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/**
* @file Core utility functions/classes for Transformers.js.
*
* These are only used internally, meaning an end-user shouldn't
* need to access anything here.
*
* @module utils/core
*/
/**
* @typedef {Object} InitiateProgressInfo
* @property {'initiate'} status
* @property {string} name The model id or directory path.
* @property {string} file The name of the file.
*/
/**
* @typedef {Object} DownloadProgressInfo
* @property {'download'} status
* @property {string} name The model id or directory path.
* @property {string} file The name of the file.
*/
/**
* @typedef {Object} ProgressStatusInfo
* @property {'progress'} status
* @property {string} name The model id or directory path.
* @property {string} file The name of the file.
* @property {number} progress A number between 0 and 100.
* @property {number} loaded The number of bytes loaded.
* @property {number} total The total number of bytes to be loaded.
*/
/**
* @typedef {Object} DoneProgressInfo
* @property {'done'} status
* @property {string} name The model id or directory path.
* @property {string} file The name of the file.
*/
/**
* @typedef {Object} ReadyProgressInfo
* @property {'ready'} status
* @property {string} task The loaded task.
* @property {string} model The loaded model.
*/
/**
* @typedef {InitiateProgressInfo | DownloadProgressInfo | ProgressStatusInfo | DoneProgressInfo | ReadyProgressInfo} ProgressInfo
*/
/**
* A callback function that is called with progress information.
* @callback ProgressCallback
* @param {ProgressInfo} progressInfo
* @returns {void}
*/
/**
* Helper function to dispatch progress callbacks.
*
* @param {ProgressCallback | null | undefined} progress_callback The progress callback function to dispatch.
* @param {ProgressInfo} data The data to pass to the progress callback function.
* @returns {void}
* @private
*/
export function dispatchCallback(progress_callback, data) {
if (progress_callback) progress_callback(data);
}
/**
* Reverses the keys and values of an object.
*
* @param {Object} data The object to reverse.
* @returns {Object} The reversed object.
* @see https://ultimatecourses.com/blog/reverse-object-keys-and-values-in-javascript
*/
export function reverseDictionary(data) {
// https://ultimatecourses.com/blog/reverse-object-keys-and-values-in-javascript
return Object.fromEntries(Object.entries(data).map(([key, value]) => [value, key]));
}
/**
* Escapes regular expression special characters from a string by replacing them with their escaped counterparts.
*
* @param {string} string The string to escape.
* @returns {string} The escaped string.
*/
export function escapeRegExp(string) {
return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
}
/**
* Check if a value is a typed array.
* @param {*} val The value to check.
* @returns {boolean} True if the value is a `TypedArray`, false otherwise.
*
* Adapted from https://stackoverflow.com/a/71091338/13989043
*/
export function isTypedArray(val) {
return val?.prototype?.__proto__?.constructor?.name === 'TypedArray';
}
/**
* Check if a value is an integer.
* @param {*} x The value to check.
* @returns {boolean} True if the value is a string, false otherwise.
*/
export function isIntegralNumber(x) {
return Number.isInteger(x) || typeof x === 'bigint'
}
/**
* Determine if a provided width or height is nullish.
* @param {*} x The value to check.
* @returns {boolean} True if the value is `null`, `undefined` or `-1`, false otherwise.
*/
export function isNullishDimension(x) {
return x === null || x === undefined || x === -1;
}
/**
* Calculates the dimensions of a nested array.
*
* @param {any[]} arr The nested array to calculate dimensions for.
* @returns {number[]} An array containing the dimensions of the input array.
*/
export function calculateDimensions(arr) {
const dimensions = [];
let current = arr;
while (Array.isArray(current)) {
dimensions.push(current.length);
current = current[0];
}
return dimensions;
}
/**
* Replicate python's .pop() method for objects.
* @param {Object} obj The object to pop from.
* @param {string} key The key to pop.
* @param {*} defaultValue The default value to return if the key does not exist.
* @returns {*} The value of the popped key.
* @throws {Error} If the key does not exist and no default value is provided.
*/
export function pop(obj, key, defaultValue = undefined) {
const value = obj[key];
if (value !== undefined) {
delete obj[key];
return value;
}
if (defaultValue === undefined) {
throw Error(`Key ${key} does not exist in object.`)
}
return defaultValue;
}
/**
* Efficiently merge arrays, creating a new copy.
* Adapted from https://stackoverflow.com/a/6768642/13989043
* @param {Array[]} arrs Arrays to merge.
* @returns {Array} The merged array.
*/
export function mergeArrays(...arrs) {
return Array.prototype.concat.apply([], arrs);
}
/**
* Compute the Cartesian product of given arrays
* @param {...Array} a Arrays to compute the product
* @returns {Array} Returns the computed Cartesian product as an array
* @private
*/
export function product(...a) {
// Cartesian product of items
// Adapted from https://stackoverflow.com/a/43053803
return a.reduce((a, b) => a.flatMap(d => b.map(e => [d, e])));
}
/**
* Calculates the index offset for a given index and window size.
* @param {number} i The index.
* @param {number} w The window size.
* @returns {number} The index offset.
*/
export function calculateReflectOffset(i, w) {
return Math.abs((i + w) % (2 * w) - w);
}
/**
* Save blob file on the web.
* @param {string} path The path to save the blob to
* @param {Blob} blob The blob to save
*/
export function saveBlob(path, blob){
// Convert the canvas content to a data URL
const dataURL = URL.createObjectURL(blob);
// Create an anchor element with the data URL as the href attribute
const downloadLink = document.createElement('a');
downloadLink.href = dataURL;
// Set the download attribute to specify the desired filename for the downloaded image
downloadLink.download = path;
// Trigger the download
downloadLink.click();
// Clean up: remove the anchor element from the DOM
downloadLink.remove();
// Revoke the Object URL to free up memory
URL.revokeObjectURL(dataURL);
}
/**
*
* @param {Object} o
* @param {string[]} props
* @returns {Object}
*/
export function pick(o, props) {
return Object.assign(
{},
...props.map((prop) => {
if (o[prop] !== undefined) {
return { [prop]: o[prop] };
}
})
);
}
/**
* Calculate the length of a string, taking multi-byte characters into account.
* This mimics the behavior of Python's `len` function.
* @param {string} s The string to calculate the length of.
* @returns {number} The length of the string.
*/
export function len(s) {
let length = 0;
for (const c of s) ++length;
return length;
}
/**
* Count the occurrences of a value in an array or string.
* This mimics the behavior of Python's `count` method.
* @param {any[]|string} arr The array or string to search.
* @param {any} value The value to count.
*/
export function count(arr, value) {
let count = 0;
for (const v of arr) {
if (v === value) ++count;
}
return count;
}
| transformers.js/src/utils/core.js/0 | {
"file_path": "transformers.js/src/utils/core.js",
"repo_id": "transformers.js",
"token_count": 2392
} | 363 |
/*
* Test that models loaded outside of the `pipeline` function work correctly (e.g., `AutoModel.from_pretrained(...)`);
*/
import { AutoTokenizer, AutoProcessor, BertForMaskedLM, GPT2LMHeadModel, T5ForConditionalGeneration, BertTokenizer, GPT2Tokenizer, T5Tokenizer, LlamaTokenizer, LlamaForCausalLM, WhisperForConditionalGeneration, WhisperProcessor, AutoModelForMaskedLM, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoModelForSpeechSeq2Seq } from "../src/transformers.js";
import { init, MAX_TEST_EXECUTION_TIME, DEFAULT_MODEL_OPTIONS } from "./init.js";
import { compare, collect_and_execute_tests } from "./test_utils.js";
// Initialise the testing environment
init();
describe("Loading different architecture types", () => {
// List all models which will be tested
const models_to_test = [
// [name, [AutoModelClass, ModelClass], [AutoProcessorClass, ProcessorClass], [modelOptions?], [modality?]]
["hf-internal-testing/tiny-random-BertForMaskedLM", [AutoModelForMaskedLM, BertForMaskedLM], [AutoTokenizer, BertTokenizer]], // Encoder-only
["hf-internal-testing/tiny-random-GPT2LMHeadModel", [AutoModelForCausalLM, GPT2LMHeadModel], [AutoTokenizer, GPT2Tokenizer]], // Decoder-only
["hf-internal-testing/tiny-random-T5ForConditionalGeneration", [AutoModelForSeq2SeqLM, T5ForConditionalGeneration], [AutoTokenizer, T5Tokenizer]], // Encoder-decoder
["onnx-internal-testing/tiny-random-LlamaForCausalLM-ONNX_external", [AutoModelForCausalLM, LlamaForCausalLM], [AutoTokenizer, LlamaTokenizer]], // Decoder-only w/ external data
["onnx-internal-testing/tiny-random-WhisperForConditionalGeneration-ONNX_external", [AutoModelForSpeechSeq2Seq, WhisperForConditionalGeneration], [AutoProcessor, WhisperProcessor], {}], // Encoder-decoder-only w/ external data
];
const texts = ["Once upon a time", "I like to eat apples"];
for (const [model_id, models, processors, modelOptions] of models_to_test) {
// Test that both the auto model and the specific model work
for (let i = 0; i < processors.length; ++i) {
const processorClassToTest = processors[i];
const modelClassToTest = models[i];
it(
`${model_id} (${modelClassToTest.name})`,
async () => {
// Load model and processor
const processor = await processorClassToTest.from_pretrained(model_id);
const model = await modelClassToTest.from_pretrained(model_id, modelOptions ?? DEFAULT_MODEL_OPTIONS);
const tests = [
texts[0], // single
texts, // batched
];
const { model_type } = model.config;
const tokenizer = model_type === "whisper" ? processor.tokenizer : processor;
const feature_extractor = model_type === "whisper" ? processor.feature_extractor : null;
for (const test of tests) {
const inputs = await tokenizer(test, { truncation: true, padding: true });
if (model.config.is_encoder_decoder) {
inputs.decoder_input_ids = inputs.input_ids;
}
if (feature_extractor) {
Object.assign(inputs, await feature_extractor(new Float32Array(16000)));
}
const output = await model(inputs);
if (output.logits) {
// Ensure correct shapes
const input_ids = inputs.input_ids ?? inputs.decoder_input_ids;
const expected_shape = [...input_ids.dims, model.config.vocab_size];
const actual_shape = output.logits.dims;
compare(expected_shape, actual_shape);
} else if (output.last_hidden_state) {
const expected_shape = [...inputs.input_ids.dims, model.config.d_model];
const actual_shape = output.last_hidden_state.dims;
compare(expected_shape, actual_shape);
} else {
console.warn("Unexpected output", output);
throw new Error("Unexpected output");
}
}
await model.dispose();
},
MAX_TEST_EXECUTION_TIME,
);
}
}
});
await collect_and_execute_tests("Model-specific tests", "modeling");
| transformers.js/tests/models.test.js/0 | {
"file_path": "transformers.js/tests/models.test.js",
"repo_id": "transformers.js",
"token_count": 1642
} | 364 |
import { CohereTokenizer, CohereModel, CohereForCausalLM } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
describe("CohereModel", () => {
const model_id = "hf-internal-testing/tiny-random-CohereModel";
/** @type {CohereModel} */
let model;
/** @type {CohereTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await CohereModel.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await CohereTokenizer.from_pretrained(model_id);
tokenizer.padding_side = "left";
}, MAX_MODEL_LOAD_TIME);
it(
"batch_size=1",
async () => {
const inputs = tokenizer("hello");
const { last_hidden_state } = await model(inputs);
expect(last_hidden_state.dims).toEqual([1, 4, 32]);
expect(last_hidden_state.mean().item()).toBeCloseTo(0.0, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size>1",
async () => {
const inputs = tokenizer(["hello", "hello world"], { padding: true });
const { last_hidden_state } = await model(inputs);
expect(last_hidden_state.dims).toEqual([2, 6, 32]);
expect(last_hidden_state.mean().item()).toBeCloseTo(9.934107758624577e-9, 5);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
describe("CohereForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-random-CohereForCausalLM";
/** @type {CohereForCausalLM} */
let model;
/** @type {CohereTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await CohereForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await CohereTokenizer.from_pretrained(model_id);
tokenizer.padding_side = "left";
}, MAX_MODEL_LOAD_TIME);
it(
"batch_size=1",
async () => {
const inputs = tokenizer("hello");
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([[5n, 203n, 790n, 87n, 87n, 87n, 87n, 87n, 87n, 87n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size>1",
async () => {
const inputs = tokenizer(["hello", "hello world"], { padding: true });
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([
[0n, 0n, 5n, 203n, 790n, 87n, 87n, 87n, 87n, 87n],
[5n, 203n, 790n, 87n, 214n, 741n, 741n, 741n, 741n, 741n],
]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/cohere/test_modeling_cohere.js/0 | {
"file_path": "transformers.js/tests/models/cohere/test_modeling_cohere.js",
"repo_id": "transformers.js",
"token_count": 1323
} | 365 |
import { GemmaTokenizer, Gemma2ForCausalLM } from "../../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js";
export default () => {
describe("Gemma2ForCausalLM", () => {
const model_id = "hf-internal-testing/tiny-random-Gemma2ForCausalLM";
/** @type {Gemma2ForCausalLM} */
let model;
/** @type {GemmaTokenizer} */
let tokenizer;
beforeAll(async () => {
model = await Gemma2ForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS);
tokenizer = await GemmaTokenizer.from_pretrained(model_id);
tokenizer.padding_side = "left";
}, MAX_MODEL_LOAD_TIME);
it(
"batch_size=1",
async () => {
const inputs = tokenizer("hello");
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([[2n, 17534n, 127534n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n]]);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"batch_size>1",
async () => {
const inputs = tokenizer(["hello", "hello world"], { padding: true });
const outputs = await model.generate({
...inputs,
max_length: 10,
});
expect(outputs.tolist()).toEqual([
[0n, 2n, 17534n, 127534n, 127534n, 215341n, 215341n, 215341n, 215341n, 215341n],
[2n, 17534n, 2134n, 107508n, 160055n, 160055n, 160055n, 160055n, 160055n, 160055n],
]);
},
MAX_TEST_EXECUTION_TIME,
);
afterAll(async () => {
await model?.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/models/gemma2/test_modeling_gemma2.js/0 | {
"file_path": "transformers.js/tests/models/gemma2/test_modeling_gemma2.js",
"repo_id": "transformers.js",
"token_count": 796
} | 366 |
import { AutoProcessor, VLChatProcessor } from "../../../src/transformers.js";
import { MAX_PROCESSOR_LOAD_TIME, MAX_TEST_EXECUTION_TIME } from "../../init.js";
export default () => {
describe("VLChatProcessor", () => {
const model_id = "onnx-community/Janus-1.3B-ONNX";
/** @type {VLChatProcessor} */
let processor;
beforeAll(async () => {
processor = await AutoProcessor.from_pretrained(model_id);
}, MAX_PROCESSOR_LOAD_TIME);
it(
"Image and text",
async () => {
// Prepare inputs
const conversation = [
{
role: "User",
content: "<image_placeholder>\nConvert the formula into latex code.",
images: ["https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/quadratic_formula.png"],
},
];
const { input_ids, attention_mask, images_seq_mask, images_emb_mask, pixel_values, original_sizes, reshaped_input_sizes } = await processor(conversation);
const num_tokens = 631;
const { num_image_tokens } = processor.config; // 576
const { image_size } = processor.image_processor.config; // 384
expect(input_ids.dims).toEqual([1, num_tokens]);
expect(attention_mask.dims).toEqual([1, num_tokens]);
expect(images_seq_mask.dims).toEqual([1, num_tokens]);
expect(images_seq_mask.to("float32").mean().item()).toBeCloseTo(num_image_tokens / num_tokens, 6);
expect(images_emb_mask.dims).toEqual([1, 1, num_image_tokens]);
expect(images_emb_mask.to("float32").mean().item()).toBeCloseTo(1);
expect(pixel_values.dims).toEqual([1, 1, 3, image_size, image_size]);
expect(pixel_values.mean().item()).toBeCloseTo(0.5999642610549927, 6);
expect(original_sizes).toEqual([[206, 767]]);
expect(reshaped_input_sizes).toEqual([[103, image_size]]);
},
MAX_TEST_EXECUTION_TIME,
);
});
};
| transformers.js/tests/models/janus/test_processor_janus.js/0 | {
"file_path": "transformers.js/tests/models/janus/test_processor_janus.js",
"repo_id": "transformers.js",
"token_count": 849
} | 367 |
import { MPNetTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = MPNetTokenizer;
export const TEST_CONFIG = {
"Xenova/all-mpnet-base-v2": {
SIMPLE: {
text: BASE_TEST_STRINGS.SIMPLE,
tokens: ["how", "are", "you", "doing", "?"],
ids: [0, 2133, 2028, 2021, 2729, 1033, 2],
decoded: "<s> how are you doing? </s>",
},
SIMPLE_WITH_PUNCTUATION: {
text: BASE_TEST_STRINGS.SIMPLE_WITH_PUNCTUATION,
tokens: ["you", "should", "'", "ve", "done", "this"],
ids: [0, 2021, 2327, 1009, 2314, 2593, 2027, 2],
decoded: "<s> you should've done this </s>",
},
NUMBERS: {
text: BASE_TEST_STRINGS.NUMBERS,
tokens: ["01", "##23", "##45", "##6", "##7", "##8", "##9", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "100", "1000"],
ids: [0, 5894, 21930, 19965, 2579, 2585, 2624, 2687, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 2188, 2535, 6698, 2],
decoded: "<s> 0123456789 0 1 2 3 4 5 6 7 8 9 10 100 1000 </s>",
},
TEXT_WITH_NUMBERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_NUMBERS,
tokens: ["the", "company", "was", "founded", "in", "2016", "."],
ids: [0, 2000, 2198, 2005, 2635, 2003, 2359, 1016, 2],
decoded: "<s> the company was founded in 2016. </s>",
},
PUNCTUATION: {
text: BASE_TEST_STRINGS.PUNCTUATION,
tokens: ["a", "'", "ll", "!", "!", "to", "?", "'", "d", "'", "'", "d", "of", ",", "can", "'", "t", "."],
ids: [0, 1041, 1009, 2226, 1003, 1003, 2004, 1033, 1009, 1044, 1009, 1009, 1044, 2001, 1014, 2068, 1009, 1060, 1016, 2],
decoded: "<s> a'll!! to?'d'' d of, can't. </s>",
},
PYTHON_CODE: {
text: BASE_TEST_STRINGS.PYTHON_CODE,
tokens: ["def", "main", "(", ")", ":", "pass"],
ids: [0, 13370, 2368, 1010, 1011, 1028, 3417, 2],
decoded: "<s> def main ( ) : pass </s>",
},
JAVASCRIPT_CODE: {
text: BASE_TEST_STRINGS.JAVASCRIPT_CODE,
tokens: ["let", "a", "=", "ob", "##j", ".", "to", "##st", "##ring", "(", ")", ";", "to", "##st", "##ring", "(", ")", ";"],
ids: [0, 2296, 1041, 1031, 27889, 3505, 1016, 2004, 3371, 4896, 1010, 1011, 1029, 2004, 3371, 4896, 1010, 1011, 1029, 2],
decoded: "<s> let a = obj. tostring ( ) ; tostring ( ) ; </s>",
},
NEWLINES: {
text: BASE_TEST_STRINGS.NEWLINES,
tokens: ["this", "is", "a", "test", "."],
ids: [0, 2027, 2007, 1041, 3235, 1016, 2],
decoded: "<s> this is a test. </s>",
},
BASIC: {
text: BASE_TEST_STRINGS.BASIC,
tokens: ["unwanted", ",", "running"],
ids: [0, 18166, 1014, 2774, 2],
decoded: "<s> unwanted, running </s>",
},
CONTROL_TOKENS: {
text: BASE_TEST_STRINGS.CONTROL_TOKENS,
tokens: ["123"],
ids: [0, 13142, 2],
decoded: "<s> 123 </s>",
},
HELLO_WORLD_TITLECASE: {
text: BASE_TEST_STRINGS.HELLO_WORLD_TITLECASE,
tokens: ["hello", "world"],
ids: [0, 7596, 2092, 2],
decoded: "<s> hello world </s>",
},
HELLO_WORLD_LOWERCASE: {
text: BASE_TEST_STRINGS.HELLO_WORLD_LOWERCASE,
tokens: ["hello", "world"],
ids: [0, 7596, 2092, 2],
decoded: "<s> hello world </s>",
},
CHINESE_ONLY: {
text: BASE_TEST_STRINGS.CHINESE_ONLY,
tokens: ["\u751f", "[UNK]", "\u7684", "\u771f", "[UNK]", "[UNK]"],
ids: [0, 1914, 104, 1920, 1925, 104, 104, 2],
decoded: "<s> \u751f [UNK] \u7684 \u771f [UNK] [UNK] </s>",
},
LEADING_SPACE: {
text: BASE_TEST_STRINGS.LEADING_SPACE,
tokens: ["leading", "space"],
ids: [0, 2881, 2690, 2],
decoded: "<s> leading space </s>",
},
TRAILING_SPACE: {
text: BASE_TEST_STRINGS.TRAILING_SPACE,
tokens: ["trailing", "space"],
ids: [0, 12546, 2690, 2],
decoded: "<s> trailing space </s>",
},
DOUBLE_SPACE: {
text: BASE_TEST_STRINGS.DOUBLE_SPACE,
tokens: ["hi", "hello"],
ids: [0, 7636, 7596, 2],
decoded: "<s> hi hello </s>",
},
CURRENCY: {
text: BASE_TEST_STRINGS.CURRENCY,
tokens: ["test", "$", "1", "r", "##2", "#", "3", "\u20ac", "##4", "\u00a35", "\u00a5", "##6", "[UNK]", "\u20b9", "##8", "\u20b1", "##9", "test"],
ids: [0, 3235, 1006, 1019, 1058, 2479, 1005, 1021, 1578, 2553, 27817, 1075, 2579, 104, 1580, 2624, 1579, 2687, 3235, 2],
decoded: "<s> test $ 1 r2 # 3 \u20ac4 \u00a35 \u00a56 [UNK] \u20b98 \u20b19 test </s>",
},
CURRENCY_WITH_DECIMALS: {
text: BASE_TEST_STRINGS.CURRENCY_WITH_DECIMALS,
tokens: ["i", "bought", "an", "apple", "for", "$", "1", ".", "00", "at", "the", "store", "."],
ids: [0, 1049, 4153, 2023, 6211, 2009, 1006, 1019, 1016, 4006, 2016, 2000, 3577, 1016, 2],
decoded: "<s> i bought an apple for $ 1. 00 at the store. </s>",
},
ELLIPSIS: {
text: BASE_TEST_STRINGS.ELLIPSIS,
tokens: ["you", "\u2026"],
ids: [0, 2021, 1533, 2],
decoded: "<s> you \u2026 </s>",
},
TEXT_WITH_ESCAPE_CHARACTERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_ESCAPE_CHARACTERS,
tokens: ["you", "\u2026"],
ids: [0, 2021, 1533, 2],
decoded: "<s> you \u2026 </s>",
},
TEXT_WITH_ESCAPE_CHARACTERS_2: {
text: BASE_TEST_STRINGS.TEXT_WITH_ESCAPE_CHARACTERS_2,
tokens: ["you", "\u2026", "you", "\u2026"],
ids: [0, 2021, 1533, 2021, 1533, 2],
decoded: "<s> you \u2026 you \u2026 </s>",
},
TILDE_NORMALIZATION: {
text: BASE_TEST_STRINGS.TILDE_NORMALIZATION,
tokens: ["weird", "\uff5e", "edge", "\uff5e", "case"],
ids: [0, 6885, 1999, 3345, 1999, 2557, 2],
decoded: "<s> weird \uff5e edge \uff5e case </s>",
},
SPIECE_UNDERSCORE: {
text: BASE_TEST_STRINGS.SPIECE_UNDERSCORE,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "."],
ids: [0, 104, 104, 104, 104, 104, 1016, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK]. </s>",
},
POPULAR_EMOJIS: {
text: BASE_TEST_STRINGS.POPULAR_EMOJIS,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]"],
ids: [0, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] </s>",
},
MULTIBYTE_EMOJIS: {
text: BASE_TEST_STRINGS.MULTIBYTE_EMOJIS,
tokens: ["[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]", "[UNK]"],
ids: [0, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 104, 2],
decoded: "<s> [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] [UNK] </s>",
},
},
};
| transformers.js/tests/models/mpnet/test_tokenization_mpnet.js/0 | {
"file_path": "transformers.js/tests/models/mpnet/test_tokenization_mpnet.js",
"repo_id": "transformers.js",
"token_count": 3834
} | 368 |
import { VitsTokenizer } from "../../../src/tokenizers.js";
import { BASE_TEST_STRINGS, VITS_TEST_STRINGS } from "../test_strings.js";
export const TOKENIZER_CLASS = VitsTokenizer;
export const TEST_CONFIG = {
"Xenova/mms-tts-eng": {
SIMPLE: {
text: BASE_TEST_STRINGS.SIMPLE,
tokens: ["k", "h", "k", "o", "k", "w", "k", " ", "k", "a", "k", "r", "k", "e", "k", " ", "k", "y", "k", "o", "k", "u", "k", " ", "k", "d", "k", "o", "k", "i", "k", "n", "k", "g", "k"],
ids: [0, 6, 0, 22, 0, 9, 0, 19, 0, 26, 0, 25, 0, 7, 0, 19, 0, 3, 0, 22, 0, 4, 0, 19, 0, 5, 0, 22, 0, 18, 0, 29, 0, 37, 0],
decoded: "how are you doing",
},
SIMPLE_WITH_PUNCTUATION: {
text: BASE_TEST_STRINGS.SIMPLE_WITH_PUNCTUATION,
tokens: ["k", "y", "k", "o", "k", "u", "k", " ", "k", "s", "k", "h", "k", "o", "k", "u", "k", "l", "k", "d", "k", "'", "k", "v", "k", "e", "k", " ", "k", "d", "k", "o", "k", "n", "k", "e", "k", " ", "k", "t", "k", "h", "k", "i", "k", "s", "k"],
ids: [0, 3, 0, 22, 0, 4, 0, 19, 0, 8, 0, 6, 0, 22, 0, 4, 0, 21, 0, 5, 0, 1, 0, 32, 0, 7, 0, 19, 0, 5, 0, 22, 0, 29, 0, 7, 0, 19, 0, 33, 0, 6, 0, 18, 0, 8, 0],
decoded: "you should've done this",
},
NUMBERS: {
text: BASE_TEST_STRINGS.NUMBERS,
tokens: ["k", "0", "k", "1", "k", "2", "k", "3", "k", "4", "k", "5", "k", "6", "k", " ", "k", "0", "k", " ", "k", "1", "k", " ", "k", "2", "k", " ", "k", "3", "k", " ", "k", "4", "k", " ", "k", "5", "k", " ", "k", "6", "k", " ", "k", " ", "k", " ", "k", " ", "k", "1", "k", "0", "k", " ", "k", "1", "k", "0", "k", "0", "k", " ", "k", "1", "k", "0", "k", "0", "k", "0", "k"],
ids: [0, 23, 0, 15, 0, 28, 0, 11, 0, 27, 0, 35, 0, 36, 0, 19, 0, 23, 0, 19, 0, 15, 0, 19, 0, 28, 0, 19, 0, 11, 0, 19, 0, 27, 0, 19, 0, 35, 0, 19, 0, 36, 0, 19, 0, 19, 0, 19, 0, 19, 0, 15, 0, 23, 0, 19, 0, 15, 0, 23, 0, 23, 0, 19, 0, 15, 0, 23, 0, 23, 0, 23, 0],
decoded: "0123456 0 1 2 3 4 5 6 10 100 1000",
},
TEXT_WITH_NUMBERS: {
text: BASE_TEST_STRINGS.TEXT_WITH_NUMBERS,
tokens: ["k", "t", "k", "h", "k", "e", "k", " ", "k", "c", "k", "o", "k", "m", "k", "p", "k", "a", "k", "n", "k", "y", "k", " ", "k", "w", "k", "a", "k", "s", "k", " ", "k", "f", "k", "o", "k", "u", "k", "n", "k", "d", "k", "e", "k", "d", "k", " ", "k", "i", "k", "n", "k", " ", "k", "2", "k", "0", "k", "1", "k", "6", "k"],
ids: [0, 33, 0, 6, 0, 7, 0, 19, 0, 12, 0, 22, 0, 17, 0, 13, 0, 26, 0, 29, 0, 3, 0, 19, 0, 9, 0, 26, 0, 8, 0, 19, 0, 20, 0, 22, 0, 4, 0, 29, 0, 5, 0, 7, 0, 5, 0, 19, 0, 18, 0, 29, 0, 19, 0, 28, 0, 23, 0, 15, 0, 36, 0],
decoded: "the company was founded in 2016",
},
NEWLINES: {
text: BASE_TEST_STRINGS.NEWLINES,
tokens: ["k", "t", "k", "h", "k", "i", "k", "s", "k", "i", "k", "s", "k", "a", "k", "t", "k", "e", "k", "s", "k", "t", "k"],
ids: [0, 33, 0, 6, 0, 18, 0, 8, 0, 18, 0, 8, 0, 26, 0, 33, 0, 7, 0, 8, 0, 33, 0],
decoded: "thisisatest",
},
CHINESE_ONLY: {
text: BASE_TEST_STRINGS.CHINESE_ONLY,
tokens: [],
ids: [],
decoded: "",
},
LEADING_SPACE: {
text: BASE_TEST_STRINGS.LEADING_SPACE,
tokens: ["k", "l", "k", "e", "k", "a", "k", "d", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 21, 0, 7, 0, 26, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "leading space",
},
TRAILING_SPACE: {
text: BASE_TEST_STRINGS.TRAILING_SPACE,
tokens: ["k", "t", "k", "r", "k", "a", "k", "i", "k", "l", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 33, 0, 25, 0, 26, 0, 18, 0, 21, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "trailing space",
},
SURROUNDING_SPACE: {
text: BASE_TEST_STRINGS.SURROUNDING_SPACE,
tokens: ["k", "s", "k", "u", "k", "r", "k", "r", "k", "o", "k", "u", "k", "n", "k", "d", "k", "i", "k", "n", "k", "g", "k", " ", "k", "s", "k", "p", "k", "a", "k", "c", "k", "e", "k"],
ids: [0, 8, 0, 4, 0, 25, 0, 25, 0, 22, 0, 4, 0, 29, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 8, 0, 13, 0, 26, 0, 12, 0, 7, 0],
decoded: "surrounding space",
},
SPECIAL_CHARACTERS: {
text: VITS_TEST_STRINGS.SPECIAL_CHARACTERS,
tokens: [],
ids: [],
decoded: "",
},
},
"Xenova/mms-tts-ron": {
SPECIAL_CHARACTERS: {
text: VITS_TEST_STRINGS.SPECIAL_CHARACTERS,
tokens: ["c", "\u0163", "c", " ", "c", "\u0163", "c"],
ids: [0, 32, 0, 28, 0, 32, 0],
decoded: "\u0163 \u0163",
},
},
};
| transformers.js/tests/models/vits/test_tokenization_vits.js/0 | {
"file_path": "transformers.js/tests/models/vits/test_tokenization_vits.js",
"repo_id": "transformers.js",
"token_count": 2597
} | 369 |
import { pipeline, ImageClassificationPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
import { load_cached_image } from "../asset_cache.js";
const PIPELINE_ID = "image-classification";
export default () => {
describe("Image Classification", () => {
const model_id = "hf-internal-testing/tiny-random-vit";
/** @type {ImageClassificationPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);
it("should be an instance of ImageClassificationPipeline", () => {
expect(pipe).toBeInstanceOf(ImageClassificationPipeline);
});
describe("batch_size=1", () => {
it(
"default (top_k=5)",
async () => {
const output = await pipe(images[0]);
const target = [
{ label: "LABEL_1", score: 0.5020533800125122 },
{ label: "LABEL_0", score: 0.4979466497898102 },
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (top_k=1)",
async () => {
const output = await pipe(images[0], { top_k: 1 });
const target = [{ label: "LABEL_1", score: 0.5020533800125122 }];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default (top_k=5)",
async () => {
const output = await pipe(images);
const target = [
[
{ label: "LABEL_1", score: 0.5020533800125122 },
{ label: "LABEL_0", score: 0.4979466497898102 },
],
[
{ label: "LABEL_1", score: 0.519227921962738 },
{ label: "LABEL_0", score: 0.4807720482349396 },
],
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (top_k=1)",
async () => {
const output = await pipe(images, { top_k: 1 });
const target = [[{ label: "LABEL_1", score: 0.5020533800125122 }], [{ label: "LABEL_1", score: 0.519227921962738 }]];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/pipelines/test_pipelines_image_classification.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_image_classification.js",
"repo_id": "transformers.js",
"token_count": 1283
} | 370 |
import { pipeline, ZeroShotImageClassificationPipeline } from "../../src/transformers.js";
import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../init.js";
import { load_cached_image } from "../asset_cache.js";
const PIPELINE_ID = "zero-shot-image-classification";
export default () => {
describe("Zero-shot Image Classification", () => {
const model_id = "hf-internal-testing/tiny-random-GroupViTModel";
// Example adapted from https://huggingface.co/docs/transformers/en/model_doc/groupvit
const labels = ["cat", "dog"];
const hypothesis_template = "a photo of a {}";
/** @type {ZeroShotImageClassificationPipeline} */
let pipe;
let images;
beforeAll(async () => {
pipe = await pipeline(PIPELINE_ID, model_id, DEFAULT_MODEL_OPTIONS);
images = await Promise.all([load_cached_image("white_image"), load_cached_image("blue_image")]);
}, MAX_MODEL_LOAD_TIME);
it("should be an instance of ZeroShotImageClassificationPipeline", () => {
expect(pipe).toBeInstanceOf(ZeroShotImageClassificationPipeline);
});
describe("batch_size=1", () => {
it(
"default",
async () => {
const output = await pipe(images[0], labels);
const target = [
{ score: 0.5990662574768066, label: "cat" },
{ score: 0.40093377232551575, label: "dog" },
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ hypothesis_template)",
async () => {
const output = await pipe(images[0], labels, { hypothesis_template });
const target = [
{ score: 0.5527022480964661, label: "cat" },
{ score: 0.44729775190353394, label: "dog" },
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
describe("batch_size>1", () => {
it(
"default",
async () => {
const output = await pipe(images, labels);
const target = [
[
{ score: 0.5990662574768066, label: "cat" },
{ score: 0.40093377232551575, label: "dog" },
],
[
{ score: 0.5006340146064758, label: "dog" },
{ score: 0.49936598539352417, label: "cat" },
],
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
it(
"custom (w/ hypothesis_template)",
async () => {
const output = await pipe(images, labels, { hypothesis_template });
const target = [
[
{ score: 0.5527022480964661, label: "cat" },
{ score: 0.44729775190353394, label: "dog" },
],
[
{ score: 0.5395973324775696, label: "cat" },
{ score: 0.46040263772010803, label: "dog" },
],
];
expect(output).toBeCloseToNested(target, 5);
},
MAX_TEST_EXECUTION_TIME,
);
});
afterAll(async () => {
await pipe.dispose();
}, MAX_MODEL_DISPOSE_TIME);
});
};
| transformers.js/tests/pipelines/test_pipelines_zero_shot_image_classification.js/0 | {
"file_path": "transformers.js/tests/pipelines/test_pipelines_zero_shot_image_classification.js",
"repo_id": "transformers.js",
"token_count": 1549
} | 371 |
# Troubleshooting
This is a document explaining how to deal with various issues on Circle-CI. The entries may include actual solutions or pointers to Issues that cover those.
