm_calc from './manifests/manifest_hf_lm.js' -import hf_lm_add from './manifests/manifest_hf_lm.js' -import hf_t5_calc from './manifests/manifest_hf_t5.js' -import hf_t5_add from './manifests/manifest_hf_t5.js' -import knn_calc from './manifests/manifest_knn.js' -import knn_add from './manifests/manifest_knn.js' -import api_calc from './manifests/manifest_api.js' -import api_add from './manifests/manifest_api.js' -import hf_faster_whisper_calc from './manifests/manifest_hf_faster_whisper.js' -import hf_faster_whisper_add from './manifests/manifest_hf_faster_whisper.js' -import hf_transformers_calc from './manifests/manifest_hf_transformers.js' -import hf_transformers_add from './manifests/manifest_hf_transformers.js' -import diffusion_calc from './manifests/manifest_diffusion.js' -import diffusion_add from './manifests/manifest_diffusion.js' -import dataset_calc from './manifests/manifest_dataset.js' -import dataset_add from './manifests/manifest_dataset.js' -import {complete, parse_templates, generate_test} from './utils.js' - -export class Generate_Manifest{ - constructor(){ - this.metadata = {} - this.hwRequirements = {} - } - - - generate_from_prompt(generate){ - let generation = this.generate(generate) - let test_generation = false - if (test_generation){ - let add_generation = this.add_generator_to_model_data(this, generation) - } - return generation - } - - main(generate){ - let generation = this.generate(generate) - let test_generation = false - if (test_generation){ - let add_generation = this.add_generator_to_model_data(this, generation) - } - return generation - } - - add_generator_to_model_data(generation){ - if (generation.skill == undefined){ - throw("skill is undefined") - } - else if (generation.skill == 'hf_transformers'){ - results = hf_transformers_add(this) - } - else if (generation.skill == 'hf_embed'){ - results = hf_embed_add(this) - } - else if (generation.skill == 'llama_cpp'){ - results = llama_cpp_add(this) - } - else if (generation.skill == 'diffusion'){ - results = diffusion_add(this) - } - else if (generation.skill == 'knn'){ - results = knn_add(this) - } - else if (generation.skill == 'api'){ - results = api_add(this) - } - else if (generation.skill == 'custom'){ - results = custom_add(this) - } - else if (generation.skill == 'hf_faster_whisper'){ - results = hf_faster_whisper_add(this) - } - else if (generation.skill == 'hf_lm'){ - results = hf_lm_add(this) - } - else if (generation.skill == 'hf_t5'){ - results = hf_t5_add(this) - } - else if (generation.skill == 'dataset'){ - results = dataset_add(this) - } - else{ - throw("skill is not defined") - } - - - return results - } - - generate(generate){ - let metadata = this.metadata - let hwRequirements = this.hwRequirements - let results - let model_types = ['hf_transformers', 'hf_embed', 'llama_cpp', 'diffusion', 'knn', 'api', 'whisper', 'hf_lm', 'hf_t5', 'dataset'] - // prompt the user for input - console.log("1. hf_transformers") - console.log("2. hf_embed") - console.log("3. llama_cpp") - console.log("4. diffusion") - console.log("5. knn") - console.log("6. api") - console.log("7. whisper") - console.log("8. hf_lm") - console.log("9. hf_t5") - console.log("10. dataset") - // request console input - let prompt = prompt_sync(({ - history: prompt_sync_history(), - autocomplete: complete(model_types), - sigint: true - })) - - let model_type = prompt("Select a model type: ") - // check if the input is valid - console.log("confirm model type: " + model_type) - if (!model_types.includes(model_type)){ - if (parseInt(model_type) > 0 && parseInt(model_type) <= 10 ){ - model_type = model_types[parseInt(model_type) - 1] - } - else{ - console.log("Invalid model type") - return - } - } - - this.skill = model_type - - if (model_type == 'hf_transformers'){ - results = hf_transformers_calc(this) - } - - if (model_type == 'hf_faster_whisper'){ - results = hf_faster_whisper_calc(this) - } - - if (model_type == 'hf_lm'){ - results = hf_lm_calc(this) - } - - if (model_type == "hf_t5"){ - results = hf_t5_calc(this) - } - - if (model_type == 'hf_embed'){ - results = hf_embed_calc(this) - } - - if (model_type == 'llama_cpp'){ - results = llama_cpp_calc(this) - } - - if (model_type == 'diffusion'){ - results = diffusion_calc(this) - } - - if (model_type == 'knn'){ - results = knn_calc(this) - } - - if (model_type == 'api'){ - results = api_calc(this) - } - - if (model_type == 'custom'){ - results = custom_calc(this) - } - - if (model_type == 'dataset'){ - results = dataset_calc(this) - } - - try{ - let test = generate_test(results) - } - catch(err){ - console.log("Error in testing the manifest ") - console.log(model_type) - console.log("results") - console.log(results) - console.log("error") - console.log(err) -// throw("Error in testing the manifest " + model_type) - } - finally{ - return results - } - - } - - custom_calc(){ - throw("custom is not yet supported") - } - -} diff --git a/huggingface_scraper/generator.js b/huggingface_scraper/generator.js deleted file mode 100644 index 75ecdd3..0000000 --- a/huggingface_scraper/generator.js +++ /dev/null @@ -1,289 +0,0 @@ - -import fs from 'fs/promises'; -import path from 'path'; -import generate_hf_transformers from './modeldata/generate_hf_transformers.json' assert { type: 'json' }; -import generate_llama_cpp from './modeldata/generate_llama_cpp.json' assert { type: 'json' }; -import generate_hf_embed from './modeldata/generate_hf_embed.json' assert { type: 'json' }; -import generate_hf_faster_whisper from './modeldata/generate_hf_faster_whisper.json' assert { type: 'json' }; -import generate_hf_lm from './modeldata/generate_hf_lm.json' assert { type: 'json' }; -import generate_hf_t5 from './modeldata/generate_hf_t5.json' assert { type: 'json' }; -import generate_hf_diffusion from './modeldata/generate_diffusion.json' assert { type: 'json' }; -import generate_api from './modeldata/generate_api.json' assert { type: 'json' }; -import generate_knn from './modeldata/generate_knn.json' assert { type: 'json' }; -import generate_dataset from './modeldata/generate_dataset.json' assert { type: 'json' }; -import { setFlagsFromString } from 'v8'; - -export function template_llama_cpp(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_llama_cpp) - let local = local_model_path - let quantizations = ['Q8_0', 'Q6_K', 'Q5_K_M','Q5_K_S', 'Q5_0', 'Q4_K_M' , 'Q4_K_S' ,'Q4_0', 'Q3_K_L' ,'Q3_K_S', 'Q3_K_M', 'Q2_K', ] - let quantiizations = quantizations.reverse() - for (var model in models){ - let this_model = models[model] - for(var quantization in quantizations){ - let this_quanitization = quantizations[quantization] - generate = {} - generate.modelName = this_model - generate.skill = "llama_cpp" - generate.quantization = this_quanitization - generate.format = generate_llama_cpp[this_model].format - generate.id = this_model + "-" + generate.quantization - generate.parameters = generate_llama_cpp[this_model].parameters - generate.source = generate_llama_cpp[this_model].source - generate.ropeScale = generate_llama_cpp[this_model].ropeScale - generate.contextSize = generate_llama_cpp[this_model].contextSize - generate.units = "tokens" - generate.destPath = local + this_model + "-" + generate.quantization - results.push(generate) - } - } - return results -} - -export function template_hf_transformers(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_transformers) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_t5" - generate.format = generate_hf_transformers[this_model].format - generate.id = this_model - generate.parameters = generate_hf_transformers[this_model].parameters - generate.source = generate_hf_transformers[this_model].source - generate.contextSize = generate_hf_transformers[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - //generate.destPath = local - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_hf_faster_whisper(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_faster_whisper) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_faster_whisper" - generate.format = generate_hf_faster_whisper[this_model].format - generate.id = this_model - generate.parameters = generate_hf_faster_whisper[this_model].parameters - generate.source = generate_hf_faster_whisper[this_model].source - generate.contextSize = generate_hf_faster_whisper[this_model].contextSize - generate.location = "huggingface" - generate.units = "seconds" - //generate.destPath = local - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_hf_embed(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_embed) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_embed" - generate.format = generate_hf_embed[this_model].format - generate.id = this_model - generate.parameters = generate_hf_embed[this_model].parameters - generate.source = generate_hf_embed[this_model].source - generate.contextSize = generate_hf_embed[this_model].contextSize - generate.dimensions = generate_hf_embed[this_model].dimensions - generate.content_type = generate_hf_embed[this_model].content_type - generate.location = "huggingface" - generate.units = "vectors" - //generate.destPath = local - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_hf_lm(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_lm) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_lm" - generate.format = generate_hf_lm[this_model].format - generate.id = this_model - generate.parameters = generate_hf_lm[this_model].parameters - generate.source = generate_hf_lm[this_model].source - generate.contextSize = generate_hf_lm[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_hf_t5(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_t5) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_t5" - generate.format = generate_hf_t5[this_model].format - generate.id = this_model - generate.parameters = generate_hf_t5[this_model].parameters - generate.source = generate_hf_t5[this_model].source - generate.contextSize = generate_hf_t5[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_hf_diffusion(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_hf_diffusion) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "hf_diffusion" - generate.format = generate_hf_diffusion[this_model].format - generate.id = this_model - generate.parameters = generate_hf_diffusion[this_model].parameters - generate.source = generate_hf_diffusion[this_model].source - generate.contextSize = generate_hf_diffusion[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_knn(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_knn) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "knn" - generate.format = generate_knn[this_model].format - generate.id = this_model - generate.parameters = generate_knn[this_model].parameters - generate.source = generate_knn[this_model].source - generate.contextSize = generate_knn[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - -export function template_api(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_api) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "api" - generate.format = generate_api[this_model].format - generate.id = this_model - generate.parameters = generate_api[this_model].parameters - generate.source = generate_api[this_model].source - generate.contextSize = generate_api[this_model].contextSize - generate.location = "huggingface" - generate.units = "tokens" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - - -export function template_dataset(local_model_path, collection_path){ - let results = [] - let generate - let models = Object.keys(generate_api) - let local = local_model_path - for (var model in models){ - let this_model = models[model] - generate = {} - generate.modelName = this_model - generate.skill = "dataset" - generate.format = generate_template[this_model].format - generate.id = this_model - generate.samples = generate_template[this_model].samples - generate.size = generate_template[this_model].size - generate.location = "huggingface" - generate.units = "MB" - generate.destPath = local + "/" + this_model - results.push(generate) - } - return results -} - - -export class Generator{ - constructor(local_model_path, collection_path){ - this.llama_cpp = {} - this.diffusion = {} - this.hf_transformers = {} - this.hf_embed = {} - this.knn = {} - this.api = {} - this.custom = {} - this.hf_lm = {} - this.hf_t5 = {} - this.hf_diffusion = {} - this.dataset = {} - this.local_model_path = local_model_path - this.collection_path = collection_path - } - - main(){ - this - this.llama_cpp = template_llama_cpp(this.local_model_path, this.collection_path) - this.hf_transformers = template_hf_transformers(this.local_model_path, this.collection_path) - this.hf_embed = template_hf_embed(this.local_model_path, this.collection_path) - this.hf_faster_whisper = template_hf_faster_whisper(this.local_model_path, this.collection_path) - this.hf_lm = template_hf_lm(this.local_model_path, this.collection_path) - this.hf_t5 = template_hf_t5(this.local_model_path, this.collection_path) - this.hf_diffusion = template_hf_diffusion(this.local_model_path, this.collection_path) - this.api = template_api(this.local_model_path, this.collection_path) - this.knn = template_knn(this.local_model_path, this.collection_path) - this.dataset = template_dataset(this.local_model_path, this.collection_path) - return this - } -} \ No newline at end of file diff --git a/huggingface_scraper/huggingface/convert_ckpt_hf.py b/huggingface_scraper/huggingface/convert_ckpt_hf.py deleted file mode 100644 index 42991ac..0000000 --- a/huggingface_scraper/huggingface/convert_ckpt_hf.py +++ /dev/null @@ -1,9 +0,0 @@ -import diffusers -import sys - -def main(source_file, output_folder): - pipe = diffusers.StableDiffusionPipeline.from_single_file(source_file) - pipe.save_pretrained(output_folder) - -if __name__ == "__main__": - main(sys.argv[1], sys.argv[2]) \ No newline at end of file diff --git a/huggingface_scraper/huggingface/convert_ckpt_safetensors.py b/huggingface_scraper/huggingface/convert_ckpt_safetensors.py deleted file mode 100644 index 8d1c8b6..0000000 --- a/huggingface_scraper/huggingface/convert_ckpt_safetensors.py +++ /dev/null @@ -1 +0,0 @@ - diff --git a/huggingface_scraper/huggingface/convert_diffusers_to_original_sdxl.py b/huggingface_scraper/huggingface/convert_diffusers_to_original_sdxl.py deleted file mode 100644 index 1f11ef4..0000000 --- a/huggingface_scraper/huggingface/convert_diffusers_to_original_sdxl.py +++ /dev/null @@ -1,340 +0,0 @@ -# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. -# *Only* converts the UNet, VAE, and Text Encoder. -# Does not convert optimizer state or any other thing. - -import argparse -import os.path as osp -import re - -import torch -from safetensors.torch import load_file, save_file - - -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), - # the following are for sdxl - ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), - ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), - ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), - ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(3): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i > 0: - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(4): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3*i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i < 2: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) -unet_conversion_map_layer.append(("output_blocks.2.2.conv.", "output_blocks.2.1.conv.")) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2*j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {sd_name: unet_state_dict[hf_name] for hf_name, sd_name in mapping.items()} - return new_state_dict - - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3-i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3-i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i+1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - # the following are for SDXL - ("q.", "to_q."), - ("k.", "to_k."), - ("v.", "to_v."), - ("proj_out.", "to_out.0."), -] - - -def reshape_weight_for_sd(w): - # convert HF linear weights to SD conv2d weights - return w.reshape(*w.shape, 1, 1) - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v) - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# - - -textenc_conversion_lst = [ - # (stable-diffusion, HF Diffusers) - ("transformer.resblocks.", "text_model.encoder.layers."), - ("ln_1", "layer_norm1"), - ("ln_2", "layer_norm2"), - (".c_fc.", ".fc1."), - (".c_proj.", ".fc2."), - (".attn", ".self_attn"), - ("ln_final.", "text_model.final_layer_norm."), - ("token_embedding.weight", "text_model.embeddings.token_embedding.weight"), - ("positional_embedding", "text_model.embeddings.position_embedding.weight"), -] -protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} -textenc_pattern = re.compile("|".join(protected.keys())) - -# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp -code2idx = {"q": 0, "k": 1, "v": 2} - - -def convert_openclip_text_enc_state_dict(text_enc_dict): - new_state_dict = {} - capture_qkv_weight = {} - capture_qkv_bias = {} - for k, v in text_enc_dict.items(): - if ( - k.endswith(".self_attn.q_proj.weight") - or k.endswith(".self_attn.k_proj.weight") - or k.endswith(".self_attn.v_proj.weight") - ): - k_pre = k[: -len(".q_proj.weight")] - k_code = k[-len("q_proj.weight")] - if k_pre not in capture_qkv_weight: - capture_qkv_weight[k_pre] = [None, None, None] - capture_qkv_weight[k_pre][code2idx[k_code]] = v - continue - - if ( - k.endswith(".self_attn.q_proj.bias") - or k.endswith(".self_attn.k_proj.bias") - or k.endswith(".self_attn.v_proj.bias") - ): - k_pre = k[: -len(".q_proj.bias")] - k_code = k[-len("q_proj.bias")] - if k_pre not in capture_qkv_bias: - capture_qkv_bias[k_pre] = [None, None, None] - capture_qkv_bias[k_pre][code2idx[k_code]] = v - continue - - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v - - for k_pre, tensors in capture_qkv_weight.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) - - for k_pre, tensors in capture_qkv_bias.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) - - return new_state_dict - - -def convert_openai_text_enc_state_dict(text_enc_dict): - return text_enc_dict - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." - ) - - args = parser.parse_args() - - assert args.model_path is not None, "Must provide a model path!" - - assert args.checkpoint_path is not None, "Must provide a checkpoint path!" - - # Path for safetensors - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") - text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") - text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "model.safetensors") - - # Load models from safetensors if it exists, if it doesn't pytorch - if osp.exists(unet_path): - unet_state_dict = load_file(unet_path, device="cpu") - else: - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") - unet_state_dict = torch.load(unet_path, map_location="cpu") - - if osp.exists(vae_path): - vae_state_dict = load_file(vae_path, device="cpu") - else: - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") - vae_state_dict = torch.load(vae_path, map_location="cpu") - - if osp.exists(text_enc_path): - text_enc_dict = load_file(text_enc_path, device="cpu") - else: - text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - - if osp.exists(text_enc_2_path): - text_enc_2_dict = load_file(text_enc_2_path, device="cpu") - else: - text_enc_2_path = osp.join(args.model_path, "text_encoder_2", "pytorch_model.bin") - text_enc_2_dict = torch.load(text_enc_2_path, map_location="cpu") - - # Convert the UNet model - unet_state_dict = convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - text_enc_dict = convert_openai_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"conditioner.embedders.0.transformer." + k: v for k, v in text_enc_dict.items()} - - text_enc_2_dict = convert_openclip_text_enc_state_dict(text_enc_2_dict) - text_enc_2_dict = {"conditioner.embedders.1.model." + k: v for k, v in text_enc_2_dict.items()} - - # Put together new checkpoint - state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict, **text_enc_2_dict} - - if args.half: - state_dict = {k: v.half() for k, v in state_dict.items()} - - if args.use_safetensors: - save_file(state_dict, args.checkpoint_path) - else: - state_dict = {"state_dict": state_dict} - torch.save(state_dict, args.checkpoint_path) diff --git a/huggingface_scraper/huggingface/convert_diffusers_to_original_stable_diffusion.py b/huggingface_scraper/huggingface/convert_diffusers_to_original_stable_diffusion.py deleted file mode 100644 index 9da4521..0000000 --- a/huggingface_scraper/huggingface/convert_diffusers_to_original_stable_diffusion.py +++ /dev/null @@ -1,333 +0,0 @@ -# Script for converting a HF Diffusers saved pipeline to a Stable Diffusion checkpoint. -# *Only* converts the UNet, VAE, and Text Encoder. -# Does not convert optimizer state or any other thing. - -import argparse -import os.path as osp -import re - -import torch -from safetensors.torch import load_file, save_file - - -# =================# -# UNet Conversion # -# =================# - -unet_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("time_embed.0.weight", "time_embedding.linear_1.weight"), - ("time_embed.0.bias", "time_embedding.linear_1.bias"), - ("time_embed.2.weight", "time_embedding.linear_2.weight"), - ("time_embed.2.bias", "time_embedding.linear_2.bias"), - ("input_blocks.0.0.weight", "conv_in.weight"), - ("input_blocks.0.0.bias", "conv_in.bias"), - ("out.0.weight", "conv_norm_out.weight"), - ("out.0.bias", "conv_norm_out.bias"), - ("out.2.weight", "conv_out.weight"), - ("out.2.bias", "conv_out.bias"), -] - -unet_conversion_map_resnet = [ - # (stable-diffusion, HF Diffusers) - ("in_layers.0", "norm1"), - ("in_layers.2", "conv1"), - ("out_layers.0", "norm2"), - ("out_layers.3", "conv2"), - ("emb_layers.1", "time_emb_proj"), - ("skip_connection", "conv_shortcut"), -] - -unet_conversion_map_layer = [] -# hardcoded number of downblocks and resnets/attentions... -# would need smarter logic for other networks. -for i in range(4): - # loop over downblocks/upblocks - - for j in range(2): - # loop over resnets/attentions for downblocks - hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." - sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." - unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) - - if i < 3: - # no attention layers in down_blocks.3 - hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." - sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." - unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) - - for j in range(3): - # loop over resnets/attentions for upblocks - hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." - sd_up_res_prefix = f"output_blocks.{3*i + j}.0." - unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) - - if i > 0: - # no attention layers in up_blocks.0 - hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." - sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." - unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) - - if i < 3: - # no downsample in down_blocks.3 - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." - sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." - unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) - - # no upsample in up_blocks.3 - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}." - unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) - -hf_mid_atn_prefix = "mid_block.attentions.0." -sd_mid_atn_prefix = "middle_block.1." -unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) - -for j in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{j}." - sd_mid_res_prefix = f"middle_block.{2*j}." - unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -def convert_unet_state_dict(unet_state_dict): - # buyer beware: this is a *brittle* function, - # and correct output requires that all of these pieces interact in - # the exact order in which I have arranged them. - mapping = {k: k for k in unet_state_dict.keys()} - for sd_name, hf_name in unet_conversion_map: - mapping[hf_name] = sd_name - for k, v in mapping.items(): - if "resnets" in k: - for sd_part, hf_part in unet_conversion_map_resnet: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - for sd_part, hf_part in unet_conversion_map_layer: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} - return new_state_dict - - -# ================# -# VAE Conversion # -# ================# - -vae_conversion_map = [ - # (stable-diffusion, HF Diffusers) - ("nin_shortcut", "conv_shortcut"), - ("norm_out", "conv_norm_out"), - ("mid.attn_1.", "mid_block.attentions.0."), -] - -for i in range(4): - # down_blocks have two resnets - for j in range(2): - hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." - sd_down_prefix = f"encoder.down.{i}.block.{j}." - vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) - - if i < 3: - hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." - sd_downsample_prefix = f"down.{i}.downsample." - vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) - - hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." - sd_upsample_prefix = f"up.{3-i}.upsample." - vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) - - # up_blocks have three resnets - # also, up blocks in hf are numbered in reverse from sd - for j in range(3): - hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." - sd_up_prefix = f"decoder.up.{3-i}.block.{j}." - vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) - -# this part accounts for mid blocks in both the encoder and the decoder -for i in range(2): - hf_mid_res_prefix = f"mid_block.resnets.{i}." - sd_mid_res_prefix = f"mid.block_{i+1}." - vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) - - -vae_conversion_map_attn = [ - # (stable-diffusion, HF Diffusers) - ("norm.", "group_norm."), - ("q.", "query."), - ("k.", "key."), - ("v.", "value."), - ("proj_out.", "proj_attn."), -] - - -def reshape_weight_for_sd(w): - # convert HF linear weights to SD conv2d weights - return w.reshape(*w.shape, 1, 1) - - -def convert_vae_state_dict(vae_state_dict): - mapping = {k: k for k in vae_state_dict.keys()} - for k, v in mapping.items(): - for sd_part, hf_part in vae_conversion_map: - v = v.replace(hf_part, sd_part) - mapping[k] = v - for k, v in mapping.items(): - if "attentions" in k: - for sd_part, hf_part in vae_conversion_map_attn: - v = v.replace(hf_part, sd_part) - mapping[k] = v - new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} - weights_to_convert = ["q", "k", "v", "proj_out"] - for k, v in new_state_dict.items(): - for weight_name in weights_to_convert: - if f"mid.attn_1.