## Circle CI
* pytest worker runs out of resident RAM and gets killed by `cgroups`: https://github.com/huggingface/transformers/issues/11408
| transformers/.circleci/TROUBLESHOOT.md/0 | {
"file_path": "transformers/.circleci/TROUBLESHOOT.md",
"repo_id": "transformers",
"token_count": 80
} | 372 |
apiVersion: 1
datasources:
- name: grafana-postgresql-datasource
uid: be28nkzirtb0gd
type: postgres
url: $GRAFANA_POSTGRES_DATASOURCE_URL
user: $GRAFANA_POSTGRES_DATASOURCE_USER
secureJsonData:
password: $GRAFANA_POSTGRES_DATASOURCE_PWD
jsonData:
database: metrics
maxOpenConns: 100
maxIdleConns: 100
maxIdleConnsAuto: true
connMaxLifetime: 14400
postgresVersion: 1000
timescaledb: false
| transformers/benchmark/grafana_datasource.yaml/0 | {
"file_path": "transformers/benchmark/grafana_datasource.yaml",
"repo_id": "transformers",
"token_count": 220
} | 373 |
FROM python:3.9-slim
ENV PYTHONDONTWRITEBYTECODE=1
ARG REF=main
USER root
RUN apt-get update && apt-get install -y libsndfile1-dev espeak-ng time git g++ cmake pkg-config openssh-client git
ENV UV_PYTHON=/usr/local/bin/python
RUN pip --no-cache-dir install uv && uv pip install --no-cache-dir -U pip setuptools
RUN uv pip install --no-deps accelerate
RUN uv pip install --no-cache-dir 'torch' 'torchvision' 'torchaudio' --index-url https://download.pytorch.org/whl/cpu
RUN uv pip install --no-cache-dir "scipy<1.13" "git+https://github.com/huggingface/transformers.git@${REF}#egg=transformers[flax,audio,sklearn,sentencepiece,vision,testing]"
# RUN pip install --no-cache-dir "scipy<1.13" "transformers[flax,testing,sentencepiece,flax-speech,vision]"
RUN uv pip uninstall transformers
RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
| transformers/docker/torch-jax-light.dockerfile/0 | {
"file_path": "transformers/docker/torch-jax-light.dockerfile",
"repo_id": "transformers",
"token_count": 334
} | 374 |
FROM intel/deep-learning-essentials:2025.1.3-0-devel-ubuntu22.04 AS base
LABEL maintainer="Hugging Face"
SHELL ["/bin/bash", "-c"]
ARG PYTHON_VER=3.11
ENV TORCH_DEVICE_BACKEND_AUTOLOAD=0
ENV DEBIAN_FRONTEND=noninteractive
RUN apt-get remove -y python3.10 && apt-get autoremove -y
RUN apt-get update && \
apt-get install -y software-properties-common && \
add-apt-repository -y ppa:deadsnakes/ppa && \
apt-get update && \
apt-get install -y python$PYTHON_VER python$PYTHON_VER-dev python3-pip && \
ln -sf /usr/bin/python$PYTHON_VER /usr/bin/python3 && \
ln -sf /usr/bin/python3 /usr/bin/python && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN apt-get update && \
apt-get -y install \
apt-utils \
build-essential \
ca-certificates \
clinfo \
curl \
git \
git-lfs \
vim \
numactl \
gnupg2 \
gpg-agent \
zlib1g-dev \
rsync \
sudo \
libnl-genl-3-200 \
xpu-smi \
unzip \
ffmpeg \
tesseract-ocr \
espeak-ng \
wget \
ncurses-term && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN apt-get update && \
apt-get install -y \
linux-headers-$(uname -r) \
linux-modules-extra-$(uname -r) \
flex bison \
intel-fw-gpu intel-i915-dkms xpu-smi \
intel-opencl-icd libze-intel-gpu1 libze1 \
intel-media-va-driver-non-free libmfx-gen1 libvpl2 \
libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
libglapi-mesa libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo intel-ocloc \
libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev libze-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN pip install --upgrade pip
RUN pip install triton==3.3.0
RUN pip install torch==2.7.0 torchvision==0.22.0 torchaudio==2.7.0 --index-url https://download.pytorch.org/whl/xpu --no-cache-dir
RUN pip install evaluate torchdata pyctcdecode pytesseract decord galore-torch fire scipy scikit-learn sentencepiece sacremoses nltk rouge_score librosa soundfile g2p_en mpi4py requests_mock
RUN pip install pretty_midi essentia resampy Levenshtein av sacrebleu phonemizer invisible_watermark schedulefree
RUN pip install gguf hqq compressed_tensors gptqmodel mergekit autoawq deepspeed torchao onnx
RUN pip install hf_transfer huggingface-hub hf-doc-builder datasets optimum-quanto timm transformers accelerate optimum peft
RUN pip install git+https://github.com/linkedin/Liger-Kernel.git --extra-index-url https://download.pytorch.org/whl/test/xpu
# install bitsandbytes
RUN pip install git+https://github.com/bitsandbytes-foundation/bitsandbytes.git
ENV OCL_ICD_VENDORS=/etc/OpenCL/vendors
ENV FI_PROVIDER_PATH=${I_MPI_ROOT}/lib/libfabric/prov:/usr/lib/x86_64-linux-gnu/libfabric
ENV CCL_ROOT=/usr/local
ENV CCL_ATL_TRANSPORT=ofi
ENV I_MPI_ROOT=/usr/local
ENV CLASSPATH=${I_MPI_ROOT}/lib/mpi.jar
ENV PATH=${I_MPI_ROOT}/bin/libfabric:${PATH}
ENV LD_LIBRARY_PATH=${I_MPI_ROOT}/lib/libfabric:${LD_LIBRARY_PATH}
RUN touch /entrypoint.sh
RUN chmod +x /entrypoint.sh
RUN echo "#!/bin/bash" >> /entrypoint.sh
RUN echo "source /opt/intel/oneapi/setvars.sh --force && /bin/bash" >> /entrypoint.sh
ENTRYPOINT ["/entrypoint.sh"]
| transformers/docker/transformers-pytorch-xpu/Dockerfile/0 | {
"file_path": "transformers/docker/transformers-pytorch-xpu/Dockerfile",
"repo_id": "transformers",
"token_count": 1558
} | 375 |
# بناء نماذج مخصصة
تم تصميم مكتبة 🤗 Transformers لتكون قابلة للتوسيع بسهولة. كل نموذج مُشفّر بالكامل في مجلد فرعي معين بالمستودع، دون أي تجريد، لذلك يمكنك بسهولة نسخ ملف النمذجة وتعديله وفقًا لاحتياجاتك.
إذا كنت تُنشئ نموذجًا جديدًا تمامًا، فقد يكون من الأسهل البدء من الصفر. في هذا البرنامج التعليمي، سنُرِيك كيفية كتابة نموذج مخصص وتكوينه ليُستخدم داخل Transformers، وكيفية مشاركته مع المجتمع (مع الكود الذي يعتمد عليه) بحيث يمكن لأي شخص استخدامه، حتى إذا لم يكن موجودًا في مكتبة 🤗 Transformers. سنرى كيفية البناء على المحولات ونوسّع الإطار باستخدام الأدوات التي يمكن استخدامها لتعديل سلوك الإطار (hooks) والتعليمات البرمجية المخصصة.
سنوضح كل هذا من خلال نموذج ResNet، بتغليف فئة ResNet من
[مكتبة timm](https://github.com/rwightman/pytorch-image-models) داخل [`PreTrainedModel`].
## كتابة إعدادات مخصصة
لنبدأ بكتابة إعدادات النموذج. إعدادات النموذج هو كائنٌ يحتوي على جميع المعلومات اللازمة لبنائه. كما سنرى لاحقًا، يتطلب النموذج كائن `config` لتهيئته، لذا يجب أن يكون هذا الكائن كاملاً.
<Tip>
تتبع النماذج في مكتبة `transformers` اتفاقية قبول كائن `config` في دالة `__init__` الخاصة بها، ثم تمرر كائن `config` بالكامل إلى الطبقات الفرعية في النموذج، بدلاً من تقسيمه إلى معامﻻت متعددة. يؤدي كتابة نموذجك بهذا الأسلوب إلى كود أبسط مع "مصدر حقيقة" واضح لأي فرط معلمات، كما يسهل إعادة استخدام الكود من نماذج أخرى في `transformers`.
</Tip>
في مثالنا، سنعدّل بعض الوسائط في فئة ResNet التي قد نرغب في ضبطها. ستعطينا التكوينات المختلفة أنواع ResNets المختلفة الممكنة. سنقوم بتخزين هذه الوسائط بعد التحقق من صحته.
```python
from transformers import PretrainedConfig
from typing import List
class ResnetConfig(PretrainedConfig):
model_type = "resnet"
def __init__(
self,
block_type="bottleneck",
layers: list[int] = [3, 4, 6, 3],
num_classes: int = 1000,
input_channels: int = 3,
cardinality: int = 1,
base_width: int = 64,
stem_width: int = 64,
stem_type: str = "",
avg_down: bool = False,
**kwargs,
):
if block_type not in ["basic", "bottleneck"]:
raise ValueError(f"`block_type` must be 'basic' or bottleneck', got {block_type}.")
if stem_type not in ["", "deep", "deep-tiered"]:
raise ValueError(f"`stem_type` must be '', 'deep' or 'deep-tiered', got {stem_type}.")
self.block_type = block_type
self.layers = layers
self.num_classes = num_classes
self.input_channels = input_channels
self.cardinality = cardinality
self.base_width = base_width
self.stem_width = stem_width
self.stem_type = stem_type
self.avg_down = avg_down
super().__init__(**kwargs)
```
الأشياء الثلاثة المهمة التي يجب تذكرها عند كتابة تكوينك الخاص هي:
- يجب أن ترث من `PretrainedConfig`،
- يجب أن تقبل دالة `__init__` الخاصة بـ `PretrainedConfig` أي معامﻻت إضافية kwargs،
- يجب تمرير هذه المعامﻻت الإضافية إلى دالة `__init__` فى الفئة الأساسية الاعلى.
يضمن الإرث حصولك على جميع الوظائف من مكتبة 🤗 Transformers، في حين أن القيدين التانى والثالث يأتيان من حقيقة أن `PretrainedConfig` لديه المزيد من الحقول أكثر من تلك التي تقوم بتعيينها. عند إعادة تحميل تكوين باستخدام طريقة `from_pretrained`، يجب أن يقبل تكوينك هذه الحقول ثم إرسالها إلى الفئة الأساسية الأعلى.
تحديد `model_type` لتكوينك (هنا `model_type="resnet"`) ليس إلزاميًا، ما لم ترغب في
تسجيل نموذجك باستخدام الفئات التلقائية (راجع القسم الأخير).
مع القيام بذلك، يمكنك بسهولة إنشاء تكوينك وحفظه مثلما تفعل مع أي تكوين نموذج آخر في
المكتبة. إليك كيفية إنشاء تكوين resnet50d وحفظه:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d_config.save_pretrained("custom-resnet")
```
سيؤدي هذا إلى حفظ ملف باسم `config.json` داخل مجلد `custom-resnet`. يمكنك بعد ذلك إعادة تحميل تكوينك باستخدام
طريقة `from_pretrained`:
```py
resnet50d_config = ResnetConfig.from_pretrained("custom-resnet")
```
يمكنك أيضًا استخدام أي طريقة أخرى من فئة [`PretrainedConfig`]، مثل [`~PretrainedConfig.push_to_hub`] لتحميل تكوينك مباشرة إلى Hub.
## كتابة نموذج مخصص
الآن بعد أن أصبح لدينا تكوين ResNet، يمكننا المتابعة لإنشاء نموذجين: الأول يستخرج الميزات المخفية من دفعة من الصور (مثل [`BertModel`]) والآخر مناسب لتصنيف الصور (مثل [`BertForSequenceClassification`]).
كما ذكرنا سابقًا، سنقوم ببناء نموذج مبسط لتسهيل الفهم في هذا المثال. الخطوة الوحيدة المطلوبة قبل كتابة هذه الفئة هي لربط أنواع وحدات البناء بفئات ذات وحدات بناء فعلية. بعد ذلك، يُعرّف النموذج من خلال التكوين عبر تمرير كل شيء إلى فئة `ResNet`:
```py
from transformers import PreTrainedModel
from timm.models.resnet import BasicBlock, Bottleneck, ResNet
from .configuration_resnet import ResnetConfig
BLOCK_MAPPING = {"basic": BasicBlock, "bottleneck": Bottleneck}
class ResnetModel(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor):
return self.model.forward_features(tensor)
```
بالنسبة للنموذج الذي سيصنف الصور، فإننا نغير فقط طريقة التقديم:
```py
import torch
class ResnetModelForImageClassification(PreTrainedModel):
config_class = ResnetConfig
def __init__(self, config):
super().__init__(config)
block_layer = BLOCK_MAPPING[config.block_type]
self.model = ResNet(
block_layer,
config.layers,
num_classes=config.num_classes,
in_chans=config.input_channels,
cardinality=config.cardinality,
base_width=config.base_width,
stem_width=config.stem_width,
stem_type=config.stem_type,
avg_down=config.avg_down,
)
def forward(self, tensor, labels=None):
logits = self.model(tensor)
if labels is not None:
loss = torch.nn.cross_entropy(logits, labels)
return {"loss": loss, "logits": logits}
return {"logits": logits}
```
في كلتا الحالتين، لاحظ كيف نرث من `PreTrainedModel` ونستدعي مُهيئ الفئة الرئيسية باستخدام `config` (كما تفعل عند إنشاء وحدة `torch.nn.Module` عادية). ليس من الضروري تعريف `config_class` إلا إذا كنت ترغب في تسجيل نموذجك مع الفئات التلقائية (راجع القسم الأخير).
<Tip>
إذا كان نموذجك مشابهًا جدًا لنموذج داخل المكتبة، فيمكنك إعادة استخدام نفس التكوين مثل هذا النموذج.
</Tip>
يمكن لنموذجك أن يعيد أي شيء تريده، ولكن إعادة قاموس مثلما فعلنا لـ
`ResnetModelForImageClassification`، مع تضمين الخسارة عند تمرير العلامات، سيجعل نموذجك قابلًا للاستخدام مباشرة داخل فئة [`Trainer`]. يعد استخدام تنسيق إخراج آخر أمرًا جيدًا طالما أنك تخطط لاستخدام حلقة تدريب خاصة بك أو مكتبة أخرى للتدريب.
الآن بعد أن أصبح لدينا فئة النموذج، دعنا ننشئ واحدة:
```py
resnet50d = ResnetModelForImageClassification(resnet50d_config)
```
يمكنك استخدام أي من طرق فئة [`PreTrainedModel`]، مثل [`~PreTrainedModel.save_pretrained`] أو
[`~PreTrainedModel.push_to_hub`]. سنستخدم الثاني في القسم التالي، وسنرى كيفية دفع أوزان النموذج مع كود نموذجنا. ولكن أولاً، دعنا نحمل بعض الأوزان المُعلمة مسبقًا داخل نموذجنا.
في حالة الاستخدام الخاصة بك، فمن المحتمل أن تقوم بتدريب نموذجك المخصص على بياناتك الخاصة. للانتقال بسرعة خلال هذا البرنامج التعليمي،
سنستخدم الإصدار المُعلم مسبقًا من resnet50d. نظرًا لأن نموذجنا هو مجرد غلاف حوله، فمن السهل نقل هذه الأوزان:
```py
import timm
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
الآن دعونا نرى كيفية التأكد من أنه عند قيامنا بـ [`~PreTrainedModel.save_pretrained`] أو [`~PreTrainedModel.push_to_hub`]، يتم حفظ كود النموذج.
## تسجيل نموذج مع كود مخصص للفئات التلقائية
إذا كنت تكتب مكتبة توسع 🤗 Transformers، فقد ترغب في توسيع الفئات التلقائية لتشمل نموذجك الخاص. يختلف هذا عن نشر الكود إلى Hub بمعنى أن المستخدمين سيحتاجون إلى استيراد مكتبتك للحصول على النماذج المخصصة (على عكس تنزيل كود النموذج تلقائيًا من Hub).
ما دام تكوينك يحتوي على معامل `model_type` مختلفة عن أنواع النماذج الحالية، وأن فئات نماذجك لديك لديها الخصائص الصحيحة `config_class`، فيمكنك ببساطة إضافتها إلى الفئات التلقائية مثل هذا:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
لاحظ أن الحجة الأولى المستخدمة عند تسجيل تكوينك المخصص لـ [`AutoConfig`] يجب أن تتطابق مع `model_type`
من تكوينك المخصص، والحجة الأولى المستخدمة عند تسجيل نماذجك المخصصة لأي فئة نموذج تلقائي يجب
أن تتطابق مع `config_class` من تلك النماذج.
## إرسال الكود إلى Hub
<Tip warning={true}>
هذا API تجريبي وقد يكون له بعض التغييرات الطفيفة في الإصدارات القادمة.
</Tip>
أولاً، تأكد من تعريف نموذجك بالكامل في ملف `.py`. يمكن أن يعتمد على الاستيراد النسبي لملفات أخرى طالما أن جميع الملفات موجودة في نفس الدليل (لا ندعم الوحدات الفرعية لهذه الميزة حتى الآن). في مثالنا، سنحدد ملف `modeling_resnet.py` وملف `configuration_resnet.py` في مجلد باسم "resnet_model" في دليل العمل الحالي. يحتوي ملف التكوين على كود لـ `ResnetConfig` ويحتوي ملف النمذجة على كود لـ `ResnetModel` و`ResnetModelForImageClassification`.
```
.
└── resnet_model
├── __init__.py
├── configuration_resnet.py
└── modeling_resnet.py
```
يمكن أن يكون ملف `__init__.py` فارغًا، فهو موجود فقط حتى يتمكن Python من اكتشاف أن `resnet_model` يمكن استخدامه كموديل.
<Tip warning={true}>
إذا كنت تقوم بنسخ ملفات النمذجة من المكتبة، فسوف تحتاج إلى استبدال جميع الواردات النسبية في أعلى الملف
لاستيرادها من حزمة `transformers`.
</Tip>
لاحظ أنه يمكنك إعادة استخدام (أو توسيع) تكوين/نموذج موجود.
لمشاركة نموذجك مع المجتمع، اتبع الخطوات التالية: أولاً، قم باستيراد نموذج ResNet والتكوين من الملفات التي تم إنشاؤها حديثًا:
```py
from resnet_model.configuration_resnet import ResnetConfig
from resnet_model.modeling_resnet import ResnetModel, ResnetModelForImageClassification
```
بعد ذلك، يجب عليك إخبار المكتبة بأنك تريد نسخ ملفات الكود الخاصة بهذه الكائنات عند استخدام طريقة `save_pretrained`
وتسجيلها بشكل صحيح باستخدام فئة تلقائية (خاصة للنماذج)، ما عليك سوى تشغيل:
```py
ResnetConfig.register_for_auto_class()
ResnetModel.register_for_auto_class("AutoModel")
ResnetModelForImageClassification.register_for_auto_class("AutoModelForImageClassification")
```
لاحظ أنه لا توجد حاجة لتحديد فئة تلقائية للتكوين (هناك فئة تلقائية واحدة فقط لها،
[`AutoConfig`]) ولكن الأمر يختلف بالنسبة للنماذج. قد يكون نموذجك المخصص مناسبًا للعديد من المهام المختلفة، لذلك يجب
تحديد أي من الفئات التلقائية هو الصحيح لنموذجك.
<Tip>
استخدم `register_for_auto_class()` إذا كنت تريد نسخ ملفات الكود. إذا كنت تفضل استخدام الكود على Hub من مستودع آخر،
فلا تحتاج إلى استدعائه. في الحالات التي يوجد فيها أكثر من فئة تلقائية واحدة، يمكنك تعديل ملف `config.json` مباشرة باستخدام
الهيكل التالي:
```json
"auto_map": {
"AutoConfig": "<your-repo-name>--<config-name>",
"AutoModel": "<your-repo-name>--<config-name>",
"AutoModelFor<Task>": "<your-repo-name>--<config-name>",
},
```
</Tip>
بعد ذلك، دعنا نقوم بإنشاء التكوين والنماذج كما فعلنا من قبل:
```py
resnet50d_config = ResnetConfig(block_type="bottleneck", stem_width=32, stem_type="deep", avg_down=True)
resnet50d = ResnetModelForImageClassification(resnet50d_config)
pretrained_model = timm.create_model("resnet50d", pretrained=True)
resnet50d.model.load_state_dict(pretrained_model.state_dict())
```
الآن لإرسال النموذج إلى Hub، تأكد من تسجيل الدخول. إما تشغيل في المحطة الأوامر الطرفية الخاصة بك:
```bash
hf auth login
```
أو من دفتر ملاحظات:
```py
from huggingface_hub import notebook_login
notebook_login()
```
يمكنك بعد ذلك الضغط على مساحة الاسم الخاصة بك (أو منظمة أنت عضو فيها) مثل هذا:
```py
resnet50d.push_to_hub("custom-resnet50d")
```
بالإضافة إلى أوزان النمذجة والتكوين بتنسيق json، فقد قام هذا أيضًا بنسخ ملفات النمذجة والتكوين `.py` في مجلد `custom-resnet50d` وتحميل النتيجة إلى Hub. يمكنك التحقق من النتيجة في هذا [مستودع النموذج](https://huggingface.co/sgugger/custom-resnet50d).
راجع [البرنامج التعليمي للمشاركة](model_sharing) لمزيد من المعلومات حول طريقة الدفع إلى المحور.
### استخدام نموذج مع كود مخصص
يمكنك استخدام أي تكوين أو نموذج أو مقسم لغوي مع ملفات برمجة مخصصة في مستودعه باستخدام الفئات التلقائية و دالة `from_pretrained`.تُفحص جميع الملفات والرموز المرفوع إلى Hub بحثًا عن البرامج الضارة (راجع وثائق [أمان Hub](https://huggingface.co/docs/hub/security#malware-scanning) لمزيد من المعلومات)، ولكن يجب عليك مراجعة كود النموذج والمؤلف لتجنب تنفيذ التعليمات البرمجية الضارة على جهازك. لتفعيل نموذج يحتوي على شفرة برمجية مخصصة، عيّن `trust_remote_code=True`:
```py
from transformers import AutoModelForImageClassification
model = AutoModelForImageClassification.from_pretrained("sgugger/custom-resnet50d", trust_remote_code=True)
```
يُنصح بشدة بتحديد رقم إصدار (commit hash) كـ `revision` للتأكد من عدم تعديل مؤلف النموذج للشفرة لاحقًابإضافة أسطر ضارة (إلا إذا كنت تثق تمامًا بمؤلفي النموذج):
```py
commit_hash = "ed94a7c6247d8aedce4647f00f20de6875b5b292"
model = AutoModelForImageClassification.from_pretrained(
"sgugger/custom-resnet50d"، trust_remote_code=True، revision=commit_hash
)
```
لاحظ وجود زرّ لنسخ رقم إصدار بسهولة عند تصفح سجل التزامات مستودع النموذج على منصة Hugging Face.
| transformers/docs/source/ar/custom_models.md/0 | {
"file_path": "transformers/docs/source/ar/custom_models.md",
"repo_id": "transformers",
"token_count": 10045
} | 376 |
# تحميل المحوّلات باستخدام 🤗 PEFT
[[open-in-colab]]
تقنية "التدريب الدقيق ذو الكفاءة البارامتيرية" (PEFT)](https://huggingface.co/blog/peft) تقوم بتجميد معلمات النموذج المُدرب مسبقًا أثناء الضبط الدقيق وتضيف عدد صغير من المعلمات القابلة للتدريب (المحولات) فوقه. يتم تدريب المحوّلات لتعلم معلومات خاصة بالمهام. وقد ثبت أن هذا النهج فعال للغاية من حيث استخدام الذاكرة مع انخفاض استخدام الكمبيوتر أثناء إنتاج نتائج قمماثلة للنموذج مضبوط دقيقًا بالكامل.
عادة ما تكون المحولات المدربة باستخدام PEFT أصغر بمقدار كبير من حيث الحجم من النموذج الكامل، مما يجعل من السهل مشاركتها وتخزينها وتحميلها.
<div class="flex flex-col justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/PEFT-hub-screenshot.png"/>
<figcaption class="text-center">تبلغ أوزان المحول لطراز OPTForCausalLM المخزن على Hub حوالي 6 ميجابايت مقارنة بالحجم الكامل لأوزان النموذج، والتي يمكن أن تكون حوالي 700 ميجابايت.</figcaption>
</div>
إذا كنت مهتمًا بمعرفة المزيد عن مكتبة 🤗 PEFT، فراجع [الوثائق](https://huggingface.co/docs/peft/index).
## الإعداد
ابدأ بتثبيت 🤗 PEFT:
```bash
pip install peft
```
إذا كنت تريد تجربة الميزات الجديدة تمامًا، فقد تكون مهتمًا بتثبيت المكتبة من المصدر:
```bash
pip install git+https://github.com/huggingface/peft.git
```
## نماذج PEFT المدعومة
يدعم 🤗 Transformers بشكلٍ أصلي بعض طرق PEFT، مما يعني أنه يمكنك تحميل أوزان المحول المخزنة محليًا أو على Hub وتشغيلها أو تدريبها ببضع سطور من التعليمات البرمجية. الطرق المدعومة هي:
- [محولات الرتبة المنخفضة](https://huggingface.co/docs/peft/conceptual_guides/lora)
- [IA3](https://huggingface.co/docs/peft/conceptual_guides/ia3)
- [AdaLoRA](https://huggingface.co/papers/2303.10512)
إذا كنت تريد استخدام طرق PEFT الأخرى، مثل تعلم المحث أو ضبط المحث، أو حول مكتبة 🤗 PEFT بشكل عام، يرجى الرجوع إلى [الوثائق](https://huggingface.co/docs/peft/index).
## تحميل محول PEFT
لتحميل نموذج محول PEFT واستخدامه من 🤗 Transformers، تأكد من أن مستودع Hub أو الدليل المحلي يحتوي على ملف `adapter_config.json` وأوزان المحوّل، كما هو موضح في صورة المثال أعلاه. بعد ذلك، يمكنك تحميل نموذج محوّل PEFT باستخدام فئة `AutoModelFor`. على سبيل المثال، لتحميل نموذج محول PEFT للنمذجة اللغوية السببية:
1. حدد معرف النموذج لPEFT
2. مرره إلى فئة [`AutoModelForCausalLM`]
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(peft_model_id)
```
<Tip>
يمكنك تحميل محول PEFT باستخدام فئة `AutoModelFor` أو فئة النموذج الأساسي مثل `OPTForCausalLM` أو `LlamaForCausalLM`.
</Tip>
يمكنك أيضًا تحميل محول PEFT عن طريق استدعاء طريقة `load_adapter`:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "facebook/opt-350m"
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.load_adapter(peft_model_id)
```
راجع قسم [وثائق API](#transformers.integrations.PeftAdapterMixin) أدناه لمزيد من التفاصيل.
## التحميل في 8 بت أو 4 بت
راجع قسم [وثائق API](#transformers.integrations.PeftAdapterMixin) أدناه لمزيد من التفاصيل.
## التحميل في 8 بت أو 4 بت
يدعم تكامل `bitsandbytes` أنواع بيانات الدقة 8 بت و4 بت، والتي تكون مفيدة لتحميل النماذج الكبيرة لأنها توفر مساحة في الذاكرة (راجع دليل تكامل `bitsandbytes` [guide](./quantization#bitsandbytes-integration) لمعرفة المزيد). أضف المعلمات`load_in_8bit` أو `load_in_4bit` إلى [`~PreTrainedModel.from_pretrained`] وقم بتعيين `device_map="auto"` لتوزيع النموذج بشكل فعال على الأجهزة لديك:
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
peft_model_id = "ybelkada/opt-350m-lora"
model = AutoModelForCausalLM.from_pretrained(peft_model_id, quantization_config=BitsAndBytesConfig(load_in_8bit=True))
```
## إضافة محول جديد
يمكنك استخدام الدالة [`~peft.PeftModel.add_adapter`] لإضافة محوّل جديد إلى نموذج يحتوي بالفعل على محوّل آخر طالما أن المحول الجديد مطابقًا للنوع الحالي. على سبيل المثال، إذا كان لديك محول LoRA موجود مرتبط بنموذج:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import LoraConfig
model_id = "facebook/opt-350m"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(
target_modules=["q_proj", "k_proj"],
init_lora_weights=False
)
model.add_adapter(lora_config, adapter_name="adapter_1")
```
لإضافة محول جديد:
```py
# قم بتعليق محول جديد بنفس التكوين
model.add_adapter(lora_config, adapter_name="adapter_2")
```
الآن يمكنك استخدام [`~peft.PeftModel.set_adapter`] لتعيين المحول الذي سيتم استخدامه:
```py
# استخدم adapter_1
model.set_adapter("adapter_1")
output = model.generate(**inputs)
print(tokenizer.decode(output_disabled[0], skip_special_tokens=True))
# استخدم adapter_2
model.set_adapter("adapter_2")
output_enabled = model.generate(**inputs)
print(tokenizer.decode(output_enabled[0], skip_special_tokens=True))
```
## تمكين وتعطيل المحولات
بمجرد إضافة محول إلى نموذج، يمكنك تمكين أو تعطيل وحدة المحول. لتمكين وحدة المحول:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import PeftConfig
model_id = "facebook/opt-350m"
adapter_model_id = "ybelkada/opt-350m-lora"
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = "Hello"
inputs = tokenizer(text, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(model_id)
peft_config = PeftConfig.from_pretrained(adapter_model_id)
# لبدء تشغيله بأوزان عشوائية
peft_config.init_lora_weights = False
model.add_adapter(peft_config)
model.enable_adapters()
output = model.generate(**inputs)
```
لإيقاف تشغيل وحدة المحول:
```py
model.disable_adapters()
output = model.generate(**inputs)
```
## تدريب محول PEFT
يدعم محول PEFT فئة [`Trainer`] بحيث يمكنك تدريب محول لحالتك الاستخدام المحددة. فهو يتطلب فقط إضافة بضع سطور أخرى من التعليمات البرمجية. على سبيل المثال، لتدريب محول LoRA:
<Tip>
إذا لم تكن معتادًا على ضبط نموذج دقيق باستخدام [`Trainer`، فراجع البرنامج التعليمي](training) لضبط نموذج مُدرب مسبقًا.
</Tip>
1. حدد تكوين المحول باستخدام نوع المهمة والمعاملات الزائدة (راجع [`~peft.LoraConfig`] لمزيد من التفاصيل حول وظيفة هذه المعلمات).
```py
from peft import LoraConfig
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM"،
)
```
2. أضف المحول إلى النموذج.
```py
model.add_adapter(peft_config)
```
3. الآن يمكنك تمرير النموذج إلى [`Trainer`]!
```py
trainer = Trainer(model=model, ...)
trainer.train()
```
لحفظ محول المدرب وتحميله مرة أخرى:
```py
model.save_pretrained(save_dir)
model = AutoModelForCausalLM.from_pretrained(save_dir)
```
## إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT
```py
model.save_pretrained(save_dir)
model = AutoModelForCausalLM.from_pretrained(save_dir)
```
## إضافة طبقات قابلة للتدريب إضافية إلى محول PEFT
يمكنك أيضًا إجراء تدريب دقيق لمحوّلات قابلة للتدريب إضافية فوق نموذج يحتوي بالفعل على محوّلات عن طريق تمرير معلم `modules_to_save` في تكوين PEFT الخاص بك. على سبيل المثال، إذا كنت تريد أيضًا ضبط دقيق لرأس النموذج اللغوي`lm_head` فوق نموذج بمحوّل LoRA:
```py
from transformers import AutoModelForCausalLM, OPTForCausalLM, AutoTokenizer
from peft import LoraConfig
model_id = "facebook/opt-350m"
model = AutoModelForCausalLM.from_pretrained(model_id)
lora_config = LoraConfig(
target_modules=["q_proj", "k_proj"],
modules_to_save=["lm_head"]،
)
model.add_adapter(lora_config)
```
## وثائق API
[[autodoc]] integrations.PeftAdapterMixin
- load_adapter
- add_adapter
- set_adapter
- disable_adapters
- enable_adapters
- active_adapters
- get_adapter_state_dict
<!--
TODO: (@younesbelkada @stevhliu)
- Link to PEFT docs for further details
- Trainer
- 8-bit / 4-bit examples ?
--> | transformers/docs/source/ar/peft.md/0 | {
"file_path": "transformers/docs/source/ar/peft.md",
"repo_id": "transformers",
"token_count": 5152
} | 377 |
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# Vortrainierte Instanzen mit einer AutoClass laden
Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforderung sein, eine für Ihren Checkpoint zu erstellen. Als Teil der 🤗 Transformers Kernphilosophie, die Bibliothek leicht, einfach und flexibel nutzbar zu machen, leitet eine `AutoClass` automatisch die richtige Architektur aus einem gegebenen Checkpoint ab und lädt sie. Mit der Methode `from_pretrained()` kann man schnell ein vortrainiertes Modell für eine beliebige Architektur laden, so dass man keine Zeit und Ressourcen aufwenden muss, um ein Modell von Grund auf zu trainieren. Die Erstellung dieser Art von Checkpoint-agnostischem Code bedeutet, dass Ihr Code, wenn er für einen Checkpoint funktioniert, auch mit einem anderen Checkpoint funktionieren wird - solange er für eine ähnliche Aufgabe trainiert wurde - selbst wenn die Architektur unterschiedlich ist.
<Tip>
Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist [BERT](https://huggingface.co/google-bert/bert-base-uncased) eine Architektur, während `google-bert/bert-base-uncased` ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
</Tip>
In dieser Anleitung lernen Sie, wie man:
* Einen vortrainierten Tokenizer lädt.
* Einen vortrainierten Merkmalsextraktor lädt.
* Einen vortrainierten Prozessor lädt.
* Ein vortrainiertes Modell lädt.
## AutoTokenizer
Nahezu jede NLP-Aufgabe beginnt mit einem Tokenizer. Ein Tokenizer wandelt Ihre Eingabe in ein Format um, das vom Modell verarbeitet werden kann.
Laden Sie einen Tokenizer mit [`AutoTokenizer.from_pretrained`]:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
```
Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:
```py
>>> sequence = "In a hole in the ground there lived a hobbit."
>>> print(tokenizer(sequence))
{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102],
'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
```
## AutoFeatureExtractor
Für Audio- und Bildverarbeitungsaufgaben verarbeitet ein Merkmalsextraktor das Audiosignal oder Bild in das richtige Eingabeformat.
Laden Sie einen Merkmalsextraktor mit [`AutoFeatureExtractor.from_pretrained`]:
```py
>>> from transformers import AutoFeatureExtractor
>>> feature_extractor = AutoFeatureExtractor.from_pretrained(
... "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
... )
```
## AutoProcessor
Multimodale Aufgaben erfordern einen Prozessor, der zwei Arten von Vorverarbeitungswerkzeugen kombiniert. Das Modell [LayoutLMV2](model_doc/layoutlmv2) beispielsweise benötigt einen Feature-Extraktor für Bilder und einen Tokenizer für Text; ein Prozessor kombiniert beide.
Laden Sie einen Prozessor mit [`AutoProcessor.from_pretrained`]:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
```
## AutoModel
<frameworkcontent>
<pt>
Mit den `AutoModelFor`-Klassen können Sie schließlich ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`AutoModelForSequenceClassification.from_pretrained`]:
```py
>>> from transformers import AutoModelForSequenceClassification
>>> model = AutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip warning={true}>
Für PyTorch-Modelle verwendet die Methode `from_pretrained()` `torch.load()`, die intern `pickle` verwendet und als unsicher bekannt ist. Generell sollte man niemals ein Modell laden, das aus einer nicht vertrauenswürdigen Quelle stammen könnte, oder das manipuliert worden sein könnte. Dieses Sicherheitsrisiko wird für öffentliche Modelle, die auf dem Hugging Face Hub gehostet werden, teilweise gemildert, da diese bei jeder Übertragung [auf Malware](https://huggingface.co/docs/hub/security-malware) gescannt werden. Siehe die [Hub-Dokumentation](https://huggingface.co/docs/hub/security) für Best Practices wie [signierte Commit-Verifizierung](https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg) mit GPG.
TensorFlow- und Flax-Checkpoints sind nicht betroffen und können in PyTorch-Architekturen mit den Kwargs `from_tf` und `from_flax` für die Methode `from_pretrained` geladen werden, um dieses Problem zu umgehen.
</Tip>
Im Allgemeinen empfehlen wir die Verwendung der Klasse "AutoTokenizer" und der Klasse "AutoModelFor", um trainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
</pt>
<tf>
Mit den Klassen `TFAutoModelFor` schließlich können Sie ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`TFAutoModelForSequenceClassification.from_pretrained`]:
```py
>>> from transformers import TFAutoModelForSequenceClassification
>>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Im Allgemeinen empfehlen wir, die Klasse "AutoTokenizer" und die Klasse "TFAutoModelFor" zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
</tf>
</frameworkcontent>
| transformers/docs/source/de/autoclass_tutorial.md/0 | {
"file_path": "transformers/docs/source/de/autoclass_tutorial.md",
"repo_id": "transformers",
"token_count": 2644
} | 378 |
- sections:
- local: index
title: Transformers
- local: installation
title: Installation
- local: quicktour
title: Quickstart
title: Get started
- isExpanded: false
sections:
- sections:
- local: models
title: Loading models
- local: custom_models
title: Customizing models
- local: how_to_hack_models
title: Customizing model components
- local: model_sharing
title: Sharing
- local: modular_transformers
title: Contributing a new model to Transformers
- local: add_new_model
title: Legacy model contribution
- local: auto_docstring
title: Documenting a model
- local: attention_interface
title: Customizing attention function
title: Models
- sections:
- local: fast_tokenizers
title: Tokenizers
- local: image_processors
title: Image processors
- local: video_processors
title: Video processors
- local: backbones
title: Backbones
- local: feature_extractors
title: Feature extractors
- local: processors
title: Processors
- local: tokenizer_summary
title: Summary of the tokenizers
- local: pad_truncation
title: Padding and truncation
title: Preprocessors
title: Base classes
- isExpanded: false
sections:
- sections:
- local: pipeline_tutorial
title: Pipeline
- local: pipeline_gradio
title: Machine learning apps
- local: pipeline_webserver
title: Web server inference
- local: add_new_pipeline
title: Adding a new pipeline
title: Pipeline API
- sections:
- local: llm_tutorial
title: Text generation
- local: generation_strategies
title: Generation strategies
- local: generation_features
title: Generation features
- local: tasks/prompting
title: Prompt engineering
- local: llm_optims
title: Optimizing inference
- local: cache_explanation
title: Caching
- local: kv_cache
title: KV cache strategies
- local: llm_tutorial_optimization
title: Getting the most out of LLMs
- local: perplexity
title: Perplexity of fixed-length models
title: LLMs
- sections:
- local: conversations
title: Chat basics
- local: chat_templating
title: Chat templates
- local: chat_templating_multimodal
title: Multimodal chat templates
- local: chat_extras
title: Tool use
- local: chat_templating_writing
title: Writing a chat template
title: Chat with models
- sections:
- local: serving
title: Serving LLMs, VLMs, and other chat-based models
- local: jan
title: Jan
- local: cursor
title: Cursor
- local: tiny_agents
title: Tiny-Agents CLI and MCP tools
- local: open_webui
title: Open WebUI
title: Serving
- sections:
- local: perf_torch_compile
title: torch.compile
- local: perf_infer_gpu_one
title: GPU
- local: perf_infer_gpu_multi
title: Distributed inference
- local: perf_infer_cpu
title: CPU
title: Optimization
- local: agents
title: Agents
- local: tools
title: Tools
- local: transformers_as_backend
title: Inference server backends
title: Inference
- isExpanded: false
sections:
- sections:
- local: trainer
title: Trainer
- local: training
title: Fine-tuning
- local: optimizers
title: Optimizers
- local: hpo_train
title: Hyperparameter search
title: Trainer API
- sections:
- local: accelerator_selection
title: Accelerator selection
- local: accelerate
title: Accelerate
- local: fsdp
title: FullyShardedDataParallel
- local: deepspeed
title: DeepSpeed
- local: debugging
title: Multi-GPU debugging
- local: perf_train_cpu_many
title: Distributed CPUs
- local: perf_train_gpu_many
title: Parallelism methods
title: Distributed training
- sections:
- local: perf_train_gpu_one
title: GPU
- local: perf_train_cpu
title: CPU
- local: perf_train_special
title: Apple Silicon
- local: perf_train_gaudi
title: Intel Gaudi
- local: perf_hardware
title: Build your own machine
title: Hardware
- local: peft
title: PEFT
- local: model_memory_anatomy
title: Model training anatomy
title: Training
- isExpanded: false
sections:
- local: quantization/overview
title: Overview
- local: quantization/selecting
title: Selecting a quantization method
- local: quantization/concept_guide
title: Quantization concepts
- local: quantization/aqlm
title: AQLM
- local: quantization/auto_round
title: AutoRound
- local: quantization/awq
title: AWQ
- local: quantization/bitnet
title: BitNet
- local: quantization/bitsandbytes
title: bitsandbytes
- local: quantization/compressed_tensors
title: compressed-tensors
- local: quantization/eetq
title: EETQ
- local: quantization/fbgemm_fp8
title: FBGEMM
- local: quantization/finegrained_fp8
title: Fine-grained FP8
- local: quantization/fp_quant
title: FP-Quant
- local: gguf
title: GGUF
- local: quantization/gptq
title: GPTQ
- local: quantization/higgs
title: HIGGS
- local: quantization/hqq
title: HQQ
- local: quantization/optimum
title: Optimum
- local: quantization/quanto
title: Quanto
- local: quantization/quark
title: Quark
- local: quantization/torchao
title: torchao
- local: quantization/spqr
title: SpQR
- local: quantization/vptq
title: VPTQ
- local: quantization/contribute
title: Contribute
title: Quantization
- isExpanded: false
sections:
- local: serialization
title: ONNX
- local: tflite
title: LiteRT
- local: executorch
title: ExecuTorch
- local: torchscript
title: TorchScript
title: Export to production
- isExpanded: false
sections:
- sections:
- sections:
- local: tasks/sequence_classification
title: Text classification
- local: tasks/token_classification
title: Token classification
- local: tasks/question_answering
title: Question answering
- local: tasks/language_modeling
title: Causal language modeling
- local: tasks/masked_language_modeling
title: Masked language modeling
- local: tasks/translation
title: Translation
- local: tasks/summarization
title: Summarization
- local: tasks/multiple_choice
title: Multiple choice
title: Natural language processing
- sections:
- local: tasks/audio_classification
title: Audio classification
- local: tasks/asr
title: Automatic speech recognition
title: Audio
- sections:
- local: tasks/image_classification
title: Image classification
- local: tasks/semantic_segmentation
title: Image segmentation
- local: tasks/video_classification
title: Video classification
- local: tasks/object_detection
title: Object detection
- local: tasks/zero_shot_object_detection
title: Zero-shot object detection
- local: tasks/zero_shot_image_classification
title: Zero-shot image classification
- local: tasks/monocular_depth_estimation
title: Depth estimation
- local: tasks/image_to_image
title: Image-to-Image
- local: tasks/image_feature_extraction
title: Image Feature Extraction
- local: tasks/mask_generation
title: Mask Generation
- local: tasks/keypoint_detection
title: Keypoint detection
- local: tasks/knowledge_distillation_for_image_classification
title: Knowledge Distillation for Computer Vision
title: Computer vision
- sections:
- local: tasks/image_captioning
title: Image captioning
- local: tasks/document_question_answering
title: Document Question Answering
- local: tasks/visual_question_answering
title: Visual Question Answering
- local: tasks/text-to-speech
title: Text to speech
- local: tasks/idefics
title: Image tasks with IDEFICS
- local: tasks/image_text_to_text
title: Image-text-to-text
- local: tasks/video_text_to_text
title: Video-text-to-text
- local: tasks/visual_document_retrieval
title: Visual Document Retrieval
title: Multimodal
title: Task recipes
- local: run_scripts
title: Training scripts
- local: glossary
title: Glossary
- local: philosophy
title: Philosophy
- local: notebooks
title: Notebooks with examples
- local: community
title: Community resources
- local: troubleshooting
title: Troubleshoot
title: Resources
- isExpanded: false
sections:
- local: contributing
title: Contribute to Transformers
- local: testing
title: Transformers model tests
- local: pr_checks
title: Pull request checks
title: Contribute
- isExpanded: false
sections:
- sections:
- local: model_doc/auto
title: Auto Classes
- local: main_classes/backbones
title: Backbones
- local: main_classes/callback
title: Callbacks
- local: main_classes/configuration
title: Configuration
- local: main_classes/data_collator
title: Data Collator
- local: main_classes/keras_callbacks
title: Keras callbacks
- local: main_classes/logging
title: Logging
- local: main_classes/model
title: Models
- local: main_classes/text_generation
title: Text Generation
- local: main_classes/onnx
title: ONNX
- local: main_classes/optimizer_schedules
title: Optimization
- local: main_classes/output
title: Model outputs
- local: main_classes/peft
title: PEFT
- local: main_classes/pipelines
title: Pipelines
- local: main_classes/processors
title: Processors
- local: main_classes/quantization
title: Quantization
- local: main_classes/tokenizer
title: Tokenizer
- local: main_classes/trainer
title: Trainer
- local: main_classes/deepspeed
title: DeepSpeed
- local: main_classes/executorch
title: ExecuTorch
- local: main_classes/feature_extractor
title: Feature Extractor
- local: main_classes/image_processor
title: Image Processor
- local: main_classes/video_processor
title: Video Processor
title: Main Classes
- sections:
- sections:
- local: model_doc/albert
title: ALBERT
- local: model_doc/arcee
title: Arcee
- local: model_doc/bamba
title: Bamba
- local: model_doc/bart
title: BART
- local: model_doc/barthez
title: BARThez
- local: model_doc/bartpho
title: BARTpho
- local: model_doc/bert
title: BERT
- local: model_doc/bert-generation
title: BertGeneration
- local: model_doc/bert-japanese
title: BertJapanese
- local: model_doc/bertweet
title: BERTweet
- local: model_doc/big_bird
title: BigBird
- local: model_doc/bigbird_pegasus
title: BigBirdPegasus
- local: model_doc/biogpt
title: BioGpt
- local: model_doc/bitnet
title: BitNet
- local: model_doc/blenderbot
title: Blenderbot
- local: model_doc/blenderbot-small
title: Blenderbot Small
- local: model_doc/bloom
title: BLOOM
- local: model_doc/bort
title: BORT
- local: model_doc/byt5
title: ByT5
- local: model_doc/camembert
title: CamemBERT
- local: model_doc/canine
title: CANINE
- local: model_doc/codegen
title: CodeGen
- local: model_doc/code_llama
title: CodeLlama
- local: model_doc/cohere
title: Cohere
- local: model_doc/cohere2
title: Cohere2
- local: model_doc/convbert
title: ConvBERT
- local: model_doc/cpm
title: CPM
- local: model_doc/cpmant
title: CPMANT
- local: model_doc/ctrl
title: CTRL
- local: model_doc/dbrx
title: DBRX
- local: model_doc/deberta
title: DeBERTa
- local: model_doc/deberta-v2
title: DeBERTa-v2
- local: model_doc/deepseek_v3
title: DeepSeek-V3
- local: model_doc/dialogpt
title: DialoGPT
- local: model_doc/diffllama
title: DiffLlama
- local: model_doc/distilbert
title: DistilBERT
- local: model_doc/doge
title: Doge
- local: model_doc/dots1
title: dots1
- local: model_doc/dpr
title: DPR
- local: model_doc/electra
title: ELECTRA
- local: model_doc/encoder-decoder
title: Encoder Decoder Models
- local: model_doc/ernie
title: ERNIE
- local: model_doc/ernie4_5
title: Ernie4_5
- local: model_doc/ernie4_5_moe
title: Ernie4_5_MoE
- local: model_doc/ernie_m
title: ErnieM
- local: model_doc/esm
title: ESM
- local: model_doc/exaone4
title: EXAONE-4.0
- local: model_doc/falcon
title: Falcon
- local: model_doc/falcon3
title: Falcon3
- local: model_doc/falcon_h1
title: FalconH1
- local: model_doc/falcon_mamba
title: FalconMamba
- local: model_doc/flan-t5
title: FLAN-T5
- local: model_doc/flan-ul2
title: FLAN-UL2
- local: model_doc/flaubert
title: FlauBERT
- local: model_doc/fnet
title: FNet
- local: model_doc/fsmt
title: FSMT
- local: model_doc/funnel
title: Funnel Transformer
- local: model_doc/fuyu
title: Fuyu
- local: model_doc/gemma
title: Gemma
- local: model_doc/gemma2
title: Gemma2
- local: model_doc/glm
title: GLM
- local: model_doc/glm4
title: glm4
- local: model_doc/glm4_moe
title: glm4_moe
- local: model_doc/openai-gpt
title: GPT
- local: model_doc/gpt_neo
title: GPT Neo
- local: model_doc/gpt_neox
title: GPT NeoX
- local: model_doc/gpt_neox_japanese
title: GPT NeoX Japanese
- local: model_doc/gptj
title: GPT-J
- local: model_doc/gpt2
title: GPT2
- local: model_doc/gpt_bigcode
title: GPTBigCode
- local: model_doc/gpt_oss
title: GptOss
- local: model_doc/gptsan-japanese
title: GPTSAN Japanese
- local: model_doc/gpt-sw3
title: GPTSw3
- local: model_doc/granite
title: Granite
- local: model_doc/granitemoe
title: GraniteMoe
- local: model_doc/granitemoehybrid
title: GraniteMoeHybrid
- local: model_doc/granitemoeshared
title: GraniteMoeShared
- local: model_doc/helium
title: Helium
- local: model_doc/herbert
title: HerBERT
- local: model_doc/hgnet_v2
title: HGNet-V2
- local: model_doc/hunyuan_v1_dense
title: HunYuanDenseV1
- local: model_doc/hunyuan_v1_moe
title: HunYuanMoEV1
- local: model_doc/ibert
title: I-BERT
- local: model_doc/jamba
title: Jamba
- local: model_doc/jetmoe
title: JetMoe
- local: model_doc/jukebox
title: Jukebox
- local: model_doc/led
title: LED
- local: model_doc/lfm2
title: LFM2
- local: model_doc/llama
title: LLaMA
- local: model_doc/llama2
title: Llama2
- local: model_doc/llama3
title: Llama3
- local: model_doc/longformer
title: Longformer
- local: model_doc/longt5
title: LongT5
- local: model_doc/luke
title: LUKE
- local: model_doc/m2m_100
title: M2M100
- local: model_doc/madlad-400
title: MADLAD-400
- local: model_doc/mamba
title: Mamba
- local: model_doc/mamba2
title: Mamba2
- local: model_doc/marian
title: MarianMT
- local: model_doc/markuplm
title: MarkupLM
- local: model_doc/mbart
title: MBart and MBart-50
- local: model_doc/mega
title: MEGA
- local: model_doc/megatron-bert
title: MegatronBERT
- local: model_doc/megatron_gpt2
title: MegatronGPT2
- local: model_doc/minimax
title: MiniMax
- local: model_doc/mistral
title: Mistral
- local: model_doc/mixtral
title: Mixtral
- local: model_doc/mluke
title: mLUKE
- local: model_doc/mobilebert
title: MobileBERT
- local: model_doc/modernbert
title: ModernBert
- local: model_doc/modernbert-decoder
title: ModernBERTDecoder
- local: model_doc/mpnet
title: MPNet
- local: model_doc/mpt
title: MPT
- local: model_doc/mra
title: MRA
- local: model_doc/mt5
title: MT5
- local: model_doc/mvp
title: MVP
- local: model_doc/myt5
title: myt5
- local: model_doc/nemotron
title: Nemotron
- local: model_doc/nezha
title: NEZHA
- local: model_doc/nllb
title: NLLB
- local: model_doc/nllb-moe
title: NLLB-MoE
- local: model_doc/nystromformer
title: Nyströmformer
- local: model_doc/olmo
title: OLMo
- local: model_doc/olmo2
title: OLMo2
- local: model_doc/olmoe
title: OLMoE
- local: model_doc/open-llama
title: Open-Llama
- local: model_doc/opt
title: OPT
- local: model_doc/pegasus
title: Pegasus
- local: model_doc/pegasus_x
title: PEGASUS-X
- local: model_doc/persimmon
title: Persimmon
- local: model_doc/phi
title: Phi
- local: model_doc/phi3
title: Phi-3
- local: model_doc/phimoe
title: PhiMoE
- local: model_doc/phobert
title: PhoBERT
- local: model_doc/plbart
title: PLBart
- local: model_doc/prophetnet
title: ProphetNet
- local: model_doc/qdqbert
title: QDQBert
- local: model_doc/qwen2
title: Qwen2
- local: model_doc/qwen2_moe
title: Qwen2MoE
- local: model_doc/qwen3
title: Qwen3
- local: model_doc/qwen3_moe
title: Qwen3MoE
- local: model_doc/rag
title: RAG
- local: model_doc/realm
title: REALM
- local: model_doc/recurrent_gemma
title: RecurrentGemma
- local: model_doc/reformer
title: Reformer
- local: model_doc/rembert
title: RemBERT
- local: model_doc/retribert
title: RetriBERT
- local: model_doc/roberta
title: RoBERTa
- local: model_doc/roberta-prelayernorm
title: RoBERTa-PreLayerNorm
- local: model_doc/roc_bert
title: RoCBert
- local: model_doc/roformer
title: RoFormer
- local: model_doc/rwkv
title: RWKV
- local: model_doc/seed_oss
title: Seed-Oss
- local: model_doc/splinter
title: Splinter
- local: model_doc/squeezebert
title: SqueezeBERT
- local: model_doc/stablelm
title: StableLm
- local: model_doc/starcoder2
title: Starcoder2
- local: model_doc/switch_transformers
title: SwitchTransformers
- local: model_doc/t5
title: T5
- local: model_doc/t5gemma
title: T5Gemma
- local: model_doc/t5v1.1
title: T5v1.1
- local: model_doc/tapex
title: TAPEX
- local: model_doc/transfo-xl
title: Transformer XL
- local: model_doc/ul2
title: UL2
- local: model_doc/umt5
title: UMT5
- local: model_doc/xmod
title: X-MOD
- local: model_doc/xglm
title: XGLM
- local: model_doc/xlm
title: XLM
- local: model_doc/xlm-prophetnet
title: XLM-ProphetNet
- local: model_doc/xlm-roberta
title: XLM-RoBERTa
- local: model_doc/xlm-roberta-xl
title: XLM-RoBERTa-XL
- local: model_doc/xlm-v
title: XLM-V
- local: model_doc/xlnet
title: XLNet
- local: model_doc/xlstm
title: xLSTM
- local: model_doc/yoso
title: YOSO
- local: model_doc/zamba
title: Zamba
- local: model_doc/zamba2
title: Zamba2
title: Text models
- sections:
- local: model_doc/aimv2
title: Aimv2
- local: model_doc/beit
title: BEiT
- local: model_doc/bit
title: BiT
- local: model_doc/conditional_detr
title: Conditional DETR
- local: model_doc/convnext
title: ConvNeXT
- local: model_doc/convnextv2
title: ConvNeXTV2
- local: model_doc/cvt
title: CvT
- local: model_doc/d_fine
title: D-FINE
- local: model_doc/dab-detr
title: DAB-DETR
- local: model_doc/deepseek_v2
title: DeepSeek-V2
- local: model_doc/deepseek_vl
title: DeepseekVL
- local: model_doc/deepseek_vl_hybrid
title: DeepseekVLHybrid
- local: model_doc/deformable_detr
title: Deformable DETR
- local: model_doc/deit
title: DeiT
- local: model_doc/depth_anything
title: Depth Anything
- local: model_doc/depth_anything_v2
title: Depth Anything V2
- local: model_doc/depth_pro
title: DepthPro
- local: model_doc/deta
title: DETA
- local: model_doc/detr
title: DETR
- local: model_doc/dinat
title: DiNAT
- local: model_doc/dinov2
title: DINOV2
- local: model_doc/dinov2_with_registers
title: DINOv2 with Registers
- local: model_doc/dinov3
title: DINOv3
- local: model_doc/dit
title: DiT
- local: model_doc/dpt
title: DPT
- local: model_doc/efficientformer
title: EfficientFormer
- local: model_doc/efficientloftr
title: EfficientLoFTR
- local: model_doc/efficientnet
title: EfficientNet
- local: model_doc/eomt
title: EoMT
- local: model_doc/focalnet
title: FocalNet
- local: model_doc/glpn
title: GLPN
- local: model_doc/hgnet_v2
title: HGNet-V2
- local: model_doc/hiera
title: Hiera
- local: model_doc/ijepa
title: I-JEPA
- local: model_doc/imagegpt
title: ImageGPT
- local: model_doc/levit
title: LeViT
- local: model_doc/lightglue
title: LightGlue
- local: model_doc/mask2former
title: Mask2Former
- local: model_doc/maskformer
title: MaskFormer
- local: model_doc/mlcd
title: MLCD
- local: model_doc/mobilenet_v1
title: MobileNetV1
- local: model_doc/mobilenet_v2
title: MobileNetV2
- local: model_doc/mobilevit
title: MobileViT
- local: model_doc/mobilevitv2
title: MobileViTV2
- local: model_doc/nat
title: NAT
- local: model_doc/poolformer
title: PoolFormer
- local: model_doc/prompt_depth_anything
title: Prompt Depth Anything
- local: model_doc/pvt
title: Pyramid Vision Transformer (PVT)
- local: model_doc/pvt_v2
title: Pyramid Vision Transformer v2 (PVTv2)
- local: model_doc/regnet
title: RegNet
- local: model_doc/resnet
title: ResNet
- local: model_doc/rt_detr
title: RT-DETR
- local: model_doc/rt_detr_v2
title: RT-DETRv2
- local: model_doc/segformer
title: SegFormer
- local: model_doc/seggpt
title: SegGpt
- local: model_doc/superglue
title: SuperGlue
- local: model_doc/superpoint
title: SuperPoint
- local: model_doc/swiftformer
title: SwiftFormer
- local: model_doc/swin
title: Swin Transformer
- local: model_doc/swinv2
title: Swin Transformer V2
- local: model_doc/swin2sr
title: Swin2SR
- local: model_doc/table-transformer
title: Table Transformer
- local: model_doc/textnet
title: TextNet
- local: model_doc/timm_wrapper
title: Timm Wrapper
- local: model_doc/upernet
title: UperNet
- local: model_doc/van
title: VAN
- local: model_doc/vit
title: Vision Transformer (ViT)
- local: model_doc/vit_hybrid
title: ViT Hybrid
- local: model_doc/vitdet
title: ViTDet
- local: model_doc/vit_mae
title: ViTMAE
- local: model_doc/vitmatte
title: ViTMatte
- local: model_doc/vit_msn
title: ViTMSN
- local: model_doc/vitpose
title: ViTPose
- local: model_doc/yolos
title: YOLOS
- local: model_doc/zoedepth
title: ZoeDepth
title: Vision models
- sections:
- local: model_doc/audio-spectrogram-transformer
title: Audio Spectrogram Transformer
- local: model_doc/bark
title: Bark
- local: model_doc/clap
title: CLAP
- local: model_doc/csm
title: CSM
- local: model_doc/dac
title: dac
- local: model_doc/dia
title: Dia
- local: model_doc/encodec
title: EnCodec
- local: model_doc/fastspeech2_conformer
title: FastSpeech2Conformer
- local: model_doc/granite_speech
title: GraniteSpeech
- local: model_doc/hubert
title: Hubert
- local: model_doc/kyutai_speech_to_text
title: Kyutai Speech-To-Text
- local: model_doc/mctct
title: MCTCT
- local: model_doc/mimi
title: Mimi
- local: model_doc/mms
title: MMS
- local: model_doc/moonshine
title: Moonshine
- local: model_doc/moshi
title: Moshi
- local: model_doc/musicgen
title: MusicGen
- local: model_doc/musicgen_melody
title: MusicGen Melody
- local: model_doc/pop2piano
title: Pop2Piano
- local: model_doc/seamless_m4t
title: Seamless-M4T
- local: model_doc/seamless_m4t_v2
title: SeamlessM4T-v2
- local: model_doc/sew
title: SEW
- local: model_doc/sew-d
title: SEW-D
- local: model_doc/speech_to_text
title: Speech2Text
- local: model_doc/speech_to_text_2
title: Speech2Text2
- local: model_doc/speecht5
title: SpeechT5
- local: model_doc/unispeech
title: UniSpeech
- local: model_doc/unispeech-sat
title: UniSpeech-SAT
- local: model_doc/univnet
title: UnivNet
- local: model_doc/vits
title: VITS
- local: model_doc/wav2vec2
title: Wav2Vec2
- local: model_doc/wav2vec2-bert
title: Wav2Vec2-BERT
- local: model_doc/wav2vec2-conformer
title: Wav2Vec2-Conformer
- local: model_doc/wav2vec2_phoneme
title: Wav2Vec2Phoneme
- local: model_doc/wavlm
title: WavLM
- local: model_doc/whisper
title: Whisper
- local: model_doc/xcodec
title: X-Codec
- local: model_doc/xls_r
title: XLS-R
- local: model_doc/xlsr_wav2vec2
title: XLSR-Wav2Vec2
title: Audio models
- sections:
- local: model_doc/timesformer
title: TimeSformer
- local: model_doc/vjepa2
title: V-JEPA 2
- local: model_doc/videomae
title: VideoMAE
- local: model_doc/vivit
title: ViViT
title: Video models
- sections:
- local: model_doc/align
title: ALIGN
- local: model_doc/altclip
title: AltCLIP
- local: model_doc/aria
title: Aria
- local: model_doc/aya_vision
title: AyaVision
- local: model_doc/blip
title: BLIP
- local: model_doc/blip-2
title: BLIP-2
- local: model_doc/bridgetower
title: BridgeTower
- local: model_doc/bros
title: BROS
- local: model_doc/chameleon
title: Chameleon
- local: model_doc/chinese_clip
title: Chinese-CLIP
- local: model_doc/clip
title: CLIP
- local: model_doc/clipseg
title: CLIPSeg
- local: model_doc/clvp
title: CLVP
- local: model_doc/cohere2_vision
title: Cohere2Vision
- local: model_doc/colpali
title: ColPali
- local: model_doc/colqwen2
title: ColQwen2
- local: model_doc/data2vec
title: Data2Vec
- local: model_doc/deplot
title: DePlot
- local: model_doc/donut
title: Donut
- local: model_doc/emu3
title: Emu3
- local: model_doc/evolla
title: Evolla
- local: model_doc/flava
title: FLAVA
- local: model_doc/florence2
title: Florence2
- local: model_doc/gemma3
title: Gemma3
- local: model_doc/gemma3n
title: Gemma3n
- local: model_doc/git
title: GIT
- local: model_doc/glm4v
title: glm4v
- local: model_doc/glm4v_moe
title: glm4v_moe
- local: model_doc/got_ocr2
title: GOT-OCR2
- local: model_doc/granitevision
title: GraniteVision
- local: model_doc/grounding-dino
title: Grounding DINO
- local: model_doc/groupvit
title: GroupViT
- local: model_doc/idefics
title: IDEFICS
- local: model_doc/idefics2
title: Idefics2
- local: model_doc/idefics3
title: Idefics3
- local: model_doc/instructblip
title: InstructBLIP
- local: model_doc/instructblipvideo
title: InstructBlipVideo
- local: model_doc/internvl
title: InternVL
- local: model_doc/janus
title: Janus
- local: model_doc/kosmos-2
title: KOSMOS-2
- local: model_doc/kosmos2_5
title: KOSMOS-2.5
- local: model_doc/layoutlm
title: LayoutLM
- local: model_doc/layoutlmv2
title: LayoutLMV2
- local: model_doc/layoutlmv3
title: LayoutLMV3
- local: model_doc/layoutxlm
title: LayoutXLM
- local: model_doc/lilt
title: LiLT
- local: model_doc/llama4
title: Llama4
- local: model_doc/llava
title: LLaVA
- local: model_doc/llava_next
title: LLaVA-NeXT
- local: model_doc/llava_next_video
title: LLaVa-NeXT-Video
- local: model_doc/llava_onevision
title: LLaVA-Onevision
- local: model_doc/lxmert
title: LXMERT
- local: model_doc/matcha
title: MatCha
- local: model_doc/metaclip_2
title: MetaCLIP 2
- local: model_doc/mgp-str
title: MGP-STR
- local: model_doc/mistral3
title: Mistral3
- local: model_doc/mllama
title: mllama
- local: model_doc/mm-grounding-dino
title: MM Grounding DINO
- local: model_doc/nougat
title: Nougat
- local: model_doc/omdet-turbo
title: OmDet-Turbo
- local: model_doc/oneformer
title: OneFormer
- local: model_doc/ovis2
title: Ovis2
- local: model_doc/owlvit
title: OWL-ViT
- local: model_doc/owlv2
title: OWLv2
- local: model_doc/paligemma
title: PaliGemma
- local: model_doc/perceiver
title: Perceiver
- local: model_doc/perception_lm
title: PerceptionLM
- local: model_doc/phi4_multimodal
title: Phi4 Multimodal
- local: model_doc/pix2struct
title: Pix2Struct
- local: model_doc/pixtral
title: Pixtral
- local: model_doc/qwen2_5_omni
title: Qwen2.5-Omni
- local: model_doc/qwen2_5_vl
title: Qwen2.5-VL
- local: model_doc/qwen2_audio
title: Qwen2Audio
- local: model_doc/qwen2_vl
title: Qwen2VL
- local: model_doc/sam2
title: SAM2
- local: model_doc/sam2_video
title: SAM2 Video
- local: model_doc/sam
title: Segment Anything
- local: model_doc/sam_hq
title: Segment Anything High Quality
- local: model_doc/shieldgemma2
title: ShieldGemma2
- local: model_doc/siglip
title: SigLIP
- local: model_doc/siglip2
title: SigLIP2
- local: model_doc/smollm3
title: SmolLM3
- local: model_doc/smolvlm
title: SmolVLM
- local: model_doc/speech-encoder-decoder
title: Speech Encoder Decoder Models
- local: model_doc/tapas
title: TAPAS
- local: model_doc/trocr
title: TrOCR
- local: model_doc/tvlt
title: TVLT
- local: model_doc/tvp
title: TVP
- local: model_doc/udop
title: UDOP
- local: model_doc/video_llava
title: VideoLlava
- local: model_doc/vilt
title: ViLT
- local: model_doc/vipllava
title: VipLlava
- local: model_doc/vision-encoder-decoder
title: Vision Encoder Decoder Models
- local: model_doc/vision-text-dual-encoder
title: Vision Text Dual Encoder
- local: model_doc/visual_bert
title: VisualBERT
- local: model_doc/voxtral
title: Voxtral
- local: model_doc/xclip
title: X-CLIP
title: Multimodal models
- sections:
- local: model_doc/decision_transformer
title: Decision Transformer
- local: model_doc/trajectory_transformer
title: Trajectory Transformer
title: Reinforcement learning models
- sections:
- local: model_doc/autoformer
title: Autoformer
- local: model_doc/informer
title: Informer
- local: model_doc/patchtsmixer
title: PatchTSMixer
- local: model_doc/patchtst
title: PatchTST
- local: model_doc/time_series_transformer
title: Time Series Transformer
- local: model_doc/timesfm
title: TimesFM
title: Time series models
- sections:
- local: model_doc/graphormer
title: Graphormer
title: Graph models
title: Models
- sections:
- local: internal/modeling_utils
title: Custom Layers and Utilities
- local: internal/model_debugging_utils
title: Utilities for Model Debugging
- local: internal/pipelines_utils
title: Utilities for pipelines
- local: internal/tokenization_utils
title: Utilities for Tokenizers
- local: internal/trainer_utils
title: Utilities for Trainer
- local: internal/generation_utils
title: Utilities for Generation
- local: internal/image_processing_utils
title: Utilities for Image Processors
- local: internal/audio_utils
title: Utilities for Audio processing
- local: internal/file_utils
title: General Utilities
- local: internal/import_utils
title: Importing Utilities
- local: internal/time_series_utils
title: Utilities for Time Series
title: Internal helpers
- sections:
- local: reference/environment_variables
title: Environment Variables
title: Reference
title: API
| transformers/docs/source/en/_toctree.yml/0 | {
"file_path": "transformers/docs/source/en/_toctree.yml",
"repo_id": "transformers",
"token_count": 16430
} | 379 |
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# Chat basics
Chat models are conversational models you can send a message to and receive a response. Most language models from mid-2023 onwards are chat models and may be referred to as "instruct" or "instruction-tuned" models. Models that do not support chat are often referred to as "base" or "pretrained" models.
Larger and newer models are generally more capable, but models specialized in certain domains (medical, legal text, non-English languages, etc.) can often outperform these larger models. Try leaderboards like [OpenLLM](https://hf.co/spaces/HuggingFaceH4/open_llm_leaderboard) and [LMSys Chatbot Arena](https://chat.lmsys.org/?leaderboard) to help you identify the best model for your use case.
This guide shows you how to quickly load chat models in Transformers from the command line, how to build and format a conversation, and how to chat using the [`TextGenerationPipeline`].
## chat CLI
After you've [installed Transformers](./installation), you can chat with a model directly from the command line. The command below launches an interactive session with a model, with a few base commands listed at the start of the session.
```bash
transformers chat Qwen/Qwen2.5-0.5B-Instruct
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers-chat-cli.png"/>
</div>
You can launch the CLI with arbitrary `generate` flags, with the format `arg_1=value_1 arg_2=value_2 ...`
```bash
transformers chat Qwen/Qwen2.5-0.5B-Instruct do_sample=False max_new_tokens=10
```
For a full list of options, run the command below.
```bash
transformers chat -h
```
The chat is implemented on top of the [AutoClass](./model_doc/auto), using tooling from [text generation](./llm_tutorial) and [chat](./chat_templating). It uses the `transformers serve` CLI under the hood ([docs](./serving.md#serve-cli)).
## TextGenerationPipeline
[`TextGenerationPipeline`] is a high-level text generation class with a "chat mode". Chat mode is enabled when a conversational model is detected and the chat prompt is [properly formatted](./llm_tutorial#wrong-prompt-format).
Chat models accept a list of messages (the chat history) as the input. Each message is a dictionary with `role` and `content` keys.
To start the chat, add a single `user` message. You can also optionally include a `system` message to give the model directions on how to behave.
```py
chat = [
{"role": "system", "content": "You are a helpful science assistant."},
{"role": "user", "content": "Hey, can you explain gravity to me?"}
]
```
Create the [`TextGenerationPipeline`] and pass `chat` to it. For large models, setting [device_map="auto"](./models#big-model-inference) helps load the model quicker and automatically places it on the fastest device available.
```py
import torch
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="HuggingFaceTB/SmolLM2-1.7B-Instruct", dtype="auto", device_map="auto")
response = pipeline(chat, max_new_tokens=512)
print(response[0]["generated_text"][-1]["content"])
```
If this works successfully, you should see a response from the model! If you want to continue the conversation,
you need to update the chat history with the model's response. You can do this either by appending the text
to `chat` (use the `assistant` role), or by reading `response[0]["generated_text"]`, which contains
the full chat history, including the most recent response.
Once you have the model's response, you can continue the conversation by appending a new `user` message to the chat history.
```py
chat = response[0]["generated_text"]
chat.append(
{"role": "user", "content": "Woah! But can it be reconciled with quantum mechanics?"}
)
response = pipeline(chat, max_new_tokens=512)
print(response[0]["generated_text"][-1]["content"])
```
By repeating this process, you can continue the conversation as long as you like, at least until the model runs out of context window
or you run out of memory.
## Performance and memory usage
Transformers load models in full `float32` precision by default, and for a 8B model, this requires ~32GB of memory! Use the `torch_dtype="auto"` argument, which generally uses `bfloat16` for models that were trained with it, to reduce your memory usage.
> [!TIP]
> Refer to the [Quantization](./quantization/overview) docs for more information about the different quantization backends available.
To lower memory usage even lower, you can quantize the model to 8-bit or 4-bit with [bitsandbytes](https://hf.co/docs/bitsandbytes/index). Create a [`BitsAndBytesConfig`] with your desired quantization settings and pass it to the pipelines `model_kwargs` parameter. The example below quantizes a model to 8-bits.
```py
from transformers import pipeline, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto", model_kwargs={"quantization_config": quantization_config})
```
In general, model size and performance are directly correlated. Larger models are slower in addition to requiring more memory because each active parameter must be read from memory for every generated token.
This is a bottleneck for LLM text generation and the main options for improving generation speed are to either quantize a model or use hardware with higher memory bandwidth. Adding more compute power doesn't meaningfully help.
You can also try techniques like [speculative decoding](./generation_strategies#speculative-decoding), where a smaller model generates candidate tokens that are verified by the larger model. If the candidate tokens are correct, the larger model can generate more than one token at a time. This significantly alleviates the bandwidth bottleneck and improves generation speed.