{weight_name}.weight" in k: - print(f"Reshaping {k} for SD format") - new_state_dict[k] = reshape_weight_for_sd(v) - return new_state_dict - - -# =========================# -# Text Encoder Conversion # -# =========================# - - -textenc_conversion_lst = [ - # (stable-diffusion, HF Diffusers) - ("resblocks.", "text_model.encoder.layers."), - ("ln_1", "layer_norm1"), - ("ln_2", "layer_norm2"), - (".c_fc.", ".fc1."), - (".c_proj.", ".fc2."), - (".attn", ".self_attn"), - ("ln_final.", "transformer.text_model.final_layer_norm."), - ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), - ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), -] -protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} -textenc_pattern = re.compile("|".join(protected.keys())) - -# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp -code2idx = {"q": 0, "k": 1, "v": 2} - - -def convert_text_enc_state_dict_v20(text_enc_dict): - new_state_dict = {} - capture_qkv_weight = {} - capture_qkv_bias = {} - for k, v in text_enc_dict.items(): - if ( - k.endswith(".self_attn.q_proj.weight") - or k.endswith(".self_attn.k_proj.weight") - or k.endswith(".self_attn.v_proj.weight") - ): - k_pre = k[: -len(".q_proj.weight")] - k_code = k[-len("q_proj.weight")] - if k_pre not in capture_qkv_weight: - capture_qkv_weight[k_pre] = [None, None, None] - capture_qkv_weight[k_pre][code2idx[k_code]] = v - continue - - if ( - k.endswith(".self_attn.q_proj.bias") - or k.endswith(".self_attn.k_proj.bias") - or k.endswith(".self_attn.v_proj.bias") - ): - k_pre = k[: -len(".q_proj.bias")] - k_code = k[-len("q_proj.bias")] - if k_pre not in capture_qkv_bias: - capture_qkv_bias[k_pre] = [None, None, None] - capture_qkv_bias[k_pre][code2idx[k_code]] = v - continue - - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) - new_state_dict[relabelled_key] = v - - for k_pre, tensors in capture_qkv_weight.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) - - for k_pre, tensors in capture_qkv_bias.items(): - if None in tensors: - raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") - relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) - new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) - - return new_state_dict - - -def convert_text_enc_state_dict(text_enc_dict): - return text_enc_dict - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.") - parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--use_safetensors", action="store_true", help="Save weights use safetensors, default is ckpt." - ) - - args = parser.parse_args() - - assert args.model_path is not None, "Must provide a model path!" - - assert args.checkpoint_path is not None, "Must provide a checkpoint path!" - - # Path for safetensors - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.safetensors") - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.safetensors") - text_enc_path = osp.join(args.model_path, "text_encoder", "model.safetensors") - - # Load models from safetensors if it exists, if it doesn't pytorch - if osp.exists(unet_path): - unet_state_dict = load_file(unet_path, device="cpu") - else: - unet_path = osp.join(args.model_path, "unet", "diffusion_pytorch_model.bin") - unet_state_dict = torch.load(unet_path, map_location="cpu") - - if osp.exists(vae_path): - vae_state_dict = load_file(vae_path, device="cpu") - else: - vae_path = osp.join(args.model_path, "vae", "diffusion_pytorch_model.bin") - vae_state_dict = torch.load(vae_path, map_location="cpu") - - if osp.exists(text_enc_path): - text_enc_dict = load_file(text_enc_path, device="cpu") - else: - text_enc_path = osp.join(args.model_path, "text_encoder", "pytorch_model.bin") - text_enc_dict = torch.load(text_enc_path, map_location="cpu") - - # Convert the UNet model - unet_state_dict = convert_unet_state_dict(unet_state_dict) - unet_state_dict = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()} - - # Convert the VAE model - vae_state_dict = convert_vae_state_dict(vae_state_dict) - vae_state_dict = {"first_stage_model." + k: v for k, v in vae_state_dict.items()} - - # Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper - is_v20_model = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict - - if is_v20_model: - # Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm - text_enc_dict = {"transformer." + k: v for k, v in text_enc_dict.items()} - text_enc_dict = convert_text_enc_state_dict_v20(text_enc_dict) - text_enc_dict = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()} - else: - text_enc_dict = convert_text_enc_state_dict(text_enc_dict) - text_enc_dict = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()} - - # Put together new checkpoint - state_dict = {**unet_state_dict, **vae_state_dict, **text_enc_dict} - if args.half: - state_dict = {k: v.half() for k, v in state_dict.items()} - - if args.use_safetensors: - save_file(state_dict, args.checkpoint_path) - else: - state_dict = {"state_dict": state_dict} - torch.save(state_dict, args.checkpoint_path) diff --git a/huggingface_scraper/huggingface/convert_diffusion.py b/huggingface_scraper/huggingface/convert_diffusion.py deleted file mode 100644 index b4307e3..0000000 --- a/huggingface_scraper/huggingface/convert_diffusion.py +++ /dev/null @@ -1,159 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Conversion script for the LDM checkpoints. """ - -import argparse - -import torch - -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--scheduler_type", - default="pndm", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--pipeline_type", - default=None, - type=str, - help=( - "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" - ". If `None` pipeline will be automatically inferred." - ), - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=( - "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" - " Base. Use 768 for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=( - "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" - " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--upcast_attention", - action="store_true", - help=( - "Whether the attention computation should always be upcasted. This is necessary when running stable" - " diffusion 2.1." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - parser.add_argument( - "--stable_unclip", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", - ) - parser.add_argument( - "--stable_unclip_prior", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", - ) - parser.add_argument( - "--clip_stats_path", - type=str, - help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", - required=False, - ) - parser.add_argument( - "--controlnet", - action="store_true", - default=None, - help="Set flag if this is a controlnet checkpoint." - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - args = parser.parse_args() - - pipe = download_from_original_stable_diffusion_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - prediction_type=args.prediction_type, - model_type=args.pipeline_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - upcast_attention=args.upcast_attention, - from_safetensors=args.from_safetensors, - device=args.device, - stable_unclip=args.stable_unclip, - stable_unclip_prior=args.stable_unclip_prior, - clip_stats_path=args.clip_stats_path, - controlnet=args.controlnet, - ) - - if args.half: - pipe.to(torch_dtype=torch.float16) - - if args.controlnet: - # only save the controlnet model - pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) - else: - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/huggingface_scraper/huggingface/convert_original_stable_diffusion_to_diffusers.py b/huggingface_scraper/huggingface/convert_original_stable_diffusion_to_diffusers.py deleted file mode 100644 index 2ca7096..0000000 --- a/huggingface_scraper/huggingface/convert_original_stable_diffusion_to_diffusers.py +++ /dev/null @@ -1,188 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Conversion script for the LDM checkpoints. """ - -import argparse -import importlib - -import torch - -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--config_files", - default=None, - type=str, - help="The YAML config file corresponding to the architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--scheduler_type", - default="pndm", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--pipeline_type", - default=None, - type=str, - help=( - "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" - ". If `None` pipeline will be automatically inferred." - ), - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=( - "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" - " Base. Use 768 for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=( - "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" - " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--upcast_attention", - action="store_true", - help=( - "Whether the attention computation should always be upcasted. This is necessary when running stable" - " diffusion 2.1." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - parser.add_argument( - "--stable_unclip", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", - ) - parser.add_argument( - "--stable_unclip_prior", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", - ) - parser.add_argument( - "--clip_stats_path", - type=str, - help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", - required=False, - ) - parser.add_argument( - "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint." - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - parser.add_argument( - "--vae_path", - type=str, - default=None, - required=False, - help="Set to a path, hub id to an already converted vae to not convert it again.", - ) - parser.add_argument( - "--pipeline_class_name", - type=str, - default=None, - required=False, - help="Specify the pipeline class name", - ) - - args = parser.parse_args() - - if args.pipeline_class_name is not None: - library = importlib.import_module("diffusers") - class_obj = getattr(library, args.pipeline_class_name) - pipeline_class = class_obj - else: - pipeline_class = None - - pipe = download_from_original_stable_diffusion_ckpt( - checkpoint_path_or_dict=args.checkpoint_path, - original_config_file=args.original_config_file, - config_files=args.config_files, - image_size=args.image_size, - prediction_type=args.prediction_type, - model_type=args.pipeline_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - upcast_attention=args.upcast_attention, - from_safetensors=args.from_safetensors, - device=args.device, - stable_unclip=args.stable_unclip, - stable_unclip_prior=args.stable_unclip_prior, - clip_stats_path=args.clip_stats_path, - controlnet=args.controlnet, - vae_path=args.vae_path, - pipeline_class=pipeline_class, - ) - - if args.half: - pipe.to(torch_dtype=torch.float16) - - if args.controlnet: - # only save the controlnet model - pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) - else: - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/huggingface_scraper/huggingface/convert_safetensor_hf.py b/huggingface_scraper/huggingface/convert_safetensor_hf.py deleted file mode 100644 index a494092..0000000 --- a/huggingface_scraper/huggingface/convert_safetensor_hf.py +++ /dev/null @@ -1,14 +0,0 @@ -from diffusers import StableDiffusionPipeline -import safetensors -import torch -import sys - -def main(source_file, output_folder): - pipe = StableDiffusionPipeline.from_single_file( - source_file, - ) - pipe.save_pretrained(output_folder) - -if __name__ == "__main__": - main(sys.argv[1], sys.argv[2]) - diff --git a/huggingface_scraper/huggingface/hf_convert_diffusion.py b/huggingface_scraper/huggingface/hf_convert_diffusion.py deleted file mode 100644 index b4307e3..0000000 --- a/huggingface_scraper/huggingface/hf_convert_diffusion.py +++ /dev/null @@ -1,159 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The HuggingFace Inc. team. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" Conversion script for the LDM checkpoints. """ - -import argparse - -import torch - -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml - parser.add_argument( - "--original_config_file", - default=None, - type=str, - help="The YAML config file corresponding to the original architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--scheduler_type", - default="pndm", - type=str, - help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']", - ) - parser.add_argument( - "--pipeline_type", - default=None, - type=str, - help=( - "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'" - ". If `None` pipeline will be automatically inferred." - ), - ) - parser.add_argument( - "--image_size", - default=None, - type=int, - help=( - "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2" - " Base. Use 768 for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--prediction_type", - default=None, - type=str, - help=( - "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable" - " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2." - ), - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--upcast_attention", - action="store_true", - help=( - "Whether the attention computation should always be upcasted. This is necessary when running stable" - " diffusion 2.1." - ), - ) - parser.add_argument( - "--from_safetensors", - action="store_true", - help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.", - ) - parser.add_argument( - "--to_safetensors", - action="store_true", - help="Whether to store pipeline in safetensors format or not.", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)") - parser.add_argument( - "--stable_unclip", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.", - ) - parser.add_argument( - "--stable_unclip_prior", - type=str, - default=None, - required=False, - help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.", - ) - parser.add_argument( - "--clip_stats_path", - type=str, - help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.", - required=False, - ) - parser.add_argument( - "--controlnet", - action="store_true", - default=None, - help="Set flag if this is a controlnet checkpoint." - ) - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - args = parser.parse_args() - - pipe = download_from_original_stable_diffusion_ckpt( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - image_size=args.image_size, - prediction_type=args.prediction_type, - model_type=args.pipeline_type, - extract_ema=args.extract_ema, - scheduler_type=args.scheduler_type, - num_in_channels=args.num_in_channels, - upcast_attention=args.upcast_attention, - from_safetensors=args.from_safetensors, - device=args.device, - stable_unclip=args.stable_unclip, - stable_unclip_prior=args.stable_unclip_prior, - clip_stats_path=args.clip_stats_path, - controlnet=args.controlnet, - ) - - if args.half: - pipe.to(torch_dtype=torch.float16) - - if args.controlnet: - # only save the controlnet model - pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) - else: - pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) diff --git a/huggingface_scraper/huggingface/huggingface_hub_upload.py b/huggingface_scraper/huggingface/huggingface_hub_upload.py deleted file mode 100644 index 21c70be..0000000 --- a/huggingface_scraper/huggingface/huggingface_hub_upload.py +++ /dev/null @@ -1,118 +0,0 @@ -from huggingface_hub import HfApi, HfFolder -import os -import sys - - -def upload_folder_to_hf_hub(folder_path, repo_name, hf_username, hf_token, this_organization): - """ - Uploads the contents of a folder to a Hugging Face Hub repository. - - Parameters: - folder_path (str): The path to the folder to upload. - repo_name (str): The name of the repository on Hugging Face Hub. - hf_username (str): Your Hugging Face username. - hf_token (str): Your Hugging Face API token. - """ - - dataset = False - buildDir = "/tmp/build/" - # Initialize Hugging Face API - api = HfApi() - HfFolder.save_token(hf_token) - # Login to Hugging Face - ## find if the repo exists - repo_id = hf_username+"/"+repo_name - print("repo_id", repo_id) - repo_exists = False - - repo_from = "" - try: - repo = api.model_info(repo_id=repo_id) - print("repo", repo) - ## try to assign repo_From repo.modelId but catch errors - repo_from = repo.modelId - if isinstance(repo_from, str) and len(repo_from) > 0: - repo_exists = True - else: - repo_exists = False - - except Exception: - pass - - - if repo_exists: - print(f"Repository {repo_id} already exists") - repo_url = " https://huggingface.co/" + repo_id - - else: - repo_url = api.create_repo( repo_id=hf_username+"/"+repo_name , private=False, exist_ok=True, token=hf_token) - print(f"Repository URL: {repo_url}") - - if(folder_path.endswith("/")): - parentname = os.path.dirname(os.path.dirname(folder_path)) - else: - parentname = os.path.dirname(folder_path) - print("parentname", parentname) - basename = folder_path.replace(parentname , "") - basename = basename.replace("/","") - print("basename", basename) - folder_path = os.path.join(parentname,basename) - print ("folder_path", folder_path) - - ##print(f"Cloning repository {folder_path} ... to {repo_url}") - ## check if repo exists in the local folder - if os.path.exists(folder_path): - print(f"folder {repo_name} already exists") - ## check if empty - if len(os.listdir(folder_path)) == 0: - command_1 = "rm -rf " + folder_path - print("Empty repo") - print("command_1", command_1) - os.system(command_1) - os.system("cp -r " + buildDir + "/" + repo_name + "/ " + folder_path ) - api.upload_folder( - folder_path=folder_path, - path_in_repo="", # root of the repo - repo_id=repo_id, - repo_type="model" - ) - else: - if os.path.exists(folder_path + "/.gitignore"): - gitIgnore = open(folder_path + "/.gitignore", "r").read().splitlines()[0] - print("gitIgnore: ", gitIgnore) - api.upload_folder( - folder_path=folder_path, - path_in_repo="", # root of the repo - repo_id=repo_id, - repo_type="model", - delete_patterns="*", - ignore_patterns="*/*" - - ) - else: - api.upload_folder( - folder_path=folder_path, - path_in_repo="", # root of the repo - repo_id=repo_id, - repo_type="model" - ) - else: - command_3 = "cp -r " + buildDir + "/" + repo_name + "/ " + folder_path - print("command_3", command_3) - os.system(command_3) - api.upload_folder( - folder_path=folder_path, - path_in_repo="", # root of the repo - repo_id=repo_id, - repo_type="model" - ) - - print(f"Folder '{folder_path}' successfully uploaded to Hugging Face Hub repository '{repo_name}'.") - -if __name__ == "__main__": - arg1 = sys.argv[1] - arg2 = sys.argv[2] - arg3 = sys.argv[3] - arg4 = sys.argv[4] - arg5 = sys.argv[5] - upload_folder_to_hf_hub(arg1, arg2, arg3, arg4, arg5) diff --git a/huggingface_scraper/huggingface/requirements.txt b/huggingface_scraper/huggingface/requirements.txt deleted file mode 100644 index c255a46..0000000 --- a/huggingface_scraper/huggingface/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -omegaconf -diffusers -git+https://github.com/huggingface/transformers -hfapi diff --git a/huggingface_scraper/ipfs.js b/huggingface_scraper/ipfs.js deleted file mode 100644 index e617ae5..0000000 --- a/huggingface_scraper/ipfs.js +++ /dev/null @@ -1,190 +0,0 @@ -import fs from 'fs' -import path from 'path' -import child_process, { exec } from 'child_process' - -export class ipfsClusterCtl { - constructor(){ - this.pinset = get_pinset() - this.exec = {} - this.stats = {} - this.queue = [] - } - - main(){ - return this - } - - check_collection(collection){ - let status = {} - let collection_keys = Object.keys(collection) - let pinset_keys = Object.keys(this.pinset) - let orphan_models = [] - let orphan_pins = [] - let active_pins = [] - let active_models = [] - for(var i = 0; i < collection_keys.length; i++){ - let this_model = collection_keys[i] - if(this_model != "cache"){ - let this_manifest = collection[this_model] - let this_id = this_manifest.id - let this_cache = this_manifest.cache - let this_ipfs_cache = this_cache.ipfs - let this_ipfs_cache_keys = Object.keys(this_ipfs_cache) - let found_all = true - for (var j = 0; j < this_ipfs_cache_keys.length ; j++){ - let this_cache_basename = this_ipfs_cache_keys[j] - let this_cache_item = this_ipfs_cache[this_cache_basename] - let this_cache_item_path = this_cache_item.path - let this_cache_item_url = this_cache_item.url - if(pinset_keys.includes(this_cache_item_path)){ - active_pins.push(this_cache_item_path) - } - else{ - found_all = false - } - } - if (found_all == true){ - active_models.push(this_model) - } - else{ - orphan_models.push(this_model) - } - } - } - - for (var i = 0; i < pinset_keys.length; i++){ - let this_pin = pinset_keys[i] - if(active_pins.includes(this_pin)){ - //pass - } - else{ - orphan_pins.push(this_pin) - } - } - - status["orphan_models"] = orphan_models - status["orphan_pins"] = orphan_pins - status["active_pins"] = active_pins - status["active_models"] = active_models - - return status - } - - execute(args){ - let command - let executable = "ipfs-cluster-ctl " - - let options = ["add", "pin", "unpin", "status", "sync", "pinset"] - - if (args.command == "add"){ - command = executable + "add " + args.file - } - - if (args.command == "pin"){ - command = executable + "pin add " + args.hash - } - - if (args.command == "unpin"){ - command = executable + "pin rm " + args.hash - } - - if (args.command == "status"){ - command = executable + "status " + args.hash - } - - if (args.command == "sync"){ - command = executable + "sync " + args.hash - } - - if (args.command == "pinset"){ - command = executable + "pin ls " + args.hash - } - - if (options.includes(args.command)){ - this.exec = command - child_process.execSync(command, (error, stdout, stderr) => { - if (error) { - console.log(`error: ${error.message}`); - return; - } - if (stderr) { - console.log(`stderr: ${stderr}`); - return; - } - console.log(`stdout: ${stdout}`); - } - ) - return this - } - else{ - console.log("command not found") - return this - } - } - -} - - - -export function get_pinset(){ - - let pinset_file = "/tmp/build/ipfs_pinset.txt" - let pinset_file_mtime = 0 - if(fs.existsSync(pinset_file)){ - pinset_file_mtime = fs.statSync(pinset_file).mtimeMs - } - let now = Date.now() - let ten_minutes_ago = now - 600000 - - let command = "ipfs-cluster-ctl pin ls > /tmp/build/ipfs_pinset.txt" - let results - try{ - if(pinset_file_mtime < ten_minutes_ago){ - results = child_process.execSync(command) - } - results = fs.readFileSync("/tmp/build/ipfs_pinset.txt") - //let command_2 = "rm /tmp/build/ipfs_pinset.txt" - //let results_2 = child_process.execSync(command_2) - } - catch(error){ - console.log("command failed") - console.log(command) - console.log(error) - } - finally{ - results = results.toString().split("\n") - let results_matrix = [] - let results_dict = {} - for(var i = 0; i < results.length; i++){ - results_matrix.push(results[i].split(" | ")) - } - for(var i = 0; i < results_matrix.length; i++){ - let this_line = results_matrix[i] - if(this_line.length > 8){ - let ipfs_url = this_line[0].replace(" ","") - let unknown_value = this_line[1] - let pin = this_line[2].replace(" ","") - let repl_factor = parseFloat(this_line[3].split(":")[1].replace(" ","").replace(" ","")) - let allocations = this_line[4].split(":")[1].replace(" ","").replace(" ","") - let recursive = this_line[5].includes("Recursive") - let metadata = this_line[6].split(":")[1].replace(" ","").replace(" ","") - let expiration = this_line[7].split(":")[1].replace(" ","").replace(" ","") - let added = this_line[8].split(":")[1].replace(" ","")+ ":" + this_line[8].split(":")[2 ].replace(" ","") - - results_dict[ipfs_url] = { - "unknown_value": unknown_value, - "repl_factor": repl_factor, - "allocations": allocations, - "recursive": recursive, - "metadata": metadata, - "expiration": expiration, - "added": added - } - } - } - - return results_dict - } - -} - diff --git a/huggingface_scraper/ipfs_kit.js b/huggingface_scraper/ipfs_kit.js deleted file mode 100644 index 4fd19fc..0000000 --- a/huggingface_scraper/ipfs_kit.js +++ /dev/null @@ -1,700 +0,0 @@ -import path from 'path'; -import * as install_ipfs from './ipfs_kit_lib/install_ipfs.js'; -import * as ipfs from './ipfs_kit_lib/ipfs.js'; -import * as IpfsClusterCtl from './ipfs_kit_lib/ipfs_cluster_ctl.js'; -import * as IpfsClusterService from './ipfs_kit_lib/ipfs_cluster_service.js'; -import * as IpfsClusterFollow from './ipfs_kit_lib/ipfs_cluster_follow.js'; -import * as ipget from './ipfs_kit_lib/ipget.js'; -import fs from 'fs'; -import util from 'util'; -import { promisify } from 'util'; -import { promises as fsPromises } from 'fs'; -import os from 'os'; -import { exec, execSync } from 'child_process'; - - -export class IpfsKit { - constructor(resources, meta) { - this.role = null; - this.ipfsGetConfig = this.ipfsGetConfig; - this.ipfsSetConfig = this.ipfsSetConfig; - this.ipfsGetConfigValue = this.ipfsGetConfigValue; - this.ipfsSetConfigValue = this.ipfsSetConfigValue; - this.testInstall = this.testInstall; - this.ipfsGet = this.ipgetDownloadObject; - if (meta !== null) { - if ('config' in meta && meta['config'] !== null) { - this.config = meta['config']; - } - if ('role' in meta && meta['role'] !== null) { - this.role = meta['role']; - if (!['master', 'worker', 'leecher'].includes(this.role)) { - this.role = 'leecher'; - } - } - if ('cluster_name' in meta && meta['cluster_name'] !== null) { - this.clusterName = meta['cluster_name']; - } - if ('ipfs_path' in meta && meta['ipfs_path'] !== null) { - this.ipfsPath = meta['ipfs_path']; - } - if (['leecher', 'worker', 'master'].includes(this.role)) { - this.ipfs = new ipfs.ipfs(resources, meta); - this.ipget = new ipget.ipget(resources, meta); - } - if (this.role === 'worker') { - this.ipfsClusterFollow = new IpfsClusterFollow.IPFSClusterFollow(resources, meta); - } - if (this.role === 'master') { - this.ipfsClusterCtl = new IpfsClusterCtl.IPFSClusterCtl(resources, meta); - this.ipfsClusterService = new IpfsClusterService.IpfsClusterService(resources, meta); - } - } - else{ - this.role = 'leecher'; - this.cluster_name = null; - } - let installIpfs = new install_ipfs.InstallIPFS(resources, meta); - this.installIpfs = installIpfs; - - } - - call(method, kwargs = {}) { - switch (method) { - case "ipfs_kit_stop": - return this.ipfsKitStop(kwargs); - case "ipfs_kit_start": - return this.ipfsKitStart(kwargs); - case "ipfs_kit_ready": - return this.ipfsKitReady(kwargs); - case "ipfs_get_pinset": - return this.ipfs.ipfsGetPinset(kwargs); - case "ipfs_follow_list": - if (this.role !