> [!TIP]
Mixture-of-Expert (MoE) models such as [Mixtral](./model_doc/mixtral), [Qwen2MoE](./model_doc/qwen2_moe), and [GPT-OSS](./model_doc/gpt-oss) have lots of parameters, but only "activate" a small fraction of them to generate each token. As a result, MoE models generally have much lower memory bandwidth requirements and can be faster than a regular LLM of the same size. However, techniques like speculative decoding are ineffective with MoE models because more parameters become activated with each new speculated token.
| transformers/docs/source/en/conversations.md/0 | {
"file_path": "transformers/docs/source/en/conversations.md",
"repo_id": "transformers",
"token_count": 1927
} | 380 |
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# Transformers
<h3 align="center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/transformers_as_a_model_definition.png"/>
</h3>
Transformers acts as the model-definition framework for state-of-the-art machine learning models in text, computer
vision, audio, video, and multimodal model, for both inference and training.
It centralizes the model definition so that this definition is agreed upon across the ecosystem. `transformers` is the
pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from `transformers`.
We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be
simple, customizable, and efficient.
There are over 1M+ Transformers [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) on the [Hugging Face Hub](https://huggingface.com/models) you can use.
Explore the [Hub](https://huggingface.com/) today to find a model and use Transformers to help you get started right away.
## Features
Transformers provides everything you need for inference or training with state-of-the-art pretrained models. Some of the main features include:
- [Pipeline](./pipeline_tutorial): Simple and optimized inference class for many machine learning tasks like text generation, image segmentation, automatic speech recognition, document question answering, and more.
- [Trainer](./trainer): A comprehensive trainer that supports features such as mixed precision, torch.compile, and FlashAttention for training and distributed training for PyTorch models.
- [generate](./llm_tutorial): Fast text generation with large language models (LLMs) and vision language models (VLMs), including support for streaming and multiple decoding strategies.
## Design
> [!TIP]
> Read our [Philosophy](./philosophy) to learn more about Transformers' design principles.
Transformers is designed for developers and machine learning engineers and researchers. Its main design principles are:
1. Fast and easy to use: Every model is implemented from only three main classes (configuration, model, and preprocessor) and can be quickly used for inference or training with [`Pipeline`] or [`Trainer`].
2. Pretrained models: Reduce your carbon footprint, compute cost and time by using a pretrained model instead of training an entirely new one. Each pretrained model is reproduced as closely as possible to the original model and offers state-of-the-art performance.
<div class="flex justify-center">
<a target="_blank" href="https://huggingface.co/support">
<img alt="HuggingFace Expert Acceleration Program" src="https://hf.co/datasets/huggingface/documentation-images/resolve/81d7d9201fd4ceb537fc4cebc22c29c37a2ed216/transformers/transformers-index.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
</a>
</div>
## Learn
If you're new to Transformers or want to learn more about transformer models, we recommend starting with the [LLM course](https://huggingface.co/learn/llm-course/chapter1/1?fw=pt). This comprehensive course covers everything from the fundamentals of how transformer models work to practical applications across various tasks. You'll learn the complete workflow, from curating high-quality datasets to fine-tuning large language models and implementing reasoning capabilities. The course contains both theoretical and hands-on exercises to build a solid foundational knowledge of transformer models as you learn. | transformers/docs/source/en/index.md/0 | {
"file_path": "transformers/docs/source/en/index.md",
"repo_id": "transformers",
"token_count": 1165
} | 381 |
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# Text generation
[[open-in-colab]]
Text generation is the most popular application for large language models (LLMs). A LLM is trained to generate the next word (token) given some initial text (prompt) along with its own generated outputs up to a predefined length or when it reaches an end-of-sequence (`EOS`) token.
In Transformers, the [`~GenerationMixin.generate`] API handles text generation, and it is available for all models with generative capabilities. This guide will show you the basics of text generation with [`~GenerationMixin.generate`] and some common pitfalls to avoid.
> [!TIP]
> You can also chat with a model directly from the command line. ([reference](./conversations.md#transformers-cli))
> ```shell
> transformers chat Qwen/Qwen2.5-0.5B-Instruct
> ```
## Default generate
Before you begin, it's helpful to install [bitsandbytes](https://hf.co/docs/bitsandbytes/index) to quantize really large models to reduce their memory usage.
```bash
!pip install -U transformers bitsandbytes
```
Bitsandbytes supports multiple backends in addition to CUDA-based GPUs. Refer to the multi-backend installation [guide](https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend) to learn more.
Load a LLM with [`~PreTrainedModel.from_pretrained`] and add the following two parameters to reduce the memory requirements.
- `device_map="auto"` enables Accelerates' [Big Model Inference](./models#big-model-inference) feature for automatically initiating the model skeleton and loading and dispatching the model weights across all available devices, starting with the fastest device (GPU).
- `quantization_config` is a configuration object that defines the quantization settings. This examples uses bitsandbytes as the quantization backend (see the [Quantization](./quantization/overview) section for more available backends) and it loads the model in [4-bits](./quantization/bitsandbytes).
```py
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto", quantization_config=quantization_config)
```
Tokenize your input, and set the [`~PreTrainedTokenizer.padding_side`] parameter to `"left"` because a LLM is not trained to continue generation from padding tokens. The tokenizer returns the input ids and attention mask.
> [!TIP]
> Process more than one prompt at a time by passing a list of strings to the tokenizer. Batch the inputs to improve throughput at a small cost to latency and memory.
```py
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
model_inputs = tokenizer(["A list of colors: red, blue"], return_tensors="pt").to(model.device)
```
Pass the inputs to [`~GenerationMixin.generate`] to generate tokens, and [`~PreTrainedTokenizer.batch_decode`] the generated tokens back to text.
```py
generated_ids = model.generate(**model_inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
"A list of colors: red, blue, green, yellow, orange, purple, pink,"
```
## Generation configuration
All generation settings are contained in [`GenerationConfig`]. In the example above, the generation settings are derived from the `generation_config.json` file of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). A default decoding strategy is used when no configuration is saved with a model.
Inspect the configuration through the `generation_config` attribute. It only shows values that are different from the default configuration, in this case, the `bos_token_id` and `eos_token_id`.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
model.generation_config
GenerationConfig {
"bos_token_id": 1,
"eos_token_id": 2
}
```
You can customize [`~GenerationMixin.generate`] by overriding the parameters and values in [`GenerationConfig`]. See [this section below](#common-options) for commonly adjusted parameters.
```py
# enable beam search sampling strategy
model.generate(**inputs, num_beams=4, do_sample=True)
```
[`~GenerationMixin.generate`] can also be extended with external libraries or custom code:
1. the `logits_processor` parameter accepts custom [`LogitsProcessor`] instances for manipulating the next token probability distribution;
2. the `stopping_criteria` parameters supports custom [`StoppingCriteria`] to stop text generation;
3. other custom generation methods can be loaded through the `custom_generate` flag ([docs](generation_strategies.md/#custom-decoding-methods)).
Refer to the [Generation strategies](./generation_strategies) guide to learn more about search, sampling, and decoding strategies.
### Saving
Create an instance of [`GenerationConfig`] and specify the decoding parameters you want.
```py
from transformers import AutoModelForCausalLM, GenerationConfig
model = AutoModelForCausalLM.from_pretrained("my_account/my_model")
generation_config = GenerationConfig(
max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id
)
```
Use [`~GenerationConfig.save_pretrained`] to save a specific generation configuration and set the `push_to_hub` parameter to `True` to upload it to the Hub.
```py
generation_config.save_pretrained("my_account/my_model", push_to_hub=True)
```
Leave the `config_file_name` parameter empty. This parameter should be used when storing multiple generation configurations in a single directory. It gives you a way to specify which generation configuration to load. You can create different configurations for different generative tasks (creative text generation with sampling, summarization with beam search) for use with a single model.
```py
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
translation_generation_config = GenerationConfig(
num_beams=4,
early_stopping=True,
decoder_start_token_id=0,
eos_token_id=model.config.eos_token_id,
pad_token=model.config.pad_token_id,
)
translation_generation_config.save_pretrained("/tmp", config_file_name="translation_generation_config.json", push_to_hub=True)
generation_config = GenerationConfig.from_pretrained("/tmp", config_file_name="translation_generation_config.json")
inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt")
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
```
## Common Options
[`~GenerationMixin.generate`] is a powerful tool that can be heavily customized. This can be daunting for a new users. This section contains a list of popular generation options that you can define in most text generation tools in Transformers: [`~GenerationMixin.generate`], [`GenerationConfig`], `pipelines`, the `chat` CLI, ...
| Option name | Type | Simplified description |
|---|---|---|
| `max_new_tokens` | `int` | Controls the maximum generation length. Be sure to define it, as it usually defaults to a small value. |
| `do_sample` | `bool` | Defines whether generation will sample the next token (`True`), or is greedy instead (`False`). Most use cases should set this flag to `True`. Check [this guide](./generation_strategies) for more information. |
| `temperature` | `float` | How unpredictable the next selected token will be. High values (`>0.8`) are good for creative tasks, low values (e.g. `<0.4`) for tasks that require "thinking". Requires `do_sample=True`. |
| `num_beams` | `int` | When set to `>1`, activates the beam search algorithm. Beam search is good on input-grounded tasks. Check [this guide](./generation_strategies) for more information. |
| `repetition_penalty` | `float` | Set it to `>1.0` if you're seeing the model repeat itself often. Larger values apply a larger penalty. |
| `eos_token_id` | `list[int]` | The token(s) that will cause generation to stop. The default value is usually good, but you can specify a different token. |
## Pitfalls
The section below covers some common issues you may encounter during text generation and how to solve them.
### Output length
[`~GenerationMixin.generate`] returns up to 20 tokens by default unless otherwise specified in a models [`GenerationConfig`]. It is highly recommended to manually set the number of generated tokens with the [`max_new_tokens`] parameter to control the output length. [Decoder-only](https://hf.co/learn/nlp-course/chapter1/6?fw=pt) models returns the initial prompt along with the generated tokens.
```py
model_inputs = tokenizer(["A sequence of numbers: 1, 2"], return_tensors="pt").to(model.device)
```
<hfoptions id="output-length">
<hfoption id="default length">
```py
generated_ids = model.generate(**model_inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'A sequence of numbers: 1, 2, 3, 4, 5'
```
</hfoption>
<hfoption id="max_new_tokens">
```py
generated_ids = model.generate(**model_inputs, max_new_tokens=50)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'A sequence of numbers: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,'
```
</hfoption>
</hfoptions>
### Decoding strategy
The default decoding strategy in [`~GenerationMixin.generate`] is *greedy search*, which selects the next most likely token, unless otherwise specified in a models [`GenerationConfig`]. While this decoding strategy works well for input-grounded tasks (transcription, translation), it is not optimal for more creative use cases (story writing, chat applications).
For example, enable a [multinomial sampling](./generation_strategies#multinomial-sampling) strategy to generate more diverse outputs. Refer to the [Generation strategy](./generation_strategies) guide for more decoding strategies.
```py
model_inputs = tokenizer(["I am a cat."], return_tensors="pt").to(model.device)
```
<hfoptions id="decoding">
<hfoption id="greedy search">
```py
generated_ids = model.generate(**model_inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
</hfoption>
<hfoption id="multinomial sampling">
```py
generated_ids = model.generate(**model_inputs, do_sample=True)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
</hfoption>
</hfoptions>
### Padding side
Inputs need to be padded if they don't have the same length. But LLMs aren't trained to continue generation from padding tokens, which means the [`~PreTrainedTokenizer.padding_side`] parameter needs to be set to the left of the input.
<hfoptions id="padding">
<hfoption id="right pad">
```py
model_inputs = tokenizer(
["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**model_inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'1, 2, 33333333333'
```
</hfoption>
<hfoption id="left pad">
```py
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
model_inputs = tokenizer(
["1, 2, 3", "A, B, C, D, E"], padding=True, return_tensors="pt"
).to(model.device)
generated_ids = model.generate(**model_inputs)
tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
'1, 2, 3, 4, 5, 6,'
```
</hfoption>
</hfoptions>
### Prompt format
Some models and tasks expect a certain input prompt format, and if the format is incorrect, the model returns a suboptimal output. You can learn more about prompting in the [prompt engineering](./tasks/prompting) guide.
For example, a chat model expects the input as a [chat template](./chat_templating). Your prompt should include a `role` and `content` to indicate who is participating in the conversation. If you try to pass your prompt as a single string, the model doesn't always return the expected output.
```py
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceH4/zephyr-7b-alpha")
model = AutoModelForCausalLM.from_pretrained(
"HuggingFaceH4/zephyr-7b-alpha", device_map="auto", load_in_4bit=True
)
```
<hfoptions id="format">
<hfoption id="no format">
```py
prompt = """How many cats does it take to change a light bulb? Reply as a pirate."""
model_inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
input_length = model_inputs.input_ids.shape[1]
generated_ids = model.generate(**model_inputs, max_new_tokens=50)
print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
"Aye, matey! 'Tis a simple task for a cat with a keen eye and nimble paws. First, the cat will climb up the ladder, carefully avoiding the rickety rungs. Then, with"
```
</hfoption>
<hfoption id="chat template">
```py
messages = [
{
"role": "system",
"content": "You are a friendly chatbot who always responds in the style of a pirate",
},
{"role": "user", "content": "How many cats does it take to change a light bulb?"},
]
model_inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
input_length = model_inputs.shape[1]
generated_ids = model.generate(model_inputs, do_sample=True, max_new_tokens=50)
print(tokenizer.batch_decode(generated_ids[:, input_length:], skip_special_tokens=True)[0])
"Arr, matey! According to me beliefs, 'twas always one cat to hold the ladder and another to climb up it an’ change the light bulb, but if yer looking to save some catnip, maybe yer can
```
</hfoption>
</hfoptions>
## Resources
Take a look below for some more specific and specialized text generation libraries.
- [Optimum](https://github.com/huggingface/optimum): an extension of Transformers focused on optimizing training and inference on specific hardware devices
- [Outlines](https://github.com/dottxt-ai/outlines): a library for constrained text generation (generate JSON files for example).
- [SynCode](https://github.com/uiuc-focal-lab/syncode): a library for context-free grammar guided generation (JSON, SQL, Python).
- [Text Generation Inference](https://github.com/huggingface/text-generation-inference): a production-ready server for LLMs.
- [Text generation web UI](https://github.com/oobabooga/text-generation-webui): a Gradio web UI for text generation.
- [logits-processor-zoo](https://github.com/NVIDIA/logits-processor-zoo): additional logits processors for controlling text generation.
| transformers/docs/source/en/llm_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/llm_tutorial.md",
"repo_id": "transformers",
"token_count": 4639
} | 382 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# PEFT
The [`~integrations.PeftAdapterMixin`] provides functions from the [PEFT](https://huggingface.co/docs/peft/index) library for managing adapters with Transformers. This mixin currently supports LoRA, IA3, and AdaLora. Prefix tuning methods (prompt tuning, prompt learning) aren't supported because they can't be injected into a torch module.
[[autodoc]] integrations.PeftAdapterMixin
- load_adapter
- add_adapter
- set_adapter
- disable_adapters
- enable_adapters
- active_adapters
- get_adapter_state_dict
| transformers/docs/source/en/main_classes/peft.md/0 | {
"file_path": "transformers/docs/source/en/main_classes/peft.md",
"repo_id": "transformers",
"token_count": 355
} | 383 |
<!--Copyright 2025 The BitNet Team and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
*This model was released on 2025-04-16 and added to Hugging Face Transformers on 2025-04-28.*
# BitNet
## Overview
Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://huggingface.co/papers/2504.12285)
➡️ **Official Inference Code:** [microsoft/BitNet (bitnet.cpp)](https://github.com/microsoft/BitNet)
## Model Variants
Several versions of the model weights are available on Hugging Face:
* [**`microsoft/bitnet-b1.58-2B-4T`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T): Contains the packed 1.58-bit weights optimized for efficient inference. **Use this for deployment.**
* [**`microsoft/bitnet-b1.58-2B-4T-bf16`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-bf16): Contains the master weights in BF16 format. **Use this only for training or fine-tuning purposes.**
* [**`microsoft/bitnet-b1.58-2B-4T-gguf`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf): Contains the model weights in GGUF format, compatible with the `bitnet.cpp` library for CPU inference.
### Model Details
* **Architecture:** Transformer-based, modified with `BitLinear` layers (BitNet framework).
* Uses Rotary Position Embeddings (RoPE).
* Uses squared ReLU (ReLU²) activation in FFN layers.
* Employs [`subln`](https://proceedings.mlr.press/v202/wang23u.html) normalization.
* No bias terms in linear or normalization layers.
* **Quantization:** Native 1.58-bit weights and 8-bit activations (W1.58A8).
* Weights are quantized to ternary values {-1, 0, +1} using absmean quantization during the forward pass.
* Activations are quantized to 8-bit integers using absmax quantization (per-token).
* **Crucially, the model was *trained from scratch* with this quantization scheme, not post-training quantized.**
* **Parameters:** ~2 Billion
* **Training Tokens:** 4 Trillion
* **Context Length:** Maximum sequence length of **4096 tokens**.
* *Recommendation:* For optimal performance on tasks requiring very long contexts (beyond the pre-training length or for specialized long-reasoning tasks), we recommend performing intermediate long-sequence adaptation/training before the final fine-tuning stage.
* **Training Stages:**
1. **Pre-training:** Large-scale training on public text/code and synthetic math data using a two-stage learning rate and weight decay schedule.
2. **Supervised Fine-tuning (SFT):** Fine-tuned on instruction-following and conversational datasets using sum loss aggregation and specific hyperparameter tuning.
3. **Direct Preference Optimization (DPO):** Aligned with human preferences using preference pairs.
* **Tokenizer:** LLaMA 3 Tokenizer (vocab size: 128,256).
## Usage tips
**VERY IMPORTANT NOTE ON EFFICIENCY**
> Please do NOT expect performance efficiency gains (in terms of speed, latency, or energy consumption) when using this model with the standard transformers library.
>
> The current execution paths within transformers do not contain the specialized, highly optimized computational kernels required to leverage the advantages of the BitNet architecture. Running the model via transformers will likely result in inference speeds and energy usage comparable to, or potentially worse than, standard full-precision models within this framework on both CPU and GPU.
>
> While you might observe reduced memory usage due to the quantized weights, the primary computational efficiency benefits are not accessible through this standard transformers usage path.
>
> For achieving the efficiency benefits demonstrated in the technical paper, you MUST use the dedicated C++ implementation: [bitnet.cpp](https://github.com/microsoft/BitNet).
### Requirements
```bash
pip install transformers
```
### Example
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "microsoft/bitnet-b1.58-2B-4T"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16
)
# Apply the chat template
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "How are you?"},
]
chat_input = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
# Generate response
chat_outputs = model.generate(chat_input, max_new_tokens=50)
response = tokenizer.decode(chat_outputs[0][chat_input.shape[-1]:], skip_special_tokens=True) # Decode only the response part
print("\nAssistant Response:", response)
```
## BitNetConfig
[[autodoc]] BitNetConfig
## BitNetModel
[[autodoc]] BitNetModel
- forward
## BitNetForCausalLM
[[autodoc]] BitNetForCausalLM
- forward
| transformers/docs/source/en/model_doc/bitnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/bitnet.md",
"repo_id": "transformers",
"token_count": 1657
} | 384 |
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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
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2021-12-18 and added to Hugging Face Transformers on 2022-11-08.*
# CLIPSeg
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://huggingface.co/papers/2112.10003) by Timo Lüddecke
and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
The abstract from the paper is the following:
*Image segmentation is usually addressed by training a
model for a fixed set of object classes. Incorporating additional classes or more complex queries later is expensive
as it requires re-training the model on a dataset that encompasses these expressions. Here we propose a system
that can generate image segmentations based on arbitrary
prompts at test time. A prompt can be either a text or an
image. This approach enables us to create a unified model
(trained once) for three common segmentation tasks, which
come with distinct challenges: referring expression segmentation, zero-shot segmentation and one-shot segmentation.
We build upon the CLIP model as a backbone which we extend with a transformer-based decoder that enables dense
prediction. After training on an extended version of the
PhraseCut dataset, our system generates a binary segmentation map for an image based on a free-text prompt or on
an additional image expressing the query. We analyze different variants of the latter image-based prompts in detail.
This novel hybrid input allows for dynamic adaptation not
only to the three segmentation tasks mentioned above, but
to any binary segmentation task where a text or image query
can be formulated. Finally, we find our system to adapt well
to generalized queries involving affordances or properties*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/clipseg_architecture.png"
alt="drawing" width="600"/>
<small> CLIPSeg overview. Taken from the <a href="https://huggingface.co/papers/2112.10003">original paper.</a> </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/timojl/clipseg).
## Usage tips
- [`CLIPSegForImageSegmentation`] adds a decoder on top of [`CLIPSegModel`]. The latter is identical to [`CLIPModel`].
- [`CLIPSegForImageSegmentation`] can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text
(provided to the model as `input_ids`) or an image (provided to the model as `conditional_pixel_values`). One can also provide custom
conditional embeddings (provided to the model as `conditional_embeddings`).
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
<PipelineTag pipeline="image-segmentation"/>
- A notebook that illustrates [zero-shot image segmentation with CLIPSeg](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/CLIPSeg/Zero_shot_image_segmentation_with_CLIPSeg.ipynb).
## CLIPSegConfig
[[autodoc]] CLIPSegConfig
- from_text_vision_configs
## CLIPSegTextConfig
[[autodoc]] CLIPSegTextConfig
## CLIPSegVisionConfig
[[autodoc]] CLIPSegVisionConfig
## CLIPSegProcessor
[[autodoc]] CLIPSegProcessor
## CLIPSegModel
[[autodoc]] CLIPSegModel
- forward
- get_text_features
- get_image_features
## CLIPSegTextModel
[[autodoc]] CLIPSegTextModel
- forward
## CLIPSegVisionModel
[[autodoc]] CLIPSegVisionModel
- forward
## CLIPSegForImageSegmentation
[[autodoc]] CLIPSegForImageSegmentation
- forward | transformers/docs/source/en/model_doc/clipseg.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/clipseg.md",
"repo_id": "transformers",
"token_count": 1318
} | 385 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
*This model was released on 2019-09-11 and added to Hugging Face Transformers on 2020-11-16.*
# CTRL
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
CTRL model was proposed in [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and
Richard Socher. It's a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).
The abstract from the paper is the following:
*Large-scale language models show promising text generation capabilities, but users cannot easily control particular
aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
via model-based source attribution.*
This model was contributed by [keskarnitishr](https://huggingface.co/keskarnitishr). The original code can be found
[here](https://github.com/salesforce/ctrl).
## Usage tips
- CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
or links to generate coherent text. Refer to the [original implementation](https://github.com/salesforce/ctrl) for
more information.
- CTRL is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
observed in the *run_generation.py* example script.
- The PyTorch models can take the `past_key_values` as input, which is the previously computed key/value attention pairs.
Using the `past_key_values` value prevents the model from re-computing
pre-computed values in the context of text generation. See the [`forward`](model_doc/ctrl#transformers.CTRLModel.forward)
method for more information on the usage of this argument.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
## CTRLConfig
[[autodoc]] CTRLConfig
## CTRLTokenizer
[[autodoc]] CTRLTokenizer
- save_vocabulary
## CTRLModel
[[autodoc]] CTRLModel
- forward
## CTRLLMHeadModel
[[autodoc]] CTRLLMHeadModel
- forward
## CTRLForSequenceClassification
[[autodoc]] CTRLForSequenceClassification
- forward
| transformers/docs/source/en/model_doc/ctrl.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/ctrl.md",
"repo_id": "transformers",
"token_count": 1024
} | 386 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
specific language governing permissions and limitations under the License. -->
*This model was released on 2021-11-30 and added to Hugging Face Transformers on 2022-08-12.*
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
</div>
# Donut
[Donut (Document Understanding Transformer)](https://huggingface.co/papers/2111.15664) is a visual document understanding model that doesn't require an Optical Character Recognition (OCR) engine. Unlike traditional approaches that extract text using OCR before processing, Donut employs an end-to-end Transformer-based architecture to directly analyze document images. This eliminates OCR-related inefficiencies making it more accurate and adaptable to diverse languages and formats.
Donut features vision encoder ([Swin](./swin)) and a text decoder ([BART](./bart)). Swin converts document images into embeddings and BART processes them into meaningful text sequences.
You can find all the original Donut checkpoints under the [Naver Clova Information Extraction](https://huggingface.co/naver-clova-ix) organization.
> [!TIP]
> Click on the Donut models in the right sidebar for more examples of how to apply Donut to different language and vision tasks.
The examples below demonstrate how to perform document understanding tasks using Donut with [`Pipeline`] and [`AutoModel`]
<hfoptions id="usage">
<hfoption id="Pipeline">
```py
# pip install datasets
import torch
from transformers import pipeline
from PIL import Image
pipeline = pipeline(
task="document-question-answering",
model="naver-clova-ix/donut-base-finetuned-docvqa",
device=0,
dtype=torch.float16
)
dataset = load_dataset("hf-internal-testing/example-documents", split="test")
image = dataset[0]["image"]
pipeline(image=image, question="What time is the coffee break?")
```
</hfoption>
<hfoption id="AutoModel">
```py
# pip install datasets
import torch
from datasets import load_dataset
from transformers import AutoProcessor, AutoModelForVision2Seq
processor = AutoProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
model = AutoModelForVision2Seq.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
dataset = load_dataset("hf-internal-testing/example-documents", split="test")
image = dataset[0]["image"]
question = "What time is the coffee break?"
task_prompt = f"<s_docvqa><s_question>{question}</s_question><s_answer>"
inputs = processor(image, task_prompt, return_tensors="pt")
outputs = model.generate(
input_ids=inputs.input_ids,
pixel_values=inputs.pixel_values,
max_length=512
)
answer = processor.decode(outputs[0], skip_special_tokens=True)
print(answer)
```
</hfoption>
</hfoptions>
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
```py
# pip install datasets torchao
import torch
from datasets import load_dataset
from transformers import TorchAoConfig, AutoProcessor, AutoModelForVision2Seq
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
processor = AutoProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
model = AutoModelForVision2Seq.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa", quantization_config=quantization_config)
dataset = load_dataset("hf-internal-testing/example-documents", split="test")
image = dataset[0]["image"]
question = "What time is the coffee break?"
task_prompt = f"<s_docvqa><s_question>{question}</s_question><s_answer>"
inputs = processor(image, task_prompt, return_tensors="pt")
outputs = model.generate(
input_ids=inputs.input_ids,
pixel_values=inputs.pixel_values,
max_length=512
)
answer = processor.decode(outputs[0], skip_special_tokens=True)
print(answer)
```
## Notes
- Use Donut for document image classification as shown below.
```py
>>> import re
>>> from transformers import DonutProcessor, VisionEncoderDecoderModel, infer_device
>>> from datasets import load_dataset
>>> import torch
>>> processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
>>> model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-rvlcdip")
>>> device = infer_device()
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # load document image
>>> dataset = load_dataset("hf-internal-testing/example-documents", split="test")
>>> image = dataset[1]["image"]
>>> # prepare decoder inputs
>>> task_prompt = "<s_rvlcdip>"
>>> decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
>>> pixel_values = processor(image, return_tensors="pt").pixel_values
>>> outputs = model.generate(
... pixel_values.to(device),
... decoder_input_ids=decoder_input_ids.to(device),
... max_length=model.decoder.config.max_position_embeddings,
... pad_token_id=processor.tokenizer.pad_token_id,
... eos_token_id=processor.tokenizer.eos_token_id,
... use_cache=True,
... bad_words_ids=[[processor.tokenizer.unk_token_id]],
... return_dict_in_generate=True,
... )
>>> sequence = processor.batch_decode(outputs.sequences)[0]
>>> sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
>>> sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
>>> print(processor.token2json(sequence))
{'class': 'advertisement'}
```
- Use Donut for document parsing as shown below.
```py
>>> import re
>>> from transformers import DonutProcessor, VisionEncoderDecoderModel, infer_device
>>> from datasets import load_dataset
>>> import torch
>>> processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
>>> model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-cord-v2")
>>> device = infer_device()
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # load document image
>>> dataset = load_dataset("hf-internal-testing/example-documents", split="test")
>>> image = dataset[2]["image"]
>>> # prepare decoder inputs
>>> task_prompt = "<s_cord-v2>"
>>> decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
>>> pixel_values = processor(image, return_tensors="pt").pixel_values
>>> outputs = model.generate(
... pixel_values.to(device),
... decoder_input_ids=decoder_input_ids.to(device),
... max_length=model.decoder.config.max_position_embeddings,
... pad_token_id=processor.tokenizer.pad_token_id,
... eos_token_id=processor.tokenizer.eos_token_id,
... use_cache=True,
... bad_words_ids=[[processor.tokenizer.unk_token_id]],
... return_dict_in_generate=True,
... )
>>> sequence = processor.batch_decode(outputs.sequences)[0]
>>> sequence = sequence.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
>>> sequence = re.sub(r"<.*?>", "", sequence, count=1).strip() # remove first task start token
>>> print(processor.token2json(sequence))
{'menu': {'nm': 'CINNAMON SUGAR', 'unitprice': '17,000', 'cnt': '1 x', 'price': '17,000'}, 'sub_total': {'subtotal_price': '17,000'}, 'total':
{'total_price': '17,000', 'cashprice': '20,000', 'changeprice': '3,000'}}
```
## DonutSwinConfig
[[autodoc]] DonutSwinConfig
## DonutImageProcessor
[[autodoc]] DonutImageProcessor
- preprocess
## DonutImageProcessorFast
[[autodoc]] DonutImageProcessorFast
- preprocess
## DonutFeatureExtractor
[[autodoc]] DonutFeatureExtractor
- __call__
## DonutProcessor
[[autodoc]] DonutProcessor
- __call__
- from_pretrained
- save_pretrained
- batch_decode
- decode
## DonutSwinModel
[[autodoc]] DonutSwinModel
- forward
## DonutSwinForImageClassification
[[autodoc]] transformers.DonutSwinForImageClassification
- forward
| transformers/docs/source/en/model_doc/donut.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/donut.md",
"repo_id": "transformers",
"token_count": 3123
} | 387 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
*This model was released on 2019-04-19 and added to Hugging Face Transformers on 2022-09-30.*
# ESM
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental
AI Research Team, providing the state-of-the-art ESMFold and ESM-2, and the previously released ESM-1b and ESM-1v.
Transformer protein language models were introduced in the paper [Biological structure and function emerge from scaling
unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott,
C. Lawrence Zitnick, Jerry Ma, and Rob Fergus.
The first version of this paper was [preprinted in 2019](https://www.biorxiv.org/content/10.1101/622803v1?versioned=true).
ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks,
and enables atomic resolution structure prediction.
It was released with the paper [Language models of protein sequences at the scale of evolution enable accurate
structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie,
Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido and Alexander Rives.
Also introduced in this paper was ESMFold. It uses an ESM-2 stem with a head that can predict folded protein
structures with state-of-the-art accuracy. Unlike [AlphaFold2](https://www.nature.com/articles/s41586-021-03819-2),
it relies on the token embeddings from the large pre-trained protein language model stem and does not perform a multiple
sequence alignment (MSA) step at inference time, which means that ESMFold checkpoints are fully "standalone" -
they do not require a database of known protein sequences and structures with associated external query tools
to make predictions, and are much faster as a result.
The abstract from
"Biological structure and function emerge from scaling unsupervised learning to 250
million protein sequences" is
*In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised
learning has led to major advances in representation learning and statistical generation. In the life sciences, the
anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling
at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To
this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250
million protein sequences spanning evolutionary diversity. The resulting model contains information about biological
properties in its representations. The representations are learned from sequence data alone. The learned representation
space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to
remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and
can be identified by linear projections. Representation learning produces features that generalize across a range of
applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and
improving state-of-the-art features for long-range contact prediction.*
The abstract from
"Language models of protein sequences at the scale of evolution enable accurate structure prediction" is
*Large language models have recently been shown to develop emergent capabilities with scale, going beyond
simple pattern matching to perform higher level reasoning and generate lifelike images and text. While
language models trained on protein sequences have been studied at a smaller scale, little is known about
what they learn about biology as they are scaled up. In this work we train models up to 15 billion parameters,
the largest language models of proteins to be evaluated to date. We find that as models are scaled they learn
information enabling the prediction of the three-dimensional structure of a protein at the resolution of
individual atoms. We present ESMFold for high accuracy end-to-end atomic level structure prediction directly
from the individual sequence of a protein. ESMFold has similar accuracy to AlphaFold2 and RoseTTAFold for
sequences with low perplexity that are well understood by the language model. ESMFold inference is an
order of magnitude faster than AlphaFold2, enabling exploration of the structural space of metagenomic
proteins in practical timescales.*
The original code can be found [here](https://github.com/facebookresearch/esm) and was
was developed by the Fundamental AI Research team at Meta AI.
ESM-1b, ESM-1v and ESM-2 were contributed to huggingface by [jasonliu](https://huggingface.co/jasonliu)
and [Matt](https://huggingface.co/Rocketknight1).
ESMFold was contributed to huggingface by [Matt](https://huggingface.co/Rocketknight1) and
[Sylvain](https://huggingface.co/sgugger), with a big thank you to Nikita Smetanin, Roshan Rao and Tom Sercu for their
help throughout the process!
## Usage tips
- ESM models are trained with a masked language modeling (MLM) objective.
- The HuggingFace port of ESMFold uses portions of the [openfold](https://github.com/aqlaboratory/openfold) library. The `openfold` library is licensed under the Apache License 2.0.
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
## EsmConfig
[[autodoc]] EsmConfig
- all
## EsmTokenizer
[[autodoc]] EsmTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## EsmModel
[[autodoc]] EsmModel
- forward
## EsmForMaskedLM
[[autodoc]] EsmForMaskedLM
- forward
## EsmForSequenceClassification
[[autodoc]] EsmForSequenceClassification
- forward
## EsmForTokenClassification
[[autodoc]] EsmForTokenClassification
- forward
## EsmForProteinFolding
[[autodoc]] EsmForProteinFolding
- forward
| transformers/docs/source/en/model_doc/esm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/esm.md",
"repo_id": "transformers",
"token_count": 1850
} | 388 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
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specific language governing permissions and limitations under the License.
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*This model was released on 2020-06-05 and added to Hugging Face Transformers on 2020-11-16.*
# Funnel Transformer
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The Funnel Transformer model was proposed in the paper [Funnel-Transformer: Filtering out Sequential Redundancy for
Efficient Language Processing](https://huggingface.co/papers/2006.03236). It is a bidirectional transformer model, like
BERT, but with a pooling operation after each block of layers, a bit like in traditional convolutional neural networks
(CNN) in computer vision.
The abstract from the paper is the following:
*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
comprehension.*
This model was contributed by [sgugger](https://huggingface.co/sgugger). The original code can be found [here](https://github.com/laiguokun/Funnel-Transformer).
## Usage tips
- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers. This way, their length is divided by 2, which speeds up the computation of the next hidden states.
The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
sequence length as the input.
- For tasks such as classification, this is not a problem, but for tasks like masked language modeling or token classification, we need a hidden state with the same sequence length as the original input. In those cases, the final hidden states are upsampled to the input sequence length and go through two additional layers. That's why there are two versions of each checkpoint. The version suffixed with “-base” contains only the three blocks, while the version without that suffix contains the three blocks and the upsampling head with its additional layers.
- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should be
used for [`FunnelModel`], [`FunnelForPreTraining`],
[`FunnelForMaskedLM`], [`FunnelForTokenClassification`] and
[`FunnelForQuestionAnswering`]. The second ones should be used for
[`FunnelBaseModel`], [`FunnelForSequenceClassification`] and
[`FunnelForMultipleChoice`].
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## FunnelConfig
[[autodoc]] FunnelConfig
## FunnelTokenizer
[[autodoc]] FunnelTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## FunnelTokenizerFast
[[autodoc]] FunnelTokenizerFast
## Funnel specific outputs
[[autodoc]] models.funnel.modeling_funnel.FunnelForPreTrainingOutput
## FunnelBaseModel
[[autodoc]] FunnelBaseModel
- forward
## FunnelModel
[[autodoc]] FunnelModel
- forward
## FunnelModelForPreTraining
[[autodoc]] FunnelForPreTraining
- forward
## FunnelForMaskedLM
[[autodoc]] FunnelForMaskedLM
- forward
## FunnelForSequenceClassification
[[autodoc]] FunnelForSequenceClassification
- forward
## FunnelForMultipleChoice
[[autodoc]] FunnelForMultipleChoice
- forward
## FunnelForTokenClassification
[[autodoc]] FunnelForTokenClassification
- forward
## FunnelForQuestionAnswering
[[autodoc]] FunnelForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/funnel.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/funnel.md",
"repo_id": "transformers",
"token_count": 1510
} | 389 |
<!--Copyright 2024 Kyutai and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2025-01-13 and added to Hugging Face Transformers on 2025-01-13.*
# Helium
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
Helium was proposed in [Announcing Helium-1 Preview](https://kyutai.org/2025/01/13/helium.html) by the Kyutai Team.
Helium-1 preview is a lightweight language model with 2B parameters, targeting edge and mobile devices.
It supports the following languages: English, French, German, Italian, Portuguese, Spanish.
- **Developed by:** Kyutai
- **Model type:** Large Language Model
- **Language(s) (NLP):** English, French, German, Italian, Portuguese, Spanish
- **License:** CC-BY 4.0
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
The model was evaluated on MMLU, TriviaQA, NaturalQuestions, ARC Easy & Challenge, Open Book QA, Common Sense QA,
Physical Interaction QA, Social Interaction QA, HellaSwag, WinoGrande, Multilingual Knowledge QA, FLORES 200.
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
We report accuracy on MMLU, ARC, OBQA, CSQA, PIQA, SIQA, HellaSwag, WinoGrande.
We report exact match on TriviaQA, NQ and MKQA.
We report BLEU on FLORES.
### English Results
| Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
|--------------|--------|--------|--------|--------|--------|
| | | | | | |
| MMLU | 51.2 | 50.4 | 53.1 | 56.6 | 61.0 |
| NQ | 17.3 | 15.1 | 17.7 | 22.0 | 13.1 |
| TQA | 47.9 | 45.4 | 49.9 | 53.6 | 35.9 |
| ARC E | 80.9 | 81.8 | 81.1 | 84.6 | 89.7 |
| ARC C | 62.7 | 64.7 | 66.0 | 69.0 | 77.2 |
| OBQA | 63.8 | 61.4 | 64.6 | 68.4 | 73.8 |
| CSQA | 65.6 | 59.0 | 64.4 | 65.4 | 72.4 |
| PIQA | 77.4 | 77.7 | 79.8 | 78.9 | 76.0 |
| SIQA | 64.4 | 57.5 | 61.9 | 63.8 | 68.7 |
| HS | 69.7 | 73.2 | 74.7 | 76.9 | 67.5 |
| WG | 66.5 | 65.6 | 71.2 | 72.0 | 64.8 |
| | | | | | |
| Average | 60.7 | 59.3 | 62.2 | 64.7 | 63.6 |
#### Multilingual Results
| Language | Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
|-----|--------------|--------|--------|--------|--------|--------|
| | | | | | | |
|German| MMLU | 45.6 | 35.3 | 45.0 | 47.5 | 49.5 |
|| ARC C | 56.7 | 38.4 | 54.7 | 58.3 | 60.2 |
|| HS | 53.5 | 33.9 | 53.4 | 53.7 | 42.8 |
|| MKQA | 16.1 | 7.1 | 18.9 | 20.2 | 10.4 |
| | | | | | | |
|Spanish| MMLU | 46.5 | 38.9 | 46.2 | 49.6 | 52.8 |
|| ARC C | 58.3 | 43.2 | 58.8 | 60.0 | 68.1 |
|| HS | 58.6 | 40.8 | 60.5 | 61.1 | 51.4 |
|| MKQA | 16.0 | 7.9 | 18.5 | 20.6 | 10.6 |
## Technical Specifications
### Model Architecture and Objective
| Hyperparameter | Value |
|--------------|--------|
| Layers | 24 |
| Heads | 20 |
| Model dimension | 2560 |
| MLP dimension | 7040 |
| Context size | 4096 |
| Theta RoPE | 100,000 |
Tips:
- This model was contributed by [Laurent Mazare](https://huggingface.co/lmz)
## Usage tips
`Helium` can be found on the [Huggingface Hub](https://huggingface.co/models?other=helium)
In the following, we demonstrate how to use `helium-1-preview` for the inference.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("kyutai/helium-1-preview-2b", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("kyutai/helium-1-preview-2b")
>>> prompt = "Give me a short introduction to large language model."
>>> model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## HeliumConfig
[[autodoc]] HeliumConfig
## HeliumModel
[[autodoc]] HeliumModel
- forward
## HeliumForCausalLM
[[autodoc]] HeliumForCausalLM
- forward
## HeliumForSequenceClassification
[[autodoc]] HeliumForSequenceClassification
- forward
## HeliumForTokenClassification
[[autodoc]] HeliumForTokenClassification
- forward
| transformers/docs/source/en/model_doc/helium.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/helium.md",
"repo_id": "transformers",
"token_count": 1986
} | 390 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2022-02-28 and added to Hugging Face Transformers on 2022-10-12.*
# LiLT
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The LiLT model was proposed in [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://huggingface.co/papers/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable [LayoutLM](layoutlm)-like document understanding for many
languages.
The abstract from the paper is the following:
*Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg"
alt="drawing" width="600"/>
<small> LiLT architecture. Taken from the <a href="https://huggingface.co/papers/2202.13669">original paper</a>. </small>
This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/jpwang/lilt).
## Usage tips
- To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the [hub](https://huggingface.co/models?search=roberta), refer to [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional).
The script will result in `config.json` and `pytorch_model.bin` files being stored locally. After doing this, one can do the following (assuming you're logged in with your HuggingFace account):
```python
from transformers import LiltModel
model = LiltModel.from_pretrained("path_to_your_files")
model.push_to_hub("name_of_repo_on_the_hub")
```
- When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
- As [lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) uses the same vocabulary as [LayoutLMv3](layoutlmv3), one can use [`LayoutLMv3TokenizerFast`] to prepare data for the model.
The same is true for [lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base): one can use [`LayoutXLMTokenizerFast`] for that model.
## Resources
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LiLT.
- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
**Documentation resources**
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
## LiltConfig
[[autodoc]] LiltConfig
## LiltModel
[[autodoc]] LiltModel
- forward
## LiltForSequenceClassification
[[autodoc]] LiltForSequenceClassification
- forward
## LiltForTokenClassification
[[autodoc]] LiltForTokenClassification
- forward
## LiltForQuestionAnswering
[[autodoc]] LiltForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/lilt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/lilt.md",
"repo_id": "transformers",
"token_count": 1387
} | 391 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2024-05-31 and added to Hugging Face Transformers on 2024-08-06.*
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
# Mamba 2
[Mamba 2](https://huggingface.co/papers/2405.21060) is based on the state space duality (SSD) framework which connects structured state space models (SSMs) and attention variants. It uses a more efficient SSD algorithm that is 2-8x faster than Mamba and modifies the architecture to enable tensor parallelism and a grouped-value attention (GVA) head structure.
You can find all the original Mamba 2 checkpoints under the [State Space Models](https://huggingface.co/state-spaces) organization, but the examples shown below use [mistralai/Mamba-Codestral-7B-v0.1](https://huggingface.co/mistralai/Mamba-Codestral-7B-v0.1) because a Hugging Face implementation isn't supported yet for the original checkpoints.
> [!TIP]
> This model was contributed by [ArthurZ](https://huggingface.co/ArthurZ).
> Click on the Mamba models in the right sidebar for more examples of how to apply Mamba to different language tasks.
The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
hfoptions id="usage">
<hfoption id="Pipeline">
```python
import torch
from transformers import pipeline
pipeline = pipeline(
task="text-generation",
model="mistralai/Mamba-Codestral-7B-v0.1",
dtype=torch.bfloat16,
device=0
)
pipeline("Plants create energy through a process known as")
```
</hfoption>
<hfoption id="AutoModel">
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1", dtype=torch.bfloat16, device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
</hfoption>
<hfoption id="transformers CLI">
```bash
echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model mistralai/Mamba-Codestral-7B-v0.1 --device 0
```
</hfoption>
</hfoptions>
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [torchao](../quantization/torchao) to only quantize the weights to 4-bit integers.
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("mistralai/Mamba-Codestral-7B-v0.1", dtype=torch.bfloat16, quantization_config=quantization_config, device_map="auto")
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## Notes
- Codestral Mamba has `groups=8` which are similar to the number of kv heads in an attention-based model.
- Codestral Mamba has two different forward passes, `torch_forward` or `cuda_kernels_forward`, and their results are expected to be slightly different.
- `torch_forward` without compilation is 3-4x faster than `cuda_kernels_forward`.
- `cuda_kernels_forward` uses the original CUDA kernels if they're available in your environment. It is slower during prefill because it requires a "warmup run" due to the higher CPU overhead (see [these](https://github.com/state-spaces/mamba/issues/389#issuecomment-2171755306) [comments](https://github.com/state-spaces/mamba/issues/355#issuecomment-2147597457) for more details).
- There are no positional embeddings in this model, but there is an `attention_mask` and a specific logic to mask out hidden states in two places in the case of batched generation (see this [comment](https://github.com/state-spaces/mamba/issues/66#issuecomment-1863563829) for more details). This (and the addition of the reimplemented Mamba 2 kernels) results in a slight discrepancy between batched and cached generation.
- The SSM algorithm heavily relies on tensor contractions, which have matmul equivalents but the order of operations is slightly different. This makes the difference greater at smaller precisions.
- Hidden states that correspond to padding tokens is shutdown in 2 places and is mostly tested with left-padding. Right-padding propagates noise down the line and is not guaranteed to yield satisfactory results. `tokenizer.padding_side = "left"` ensures you are using the correct padding side.
- The example below demonstrates how to fine-tune Mamba 2 with [PEFT](https://huggingface.co/docs/peft).
```python
from datasets import load_dataset
from peft import LoraConfig
from trl import SFTConfig, SFTTrainer
model_id = "mistralai/Mamba-Codestral-7B-v0.1"
dataset = load_dataset("Abirate/english_quotes", split="train")
training_args = SFTConfig(dataset_text_field="quote", gradient_checkpointing=True, per_device_train_batch_size=4)
lora_config = LoraConfig(target_modules=["x_proj", "embeddings", "in_proj", "out_proj"])
trainer = SFTTrainer(
model=model_id,
args=training_args,
train_dataset=dataset,
peft_config=lora_config,
)
trainer.train()
```
## Mamba2Config
[[autodoc]] Mamba2Config
## Mamba2Model
[[autodoc]] Mamba2Model
- forward
## Mamba2LMHeadModel
[[autodoc]] Mamba2ForCausalLM
- forward
| transformers/docs/source/en/model_doc/mamba2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mamba2.md",
"repo_id": "transformers",
"token_count": 2044
} | 392 |
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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
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2025-01-30 and added to Hugging Face Transformers on 2025-03-18.*
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
</div>
# Mistral 3
[Mistral 3](https://mistral.ai/news/mistral-small-3) is a latency optimized model with a lot fewer layers to reduce the time per forward pass. This model adds vision understanding and supports long context lengths of up to 128K tokens without compromising performance.
You can find the original Mistral 3 checkpoints under the [Mistral AI](https://huggingface.co/mistralai/models?search=mistral-small-3) organization.
> [!TIP]
> This model was contributed by [cyrilvallez](https://huggingface.co/cyrilvallez) and [yonigozlan](https://huggingface.co/yonigozlan).
> Click on the Mistral3 models in the right sidebar for more examples of how to apply Mistral3 to different tasks.
The example below demonstrates how to generate text for an image with [`Pipeline`] and the [`AutoModel`] class.
<hfoptions id="usage">
<hfoption id="Pipeline">
```py
import torch
from transformers import pipeline
messages = [
{"role": "user",
"content":[
{"type": "image",
"image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",},
{"type": "text", "text": "Describe this image."}
,]
,}
,]
pipeline = pipeline(
task="image-text-to-text",
model="mistralai/Mistral-Small-3.1-24B-Instruct-2503",
dtype=torch.bfloat16,
device=0
)
outputs = pipeline(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]
'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'
```
</hfoption>
<hfoption id="AutoModel">
```py
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, infer_device
torch_device = infer_device()
model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(
model_checkpoint,
device_map=torch_device,
dtype=torch.bfloat16
)
messages = [
{"role": "user",
"content":[
{"type": "image",
"image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",},
{"type": "text", "text": "Describe this image."}
,]
,}
,]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True, return_dict=True,
return_tensors="pt").to(model.device, dtype=torch.bfloat16)
generate_ids = model.generate(**inputs, max_new_tokens=20)
decoded_output = processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
decoded_output
'The image depicts a vibrant and lush garden scene featuring a variety of wildflowers and plants. The central focus is on a large, pinkish-purple flower, likely a Greater Celandine (Chelidonium majus), with a'
```
</hfoption>
</hfoptions>
## Notes
- Mistral 3 supports text-only generation.
```py
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, infer_device
torch_device = infer_device()
model_checkpoint = ".mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, dtype=torch.bfloat16)
SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": user_prompt},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(0, dtype=torch.float16)
generate_ids = model.generate(**inputs, max_new_tokens=50, do_sample=False)
decoded_output = processor.batch_decode(generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True)[0]
print(decoded_output)
"1. À plus tard!
2. Salut, à plus!
3. À toute!
4. À la prochaine!
5. Je me casse, à plus!
```
/\_/\
( o.o )
> ^ <
```"
````
- Mistral 3 accepts batched image and text inputs.
```py
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, infer_device
torch_device = infer_device()
model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, dtype=torch.bfloat16)
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Describe this image"},
],
},
],
]
inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
output = model.generate(**inputs, max_new_tokens=25)
decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
decoded_outputs
["Write a haiku for this imageCalm waters reflect\nWhispers of the forest's breath\nPeace on wooden path"
, "Describe this imageThe image depicts a vibrant street scene in what appears to be a Chinatown district. The focal point is a traditional Chinese"]
```
- Mistral 3 also supported batched image and text inputs with a different number of images for each text. The example below quantizes the model with bitsandbytes.
```py
import torch
from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig, infer_device
torch_device = infer_device()
model_checkpoint = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
processor = AutoProcessor.from_pretrained(model_checkpoint)
quantization_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForImageTextToText.from_pretrained(
model_checkpoint, quantization_config=quantization_config
)
messages = [
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
{"type": "text", "text": "Write a haiku for this image"},
],
},
],
[
{
"role": "user",
"content": [
{"type": "image", "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"},
{"type": "image", "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"},
{"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
],
},
],
]
inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)
output = model.generate(**inputs, max_new_tokens=25)
decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
decoded_outputs
["Write a haiku for this imageSure, here is a haiku inspired by the image:\n\nCalm lake's wooden path\nSilent forest stands guard\n", "These images depict two different landmarks. Can you identify them? Certainly! The images depict two iconic landmarks:\n\n1. The first image shows the Statue of Liberty in New York City."]
```
## Mistral3Config
[[autodoc]] Mistral3Config
## MistralCommonTokenizer
[[autodoc]] MistralCommonTokenizer
## Mistral3Model
[[autodoc]] Mistral3Model
## Mistral3ForConditionalGeneration
[[autodoc]] Mistral3ForConditionalGeneration
- forward
| transformers/docs/source/en/model_doc/mistral3.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mistral3.md",
"repo_id": "transformers",
"token_count": 3251
} | 393 |
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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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
*This model was released on 2020-04-20 and added to Hugging Face Transformers on 2020-12-09.*
# MPNet
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The MPNet model was proposed in [MPNet: Masked and Permuted Pre-training for Language Understanding](https://huggingface.co/papers/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
MPNet adopts a novel pre-training method, named masked and permuted language modeling, to inherit the advantages of
masked language modeling and permuted language modeling for natural language understanding.
The abstract from the paper is the following:
*BERT adopts masked language modeling (MLM) for pre-training and is one of the most successful pre-training models.
Since BERT neglects dependency among predicted tokens, XLNet introduces permuted language modeling (PLM) for
pre-training to address this problem. However, XLNet does not leverage the full position information of a sentence and
thus suffers from position discrepancy between pre-training and fine-tuning. In this paper, we propose MPNet, a novel
pre-training method that inherits the advantages of BERT and XLNet and avoids their limitations. MPNet leverages the
dependency among predicted tokens through permuted language modeling (vs. MLM in BERT), and takes auxiliary position
information as input to make the model see a full sentence and thus reducing the position discrepancy (vs. PLM in
XLNet). We pre-train MPNet on a large-scale dataset (over 160GB text corpora) and fine-tune on a variety of
down-streaming tasks (GLUE, SQuAD, etc). Experimental results show that MPNet outperforms MLM and PLM by a large
margin, and achieves better results on these tasks compared with previous state-of-the-art pre-trained methods (e.g.,
BERT, XLNet, RoBERTa) under the same model setting.*
The original code can be found [here](https://github.com/microsoft/MPNet).
## Usage tips
MPNet doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
separate your segments with the separation token `tokenizer.sep_token` (or `[sep]`).
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## MPNetConfig
[[autodoc]] MPNetConfig
## MPNetTokenizer
[[autodoc]] MPNetTokenizer
- build_inputs_with_special_tokens
- get_special_tokens_mask
- create_token_type_ids_from_sequences
- save_vocabulary
## MPNetTokenizerFast
[[autodoc]] MPNetTokenizerFast
## MPNetModel
[[autodoc]] MPNetModel
- forward
## MPNetForMaskedLM
[[autodoc]] MPNetForMaskedLM
- forward
## MPNetForSequenceClassification
[[autodoc]] MPNetForSequenceClassification
- forward
## MPNetForMultipleChoice
[[autodoc]] MPNetForMultipleChoice
- forward
## MPNetForTokenClassification
[[autodoc]] MPNetForTokenClassification
- forward
## MPNetForQuestionAnswering
[[autodoc]] MPNetForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/mpnet.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/mpnet.md",
"repo_id": "transformers",
"token_count": 1144
} | 394 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
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*This model was released on 2024-12-31 and added to Hugging Face Transformers on 2024-11-25.*
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
</div>
# OLMo2
[OLMo2](https://huggingface.co/papers/2501.00656) improves on [OLMo](./olmo) by changing the architecture and training recipes of the original models. This includes excluding all biases to improve training stability, non-parametric layer norm, SwiGLU activation function, rotary positional embeddings, and a modified BPE-based tokenizer that masks personal identifiable information. It is pretrained on [Dolma](https://huggingface.co/datasets/allenai/dolma), a dataset of 3T tokens.
You can find all the original OLMo2 checkpoints under the [OLMo2](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc) collection.
> [!TIP]
> Click on the OLMo2 models in the right sidebar for more examples of how to apply OLMo2 to different language tasks.
The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`] and from the command line.
<hfoptions id="usage">
<hfoption id="Pipeline">
```py
import torch
from transformers import pipeline
pipe = pipeline(
task="text-generation",
model="allenai/OLMo-2-0425-1B",
dtype=torch.float16,
device=0,
)
result = pipe("Plants create energy through a process known as")
print(result)
```
</hfoption>
<hfoption id="AutoModel">
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"allenai/OLMo-2-0425-1B"
)
model = AutoModelForCausalLM.from_pretrained(
"allenai/OLMo-2-0425-1B",
dtype=torch.float16,
device_map="auto",
attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
</hfoption>
<hfoption id="transformers CLI">
```bash
echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model allenai/OLMo-2-0425-1B --device 0
```
</hfoption>
</hfoptions>
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [torchao](../quantization/torchao) to only quantize the weights to 4-bits.
```py
#pip install torchao
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
torchao_config = TorchAoConfig(
"int4_weight_only",
group_size=128
)
tokenizer = AutoTokenizer.from_pretrained(
"allenai/OLMo-2-0425-1B"
)
model = AutoModelForCausalLM.from_pretrained(
"allenai/OLMo-2-0425-1B",
quantization_config=torchao_config,
dtype=torch.bfloat16,
device_map="auto",
attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## Notes
- OLMo2 uses RMSNorm instead of standard layer norm. The RMSNorm is applied to attention queries and keys, and it is applied after the attention and feedforward layers rather than before.
- OLMo2 requires Transformers v4.48 or higher.
- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`].
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")
```
## Olmo2Config
[[autodoc]] Olmo2Config
## Olmo2Model
[[autodoc]] Olmo2Model
- forward
## Olmo2ForCausalLM
[[autodoc]] Olmo2ForCausalLM
- forward
| transformers/docs/source/en/model_doc/olmo2.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/olmo2.md",
"repo_id": "transformers",
"token_count": 1719
} | 395 |
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
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*This model was released on 2025-04-17 and added to Hugging Face Transformers on 2025-07-11.*
# PerceptionLM
## Overview
The [PerceptionLM](https://huggingface.co/papers/2504.13180) model was proposed in [PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding](https://ai.meta.com/research/publications/perceptionlm-open-access-data-and-models-for-detailed-visual-understanding/) by Jang Hyun Cho et al. It's a fully open, reproducible model for transparent research in image and video understanding. PLM consists of
a vision encoder with a small scale (<8B parameters) LLM decoder.
The abstract from the paper is the following:
*Vision-language models are integral to computer vision research, yet many high-performing models
remain closed-source, obscuring their data, design and training recipe. The research community
has responded by using distillation from black-box models to label training data, achieving strong
benchmark results, at the cost of measurable scientific progress. However, without knowing the details
of the teacher model and its data sources, scientific progress remains difficult to measure. In this
paper, we study building a Perception Language Model (PLM) in a fully open and reproducible
framework for transparent research in image and video understanding. We analyze standard training
pipelines without distillation from proprietary models and explore large-scale synthetic data to identify
critical data gaps, particularly in detailed video understanding. To bridge these gaps, we release 2.8M
human-labeled instances of fine-grained video question-answer pairs and spatio-temporally grounded
video captions. Additionally, we introduce PLM–VideoBench, a suite for evaluating challenging video
understanding tasks focusing on the ability to reason about “what”, “where”, “when”, and “how” of a
video. We make our work fully reproducible by providing data, training recipes, code & models.*
This model was contributed by [shumingh](https://huggingface.co/shumingh).
The original code can be found [here](https://github.com/facebookresearch/perception_models).
## PerceptionLMConfig
[[autodoc]] PerceptionLMConfig
## PerceptionLMProcessor
[[autodoc]] PerceptionLMProcessor
## PerceptionLMImageProcessorFast
[[autodoc]] PerceptionLMImageProcessorFast
## PerceptionLMVideoProcessor
[[autodoc]] PerceptionLMVideoProcessor
## PerceptionLMModel
[[autodoc]] PerceptionLMModel
## PerceptionLMForConditionalGeneration
[[autodoc]] PerceptionLMForConditionalGeneration
- forward
| transformers/docs/source/en/model_doc/perception_lm.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/perception_lm.md",
"repo_id": "transformers",
"token_count": 812
} | 396 |
<!--Copyright 2021 NVIDIA Corporation and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
*This model was released on 2020-04-20 and added to Hugging Face Transformers on 2023-06-20.*
# QDQBERT
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
<Tip warning={true}>
This model is in maintenance mode only, we don't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Tip>
## Overview
The QDQBERT model can be referenced in [Integer Quantization for Deep Learning Inference: Principles and Empirical
Evaluation](https://huggingface.co/papers/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius
Micikevicius.
The abstract from the paper is the following:
*Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by
taking advantage of high throughput integer instructions. In this paper we review the mathematical aspects of
quantization parameters and evaluate their choices on a wide range of neural network models for different application
domains, including vision, speech, and language. We focus on quantization techniques that are amenable to acceleration
by processors with high-throughput integer math pipelines. We also present a workflow for 8-bit quantization that is
able to maintain accuracy within 1% of the floating-point baseline on all networks studied, including models that are
more difficult to quantize, such as MobileNets and BERT-large.*
This model was contributed by [shangz](https://huggingface.co/shangz).
## Usage tips
- QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to (i) linear layer
inputs and weights, (ii) matmul inputs, (iii) residual add inputs, in BERT model.
- QDQBERT requires the dependency of [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). To install `pip install pytorch-quantization --extra-index-url https://pypi.ngc.nvidia.com`
- QDQBERT model can be loaded from any checkpoint of HuggingFace BERT model (for example *google-bert/bert-base-uncased*), and
perform Quantization Aware Training/Post Training Quantization.
- A complete example of using QDQBERT model to perform Quatization Aware Training and Post Training Quantization for
SQUAD task can be found at https://github.com/huggingface/transformers-research-projects/tree/main/quantization-qdqbert.
### Set default quantizers
QDQBERT model adds fake quantization operations (pair of QuantizeLinear/DequantizeLinear ops) to BERT by
`TensorQuantizer` in [Pytorch Quantization Toolkit](https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization). `TensorQuantizer` is the module
for quantizing tensors, with `QuantDescriptor` defining how the tensor should be quantized. Refer to [Pytorch
Quantization Toolkit userguide](https://docs.nvidia.com/deeplearning/tensorrt/pytorch-quantization-toolkit/docs/userguide.html) for more details.
Before creating QDQBERT model, one has to set the default `QuantDescriptor` defining default tensor quantizers.
Example:
```python
>>> import pytorch_quantization.nn as quant_nn
>>> from pytorch_quantization.tensor_quant import QuantDescriptor
>>> # The default tensor quantizer is set to use Max calibration method
>>> input_desc = QuantDescriptor(num_bits=8, calib_method="max")
>>> # The default tensor quantizer is set to be per-channel quantization for weights
>>> weight_desc = QuantDescriptor(num_bits=8, axis=((0,)))
>>> quant_nn.QuantLinear.set_default_quant_desc_input(input_desc)
>>> quant_nn.QuantLinear.set_default_quant_desc_weight(weight_desc)
```
### Calibration
Calibration is the terminology of passing data samples to the quantizer and deciding the best scaling factors for
tensors. After setting up the tensor quantizers, one can use the following example to calibrate the model:
```python
>>> # Find the TensorQuantizer and enable calibration
>>> for name, module in model.named_modules():
... if name.endswith("_input_quantizer"):
... module.enable_calib()
... module.disable_quant() # Use full precision data to calibrate
>>> # Feeding data samples
>>> model(x)
>>> # ...
>>> # Finalize calibration
>>> for name, module in model.named_modules():
... if name.endswith("_input_quantizer"):
... module.load_calib_amax()
... module.enable_quant()
>>> # If running on accelerator, it needs to call `.to(xx)` again because new tensors will be created by calibration process
>>> from transformers import infer_device
>>> device = infer_device()
>>> model.to(device)
>>> # Keep running the quantized model
>>> # ...
```
### Export to ONNX
The goal of exporting to ONNX is to deploy inference by [TensorRT](https://developer.nvidia.com/tensorrt). Fake
quantization will be broken into a pair of QuantizeLinear/DequantizeLinear ONNX ops. After setting static member of
TensorQuantizer to use Pytorch’s own fake quantization functions, fake quantized model can be exported to ONNX, follow
the instructions in [torch.onnx](https://pytorch.org/docs/stable/onnx.html). Example:
```python
>>> from pytorch_quantization.nn import TensorQuantizer
>>> TensorQuantizer.use_fb_fake_quant = True
>>> # Load the calibrated model
>>> ...
>>> # ONNX export
>>> torch.onnx.export(...)
```
## Resources
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multiple_choice)
## QDQBertConfig
[[autodoc]] QDQBertConfig
## QDQBertModel
[[autodoc]] QDQBertModel
- forward
## QDQBertLMHeadModel
[[autodoc]] QDQBertLMHeadModel
- forward
## QDQBertForMaskedLM
[[autodoc]] QDQBertForMaskedLM
- forward
## QDQBertForSequenceClassification
[[autodoc]] QDQBertForSequenceClassification
- forward
## QDQBertForNextSentencePrediction
[[autodoc]] QDQBertForNextSentencePrediction
- forward
## QDQBertForMultipleChoice
[[autodoc]] QDQBertForMultipleChoice
- forward
## QDQBertForTokenClassification
[[autodoc]] QDQBertForTokenClassification
- forward
## QDQBertForQuestionAnswering
[[autodoc]] QDQBertForQuestionAnswering
- forward
| transformers/docs/source/en/model_doc/qdqbert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/qdqbert.md",
"repo_id": "transformers",
"token_count": 2185
} | 397 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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*This model was released on 2020-06-12 and added to Hugging Face Transformers on 2023-06-20.*
# RetriBERT
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`.
</Tip>
## Overview
The [RetriBERT](https://huggingface.co/yjernite/retribert-base-uncased/tree/main) model was proposed in the blog post [Explain Anything Like I'm Five: A Model for Open Domain Long Form
Question Answering](https://yjernite.github.io/lfqa.html). RetriBERT is a small model that uses either a single or
pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
This model was contributed by [yjernite](https://huggingface.co/yjernite). Code to train and use the model can be
found [here](https://github.com/huggingface/transformers/tree/main/examples/research-projects/distillation).
## RetriBertConfig
[[autodoc]] RetriBertConfig
## RetriBertTokenizer
[[autodoc]] RetriBertTokenizer
## RetriBertTokenizerFast
[[autodoc]] RetriBertTokenizerFast
## RetriBertModel
[[autodoc]] RetriBertModel
- forward
| transformers/docs/source/en/model_doc/retribert.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/retribert.md",
"repo_id": "transformers",
"token_count": 660
} | 398 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
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specific language governing permissions and limitations under the License.
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*This model was released on 2023-04-06 and added to Hugging Face Transformers on 2024-02-26.*
# SegGPT
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The SegGPT model was proposed in [SegGPT: Segmenting Everything In Context](https://huggingface.co/papers/2304.03284) by Xinlong Wang, Xiaosong Zhang, Yue Cao, Wen Wang, Chunhua Shen, Tiejun Huang. SegGPT employs a decoder-only Transformer that can generate a segmentation mask given an input image, a prompt image and its corresponding prompt mask. The model achieves remarkable one-shot results with 56.1 mIoU on COCO-20 and 85.6 mIoU on FSS-1000.
The abstract from the paper is the following:
*We present SegGPT, a generalist model for segmenting everything in context. We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images. The training of SegGPT is formulated as an in-context coloring problem with random color mapping for each data sample. The objective is to accomplish diverse tasks according to the context, rather than relying on specific colors. After training, SegGPT can perform arbitrary segmentation tasks in images or videos via in-context inference, such as object instance, stuff, part, contour, and text. SegGPT is evaluated on a broad range of tasks, including few-shot semantic segmentation, video object segmentation, semantic segmentation, and panoptic segmentation. Our results show strong capabilities in segmenting in-domain and out-of*
Tips:
- One can use [`SegGptImageProcessor`] to prepare image input, prompt and mask to the model.
- One can either use segmentation maps or RGB images as prompt masks. If using the latter make sure to set `do_convert_rgb=False` in the `preprocess` method.
- It's highly advisable to pass `num_labels` when using `segmentation_maps` (not considering background) during preprocessing and postprocessing with [`SegGptImageProcessor`] for your use case.
- When doing inference with [`SegGptForImageSegmentation`] if your `batch_size` is greater than 1 you can use feature ensemble across your images by passing `feature_ensemble=True` in the forward method.
Here's how to use the model for one-shot semantic segmentation:
```python
import torch
from datasets import load_dataset
from transformers import SegGptImageProcessor, SegGptForImageSegmentation
checkpoint = "BAAI/seggpt-vit-large"
image_processor = SegGptImageProcessor.from_pretrained(checkpoint)
model = SegGptForImageSegmentation.from_pretrained(checkpoint)
dataset_id = "EduardoPacheco/FoodSeg103"
ds = load_dataset(dataset_id, split="train")
# Number of labels in FoodSeg103 (not including background)
num_labels = 103
image_input = ds[4]["image"]
ground_truth = ds[4]["label"]
image_prompt = ds[29]["image"]
mask_prompt = ds[29]["label"]
inputs = image_processor(
images=image_input,
prompt_images=image_prompt,
segmentation_maps=mask_prompt,
num_labels=num_labels,
return_tensors="pt"
)
with torch.no_grad():
outputs = model(**inputs)
target_sizes = [image_input.size[::-1]]
mask = image_processor.post_process_semantic_segmentation(outputs, target_sizes, num_labels=num_labels)[0]
```
This model was contributed by [EduardoPacheco](https://huggingface.co/EduardoPacheco).
The original code can be found [here]([(https://github.com/baaivision/Painter/tree/main)).
## SegGptConfig
[[autodoc]] SegGptConfig
## SegGptImageProcessor
[[autodoc]] SegGptImageProcessor
- preprocess
- post_process_semantic_segmentation
## SegGptModel
[[autodoc]] SegGptModel
- forward
## SegGptForImageSegmentation
[[autodoc]] SegGptForImageSegmentation
- forward
| transformers/docs/source/en/model_doc/seggpt.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/seggpt.md",
"repo_id": "transformers",
"token_count": 1352
} | 399 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the MIT License; you may not use this file except in compliance with
the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2019-11-26 and added to Hugging Face Transformers on 2025-01-20.*
<div style="float: right;">
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white" >
</div>
</div>
# SuperGlue
[SuperGlue](https://huggingface.co/papers/1911.11763) is a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points. Assignments are estimated by solving a differentiable optimal transport problem, whose costs are predicted by a graph neural network. SuperGlue introduces a flexible context aggregation mechanism based on attention, enabling it to reason about the underlying 3D scene and feature assignments jointly. Paired with the [SuperPoint model](https://huggingface.co/magic-leap-community/superpoint), it can be used to match two images and estimate the pose between them. This model is useful for tasks such as image matching, homography estimation, etc.
You can find all the original SuperGlue checkpoints under the [Magic Leap Community](https://huggingface.co/magic-leap-community) organization.
> [!TIP]
> This model was contributed by [stevenbucaille](https://huggingface.co/stevenbucaille).
>
> Click on the SuperGlue models in the right sidebar for more examples of how to apply SuperGlue to different computer vision tasks.
The example below demonstrates how to match keypoints between two images with the [`AutoModel`] class.
<hfoptions id="usage">
<hfoption id="AutoModel">
```py
from transformers import AutoImageProcessor, AutoModel
import torch
from PIL import Image
import requests
url_image1 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_98169888_3347710852.jpg"
image1 = Image.open(requests.get(url_image1, stream=True).raw)
url_image2 = "https://raw.githubusercontent.com/magicleap/SuperGluePretrainedNetwork/refs/heads/master/assets/phototourism_sample_images/united_states_capitol_26757027_6717084061.jpg"
image2 = Image.open(requests.get(url_image2, stream=True).raw)
images = [image1, image2]
processor = AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor")
model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")
inputs = processor(images, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
# Post-process to get keypoints and matches
image_sizes = [[(image.height, image.width) for image in images]]
processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
```
</hfoption>
</hfoptions>
## Notes
- SuperGlue performs feature matching between two images simultaneously, requiring pairs of images as input.
```python
from transformers import AutoImageProcessor, AutoModel
import torch
from PIL import Image
import requests
processor = AutoImageProcessor.from_pretrained("magic-leap-community/superglue_outdoor")
model = AutoModel.from_pretrained("magic-leap-community/superglue_outdoor")
# SuperGlue requires pairs of images
images = [image1, image2]
inputs = processor(images, return_tensors="pt")
outputs = model(**inputs)
# Extract matching information
keypoints0 = outputs.keypoints0 # Keypoints in first image
keypoints1 = outputs.keypoints1 # Keypoints in second image
matches = outputs.matches # Matching indices
matching_scores = outputs.matching_scores # Confidence scores
```
- The model outputs matching indices, keypoints, and confidence scores for each match.
- For better visualization and analysis, use the [`SuperGlueImageProcessor.post_process_keypoint_matching`] method to get matches in a more readable format.
```py
# Process outputs for visualization
image_sizes = [[(image.height, image.width) for image in images]]
processed_outputs = processor.post_process_keypoint_matching(outputs, image_sizes, threshold=0.2)
for i, output in enumerate(processed_outputs):
print(f"For the image pair {i}")
for keypoint0, keypoint1, matching_score in zip(
output["keypoints0"], output["keypoints1"], output["matching_scores"]
):
print(f"Keypoint at {keypoint0.numpy()} matches with keypoint at {keypoint1.numpy()} with score {matching_score}")
```
- Visualize the matches between the images using the built-in plotting functionality.