== "master") { - return this.ipfsClusterCtl.ipfsFollowList(kwargs); - } else { - throw new Error("role is not master"); - } - case "ipfs_follow_ls": - if (this.role !== "master") { - return this.ipfsClusterFollow.ipfsFollowLs(kwargs); - } else { - throw new Error("role is not master"); - } - case "ipfs_follow_info": - if (this.role !== "master") { - return this.ipfsClusterFollow.ipfsFollowInfo(kwargs); - } else { - throw new Error("role is not master"); - } - case "ipfs_cluster_get_pinset": - return this.ipfsClusterGetPinset(kwargs); - case "ipfs_ls_pinset": - return this.ipfs.ipfsLsPinset(kwargs); - case "ipfs_cluster_ctl_add_pin": - if (this.role === "master") { - return this.ipfsClusterCtl.ipfsClusterCtlAddPin(kwargs); - } else { - throw new Error("role is not master"); - } - case "ipfs_cluster_ctl_rm_pin": - if (this.role === "master") { - return this.ipfsClusterCtl.ipfsClusterCtlRmPin(kwargs); - } else { - throw new Error("role is not master"); - } - case "ipfs_add_pin": - return this.ipfs.ipfsAddPin(kwargs); - case "ipts_ls_pin": - return this.ipfs.ipfsLsPin(kwargs); - case "ipfs_remove_pin": - return this.ipfs.ipfsRemovePin(kwargs); - case "ipfs_get": - return this.ipfs.ipfsGet(kwargs); - case "ipget_download_object": - this.method = 'download_object'; - return this.ipget.ipgetDownloadObject(kwargs); - case "ipfs_upload_object": - this.method = 'ipfs_upload_object'; - return this.ipfs.ipfsUploadObject(kwargs); - case "loadCollection": - return this.loadCollection(kwargs); - default: - return; - } - } - - ipfsKitReady(kwargs = {}) { - let cluster_name; - if (kwargs.cluster_name) { - cluster_name = kwargs.cluster_name; - } else if (this.clusterName !== null) { - cluster_name = this.clusterName; - } else if (this.role !== "leecher") { - throw new Error("cluster_name is not defined"); - } - - let ready = false; - let ipfs_ready = false; - let ipfs_cluster_ready = false; - - const command1 = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - const execute1 = execSync(command1, (error, stdout, stderr) => { - if (error) { - console.log("command failed " + error); - reject(error); - } else { - resolve(stdout.trim()); - } - }).toString().trim(); - - if (parseInt(execute1) > 0) { - ipfs_ready = true; - } - - if (this.role === "master") { - return this.ipfsClusterService.ipfsClusterServiceReady(); - } - else if (this.role === "worker") { - const data_ipfs_follow = this.ipfsClusterFollow.ipfsFollowInfo(); - if (data_ipfs_follow.cluster_peer_online && data_ipfs_follow.ipfs_peer_online) { - if (data_ipfs_follow.cluster_name === cluster_name) { - if (data_ipfs_follow.cluster_peer_online === 'true' && data_ipfs_follow.ipfs_peer_online === 'true') { - ipfs_cluster_ready = true; - } - } - } - } - - if (this.role === "leecher" && ipfs_ready) { - ready = true; - } else if (this.role !== "leecher" && ipfs_ready && ipfs_cluster_ready) { - this.ipfsFollowInfo = data_ipfs_follow; - ready = true; - } else { - ready = { - "ipfs": ipfs_ready, - "ipfs_cluster": ipfs_cluster_ready - }; - } - - return ready; - } - - - async loadCollection(cid, kwargs = {}) { - if (!cid) { - throw new Error("collection is None"); - } - - let dstPath; - if (kwargs.path) { - dstPath = kwargs.path; - } else { - dstPath = this.ipfsPath; - if (!fs.existsSync(dstPath)) { - await mkdir(dstPath); - } - dstPath = path.join(dstPath, "pins"); - if (!fs.existsSync(dstPath)) { - await mkdir(dstPath); - } - dstPath = path.join(dstPath, cid); - } - - let ipget; - try { - ipget = await this.ipget.ipgetDownloadObject({cid: cid, path: dstPath}); - } catch (e) { - ipget = e.toString(); - } - - let collection = await readFile(dstPath, 'utf8'); - - try { - let collectionJson = JSON.parse(collection); - collection = collectionJson; - } catch (e) { - collectionJson = e; - } - - return collection; - } - - - async ipfsAddPin(pin, kwargs = {}) { - let dstPath; - if (kwargs.path) { - dstPath = kwargs.path; - } else { - dstPath = this.ipfsPath; - if (!fs.existsSync(dstPath)) { - await mkdir(dstPath); - } - dstPath = path.join(dstPath, "pins"); - if (!fs.existsSync(dstPath)) { - await mkdir(dstPath); - } - dstPath = path.join(dstPath, pin); - } - - let ipget; - try { - ipget = await this.ipget.ipgetDownloadObject({cid: pin, path: dstPath}); - } catch (e) { - ipget = e.toString(); - } - - let result1 = null; - let result2 = null; - if (this.role === "master") { - result1 = await this.ipfsClusterCtl.ipfsClusterCtlAddPin(dstPath, kwargs); - result2 = await this.ipfs.ipfsAddPin(pin, kwargs); - } else if (this.role === "worker" || this.role === "leecher") { - result2 = await this.ipfs.ipfsAddPin(pin, kwargs); - } - - let results = { - "ipfs_cluster_ctl_add_pin": result1, - "ipfs_add_pin": result2 - }; - - return results; - } - - - async ipfsAddPath(path, kwargs = {}) { - let result1 = null; - let result2 = null; - if (this.role === "master") { - result2 = await this.ipfs.ipfsAddPath(path, kwargs); - result1 = await this.ipfsClusterCtl.ipfsClusterCtlAddPath(path, kwargs); - } else if (this.role === "worker" || this.role === "leecher") { - result2 = await this.ipfs.ipfsAddPath(path, kwargs); - } - - let results = { - "ipfs_cluster_ctl_add_path": result1, - "ipfs_add_path": result2 - }; - - return results; - } - - async ipfsLsPath(path, kwargs = {}) { - let result1 = await this.ipfs.ipfsLsPath(path, kwargs); - result1 = result1.filter(item => item !== ""); - - let results = { - "ipfs_ls_path": result1 - }; - - return results; - } - - async nameResolve(kwargs = {}) { - let result1 = await this.ipfs.ipfsNameResolve(kwargs); - let results = { - "ipfs_name_resolve": result1 - }; - return results; - } - - async namePublish(path, kwargs = {}) { - let result1 = await this.ipfs.ipfsNamePublish(path, kwargs); - let results = { - "ipfs_name_publish": result1 - }; - return results; - } - - async ipfsRemovePath(path, kwargs = {}) { - let ipfsClusterPath = null; - let ipfsPins = null; - let ipfsClusterPins = null; - let result1 = null; - let result2 = null; - if (this.role === "master") { - result1 = await this.ipfsClusterCtl.ipfsClusterCtlRemovePath(path, kwargs); - result2 = await this.ipfs.ipfsRemovePath(path, kwargs); - } else if (this.role === "worker" || this.role === "leecher") { - result2 = await this.ipfs.ipfsRemovePath(path, kwargs); - } - - let results = { - "ipfs_cluster_ctl_rm_path": result1, - "ipfs_rm_path": result2 - }; - return results; - } - - async ipfsRemovePin(pin, kwargs = {}) { - let result1 = null; - let result2 = null; - if (this.role === "master") { - result1 = await this.ipfsClusterCtl.ipfsClusterCtlRemovePin(pin, kwargs); - result2 = await this.ipfs.ipfsRemovePin(pin, kwargs); - } else if (this.role === "worker" || this.role === "leecher") { - result2 = await this.ipfs.ipfsRemovePin(pin, kwargs); - } - - let results = { - "ipfs_cluster_ctl_rm_pin": result1, - "ipfs_rm_pin": result2 - }; - return results; - } - - async testInstall(kwargs = {}) { - if (this.role === "master") { - return { - "ipfs_cluster_service": await this.installIpfs.ipfsClusterServiceTestInstall(), - "ipfs_cluster_ctl": await this.installIpfs.ipfsClusterCtlTestInstall(), - "ipfs": await this.installIpfs.ipfsTestInstall() - }; - } else if (this.role === "worker") { - return { - "ipfs_cluster_follow": await this.installIpfs.ipfsClusterFollowTestInstall(), - "ipfs": await this.installIpfs.ipfsTestInstall() - }; - } else if (this.role === "leecher") { - return await this.installIpfs.ipfsTestInstall(); - } else { - throw new Error("role is not master, worker, or leecher"); - } - } - - async ipfsGetPinset(kwargs = {}) { - let ipfsPinset = await this.ipfs.ipfsGetPinset(kwargs); - - let ipfsCluster = null; - if (this.role === "master") { - ipfsCluster = await this.ipfsClusterCtl.ipfsClusterGetPinset(kwargs); - } else if (this.role === "worker") { - ipfsCluster = await this.ipfsClusterFollow.ipfsFollowList(kwargs); - } else if (this.role === "leecher") { - // do nothing - } - - let results = { - "ipfs_cluster": ipfsCluster, - "ipfs": ipfsPinset - }; - return results; - } - - async ipfsKitStop(kwargs = {}) { - let ipfsClusterService = null; - let ipfsClusterFollow = null; - let ipfs = null; - - if (this.role === "master") { - try { - ipfsClusterService = await this.ipfsClusterService.ipfs_cluster_service_stop(); - } catch (e) { - console.log(e) - ipfsClusterService = e.toString(); - } - try { - ipfs = await this.ipfs.daemon_stop(); - } catch (e) { - console.log(e) - ipfs = e.toString(); - } - } - if (this.role === "worker") { - try { - ipfsClusterFollow = await this.ipfsClusterFollow.ipfsFollowStop(); - } catch (e) { - console.log(e); - ipfsClusterFollow = e.toString(); - } - try { - ipfs = await this.ipfs.daemon_stop(); - } catch (e) { - console.log(e); - ipfs = e.toString(); - } - } - if (this.role === "leecher") { - try { - ipfs = await this.ipfs.daemon_stop(); - } catch (e) { - console.log(e); - ipfs = e.toString(); - } - } - - let results = { - "ipfs_cluster_service": ipfsClusterService, - "ipfs_cluster_follow": ipfsClusterFollow, - "ipfs": ipfs - }; - return results; - } - - async ipfsKitStart(kwargs = {}) { - let ipfsClusterService = null; - let ipfsClusterFollow = null; - let ipfs = null; - - if (this.role === "master") { - try { - ipfs = await this.ipfs.daemon_start(); - } catch (e) { - console.log(e) - ipfs = e.toString(); - } - try { - ipfsClusterService = await this.ipfsClusterService.ipfs_cluster_service_start(); - } catch (e) { - ipfsClusterService = e.toString(); - } - } - - if (this.role === "worker") { - try { - ipfs = this.ipfs.daemon_start(); - } catch (e) { - console.log(e); - ipfs = e.toString(); - } - try { - ipfsClusterFollow = await this.ipfsClusterFollow.ipfsFollowStart(); - } catch (e) { - console.log(e); - ipfsClusterFollow = e.toString(); - } - } - - if (this.role === "leecher") { - try { - ipfs = await this.ipfs.daemon_start(); - } catch (e) { - console.log(e); - ipfs = e.toString(); - } - } - - let results = { - "ipfs_cluster_service": ipfsClusterService, - "ipfs_cluster_follow": ipfsClusterFollow, - "ipfs": ipfs - }; - return results; - } - - async ipfsGetConfig(kwargs = {}) { - const command = "ipfs config show"; - let results = ""; - - try { - const { stdout } = await exec(command); - results = JSON.parse(stdout); - } catch (error) { - console.log("command failed"); - console.log(command); - console.log(error); - } finally { - this.ipfsConfig = results; - } - - return results; - } - - async ipfsSetConfig(newConfig, kwargs = {}) { - const filename = path.join(os.tmpdir(), `${Date.now()}.json`); - fs.writeFileSync(filename, JSON.stringify(newConfig)); - - const command = "ipfs config replace " + filename; - let results = ""; - - try { - const { stdout } = await exec(command); - results = JSON.parse(stdout); - } catch (error) { - console.log("command failed"); - console.log(command); - console.log(error); - } finally { - this.ipfsConfig = results; - } - - return results; - } - - async ipfsGetConfigValue(key, kwargs = {}) { - let command = null; - let results = null; - - try { - command = await this.ipfsExecute({ - "command": "config", - "key": key - }); - results = JSON.parse(command); - } catch (error) { - console.log("command failed"); - console.log(command); - console.log(error); - } - - const query = "ipfs config " + key; - - try { - const { stdout } = await exec(query); - results = JSON.parse(stdout); - } catch (error) { - console.log("command failed"); - console.log(query); - console.log(error); - throw new Error("command failed"); - } - - return results; - } - - async ipfsSetConfigValue(key, value, kwargs = {}) { - let command = null; - let results = null; - - try { - command = await this.ipfsExecute({ - "command": "config", - "key": key, - "value": value - }); - results = JSON.parse(command); - } catch (error) { - console.log("command failed"); - console.log(command); - console.log(error); - } - - const query = "ipfs config " + key + " " + value; - - try { - const { stdout } = await exec(query); - results = JSON.parse(stdout); - } catch (error) { - console.log("command failed"); - console.log(query); - console.log(error); - throw new Error("command failed"); - } - - return results; - } - - - - checkCollection(collection) { - let status = {}; - let collectionKeys = Object.keys(collection); - let pinsetKeys = Object.keys(this.pinset); - let orphanModels = []; - let orphanPins = []; - let activePins = []; - let activeModels = []; - - for (let thisModel of collectionKeys) { - if (thisModel !== "cache") { - let thisManifest = collection[thisModel]; - let thisId = thisManifest["id"]; - let thisCache = thisManifest["cache"]; - let thisIpfsCache = thisCache["ipfs"]; - let thisIpfsCacheKeys = Object.keys(thisIpfsCache); - let foundAll = true; - - for (let thisCacheBasename of thisIpfsCacheKeys) { - let thisCacheItem = thisIpfsCache[thisCacheBasename]; - let thisCacheItemPath = thisCacheItem["path"]; - let thisCacheItemUrl = thisCacheItem["url"]; - - if (pinsetKeys.includes(thisCacheItemPath)) { - activePins.push(thisCacheItemPath); - } else { - foundAll = false; - } - } - - if (foundAll) { - activeModels.push(thisModel); - } else { - orphanModels.push(thisModel); - } - } - } - - for (let thisPin of pinsetKeys) { - if (!activePins.includes(thisPin)) { - orphanPins.push(thisPin); - } - } - - status["orphanModels"] = orphanModels; - status["orphanPins"] = orphanPins; - status["activePins"] = activePins; - status["activeModels"] = activeModels; - - return status; - } - - - - - async ipfsUploadObject(kwargs) { - return this.uploadObject(kwargs['file']); - } - - async ipgetDownloadObject(kwargs) { - return this.ipget.ipgetDownloadObject(kwargs); - } - - async updateCollectionIpfs(collection, collectionPath) { - let thisCollectionIpfs = null; - let thisCollectionPath = collectionPath; - - const command1 = "ipfs add -r " + thisCollectionPath; - try { - const { stdout: results1 } = await exec(command1); - const resultsMatrix = results1.split("\n").map(line => line.split(" ")); - - if (resultsMatrix.length === 2) { - thisCollectionIpfs = resultsMatrix[resultsMatrix.length - 2][1]; - } - - const metadata = ["path=/collection.json"]; - let argstring = ""; - for (let i = 0; i < metadata.length; i++) { - argstring += " --metadata " + metadata[i]; - } - - const command2 = "ipfs-cluster-ctl pin add " + thisCollectionIpfs + argstring; - const { stdout: results2 } = await exec(command2); - - if ("cache" in collection) { - collection["cache"]["ipfs"] = thisCollectionIpfs; - } else { - collection["cache"] = {}; - collection["cache"]["ipfs"] = thisCollectionIpfs; - } - return thisCollectionIpfs; - - } catch (error) { - console.log("ipfs add failed"); - console.log(command1); - console.log(error); - } - } - -} - - -async function main() { - const ipfs = new IpfsKit(null, null); - const results = await ipfs.testInstall(); - console.log(results); -} - -main(); \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/cluster.lock b/huggingface_scraper/ipfs_kit_lib/cluster.lock deleted file mode 100755 index e69de29..0000000 diff --git a/huggingface_scraper/ipfs_kit_lib/config b/huggingface_scraper/ipfs_kit_lib/config deleted file mode 100755 index ef35452..0000000 --- a/huggingface_scraper/ipfs_kit_lib/config +++ /dev/null @@ -1,147 +0,0 @@ -{ - "API": { - "HTTPHeaders": {} - }, - "Addresses": { - "API": "/ip4/127.0.0.1/tcp/5001", - "Announce": [], - "AppendAnnounce": [], - "Gateway": "/ip4/127.0.0.1/tcp/8080", - "NoAnnounce": [], - "Swarm": [ - "/ip4/0.0.0.0/tcp/4001", - "/ip6/::/tcp/4001", - "/ip4/0.0.0.0/udp/4001/quic-v1", - "/ip4/0.0.0.0/udp/4001/quic-v1/webtransport", - "/ip6/::/udp/4001/quic-v1", - "/ip6/::/udp/4001/quic-v1/webtransport" - ] - }, - "AutoNAT": {}, - "Bootstrap": [ - "/ip4/104.131.131.82/tcp/4001/p2p/QmaCpDMGvV2BGHeYERUEnRQAwe3N8SzbUtfsmvsqQLuvuJ", - "/ip4/104.131.131.82/udp/4001/quic-v1/p2p/QmaCpDMGvV2BGHeYERUEnRQAwe3N8SzbUtfsmvsqQLuvuJ", - "/dnsaddr/bootstrap.libp2p.io/p2p/QmNnooDu7bfjPFoTZYxMNLWUQJyrVwtbZg5gBMjTezGAJN", - "/dnsaddr/bootstrap.libp2p.io/p2p/QmQCU2EcMqAqQPR2i9bChDtGNJchTbq5TbXJJ16u19uLTa", - "/dnsaddr/bootstrap.libp2p.io/p2p/QmbLHAnMoJPWSCR5Zhtx6BHJX9KiKNN6tpvbUcqanj75Nb", - "/dnsaddr/bootstrap.libp2p.io/p2p/QmcZf59bWwK5XFi76CZX8cbJ4BhTzzA3gU1ZjYZcYW3dwt" - ], - "DNS": { - "Resolvers": {} - }, - "Datastore": { - "BloomFilterSize": 0, - "GCPeriod": "1h", - "HashOnRead": false, - "Spec": { - "mounts": [ - { - "child": { - "path": "blocks", - "shardFunc": "/repo/flatfs/shard/v1/next-to-last/2", - "sync": true, - "type": "flatfs" - }, - "mountpoint": "/blocks", - "prefix": "flatfs.datastore", - "type": "measure" - }, - { - "child": { - "compression": "none", - "path": "datastore", - "type": "levelds" - }, - "mountpoint": "/", - "prefix": "leveldb.datastore", - "type": "measure" - } - ], - "type": "mount" - }, - "StorageGCWatermark": 90, - "StorageMax": "10GB" - }, - "Discovery": { - "MDNS": { - "Enabled": true - } - }, - "Experimental": { - "FilestoreEnabled": false, - "GraphsyncEnabled": false, - "Libp2pStreamMounting": false, - "OptimisticProvide": false, - "OptimisticProvideJobsPoolSize": 0, - "P2pHttpProxy": false, - "StrategicProviding": false, - "UrlstoreEnabled": false - }, - "Gateway": { - "APICommands": [], - "DeserializedResponses": null, - "DisableHTMLErrors": null, - "ExposeRoutingAPI": null, - "HTTPHeaders": {}, - "NoDNSLink": false, - "NoFetch": false, - "PathPrefixes": [], - "PublicGateways": null, - "RootRedirect": "" - }, - "Identity": { - "PeerID": "12D3KooWMMVhBAXPGt5QsgYyNNfpoobV6ZfMwJV4j6qsqQkGctis", - "PrivKey": "CAESQKjZx7ETZ/wbHmoBhAexrAg3m0rsC5DPzH07UgFmldotq2oRx/UN4JXlIBz17kWUaIw2+EbddLMGV/SJTlO9brA=" - }, - "Internal": {}, - "Ipns": { - "RecordLifetime": "", - "RepublishPeriod": "", - "ResolveCacheSize": 128 - }, - "Migration": { - "DownloadSources": [], - "Keep": "" - }, - "Mounts": { - "FuseAllowOther": false, - "IPFS": "/ipfs", - "IPNS": "/ipns" - }, - "Peering": { - "Peers": null - }, - "Pinning": { - "RemoteServices": {} - }, - "Plugins": { - "Plugins": null - }, - "Provider": { - "Strategy": "" - }, - "Pubsub": { - "DisableSigning": false, - "Router": "" - }, - "Reprovider": {}, - "Routing": { - "AcceleratedDHTClient": false, - "Methods": null, - "Routers": null - }, - "Swarm": { - "AddrFilters": null, - "ConnMgr": {}, - "DisableBandwidthMetrics": false, - "DisableNatPortMap": false, - "RelayClient": {}, - "RelayService": {}, - "ResourceMgr": {}, - "Transports": { - "Multiplexers": {}, - "Network": {}, - "Security": {} - } - } -} diff --git a/huggingface_scraper/ipfs_kit_lib/datastore_spec b/huggingface_scraper/ipfs_kit_lib/datastore_spec deleted file mode 100644 index 7bf9626..0000000 --- a/huggingface_scraper/ipfs_kit_lib/datastore_spec +++ /dev/null @@ -1 +0,0 @@ -{"mounts":[{"mountpoint":"/blocks","path":"blocks","shardFunc":"/repo/flatfs/shard/v1/next-to-last/2","type":"flatfs"},{"mountpoint":"/","path":"datastore","type":"levelds"}],"type":"mount"} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/identity.json b/huggingface_scraper/ipfs_kit_lib/identity.json deleted file mode 100755 index 8488599..0000000 --- a/huggingface_scraper/ipfs_kit_lib/identity.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "id": "12D3KooWECihD8h6TooNoqJAVFDsP5MgUiVcy7XYRRmkFM3yyYME", - "private_key": "CAESQCdeFjj9HSI9+LDwsHA84hhfKj7GoLVWTL3yD6FWUqCXQSi7xqLG+6V+9ROfWtlfs3wR8UsuifuWSVoSHkfaPnE=" -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/install_ipfs.js b/huggingface_scraper/ipfs_kit_lib/install_ipfs.js deleted file mode 100644 index 7a58796..0000000 --- a/huggingface_scraper/ipfs_kit_lib/install_ipfs.js +++ /dev/null @@ -1,1184 +0,0 @@ - -import { execSync, exec } from 'child_process'; -import fs from 'fs'; -import os from 'os'; -import path from 'path'; -import * as test_fio from './test_fio.js'; -import util from 'util'; -import process from 'process'; -import { ChildProcess } from 'child_process'; -import { spawnSync } from 'child_process'; -import { spawn } from 'child_process'; -import { run } from 'shutil'; -import { randomUUID } from 'crypto'; -import crypto from 'crypto'; - -// TODO: This fails if aria2c is not installed but doesn't fail gracefully and in a way that diagnoses the problem to the user -// Either add a check for aria2c and report to user or add aria2c to the install that is ran before hand - -export class InstallIPFS { - constructor(resources, meta = null) { - this.resources = resources; - this.meta = meta; - this.thisDir = path.dirname(new URL(import.meta.url).pathname); - this.ipfsTestInstall = this.ipfsTestInstall.bind(this); - this.env = process.env; - this.ipfs_dist_tar = "https://dist.ipfs.tech/kubo/v0.26.0/kubo_v0.26.0_linux-amd64.tar.gz"; - this.ipfs_follow_dist_tar = "https://dist.ipfs.tech/ipfs-cluster-follow/v1.0.8/ipfs-cluster-follow_v1.0.8_linux-amd64.tar.gz"; - this.ipfs_cluster_dist_tar = "https://dist.ipfs.tech/ipfs-cluster-ctl/v1.0.8/ipfs-cluster-ctl_v1.0.8_linux-amd64.tar.gz"; - this.ipfs_cluster_service_dist_tar = "https://dist.ipfs.tech/ipfs-cluster-service/v1.0.8/ipfs-cluster-service_v1.0.8_linux-amd64.tar.gz"; - if (meta !== null) { - this.config = meta.config ? meta.config : null; - this.secret = meta.secret ? meta.secret : null; - - if (this.secret == null) { - // generate 64 character hex string - this.secret = crypto.randomBytes(32).toString('hex'); - } - - if (meta.role) { - this.role = meta.role; - if (!['master', 'worker', 'leecher'].includes(this.role)) { - throw new Error("role is not either master, worker, leecher"); - } - } else { - this.role = "leecher"; - } - - if (meta.ipfsPath) { - this.ipfsPath = meta.ipfsPath; - if (!fs.existsSync(this.ipfsPath)) { - fs.mkdirSync(this.ipfsPath, { recursive: true }); - } - let testDisk = new test_fio.TestFio(); - this.diskName = testDisk.disk_device_name_from_location(this.ipfsPath); - this.diskStats = { - disk_size: testDisk.disk_device_total_capacity(this.diskName), - disk_used: testDisk.disk_device_used_capacity(this.diskName), - disk_avail: testDisk.disk_device_avail_capacity(this.diskName), - disk_name: this.diskName - }; - } else { - if (os.userInfo().username === "root") { - this.ipfsPath = "/root/ipfs"; - } else { - this.ipfsPath = path.join(path.join(os.homedir(),".cache"), "ipfs"); - } - let testDisk = new test_fio.TestFio(); - this.diskName = testDisk.disk_device_name_from_location(this.ipfsPath); - this.diskStats = { - disk_size: testDisk.disk_device_total_capacity(this.diskName), - disk_used: testDisk.disk_device_used_capacity(this.diskName), - disk_avail: testDisk.disk_device_avail_capacity(this.diskName), - disk_name: this.diskName - }; - } - - this.clusterName = meta.clusterName ? meta.clusterName : null; - this.clusterLocation = meta.clusterLocation ? meta.clusterLocation : "/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv"; - - if (['leecher', 'worker', 'master'].includes(this.role) && this.ipfsPath) { - // Bind the methods for installing and configuring IPFS - this.ipfs_install_command = this.installIPFSDaemon.bind(this); - this.ipfs_config_command = this.configIPFS.bind(this); - } - - if (this.role === "worker" && this.cluster_name && this.ipfsPath) { - // Bind methods for worker role - this.clusterInstall = this.installIPFSClusterFollow.bind(this); - this.clusterConfig = this.configIPFSClusterFollow.bind(this); - } - - if (this.role === "master" && this.cluster_name && this.ipfsPath) { - // Bind methods for master role - this.clusterCtlInstall = this.installIPFSClusterCtl.bind(this); - this.clusterCtlConfig = this.configIPFSClusterCtl.bind(this); - this.clusterService_install = this.installIPFSClusterService.bind(this); - this.clusterServiceConfig = this.configIPFSClusterService.bind(this); - } - } - } - - async installIPFSDaemon(options = {}) { - let detect = ''; - try { - detect = execSync("which ipfs").toString().trim(); - if (detect.length > 0) { - return true; - } - } catch (e) { - console.error(e); - detect = ''; - } - let thisDir = this.thisDir || path.dirname(new URL(import.meta.url).pathname); - - if (!detect) { - try { - const tmpDir = fs.mkdtempSync(path.join(os.tmpdir(), 'ipfs-')); - const tarFile = path.join(tmpDir, "kubo.tar.gz"); - execSync(`wget ${this.ipfs_dist_tar} -O ${tarFile}`); - execSync(`tar -xvzf ${tarFile} -C ${tmpDir}`); - execSync(`cd ${tmpDir}/kubo && sudo bash install.sh`); - const results = execSync("ipfs --version").toString().trim(); - const serviceConfig = fs.readFileSync(path.join(thisDir, 'ipfs.service')).toString(); - fs.writeFileSync("/etc/systemd/system/ipfs.service", serviceConfig); - execSync("systemctl enable ipfs"); - return results.includes("ipfs"); - } catch (e) { - console.error(e); - return false; - } - } - } - - installIPFSClusterFollow(options = {}) { - // Check if ipfs-cluster-follow is already installed - let followCmdExists = execSync('which ipfs-cluster-follow') - if (followCmdExists.length > 0) { - console.log('ipfs-cluster-follow is already installed.'); - return true; - } else { - console.log('ipfs-cluster-follow is not installed, proceeding with installation.'); - // Downloading tarball - const url = this.ipfs_follow_dist_tar; - const tmpDir = fs.mkdtempSync(path.join(os.tmpdir(), 'ipfs-cluster-follow-')); - const tarPath = path.join(tmpDir, url.split('/')[-1]); - let thisDir = this.thisDir || path.dirname(new URL(import.meta.url).pathname); - - const file = fs.createWriteStream(tarPath); - https.get(url, function(response) { - response.pipe(file); - file.on('finish', () => { - file.close(); - console.log('Download completed.'); - - // Extracting tarball - tar.x({ - file: tarPath, - C: tmpDir, - }).then(() => { - console.log('Extraction completed.'); - const binPath = path.join(tmpDir, 'ipfs-cluster-follow', 'ipfs-cluster-follow'); - execSync(`sudo mv ${binPath} /usr/local/bin/ipfs-cluster-follow`); - try { - // Verify installation - const version = execSync('ipfs-cluster-follow --version').toString().trim(); - console.log(`Installed ipfs-cluster-follow version: ${version}`); - if (os.userInfo().username == "root") { - let serviceConfig = fs.readFileSync(path.join(thisDir, 