```py
# Easy visualization using the built-in plotting method
processor.visualize_keypoint_matching(images, processed_outputs)
```
<div class="flex justify-center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/632885ba1558dac67c440aa8/01ZYaLB1NL5XdA8u7yCo4.png">
</div>
## Resources
- Refer to the [original SuperGlue repository](https://github.com/magicleap/SuperGluePretrainedNetwork) for more examples and implementation details.
## SuperGlueConfig
[[autodoc]] SuperGlueConfig
## SuperGlueImageProcessor
[[autodoc]] SuperGlueImageProcessor
- preprocess
- post_process_keypoint_matching
- visualize_keypoint_matching
<frameworkcontent>
<pt>
## SuperGlueForKeypointMatching
[[autodoc]] SuperGlueForKeypointMatching
- forward
</pt>
</frameworkcontent> | transformers/docs/source/en/model_doc/superglue.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/superglue.md",
"repo_id": "transformers",
"token_count": 1866
} | 400 |
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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*This model was released on 2025-07-15 and added to Hugging Face Transformers on 2025-07-18.*
# Voxtral
Voxtral is an upgrade of [Ministral 3B and Mistral Small 3B](https://mistral.ai/news/ministraux), extending its language capabilities with audio input support. It is designed to handle tasks such as speech transcription, translation, and audio understanding.
You can read more in Mistral's [realease blog post](https://mistral.ai/news/voxtral).
The model is available in two checkpoints:
- 3B: [mistralai/Voxtral-Mini-3B-2507](https://huggingface.co/mistralai/Voxtral-Mini-3B-2507)
- 24B: [mistralai/Voxtral-Small-24B-2507](https://huggingface.co/mistralai/Voxtral-Small-24B-2507)
## Key Features
Voxtral builds on Ministral-3B by adding audio processing capabilities:
- **Transcription mode**: Includes a dedicated mode for speech transcription. By default, Voxtral detects the spoken language and transcribes it accordingly.
- **Long-form context**: With a 32k token context window, Voxtral can process up to 30 minutes of audio for transcription or 40 minutes for broader audio understanding.
- **Integrated Q&A and summarization**: Supports querying audio directly and producing structured summaries without relying on separate ASR and language models.
- **Multilingual support**: Automatically detects language and performs well across several widely spoken languages, including English, Spanish, French, Portuguese, Hindi, German, Dutch, and Italian.
- **Function calling via voice**: Can trigger functions or workflows directly from spoken input based on detected user intent.
- **Text capabilities**: Maintains the strong text processing performance of its Ministral-3B foundation.
## Usage
### Audio Instruct Mode
The model supports audio-text instructions, including multi-turn and multi-audio interactions, all processed in batches.
➡️ audio + text instruction
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"url": "https://huggingface.co/datasets/eustlb/audio-samples/resolve/main/dude_where_is_my_car.wav",
},
{"type": "text", "text": "What can you tell me about this audio?"},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
```
➡️ multi-audio + text instruction
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/mary_had_lamb.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
{"type": "text", "text": "What sport and what nursery rhyme are referenced?"},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
```
➡️ multi-turn:
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
},
{"type": "text", "text": "Describe briefly what you can hear."},
],
},
{
"role": "assistant",
"content": "The audio begins with the speaker delivering a farewell address in Chicago, reflecting on his eight years as president and expressing gratitude to the American people. The audio then transitions to a weather report, stating that it was 35 degrees in Barcelona the previous day, but the temperature would drop to minus 20 degrees the following day.",
},
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
},
{"type": "text", "text": "Ok, now compare this new audio with the previous one."},
],
},
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
```
➡️ text only:
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What if a cyber brain could possibly generate its own ghost, and create a soul all by itself?",
},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
```
➡️ audio only:
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversation = [
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/dude_where_is_my_car.wav",
},
],
}
]
inputs = processor.apply_chat_template(conversation)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated response:")
print("=" * 80)
print(decoded_outputs[0])
print("=" * 80)
```
➡️ batched inference!
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device()
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
conversations = [
[
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3",
},
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3",
},
{
"type": "text",
"text": "Who's speaking in the speach and what city's weather is being discussed?",
},
],
}
],
[
{
"role": "user",
"content": [
{
"type": "audio",
"path": "https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/winning_call.mp3",
},
{"type": "text", "text": "What can you tell me about this audio?"},
],
}
],
]
inputs = processor.apply_chat_template(conversations)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated responses:")
print("=" * 80)
for decoded_output in decoded_outputs:
print(decoded_output)
print("=" * 80)
```
### Transcription Mode
Use the model to transcribe audio (supports English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian)!
```python
import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor, infer_device
device = infer_device()
repo_id = "mistralai/Voxtral-Mini-3B-2507"
processor = AutoProcessor.from_pretrained(repo_id)
model = VoxtralForConditionalGeneration.from_pretrained(repo_id, dtype=torch.bfloat16, device_map=device)
inputs = processor.apply_transcription_request(language="en", audio="https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/obama.mp3", model_id=repo_id)
inputs = inputs.to(device, dtype=torch.bfloat16)
outputs = model.generate(**inputs, max_new_tokens=500)
decoded_outputs = processor.batch_decode(outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True)
print("\nGenerated responses:")
print("=" * 80)
for decoded_output in decoded_outputs:
print(decoded_output)
print("=" * 80)
```
This model was contributed by [Eustache Le Bihan](https://huggingface.co/eustlb).
## VoxtralConfig
[[autodoc]] VoxtralConfig
## VoxtralEncoderConfig
[[autodoc]] VoxtralEncoderConfig
## VoxtralProcessor
[[autodoc]] VoxtralProcessor
## VoxtralEncoder
[[autodoc]] VoxtralEncoder
- forward
## VoxtralForConditionalGeneration
[[autodoc]] VoxtralForConditionalGeneration
- forward
| transformers/docs/source/en/model_doc/voxtral.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/voxtral.md",
"repo_id": "transformers",
"token_count": 4849
} | 401 |
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*This model was released on 2021-11-17 and added to Hugging Face Transformers on 2023-06-20.*
# XLS-R
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## Overview
The XLS-R model was proposed in [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://huggingface.co/papers/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman
Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
The abstract from the paper is the following:
*This paper presents XLS-R, a large-scale model for cross-lingual speech representation learning based on wav2vec 2.0.
We train models with up to 2B parameters on nearly half a million hours of publicly available speech audio in 128
languages, an order of magnitude more public data than the largest known prior work. Our evaluation covers a wide range
of tasks, domains, data regimes and languages, both high and low-resource. On the CoVoST-2 speech translation
benchmark, we improve the previous state of the art by an average of 7.4 BLEU over 21 translation directions into
English. For speech recognition, XLS-R improves over the best known prior work on BABEL, MLS, CommonVoice as well as
VoxPopuli, lowering error rates by 14-34% relative on average. XLS-R also sets a new state of the art on VoxLingua107
language identification. Moreover, we show that with sufficient model size, cross-lingual pretraining can outperform
English-only pretraining when translating English speech into other languages, a setting which favors monolingual
pretraining. We hope XLS-R can help to improve speech processing tasks for many more languages of the world.*
Relevant checkpoints can be found under https://huggingface.co/models?other=xls_r.
The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
## Usage tips
- XLS-R is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
- XLS-R model was trained using connectionist temporal classification (CTC) so the model output has to be decoded using
[`Wav2Vec2CTCTokenizer`].
<Tip>
XLS-R's architecture is based on the Wav2Vec2 model, refer to [Wav2Vec2's documentation page](wav2vec2) for API reference.
</Tip>
| transformers/docs/source/en/model_doc/xls_r.md/0 | {
"file_path": "transformers/docs/source/en/model_doc/xls_r.md",
"repo_id": "transformers",
"token_count": 879
} | 402 |
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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
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# Pipeline
The [`Pipeline`] is a simple but powerful inference API that is readily available for a variety of machine learning tasks with any model from the Hugging Face [Hub](https://hf.co/models).
Tailor the [`Pipeline`] to your task with task specific parameters such as adding timestamps to an automatic speech recognition (ASR) pipeline for transcribing meeting notes. [`Pipeline`] supports GPUs, Apple Silicon, and half-precision weights to accelerate inference and save memory.
<Youtube id=tiZFewofSLM/>
Transformers has two pipeline classes, a generic [`Pipeline`] and many individual task-specific pipelines like [`TextGenerationPipeline`] or [`VisualQuestionAnsweringPipeline`]. Load these individual pipelines by setting the task identifier in the `task` parameter in [`Pipeline`]. You can find the task identifier for each pipeline in their API documentation.
Each task is configured to use a default pretrained model and preprocessor, but this can be overridden with the `model` parameter if you want to use a different model.
For example, to use the [`TextGenerationPipeline`] with [Gemma 2](./model_doc/gemma2), set `task="text-generation"` and `model="google/gemma-2-2b"`.
```py
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b")
pipeline("the secret to baking a really good cake is ")
[{'generated_text': 'the secret to baking a really good cake is 1. the right ingredients 2. the'}]
```
When you have more than one input, pass them as a list.
```py
from transformers import pipeline, infer_device
device = infer_device()
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b", device=device)
pipeline(["the secret to baking a really good cake is ", "a baguette is "])
[[{'generated_text': 'the secret to baking a really good cake is 1. the right ingredients 2. the'}],
[{'generated_text': 'a baguette is 100% bread.\n\na baguette is 100%'}]]
```
This guide will introduce you to the [`Pipeline`], demonstrate its features, and show how to configure its various parameters.
## Tasks
[`Pipeline`] is compatible with many machine learning tasks across different modalities. Pass an appropriate input to the pipeline and it will handle the rest.
Here are some examples of how to use [`Pipeline`] for different tasks and modalities.
<hfoptions id="tasks">
<hfoption id="summarization">
```py
from transformers import pipeline
pipeline = pipeline(task="summarization", model="google/pegasus-billsum")
pipeline("Section was formerly set out as section 44 of this title. As originally enacted, this section contained two further provisions that 'nothing in this act shall be construed as in any wise affecting the grant of lands made to the State of California by virtue of the act entitled 'An act authorizing a grant to the State of California of the Yosemite Valley, and of the land' embracing the Mariposa Big-Tree Grove, approved June thirtieth, eighteen hundred and sixty-four; or as affecting any bona-fide entry of land made within the limits above described under any law of the United States prior to the approval of this act.' The first quoted provision was omitted from the Code because the land, granted to the state of California pursuant to the Act cite, was receded to the United States. Resolution June 11, 1906, No. 27, accepted the recession.")
[{'summary_text': 'Instructs the Secretary of the Interior to convey to the State of California all right, title, and interest of the United States in and to specified lands which are located within the Yosemite and Mariposa National Forests, California.'}]
```
</hfoption>
<hfoption id="automatic speech recognition">
```py
from transformers import pipeline
pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-large-v3")
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
```
</hfoption>
<hfoption id="image classification">
```py
from transformers import pipeline
pipeline = pipeline(task="image-classification", model="google/vit-base-patch16-224")
pipeline(images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
[{'label': 'lynx, catamount', 'score': 0.43350091576576233},
{'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
'score': 0.034796204417943954},
{'label': 'snow leopard, ounce, Panthera uncia',
'score': 0.03240183740854263},
{'label': 'Egyptian cat', 'score': 0.02394474856555462},
{'label': 'tiger cat', 'score': 0.02288915030658245}]
```
</hfoption>
<hfoption id="visual question answering">
```py
from transformers import pipeline
pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base")
pipeline(
image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg",
question="What is in the image?",
)
[{'answer': 'statue of liberty'}]
```
</hfoption>
</hfoptions>
## Parameters
At a minimum, [`Pipeline`] only requires a task identifier, model, and the appropriate input. But there are many parameters available to configure the pipeline with, from task-specific parameters to optimizing performance.
This section introduces you to some of the more important parameters.
### Device
[`Pipeline`] is compatible with many hardware types, including GPUs, CPUs, Apple Silicon, and more. Configure the hardware type with the `device` parameter. By default, [`Pipeline`] runs on a CPU which is given by `device=-1`.
<hfoptions id="device">
<hfoption id="GPU">
To run [`Pipeline`] on a GPU, set `device` to the associated CUDA device id. For example, `device=0` runs on the first GPU.
```py
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b", device=0)
pipeline("the secret to baking a really good cake is ")
```
You could also let [Accelerate](https://hf.co/docs/accelerate/index), a library for distributed training, automatically choose how to load and store the model weights on the appropriate device. This is especially useful if you have multiple devices. Accelerate loads and stores the model weights on the fastest device first, and then moves the weights to other devices (CPU, hard drive) as needed. Set `device_map="auto"` to let Accelerate choose the device.
> [!TIP]
> Make sure have [Accelerate](https://hf.co/docs/accelerate/basic_tutorials/install) is installed.
>
> ```py
> !pip install -U accelerate
> ```
```py
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b", device_map="auto")
pipeline("the secret to baking a really good cake is ")
```
</hfoption>
<hfoption id="Apple silicon">
To run [`Pipeline`] on Apple silicon, set `device="mps"`.
```py
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b", device="mps")
pipeline("the secret to baking a really good cake is ")
```
</hfoption>
</hfoptions>
### Batch inference
[`Pipeline`] can also process batches of inputs with the `batch_size` parameter. Batch inference may improve speed, especially on a GPU, but it isn't guaranteed. Other variables such as hardware, data, and the model itself can affect whether batch inference improves speed. For this reason, batch inference is disabled by default.
In the example below, when there are 4 inputs and `batch_size` is set to 2, [`Pipeline`] passes a batch of 2 inputs to the model at a time.
```py
from transformers import pipeline, infer_device()
device = infer_device()
pipeline = pipeline(task="text-generation", model="google/gemma-2-2b", device=device, batch_size=2)
pipeline(["the secret to baking a really good cake is", "a baguette is", "paris is the", "hotdogs are"])
[[{'generated_text': 'the secret to baking a really good cake is to use a good cake mix.\n\ni’'}],
[{'generated_text': 'a baguette is'}],
[{'generated_text': 'paris is the most beautiful city in the world.\n\ni’ve been to paris 3'}],
[{'generated_text': 'hotdogs are a staple of the american diet. they are a great source of protein and can'}]]
```
Another good use case for batch inference is for streaming data in [`Pipeline`].
```py
from transformers import pipeline, infer_device
from transformers.pipelines.pt_utils import KeyDataset
import datasets
device = infer_device()
# KeyDataset is a utility that returns the item in the dict returned by the dataset
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
pipeline = pipeline(task="text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", device=device)
for out in pipeline(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
print(out)
```
Keep the following general rules of thumb in mind for determining whether batch inference can help improve performance.
1. The only way to know for sure is to measure performance on your model, data, and hardware.
2. Don't batch inference if you're constrained by latency (a live inference product for example).
3. Don't batch inference if you're using a CPU.
4. Don't batch inference if you don't know the `sequence_length` of your data. Measure performance, iteratively add to `sequence_length`, and include out-of-memory (OOM) checks to recover from failures.
5. Do batch inference if your `sequence_length` is regular, and keep pushing it until you reach an OOM error. The larger the GPU, the more helpful batch inference is.
6. Do make sure you can handle OOM errors if you decide to do batch inference.
### Task-specific parameters
[`Pipeline`] accepts any parameters that are supported by each individual task pipeline. Make sure to check out each individual task pipeline to see what type of parameters are available. If you can't find a parameter that is useful for your use case, please feel free to open a GitHub [issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=feature&template=feature-request.yml) to request it!
The examples below demonstrate some of the task-specific parameters available.
<hfoptions id="task-specific-parameters">
<hfoption id="automatic speech recognition">
Pass the `return_timestamps="word"` parameter to [`Pipeline`] to return when each word was spoken.
```py
from transformers import pipeline
pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-large-v3")
pipeline(audio="https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac", return_timestamp="word")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.',
'chunks': [{'text': ' I', 'timestamp': (0.0, 1.1)},
{'text': ' have', 'timestamp': (1.1, 1.44)},
{'text': ' a', 'timestamp': (1.44, 1.62)},
{'text': ' dream', 'timestamp': (1.62, 1.92)},
{'text': ' that', 'timestamp': (1.92, 3.7)},
{'text': ' one', 'timestamp': (3.7, 3.88)},
{'text': ' day', 'timestamp': (3.88, 4.24)},
{'text': ' this', 'timestamp': (4.24, 5.82)},
{'text': ' nation', 'timestamp': (5.82, 6.78)},
{'text': ' will', 'timestamp': (6.78, 7.36)},
{'text': ' rise', 'timestamp': (7.36, 7.88)},
{'text': ' up', 'timestamp': (7.88, 8.46)},
{'text': ' and', 'timestamp': (8.46, 9.2)},
{'text': ' live', 'timestamp': (9.2, 10.34)},
{'text': ' out', 'timestamp': (10.34, 10.58)},
{'text': ' the', 'timestamp': (10.58, 10.8)},
{'text': ' true', 'timestamp': (10.8, 11.04)},
{'text': ' meaning', 'timestamp': (11.04, 11.4)},
{'text': ' of', 'timestamp': (11.4, 11.64)},
{'text': ' its', 'timestamp': (11.64, 11.8)},
{'text': ' creed.', 'timestamp': (11.8, 12.3)}]}
```
</hfoption>
<hfoption id="text generation">
Pass `return_full_text=False` to [`Pipeline`] to only return the generated text instead of the full text (prompt and generated text).
[`~TextGenerationPipeline.__call__`] also supports additional keyword arguments from the [`~GenerationMixin.generate`] method. To return more than one generated sequence, set `num_return_sequences` to a value greater than 1.
```py
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="openai-community/gpt2")
pipeline("the secret to baking a good cake is", num_return_sequences=4, return_full_text=False)
[{'generated_text': ' how easy it is for me to do it with my hands. You must not go nuts, or the cake is going to fall out.'},
{'generated_text': ' to prepare the cake before baking. The key is to find the right type of icing to use and that icing makes an amazing frosting cake.\n\nFor a good icing cake, we give you the basics'},
{'generated_text': " to remember to soak it in enough water and don't worry about it sticking to the wall. In the meantime, you could remove the top of the cake and let it dry out with a paper towel.\n"},
{'generated_text': ' the best time to turn off the oven and let it stand 30 minutes. After 30 minutes, stir and bake a cake in a pan until fully moist.\n\nRemove the cake from the heat for about 12'}]
```
</hfoption>
</hfoptions>
## Chunk batching
There are some instances where you need to process data in chunks.
- for some data types, a single input (for example, a really long audio file) may need to be chunked into multiple parts before it can be processed
- for some tasks, like zero-shot classification or question answering, a single input may need multiple forward passes which can cause issues with the `batch_size` parameter
The [ChunkPipeline](https://github.com/huggingface/transformers/blob/99e0ab6ed888136ea4877c6d8ab03690a1478363/src/transformers/pipelines/base.py#L1387) class is designed to handle these use cases. Both pipeline classes are used in the same way, but since [ChunkPipeline](https://github.com/huggingface/transformers/blob/99e0ab6ed888136ea4877c6d8ab03690a1478363/src/transformers/pipelines/base.py#L1387) can automatically handle batching, you don't need to worry about the number of forward passes your inputs trigger. Instead, you can optimize `batch_size` independently of the inputs.
The example below shows how it differs from [`Pipeline`].
```py
# ChunkPipeline
all_model_outputs = []
for preprocessed in pipeline.preprocess(inputs):
model_outputs = pipeline.model_forward(preprocessed)
all_model_outputs.append(model_outputs)
outputs =pipeline.postprocess(all_model_outputs)
# Pipeline
preprocessed = pipeline.preprocess(inputs)
model_outputs = pipeline.forward(preprocessed)
outputs = pipeline.postprocess(model_outputs)
```
## Large datasets
For inference with large datasets, you can iterate directly over the dataset itself. This avoids immediately allocating memory for the entire dataset, and you don't need to worry about creating batches yourself. Try [Batch inference](#batch-inference) with the `batch_size` parameter to see if it improves performance.
```py
from transformers.pipelines.pt_utils import KeyDataset
from transformers import pipeline, infer_device
from datasets import load_dataset
device = infer_device()
dataset = datasets.load_dataset("imdb", name="plain_text", split="unsupervised")
pipeline = pipeline(task="text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english", device=device)
for out in pipeline(KeyDataset(dataset, "text"), batch_size=8, truncation="only_first"):
print(out)
```
Other ways to run inference on large datasets with [`Pipeline`] include using an iterator or generator.
```py
def data():
for i in range(1000):
yield f"My example {i}"
pipeline = pipeline(model="openai-community/gpt2", device=0)
generated_characters = 0
for out in pipeline(data()):
generated_characters += len(out[0]["generated_text"])
```
## Large models
[Accelerate](https://hf.co/docs/accelerate/index) enables a couple of optimizations for running large models with [`Pipeline`]. Make sure Accelerate is installed first.
```py
!pip install -U accelerate
```
The `device_map="auto"` setting is useful for automatically distributing the model across the fastest devices (GPUs) first before dispatching to other slower devices if available (CPU, hard drive).
[`Pipeline`] supports half-precision weights (torch.float16), which can be significantly faster and save memory. Performance loss is negligible for most models, especially for larger ones. If your hardware supports it, you can enable torch.bfloat16 instead for more range.
> [!TIP]
> Inputs are internally converted to torch.float16 and it only works for models with a PyTorch backend.
Lastly, [`Pipeline`] also accepts quantized models to reduce memory usage even further. Make sure you have the [bitsandbytes](https://hf.co/docs/bitsandbytes/installation) library installed first, and then add `load_in_8bit=True` to `model_kwargs` in the pipeline.
```py
import torch
from transformers import pipeline, BitsAndBytesConfig
pipeline = pipeline(model="google/gemma-7b", dtype=torch.bfloat16, device_map="auto", model_kwargs={"quantization_config": BitsAndBytesConfig(load_in_8bit=True)})
pipeline("the secret to baking a good cake is ")
[{'generated_text': 'the secret to baking a good cake is 1. the right ingredients 2. the right'}]
```
| transformers/docs/source/en/pipeline_tutorial.md/0 | {
"file_path": "transformers/docs/source/en/pipeline_tutorial.md",
"repo_id": "transformers",
"token_count": 5405
} | 403 |
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# GPTQ
The [GPTQModel](https://github.com/ModelCloud/GPTQModel) and [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) implements the GPTQ algorithm, a post-training quantization technique where each row of the weight matrix is quantized independently to find a version of the weights that minimizes the error. These weights are quantized to int4, but they're restored to fp16 on the fly during inference. This can save memory usage by 4x because the int4 weights are dequantized in a fused kernel rather than a GPU's global memory. Inference is also faster because a lower bitwidth takes less time to communicate.
> [!WARNING]
> AutoGPTQ is likely to be deprecated in the future due to lack of continued support for new models and features. See the [GPTQModel](#gptqmodel) section for more details.
Install Accelerate, Transformers and Optimum first.
```bash
pip install --upgrade accelerate optimum transformers
```
Then run the command below to install a GPTQ library.
<hfoptions id="install">
<hfoption id="GPTQmodel">
```bash
pip install gptqmodel --no-build-isolation
```
</hfoption>
<hfoption id="AutoGPTQ">
```bash
pip install auto-gptq --no-build-isolation
```
</hfoption>
</hfoptions>
Create a [`GPTQConfig`] class and set the number of bits to quantize to, a dataset to calbrate the weights for quantization, and a tokenizer to prepare the dataset.
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
tokenizer = AutoTokenizer.from_pretrained("facebook/opt-125m")
gptq_config = GPTQConfig(bits=4, dataset="c4", tokenizer=tokenizer)
```
You can pass your own dataset as a list of strings, but it is highly recommended to use the same dataset from the GPTQ paper.
```py
dataset = ["auto-gptq is an easy-to-use model quantization library with user-friendly apis, based on GPTQ algorithm."]
gptq_config = GPTQConfig(bits=4, dataset=dataset, tokenizer=tokenizer)
```
Load a model to quantize and pass [`GPTQConfig`] to [`~AutoModelForCausalLM.from_pretrained`]. Set `device_map="auto"` to automatically offload the model to a CPU to help fit the model in memory, and allow the model modules to be moved between the CPU and GPU for quantization.
```py
quantized_model = AutoModelForCausalLM.from_pretrained("facebook/opt-125m", device_map="auto", quantization_config=gptq_config)
```
If you're running out of memory because a dataset is too large (disk offloading is not supported), try passing the `max_memory` parameter to allocate the amount of memory to use on your device (GPU and CPU).
```py
quantized_model = AutoModelForCausalLM.from_pretrained(
"facebook/opt-125m",
device_map="auto",
max_memory={0: "30GiB", 1: "46GiB", "cpu": "30GiB"},
quantization_config=gptq_config
)
```
> [!WARNING]
> Depending on your hardware, it can take some time to quantize a model from scratch. It can take ~5 minutes to quantize the [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) model on a free-tier Google Colab GPU, but it'll take ~4 hours to quantize a 175B parameter model on a NVIDIA A100. Before you quantize a model, it is a good idea to check the Hub if a GPTQ-quantized version of the model already exists.
Once a model is quantized, you can use [`~PreTrainedModel.push_to_hub`] to push the model and tokenizer to the Hub where it can be easily shared and accessed. This saves the [`GPTQConfig`].
```py
quantized_model.push_to_hub("opt-125m-gptq")
tokenizer.push_to_hub("opt-125m-gptq")
```
[`~PreTrainedModel.save_pretrained`] saves a quantized model locally. If the model was quantized with the `device_map` parameter, make sure to move the entire model to a GPU or CPU before saving it. The example below saves the model on a CPU.
```py
quantized_model.save_pretrained("opt-125m-gptq")
tokenizer.save_pretrained("opt-125m-gptq")
# if quantized with device_map set
quantized_model.to("cpu")
quantized_model.save_pretrained("opt-125m-gptq")
```
Reload a quantized model with [`~PreTrainedModel.from_pretrained`], and set `device_map="auto"` to automatically distribute the model on all available GPUs to load the model faster without using more memory than needed.
```py
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto")
```
## Marlin
[Marlin](https://github.com/IST-DASLab/marlin) is a 4-bit only CUDA GPTQ kernel, highly optimized for the NVIDIA A100 GPU (Ampere) architecture. Loading, dequantization, and execution of post-dequantized weights are highly parallelized, offering a substantial inference improvement versus the original CUDA GPTQ kernel. Marlin is only available for quantized inference and does not support model quantization.
Marlin inference can be activated with the `backend` parameter in [`GPTQConfig`].
```py
from transformers import AutoModelForCausalLM, GPTQConfig
model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto", quantization_config=GPTQConfig(bits=4, backend="marlin"))
```
## ExLlama
> [!WARNING]
> Only 4-bit models are supported, and we recommend deactivating the ExLlama kernels if you're finetuning a quantized model with PEFT.
[ExLlama](https://github.com/turboderp/exllama) is a Python/C++/CUDA implementation of the [Llama](model_doc/llama) model that is designed for faster inference with 4-bit GPTQ weights (check out these [benchmarks](https://github.com/huggingface/optimum/tree/main/tests/benchmark#gptq-benchmark)). The ExLlama kernel is activated by default when you create a [`GPTQConfig`] object.
To boost inference speed even further, use the [ExLlamaV2](https://github.com/turboderp/exllamav2) kernels by configuring the `exllama_config` parameter in [`GPTQConfig`].
```py
import torch
from transformers import AutoModelForCausalLM, GPTQConfig
gptq_config = GPTQConfig(bits=4, exllama_config={"version":2})
model = AutoModelForCausalLM.from_pretrained(
"{your_username}/opt-125m-gptq",
device_map="auto",
quantization_config=gptq_config
)
```
The ExLlama kernels are only supported when the entire model is on the GPU. If you're doing inference on a CPU with AutoGPTQ 0.4.2+, disable the ExLlama kernel in [`GPTQConfig`]. This overwrites the attributes related to the ExLlama kernels in the quantization config of the `config.json` file.
```py
import torch
from transformers import AutoModelForCausalLM, GPTQConfig
gptq_config = GPTQConfig(bits=4, use_exllama=False)
model = AutoModelForCausalLM.from_pretrained(
"{your_username}/opt-125m-gptq",
device_map="cpu",
quantization_config=gptq_config
)
```
## GPTQModel
It is recommended to use GPTQModel, originally a maintained fork of AutoGPTQ, because it has since diverged from AutoGTPQ with some significant features. GPTQModel has faster quantization, lower memory usage, and more accurate default quantization.
GPTQModel provides asymmetric quantization which can potentially lower quantization errors compared to symmetric quantization. It is not backward compatible with AutoGPTQ, and not all kernels (Marlin) support asymmetric quantization.
GPTQModel also has broader support for the latest LLM models, multimodal models (Qwen2-VL and Ovis1.6-VL), platforms (Linux, macOS, Windows 11), and hardware (AMD ROCm, Apple Silicon, Intel/AMD CPUs, and Intel Datacenter Max/Arc GPUs, etc.).
The Marlin kernels are also updated for A100 GPUs and other kernels are updated to include auto-padding for legacy models and models with non-uniform in/out-features.
## Resources
Run the GPTQ quantization with PEFT [notebook](https://colab.research.google.com/drive/1_TIrmuKOFhuRRiTWN94iLKUFu6ZX4ceb?usp=sharing) for a hands-on experience, and read [Making LLMs lighter with AutoGPTQ and transformers](https://huggingface.co/blog/gptq-integration) to learn more about the AutoGPTQ integration.
| transformers/docs/source/en/quantization/gptq.md/0 | {
"file_path": "transformers/docs/source/en/quantization/gptq.md",
"repo_id": "transformers",
"token_count": 2618
} | 404 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Automatic speech recognition
[[open-in-colab]]
<Youtube id="TksaY_FDgnk"/>
Automatic speech recognition (ASR) converts a speech signal to text, mapping a sequence of audio inputs to text outputs. Virtual assistants like Siri and Alexa use ASR models to help users every day, and there are many other useful user-facing applications like live captioning and note-taking during meetings.
This guide will show you how to:
1. Fine-tune [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) on the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset to transcribe audio to text.
2. Use your fine-tuned model for inference.
<Tip>
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/automatic-speech-recognition)
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install transformers datasets evaluate jiwer
```
We encourage you to login to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to login:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load MInDS-14 dataset
Start by loading a smaller subset of the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset from the 🤗 Datasets library. This will give you a chance to experiment and make sure everything works before spending more time training on the full dataset.
```py
>>> from datasets import load_dataset, Audio
>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
```
Split the dataset's `train` split into a train and test set with the [`~Dataset.train_test_split`] method:
```py
>>> minds = minds.train_test_split(test_size=0.2)
```
Then take a look at the dataset:
```py
>>> minds
DatasetDict({
train: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 16
})
test: Dataset({
features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
num_rows: 4
})
})
```
While the dataset contains a lot of useful information, like `lang_id` and `english_transcription`, this guide focuses on the `audio` and `transcription`. Remove the other columns with the [`~datasets.Dataset.remove_columns`] method:
```py
>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
```
Review the example again:
```py
>>> minds["train"][0]
{'audio': {'array': array([-0.00024414, 0. , 0. , ..., 0.00024414,
0.00024414, 0.00024414], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 8000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
There are two fields:
- `audio`: a 1-dimensional `array` of the speech signal that must be called to load and resample the audio file.
- `transcription`: the target text.
## Preprocess
The next step is to load a Wav2Vec2 processor to process the audio signal:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
```
The MInDS-14 dataset has a sampling rate of 8000Hz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000Hz to use the pretrained Wav2Vec2 model:
```py
>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
>>> minds["train"][0]
{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
2.78103951e-04, 2.38446111e-04, 1.18740834e-04], dtype=float32),
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'sampling_rate': 16000},
'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
```
As you can see in the `transcription` above, the text contains a mix of uppercase and lowercase characters. The Wav2Vec2 tokenizer is only trained on uppercase characters so you'll need to make sure the text matches the tokenizer's vocabulary:
```py
>>> def uppercase(example):
... return {"transcription": example["transcription"].upper()}
>>> minds = minds.map(uppercase)
```
Now create a preprocessing function that:
1. Calls the `audio` column to load and resample the audio file.
2. Extracts the `input_values` from the audio file and tokenize the `transcription` column with the processor.
```py
>>> def prepare_dataset(batch):
... audio = batch["audio"]
... batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"])
... batch["input_length"] = len(batch["input_values"][0])
... return batch
```
To apply the preprocessing function over the entire dataset, use 🤗 Datasets [`~datasets.Dataset.map`] function. You can speed up `map` by increasing the number of processes with the `num_proc` parameter. Remove the columns you don't need with the [`~datasets.Dataset.remove_columns`] method:
```py
>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
```
🤗 Transformers doesn't have a data collator for ASR, so you'll need to adapt the [`DataCollatorWithPadding`] to create a batch of examples. It'll also dynamically pad your text and labels to the length of the longest element in its batch (instead of the entire dataset) so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.
Unlike other data collators, this specific data collator needs to apply a different padding method to `input_values` and `labels`:
```py
>>> import torch
>>> from dataclasses import dataclass, field
>>> from typing import Any, Dict, List, Optional, Union
>>> @dataclass
... class DataCollatorCTCWithPadding:
... processor: AutoProcessor
... padding: Union[bool, str] = "longest"
... def __call__(self, features: list[dict[str, Union[list[int], torch.Tensor]]]) -> dict[str, torch.Tensor]:
... # split inputs and labels since they have to be of different lengths and need
... # different padding methods
... input_features = [{"input_values": feature["input_values"][0]} for feature in features]
... label_features = [{"input_ids": feature["labels"]} for feature in features]
... batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
... labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
... # replace padding with -100 to ignore loss correctly
... labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
... batch["labels"] = labels
... return batch
```
Now instantiate your `DataCollatorForCTCWithPadding`:
```py
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
```
## Evaluate
Including a metric during training is often helpful for evaluating your model's performance. You can quickly load an evaluation method with the 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) library. For this task, load the [word error rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) metric (refer to the 🤗 Evaluate [quick tour](https://huggingface.co/docs/evaluate/a_quick_tour) to learn more about loading and computing metrics):
```py
>>> import evaluate
>>> wer = evaluate.load("wer")
```
Then create a function that passes your predictions and labels to [`~evaluate.EvaluationModule.compute`] to calculate the WER:
```py
>>> import numpy as np
>>> def compute_metrics(pred):
... pred_logits = pred.predictions
... pred_ids = np.argmax(pred_logits, axis=-1)
... pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
... pred_str = processor.batch_decode(pred_ids)
... label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
... wer = wer.compute(predictions=pred_str, references=label_str)
... return {"wer": wer}
```
Your `compute_metrics` function is ready to go now, and you'll return to it when you setup your training.
## Train
<frameworkcontent>
<pt>
<Tip>
If you aren't familiar with finetuning a model with the [`Trainer`], take a look at the basic tutorial [here](../training#train-with-pytorch-trainer)!
</Tip>
You are now ready to start training your model! Load Wav2Vec2 with [`AutoModelForCTC`]. Specify the reduction to apply with the `ctc_loss_reduction` parameter. It is often better to use the average instead of the default summation:
```py
>>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
>>> model = AutoModelForCTC.from_pretrained(
... "facebook/wav2vec2-base",
... ctc_loss_reduction="mean",
... pad_token_id=processor.tokenizer.pad_token_id,
... )
```
At this point, only three steps remain:
1. Define your training hyperparameters in [`TrainingArguments`]. The only required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the WER and save the training checkpoint.
2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
3. Call [`~Trainer.train`] to fine-tune your model.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_asr_mind_model",
... per_device_train_batch_size=8,
... gradient_accumulation_steps=2,
... learning_rate=1e-5,
... warmup_steps=500,
... max_steps=2000,
... gradient_checkpointing=True,
... fp16=True,
... group_by_length=True,
... eval_strategy="steps",
... per_device_eval_batch_size=8,
... save_steps=1000,
... eval_steps=1000,
... logging_steps=25,
... load_best_model_at_end=True,
... metric_for_best_model="wer",
... greater_is_better=False,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=encoded_minds["train"],
... eval_dataset=encoded_minds["test"],
... processing_class=processor,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
Once training is completed, share your model to the Hub with the [`~transformers.Trainer.push_to_hub`] method so it can be accessible to everyone:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<Tip>
For a more in-depth example of how to fine-tune a model for automatic speech recognition, take a look at this blog [post](https://huggingface.co/blog/fine-tune-wav2vec2-english) for English ASR and this [post](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for multilingual ASR.