'ipfs_clusterFollow.service')).toString(); - fs.writeFileSync('/etc/systemd/system/ipfs-cluster-follow.service', serviceConfig); - execSync('systemctl enable ipfs-cluster-follow'); - console.log('ipfs-cluster-follow service enabled.'); - } - else{ - console.log('Please run as root user to enable systemd service'); - } - } catch (e) { - console.error('Error verifying ipfs-cluster-follow installation:', e); - return false; - } - }).catch((err) => { - console.error('Error extracting file:', err); - }); - }); - }).on('error', (err) => { - // Handle errors - console.error('Error downloading file:', err); - fs.unlink(dest); - }); - } - return results - } - - async installIPFSClusterCtl(options = {}) { - try { - const detect = execSync("which ipfs-cluster-ctl").toString().trim(); - if (detect) { - console.log('ipfs-cluster-ctl is already installed.'); - return true; - } - } catch (e) { - console.error(e.message); - } - - const tmpDir = fs.mkdtempSync(path.join(os.tmpdir(), 'ipfs-cluster-ctl-')); - const tarPath = path.join(tmpDir, 'ipfs-cluster-ctl.tar.gz'); - const url = "https://dist.ipfs.tech/ipfs-cluster-ctl/v1.0.8/ipfs-cluster-ctl_v1.0.8_linux-amd64.tar.gz"; - - // Download and extract the tarball - const file = fs.createWriteStream(tarPath); - https.get(url, (response) => { - response.pipe(file); - file.on('finish', () => { - file.close(); - console.log('Download completed.'); - tar.x({ - file: tarPath, - C: tmpDir, - }).then(() => { - console.log('Extraction completed.'); - const binPath = path.join(tmpDir, 'ipfs-cluster-ctl', 'ipfs-cluster-ctl'); - execSync(`sudo mv ${binPath} /usr/local/bin/ipfs-cluster-ctl`); - try { - // Verify installation - const version = execSync('ipfs-cluster-ctl --version').toString().trim(); - console.log(`Installed ipfs-cluster-ctl version: ${version}`); - return true; - } catch (e) { - console.error('Error verifying ipfs-cluster-ctl installation:', e); - return false; - } - }).catch((err) => { - console.error('Error extracting file:', err); - }); - }); - }).on('error', (err) => { - console.error('Error downloading file:', err); - fs.unlink(tarPath, (err) => { - if (err) console.error(`Error removing temporary tarball: ${err}`); - }); - }); - } - - async installIPFSClusterService(options = {}) { - try { - const detect = execSync("which ipfs-cluster-service").toString().trim(); - if (detect) { - console.log('ipfs-cluster-service is already installed.'); - return true; - } - } catch (e) { - console.error(e.message); - } - - const tmpDir = fs.mkdtempSync(path.join(os.tmpdir(), 'ipfs-cluster-service-')); - const tarPath = path.join(tmpDir, 'ipfs-cluster-service.tar.gz'); - const url = "https://dist.ipfs.tech/ipfs-cluster-service/v1.0.8/ipfs-cluster-service_v1.0.8_linux-amd64.tar.gz"; - let thisDir = this.thisDir || path.dirname(new URL(import.meta.url).pathname); - - // Download and extract the tarball - const file = fs.createWriteStream(tarPath); - https.get(url, (response) => { - response.pipe(file); - file.on('finish', () => { - file.close(); - console.log('Download completed.'); - tar.x({ - file: tarPath, - C: tmpDir, - }).then(() => { - console.log('Extraction completed.'); - const binPath = path.join(tmpDir, 'ipfs-cluster-service', 'ipfs-cluster-service'); - execSync(`sudo mv ${binPath} /usr/local/bin/ipfs-cluster-service`); - try { - // Verify installation - const version = execSync('ipfs-cluster-service --version').toString().trim(); - console.log(`Installed ipfs-cluster-service version: ${version}`); - // if root user, write and enable systemd service - if (os.userInfo().username == "root") { - let serviceConfig = fs.readFileSync(path.join(thisDir, 'ipfs_clusterFollow.service')).toString(); - fs.writeFileSync('/etc/systemd/system/ipfs-cluster-follow.service', serviceConfig); - execSync('systemctl enable ipfs-cluster-service'); - console.log('ipfs-cluster-service service enabled.'); - execSync('systemctl daemon-reload'); - console.log('systemctl daemon reloaded.'); - } - else{ - console.log('Please run as root user to enable systemd service'); - } - return true; - } catch (e) { - console.error('Error verifying ipfs-cluster-service installation:', e); - return false; - } - }).catch((err) => { - console.error('Error extracting file:', err); - }); - }); - }).on('error', (err) => { - console.error('Error downloading file:', err); - fs.unlink(tarPath, (err) => { - if (err) console.error(`Error removing temporary tarball: ${err}`); - }); - }); - } - - async installIPGet(options = {}) { - try { - // Check if ipget is already installed - const detect = execSync("which ipget").toString().trim(); - if (detect) { - //console.log('ipget is already installed.'); - return true; - } - } catch (e) { - console.error(e.message); - } - - // Prepare for download and extraction - const tmpDir = fs.mkdtempSync(path.join(os.tmpdir(), 'ipget-')); - const tarPath = path.join(tmpDir, 'ipget.tar.gz'); - const url = "https://dist.ipfs.tech/ipget/v0.10.0/ipget_v0.10.0_linux-amd64.tar.gz"; - - // Download the tarball - https.get(url, (response) => { - const fileStream = fs.createWriteStream(tarPath); - response.pipe(fileStream); - fileStream.on('finish', () => { - fileStream.close(); - console.log('Downloaded ipget tarball.'); - - // Extract the tarball - tar.x({ - file: tarPath, - C: tmpDir, - }).then(() => { - console.log('Extracted ipget.'); - - // Move to bin and install - if (os.userInfo().username == "root") { - const binPath = path.join(tmpDir, 'ipget', 'ipget'); - execSync(`sudo mv ${binPath} /usr/local/bin/ipget`); - const installScriptPath = path.join(tmpDir, 'ipget', 'install.sh'); - execSync(`cd ${tmpDir}/ipget && sudo bash install.sh`); - - // Update system settings - execSync('sudo sysctl -w net.core.rmem_max=2500000'); - execSync('sudo sysctl -w net.core.wmem_max=2500000'); - } - else{ - console.log('Please run as root user to install ipget'); - execSync(`cd ${tmpDir}/ipget && bash install.sh`); - } - - // Verify installation - try { - const version = execSync('ipget --version').toString().trim(); - console.log(`Installed ipget version: ${version}`); - return true; - } catch (verificationError) { - console.error('Error verifying ipget installation:', verificationError); - return false; - } - }).catch((extractionError) => { - console.error('Error extracting ipget:', extractionError); - }); - }); - }).on('error', (downloadError) => { - console.error('Error downloading ipget:', downloadError); - }); - } - - async configIPFSClusterService(options = {}) { - let clusterName = options.clusterName || this.clusterName; - let diskStats = options.diskStats || this.diskStats; - let ipfsPath = options.ipfsPath || this.ipfsPath; - let secret = options.secret || this.secret; - - if (!diskStats) throw new Error("diskStats is None"); - if (!ipfsPath) throw new Error("ipfsPath is None"); - if (!clusterName) throw new Error("clusterName is None"); - if (!secret) throw new Error("secret is None"); - - let thisDir = this.thisDir || path.dirname(new URL(import.meta.url).pathname); - let homeDir = os.homedir(); - let clusterPath = path.join(ipfsPath, clusterName); - let servicePath; - let run_daemon; - let initClusterDaemonResults; - let results = {} - if (os.userInfo().username == "root") { - servicePath = path.join("/root", ".ipfs-cluster"); - } else { - servicePath = path.join(homeDir, ".ipfs-cluster"); - } - if (clusterName && ipfsPath && diskStats && secret) { - try{ - if (os.userInfo().username == "root") { - const command0 = "systemctl enable ipfs-cluster-service"; - const results0 = execSync(command0, { shell: true }); - const initClusterDaemon = `IPFS_PATH=${ipfsPath} ipfs-cluster-service init -f`; - initClusterDaemonResults = execSync(initClusterDaemonResults, { shell: true }).toString(); - } else { - const initClusterDaemon = `IPFS_PATH=${ipfsPath} ipfs-cluster-service init -f`; - initClusterDaemonResults = execSync(command1, { shell: true }).toString(); - } - - results["initClusterDaemonResults"] = initClusterDaemonResults - } - catch(e){ - console.error(e); - throw new Error("Error configuring IPFS Cluster Service"); - } - - try{ - let serviceConfig = fs.readFileSync(path.join(thisDir, 'service.json')).toString(); - let workerID = "worker-" + crypto.randomUUID(); - serviceConfig = serviceConfig.replace('"cluster_name": "ipfs-cluster"', 'cluster_name": "'+clusterName+'"'); - serviceConfig = serviceConfig.replace('"secret": "96d5952479d0a2f9fbf55076e5ee04802f15ae5452b5faafc98e2bd48cf564d3"', '"secret": "'+ secret +'"'); - fs.writeFileSync(path.join(followPath, 'service.json'), serviceConfig); - let peerStore = fs.readFileSync(path.join(thisDir, 'peerstore')).toString(); - fs.writeFileSync(path.join(followPath, 'peerstore'), peerStore); - - let pebbleLink = path.join(servicePath, "pebble"); - let pebbleDir = path.join(clusterPath, "pebble"); - if (clusterPath != followPath) { - if (fs.existsSync(pebbleLink)) { - fs.unlinkSync(pebbleLink); - } - if (!fs.existsSync(pebbleDir)) { - fs.mkdirSync(pebbleDir, { recursive: true }); - } - fs.symlinkSync(pebbleDir, pebbleLink); - } - - if (os.userInfo().username == "root") { - let serviceFile = fs.readFileSync(path.join(thisDir, 'ipfs_cluster.service')).toString(); - fs.writeFileSync("/etc/systemd/system/ipfs-cluster-service.service", new_service) - execSync("systemctl enable ipfs-cluster-service"); - execSync("systemctl daemon-reload"); - } - - } - catch(e){ - console.error(e); - throw new Error("Error configuring IPFS Cluster Service"); - } - - try{ - let run_daemon_results - if (os.userInfo().username == "root") { - let reloadDaemon = "systemctl daemon-reload"; - let reloadDaemonResults = execSync(reloadDaemon); - - // Enable service - let enableDaemon = "systemctl enable ipfs-cluster-service"; - let enableDaemonResults = execSync(enableDaemon); - - // Start daemon - let startDaemon = "systemctl start ipfs-cluster-service"; - let startDaemonResults = execSync(startDaemon); - await new Promise(resolve => setTimeout(resolve, 2000)); - run_daemon = execSync("systemctl status ipfs-cluster-service | grep Active | awk '{print $2}'").toString(); - } - else{ - let run_daemon_cmd = "ipfs-cluster-service daemon"; - run_daemon = exec( - run_daemon_cmd, - (error, stdout, stderr) => { - console.log(stdout); - } - ); - await new Promise(resolve => setTimeout(resolve, 2000)); - run_daemon = run_daemon.stderr.read(); - } - // Check if daemon is running - results["run_daemon"] = run_daemon; - } - catch(e){ - console.log(e); - return e.toString(); - } - } - return results; - } - - async configIPFSClusterCtl(options = {}) { - let results = {}; - let run_daemon_cmd = "ipfs-cluster-ctl status"; - run_daemon = execSync(run_daemon_cmd).toString(); - - findDaemon = "ps -ef | grep ipfs-cluster-service | grep -v grep | wc -l"; - findDaemonResuls = execSync(findDaemon).toString(); - - if (parseInt(findDaemonResuls) == 0) { - console.log("ipfs-cluster-service daemon is not running"); - throw new Error("ipfs-cluster-service daemon is not running"); - } - else{ - let killDaemon = "ps -ef | grep ipfs-cluster-service | grep -v grep | awk '{print $2}' | xargs kill -9"; - let killDaemonResults = execSync(killDaemon); - } - - results["run_daemon"] = run_daemon; - - return results; - } - - async configIPFSClusterFollow(options = {}) { - let clusterName = options.clusterName || this.clusterName; - let diskStats = options.diskStats || this.diskStats; - let ipfsPath = options.ipfsPath || this.ipfsPath; - let secret = options.secret || this.secret; - - if (!diskStats) throw new Error("diskStats is None"); - if (!ipfsPath) throw new Error("ipfsPath is None"); - if (!clusterName) throw new Error("clusterName is None"); - if (!secret) throw new Error("secret is None"); - - let thisDir = this.thisDir || path.dirname(new URL(import.meta.url).pathname); - let homeDir = os.homedir(); - let clusterPath = path.join(ipfsPath, clusterName); - let followPath; - let run_daemon; - let followInitCmdResults; - if (os.userInfo().username == "root") { - followPath = path.join("/root", ".ipfs-cluster-follow", clusterName); - } else { - followPath = path.join(homeDir, ".ipfs-cluster-follow", clusterName); - } - if (clusterName && ipfsPath && diskStats && secret) { - try { - let rm_command = `rm -rf ${followPath}`; - execSync(rm_command); - let followInitCmd = `ipfs-cluster-follow ${clusterName} init ${ipfsPath}`; - followInitCmdResults = execSync(followInitCmd).toString() - if (!fs.existsSync(clusterPath)) { - fs.mkdirSync(clusterPath, { recursive: true }); - } - if (!fs.existsSync(followPath)) { - fs.mkdirSync(followPath, { recursive: true }); - } - let serviceConfig = fs.readFileSync(path.join(thisDir, 'service_follower.json')).toString(); - let workerID = "worker-" + crypto.randomUUID(); - serviceConfig = serviceConfig.replace('"peername": "worker"', `peername": `+workerID+`"`); - serviceConfig = serviceConfig.replace('"cluster_name": "ipfs-cluster"', 'cluster_name": "'+clusterName+'"'); - serviceConfig = serviceConfig.replace('"secret": "96d5952479d0a2f9fbf55076e5ee04802f15ae5452b5faafc98e2bd48cf564d3"', '"secret": "'+ secret +'"'); - fs.writeFileSync(path.join(followPath, 'service.json'), serviceConfig); - let peerStore = fs.readFileSync(path.join(thisDir, 'peerstore')).toString(); - fs.writeFileSync(path.join(followPath, 'peerstore'), peerStore); - - // Link the pebble directory if needed - let pebbleLink = path.join(followPath, "pebble"); - let pebbleDir = path.join(clusterPath, "pebble"); - if (clusterPath != followPath) { - if (fs.existsSync(pebbleLink)) { - fs.unlinkSync(pebbleLink); - } - if (!fs.existsSync(pebbleDir)) { - fs.mkdirSync(pebbleDir, { recursive: true }); - } - fs.symlinkSync(pebbleDir, pebbleLink); - } - - if (os.userInfo().username == "root") { - let serviceFile = fs.readFileSync(path.join(thisDir, 'ipfs_clusterFollow.service')).toString(); - let new_service = serviceFile.replace("ExecStart=/usr/local/bin/ipfs-cluster-follow run","ExecStart=/usr/local/bin/ipfs-cluster-follow "+ clusterName + " run") - new_service = new_service.replace("Description=IPFS Cluster Follow","Description=IPFS Cluster Follow "+ clusterName) - fs.writeFileSync("/etc/systemd/system/ipfs-cluster-follow@"+clusterName+".service", new_service) - execSync("systemctl enable ipfs-cluster-follow@"+clusterName); - execSync("systemctl daemon-reload"); - } - - } catch (e) { - console.error(e); - } - } - - try{ - let findDaemon = "ps -ef | grep ipfs-cluster-follow | grep -v grep | wc -l"; - let findDaemonResuls = execSync(findDaemon).toString(); - if (parseInt(findDaemonResuls) > 0) { - killDaemon = "ps -ef | grep ipfs-cluster-follow | grep -v grep | awk '{print $2}' | xargs kill -9"; - killDaemonResults = execSync(killDaemon); - } - let run_daemon_results - if (os.userInfo().username == "root") { - let reloadDaemon = "systemctl daemon-reload"; - let reloadDaemonResults = execSync(reloadDaemon); - - // Enable service - let enableDaemon = "systemctl enable ipfs-cluster-follow@"+ clusterName; - let enableDaemonResults = execSync(enableDaemon); - - // Start daemon - let startDaemon = "systemctl start ipfs-cluster-follow@" + clusterName; - let startDaemonResults = execSync(startDaemon); - await new Promise(resolve => setTimeout(resolve, 2000)); - run_daemon = execSync("systemctl status ipfs-cluster-follow@"+ clusterName +" | grep Active | awk '{print $2}'").toString(); - } - else{ - let run_daemon_cmd = "ipfs-cluster-follow " + clusterName + " run"; - run_daemon = exec( - run_daemon_cmd, - (error, stdout, stderr) => { - console.log(stdout); - } - ); - await new Promise(resolve => setTimeout(resolve, 2000)); - run_daemon = run_daemon.stderr.read(); - } - // Check if daemon is running - await new Promise(resolve => setTimeout(resolve, 2000)); - - findDaemon = "ps -ef | grep ipfs-cluster-follow | grep -v grep | wc -l"; - findDaemonResuls = execSync(findDaemon).toString(); - - if (parseInt(findDaemonResuls) == 0) { - console.log("ipfs-cluster-follow daemon is not running"); - throw new Error("ipfs-cluster-follow daemon is not running"); - } - else{ - let killDaemon = "ps -ef | grep ipfs-cluster-follow | grep -v grep | awk '{print $2}' | xargs kill -9"; - let killDaemonResults = execSync(killDaemon); - } - - } - catch(e){ - console.log(e); - return e.toString(); - } - - let results = { - "followInitCmdResults": followInitCmdResults, - "run_daemon": run_daemon - }; - - return results; - } - - async configIPFS(options = {}) { - let diskStats = options.diskStats || this.diskStats; - let ipfsPath = options.ipfsPath || this.ipfsPath; - let identity - let config - let peerId - let run_daemon - let public_key - let ipfs_daemon - if (!diskStats) throw new Error("diskStats is None"); - if (!ipfsPath) throw new Error("ipfsPath is None"); - - ipfsPath = path.join(ipfsPath, "ipfs"); - fs.mkdirSync(ipfsPath, { recursive: true }); - - let ipfsDirContents = fs.readdirSync(ipfsPath); - if (ipfsDirContents.length > 0) { - console.log("IPFS directory is not empty. Clearing contents..."); - for (let thisFile of ipfsDirContents) { - let delfile = path.join(ipfsPath, thisFile); - if (fs.existsSync(delfile)) { - if (fs.lstatSync(delfile).isFile()) { - fs.unlinkSync(delfile); - } - else if(fs.lstatSync(delfile).isDirectory()) { - fs.rmSync(delfile, { - recursive: true - }); - } - else { - console.log(`Unknown file type: ${delfile}`); - } - } - } - } - - let results = { - config: null, - identity: null, - public_key: null - }; - - if (diskStats && ipfsPath) { - try { - execSync(`IPFS_PATH=${ipfsPath} ipfs init --profile=badgerds`); - peerId = JSON.parse(execSync(`IPFS_PATH=${ipfsPath} ipfs id`).toString()); - execSync(`IPFS_PATH=${ipfsPath} ipfs config profile apply badgerds`); - - // Calculate available disk space and adjust storage allocation - let diskAvailable = parseFloat(diskStats.disk_avail); - let minFreeSpace = 32 * 1024 * 1024 * 1024; // 32 GB - if (diskAvailable > minFreeSpace) { - let allocate = Math.ceil(((diskAvailable - minFreeSpace) * 0.8) / 1024 / 1024 / 1024); - execSync(`IPFS_PATH=${ipfsPath} ipfs config Datastore.StorageMax ${allocate}GB`); - } - - // Load peer list and add to bootstrap - let peerListPath = path.join(process.cwd(), "peerstore"); - if (fs.existsSync(peerListPath)) { - let peerList = fs.readFileSync(peerListPath).toString().split("\n"); - peerList.forEach(peer => { - if (peer) { - execSync(`IPFS_PATH=${ipfsPath} ipfs bootstrap add ${peer}`); - } - }); - } - - //Assuming ipfs_service_text contains the systemd service configuration - if (os.userInfo().username == "root") { - const original_service = fs.readFileSync("/etc/systemd/system/ipfs.service").toString(); - const new_service_text = original_service.replace("ExecStart=","ExecStart= bash -c \"export IPFS_PATH="+ ipfsPath + " && "); - fs.writeFileSync("/etc/systemd/system/ipfs.service", new_service_text); - } - - results.config = execSync(`IPFS_PATH=${ipfsPath} ipfs config show`).toString(); - results.identity = peerId.ID; - results.public_key = peerId.PublicKey - results.AgentVersion = peerId.AgentVersion - results.Addresses = peerId.Addresses - } catch (error) { - console.error('Error configuring IPFS:', error); - } - } - if (os.userInfo().username == "root") { - try { - // Reload daemon - let reloadDaemon = "systemctl daemon-reload"; - let reloadDaemonResults = execSync(reloadDaemon); - - // Enable service - let enableDaemon = "systemctl enable ipfs"; - let enableDaemonResults = execSync(enableDaemon); - - - // Check if daemon is running - let findDaemon = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - let findDaemonResuls = execSync(findDaemon).toString(); - if (parseInt(findDaemonResuls) > 0) { - execSync("systemctl stop ipfs"); - let findDaemonResuls = execSync(findDaemon).toString(); - } - // Start daemon - let startDaemon = "systemctl start ipfs"; - let startDaemonResults = execSync(startDaemon); - - if (parseInt(findDaemonResuls) == 0) { - // Downloads image from ipfs as a test - let testDaemon = `bash -c "export IPFS_PATH=${ipfsPath} && ipfs cat /ipfs/QmSgvgwxZGaBLqkGyWemEDqikCqU52XxsYLKtdy3vGZ8uq > ${ipfsPath}/test.jpg"`; - let testDaemonResults = execSync(testDaemon); - - // Time out for 2 seconds to allow the file to download - await new Promise(resolve => setTimeout(resolve, 5000)); - - if (fs.existsSync(`${ipfsPath}/test.jpg`)) { - if (fs.statSync(`${ipfsPath}/test.jpg`).size > 0) { - fs.unlinkSync(`${ipfsPath}/test.jpg`); - } else { - throw new Error("ipfs failed to download test file"); - } - fs.unlinkSync(`${ipfsPath}/test.jpg`); - } - } else { - throw new Error("ipfs failed to download test file"); - } - } catch (e) { - console.log(e); - return e.toString(); - } finally { - let stopDaemon = "systemctl stop ipfs"; - let stopDaemonResults = execSync(stopDaemon); - } - } else { - let findDaemon = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - let findDaemonResuls = execSync(findDaemon).toString(); - if (parseInt(findDaemonResuls) > 0) { - execSync("ps -ef | grep ipfs | grep daemon | grep -v grep | awk '{print $2}' | xargs kill -9"); - findDaemonResuls = execSync(findDaemon).toString(); - } - - console.warn('You need to be root to write to /etc/systemd/system/ipfs.service'); - let run_daemon_cmd = `IPFS_PATH=${ipfsPath} ipfs daemon --enable-pubsub-experiment --enable-gc`; - run_daemon = exec( - run_daemon_cmd, - (error, stdout, stderr) => { - if (stdout.length > 0) { - console.log(stdout); - } - if (stderr.length > 0) { - console.error(stderr); - //throw new Error(stderr); - } - } - ); - await new Promise(resolve => setTimeout(resolve, 2000)); - findDaemon = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - findDaemonResuls = execSync(findDaemon).toString(); - run_daemon = run_daemon.stderr.read(); - try{ - // Start the daemon - let testDaemon = `bash -c 'IPFS_PATH=${ipfsPath} ipfs cat /ipfs/QmSgvgwxZGaBLqkGyWemEDqikCqU52XxsYLKtdy3vGZ8uq' > ${ipfsPath}/test.jpg`; - let testDaemonResults = execSync(testDaemon).toString(); - - // Time out for 2 seconds to allow the file to download - await new Promise(resolve => setTimeout(resolve, 2000)); - - if (fs.existsSync(`${ipfsPath}/test.jpg`)) { - if (fs.statSync(`${ipfsPath}/test.jpg`).size > 0) { - fs.unlinkSync(`${ipfsPath}/test.jpg`); - } else { - fs.unlinkSync(`${ipfsPath}/test.jpg`); - throw new Error("ipfs failed to download test file"); - } - } - else { - throw new Error("ipfs failed to download test file"); - } - } - catch(e){ - console.log(e); - return e.toString(); - } - } - if (results.identity != undefined && results.identity.length == 52) { - identity = results.identity ; - config = JSON.parse(results.config.replace("\n", "")); - public_key = results.public_key; - ipfs_daemon = run_daemon; - } - results = { - "config": config, - "identity": identity, - "public_key": public_key, - "ipfs_daemon": ipfs_daemon - }; - return results; - } - - async runIPFSClusterService(options = {}) { - let ipfsPath = options.ipfsPath || this.ipfsPath; - ipfsPath = path.join(ipfsPath, "ipfs"); - fs.mkdirSync(ipfsPath, { recursive: true }); - - const command = `IPFS_CLUSTER_PATH=${ipfsPath} ipfs-cluster-service`; - const process = exec(command); - - process.stdout.on('data', (data) => { - console.log(`stdout: ${data}`); - }); - - process.stderr.on('data', (data) => { - console.error(`stderr: ${data}`); - }); - - process.on('close', (code) => { - console.log(`ipfs-cluster-service process exited with code ${code}`); - }); - - return process; - } - - async runIPFSClusterCtl(options = {}) { - let ipfsPath = options.ipfsPath || this.ipfsPath; - ipfsPath = path.join(ipfsPath, "ipfs"); - fs.mkdirSync(ipfsPath, { recursive: true }); - - const command = `IPFS_CLUSTER_PATH=${ipfsPath} ipfs-cluster-ctl`; - const process = exec(command); - - process.stdout.on('data', (data) => { - console.log(`stdout: ${data}`); - }); - - process.stderr.on('data', (data) => { - console.error(`stderr: ${data}`); - }); - - process.on('close', (code) => { - console.log(`ipfs-cluster-ctl process exited with code ${code}`); - }); - - return process; - } - - - async ensureDirSync(dirPath) { - if (!fs.existsSync(dirPath)) { - fs.mkdirSync(dirPath, { recursive: true }); - } - } - - async runIPFSClusterFollow(options = {}) { - this.ensureDirSync(this.ipfsPath); - const command = "ipfs-cluster-follow"; - let cluster_name = options.cluster_name || this.cluster_name; - const args = [ "init", cluster_name]; // Example, adjust as needed - const env = {ipfsPath: this.ipfsPath }; - const export_command = Object.entries(env).map(([key, value]) => `export ${key}=${value}`).join(" && "); - const process_command = export_command + command + " " + args.join(" "); - try { - const { stdout, stderr } = await exec(process_command); - console.log(stdout); - console.error(stderr); - return true; - } catch (error) { - console.error(error); - } - } - - async runIPFSDaemon(options = {}) { - const ipfs_path = path.join( this.ipfsPath , "ipfs" ) - const psDaemon = "ps -ef | grep ipfs | grep daemon | grep -v grep | awk '{print $2}' | wc -l"; - const psDaemonResults = execSync(psDaemon).toString(); - if (parseInt(psDaemonResults) > 0) { - const killDaemon = "ps -ef | grep ipfs | grep daemon | grep -v grep | awk '{print $2}' | xargs kill -9"; - const killDaemonResults = execSync(killDaemon); - } - const lockFile = path.join(ipfs_path, "repo.lock"); - if (fs.existsSync(lockFile)) { - fs.unlinkSync(lockFile); - } - this.ensureDirSync(this.ipfsPath); - const command = "ipfs"; - const args = ["daemon", "--enable-pubsub-experiment", "--enable-gc"]; - const env = {IPFS_PATH: ipfs_path }; - const export_command = Object.entries(env).map(([key, value]) => ` ${key}=${value}`).join(" "); - const process_command = export_command + " " + command + " " + args.join(" "); - try { - let run_daemon = exec( - process_command, - (error, stdout, stderr) => { - if (stdout.length > 0) { - console.log(stdout); - } - if (stderr.length > 0) { - console.error(stderr); - } - } - ); - return true; - } catch (error) { - console.error(error); - } - finally { - return true; - } - } - - - async killProcessByPattern(pattern) { - try { - // Using pgrep and pkill for more precise process management - const pids = execSync(`pgrep -f '${pattern}'`).toString().trim(); - if (pids) { - execSync(`pkill -f '${pattern}'`); - } - return true; - } catch (error) { - console.error(`Failed to kill process with pattern ${pattern}: ${error}`); - return false; - } - } - - async removeDirectorySync(dirPath) { - // Recursive removal using rmSync in newer Node.js versions, for older versions consider rimraf package - try { - fs.rmSync(dirPath, { recursive: true, force: true }); - return true; - } catch (error) { - console.error(`Failed to remove directory ${dirPath}: ${error}`); - return false; - } - } - - async uninstallIPFS(options = {}) { - await this.killProcessByPattern('ipfs.*daemon'); - await this.killProcessByPattern('ipfs-cluster-follow'); - await this.removeDirectorySync(this.ipfsPath); - await this.removeDirectorySync(path.join(os.homedir(), '.ipfs-cluster-follow', 'ipfs_cluster', 'api-socket')); - return true; - } - - - async killProcessByPattern(pattern) { - try { - // Using pgrep and pkill for more precise process management - const pids = execSync(`pgrep -f '${pattern}'`).toString().trim(); - if (pids) { - execSync(`pkill -f '${pattern}'`); - } - return true; - } catch (error) { - console.error(`Failed to kill process with pattern ${pattern}: ${error}`); - return false; - } - } - - async removeDirectorySync(dirPath) { - // Recursive removal using rmSync in newer Node.js versions, for older versions consider rimraf package - try { - fs.rmSync(dirPath, { recursive: true, force: true }); - return true; - } catch (error) { - console.error(`Failed to remove directory ${dirPath}: ${error}`); - return false; - } - } - - async uninstallIPFS(options = {}) { - await this.killProcessByPattern('ipfs.*daemon'); - await this.killProcessByPattern('ipfs-cluster-follow'); - await this.removeDirectorySync(this.ipfsPath); - await this.removeDirectorySync(path.join(os.homedir(), '.ipfs-cluster-follow', 'ipfs_cluster', 'api-socket')); - return true; - } - - async testUninstall(options = {}) { - if (['leecher', 'worker', 'master'].includes(this.role)) { - this.uninstallIPFS(); - } - if (this.role === "master") { - this.uninstallIPFSClusterService(); - this.uninstallIPFSClusterCtl(); - } - if (this.role === "worker") { - this.uninstallIPFSClusterFollow(); - } - } - - async installExecutables(options = {}) { - let results = {}; - if (['leecher', 'worker', 'master'].includes(this.role)) { - let ipfs = await this.installIPFSDaemon(); - results["ipfs"] = ipfs; - } - if (this.role === "master") { - let clusterService = await this.installIPFSClusterService(); - let clusterCtl = await this.installIPFSClusterCtl(); - results["clusterService"] = clusterService; - results["clusterCtl"] = clusterCtl; - } - if (this.role === "worker") { - let clusterFollow = await this.installIPFSClusterFollow(); - results["clusterFollow"] = clusterFollow; - } - return results; - } - - - async configExecutables(options = {}) { - let results = {}; - if (['leecher', 'worker', 'master'].includes(this.role)) { - let ipfsConfig = await this.configIPFS(); - results["ipfs_config"] = ipfsConfig.config; - } - if (this.role === "master") { - let clusterServiceConfig = await this.configIPFSClusterService(); - let clusterCtlConfig = await this.configIPFSClusterCtl(); - results["clusterServiceConfig"] = clusterServiceConfig.config; - results["clusterCtlConfig"] = clusterCtlConfig.config; - } - if (this.role === "worker") { - let clusterFollowConfig = this.configIPFSClusterFollow(); - results["clusterFollowConfig"] = clusterFollowConfig.config; - } - return results; - } - - async ipfsTestInstall() { - try { - execSync('which ipfs'); - return true; - } catch (error) { - return false; - } - } - - async ipfsClusterServiceTestInstall() { - try { - execSync('which ipfs-cluster-service'); - return true; - } catch (error) { - return false; - } - } - - async ipfsClusterFollowTestInstall() { - try { - execSync('which ipfs-cluster-follow'); - return true; - } catch (error) { - return false; - } - } - - async ipfsClusterCtlTestInstall() { - try { - execSync('which ipfs-cluster-ctl'); - return true; - } catch (error) { - return false; - } - } - - async ipgetTestInstall() { - try { - execSync('which ipget'); - return true; - } catch (error) { - return false; - } - } - - async installAndConfigure() { - let results = {}; - let options = {diskStats: this.diskStats, ipfsPath: this.ipfsPath, clusterName: this.clusterName, clusterLocation: this.clusterLocation, secret: this.secret}; - try { - if (['leecher', 'worker', 'master'].includes(this.role)) { - // Assuming these methods are implemented and properly handle async operations - this.installIPGet(); - this.installIPFSDaemon(); - const ipfsConfig = await this.configIPFS(options) - results.ipfs = true; // Assuming installation success - results.ipfs_config = ipfsConfig; - //await this.runIPFSDaemon(); - } - if (this.role === 'master') { - const clusterService = this.installIPFSClusterService(options); - const clusterCtl = this.installIPFSClusterCtl(options); - const clusterServiceConfig = await this.configIPFSClusterService(options); - const clusterCtlConfig = await this.configIPFSClusterCtl(options); - results.clusterService = clusterService; - results.clusterCtl = clusterCtl; - results.clusterServiceConfig = clusterServiceConfig; - results.clusterCtlConfig = clusterCtlConfig; - //await this.runIPFSClusterService(options); - } - if (this.role === 'worker') { - const clusterFollow = this.installIPFSClusterFollow(options); - const clusterFollowConfig = await this.configIPFSClusterFollow(options); - results.clusterFollow = clusterFollow; - results.clusterFollowConfig = clusterFollowConfig; - //await this.runIPFSClusterFollow(options); - } - - // Systemctl daemon reload - if (os.userInfo().username == "root") { - exec('systemctl daemon-reload'); - results.systemctl_reload = true; - } - } catch (error) { - console.error('Error during installation and configuration:', error); - return null; // Or handle the error as needed - } - - return results; - } - -} -// run this if the script is run directly - -async function main(){ - const meta = { - role: "master", - clusterName: "cloudkit_storage", - clusterLocation: "/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv", - secret: "96d5952479d0a2f9fbf55076e5ee04802f15ae5452b5faafc98e2bd48cf564d3", - }; - - // Initialize the IPFS configuration manager with the provided metadata - const install_ipfs = new InstallIPFS(undefined, meta); - - // Execute the installation and configuration process - async function runInstallationAndConfiguration() { - try { - const results = await install_ipfs.installAndConfigure(); - console.log('Installation and Configuration Results:', results); - } catch (error) { - console.error('An error occurred during the installation and configuration process:', error); - } - } - - await runInstallationAndConfiguration(); -} - -main(); \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-follow.service b/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-follow.service deleted file mode 100644 index 2a86006..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-follow.service +++ /dev/null @@ -1,12 +0,0 @@ -[Unit] -Description=IPFS Cluster Follow Daemon -After=network.target - -[Service] -ExecStart=/usr/local/bin/ipfs-cluster-follow run -Restart=on-failure -User=root -Group=root - -[Install] -WantedBy=multi-user.target diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-service.service b/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-service.service deleted file mode 100644 index 13fd35a..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs-cluster-service.service +++ /dev/null @@ -1,12 +0,0 @@ -[Unit] -Description=IPFS Cluster Daemon -After=network.target - -[Service] -ExecStart=/usr/local/bin/ipfs-cluster-service daemon -Restart=on-failure -User=root -Group=root - -[Install] -WantedBy=multi-user.target diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs.js b/huggingface_scraper/ipfs_kit_lib/ipfs.js deleted file mode 100644 index c1aa767..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs.js +++ /dev/null @@ -1,685 +0,0 @@ -import { exec, execSync } from 'child_process'; -import fs from 'fs'; -import os from 'os'; -import path from 'path'; - -export class ipfs { - constructor(resources, meta) { - if (meta !== null) { - if ('config' in meta) { - if (meta['config'] !== null) { - this.config = meta['config']; - } - } - if ('role' in meta) { - if (meta['role'] !== null) { - this.role = meta['role']; - if (!['master', 'worker', 'leecher'].includes(this.role)) { - throw new Error('role is not either master, worker, leecher'); - } else { - this.role = 'leecher'; - } - } - } - if ('cluster_name' in meta) { - if (meta['cluster_name'] !== null) { - this.cluster_name = meta['cluster_name']; - } - } - if ('ipfs_path' in meta) { - if (meta['ipfs_path'] !== null) { - this.ipfsPath = meta['ipfs_path']; - } - } - if (this.role === 'leecher' || this.role === 'worker' || this.role === 'master') { - this.commands = {}; - } - } - } - - async daemon_start(kwargs = {}) { - let cluster_name; - if ('cluster_name' in this) { - cluster_name = this.cluster_name; - } - if ('cluster_name' in kwargs) { - cluster_name = kwargs['cluster_name']; - } - - let results1 = null; - let results2 = null; - let ipfs_ready = false; - - // Run this if root and check if it passes - if (os.userInfo().uid === 0) { - try { - const command1 = "systemctl start ipfs"; - exec(command1, (error, stdout, stderr) => { - if (error) { - console.log(`Error starting ipfs: ${error.message}`); - results1 = error.message; - } else { - results1 = stdout; - - const command2 = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - exec(command2, (error, stdout, stderr) => { - if (!error && parseInt(stdout.trim()) > 0) { - ipfs_ready = true; - } - }); - } - }); - } catch (error) { - results1 = error.message; - } - } - - // Run this if user is not root or root user fails check if it passes - if (os.userInfo().uid !== 0 || ipfs_ready === false) { - try { - const command2 = `export IPFS_PATH=${path.resolve(path.join(this.ipfsPath,"ipfs"))} && ipfs daemon --enable-gc --enable-pubsub-experiment`; - //const execute2 = execSync(command2); - const execute2 = exec(command2, (error, stdout, stderr) => { - if (error) { - console.log(`Error starting ipfs: ${error.message}`); - results1 = error.message; - } - }); - //results2 = execute2.toString(); - } catch (error) { - console.log(`Error starting ipfs: ${error.message}`); - results2 = error.message; - } - } - - const results = { - "systemctl": results1, - "bash": results2 - }; - - return results; - } - - - async daemon_stop(kwargs = {}) { - let cluster_name; - if ('cluster_name' in this) { - cluster_name = this.cluster_name; - } - if ('cluster_name' in kwargs) { - cluster_name = kwargs['cluster_name']; - } - - let results1 = null; - let results2 = null; - let ipfs_ready = false; - - // Run this if root and check if it passes - if (os.userInfo().uid === 0) { - try { - const command1 = "systemctl stop ipfs"; - exec(command1, (error, stdout, stderr) => { - if (error) { - results1 = error.message; - } else { - results1 = stdout; - - const command2 = "ps -ef | grep ipfs | grep daemon | grep -v grep | wc -l"; - exec(command2, (error, stdout, stderr) => { - if (!error && parseInt(stdout.trim()) === 0) { - ipfs_ready = true; - } - }); - } - }); - } catch (error) { - results1 = error.message; - } - } - - // Run this if user is not root or root user fails check if it passes - if (os.userInfo().uid !== 0 || ipfs_ready === false) { - try { - const command2 = "ps -ef | grep ipfs | grep daemon | grep -v grep | awk '{print $2}' | xargs kill -9"; - exec(command2, (error, stdout, stderr) => { - if (error) { - results2 = error.message; - } else { - results2 = stdout; - } - }); - } catch (error) { - results2 = error.message; - } - } - - const results = { - "systemctl": results1, - "bash": results2 - }; - - return results; - } - - - async ipfs_resize(size, kwargs = {}) { - const command1 = this.daemon_stop(); - const command2 = `ipfs config --json Datastore.StorageMax ${size}GB`; - const results1 = await new Promise((resolve, reject) => { - exec(command2, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - const command3 = this.daemon_start(); - return results1; - } - - async ipfs_ls_pin(kwargs = {}) { - if ('hash' in kwargs) { - const hash = kwargs['hash']; - let request1 = null; - try { - request1 = await this.ipfs_execute({ - "command": "cat", - "hash": hash - }); - } catch (error) { - console.error(error); - } - if (request1 !== null) { - return request1; - } - let request2 = null; - try { - const command = `ipfs cat ${hash}`; - request2 = await new Promise((resolve, reject) => { - exec(command, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - } catch (error) { - console.error(error); - } - if (request2 !== null) { - return request2; - } - } - throw new Error("hash not found"); - } - - - async ipfs_get_pinset(kwargs = {}) { - const this_tempfile = path.join(os.tmpdir(), 'temp.txt'); - const command = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs pin ls -s > ${this_tempfile}`; - await new Promise((resolve, reject) => { - exec(command, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - const file_data = fs.readFileSync(this_tempfile, 'utf8'); - const pinset = {}; - const parse_results = file_data.split("\n"); - for (let i = 0; i < parse_results.length; i++) { - const data = parse_results[i].split(" "); - if (data.length > 1) { - pinset[data[0]] = data[1]; - } - } - return pinset; - } - - async ipfs_add_pin(pin, kwargs = {}) { - const dirname = path.dirname(__filename); - let result1; - try { - const command1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && cd ${this.ipfsPath}ipfs/ && ipfs pin add ${pin}`; - result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - } catch (error) { - result1 = error; - } - return result1; - } - - async ipfs_mkdir(path, kwargs = {}) { - const this_path_split = path.split("/"); - let this_path = ""; - const results = []; - for (let i = 0; i < this_path_split.length; i++) { - this_path += this_path_split[i] + "/"; - const command1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs files mkdir ${this_path}`; - const result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - results.push(result1); - } - return results; - } - - - async ipfs_add_path2(path, kwargs = {}) { - let ls_dir = []; - if (!fs.existsSync(path)) { - throw new Error("path not found"); - } - if (fs.lstatSync(path).isFile()) { - ls_dir = [path]; - await this.ipfs_mkdir(path.dirname(path), kwargs); - } else if (fs.lstatSync(path).isDirectory()) { - await this.ipfs_mkdir(path, kwargs); - ls_dir = fs.readdirSync(path).map(file => path.join(path, file)); - } - const results1 = []; - for (let i = 0; i < ls_dir.length; i++) { - let argstring = ` --to-files=${ls_dir[i]} `; - const command1 = `ipfs add ${argstring}${ls_dir[i]}`; - const result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - results1.push(result1); - } - return results1; - } - - async ipfs_add_path(path, kwargs = {}) { - let argstring = ""; - let ls_dir = path; - if (!fs.existsSync(path)) { - throw new Error("path not found"); - } - if (fs.lstatSync(path).isFile()) { - await this.ipfs_mkdir(path.dirname(path), kwargs); - } else if (fs.lstatSync(path).isDirectory()) { - await this.ipfs_mkdir(path, kwargs); - } - argstring += `--recursive --to-files=${ls_dir} `; - const command1 = `ipfs add ${argstring}${ls_dir}`; - const result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - const results = {}; - const result_split = result1.split("\n"); - for (let i = 0; i < result_split.length; i++) { - const parts = result_split[i].split(" "); - if (parts.length > 1) { - results[parts[2]] = parts[1]; - } - } - return results; - } - - - async ipfs_remove_path(path, kwargs = {}) { - let result1 = null; - let result2 = null; - const stats = await this.ipfs_stat_path(path, kwargs); - if (Object.keys(stats).length === 0) { - throw new Error("path not found"); - } - const pin = stats['pin']; - if (stats["type"] === "file") { - const command1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs files rm ${path}`; - result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - const command2 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs pin rm ${pin}`; - result2 = await new Promise((resolve, reject) => { - exec(command2, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - result2 = result2.split("\n"); - } else if (stats["type"] === "directory") { - const contents = await this.ipfs_ls_path(path, kwargs); - for (let i = 0; i < contents.length; i++) { - if (contents[i].length > 0) { - result1 = await this.ipfs_remove_path(`${path}/${contents[i]}`, kwargs); - } - } - } else { - throw new Error("unknown path type"); - } - const results = { - "files_rm": result1, - "pin_rm": result2 - }; - return results; - } - - async ipfs_stat_path(path, kwargs = {}) { - try { - const stat1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs files stat ${path}`; - const results1 = await new Promise((resolve, reject) => { - exec(stat1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - const results1Split = results1.split("\n"); - if (results1Split.length > 0 && Array.isArray(results1Split)) { - const pin = results1Split[0]; - const size = parseFloat(results1Split[1].split(": ")[1]); - const culumulative_size = parseFloat(results1Split[2].split(": ")[1]); - const child_blocks = parseFloat(results1Split[3].split(": ")[1]); - const type = results1Split[4].split(": ")[1]; - const results = { - "pin": pin, - "size": size, - "culumulative_size": culumulative_size, - "child_blocks": child_blocks, - "type": type - }; - return results; - } else { - return false; - } - } catch (error) { - console.error(error.message); - return false; - } - } - - - async ipfs_name_resolve(kwargs = {}) { - let result1 = null; - try { - const command1 = `export IPFS_PATH=${this.ipfsPath}/ipfs/ && ipfs name resolve ${kwargs['path']}`; - result1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - } catch (error) { - result1 = error.message; - } - return result1; - } - - async ipfs_name_publish(path, kwargs = {}) { - if (!fs.existsSync(path)) { - throw new Error("path not found"); - } - let results1 = null; - let results2 = null; - try { - const command1 = `export IPFS_PATH=${this.ipfsPath}/ipfs/ && ipfs add --cid-version 1 ${path}`; - results1 = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - results1 = results1.trim(); - const cid = results1.split(" ")[1]; - const fname = results1.split(" ")[2]; - results1 = { - [fname]: cid - }; - } catch (error) { - results1 = error.message; - } - - try { - const command2 = `export IPFS_PATH=${this.ipfsPath}/ipfs/ && ipfs name publish ${results1[Object.keys(results1)[0]]}`; - results2 = await new Promise((resolve, reject) => { - exec(command2, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - results2 = results2.split(":")[0].split(" ")[results2.split(":")[0].split(" ").length - 1]; - } catch (error) { - results2 = error.message; - } - - const results = { - "add": results1, - "publish": results2 - }; - return results; - } - - - async ipfs_ls_path(path, kwargs = {}) { - let results1 = null; - try { - const stat1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs files ls ${path}`; - results1 = await new Promise((resolve, reject) => { - exec(stat1, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - results1 = results1.split("\n"); - } catch (error) { - results1 = error.message; - } - if (results1.length > 0 && Array.isArray(results1)) { - return results1; - } else { - return false; - } - } - - async ipfs_remove_pin(cid, kwargs = {}) { - let result1 = null; - let stdout = null; - let stderr = null; - try { - const command1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs pin rm ${cid}`; - const output = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject({ stdout, stderr }); - } else { - resolve({ stdout, stderr }); - } - }); - }); - stdout = output.stdout; - stderr = output.stderr; - } catch (error) { - result1 = error.message; - } - if (stdout && stdout.includes("unpinned")) { - result1 = true; - } - return result1; - } - - - async ipfs_remove_pin(cid, kwargs = {}) { - let result1 = null; - let stdout = null; - let stderr = null; - try { - const command1 = `export IPFS_PATH=${this.ipfsPath}ipfs/ && ipfs pin rm ${cid}`; - const output = await new Promise((resolve, reject) => { - exec(command1, (error, stdout, stderr) => { - if (error) { - reject({ stdout, stderr }); - } else { - resolve({ stdout, stderr }); - } - }); - }); - stdout = output.stdout; - stderr = output.stderr; - } catch (error) { - result1 = error.message; - } - if (stdout && stdout.includes("unpinned")) { - result1 = true; - } - return result1; - } - - async test_ipfs() { - let detect = null; - try { - detect = await new Promise((resolve, reject) => { - exec("which ipfs", (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - } catch (error) { - detect = error.message; - } - if (detect && detect.length > 0) { - return true; - } else { - return false; - } - } - - async ipfs_execute(command, kwargs = {}) { - if (typeof kwargs !== 'object') { - throw new Error("kwargs must be an object"); - } - - const executable = "ipfs "; - const options = ["add", "pin", "unpin", "get", "cat", "ls", "refs", "refs-local", "refs-local-recursive", "refs-remote", "refs-remote-recursive", "repo", "version"]; - let execute = ""; - - if (command === "add") { - execute = `${executable}add ${kwargs.file}`; - } - - if (!kwargs.hash) { - throw new Error("hash not found in kwargs"); - } - - if (command === "get") { - execute = `${executable}get ${kwargs.hash} -o ${kwargs.file}`; - } - - if (command === "pin") { - execute = `${executable}pin add ${kwargs.hash}`; - } - - if (command === "unpin") { - execute = `${executable}pin rm ${kwargs.hash}`; - } - - if (command === "cat") { - execute = `${executable}cat ${kwargs.hash}`; - } - - if (command === "ls") { - execute = `${executable}ls ${kwargs.hash}`; - } - - if (command === "refs") { - execute = `${executable}refs ${kwargs.hash}`; - } - - if (command === "refs-local") { - execute = `${executable}refs local ${kwargs.hash}`; - } - - if (command === "refs-local-recursive") { - execute = `${executable}refs local --recursive ${kwargs.hash}`; - } - - if (command === "refs-remote") { - execute = `${executable}refs remote ${kwargs.hash}`; - } - - if (command === "refs-remote-recursive") { - execute = `${executable}refs remote --recursive ${kwargs.hash}`; - } - - if (command === "repo") { - execute = `${executable}repo ${kwargs.hash}`; - } - - if (command === "version") { - execute = `${executable}version ${kwargs.hash}`; - } - - try { - const output = await new Promise((resolve, reject) => { - exec(execute, (error, stdout, stderr) => { - if (error) { - reject(error.message); - } else { - resolve(stdout); - } - }); - }); - console.log(`stdout: ${output}`); - return output; - } catch (error) { - console.log(`error: ${error}`); - return error; - } - } - -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs.service b/huggingface_scraper/ipfs_kit_lib/ipfs.service deleted file mode 100644 index de67202..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs.service +++ /dev/null @@ -1,11 +0,0 @@ -Description=IPFS Daemon -After=network.target - -[Service] -ExecStart=/usr/local/bin/ipfs daemon --enable-gc --enable-pubsub-experiment \" -Restart=on-failure -User=root -Group=root - -[Install] -WantedBy=multi-user.target \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_ctl.js b/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_ctl.js deleted file mode 100644 index b90243b..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_ctl.js +++ /dev/null @@ -1,257 +0,0 @@ -export class IPFSClusterCtl { - constructor(resources, meta = null) { - this.config = {}; - this.role = "leecher"; // Default role - - if (meta !== null) { - if ("config" in meta && meta['config'] !== null) { - this.config = meta['config']; - } - if ("role" in meta && meta['role'] !== null) { - if (["master", "worker", "leecher"].includes(meta['role'])) { - this.role = meta['role']; - } else { - throw new Error("role is not either master, worker, leecher"); - } - } - } - - // Any additional setup for roles can be added here - if (["leecher", "worker", "master"].includes(this.role)) { - // Perform role-specific initialization - } - } - - // Helper function to recursively walk through directory - walkSync(dir, fileList = []) { - fs.readdirSync(dir).forEach(file => { - const filePath = path.join(dir, file); - if (fs.statSync(filePath).isDirectory()) { - this.walkSync(filePath, fileList); - } else { - fileList.push(filePath); - } - }); - return fileList; - } - - ipfsClusterCtlAddPin(dirPath, metadata = {}) { - if (!fs.existsSync(dirPath)) { - throw new Error("Path not found"); - } - - const files = this.walkSync(dirPath); - const results = files.map(file => { - const relativePath = path.relative(dirPath, file); - let argString = metadata[relativePath] ? ` --metadata ${metadata[relativePath]}` : ""; - let command = `ipfs-cluster-ctl pin add ${file}${argString}`; - try { - const output = execSync(command).toString(); - return output; - } catch (error) { - console.error(`Failed to execute command for file ${file}: ${error}`); - return null; - } - }); - - return results.filter(result => result !== null); - } - - - ipfsClusterCtlRemovePin(dirPath) { - if (!fs.existsSync(dirPath)) { - throw new Error("Path not found"); - } - - const files = this.walkSync(dirPath); - const results = files.map(file => { - let command = `ipfs-cluster-ctl pin rm ${file}`; - try { - const output = execSync(command).toString(); - return `Unpinned: ${file}`; - } catch (error) { - console.error(`Failed to execute command for file ${file}: ${error}`); - return `Failed to unpin: ${file}`; - } - }); - - return results; - } - - - // Simplified directory traversal for demonstration - getDirectories(basePath) { - return fs.readdirSync(basePath, { withFileTypes: true }) - .filter(dirent => dirent.isDirectory()) - .map(dirent => path.join(basePath, dirent.name)); - } - - ipfsClusterCtlAddPinRecursive(dirPath, metadata = {}) { - if (!fs.existsSync(dirPath)) { - throw new Error("Path not found"); - } - - // If the path is a directory, traverse and pin each subdirectory recursively. - // Otherwise, pin the file directly. - let targets = fs.statSync(dirPath).isDirectory() ? this.getDirectories(dirPath) : [dirPath]; - const results = targets.map(target => { - const relativePath = path.relative(dirPath, target); - let argString = metadata[relativePath] ? ` --metadata ${metadata[relativePath]}` : ""; - let command = `ipfs-cluster-ctl pin add -r ${target}${argString}`; - - try { - const output = execSync(command, { encoding: 'utf-8' }); - return `Pinned: ${target}`; - } catch (error) { - console.error(`Failed to execute command for ${target}: ${error.message}`); - return `Failed to pin: ${target}`; - } - }); - - return results; - } - - - ipfs_cluster_ctl_execute(args) { - if (!this.options.includes(args[0])) { - console.error(`"${args[0]}" is not a valid command.