</Tip>
## Inference
Great, now that you've fine-tuned a model, you can use it for inference!
Load an audio file you'd like to run inference on. Remember to resample the sampling rate of the audio file to match the sampling rate of the model if you need to!
```py
>>> from datasets import load_dataset, Audio
>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
>>> sampling_rate = dataset.features["audio"].sampling_rate
>>> audio_file = dataset[0]["audio"]["path"]
```
The simplest way to try out your fine-tuned model for inference is to use it in a [`pipeline`]. Instantiate a `pipeline` for automatic speech recognition with your model, and pass your audio file to it:
```py
>>> from transformers import pipeline
>>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
>>> transcriber(audio_file)
{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
```
<Tip>
The transcription is decent, but it could be better! Try finetuning your model on more examples to get even better results!
</Tip>
You can also manually replicate the results of the `pipeline` if you'd like:
<frameworkcontent>
<pt>
Load a processor to preprocess the audio file and transcription and return the `input` as PyTorch tensors:
```py
>>> from transformers import AutoProcessor
>>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
```
Pass your inputs to the model and return the logits:
```py
>>> from transformers import AutoModelForCTC
>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
Get the predicted `input_ids` with the highest probability, and use the processor to decode the predicted `input_ids` back into text:
```py
>>> import torch
>>> predicted_ids = torch.argmax(logits, dim=-1)
>>> transcription = processor.batch_decode(predicted_ids)
>>> transcription
['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
```
</pt>
</frameworkcontent>
| transformers/docs/source/en/tasks/asr.md/0 | {
"file_path": "transformers/docs/source/en/tasks/asr.md",
"repo_id": "transformers",
"token_count": 4909
} | 405 |
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# Object detection
[[open-in-colab]]
Object detection is the computer vision task of detecting instances (such as humans, buildings, or cars) in an image. Object detection models receive an image as input and output
coordinates of the bounding boxes and associated labels of the detected objects. An image can contain multiple objects,
each with its own bounding box and a label (e.g. it can have a car and a building), and each object can
be present in different parts of an image (e.g. the image can have several cars).
This task is commonly used in autonomous driving for detecting things like pedestrians, road signs, and traffic lights.
Other applications include counting objects in images, image search, and more.
In this guide, you will learn how to:
1. Finetune [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a model that combines a convolutional
backbone with an encoder-decoder Transformer, on the [CPPE-5](https://huggingface.co/datasets/cppe-5)
dataset.
2. Use your finetuned model for inference.
<Tip>
To see all architectures and checkpoints compatible with this task, we recommend checking the [task-page](https://huggingface.co/tasks/object-detection)
</Tip>
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q datasets transformers accelerate timm
pip install -q -U albumentations>=1.4.5 torchmetrics pycocotools
```
You'll use 🤗 Datasets to load a dataset from the Hugging Face Hub, 🤗 Transformers to train your model,
and `albumentations` to augment the data.
We encourage you to share your model with the community. Log in to your Hugging Face account to upload it to the Hub.
When prompted, enter your token to log in:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
To get started, we'll define global constants, namely the model name and image size. For this tutorial, we'll use the conditional DETR model due to its faster convergence. Feel free to select any object detection model available in the `transformers` library.
```py
>>> MODEL_NAME = "microsoft/conditional-detr-resnet-50" # or "facebook/detr-resnet-50"
>>> IMAGE_SIZE = 480
```
## Load the CPPE-5 dataset
The [CPPE-5 dataset](https://huggingface.co/datasets/cppe-5) contains images with
annotations identifying medical personal protective equipment (PPE) in the context of the COVID-19 pandemic.
Start by loading the dataset and creating a `validation` split from `train`:
```py
>>> from datasets import load_dataset
>>> cppe5 = load_dataset("cppe-5")
>>> if "validation" not in cppe5:
... split = cppe5["train"].train_test_split(0.15, seed=1337)
... cppe5["train"] = split["train"]
... cppe5["validation"] = split["test"]
>>> cppe5
DatasetDict({
train: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 850
})
test: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 29
})
validation: Dataset({
features: ['image_id', 'image', 'width', 'height', 'objects'],
num_rows: 150
})
})
```
You'll see that this dataset has 1000 images for train and validation sets and a test set with 29 images.
To get familiar with the data, explore what the examples look like.
```py
>>> cppe5["train"][0]
{
'image_id': 366,
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=500x290>,
'width': 500,
'height': 500,
'objects': {
'id': [1932, 1933, 1934],
'area': [27063, 34200, 32431],
'bbox': [[29.0, 11.0, 97.0, 279.0],
[201.0, 1.0, 120.0, 285.0],
[382.0, 0.0, 113.0, 287.0]],
'category': [0, 0, 0]
}
}
```
The examples in the dataset have the following fields:
- `image_id`: the example image id
- `image`: a `PIL.Image.Image` object containing the image
- `width`: width of the image
- `height`: height of the image
- `objects`: a dictionary containing bounding box metadata for the objects in the image:
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [COCO format](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) )
- `category`: the object's category, with possible values including `Coverall (0)`, `Face_Shield (1)`, `Gloves (2)`, `Goggles (3)` and `Mask (4)`
You may notice that the `bbox` field follows the COCO format, which is the format that the DETR model expects.
However, the grouping of the fields inside `objects` differs from the annotation format DETR requires. You will
need to apply some preprocessing transformations before using this data for training.
To get an even better understanding of the data, visualize an example in the dataset.
```py
>>> import numpy as np
>>> import os
>>> from PIL import Image, ImageDraw
>>> image = cppe5["train"][2]["image"]
>>> annotations = cppe5["train"][2]["objects"]
>>> draw = ImageDraw.Draw(image)
>>> categories = cppe5["train"].features["objects"]["category"].feature.names
>>> id2label = {index: x for index, x in enumerate(categories, start=0)}
>>> label2id = {v: k for k, v in id2label.items()}
>>> for i in range(len(annotations["id"])):
... box = annotations["bbox"][i]
... class_idx = annotations["category"][i]
... x, y, w, h = tuple(box)
... # Check if coordinates are normalized or not
... if max(box) > 1.0:
... # Coordinates are un-normalized, no need to re-scale them
... x1, y1 = int(x), int(y)
... x2, y2 = int(x + w), int(y + h)
... else:
... # Coordinates are normalized, re-scale them
... x1 = int(x * width)
... y1 = int(y * height)
... x2 = int((x + w) * width)
... y2 = int((y + h) * height)
... draw.rectangle((x, y, x + w, y + h), outline="red", width=1)
... draw.text((x, y), id2label[class_idx], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/oVQb9SF.png" alt="CPPE-5 Image Example"/>
</div>
To visualize the bounding boxes with associated labels, you can get the labels from the dataset's metadata, specifically
the `category` field.
You'll also want to create dictionaries that map a label id to a label class (`id2label`) and the other way around (`label2id`).
You can use them later when setting up the model. Including these maps will make your model reusable by others if you share
it on the Hugging Face Hub. Please note that, the part of above code that draws the bounding boxes assume that it is in `COCO` format `(x_min, y_min, width, height)`. It has to be adjusted to work for other formats like `(x_min, y_min, x_max, y_max)`.
As a final step of getting familiar with the data, explore it for potential issues. One common problem with datasets for
object detection is bounding boxes that "stretch" beyond the edge of the image. Such "runaway" bounding boxes can raise
errors during training and should be addressed. There are a few examples with this issue in this dataset.
To keep things simple in this guide, we will set `clip=True` for `BboxParams` in transformations below.
## Preprocess the data
To finetune a model, you must preprocess the data you plan to use to match precisely the approach used for the pre-trained model.
[`AutoImageProcessor`] takes care of processing image data to create `pixel_values`, `pixel_mask`, and
`labels` that a DETR model can train with. The image processor has some attributes that you won't have to worry about:
- `image_mean = [0.485, 0.456, 0.406 ]`
- `image_std = [0.229, 0.224, 0.225]`
These are the mean and standard deviation used to normalize images during the model pre-training. These values are crucial
to replicate when doing inference or finetuning a pre-trained image model.
Instantiate the image processor from the same checkpoint as the model you want to finetune.
```py
>>> from transformers import AutoImageProcessor
>>> MAX_SIZE = IMAGE_SIZE
>>> image_processor = AutoImageProcessor.from_pretrained(
... MODEL_NAME,
... do_resize=True,
... size={"max_height": MAX_SIZE, "max_width": MAX_SIZE},
... do_pad=True,
... pad_size={"height": MAX_SIZE, "width": MAX_SIZE},
... )
```
Before passing the images to the `image_processor`, apply two preprocessing transformations to the dataset:
- Augmenting images
- Reformatting annotations to meet DETR expectations
First, to make sure the model does not overfit on the training data, you can apply image augmentation with any data augmentation library. Here we use [Albumentations](https://albumentations.ai/docs/).
This library ensures that transformations affect the image and update the bounding boxes accordingly.
The 🤗 Datasets library documentation has a detailed [guide on how to augment images for object detection](https://huggingface.co/docs/datasets/object_detection),
and it uses the exact same dataset as an example. Apply some geometric and color transformations to the image. For additional augmentation options, explore the [Albumentations Demo Space](https://huggingface.co/spaces/qubvel-hf/albumentations-demo).
```py
>>> import albumentations as A
>>> train_augment_and_transform = A.Compose(
... [
... A.Perspective(p=0.1),
... A.HorizontalFlip(p=0.5),
... A.RandomBrightnessContrast(p=0.5),
... A.HueSaturationValue(p=0.1),
... ],
... bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True, min_area=25),
... )
>>> validation_transform = A.Compose(
... [A.NoOp()],
... bbox_params=A.BboxParams(format="coco", label_fields=["category"], clip=True),
... )
```
The `image_processor` expects the annotations to be in the following format: `{'image_id': int, 'annotations': list[Dict]}`,
where each dictionary is a COCO object annotation. Let's add a function to reformat annotations for a single example:
```py
>>> def format_image_annotations_as_coco(image_id, categories, areas, bboxes):
... """Format one set of image annotations to the COCO format
... Args:
... image_id (str): image id. e.g. "0001"
... categories (list[int]): list of categories/class labels corresponding to provided bounding boxes
... areas (list[float]): list of corresponding areas to provided bounding boxes
... bboxes (list[tuple[float]]): list of bounding boxes provided in COCO format
... ([center_x, center_y, width, height] in absolute coordinates)
... Returns:
... dict: {
... "image_id": image id,
... "annotations": list of formatted annotations
... }
... """
... annotations = []
... for category, area, bbox in zip(categories, areas, bboxes):
... formatted_annotation = {
... "image_id": image_id,
... "category_id": category,
... "iscrowd": 0,
... "area": area,
... "bbox": list(bbox),
... }
... annotations.append(formatted_annotation)
... return {
... "image_id": image_id,
... "annotations": annotations,
... }
```
Now you can combine the image and annotation transformations to use on a batch of examples:
```py
>>> def augment_and_transform_batch(examples, transform, image_processor, return_pixel_mask=False):
... """Apply augmentations and format annotations in COCO format for object detection task"""
... images = []
... annotations = []
... for image_id, image, objects in zip(examples["image_id"], examples["image"], examples["objects"]):
... image = np.array(image.convert("RGB"))
... # apply augmentations
... output = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
... images.append(output["image"])
... # format annotations in COCO format
... formatted_annotations = format_image_annotations_as_coco(
... image_id, output["category"], objects["area"], output["bboxes"]
... )
... annotations.append(formatted_annotations)
... # Apply the image processor transformations: resizing, rescaling, normalization
... result = image_processor(images=images, annotations=annotations, return_tensors="pt")
... if not return_pixel_mask:
... result.pop("pixel_mask", None)
... return result
```
Apply this preprocessing function to the entire dataset using 🤗 Datasets [`~datasets.Dataset.with_transform`] method. This method applies
transformations on the fly when you load an element of the dataset.
At this point, you can check what an example from the dataset looks like after the transformations. You should see a tensor
with `pixel_values`, a tensor with `pixel_mask`, and `labels`.
```py
>>> from functools import partial
>>> # Make transform functions for batch and apply for dataset splits
>>> train_transform_batch = partial(
... augment_and_transform_batch, transform=train_augment_and_transform, image_processor=image_processor
... )
>>> validation_transform_batch = partial(
... augment_and_transform_batch, transform=validation_transform, image_processor=image_processor
... )
>>> cppe5["train"] = cppe5["train"].with_transform(train_transform_batch)
>>> cppe5["validation"] = cppe5["validation"].with_transform(validation_transform_batch)
>>> cppe5["test"] = cppe5["test"].with_transform(validation_transform_batch)
>>> cppe5["train"][15]
{'pixel_values': tensor([[[ 1.9235, 1.9407, 1.9749, ..., -0.7822, -0.7479, -0.6965],
[ 1.9578, 1.9749, 1.9920, ..., -0.7993, -0.7650, -0.7308],
[ 2.0092, 2.0092, 2.0263, ..., -0.8507, -0.8164, -0.7822],
...,
[ 0.0741, 0.0741, 0.0741, ..., 0.0741, 0.0741, 0.0741],
[ 0.0741, 0.0741, 0.0741, ..., 0.0741, 0.0741, 0.0741],
[ 0.0741, 0.0741, 0.0741, ..., 0.0741, 0.0741, 0.0741]],
[[ 1.6232, 1.6408, 1.6583, ..., 0.8704, 1.0105, 1.1331],
[ 1.6408, 1.6583, 1.6758, ..., 0.8529, 0.9930, 1.0980],
[ 1.6933, 1.6933, 1.7108, ..., 0.8179, 0.9580, 1.0630],
...,
[ 0.2052, 0.2052, 0.2052, ..., 0.2052, 0.2052, 0.2052],
[ 0.2052, 0.2052, 0.2052, ..., 0.2052, 0.2052, 0.2052],
[ 0.2052, 0.2052, 0.2052, ..., 0.2052, 0.2052, 0.2052]],
[[ 1.8905, 1.9080, 1.9428, ..., -0.1487, -0.0964, -0.0615],
[ 1.9254, 1.9428, 1.9603, ..., -0.1661, -0.1138, -0.0790],
[ 1.9777, 1.9777, 1.9951, ..., -0.2010, -0.1138, -0.0790],
...,
[ 0.4265, 0.4265, 0.4265, ..., 0.4265, 0.4265, 0.4265],
[ 0.4265, 0.4265, 0.4265, ..., 0.4265, 0.4265, 0.4265],
[ 0.4265, 0.4265, 0.4265, ..., 0.4265, 0.4265, 0.4265]]]),
'labels': {'image_id': tensor([688]), 'class_labels': tensor([3, 4, 2, 0, 0]), 'boxes': tensor([[0.4700, 0.1933, 0.1467, 0.0767],
[0.4858, 0.2600, 0.1150, 0.1000],
[0.4042, 0.4517, 0.1217, 0.1300],
[0.4242, 0.3217, 0.3617, 0.5567],
[0.6617, 0.4033, 0.5400, 0.4533]]), 'area': tensor([ 4048., 4140., 5694., 72478., 88128.]), 'iscrowd': tensor([0, 0, 0, 0, 0]), 'orig_size': tensor([480, 480])}}
```
You have successfully augmented the individual images and prepared their annotations. However, preprocessing isn't
complete yet. In the final step, create a custom `collate_fn` to batch images together.
Pad images (which are now `pixel_values`) to the largest image in a batch, and create a corresponding `pixel_mask`
to indicate which pixels are real (1) and which are padding (0).
```py
>>> import torch
>>> def collate_fn(batch):
... data = {}
... data["pixel_values"] = torch.stack([x["pixel_values"] for x in batch])
... data["labels"] = [x["labels"] for x in batch]
... if "pixel_mask" in batch[0]:
... data["pixel_mask"] = torch.stack([x["pixel_mask"] for x in batch])
... return data
```
## Preparing function to compute mAP
Object detection models are commonly evaluated with a set of <a href="https://cocodataset.org/#detection-eval">COCO-style metrics</a>. We are going to use `torchmetrics` to compute `mAP` (mean average precision) and `mAR` (mean average recall) metrics and will wrap it to `compute_metrics` function in order to use in [`Trainer`] for evaluation.
Intermediate format of boxes used for training is `YOLO` (normalized) but we will compute metrics for boxes in `Pascal VOC` (absolute) format in order to correctly handle box areas. Let's define a function that converts bounding boxes to `Pascal VOC` format:
```py
>>> from transformers.image_transforms import center_to_corners_format
>>> def convert_bbox_yolo_to_pascal(boxes, image_size):
... """
... Convert bounding boxes from YOLO format (x_center, y_center, width, height) in range [0, 1]
... to Pascal VOC format (x_min, y_min, x_max, y_max) in absolute coordinates.
... Args:
... boxes (torch.Tensor): Bounding boxes in YOLO format
... image_size (tuple[int, int]): Image size in format (height, width)
... Returns:
... torch.Tensor: Bounding boxes in Pascal VOC format (x_min, y_min, x_max, y_max)
... """
... # convert center to corners format
... boxes = center_to_corners_format(boxes)
... # convert to absolute coordinates
... height, width = image_size
... boxes = boxes * torch.tensor([[width, height, width, height]])
... return boxes
```
Then, in `compute_metrics` function we collect `predicted` and `target` bounding boxes, scores and labels from evaluation loop results and pass it to the scoring function.
```py
>>> import numpy as np
>>> from dataclasses import dataclass
>>> from torchmetrics.detection.mean_ap import MeanAveragePrecision
>>> @dataclass
>>> class ModelOutput:
... logits: torch.Tensor
... pred_boxes: torch.Tensor
>>> @torch.no_grad()
>>> def compute_metrics(evaluation_results, image_processor, threshold=0.0, id2label=None):
... """
... Compute mean average mAP, mAR and their variants for the object detection task.
... Args:
... evaluation_results (EvalPrediction): Predictions and targets from evaluation.
... threshold (float, optional): Threshold to filter predicted boxes by confidence. Defaults to 0.0.
... id2label (Optional[dict], optional): Mapping from class id to class name. Defaults to None.
... Returns:
... Mapping[str, float]: Metrics in a form of dictionary {<metric_name>: <metric_value>}
... """
... predictions, targets = evaluation_results.predictions, evaluation_results.label_ids
... # For metric computation we need to provide:
... # - targets in a form of list of dictionaries with keys "boxes", "labels"
... # - predictions in a form of list of dictionaries with keys "boxes", "scores", "labels"
... image_sizes = []
... post_processed_targets = []
... post_processed_predictions = []
... # Collect targets in the required format for metric computation
... for batch in targets:
... # collect image sizes, we will need them for predictions post processing
... batch_image_sizes = torch.tensor(np.array([x["orig_size"] for x in batch]))
... image_sizes.append(batch_image_sizes)
... # collect targets in the required format for metric computation
... # boxes were converted to YOLO format needed for model training
... # here we will convert them to Pascal VOC format (x_min, y_min, x_max, y_max)
... for image_target in batch:
... boxes = torch.tensor(image_target["boxes"])
... boxes = convert_bbox_yolo_to_pascal(boxes, image_target["orig_size"])
... labels = torch.tensor(image_target["class_labels"])
... post_processed_targets.append({"boxes": boxes, "labels": labels})
... # Collect predictions in the required format for metric computation,
... # model produce boxes in YOLO format, then image_processor convert them to Pascal VOC format
... for batch, target_sizes in zip(predictions, image_sizes):
... batch_logits, batch_boxes = batch[1], batch[2]
... output = ModelOutput(logits=torch.tensor(batch_logits), pred_boxes=torch.tensor(batch_boxes))
... post_processed_output = image_processor.post_process_object_detection(
... output, threshold=threshold, target_sizes=target_sizes
... )
... post_processed_predictions.extend(post_processed_output)
... # Compute metrics
... metric = MeanAveragePrecision(box_format="xyxy", class_metrics=True)
... metric.update(post_processed_predictions, post_processed_targets)
... metrics = metric.compute()
... # Replace list of per class metrics with separate metric for each class
... classes = metrics.pop("classes")
... map_per_class = metrics.pop("map_per_class")
... mar_100_per_class = metrics.pop("mar_100_per_class")
... for class_id, class_map, class_mar in zip(classes, map_per_class, mar_100_per_class):
... class_name = id2label[class_id.item()] if id2label is not None else class_id.item()
... metrics[f"map_{class_name}"] = class_map
... metrics[f"mar_100_{class_name}"] = class_mar
... metrics = {k: round(v.item(), 4) for k, v in metrics.items()}
... return metrics
>>> eval_compute_metrics_fn = partial(
... compute_metrics, image_processor=image_processor, id2label=id2label, threshold=0.0
... )
```
## Training the detection model
You have done most of the heavy lifting in the previous sections, so now you are ready to train your model!
The images in this dataset are still quite large, even after resizing. This means that finetuning this model will
require at least one GPU.
Training involves the following steps:
1. Load the model with [`AutoModelForObjectDetection`] using the same checkpoint as in the preprocessing.
2. Define your training hyperparameters in [`TrainingArguments`].
3. Pass the training arguments to [`Trainer`] along with the model, dataset, image processor, and data collator.
4. Call [`~Trainer.train`] to finetune your model.
When loading the model from the same checkpoint that you used for the preprocessing, remember to pass the `label2id`
and `id2label` maps that you created earlier from the dataset's metadata. Additionally, we specify `ignore_mismatched_sizes=True` to replace the existing classification head with a new one.
```py
>>> from transformers import AutoModelForObjectDetection
>>> model = AutoModelForObjectDetection.from_pretrained(
... MODEL_NAME,
... id2label=id2label,
... label2id=label2id,
... ignore_mismatched_sizes=True,
... )
```
In the [`TrainingArguments`] use `output_dir` to specify where to save your model, then configure hyperparameters as you see fit. For `num_train_epochs=30` training will take about 35 minutes in Google Colab T4 GPU, increase the number of epoch to get better results.
Important notes:
- Do not remove unused columns because this will drop the image column. Without the image column, you
can't create `pixel_values`. For this reason, set `remove_unused_columns` to `False`.
- Set `eval_do_concat_batches=False` to get proper evaluation results. Images have different number of target boxes, if batches are concatenated we will not be able to determine which boxes belongs to particular image.
If you wish to share your model by pushing to the Hub, set `push_to_hub` to `True` (you must be signed in to Hugging
Face to upload your model).
```py
>>> from transformers import TrainingArguments
>>> training_args = TrainingArguments(
... output_dir="detr_finetuned_cppe5",
... num_train_epochs=30,
... fp16=False,
... per_device_train_batch_size=8,
... dataloader_num_workers=4,
... learning_rate=5e-5,
... lr_scheduler_type="cosine",
... weight_decay=1e-4,
... max_grad_norm=0.01,
... metric_for_best_model="eval_map",
... greater_is_better=True,
... load_best_model_at_end=True,
... eval_strategy="epoch",
... save_strategy="epoch",
... save_total_limit=2,
... remove_unused_columns=False,
... eval_do_concat_batches=False,
... push_to_hub=True,
... )
```
Finally, bring everything together, and call [`~transformers.Trainer.train`]:
```py
>>> from transformers import Trainer
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=cppe5["train"],
... eval_dataset=cppe5["validation"],
... processing_class=image_processor,
... data_collator=collate_fn,
... compute_metrics=eval_compute_metrics_fn,
... )
>>> trainer.train()
```
<div>
<progress value='3210' max='3210' style='width:300px; height:20px; vertical-align: middle;'></progress>
[3210/3210 26:07, Epoch 30/30]
</div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: left;">
<th>Epoch</th>
<th>Training Loss</th>
<th>Validation Loss</th>
<th>Map</th>
<th>Map 50</th>
<th>Map 75</th>
<th>Map Small</th>
<th>Map Medium</th>
<th>Map Large</th>
<th>Mar 1</th>
<th>Mar 10</th>
<th>Mar 100</th>
<th>Mar Small</th>
<th>Mar Medium</th>
<th>Mar Large</th>
<th>Map Coverall</th>
<th>Mar 100 Coverall</th>
<th>Map Face Shield</th>
<th>Mar 100 Face Shield</th>
<th>Map Gloves</th>
<th>Mar 100 Gloves</th>
<th>Map Goggles</th>
<th>Mar 100 Goggles</th>
<th>Map Mask</th>
<th>Mar 100 Mask</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>No log</td>
<td>2.629903</td>
<td>0.008900</td>
<td>0.023200</td>
<td>0.006500</td>
<td>0.001300</td>
<td>0.002800</td>
<td>0.020500</td>
<td>0.021500</td>
<td>0.070400</td>
<td>0.101400</td>
<td>0.007600</td>
<td>0.106200</td>
<td>0.096100</td>
<td>0.036700</td>
<td>0.232000</td>
<td>0.000300</td>
<td>0.019000</td>
<td>0.003900</td>
<td>0.125400</td>
<td>0.000100</td>
<td>0.003100</td>
<td>0.003500</td>
<td>0.127600</td>
</tr>
<tr>
<td>2</td>
<td>No log</td>
<td>3.479864</td>
<td>0.014800</td>
<td>0.034600</td>
<td>0.010800</td>
<td>0.008600</td>
<td>0.011700</td>
<td>0.012500</td>
<td>0.041100</td>
<td>0.098700</td>
<td>0.130000</td>
<td>0.056000</td>
<td>0.062200</td>
<td>0.111900</td>
<td>0.053500</td>
<td>0.447300</td>
<td>0.010600</td>
<td>0.100000</td>
<td>0.000200</td>
<td>0.022800</td>
<td>0.000100</td>
<td>0.015400</td>
<td>0.009700</td>
<td>0.064400</td>
</tr>
<tr>
<td>3</td>
<td>No log</td>
<td>2.107622</td>
<td>0.041700</td>
<td>0.094000</td>
<td>0.034300</td>
<td>0.024100</td>
<td>0.026400</td>
<td>0.047400</td>
<td>0.091500</td>
<td>0.182800</td>
<td>0.225800</td>
<td>0.087200</td>
<td>0.199400</td>
<td>0.210600</td>
<td>0.150900</td>
<td>0.571200</td>
<td>0.017300</td>
<td>0.101300</td>
<td>0.007300</td>
<td>0.180400</td>
<td>0.002100</td>
<td>0.026200</td>
<td>0.031000</td>
<td>0.250200</td>
</tr>
<tr>
<td>4</td>
<td>No log</td>
<td>2.031242</td>
<td>0.055900</td>
<td>0.120600</td>
<td>0.046900</td>
<td>0.013800</td>
<td>0.038100</td>
<td>0.090300</td>
<td>0.105900</td>
<td>0.225600</td>
<td>0.266100</td>
<td>0.130200</td>
<td>0.228100</td>
<td>0.330000</td>
<td>0.191000</td>
<td>0.572100</td>
<td>0.010600</td>
<td>0.157000</td>
<td>0.014600</td>
<td>0.235300</td>
<td>0.001700</td>
<td>0.052300</td>
<td>0.061800</td>
<td>0.313800</td>
</tr>
<tr>
<td>5</td>
<td>3.889400</td>
<td>1.883433</td>
<td>0.089700</td>
<td>0.201800</td>
<td>0.067300</td>
<td>0.022800</td>
<td>0.065300</td>
<td>0.129500</td>
<td>0.136000</td>
<td>0.272200</td>
<td>0.303700</td>
<td>0.112900</td>
<td>0.312500</td>
<td>0.424600</td>
<td>0.300200</td>
<td>0.585100</td>
<td>0.032700</td>
<td>0.202500</td>
<td>0.031300</td>
<td>0.271000</td>
<td>0.008700</td>
<td>0.126200</td>
<td>0.075500</td>
<td>0.333800</td>
</tr>
<tr>
<td>6</td>
<td>3.889400</td>
<td>1.807503</td>
<td>0.118500</td>
<td>0.270900</td>
<td>0.090200</td>
<td>0.034900</td>
<td>0.076700</td>
<td>0.152500</td>
<td>0.146100</td>
<td>0.297800</td>
<td>0.325400</td>
<td>0.171700</td>
<td>0.283700</td>
<td>0.545900</td>
<td>0.396900</td>
<td>0.554500</td>
<td>0.043000</td>
<td>0.262000</td>
<td>0.054500</td>
<td>0.271900</td>
<td>0.020300</td>
<td>0.230800</td>
<td>0.077600</td>
<td>0.308000</td>
</tr>
<tr>
<td>7</td>
<td>3.889400</td>
<td>1.716169</td>
<td>0.143500</td>
<td>0.307700</td>
<td>0.123200</td>
<td>0.045800</td>
<td>0.097800</td>
<td>0.258300</td>
<td>0.165300</td>
<td>0.327700</td>
<td>0.352600</td>
<td>0.140900</td>
<td>0.336700</td>
<td>0.599400</td>
<td>0.442900</td>
<td>0.620700</td>
<td>0.069400</td>
<td>0.301300</td>
<td>0.081600</td>
<td>0.292000</td>
<td>0.011000</td>
<td>0.230800</td>
<td>0.112700</td>
<td>0.318200</td>
</tr>
<tr>
<td>8</td>
<td>3.889400</td>
<td>1.679014</td>
<td>0.153000</td>
<td>0.355800</td>
<td>0.127900</td>
<td>0.038700</td>
<td>0.115600</td>
<td>0.291600</td>
<td>0.176000</td>
<td>0.322500</td>
<td>0.349700</td>
<td>0.135600</td>
<td>0.326100</td>
<td>0.643700</td>
<td>0.431700</td>
<td>0.582900</td>
<td>0.069800</td>
<td>0.265800</td>
<td>0.088600</td>
<td>0.274600</td>
<td>0.028300</td>
<td>0.280000</td>
<td>0.146700</td>
<td>0.345300</td>
</tr>
<tr>
<td>9</td>
<td>3.889400</td>
<td>1.618239</td>
<td>0.172100</td>
<td>0.375300</td>
<td>0.137600</td>
<td>0.046100</td>
<td>0.141700</td>
<td>0.308500</td>
<td>0.194000</td>
<td>0.356200</td>
<td>0.386200</td>
<td>0.162400</td>
<td>0.359200</td>
<td>0.677700</td>
<td>0.469800</td>
<td>0.623900</td>
<td>0.102100</td>
<td>0.317700</td>
<td>0.099100</td>
<td>0.290200</td>
<td>0.029300</td>
<td>0.335400</td>
<td>0.160200</td>
<td>0.364000</td>
</tr>
<tr>
<td>10</td>
<td>1.599700</td>
<td>1.572512</td>
<td>0.179500</td>
<td>0.400400</td>
<td>0.147200</td>
<td>0.056500</td>
<td>0.141700</td>
<td>0.316700</td>
<td>0.213100</td>
<td>0.357600</td>
<td>0.381300</td>
<td>0.197900</td>
<td>0.344300</td>
<td>0.638500</td>
<td>0.466900</td>
<td>0.623900</td>
<td>0.101300</td>
<td>0.311400</td>
<td>0.104700</td>
<td>0.279500</td>
<td>0.051600</td>
<td>0.338500</td>
<td>0.173000</td>
<td>0.353300</td>
</tr>
<tr>
<td>11</td>
<td>1.599700</td>
<td>1.528889</td>
<td>0.192200</td>
<td>0.415000</td>
<td>0.160800</td>
<td>0.053700</td>
<td>0.150500</td>
<td>0.378000</td>
<td>0.211500</td>
<td>0.371700</td>
<td>0.397800</td>
<td>0.204900</td>
<td>0.374600</td>
<td>0.684800</td>
<td>0.491900</td>
<td>0.632400</td>
<td>0.131200</td>
<td>0.346800</td>
<td>0.122000</td>
<td>0.300900</td>
<td>0.038400</td>
<td>0.344600</td>
<td>0.177500</td>
<td>0.364400</td>
</tr>
<tr>
<td>12</td>
<td>1.599700</td>
<td>1.517532</td>
<td>0.198300</td>
<td>0.429800</td>
<td>0.159800</td>
<td>0.066400</td>
<td>0.162900</td>
<td>0.383300</td>
<td>0.220700</td>
<td>0.382100</td>
<td>0.405400</td>
<td>0.214800</td>
<td>0.383200</td>
<td>0.672900</td>
<td>0.469000</td>
<td>0.610400</td>
<td>0.167800</td>
<td>0.379700</td>
<td>0.119700</td>
<td>0.307100</td>
<td>0.038100</td>
<td>0.335400</td>
<td>0.196800</td>
<td>0.394200</td>
</tr>
<tr>
<td>13</td>
<td>1.599700</td>
<td>1.488849</td>
<td>0.209800</td>
<td>0.452300</td>
<td>0.172300</td>
<td>0.094900</td>
<td>0.171100</td>
<td>0.437800</td>
<td>0.222000</td>
<td>0.379800</td>
<td>0.411500</td>
<td>0.203800</td>
<td>0.397300</td>
<td>0.707500</td>
<td>0.470700</td>
<td>0.620700</td>
<td>0.186900</td>
<td>0.407600</td>
<td>0.124200</td>
<td>0.306700</td>
<td>0.059300</td>
<td>0.355400</td>
<td>0.207700</td>
<td>0.367100</td>
</tr>
<tr>
<td>14</td>
<td>1.599700</td>
<td>1.482210</td>
<td>0.228900</td>
<td>0.482600</td>
<td>0.187800</td>
<td>0.083600</td>
<td>0.191800</td>
<td>0.444100</td>
<td>0.225900</td>
<td>0.376900</td>
<td>0.407400</td>
<td>0.182500</td>
<td>0.384800</td>
<td>0.700600</td>
<td>0.512100</td>
<td>0.640100</td>
<td>0.175000</td>
<td>0.363300</td>
<td>0.144300</td>
<td>0.300000</td>
<td>0.083100</td>
<td>0.363100</td>
<td>0.229900</td>
<td>0.370700</td>
</tr>
<tr>
<td>15</td>
<td>1.326800</td>
<td>1.475198</td>
<td>0.216300</td>
<td>0.455600</td>
<td>0.174900</td>
<td>0.088500</td>
<td>0.183500</td>
<td>0.424400</td>
<td>0.226900</td>
<td>0.373400</td>
<td>0.404300</td>
<td>0.199200</td>
<td>0.396400</td>
<td>0.677800</td>
<td>0.496300</td>
<td>0.633800</td>
<td>0.166300</td>
<td>0.392400</td>
<td>0.128900</td>
<td>0.312900</td>
<td>0.085200</td>
<td>0.312300</td>
<td>0.205000</td>
<td>0.370200</td>
</tr>
<tr>
<td>16</td>
<td>1.326800</td>
<td>1.459697</td>
<td>0.233200</td>
<td>0.504200</td>
<td>0.192200</td>
<td>0.096000</td>
<td>0.202000</td>
<td>0.430800</td>
<td>0.239100</td>
<td>0.382400</td>
<td>0.412600</td>
<td>0.219500</td>
<td>0.403100</td>
<td>0.670400</td>
<td>0.485200</td>
<td>0.625200</td>
<td>0.196500</td>
<td>0.410100</td>
<td>0.135700</td>
<td>0.299600</td>
<td>0.123100</td>
<td>0.356900</td>
<td>0.225300</td>
<td>0.371100</td>
</tr>
<tr>
<td>17</td>
<td>1.326800</td>
<td>1.407340</td>
<td>0.243400</td>
<td>0.511900</td>
<td>0.204500</td>
<td>0.121000</td>
<td>0.215700</td>
<td>0.468000</td>
<td>0.246200</td>
<td>0.394600</td>
<td>0.424200</td>
<td>0.225900</td>
<td>0.416100</td>
<td>0.705200</td>
<td>0.494900</td>
<td>0.638300</td>
<td>0.224900</td>
<td>0.430400</td>
<td>0.157200</td>
<td>0.317900</td>
<td>0.115700</td>
<td>0.369200</td>
<td>0.224200</td>
<td>0.365300</td>
</tr>
<tr>
<td>18</td>
<td>1.326800</td>
<td>1.419522</td>
<td>0.245100</td>
<td>0.521500</td>
<td>0.210000</td>
<td>0.116100</td>
<td>0.211500</td>
<td>0.489900</td>
<td>0.255400</td>
<td>0.391600</td>
<td>0.419700</td>
<td>0.198800</td>
<td>0.421200</td>
<td>0.701400</td>
<td>0.501800</td>
<td>0.634200</td>
<td>0.226700</td>
<td>0.410100</td>
<td>0.154400</td>
<td>0.321400</td>
<td>0.105900</td>
<td>0.352300</td>
<td>0.236700</td>
<td>0.380400</td>
</tr>
<tr>
<td>19</td>
<td>1.158600</td>
<td>1.398764</td>
<td>0.253600</td>
<td>0.519200</td>
<td>0.213600</td>
<td>0.135200</td>
<td>0.207700</td>
<td>0.491900</td>
<td>0.257300</td>
<td>0.397300</td>
<td>0.428000</td>
<td>0.241400</td>
<td>0.401800</td>
<td>0.703500</td>
<td>0.509700</td>
<td>0.631100</td>
<td>0.236700</td>
<td>0.441800</td>
<td>0.155900</td>
<td>0.330800</td>
<td>0.128100</td>
<td>0.352300</td>
<td>0.237500</td>
<td>0.384000</td>
</tr>
<tr>
<td>20</td>
<td>1.158600</td>
<td>1.390591</td>
<td>0.248800</td>
<td>0.520200</td>
<td>0.216600</td>
<td>0.127500</td>
<td>0.211400</td>
<td>0.471900</td>
<td>0.258300</td>
<td>0.407000</td>
<td>0.429100</td>
<td>0.240300</td>
<td>0.407600</td>
<td>0.708500</td>
<td>0.505800</td>
<td>0.623400</td>
<td>0.235500</td>
<td>0.431600</td>
<td>0.150000</td>
<td>0.325000</td>
<td>0.125700</td>
<td>0.375400</td>
<td>0.227200</td>
<td>0.390200</td>
</tr>
<tr>
<td>21</td>
<td>1.158600</td>
<td>1.360608</td>
<td>0.262700</td>
<td>0.544800</td>
<td>0.222100</td>
<td>0.134700</td>
<td>0.230000</td>
<td>0.487500</td>
<td>0.269500</td>
<td>0.413300</td>
<td>0.436300</td>
<td>0.236200</td>
<td>0.419100</td>
<td>0.709300</td>
<td>0.514100</td>
<td>0.637400</td>
<td>0.257200</td>
<td>0.450600</td>
<td>0.165100</td>
<td>0.338400</td>
<td>0.139400</td>
<td>0.372300</td>
<td>0.237700</td>
<td>0.382700</td>
</tr>
<tr>
<td>22</td>
<td>1.158600</td>
<td>1.368296</td>
<td>0.262800</td>
<td>0.542400</td>
<td>0.236400</td>
<td>0.137400</td>
<td>0.228100</td>
<td>0.498500</td>
<td>0.266500</td>
<td>0.409000</td>
<td>0.433000</td>
<td>0.239900</td>
<td>0.418500</td>
<td>0.697500</td>
<td>0.520500</td>
<td>0.641000</td>
<td>0.257500</td>
<td>0.455700</td>
<td>0.162600</td>
<td>0.334800</td>
<td>0.140200</td>
<td>0.353800</td>
<td>0.233200</td>
<td>0.379600</td>
</tr>
<tr>
<td>23</td>
<td>1.158600</td>
<td>1.368176</td>
<td>0.264800</td>
<td>0.541100</td>
<td>0.233100</td>
<td>0.138200</td>
<td>0.223900</td>
<td>0.498700</td>
<td>0.272300</td>
<td>0.407400</td>
<td>0.434400</td>
<td>0.233100</td>
<td>0.418300</td>
<td>0.702000</td>
<td>0.524400</td>
<td>0.642300</td>
<td>0.262300</td>
<td>0.444300</td>
<td>0.159700</td>
<td>0.335300</td>
<td>0.140500</td>
<td>0.366200</td>
<td>0.236900</td>
<td>0.384000</td>
</tr>
<tr>
<td>24</td>
<td>1.049700</td>
<td>1.355271</td>
<td>0.269700</td>
<td>0.549200</td>
<td>0.239100</td>
<td>0.134700</td>
<td>0.229900</td>
<td>0.519200</td>
<td>0.274800</td>
<td>0.412700</td>
<td>0.437600</td>
<td>0.245400</td>
<td>0.417200</td>
<td>0.711200</td>
<td>0.523200</td>
<td>0.644100</td>
<td>0.272100</td>
<td>0.440500</td>
<td>0.166700</td>
<td>0.341500</td>
<td>0.137700</td>
<td>0.373800</td>
<td>0.249000</td>
<td>0.388000</td>
</tr>
<tr>
<td>25</td>
<td>1.049700</td>
<td>1.355180</td>
<td>0.272500</td>
<td>0.547900</td>
<td>0.243800</td>
<td>0.149700</td>
<td>0.229900</td>
<td>0.523100</td>
<td>0.272500</td>
<td>0.415700</td>
<td>0.442200</td>
<td>0.256200</td>
<td>0.420200</td>
<td>0.705800</td>
<td>0.523900</td>
<td>0.639600</td>
<td>0.271700</td>
<td>0.451900</td>
<td>0.166300</td>
<td>0.346900</td>
<td>0.153700</td>
<td>0.383100</td>
<td>0.247000</td>
<td>0.389300</td>
</tr>
<tr>
<td>26</td>
<td>1.049700</td>
<td>1.349337</td>
<td>0.275600</td>
<td>0.556300</td>
<td>0.246400</td>
<td>0.146700</td>
<td>0.234800</td>
<td>0.516300</td>
<td>0.274200</td>
<td>0.418300</td>
<td>0.440900</td>
<td>0.248700</td>
<td>0.418900</td>
<td>0.705800</td>
<td>0.523200</td>
<td>0.636500</td>
<td>0.274700</td>
<td>0.440500</td>
<td>0.172400</td>
<td>0.349100</td>
<td>0.155600</td>
<td>0.384600</td>
<td>0.252300</td>
<td>0.393800</td>
</tr>
<tr>
<td>27</td>
<td>1.049700</td>
<td>1.350782</td>
<td>0.275200</td>
<td>0.548700</td>
<td>0.246800</td>
<td>0.147300</td>
<td>0.236400</td>
<td>0.527200</td>
<td>0.280100</td>
<td>0.416200</td>
<td>0.442600</td>
<td>0.253400</td>
<td>0.424000</td>
<td>0.710300</td>
<td>0.526600</td>
<td>0.640100</td>
<td>0.273200</td>
<td>0.445600</td>
<td>0.167000</td>
<td>0.346900</td>
<td>0.160100</td>
<td>0.387700</td>
<td>0.249200</td>
<td>0.392900</td>
</tr>
<tr>
<td>28</td>
<td>1.049700</td>
<td>1.346533</td>
<td>0.277000</td>
<td>0.552800</td>
<td>0.252900</td>
<td>0.147400</td>
<td>0.240000</td>
<td>0.527600</td>
<td>0.280900</td>
<td>0.420900</td>
<td>0.444100</td>
<td>0.255500</td>
<td>0.424500</td>
<td>0.711200</td>
<td>0.530200</td>
<td>0.646800</td>
<td>0.277400</td>
<td>0.441800</td>
<td>0.170900</td>
<td>0.346900</td>
<td>0.156600</td>
<td>0.389200</td>
<td>0.249600</td>
<td>0.396000</td>
</tr>
<tr>
<td>29</td>
<td>0.993700</td>
<td>1.346575</td>
<td>0.277100</td>
<td>0.554800</td>
<td>0.252900</td>
<td>0.148400</td>
<td>0.239700</td>
<td>0.523600</td>
<td>0.278400</td>
<td>0.420000</td>
<td>0.443300</td>
<td>0.256300</td>
<td>0.424000</td>
<td>0.705600</td>
<td>0.529600</td>
<td>0.647300</td>
<td>0.273900</td>
<td>0.439200</td>
<td>0.174300</td>
<td>0.348700</td>
<td>0.157600</td>
<td>0.386200</td>
<td>0.250100</td>
<td>0.395100</td>
</tr>
<tr>
<td>30</td>
<td>0.993700</td>
<td>1.346446</td>
<td>0.277400</td>
<td>0.554700</td>
<td>0.252700</td>
<td>0.147900</td>
<td>0.240800</td>
<td>0.523600</td>
<td>0.278800</td>
<td>0.420400</td>
<td>0.443300</td>
<td>0.256100</td>
<td>0.424200</td>
<td>0.705500</td>
<td>0.530100</td>
<td>0.646800</td>
<td>0.275600</td>
<td>0.440500</td>
<td>0.174500</td>
<td>0.348700</td>
<td>0.157300</td>
<td>0.386200</td>
<td>0.249200</td>
<td>0.394200</td>
</tr>
</tbody>
</table><p>
If you have set `push_to_hub` to `True` in the `training_args`, the training checkpoints are pushed to the
Hugging Face Hub. Upon training completion, push the final model to the Hub as well by calling the [`~transformers.Trainer.push_to_hub`] method.