`); - return; - } - - let command = `${this.executable}${args[0]}`; - - if (args[1]) { - command += ` ${args[1]}`; - - // Validate subcommands for certain options - switch (args[0]) { - case "peers": - if (!["ls", "rm"].includes(args[1])) { - throw new Error("Invalid option for 'peers'"); - } - break; - case "pin": - if (!["add", "rm", "ls", "update"].includes(args[1])) { - throw new Error("Invalid option for 'pin'"); - } - break; - case "health": - if (!["graph", "metrics", "alerts"].includes(args[1])) { - throw new Error("Invalid option for 'health'"); - } - break; - case "ipfs": - if (args[1] !== "gc") { - throw new Error("Invalid option for 'ipfs'"); - } - break; - } - } - - // Special handling for the 'add' command with multiple options - if (args[0] === "add" && args.length > 2) { - const path = args.pop(); // Assuming the last argument is always the path - const options = args.slice(1).join(" "); - command = `${this.executable}add ${options} ${path}`; - } - - exec(command, (error, stdout, stderr) => { - if (error) { - console.error(`Error executing command: ${error}`); - return; - } - if (stderr) { - console.error(`stderr: ${stderr}`); - return; - } - console.log(`stdout: ${stdout}`); - }); - } - - - getPinset() { - // Create a temporary file path - const tempFile = path.join(os.tmpdir(), `pinset-${Date.now()}.txt`); - try { - // Redirect output of the command to the temporary file - execSync(`ipfs-cluster-ctl pin ls > ${tempFile}`); - - // Read and parse the temporary file - const fileData = fs.readFileSync(tempFile, 'utf8'); - const pinset = this.parsePinsetData(fileData); - - // Clean up the temporary file - fs.unlinkSync(tempFile); - - return pinset; - } catch (error) { - console.error(`Failed to get pinset: ${error.message}`); - // Clean up the temporary file in case of an error - if (fs.existsSync(tempFile)) { - fs.unlinkSync(tempFile); - } - return {}; - } - } - - parsePinsetData(fileData) { - const pinset = {}; - const parseResults = fileData.split("\n"); - parseResults.forEach(resultLine => { - const resultsList = resultLine.split(" | "); - if (resultsList.length > 1) { // Ensure it's not an empty line - const resultDict = {}; - resultsList.forEach(cell => { - const cellSplit = cell.split(":").map(part => part.trim()); - if (cellSplit.length > 1) { - resultDict[cellSplit[0]] = cellSplit[1]; - } - }); - if (Object.keys(resultDict).length > 0) { - pinset[resultsList[0]] = resultDict; - } - } - }); - return pinset; - } - - ipfsClusterCtlStatus() { - try { - const command = "ipfs-cluster-ctl status"; - const results = execSync(command, { encoding: 'utf8' }); // Directly get the output as a string - return results; - } catch (error) { - console.error(`Error executing ipfs-cluster-ctl status: ${error.message}`); - return null; - } - } - - testIPFSClusterCtl() { - return new Promise((resolve, reject) => { - exec("which ipfs-cluster-ctl", (error, stdout) => { - if (error) { - resolve(false); - } else { - resolve(stdout.trim().length > 0); - } - }); - }); - } -} - -function main() -{ - (async () => { - const thisIpfsClusterCtl = new IPFSClusterCtl(); - const results = await thisIpfsClusterCtl.testIPFSClusterCtl(); - console.log(results); - - if (results) { - const status = thisIpfsClusterCtl.ipfsClusterCtlStatus(); - console.log(status); - } else { - console.log("ipfs-cluster-ctl is not installed."); - } - })(); - -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_follow.js b/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_follow.js deleted file mode 100644 index 441de61..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_follow.js +++ /dev/null @@ -1,251 +0,0 @@ -import { execSync, exec } from 'child_process'; -import os from 'os'; -import path, { parse } from 'path'; -import fs from 'fs'; -import * as install_ipfs from './install_ipfs.js'; - -export class IPFSClusterFollow { - constructor(resources, meta) { - // Default values - this.config = {}; - this.role = 'leecher'; // default role - this.clusterName = ''; - - // Set from meta if provided - if (meta.config) { - this.config = meta.config; - } - - if (meta.role && ['master', 'worker', 'leecher'].includes(meta.role)) { - this.role = meta.role; - } else if (meta.role) { - throw new Error("role is not either master, worker, leecher"); - } - - if (meta.ipfs_path) { - this.ipfsPath = meta.ipfs_path; - } - - if (meta.cluster_name) { - this.clusterName = meta.cluster_name; - } - } - - async ipfsFollowStart(clusterName = this.clusterName) { - let results = { systemctl: false, bash: false }; - let detectResults - if (os.userInfo().uid === 0) { - try { - // Attempt to start the ipfs-cluster-follow service - let systemctlStart = execSync("systemctl start ipfs-cluster-follow").toString(); - results.systemctl = systemctlStart; - // Check if the service is running - detectResults = execSync("ps -ef | grep ipfs-cluster-follow | grep -v grep").toString().trim(); - - if (detectResults.length === 0) { - const homeDir = os.homedir(); - const followDir = path.join(homeDir, ".ipfs-cluster-follow", clusterName); - // Check for and remove the api-socket file if it exists - const apiSocketPath = path.join(followDir, "api-socket"); - if (fs.existsSync(apiSocketPath)) { - fs.unlinkSync(apiSocketPath); - results.bash = true; - } - // Attempt to start the ipfs-cluster-follow service - let systemctlStart = execSync("systemctl start ipfs-cluster-follow").toString(); - results.systemctl = systemctlStart; - // Check if the service is running - detectResults = execSync("ps -ef | grep ipfs-cluster-follow | grep -v grep").toString().trim(); - } - } - catch (error) { - console.log(`Error starting ipfs-cluster-follow: ${error.message}`); - results.systemctl = `Error: ${error.message}`; - } - } - if (os.userInfo().uid != 0 || results.systemctl.includes('Error') || detectResults.length === 0) { - try { - // Attempt to run the ipfs-cluster-follow command - const commandRun = `/usr/local/bin/ipfs-cluster-follow ${clusterName} run`; - const process = exec(commandRun, (error, stdout, stderr) => { - if (error) { - console.error(`Error running ipfs-cluster-follow: ${error.message}`); - if (error.message.includes("command not found")){ - let InstallIPFS = new install_ipfs.InstallIPFS(); - let installResults = InstallIPFS.installIPFSClusterFollow(); - let configResults = InstallIPFS.configIPFSClusterFollow(); - console.log(`Install IPFS Cluster Follow results: ${installResults}`); - console.log(`Configure IPFS Cluster Follow results: ${configResults}`); - console.log(`Attempting to run ipfs-cluster-follow again`); - exec(commandRun, (error, stdout, stderr) => { - if (error) { - console.error(`Error running ipfs-cluster-follow: ${error.message}`); - } - console.log(`ipfs-cluster-follow output: ${stdout}`); - }); - } - return; - } - console.log(`ipfs-cluster-follow output: ${stdout}`); - }); - } - catch (error) { - console.log(`Error running ipfs-cluster-follow: ${error.message}`); - console.error(`Error in ipfsFollowStart: ${error.message}`); - } - } - return results; - } - - async ipfsFollowStop() { - let results = { systemctl: '', bash: '', 'api-socket': '' }; - let clusterName = this.clusterName; - - if (os.userInfo().uid === 0) { - try { - // Attempt to stop the ipfs-cluster-follow service - const systemctlStop = execSync("systemctl stop ipfs-cluster-follow").toString(); - results.systemctl = systemctlStop; - } catch (error) { - console.log(`Error stopping ipfs-cluster-follow: ${error.message}`) - results.systemctl = `Error: ${error.message}`; - } - } - if (os.userInfo().uid != 0 || results.systemctl.includes('Error')){ - try { - // Forcefully kill the ipfs-cluster-follow process if it's still running - const listCommand = "ps -ef | grep ipfs-cluster-follow | grep -v grep | wc -l"; - const listResults = execSync(listCommand).toString().trim(); - if (parseInt(listResults) > 0) { - const killCommand = "ps -ef | grep ipfs-cluster-follow | grep -v grep | awk '{print $2}' | xargs kill -9"; - const killResults = execSync(killCommand).toString(); - results.bash = killResults; - } - else{ - results.bash = "No ipfs-cluster-follow process found to kill"; - } - } catch (error) { - console.log(`Error killing ipfs-cluster-follow: ${error.message}`) - results.bash = `Error: ${error.message}`; - } - } - - try { - // Remove the api-socket file - const apiSocketPath = path.join(os.homedir(), ".ipfs-cluster-follow", clusterName, "api-socket"); - if (fs.existsSync(apiSocketPath)) { - fs.unlinkSync(apiSocketPath); - results['api-socket'] = 'Removed api-socket'; - } else { - //console.log("api-socket not found, deleting not necessary"); - results['api-socket'] = 'api-socket not found, deleting not necessary'; - } - } catch (error) { - console.error(`Error removing api-socket: ${error.message}`); - results['api-socket'] = `Error removing api-socket: ${error.message}`; - } - - return results; - } - - async ipfsFollowList(clusterName = this.clusterName) { - let command = `ipfs-cluster-follow ${clusterName} list`; - - try { - let results = execSync(command, { encoding: 'utf8' }).trim(); - - if (results.length > 0) { - let resultsArray = results.split("\n"); - let resultsDict = {}; - - resultsArray.forEach((result) => { - // Replace multiple spaces with a single space - result = result.replace(/\s\s+/g, ' '); - let parts = result.split(" "); - - // Assuming the first element is the value and the second is the key - if (parts.length >= 2) { - let key = parts[1]; - let value = parts[0]; - resultsDict[key] = value; - } - }); - - return resultsDict; - } else { - return false; - } - } catch (error) { - console.error(`Error executing ipfs-cluster-follow list: ${error.message}`); - return false; - } - } - - - ipfsFollowInfo(kwargs = {}) { - let clusterName = this.clusterName; - let resultsDict = {}; - - try { - const command = `ipfs-cluster-follow ${clusterName} info`; - let results = execSync(command, { encoding: 'utf8' }).trim().split("\n"); - - if (results.length > 0) { - resultsDict = { - clusterName: clusterName, - configFolder: results[2].split(": ")[1], - configSource: results[3].split(": ")[1], - clusterPeerOnline: results[4].split(": ")[1], - ipfsPeerOnline: results[5].split(": ")[1], - }; - } - } catch (error) { - console.error(`Error executing ipfs-cluster-follow info: ${error.message}`); - if (error.message.includes("500")){ - let InstallIPFS = new install_ipfs.InstallIPFS(undefined, { role: 'leecher', cluster_name: clusterName, ipfs_path: this.ipfsPath }); - let installResults = InstallIPFS.installIPFSClusterFollow(); - let configResults = InstallIPFS.configIPFSClusterFollow(); - console.log(`Install IPFS Cluster Follow results: ${installResults}`); - console.log(`Configure IPFS Cluster Follow results: ${configResults}`); - console.log(`Attempting to run ipfs-cluster-follow info again`); - execSync(command, { encoding: 'utf8' }); - } - } - - return resultsDict; - } - - async ipfs_follow_run(kwargs = {}) { - let cluster_name = this.cluster_name; - if ('cluster_name' in kwargs) { - cluster_name = kwargs['cluster_name']; - } - - const command = `ipfs-cluster-follow ${cluster_name} run`; - let results = execSync(command).toString(); - results = results.split("\n"); - return results; - } - - async test_ipfs_cluster_follow() { - let detect; - try { - detect = execSync('which ipfs-cluster-follow').toString(); - } catch (error) { - detect = ''; - } - return detect.length > 0; - } - -} - -async function main(){ - - const meta = { - cluster_name: "test" - }; - - const thisIpfsClusterFollow = new IpfsClusterFollow(meta); - const results = await thisIpfsClusterFollow.test_ipfs_cluster_follow(); - console.log(results); -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_service.js b/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_service.js deleted file mode 100644 index 3af0da0..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipfs_cluster_service.js +++ /dev/null @@ -1,53 +0,0 @@ - -import { execSync } from 'child_process'; - -export class IpfsClusterService { - constructor(resources, meta = null) { - this.role = "leecher"; - if (meta !== null) { - if ('config' in meta && meta['config'] !== null) { - this.config = meta['config']; - } - if ('role' in meta && meta['role'] !== null) { - if (!["master", "worker", "leecher"].includes(meta['role'])) { - throw new Error("role is not either master, worker, leecher"); - } - this.role = meta['role']; - } - } - } - - async test_ipfs_cluster_service() { - let detect; - try { - detect = execSync('which ipfs-cluster-service').toString(); - } catch (error) { - detect = ''; - } - return detect.length > 0; - } - - async ipfs_cluster_service_start() { - const command = "systemctl start ipfs-cluster-service"; - const results = execSync(command).toString(); - return results; - } - - async ipfs_cluster_service_stop() { - const command = "systemctl stop ipfs-cluster-service"; - const results = execSync(command).toString(); - return results; - } - - async ipfs_cluster_service_status() { - const command = "ipfs-cluster-service status"; - const results = execSync(command).toString(); - return results; - } -} - -async function main(){ - const thisIpfsClusterService = new IpfsClusterService(); - const results = await thisIpfsClusterService.test_ipfs_cluster_service(); - console.log(results); -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/ipget.js b/huggingface_scraper/ipfs_kit_lib/ipget.js deleted file mode 100644 index 5e8c884..0000000 --- a/huggingface_scraper/ipfs_kit_lib/ipget.js +++ /dev/null @@ -1,94 +0,0 @@ -import { execSync } from 'child_process'; - -export class ipget { - constructor(resources, meta = null) { - if (meta !== null) { - if ('config' in meta) { - if (meta['config'] !== null) { - this.config = meta['config']; - } - } - if ('role' in meta) { - if (meta['role'] !== null) { - this.role = meta['role']; - if (!["master", "worker", "leecher"].includes(this.role)) { - throw new Error("role is not either master, worker, leecher"); - } else { - this.role = "leecher"; - } - } - } - if ('cluster_name' in meta) { - if (meta['cluster_name'] !== null) { - this.cluster_name = meta['cluster_name']; - } - } - if ('ipfs_path' in meta) { - if (meta['ipfs_path'] !== null) { - this.ipfsPath = meta['ipfs_path']; - } - } - if (this.role === "leecher" || this.role === "worker" || this.role === "master") { - // pass - } - } - } - - - async ipget_download_object(kwargs = {}) { - // NOTE: Make sure this function can download both files and folders - if (!kwargs.cid) { - throw new Error("cid not found in kwargs"); - } - if (!kwargs.path) { - throw new Error("path not found in kwargs"); - } - if (fs.existsSync(kwargs.path)) { - // pass - } - //check if folder exists - if (!fs.existsSync(path.dirname(kwargs.path))) { - fs.mkdirSync(path.dirname(kwargs.path), { recursive: true }); - } - - const command = `export IPFS_PATH=${this.ipfsPath} && ipfs get ${kwargs.cid} -o ${kwargs.path}`; - const process = exec(command); - - const start_time = Date.now(); - const timeout = 5000; // 5 seconds - - return new Promise((resolve, reject) => { - process.on('exit', (code, signal) => { - if (signal) { - reject(new Error("Command timed out")); - } else { - const stats = fs.statSync(kwargs.path); - const metadata = { - "cid": kwargs.cid, - "path": kwargs.path, - "mtime": stats.mtimeMs, - "filesize": stats.size - }; - resolve(metadata); - } - }); - - setTimeout(() => { - process.kill(); - }, timeout); - }); - } - - async test_ipget() { - try { - execSync('which ipget'); - const ipget_download_object = await this.ipget_download_object({ - cid: "QmccfbkWLYs9K3yucc6b3eSt8s8fKcyRRt24e3CDaeRhM1", - path: "/tmp/test" - }); - return true; - } catch (error) { - return false; - } - } -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/peerstore b/huggingface_scraper/ipfs_kit_lib/peerstore deleted file mode 100755 index 40337bb..0000000 --- a/huggingface_scraper/ipfs_kit_lib/peerstore +++ /dev/null @@ -1,28 +0,0 @@ -/ip4/172.29.29.10/tcp/9096/p2p/12D3KooWNJN6azoq29bY4J3GjE8xQHdYZeQ72Ga6cXnnQ11Jx8fP -/ip4/192.168.0.20/tcp/57468/p2p/12D3KooWNJN6azoq29bY4J3GjE8xQHdYZeQ72Ga6cXnnQ11Jx8fP -/ip4/97.120.209.166/tcp/18100/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea/p2p-circuit/p2p/12D3KooWNJN6azoq29bY4J3GjE8xQHdYZeQ72Ga6cXnnQ11Jx8fP -/ip4/97.120.209.166/tcp/28143/p2p/12D3KooWECihD8h6TooNoqJAVFDsP5MgUiVcy7XYRRmkFM3yyYME/p2p-circuit/p2p/12D3KooWNJN6azoq29bY4J3GjE8xQHdYZeQ72Ga6cXnnQ11Jx8fP -/ip4/97.120.209.166/tcp/38053/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea/p2p-circuit/p2p/12D3KooWNJN6azoq29bY4J3GjE8xQHdYZeQ72Ga6cXnnQ11Jx8fP -/ip4/10.11.0.1/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/10.120.0.2/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/10.46.0.5/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/127.0.0.1/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/172.17.0.1/tcp/9096/p2p/12D3KooWDYKMnVLKnP2SmM8umJEEKdhug93QYybmNUEiSe1Kwjmu -/ip4/10.11.0.1/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/10.120.0.2/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/10.46.0.5/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/127.0.0.1/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/172.17.0.1/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv -/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWKw9XCkdfnf8CkAseryCgS3VVoGQ6HUAkY91Qc6Fvn4yv/p2p-circuit/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea -/ip4/25.18.152.214/tcp/9096/p2p/12D3KooWECihD8h6TooNoqJAVFDsP5MgUiVcy7XYRRmkFM3yyYME/p2p-circuit/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea -/ip4/97.120.166.109/tcp/33661/p2p/12D3KooWECihD8h6TooNoqJAVFDsP5MgUiVcy7XYRRmkFM3yyYME/p2p-circuit/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea -/ip4/97.120.209.166/tcp/18100/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea -/ip4/97.120.209.166/tcp/38053/p2p/12D3KooWJ6mj5yii47Hedtfajahmcq1jhU1CmycvKoKoRFWyY4Ea -/ip4/10.11.0.1/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV -/ip4/10.120.0.2/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV -/ip4/10.46.0.5/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV -/ip4/127.0.0.1/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV -/ip4/167.99.96.231/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV -/ip4/172.17.0.1/tcp/9096/p2p/12D3KooWFHz8Ze2LyrkkL7q3AqMU6TASaYcW2nmzpYwQodsq2SCV diff --git a/huggingface_scraper/ipfs_kit_lib/service.json b/huggingface_scraper/ipfs_kit_lib/service.json deleted file mode 100755 index 81b6568..0000000 --- a/huggingface_scraper/ipfs_kit_lib/service.json +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cluster": { - "peername": "master", - "secret": "96d5952479d0a2f9fbf55076e5ee04802f15ae5452b5faafc98e2bd48cf564d3", - "leave_on_shutdown": false, - "listen_multiaddress": [ - "/ip4/0.0.0.0/tcp/9096", - "/ip4/0.0.0.0/udp/9096/quic" - ], - "enable_relay_hop": true, - "connection_manager": { - "high_water": 400, - "low_water": 100, - "grace_period": "2m0s" - }, - "dial_peer_timeout": "3s", - "state_sync_interval": "5m0s", - "pin_recover_interval": "12m0s", - "replication_factor_min": -1, - "replication_factor_max": -1, - "monitor_ping_interval": "15s", - "peer_watch_interval": "5s", - "mdns_interval": "10s", - "pin_only_on_trusted_peers": false, - "pin_only_on_untrusted_peers": false, - "disable_repinning": true, - "peer_addresses": [] - }, - "consensus": { - "crdt": { - "cluster_name": "ipfs-cluster", - "trusted_peers": [ - "*" - ], - "batching": { - "max_batch_size": 0, - "max_batch_age": "0s" - }, - "repair_interval": "1h0m0s" - } - }, - "api": { - "ipfsproxy": { - "listen_multiaddress": "/ip4/127.0.0.1/tcp/9095", - "node_multiaddress": "/ip4/127.0.0.1/tcp/5001", - "log_file": "", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "1m0s", - "max_header_bytes": 4096 - }, - "pinsvcapi": { - "http_listen_multiaddress": "/ip4/127.0.0.1/tcp/9097", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "2m0s", - "max_header_bytes": 4096, - "basic_auth_credentials": null, - "http_log_file": "", - "headers": {}, - "cors_allowed_origins": [ - "*" - ], - "cors_allowed_methods": [ - "GET" - ], - "cors_allowed_headers": [], - "cors_exposed_headers": [ - "Content-Type", - "X-Stream-Output", - "X-Chunked-Output", - "X-Content-Length" - ], - "cors_allow_credentials": true, - "cors_max_age": "0s" - }, - "restapi": { - "http_listen_multiaddress": "/ip4/127.0.0.1/tcp/9094", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "2m0s", - "max_header_bytes": 4096, - "basic_auth_credentials": null, - "http_log_file": "", - "headers": {}, - "cors_allowed_origins": [ - "*" - ], - "cors_allowed_methods": [ - "GET" - ], - "cors_allowed_headers": [], - "cors_exposed_headers": [ - "Content-Type", - "X-Stream-Output", - "X-Chunked-Output", - "X-Content-Length" - ], - "cors_allow_credentials": true, - "cors_max_age": "0s" - } - }, - "ipfs_connector": { - "ipfshttp": { - "node_multiaddress": "/ip4/127.0.0.1/tcp/5001", - "connect_swarms_delay": "30s", - "ipfs_request_timeout": "5m0s", - "pin_timeout": "2m0s", - "unpin_timeout": "3h0m0s", - "repogc_timeout": "24h0m0s", - "informer_trigger_interval": 0 - } - }, - "pin_tracker": { - "stateless": { - "concurrent_pins": 10, - "priority_pin_max_age": "24h0m0s", - "priority_pin_max_retries": 5 - } - }, - "monitor": { - "pubsubmon": { - "check_interval": "15s" - } - }, - "allocator": { - "balanced": { - "allocate_by": [ - "tag:group", - "freespace" - ] - } - }, - "informer": { - "disk": { - "metric_ttl": "30s", - "metric_type": "freespace" - }, - "pinqueue": { - "metric_ttl": "30s", - "weight_bucket_size": 100000 - }, - "tags": { - "metric_ttl": "30s", - "tags": { - "group": "default" - } - } - }, - "observations": { - "metrics": { - "enable_stats": false, - "prometheus_endpoint": "/ip4/127.0.0.1/tcp/8888", - "reporting_interval": "2s" - }, - "tracing": { - "enable_tracing": false, - "jaeger_agent_endpoint": "/ip4/0.0.0.0/udp/6831", - "sampling_prob": 0.3, - "service_name": "cluster-daemon" - } - }, - "datastore": { - "pebble": { - "pebble_options": { - "cache_size_bytes": 1073741824, - "bytes_per_sync": 1048576, - "disable_wal": false, - "flush_delay_delete_range": 0, - "flush_delay_range_key": 0, - "flush_split_bytes": 4194304, - "format_major_version": 1, - "l0_compaction_file_threshold": 750, - "l0_compaction_threshold": 4, - "l0_stop_writes_threshold": 12, - "l_base_max_bytes": 134217728, - "max_open_files": 1000, - "mem_table_size": 67108864, - "mem_table_stop_writes_threshold": 20, - "read_only": false, - "wal_bytes_per_sync": 0, - "levels": [ - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 4194304 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 8388608 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 16777216 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 33554432 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 67108864 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 134217728 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 268435456 - } - ], - "max_concurrent_compactions": 5 - } - } - } -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/service_follower.json b/huggingface_scraper/ipfs_kit_lib/service_follower.json deleted file mode 100644 index 7e24995..0000000 --- a/huggingface_scraper/ipfs_kit_lib/service_follower.json +++ /dev/null @@ -1,261 +0,0 @@ -{ - "cluster": { - "peername": "worker", - "secret": "96d5952479d0a2f9fbf55076e5ee04802f15ae5452b5faafc98e2bd48cf564d3", - "leave_on_shutdown": false, - "listen_multiaddress": [ - "/ip4/0.0.0.0/tcp/9096", - "/ip4/0.0.0.0/udp/9096/quic" - ], - "enable_relay_hop": true, - "connection_manager": { - "high_water": 400, - "low_water": 100, - "grace_period": "2m0s" - }, - "dial_peer_timeout": "3s", - "state_sync_interval": "5m0s", - "pin_recover_interval": "12m0s", - "replication_factor_min": -1, - "replication_factor_max": -1, - "monitor_ping_interval": "15s", - "peer_watch_interval": "5s", - "mdns_interval": "10s", - "pin_only_on_trusted_peers": false, - "pin_only_on_untrusted_peers": false, - "disable_repinning": true, - "peer_addresses": [] - }, - "consensus": { - "crdt": { - "cluster_name": "ipfs-cluster", - "trusted_peers": [ - "*" - ], - "batching": { - "max_batch_size": 0, - "max_batch_age": "0s" - }, - "repair_interval": "1h0m0s" - } - }, - "api": { - "ipfsproxy": { - "listen_multiaddress": "/ip4/127.0.0.1/tcp/9095", - "node_multiaddress": "/ip4/127.0.0.1/tcp/5001", - "log_file": "", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "1m0s", - "max_header_bytes": 4096 - }, - "pinsvcapi": { - "http_listen_multiaddress": "/ip4/127.0.0.1/tcp/9097", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "2m0s", - "max_header_bytes": 4096, - "basic_auth_credentials": null, - "http_log_file": "", - "headers": {}, - "cors_allowed_origins": [ - "*" - ], - "cors_allowed_methods": [ - "GET" - ], - "cors_allowed_headers": [], - "cors_exposed_headers": [ - "Content-Type", - "X-Stream-Output", - "X-Chunked-Output", - "X-Content-Length" - ], - "cors_allow_credentials": true, - "cors_max_age": "0s" - }, - "restapi": { - "http_listen_multiaddress": "/ip4/127.0.0.1/tcp/9094", - "read_timeout": "0s", - "read_header_timeout": "5s", - "write_timeout": "0s", - "idle_timeout": "2m0s", - "max_header_bytes": 4096, - "basic_auth_credentials": null, - "http_log_file": "", - "headers": {}, - "cors_allowed_origins": [ - "*" - ], - "cors_allowed_methods": [ - "GET" - ], - "cors_allowed_headers": [], - "cors_exposed_headers": [ - "Content-Type", - "X-Stream-Output", - "X-Chunked-Output", - "X-Content-Length" - ], - "cors_allow_credentials": true, - "cors_max_age": "0s" - } - }, - "ipfs_connector": { - "ipfshttp": { - "node_multiaddress": "/ip4/127.0.0.1/tcp/5001", - "connect_swarms_delay": "30s", - "ipfs_request_timeout": "5m0s", - "pin_timeout": "2m0s", - "unpin_timeout": "3h0m0s", - "repogc_timeout": "24h0m0s", - "informer_trigger_interval": 0 - } - }, - "pin_tracker": { - "stateless": { - "concurrent_pins": 10, - "priority_pin_max_age": "24h0m0s", - "priority_pin_max_retries": 5 - } - }, - "monitor": { - "pubsubmon": { - "check_interval": "15s" - } - }, - "allocator": { - "balanced": { - "allocate_by": [ - "tag:group", - "freespace" - ] - } - }, - "informer": { - "disk": { - "metric_ttl": "30s", - "metric_type": "freespace" - }, - "pinqueue": { - "metric_ttl": "30s", - "weight_bucket_size": 100000 - }, - "tags": { - "metric_ttl": "30s", - "tags": { - "group": "default" - } - } - }, - "observations": { - "metrics": { - "enable_stats": false, - "prometheus_endpoint": "/ip4/127.0.0.1/tcp/8888", - "reporting_interval": "2s" - }, - "tracing": { - "enable_tracing": false, - "jaeger_agent_endpoint": "/ip4/0.0.0.0/udp/6831", - "sampling_prob": 0.3, - "service_name": "cluster-daemon" - } - }, - "datastore": { - "pebble": { - "pebble_options": { - "cache_size_bytes": 1073741824, - "bytes_per_sync": 1048576, - "disable_wal": false, - "flush_delay_delete_range": 0, - "flush_delay_range_key": 0, - "flush_split_bytes": 4194304, - "format_major_version": 1, - "l0_compaction_file_threshold": 750, - "l0_compaction_threshold": 4, - "l0_stop_writes_threshold": 12, - "l_base_max_bytes": 134217728, - "max_open_files": 1000, - "mem_table_size": 67108864, - "mem_table_stop_writes_threshold": 20, - "read_only": false, - "wal_bytes_per_sync": 0, - "levels": [ - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 4194304 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 8388608 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 16777216 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 33554432 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 67108864 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 134217728 - }, - { - "block_restart_interval": 16, - "block_size": 4096, - "block_size_threshold": 90, - "compression": 2, - "filter_type": 0, - "filter_policy": 10, - "index_block_size": 4096, - "target_file_size": 268435456 - } - ], - "max_concurrent_compactions": 5 - } - } - } -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_kit_lib/test_fio.js b/huggingface_scraper/ipfs_kit_lib/test_fio.js deleted file mode 100644 index fe7031c..0000000 --- a/huggingface_scraper/ipfs_kit_lib/test_fio.js +++ /dev/null @@ -1,140 +0,0 @@ -import { execSync } from 'child_process'; - -export class TestFio { - constructor(resources, meta = null) { - // constructor body - } - - call(method, kwargs = {}) { - if (method === "test") { - return this.test(kwargs); - } - } - - disk_device_name_from_location(location) { - let directory_tree = location.split("/"); - - const command = "df -h"; - let df = execSync(command).toString(); - df = df.split("\n"); - for (let line of df) { - if (line.includes(location)) { - const device = line.split(" ")[0]; - return device; - } else { - while (directory_tree.length > 1) { - directory_tree.pop(); - location = directory_tree.join("/"); - for (let line of df) { - if (directory_tree.length === 1 && location === "") { - location = "/"; - } - if (line.includes(location)) { - while (line.includes(" ")) { - line = line.replace(" ", " "); - } - const mount = line.split(" "); - if (mount[5] === location) { - const device = mount[0]; - return device; - } - } - } - } - } - } - return "rootfs"; - } - - - disk_device_total_capacity(device) { - const command = "df -h"; - let df = execSync(command).toString(); - df = df.split("\n"); - for (let line of df) { - if (line.includes(device)) { - while (line.includes(" ")) { - line = line.replace(" ", " "); - } - const capacity = line.split(" ")[1]; - return capacity; - } - } - return null; - } - - disk_device_used_capacity(device) { - const command = "df -h"; - let df = execSync(command).toString(); - df = df.split("\n"); - for (let line of df) { - if (line.includes(device)) { - while (line.includes(" ")) { - line = line.replace(" ", " "); - } - const capacity = line.split(" ")[2]; - return capacity; - } - } - return null; - } - - disk_device_avail_capacity(device) { - const command = "df -h"; - let df = execSync(command).toString(); - df = df.split("\n"); - for (let line of df) { - if (line.includes(device)) { - while (line.includes(" ")) { - line = line.replace(" ", " "); - } - const capacity = line.split(" ")[3]; - return capacity; - } - } - return null; - } - - disk_speed_4k(location) { - const tempFile = tmp.fileSync({ postfix: '.iso', dir: location }); - const timestamp_0 = Date.now(); - const command = `dd if=/dev/zero of=${tempFile.name} bs=4k count=8k conv=fdatasync`; - execSync(command); - const timestamp_1 = Date.now(); - const write_speed = 32 / ((timestamp_1 - timestamp_0) / 1000); - const command2 = `dd if=${tempFile.name} of=/dev/null bs=4k`; - execSync(command2); - const timestamp_2 = Date.now(); - const read_speed = 32 / ((timestamp_2 - timestamp_1) / 1000); - fs.unlinkSync(tempFile.name); - return { read_speed, write_speed }; - } - - - stats(location, kwargs = {}) { - const disk_device = this.disk_device_name_from_location(location); - const disk_capacity = this.disk_device_total_capacity(disk_device); - const disk_used = this.disk_device_used_capacity(disk_device); - const disk_avail = this.disk_device_avail_capacity(disk_device); - const { read_speed: disk_read_speed, write_speed: disk_write_speed } = this.disk_speed_4k(location); - const results = { - disk_device, - disk_capacity, - disk_used, - disk_avail, - disk_write_speed, - disk_read_speed - }; - return results; - } - - -} - -function test(){ - const thisTest = new TestFio(null); - const results = thisTest.test("/tmp/"); - console.log(results); - console.log("Test complete"); - //process.exit(0); -} \ No newline at end of file diff --git a/huggingface_scraper/ipfs_test.js b/huggingface_scraper/ipfs_test.js deleted file mode 100644 index 3de4e9e..0000000 --- a/huggingface_scraper/ipfs_test.js +++ /dev/null @@ -1,22 +0,0 @@ -import { ipfsClusterCtl } from "./ipfs.js" -import fs from 'fs' - - -export function test1(){ - let collection_path = "/storage/cloudkit-models/collection.json" - let collection = fs.readFileSync(collection_path) - collection = JSON.parse(collection) - let this_cluster = new ipfsClusterCtl() - let status = this_cluster.check_collection(collection) - return status -} - - -export function ipfsClusterStatus(collection){ - let this_cluster = new ipfsClusterCtl() - let status = this_cluster.check_collection(collection) - return status -} - -let results = test1() -console.log(results) \ No newline at end of file diff --git a/huggingface_scraper/libllama/avx2/convert-ggml-gguf.py b/huggingface_scraper/libllama/avx2/convert-ggml-gguf.py deleted file mode 100644 index 5a7483b..0000000 --- a/huggingface_scraper/libllama/avx2/convert-ggml-gguf.py +++ /dev/null @@ -1,1225 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import argparse -import concurrent.futures -import copy -import enum -import faulthandler -import functools -import io -import itertools -import json -import math -import mmap -import pickle -import re -import signal -import struct -import sys -import time -import zipfile -from abc import ABCMeta, abstractmethod -from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor -from dataclasses import dataclass -from pathlib import Path -from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar - -import numpy as np -from sentencepiece import SentencePieceProcessor # type: ignore[import] - -import os -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - -if TYPE_CHECKING: - from typing import TypeAlias - -if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): - faulthandler.register(signal.SIGUSR1) - -NDArray: TypeAlias = 'np.ndarray[Any, Any]' - -ARCH=gguf.MODEL_ARCH.LLAMA -NAMES=gguf.MODEL_TENSOR_NAMES[ARCH] - -DEFAULT_CONCURRENCY = 8 -# -# data types -# - -@dataclass(frozen=True) -class DataType: - name: str - dtype: np.dtype[Any] - valid_conversions: list[str] - - def elements_to_bytes(self, n_elements: int) -> int: - return n_elements * self.dtype.itemsize - -@dataclass(frozen=True) -class UnquantizedDataType(DataType): - pass - -DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) -DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) -DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) -DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) - -@dataclass(frozen=True) -class QuantizedDataType(DataType): - block_size: int - quantized_dtype: np.dtype[Any] - ggml_type: gguf.GGMLQuantizationType - - def quantize(self, arr: NDArray) -> NDArray: - raise NotImplementedError(f'Quantization for {self.name} not implemented') - - def elements_to_bytes(self, n_elements: int) -> int: - assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}' - return self.quantized_dtype.itemsize * (n_elements // self.block_size) - -@dataclass(frozen=True) -class Q8_0QuantizedDataType(QuantizedDataType): - # Mini Q8_0 quantization in Python! - def quantize(self, arr: NDArray) -> NDArray: - assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}' - assert arr.dtype == np.float32, f'Bad array type {arr.dtype}' - n_blocks = arr.size // self.block_size - blocks = arr.reshape((n_blocks, self.block_size)) - # Much faster implementation of block quantization contributed by @Cebtenzzre - def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]: - d = abs(blocks).max(axis = 1) / np.float32(127) - with np.errstate(divide = 'ignore'): - qs = (blocks / d[:, None]).round() - qs[d == 0] = 0 - yield from zip(d, qs) - return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype) - -DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', - dtype = np.dtype(np.float32), valid_conversions = [], - ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32, - quantized_dtype = np.dtype([('d', ' DataType: - dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) - if dt is None: - raise ValueError(self) - # 1D tensors are always F32. - return dt if len(tensor.shape) > 1 else DT_F32 - -GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { - GGMLFileType.AllF32 : DT_F32, - GGMLFileType.MostlyF16 : DT_F16, - GGMLFileType.MostlyQ8_0: DT_Q8_0, -} - -# -# hparams loading -# - -@dataclass -class Params: - n_vocab: int - n_embd: int - n_mult: int - n_layer: int - n_ctx: int - n_ff: int - n_head: int - n_head_kv: int - f_norm_eps: float - - f_rope_freq_base: float | None = None - f_rope_scale: float | None = None - - ftype: GGMLFileType | None = None - - # path to the directory containing the model files - path_model: Path | None = None - - @staticmethod - def find_n_mult(n_ff: int, n_embd: int) -> int: - # hardcoded magic range - for n_mult in range(8192, 1, -1): - calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult - if calc_ff == n_ff: - return n_mult - raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") - - @staticmethod - def guessed(model: LazyModel) -> Params: - # try transformer naming first - n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape - - # try transformer naming first - if "model.layers.0.self_attn.q_proj.weight" in model: - n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model) - elif "model.layers.0.self_attn.W_pack.weight" in model: # next: try baichuan naming - n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.W_pack.weight" not in model) - else: - n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) - - if n_layer < 1: - raise Exception("failed to guess 'n_layer'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") - - n_head = n_embd // 128 # guessed - n_mult = 256 # guessed - - # TODO: verify this - n_ff = int(2 * (4 * n_embd) / 3) - n_ff = n_mult * ((n_ff + n_mult - 1) // n_mult) - - return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_layer = n_layer, - n_ctx = -1, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head, - f_norm_eps = 1e-5, - ) - - @staticmethod - def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: - config = json.load(open(config_path)) - - n_vocab = config["vocab_size"] - n_embd = config["hidden_size"] - n_layer = config["num_hidden_layers"] - n_ff = config["intermediate_size"] - n_head = config["num_attention_heads"] - n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head - f_norm_eps = config["rms_norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - - rope_scaling = config.get("rope_scaling") - if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear": - f_rope_scale = config["rope_scaling"].get("factor") - else: - f_rope_scale = None - - n_mult = Params.find_n_mult(n_ff, n_embd) - - if "max_sequence_length" in config: - n_ctx = config["max_sequence_length"] - elif "max_position_embeddings" in config: - n_ctx = config["max_position_embeddings"] - else: - raise Exception("failed to guess 'n_ctx'. This model is unknown or unsupported.\n" - "Suggestion: provide 'config.json' of the model in the same directory containing model files.") - - return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_layer = n_layer, - n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, - f_rope_scale = f_rope_scale, - ) - - # LLaMA v2 70B params.json - # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 - @staticmethod - def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: - config = json.load(open(config_path)) - - n_vocab = config["vocab_size"] if "vocab_size" in config else -1 - n_embd = config["dim"] - n_layer = config["n_layers"] - n_mult = config["multiple_of"] - n_ff = -1 - n_head = config["n_heads"] - n_head_kv = config["n_kv_heads"] if "n_kv_heads" in config else n_head - f_norm_eps = config["norm_eps"] - f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - - # hack to determine LLaMA v1 vs v2 vs CodeLlama - if f_rope_freq_base and f_rope_freq_base == 1000000: - # CodeLlama - n_ctx = 16384 - elif config["norm_eps"] == 1e-05: - # LLaMA v2 - n_ctx = 4096 - else: - # LLaMA v1 - n_ctx = 2048 - - if n_vocab == -1: - n_vocab = model["tok_embeddings.weight"].shape[0] - - if n_ff == -1: - n_ff = model["layers.0.feed_forward.w1.weight"].shape[0] - - return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_layer = n_layer, - n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, - f_rope_freq_base = f_rope_freq_base, - ) - - @staticmethod - def load(model_plus: ModelPlus) -> Params: - hf_config_path = model_plus.paths[0].parent / "config.json" - orig_config_path = model_plus.paths[0].parent / "params.json" - - if hf_config_path.exists(): - params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) - elif orig_config_path.exists(): - params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) - elif model_plus.format != 'none': - params = Params.guessed(model_plus.model) - else: - raise ValueError('Cannot guess params when model format is none') - - params.path_model = model_plus.paths[0].parent - - return params - - -# -# vocab -# - -class BpeVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: - self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - added_tokens: dict[str, int] - if fname_added_tokens is not None: - # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. - added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) - else: - # Fall back to trying to find the added tokens in tokenizer.json - tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json' - if not tokenizer_json_file.is_file(): - added_tokens = {} - else: - tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) - added_tokens = dict( - (item['content'], item['id']) - for item in tokenizer_json.get('added_tokens', []) - # Added tokens here can be duplicates of the main vocabulary. - if item['content'] not in self.bpe_tokenizer ) - - vocab_size: int = len(self.bpe_tokenizer) - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - expected_end_id = vocab_size + len(actual_ids) - 1 - raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base: int = vocab_size - self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.bpe_tokenizer - from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] - byte_encoder = tokenization_gpt2.bytes_to_unicode() - byte_decoder = {v: k for k, v in byte_encoder.items()} - score = 0.0 - for i, item in enumerate(tokenizer): - text: bytes = item.encode("utf-8") - # FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior? - if i <= 258 and text.startswith(b'<') and text.endswith(b'>'): - if i == 0 and text == b'': - toktype = gguf.TokenType.UNKNOWN - elif i == 1 or i == 2: - toktype = gguf.TokenType.CONTROL - elif i >= 3 and text.startswith(b'<0x'): - toktype = gguf.TokenType.BYTE - else: - toktype = gguf.TokenType.NORMAL - else: - toktype = gguf.TokenType.NORMAL - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.bpe_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) - added_tokens: dict[str, int] - if fname_added_tokens is not None: - added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) - else: - added_tokens = {} - - vocab_size: int = self.sentencepiece_tokenizer.vocab_size() - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base: int = vocab_size - self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - self.fname_added_tokens = fname_added_tokens - - def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(i) - text: bytes = piece.encode("utf-8") - score: float = tokenizer.get_score(i) - - toktype = gguf.TokenType.NORMAL - if tokenizer.is_unknown(i): - toktype = gguf.TokenType.UNKNOWN - if tokenizer.is_control(i): - toktype = gguf.TokenType.CONTROL - - # NOTE: I think added_tokens are user defined. - # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto - # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED - - if tokenizer.is_unused(i): - toktype = gguf.TokenType.UNUSED - if tokenizer.is_byte(i): - toktype = gguf.TokenType.BYTE - - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - -Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab' - -# -# data loading -# TODO: reuse (probably move to gguf.py?) -# - -def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: - #print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) ) - if n_head_kv is not None and n_head != n_head_kv: - n_head //= n_head_kv - return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) - - -class Tensor(metaclass=ABCMeta): - data_type: DataType - - @abstractmethod - def astype(self, data_type: DataType) -> Tensor: ... - @abstractmethod - def permute(self, n_head: int, n_head_kv: int) -> Tensor: ... - @abstractmethod - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ... - @abstractmethod - def part(self, n_part: int) -> UnquantizedTensor: ... - @abstractmethod - def to_ggml(self) -> GGMLCompatibleTensor: ... - - -def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: - assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}" - fp32_arr = bf16_arr.astype(np.uint32) << 16 - return fp32_arr.view(np.float32) - - -class UnquantizedTensor(Tensor): - def __init__(self, ndarray: NDArray) -> None: - assert isinstance(ndarray, np.ndarray) - self.ndarray = ndarray - self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] - - def astype(self, data_type: DataType) -> Tensor: - dtype = data_type.dtype - if self.data_type == DT_BF16: - self.ndarray = bf16_to_fp32(self.ndarray) - return UnquantizedTensor(self.ndarray.astype(dtype)) - - def to_ggml(self) -> UnquantizedTensor: - return self - - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: - r = self.ndarray.shape[0] // 3 - return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) - - def part(self, n_part: int) -> UnquantizedTensor: - r = self.ndarray.shape[0] // 3 - return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) - - def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor: - return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv)) - - -def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray: - tensor = lazy_tensor.load() - assert isinstance(tensor, UnquantizedTensor) - - # double-check: - actual_shape = list(tensor.ndarray.shape) - assert actual_shape == lazy_tensor.shape, (actual_shape, lazy_tensor.shape) - if expected_dtype is not None and expected_dtype != tensor.ndarray.dtype: - if convert: - tensor.ndarray = tensor.ndarray.astype(expected_dtype) - else: - raise ValueError(f'expected this tensor to have dtype {expected_dtype}, got {tensor.ndarray.dtype}') - - return tensor.ndarray - - -GGMLCompatibleTensor = UnquantizedTensor - - -@dataclass -class LazyTensor: - _load: Callable[[], Tensor] - shape: list[int] - data_type: DataType - description: str - - def load(self) -> Tensor: - ret = self._load() - # Should be okay if it maps to the same numpy type? - assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \ - (self.data_type, ret.data_type, self.description) - return ret - - def astype(self, data_type: DataType) -> LazyTensor: - self.validate_conversion_to(data_type) - - def load() -> Tensor: - return self.load().astype(data_type) - return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') - - def validate_conversion_to(self, data_type: DataType) -> None: - if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions: - raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') - - -LazyModel: TypeAlias = 'dict[str, LazyTensor]' - - -@dataclass -class ModelPlus: - model: LazyModel - paths: list[Path] # Where this was read from. - format: Literal['ggml', 'torch', 'safetensors', 'none'] - vocab: Vocab | None # For GGML models (which have vocab built in), the vocab. - - -def merge_sharded(models: list[LazyModel]) -> LazyModel: - # Original LLaMA models have each file contain one part of each tensor. - # Use a dict instead of a set to preserve order. - names = {name: None for model in models for name in model} - - def convert(name: str) -> LazyTensor: - lazy_tensors: list[LazyTensor] = [model[name] for model in models] - if len(lazy_tensors) == 1: - # only one file; don't go through this procedure since there might - # be quantized tensors - return lazy_tensors[0] - if len(lazy_tensors[0].shape) == 1: - # the tensor is just duplicated in every file - return lazy_tensors[0] - if name.startswith('tok_embeddings.') or \ - name.endswith('.attention.wo.weight') or \ - name.endswith('.feed_forward.w2.weight'): - # split by columns - axis = 1 - else: - # split by rows - axis = 0 - concatenated_shape = list(lazy_tensors[0].shape) - concatenated_shape[axis] = sum(tensor.shape[axis] for tensor in lazy_tensors) - - def load() -> UnquantizedTensor: - ndarrays = [load_unquantized(tensor) for tensor in lazy_tensors] - concatenated: NDArray = np.concatenate(ndarrays, axis=axis) - return UnquantizedTensor(concatenated) - description = 'concatenated[[' + '] | ['.join(lt.description for lt in lazy_tensors) + ']]' - return LazyTensor(load, concatenated_shape, lazy_tensors[0].data_type, description) - return {name: convert(name) for name in names} - - -def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: - formats = set(mp.format for mp in models_plus) - assert len(formats) == 1, "different formats?" - format = formats.pop() - paths = [path for mp in models_plus for path in mp.paths] - # Use the first non-None vocab, if any. - try: - vocab = next(mp.vocab for mp in models_plus if mp.vocab is not None) - except StopIteration: - vocab = None - - if any("model.embed_tokens.weight" in mp.model for mp in models_plus): - # Transformers models put different tensors in different files, but - # don't split indivdual tensors between files. - model: LazyModel = {} - for mp in models_plus: - model.update(mp.model) - else: - model = merge_sharded([mp.model for mp in models_plus]) - - return ModelPlus(model, paths, format, vocab) - - -def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTensor: - def load() -> Tensor: - return lazy_tensor.load().permute(n_head, n_head_kv) - return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) - -def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor: - def load() -> Tensor: - return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv) - s = lazy_tensor.shape.copy() - s[0] = s[0] // 3 - return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) - -def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: - def load() -> Tensor: - return lazy_tensor.load().part(n_part) - s = lazy_tensor.shape.copy() - s[0] = s[0] // 3 - return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) - - -# Functionality that simulates `torch.load` but where individual tensors are -# only loaded into memory on demand, not all at once. -# PyTorch can't do this natively as of time of writing: -# - https://github.com/pytorch/pytorch/issues/64327 -# This allows us to de-shard without multiplying RAM usage, and also -# conveniently drops the PyTorch dependency (though we still need numpy). - - -@dataclass -class LazyStorageKind: - data_type: DataType - - -@dataclass -class LazyStorage: - load: Callable[[int, int], NDArray] - kind: LazyStorageKind - description: str - - -class LazyUnpickler(pickle.Unpickler): - def __init__(self, fp: IO[bytes], data_base_path: str, zip_file: zipfile.ZipFile): - super().__init__(fp) - self.data_base_path = data_base_path - self.zip_file = zip_file - - def persistent_load(self, pid: Any) -> Any: - assert pid[0] == 'storage' - assert isinstance(pid[1], LazyStorageKind) - data_type = pid[1].data_type - filename_stem = pid[2] - filename = self.data_base_path + '/' + filename_stem - info = self.zip_file.getinfo(filename) - - def load(offset: int, elm_count: int) -> NDArray: - dtype = data_type.dtype - fp = self.zip_file.open(info) - fp.seek(offset * dtype.itemsize) - size = elm_count * dtype.itemsize - data = fp.read(size) - assert len(data) == size - return np.frombuffer(data, dtype) - description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' - return LazyStorage(load=load, kind=pid[1], description=description) - - @staticmethod - def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, - # pyright: ignore[reportSelfClsParameterName] - requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: - assert isinstance(storage, LazyStorage) - - def load() -> UnquantizedTensor: - elm_count = stride[0] * size[0] - return UnquantizedTensor(storage.load(storage_offset, elm_count).reshape(size)) - description = f'pickled storage_offset={storage_offset} in {storage.description}' - return LazyTensor(load, list(size), storage.kind.data_type, description) - - @staticmethod - def rebuild_from_type_v2(func, new_type, args, state): - return func(*args) - - CLASSES: dict[tuple[str, str], Any] = { - # getattr used here as a workaround for mypy not being smart enough to detrmine - # the staticmethods have a __func__ attribute. - ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), - ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), - ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), - ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), - ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), - ('torch', 'IntStorage'): LazyStorageKind(DT_I32), - ('torch', 'Tensor'): LazyTensor, - } - - def find_class(self, module: str, name: str) -> Any: - if not module.startswith('torch'): - return super().find_class(module, name) - return self.CLASSES[(module, name)] - - -def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: - zf = zipfile.ZipFile(outer_fp) - pickle_paths = [name for name in zf.namelist() if name.endswith('.pkl')] - assert len(pickle_paths) == 1, pickle_paths - pickle_fp = zf.open(pickle_paths[0], 'r') - unpickler = LazyUnpickler(pickle_fp, - data_base_path=pickle_paths[0][:-4], - zip_file=zf) - model = unpickler.load() - as_dict = dict(model.items()) - return ModelPlus(model=as_dict, paths=[path], format='torch', vocab=None) - - -def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: - header_size, = struct.unpack(' LazyTensor: - data_type = SAFETENSORS_DATA_TYPES[info['dtype']] - numpy_dtype = data_type.dtype - shape: list[int] = info['shape'] - begin, end = info['data_offsets'] - assert 0 <= begin <= end <= len(byte_buf) - assert end - begin == math.prod(shape) * numpy_dtype.itemsize - buf = byte_buf[begin:end] - - def load() -> UnquantizedTensor: - return UnquantizedTensor(np.frombuffer(buf, dtype=numpy_dtype).reshape(shape)) - description = f'safetensors begin={begin} end={end} type={data_type} path={path}' - return LazyTensor(load, shape, data_type, description) - model = {name: convert(info) for (name, info) in header.items() if name != '__metadata__'} - return ModelPlus(model=model, paths=[path], format='safetensors', vocab=None) - - -def must_read(fp: IO[bytes], length: int) -> bytes: - ret = fp.read(length) - if len(ret) < length: - raise Exception("unexpectedly reached end of file") - return ret - - -@functools.lru_cache(maxsize=None) -def lazy_load_file(path: Path) -> ModelPlus: - fp = open(path, 'rb') - first8 = fp.read(8) - fp.seek(0) - if first8[:2] == b'PK': - # A zip file, i.e. PyTorch format - return lazy_load_torch_file(fp, path) - elif struct.unpack(' Iterable[Out]: - '''Parallel map, but with backpressure. If the caller doesn't call `next` - fast enough, this will stop calling `func` at some point rather than - letting results pile up in memory. Specifically, there is a max of one - output value buffered per thread.''' - if concurrency < 2: - yield from map(func, iterable) - # Not reached. - iterable = iter(iterable) - executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor] - if use_processpool_executor: - executor_class = ProcessPoolExecutor - else: - executor_class = ThreadPoolExecutor - with executor_class(max_workers = max_workers) as executor: - futures: list[concurrent.futures.Future[Out]] = [] - done = False - for _ in range(concurrency): - try: - futures.append(executor.submit(func, next(iterable))) - except StopIteration: - done = True - break - - while futures: - result = futures.pop(0).result() - while not done and len(futures) < concurrency: - try: - futures.append(executor.submit(func, next(iterable))) - except StopIteration: - done = True - break - yield result - -def check_vocab_size(params: Params, vocab: Vocab) -> None: - if params.n_vocab != vocab.vocab_size: - assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab) - if params.n_vocab == vocab.vocab_size_base: - print("Ignoring added_tokens.json since model matches vocab size without it.") - vocab.added_tokens_list = [] - vocab.vocab_size = vocab.vocab_size_base - return - msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}" - if vocab.fname_added_tokens is not None: - msg += f" combined with {vocab.fname_added_tokens}" - msg += f" has {vocab.vocab_size})." - if vocab.vocab_size < params.n_vocab < vocab.vocab_size + 20 and vocab.fname_added_tokens is None: - msg += f" Most likely you are missing added_tokens.json (should be in {vocab.fname_tokenizer.parent})." - raise Exception(msg) - - -class OutputFile: - def __init__(self, fname_out: Path) -> None: - self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - - def add_meta_arch(self, params: Params) -> None: - name = "LLaMA" - - # TODO: better logic to determine model name - if (params.n_ctx == 4096): - name = "LLaMA v2" - elif params.path_model: - name = str(params.path_model.parent).split('/')[-1] - - self.gguf.add_name (name) - self.gguf.add_context_length (params.n_ctx) - self.gguf.add_embedding_length (params.n_embd) - self.gguf.add_block_count (params.n_layer) - self.gguf.add_feed_forward_length (params.n_ff) - self.gguf.add_rope_dimension_count(params.n_embd // params.n_head) - self.gguf.add_head_count (params.n_head) - self.gguf.add_head_count_kv (params.n_head_kv) - self.gguf.add_layer_norm_rms_eps (params.f_norm_eps) - - if params.f_rope_freq_base: - self.gguf.add_rope_freq_base(params.f_rope_freq_base) - - if params.f_rope_scale: - self.gguf.add_rope_scale_linear(params.f_rope_scale) - - if params.ftype: - self.gguf.add_file_type(params.ftype) - - def add_meta_vocab(self, vocab: Vocab) -> None: - tokens = [] - scores = [] - toktypes = [] - # NOTE: `all_tokens` returns the base vocabulary and added tokens - for text, score, toktype in vocab.all_tokens(): - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - if isinstance(vocab, SentencePieceVocab): - self.gguf.add_tokenizer_model("llama") - elif isinstance(vocab, BpeVocab): - self.gguf.add_tokenizer_model("gpt2") - else: - raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab') - self.gguf.add_token_list(tokens) - self.gguf.add_token_scores(scores) - self.gguf.add_token_types(toktypes) - - def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None: - svocab.add_to_gguf(self.gguf) - - def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: - n_elements = int(np.prod(tensor.shape)) - raw_dtype = getattr(tensor.data_type, 'ggml_type', None) - data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype - data_nbytes = tensor.data_type.elements_to_bytes(n_elements) - self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype) - - def write_meta(self) -> None: - self.gguf.write_header_to_file() - self.gguf.write_kv_data_to_file() - - def write_tensor_info(self) -> None: - self.gguf.write_ti_data_to_file() - - def close(self) -> None: - self.gguf.close() - - @staticmethod - def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None: - check_vocab_size(params, vocab) - - of = OutputFile(fname_out) - - # meta data - of.add_meta_arch(params) - of.add_meta_vocab(vocab) - of.add_meta_special_vocab(svocab) - - of.write_meta() - - of.close() - - @staticmethod - def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]: - name, lazy_tensor = item - tensor = lazy_tensor.load().to_ggml() - return (lazy_tensor.data_type, tensor.ndarray) - - @staticmethod - def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: - dt, arr = item - if not isinstance(dt, QuantizedDataType): - return arr - return dt.quantize(arr) - - @staticmethod - def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None: - check_vocab_size(params, vocab) - - of = OutputFile(fname_out) - - # meta data - of.add_meta_arch(params) - of.add_meta_vocab(vocab) - of.add_meta_special_vocab(svocab) - - # tensor info - for name, lazy_tensor in model.items(): - of.add_tensor_info(name, lazy_tensor) - - of.write_meta() - of.write_tensor_info() - - # tensor data - ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) - if ftype == GGMLFileType.MostlyQ8_0: - ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True) - else: - ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) - - start = time.time() - for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): - elapsed = time.time() - start - size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) - padi = len(str(len(model))) - print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}") - of.gguf.write_tensor_data(ndarray) - - of.close() - -def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: - wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type - - if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): - return GGMLFileType.AllF32 - if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)): - return GGMLFileType.MostlyF16 - if output_type_str == "q8_0": - return GGMLFileType.MostlyQ8_0 - - name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} - - raise Exception(f"Unexpected combination of types: {name_to_type}") - -def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel: - return {name: tensor.astype(output_type.type_for_tensor(name, tensor)) - for (name, tensor) in model.items()} - -def convert_model_names(model: LazyModel, params: Params) -> LazyModel: - tmap = gguf.TensorNameMap(ARCH, params.n_layer) - should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) - - tmp = model - - # HF models permut or pack some of the tensors, so we need to undo that - for i in itertools.count(): - if f"model.layers.{i}.self_attn.q_proj.weight" in model: - print(f"Permuting layer {i}") - tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head) - tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv) - #tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] - elif f"model.layers.{i}.self_attn.W_pack.weight" in model: - print(f"Unpacking and permuting layer {i}") - tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) - tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) - tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) - del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] - else: - break - - out: LazyModel = {} - for name, lazy_tensor in model.items(): - tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) - if name_new is None: - raise Exception(f"Unexpected tensor name: {name}") - - if tensor_type in should_skip: - print(f"skipping tensor {name_new}") - continue - - print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") - out[name_new] = lazy_tensor - - return out - -def nth_multifile_path(path: Path, n: int) -> Path | None: - '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return - the nth path in the model. - ''' - # Support the following patterns: - patterns: list[tuple[str, str]] = [ - # - x.00.pth, x.01.pth, etc. - (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), - # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. - (r'-[0-9]{5}-of-(.*)$', fr'-{n:05}-of-\1'), - # x.bin, x.bin.1, etc. - (r'(\.[0-9]+)?$', r'\1' if n == 0 else fr'\1.{n}') - ] - for regex, replacement in patterns: - if re.search(regex, path.name): - new_path = path.with_name(re.sub(regex, replacement, path.name)) - if new_path.exists(): - return new_path - return None - - -def find_multifile_paths(path: Path) -> list[Path]: - '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return - the whole list of paths in the model. - ''' - ret: list[Path] = [] - for i in itertools.count(): - nth_path = nth_multifile_path(path, i) - if nth_path is None: - break - ret.append(nth_path) - if not ret: - # No matches. This should only happen if the file was named, e.g., - # foo.0, and there was no file named foo. Oh well, try to process it - # as a single file. - return [path] - return ret - - -def load_some_model(path: Path) -> ModelPlus: - '''Load a model of any supported format.''' - # Be extra-friendly and accept either a file or a directory: - if path.is_dir(): - # Check if it's a set of safetensors files first - files = list(path.glob("model-00001-of-*.safetensors")) - if not files: - # Try the PyTorch patterns too, with lower priority - globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"] - files = [file for glob in globs for file in path.glob(glob)] - if not files: - raise Exception(f"Can't find model in directory {path}") - if len(files) > 1: - raise Exception(f"Found multiple models in {path}, not sure which to pick: {files}") - path = files[0] - - paths = find_multifile_paths(path) - models_plus: list[ModelPlus] = [] - for path in paths: - print(f"Loading model file {path}") - models_plus.append(lazy_load_file(path)) - - model_plus = merge_multifile_models(models_plus) - return model_plus - - -def load_vocab(path: Path, vocabtype: str | None) -> Vocab: - # Be extra-friendly and accept either a file or a directory. Also, if it's - # a directory, it might be the model directory, and tokenizer.model might - # be in the parent of that. - if path.is_dir(): - vocab_file = "tokenizer.model" - if vocabtype == 'bpe': - vocab_file = "vocab.json" - path2 = path / vocab_file - # Use `.parent` instead of /.. to handle the symlink case better. - path3 = path.parent / vocab_file - if path2.exists(): - path = path2 - elif path3.exists(): - path = path3 - else: - raise FileNotFoundError( - f"Could not find {vocab_file} in {path} or its parent; " - "if it's in another directory, pass the directory as --vocab-dir") - - print(f"Loading vocab file '{path}', type '{vocabtype}'") - - added_tokens_path = path.parent / "added_tokens.json" - if vocabtype == "bpe": - return BpeVocab(path, added_tokens_path if added_tokens_path.exists() else None) - elif vocabtype == "spm": - return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) - else: - raise ValueError(f"Unsupported vocabulary type {vocabtype}") - - -def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: - namestr = { - GGMLFileType.AllF32: "f32", - GGMLFileType.MostlyF16: "f16", - GGMLFileType.MostlyQ8_0:"q8_0", - }[file_type] - ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" - if ret in model_paths: - sys.stderr.write( - f"Error: Default output path ({ret}) would overwrite the input. " - "Please explicitly specify a path using --outfile.\n") - sys.exit(1) - return ret - - -def do_dump_model(model_plus: ModelPlus) -> None: - print(f"model_plus.paths = {model_plus.paths!r}") - print(f"model_plus.format = {model_plus.format!r}") - print(f"model_plus.vocab = {model_plus.vocab!r}") - for name, lazy_tensor in model_plus.model.items(): - print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") - - -def main(args_in: list[str] | None = None) -> None: - parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") - parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") - parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm") - parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") - parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY) - args = parser.parse_args(args_in) - - if args.dump_single: - model_plus = lazy_load_file(args.model) - do_dump_model(model_plus) - return - - if not args.vocab_only: - model_plus = load_some_model(args.model) - else: - model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) - - if args.dump: - do_dump_model(model_plus) - return - - params = Params.load(model_plus) - if params.n_ctx == -1: - if args.ctx is None: - raise Exception("The model doesn't have a context size, and you didn't specify one with --ctx\n" - "Please specify one with --ctx:\n" - " - LLaMA v1: --ctx 2048\n" - " - LLaMA v2: --ctx 4096\n") - params.n_ctx = args.ctx - - if args.outtype: - params.ftype = { - "f32": GGMLFileType.AllF32, - "f16": GGMLFileType.MostlyF16, - "q8_0": GGMLFileType.MostlyQ8_0, - }[args.outtype] - - print(f"params = {params}") - - vocab: Vocab - if args.vocab_only: - assert args.outfile, "need --outfile if using --vocab-only" - # FIXME: Try to respect vocab_dir somehow? - vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') - outfile = args.outfile - OutputFile.write_vocab_only(outfile, params, vocab, special_vocab) - print(f"Wrote {outfile}") - return - - if model_plus.vocab is not None and args.vocab_dir is None: - vocab = model_plus.vocab - else: - vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = load_vocab(vocab_dir, args.vocabtype) - # FIXME: Try to respect vocab_dir somehow? - special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') - - model = model_plus.model - model = convert_model_names(model, params) - ftype = pick_output_type(model, args.outtype) - model = convert_to_output_type(model, ftype) - outfile = args.outfile or default_outfile(model_plus.paths, ftype) - - params.ftype = ftype - print(f"Writing {outfile}, format {ftype}") - - OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency) - print(f"Wrote {outfile}") - - -if __name__ == '__main__': - main() diff --git a/huggingface_scraper/libllama/avx2/convert-llama-ggmlv3-to-gguf.py b/huggingface_scraper/libllama/avx2/convert-llama-ggmlv3-to-gguf.py deleted file mode 100644 index 08ba0c4..0000000 --- a/huggingface_scraper/libllama/avx2/convert-llama-ggmlv3-to-gguf.py +++ /dev/null @@ -1,353 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import argparse -import math -import struct -import sys -from pathlib import Path - -import numpy as np - -import os -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - -# Note: Does not support GGML_QKK_64 -QK_K = 256 -# Items here are (block size, type size) -GGML_QUANT_SIZES = { - gguf.GGMLQuantizationType.F32 : (1, 4), - gguf.GGMLQuantizationType.F16 : (1, 2), - gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16), - gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16), - gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16), - gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16), - gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32), - gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32), - gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4), - gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12), - gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), - gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), - gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8), -} - -class Hyperparameters: - def __init__(self): - self.n_vocab = self.n_embd = self.n_mult = self.n_head = self.n_layer = self.n_rot = self.ftype = 0 - self.n_ff = 0 - - def set_n_ff(self, model): - ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight') - assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor' - ff_tensor = model.tensors[ff_tensor_idx] - self.n_ff = ff_tensor.dims[1] - - def load(self, data, offset): - ( - self.n_vocab, - self.n_embd, - self.n_mult, - self.n_head, - self.n_layer, - self.n_rot, - self.ftype, - ) = struct.unpack('<7I', data[offset:offset + (4 * 7)]) - return 4 * 7 - - def __str__(self): - return f'' - -class Vocab: - def __init__(self): - self.items = [] - - def load(self, data, offset, n_vocab): - orig_offset = offset - for _ in range(n_vocab): - itemlen = struct.unpack('= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}' - assert name_len < 4096, 'Absurd tensor name length' - quant = GGML_QUANT_SIZES.get(dtype) - assert quant is not None, 'Unknown tensor type' - (blksize, tysize) = quant - offset += 12 - self.dtype= dtype - self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)]) - offset += 4 * n_dims - self.name = bytes(data[offset:offset + name_len]) - offset += name_len - pad = ((offset + 31) & ~31) - offset - offset += pad - n_elems = np.prod(self.dims) - n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize) - self.start_offset = offset - self.len_bytes = n_bytes - offset += n_bytes - # print(n_dims, name_len, dtype, self.dims, self.name, pad) - return offset - orig_offset - -class GGMLV3Model: - def __init__(self): - self.hyperparameters = None - self.vocab = None - self.tensor_map = {} - self.tensors = [] - - def validate_header(self, data, offset): - if bytes(data[offset:offset + 4]) != b'tjgg' or struct.unpack(' 0: - gguf_writer.add_token_types(toktypes) - return - print(f'* Adding {hp.n_vocab} vocab item(s)') - assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab' - for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items): - tt = 1 # Normal - # Special handling for UNK, BOS, EOS tokens. - if tokid <= 2: - if tokid == 0: - vbytes = b'' - tt = 2 - elif tokid == 1: - vbytes = b'' - tt = 3 - else: - vbytes = b'' - tt = 3 - elif len(vbytes) == 0: - tt = 3 # Control - elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1: - vbytes = bytes(f'<0x{vbytes[0]:02X}>', encoding = 'UTF-8') - tt = 6 # Byte - else: - vbytes = vbytes.replace(b' ', b'\xe2\x96\x81') - toktypes.append(tt) - tokens.append(vbytes) - scores.append(vscore) - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - gguf_writer.add_unk_token_id(0) - gguf_writer.add_bos_token_id(1) - gguf_writer.add_eos_token_id(2) - - def add_tensors(self, gguf_writer): - tensor_map = self.name_map - data = self.data - print(f'* Adding {len(self.model.tensors)} tensor(s)') - for tensor in self.model.tensors: - name = str(tensor.name, 'UTF-8') - mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - assert mapped_name is not None, f'Bad name {name}' - tempdims = list(tensor.dims[:]) - if len(tempdims) > 1: - temp = tempdims[1] - tempdims[1] = tempdims[0] - tempdims[0] = temp - # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') - gguf_writer.add_tensor(mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], raw_shape = tempdims, raw_dtype = tensor.dtype) - -def handle_metadata(cfg, hp): - import convert - assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory' - hf_config_path = cfg.model_metadata_dir / "config.json" - orig_config_path = cfg.model_metadata_dir / "params.json" - # We pass a fake model here. "original" mode will check the shapes of some - # tensors if information is missing in the .json file: other than that, the - # model data isn't used so this should be safe (at least for now). - fakemodel = { - 'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor), - 'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor), - } - fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab] - fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff] - if hf_config_path.exists(): - params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path) - elif orig_config_path.exists(): - params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path) - else: - raise ValueError('Unable to load metadata') - vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype) - # FIXME: Respect cfg.vocab_dir? - svocab = gguf.SpecialVocab(cfg.model_metadata_dir) - convert.check_vocab_size(params, vocab) - return (params, vocab, svocab) - -def handle_args(): - parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') - parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename') - parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') - parser.add_argument('--name', help = 'Set model name') - parser.add_argument('--desc', help = 'Set model description') - parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') - parser.add_argument('--eps', default = '5.0e-06', help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2') - parser.add_argument('--context-length', '-c', type=int, default = 2048, help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096') - parser.add_argument('--model-metadata-dir', '-m', type = Path, help ='Load HuggingFace/.pth vocab and metadata from the specified directory') - parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") - parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)", default="spm") - return parser.parse_args() - -def main(): - cfg = handle_args() - print(f'* Using config: {cfg}') - print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n') - data = np.memmap(cfg.input, mode = 'r') - model = GGMLV3Model() - print('* Scanning GGML input file') - offset = model.load(data, 0) - print(f'* GGML model hyperparameters: {model.hyperparameters}') - vocab_override = None - params_override = None - special_vocab = None - if cfg.model_metadata_dir is not None: - (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters) - print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') - print(f'* Overriding params: {params_override}') - print(f'* Overriding vocab: {vocab_override}') - print(f'* Special vocab: {special_vocab}') - else: - print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') - converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab) - converter.save() - print(f'* Successful completion. Output saved to: {cfg.output}') - -if __name__ == '__main__': - main() diff --git a/huggingface_scraper/libllama/avx2/convert_models.py b/huggingface_scraper/libllama/avx2/convert_models.py deleted file mode 100644 index 8441952..0000000 --- a/huggingface_scraper/libllama/avx2/convert_models.py +++ /dev/null @@ -1,35 +0,0 @@ -import os - -def main(): - cwd = os.getcwd() - dir = os.path.dirname(__file__) - os.chdir(dir) - models_dir = os.path.join(dir, 'models') - models = os.listdir(models_dir) - for model in models: - if os.path.isdir(os.path.join(models_dir, model)): - this_dir = os.path.join(models_dir, model) - if(os.path.isfile(os.path.join(this_dir, 'checkpoint.bin.bak'))): - print("backup found") - if(os.path.isfile(os.path.join(this_dir, 'checkpoint.bin'))): - print("original found") - pass - else: - print("no original found") - # rename - os.system('mv ' + os.path.join(this_dir, 'checkpoint.bin.bak' ) + ' ' + os.path.join(this_dir, 'checkpoint.bin')) - pass - - if(os.path.isfile(os.path.join(this_dir, 'checkpoint.bin'))): - os.chdir(this_dir) - os.system('python ../../skillset/convert-llama-ggmlv3-to-gguf.py --input checkpoint.bin --output checkpoint.gguf.bin ; mv checkpoint.bin checkpoint.bin.bak ; mv checkpoint.gguf.bin checkpoint.bin') - os.chdir(dir) - else: - print("skipping " + model) - print("no checkpoint.bin found") - print(os.path.join(this_dir, 'checkpoint.bin')) - print("done") - return - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/huggingface_scraper/libllama/avx2/libllama.so b/huggingface_scraper/libllama/avx2/libllama.so deleted file mode 100644 index 48659c939ecad3e34475b475902e3c88f4683d1c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 5039960 zcmeEP33yZ0^1m$=3nGL?kxjx5qSCabfS`sYKq*V5;KE{>rfCVZX=u~3`LuvwQPhek zAT9+&Sp@2aC@vNEy*@>s3sM&z?)JF?D*k_SW^S6?-ax7F^nKs|d>qouojYgFoH=vm z%vo}kWAyOosHg^-qz2jrT9p0;pLY5CUmuMQLhV|A?G!xetaVbjF~KLb|FG_0YWk;U 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