```py
>>> trainer.push_to_hub()
```
## Evaluate
```py
>>> from pprint import pprint
>>> metrics = trainer.evaluate(eval_dataset=cppe5["test"], metric_key_prefix="test")
>>> pprint(metrics)
{'epoch': 30.0,
'test_loss': 1.0877351760864258,
'test_map': 0.4116,
'test_map_50': 0.741,
'test_map_75': 0.3663,
'test_map_Coverall': 0.5937,
'test_map_Face_Shield': 0.5863,
'test_map_Gloves': 0.3416,
'test_map_Goggles': 0.1468,
'test_map_Mask': 0.3894,
'test_map_large': 0.5637,
'test_map_medium': 0.3257,
'test_map_small': 0.3589,
'test_mar_1': 0.323,
'test_mar_10': 0.5237,
'test_mar_100': 0.5587,
'test_mar_100_Coverall': 0.6756,
'test_mar_100_Face_Shield': 0.7294,
'test_mar_100_Gloves': 0.4721,
'test_mar_100_Goggles': 0.4125,
'test_mar_100_Mask': 0.5038,
'test_mar_large': 0.7283,
'test_mar_medium': 0.4901,
'test_mar_small': 0.4469,
'test_runtime': 1.6526,
'test_samples_per_second': 17.548,
'test_steps_per_second': 2.42}
```
These results can be further improved by adjusting the hyperparameters in [`TrainingArguments`]. Give it a go!
## Inference
Now that you have finetuned a model, evaluated it, and uploaded it to the Hugging Face Hub, you can use it for inference.
```py
>>> import torch
>>> import requests
>>> from PIL import Image, ImageDraw
>>> from transformers import AutoImageProcessor, AutoModelForObjectDetection
>>> url = "https://images.pexels.com/photos/8413299/pexels-photo-8413299.jpeg?auto=compress&cs=tinysrgb&w=630&h=375&dpr=2"
>>> image = Image.open(requests.get(url, stream=True).raw)
```
Load model and image processor from the Hugging Face Hub (skip to use already trained in this session):
```py
>>> from transformers import infer_device
>>> device = infer_device()
>>> model_repo = "qubvel-hf/detr_finetuned_cppe5"
>>> image_processor = AutoImageProcessor.from_pretrained(model_repo)
>>> model = AutoModelForObjectDetection.from_pretrained(model_repo)
>>> model = model.to(device)
```
And detect bounding boxes:
```py
>>> with torch.no_grad():
... inputs = image_processor(images=[image], return_tensors="pt")
... outputs = model(**inputs.to(device))
... target_sizes = torch.tensor([[image.size[1], image.size[0]]])
... results = image_processor.post_process_object_detection(outputs, threshold=0.3, target_sizes=target_sizes)[0]
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... print(
... f"Detected {model.config.id2label[label.item()]} with confidence "
... f"{round(score.item(), 3)} at location {box}"
... )
Detected Gloves with confidence 0.683 at location [244.58, 124.33, 300.35, 185.13]
Detected Mask with confidence 0.517 at location [143.73, 64.58, 219.57, 125.89]
Detected Gloves with confidence 0.425 at location [179.15, 155.57, 262.4, 226.35]
Detected Coverall with confidence 0.407 at location [307.13, -1.18, 477.82, 318.06]
Detected Coverall with confidence 0.391 at location [68.61, 126.66, 309.03, 318.89]
```
Let's plot the result:
```py
>>> draw = ImageDraw.Draw(image)
>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
... box = [round(i, 2) for i in box.tolist()]
... x, y, x2, y2 = tuple(box)
... draw.rectangle((x, y, x2, y2), outline="red", width=1)
... draw.text((x, y), model.config.id2label[label.item()], fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://i.imgur.com/oDUqD0K.png" alt="Object detection result on a new image"/>
</div>
| transformers/docs/source/en/tasks/object_detection.md/0 | {
"file_path": "transformers/docs/source/en/tasks/object_detection.md",
"repo_id": "transformers",
"token_count": 23685
} | 406 |
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# LiteRT
[LiteRT](https://ai.google.dev/edge/litert) (previously known as TensorFlow Lite) is a high-performance runtime designed for on-device machine learning.
The [Optimum](https://huggingface.co/docs/optimum/index) library exports a model to LiteRT for [many architectures](https://huggingface.co/docs/optimum/exporters/onnx/overview).
The benefits of exporting to LiteRT include the following.
- Low-latency, privacy-focused, no internet connectivity required, and reduced model size and power consumption for on-device machine learning.
- Broad platform, model framework, and language support.
- Hardware acceleration for GPUs and Apple Silicon.
Export a Transformers model to LiteRT with the Optimum CLI.
Run the command below to install Optimum and the [exporters](https://huggingface.co/docs/optimum/exporters/overview) module for LiteRT.
```bash
pip install optimum[exporters-tf]
```
> [!TIP]
> Refer to the [Export a model to TFLite with optimum.exporters.tflite](https://huggingface.co/docs/optimum/main/en/exporters/tflite/usage_guides/export_a_model) guide for all available arguments or with the command below.
> ```bash
> optimum-cli export tflite --help
> ```
Set the `--model` argument to export a from the Hub.
```bash
optimum-cli export tflite --model google-bert/bert-base-uncased --sequence_length 128 bert_tflite/
```
You should see logs indicating the progress and showing where the resulting `model.tflite` is saved.
```bash
Validating TFLite model...
-[✓] TFLite model output names match reference model (logits)
- Validating TFLite Model output "logits":
-[✓] (1, 128, 30522) matches (1, 128, 30522)
-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
- logits: max diff = 5.817413330078125e-05.
The exported model was saved at: bert_tflite
```
For local models, make sure the model weights and tokenizer files are saved in the same directory, for example `local_path`. Pass the directory to the `--model` argument and use `--task` to indicate the [task](https://huggingface.co/docs/optimum/exporters/task_manager) a model can perform. If `--task` isn't provided, the model architecture without a task-specific head is used.
```bash
optimum-cli export tflite --model local_path --task question-answering google-bert/bert-base-uncased --sequence_length 128 bert_tflite/
```
| transformers/docs/source/en/tflite.md/0 | {
"file_path": "transformers/docs/source/en/tflite.md",
"repo_id": "transformers",
"token_count": 953
} | 407 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# BERTología
Hay un creciente campo de estudio empeñado en la investigación del funcionamiento interno de los transformers de gran escala como BERT
(que algunos llaman "BERTología"). Algunos buenos ejemplos de este campo son:
- BERT Rediscovers the Classical NLP Pipeline por Ian Tenney, Dipanjan Das, Ellie Pavlick:
https://huggingface.co/papers/1905.05950
- Are Sixteen Heads Really Better than One? por Paul Michel, Omer Levy, Graham Neubig: https://huggingface.co/papers/1905.10650
- What Does BERT Look At? An Analysis of BERT's Attention por Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D.
Manning: https://huggingface.co/papers/1906.04341
- CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure: https://huggingface.co/papers/2210.04633
Para asistir al desarrollo de este nuevo campo, hemos incluido algunas features adicionales en los modelos BERT/GPT/GPT-2 para
ayudar a acceder a las representaciones internas, principalmente adaptado de la gran obra de Paul Michel
(https://huggingface.co/papers/1905.10650):
- accediendo a todos los hidden-states de BERT/GPT/GPT-2,
- accediendo a todos los pesos de atención para cada head de BERT/GPT/GPT-2,
- adquiriendo los valores de salida y gradientes de las heads para poder computar la métrica de importancia de las heads y realizar la poda de heads como se explica
en https://huggingface.co/papers/1905.10650.
Para ayudarte a entender y usar estas features, hemos añadido un script específico de ejemplo: [bertology.py](https://github.com/huggingface/transformers-research-projects/tree/main/bertology/run_bertology.py) mientras extraes información y cortas un modelo pre-entrenado en
GLUE.
| transformers/docs/source/es/bertology.md/0 | {
"file_path": "transformers/docs/source/es/bertology.md",
"repo_id": "transformers",
"token_count": 764
} | 408 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Perplejidad de los modelos de longitud fija
[[open-in-colab]]
La perplejidad, perplexity en inglés (PPL), es una de las métricas más comunes para evaluar modelos de lenguaje. Antes de sumergirnos, debemos tener en cuenta que esta métrica se aplica específicamente a modelos de lenguaje clásicos (a veces llamados modelos autorregresivos o causales) y no está bien definida para modelos de lenguaje enmascarados como BERT (ver [resumen del modelo](model_summary)).
La perplejidad se define como la media negativa exponenciada del log-likelihood de una secuencia. Si tenemos una secuencia tokenizada \\(X = (x_0, x_1, \dots, x_t)\\), entonces la perplejidad de \\(X\\) es,
$$\text{PPL}(X) = \exp \left\{ {-\frac{1}{t}\sum_i^t \log p_\theta (x_i|x_{<i}) } \right\}$$
donde \\(\log p_\theta (x_i|x_{<i})\\) es el log-likelihood del token i-ésimo condicionado a los tokens precedentes \\(x_{<i}\\) según nuestro modelo. De manera intuitiva, se puede pensar en esto como una evaluación de la capacidad del modelo para predecir de manera uniforme entre el conjunto de tokens especificados en un corpus. Es importante destacar que el procedimiento de tokenización tiene un impacto directo en la perplejidad de un modelo, lo cual siempre debe tenerse en cuenta al comparar diferentes modelos.
Esto también es equivalente a la exponenciación de la entropía cruzada entre los datos y las predicciones del modelo. Para obtener más intuición sobre la perplejidad y su relación con los Bits Por Carácter (BPC) y la compresión de datos, echa un vistazo a esta [fantástica publicación en el blog de "The Gradient"](https://thegradient.pub/understanding-evaluation-metrics-for-language-models/).
## Cálculo de PPL con modelos de longitud fija
Si no estuviéramos limitados por el tamaño del contexto de un modelo, evaluaríamos la perplejidad (PPL) del modelo auto regresivamente factorizando una secuencia y condicionándonos en toda la subsecuencia precedente en cada paso, como se muestra a continuación.
<img width="600" alt="Full decomposition of a sequence with unlimited context length" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_full.gif"/>
Sin embargo, al trabajar con modelos aproximados, generalmente tenemos una restricción en la cantidad de tokens que el modelo puede procesar. La versión más grande de [GPT-2](model_doc/gpt2), por ejemplo, tiene una longitud fija de 1024 tokens, por lo que no podemos calcular \\(p_\theta(x_t|x_{<t})\\) directamente cuando \\(t\\) es mayor que 1024.
En cambio, la secuencia se divide típicamente en subsecuencias iguales al tamaño máximo de entrada del modelo. Si el tamaño máximo de entrada, de un modelo es \\(k\\), entonces aproximamos la probabilidad de un token \\(x_t\\) condicionándonos solo en los \\(k-1\\) tokens que lo preceden en lugar de todo el contexto. Al evaluar la perplejidad del modelo en una secuencia, un enfoque tentador pero sub óptimo es dividir la secuencia en fragmentos independientes y sumar los log-likelihood descompuestos de cada segmento de manera independiente.
<img width="600" alt="Suboptimal PPL not taking advantage of full available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_chunked.gif"/>
Esto es rápido de calcular, ya que la perplejidad de cada segmento se puede calcular en un solo pase hacia adelante, pero sirve como una aproximación pobre de la perplejidad completamente factorizada y generalmente dará como resultado una PPL más alta (peor) porque el modelo tendrá menos contexto en la mayoría de los pasos de predicción.
En cambio, la PPL de modelos de longitud fija debería evaluarse con una estrategia de ventana deslizante. Esto implica deslizar repetidamente la ventana de contexto para que el modelo tenga más contexto al hacer cada predicción.
<img width="600" alt="Sliding window PPL taking advantage of all available context" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ppl_sliding.gif"/>
Esta es una aproximación más cercana a la verdadera descomposición de la probabilidad de la secuencia y generalmente dará como resultado una puntuación más favorable. La desventaja es que requiere un pase hacia adelante separado para cada token en el corpus. Un buen compromiso práctico es emplear una ventana deslizante estratificada, moviendo el contexto con pasos más grandes en lugar de deslizarse de 1 token a la vez. Esto permite que la computación avance mucho más rápido, mientras le da al modelo un contexto amplio para hacer
predicciones en cada paso.
## Ejemplo: Cálculo de la perplejidad con GPT-2 en 🤗 Transformers
Demostremos este proceso con GPT-2.
```python
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
device = "cuda"
model_id = "openai-community/gpt2-large"
model = GPT2LMHeadModel.from_pretrained(model_id).to(device)
tokenizer = GPT2TokenizerFast.from_pretrained(model_id)
```
Carguemos el conjunto de datos WikiText-2 y evaluemos la perplejidad utilizando algunas estrategias de ventana deslizante diferentes. Dado que este conjunto de datos es pequeño y solo estamos realizando un pase hacia adelante sobre el conjunto, podemos cargar y codificar todo el conjunto de datos en la memoria.
```python
from datasets import load_dataset
test = load_dataset("wikitext", "wikitext-2-raw-v1", split="test")
encodings = tokenizer("\n\n".join(test["text"]), return_tensors="pt")
```
Con 🤗 Transformers, simplemente podemos pasar los `input_ids` como las `labels` a nuestro modelo, y la media negativa del log-likelihood para cada token se devuelve como la pérdida. Sin embargo, con nuestro enfoque de ventana deslizante, hay superposición en los tokens que pasamos al modelo en cada iteración. No queremos que el log-likelihood de los tokens que estamos tratando solo como contexto se incluya en nuestra pérdida, por lo que podemos establecer estos objetivos en `-100` para que se ignoren. El siguiente es un ejemplo de cómo podríamos hacer esto con un paso de `512`. Esto significa que el modelo tendrá al menos `512` tokens como contexto al calcular el log-likelihood condicional de cualquier token (siempre que haya `512` tokens precedentes disponibles para condicionar).
```python
import torch
from tqdm import tqdm
max_length = model.config.n_positions
stride = 512
seq_len = encodings.input_ids.size(1)
nlls = []
prev_end_loc = 0
for begin_loc in tqdm(range(0, seq_len, stride)):
end_loc = min(begin_loc + max_length, seq_len)
trg_len = end_loc - prev_end_loc # puede ser diferente del paso en el último bucle
input_ids = encodings.input_ids[:, begin_loc:end_loc].to(device)
target_ids = input_ids.clone()
target_ids[:, :-trg_len] = -100
with torch.no_grad():
outputs = model(input_ids, labels=target_ids)
# la pérdida se calcula utilizando CrossEntropyLoss, que promedia las etiquetas válidas
# N.B. el modelo solo calcula la pérdida sobre trg_len - 1 etiquetas, porque desplaza las etiqueta internamente
# a la izquierda por 1.
neg_log_likelihood = outputs.loss
nlls.append(neg_log_likelihood)
prev_end_loc = end_loc
if end_loc == seq_len:
break
ppl = torch.exp(torch.stack(nlls).mean())
```
Ejecuta esto con la longitud de paso igual a la longitud máxima de entrada es equivalente a la estrategia sub óptima,
sin ventana deslizante, que discutimos anteriormente. Cuanto menor sea el paso, más contexto tendrá el modelo para
realizar cada predicción y, por lo general, mejor será la perplejidad informada.
Cuando ejecutamos lo anterior con `stride = 1024`, es decir, sin superposición, la PPL resultante es `19.44`, que es
aproximadamente la misma que la `19.93` informada en el artículo de GPT-2. Al utilizar `stride = 512` y, por lo tanto,
emplear nuestra estrategia de ventana deslizante, esto disminuye a `16.45`. Esto no solo es una puntuación más favorable, sino que se calcula de una manera más cercana a la verdadera descomposición autorregresiva de la probabilidad de una secuencia.
| transformers/docs/source/es/perplexity.md/0 | {
"file_path": "transformers/docs/source/es/perplexity.md",
"repo_id": "transformers",
"token_count": 3119
} | 409 |
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# Selección múltiple
La tarea de selección múltiple es parecida a la de responder preguntas, con la excepción de que se dan varias opciones de respuesta junto con el contexto. El modelo se entrena para escoger la respuesta correcta
entre varias opciones a partir del contexto dado.
Esta guía te mostrará como hacerle fine-tuning a [BERT](https://huggingface.co/google-bert/bert-base-uncased) en la configuración `regular` del dataset [SWAG](https://huggingface.co/datasets/swag), de forma
que seleccione la mejor respuesta a partir de varias opciones y algún contexto.
## Cargar el dataset SWAG
Carga el dataset SWAG con la biblioteca 🤗 Datasets:
```py
>>> from datasets import load_dataset
>>> swag = load_dataset("swag", "regular")
```
Ahora, échale un vistazo a un ejemplo del dataset:
```py
>>> swag["train"][0]
{'ending0': 'passes by walking down the street playing their instruments.',
'ending1': 'has heard approaching them.',
'ending2': "arrives and they're outside dancing and asleep.",
'ending3': 'turns the lead singer watches the performance.',
'fold-ind': '3416',
'gold-source': 'gold',
'label': 0,
'sent1': 'Members of the procession walk down the street holding small horn brass instruments.',
'sent2': 'A drum line',
'startphrase': 'Members of the procession walk down the street holding small horn brass instruments. A drum line',
'video-id': 'anetv_jkn6uvmqwh4'}
```
Los campos `sent1` y `sent2` muestran cómo comienza una oración, y cada campo `ending` indica cómo podría terminar. Dado el comienzo de la oración, el modelo debe escoger el final de oración correcto indicado por el campo `label`.
## Preprocesmaiento
Carga el tokenizer de BERT para procesar el comienzo de cada oración y los cuatro finales posibles:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
```
La función de preprocesmaiento debe hacer lo siguiente:
1. Hacer cuatro copias del campo `sent1` de forma que se pueda combinar cada una con el campo `sent2` para recrear la forma en que empieza la oración.
2. Combinar `sent2` con cada uno de los cuatro finales de oración posibles.
3. Aplanar las dos listas para que puedas tokenizarlas, y luego des-aplanarlas para que cada ejemplo tenga los campos `input_ids`, `attention_mask` y `labels` correspondientes.
```py
>>> ending_names = ["ending0", "ending1", "ending2", "ending3"]
>>> def preprocess_function(examples):
... first_sentences = [[context] * 4 for context in examples["sent1"]]
... question_headers = examples["sent2"]
... second_sentences = [
... [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
... ]
... first_sentences = sum(first_sentences, [])
... second_sentences = sum(second_sentences, [])
... tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True)
... return {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
```
Usa la función [`~datasets.Dataset.map`] de 🤗 Datasets para aplicarle la función de preprocesamiento al dataset entero. Puedes acelerar la función `map` haciendo `batched=True` para procesar varios elementos del dataset a la vez.
```py
tokenized_swag = swag.map(preprocess_function, batched=True)
```
Para crear un lote de ejemplos para selección múltiple, este también le *añadirá relleno de manera dinámica* a tu texto y a las etiquetas para que tengan la longitud del elemento más largo en su lote, de forma que tengan una longitud uniforme. Aunque es posible rellenar el texto en la función `tokenizer` haciendo
`padding=True`, el rellenado dinámico es más eficiente.
El [`DataCollatorForMultipleChoice`] aplanará todas las entradas del modelo, les aplicará relleno y luego des-aplanará los resultados.
```py
>>> from transformers import DataCollatorForMultipleChoice
>>> collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
```
## Entrenamiento
<frameworkcontent>
<pt>
Carga el modelo BERT con [`AutoModelForMultipleChoice`]:
```py
>>> from transformers import AutoModelForMultipleChoice, TrainingArguments, Trainer
>>> model = AutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
```
<Tip>
Para familiarizarte con el fine-tuning con [`Trainer`], ¡mira el tutorial básico [aquí](../training#finetune-with-trainer)!
</Tip>
En este punto, solo quedan tres pasos:
1. Definir tus hiperparámetros de entrenamiento en [`TrainingArguments`].
2. Pasarle los argumentos del entrenamiento al [`Trainer`] jnto con el modelo, el dataset, el tokenizer y el collator de datos.
3. Invocar el método [`~Trainer.train`] para realizar el fine-tuning del modelo.
```py
>>> training_args = TrainingArguments(
... output_dir="./results",
... eval_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=3,
... weight_decay=0.01,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_swag["train"],
... eval_dataset=tokenized_swag["validation"],
... processing_class=tokenizer,
... data_collator=collator,
... )
>>> trainer.train()
```
</pt>
<tf>
Para realizar el fine-tuning de un modelo en TensorFlow, primero convierte tus datasets al formato `tf.data.Dataset` con el método [`~TFPreTrainedModel.prepare_tf_dataset`].
```py
>>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_swag["train"],
... shuffle=True,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_swag["validation"],
... shuffle=False,
... batch_size=batch_size,
... collate_fn=data_collator,
... )
```
<Tip>
Para familiarizarte con el fine-tuning con Keras, ¡mira el tutorial básico [aquí](training#finetune-with-keras)!
</Tip>
Prepara una función de optimización, un programa para la tasa de aprendizaje y algunos hiperparámetros de entrenamiento:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 2
>>> total_train_steps = (len(tokenized_swag["train"]) // batch_size) * num_train_epochs
>>> optimizer, schedule = create_optimizer(init_lr=5e-5, num_warmup_steps=0, num_train_steps=total_train_steps)
```
Carga el modelo BERT con [`TFAutoModelForMultipleChoice`]:
```py
>>> from transformers import TFAutoModelForMultipleChoice
>>> model = TFAutoModelForMultipleChoice.from_pretrained("google-bert/bert-base-uncased")
```
Configura el modelo para entrenarlo con [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
```py
>>> model.compile(optimizer=optimizer)
```
Invoca el método [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) para realizar el fine-tuning del modelo:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)
```
</tf>
</frameworkcontent>
| transformers/docs/source/es/tasks/multiple_choice.md/0 | {
"file_path": "transformers/docs/source/es/tasks/multiple_choice.md",
"repo_id": "transformers",
"token_count": 2812
} | 410 |
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# Ce que 🤗 Transformers peut faire
🤗 Transformers est une bibliothèque de modèles préentraînés à la pointe de la technologie pour les tâches de traitement du langage naturel (NLP), de vision par ordinateur et de traitement audio et de la parole. Non seulement la bibliothèque contient des modèles Transformer, mais elle inclut également des modèles non-Transformer comme des réseaux convolutionnels modernes pour les tâches de vision par ordinateur. Si vous regardez certains des produits grand public les plus populaires aujourd'hui, comme les smartphones, les applications et les téléviseurs, il est probable qu'une technologie d'apprentissage profond soit derrière. Vous souhaitez supprimer un objet de fond d'une photo prise avec votre smartphone ? C'est un exemple de tâche de segmentation panoptique (ne vous inquiétez pas si vous ne savez pas encore ce que cela signifie, nous le décrirons dans les sections suivantes !).
Cette page fournit un aperçu des différentes tâches de traitement de la parole et de l'audio, de vision par ordinateur et de NLP qui peuvent être résolues avec la bibliothèque 🤗 Transformers en seulement trois lignes de code !
## Audio
Les tâches de traitement audio et de la parole sont légèrement différentes des autres modalités principalement parce que l'audio en tant que donnée d'entrée est un signal continu. Contrairement au texte, un signal audio brut ne peut pas discrétisé de la manière dont une phrase peut être divisée en mots. Pour contourner cela, le signal audio brut est généralement échantillonné à intervalles réguliers. Si vous prenez plus d'échantillons dans un intervalle, le taux d'échantillonnage est plus élevé et l'audio ressemble davantage à la source audio originale.
Les approches précédentes prétraitaient l'audio pour en extraire des caractéristiques utiles. Il est maintenant plus courant de commencer les tâches de traitement audio et de la parole en donnant directement le signal audio brut à un encodeur de caractéristiques (*feature encoder* en anglais) pour extraire une représentation de l'audio. Cela correspond à l'étape de prétraitement et permet au modèle d'apprendre les caractéristiques les plus essentielles du signal.
### Classification audio
La classification audio est une tâche qui consiste à attribuer une classe, parmi un ensemble de classes prédéfini, à un audio. La classification audio englobe de nombreuses applications spécifiques, dont certaines incluent :
* la classification d'environnements sonores : attribuer une classe (catégorie) à l'audio pour indiquer l'environnement associé, tel que "bureau", "plage" ou "stade".
* la détection d'événements sonores : étiqueter l'audio avec une étiquette d'événement sonore ("klaxon de voiture", "appel de baleine", "verre brisé")
* l'identification d'éléments sonores : attribuer des tags (*étiquettes* en français) à l'audio pour marquer des sons spécifiques, comme "chant des oiseaux" ou "identification du locuteur lors d'une réunion".
* la classification musicale : attribuer un genre à la musique, comme "metal", "hip-hop" ou "country".
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="audio-classification", model="superb/hubert-base-superb-er")
>>> preds = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4532, 'label': 'hap'},
{'score': 0.3622, 'label': 'sad'},
{'score': 0.0943, 'label': 'neu'},
{'score': 0.0903, 'label': 'ang'}]
```
### Reconnaissance vocale
La reconnaissance vocale (*Automatic Speech Recognition* ou ASR en anglais) transcrit la parole en texte. C'est l'une des tâches audio les plus courantes en partie parce que la parole est une forme de communication la plus naturelle pour nous, humains. Aujourd'hui, les systèmes ASR sont intégrés dans des produits technologiques "intelligents" comme les enceintes, les téléphones et les voitures. Il est désormais possible de demander à nos assistants virtuels de jouer de la musique, de définir des rappels et de nous indiquer la météo.
Mais l'un des principaux défis auxquels les architectures Transformer contribuent à résoudre est celui des langues à faibles ressources, c'est-à-dire des langues pour lesquelles il existe peu de données étiquetées. En préentraînant sur de grandes quantités de données vocales d'un autre language plus ou moins similaire, le réglage fin (*fine-tuning* en anglais) du modèle avec seulement une heure de données vocales étiquetées dans une langue à faibles ressources peut tout de même produire des résultats de haute qualité comparés aux systèmes ASR précédents entraînés sur 100 fois plus de données étiquetées.
```py
>>> from transformers import pipeline
>>> transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-small")
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
```
## Vision par ordinateur
L'une des premières réussites en vision par ordinateur a été la reconnaissance des numéros de code postal à l'aide d'un [réseau de neurones convolutionnel (CNN)](glossary#convolution). Une image est composée de pixels, chacun ayant une valeur numérique, ce qui permet de représenter facilement une image sous forme de matrice de valeurs de pixels. Chaque combinaison de valeurs de pixels correspond aux couleurs d'une image.
Il existe deux approches principales pour résoudre les tâches de vision par ordinateur :
1. Utiliser des convolutions pour apprendre les caractéristiques hiérarchiques d'une image, des détails de bas niveau aux éléments abstraits de plus haut niveau.
2. Diviser l'image en morceaux (*patches* en anglais) et utiliser un Transformer pour apprendre progressivement comment chaque morceau est lié aux autres pour former l'image complète. Contrairement à l'approche ascendante des CNNs, cette méthode ressemble à un processus où l'on démarre avec une image floue pour ensuite la mettre au point petit à petit.
### Classification d'images
La classification d'images consiste à attribuer une classe, parmi un ensemble de classes prédéfini, à toute une image. Comme pour la plupart des tâches de classification, les cas d'utilisation pratiques sont nombreux, notamment :
- Santé : classification d'images médicales pour détecter des maladies ou surveiller l'état de santé des patients.
- Environnement : classification d'images satellites pour suivre la déforestation, aider à la gestion des terres ou détecter les incendies de forêt.
- Agriculture : classification d'images de cultures pour surveiller la santé des plantes ou des images satellites pour analyser l'utilisation des terres.
- Écologie : classification d'images d'espèces animales ou végétales pour suivre les populations fauniques ou les espèces menacées.
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="image-classification")
>>> preds = classifier(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> print(*preds, sep="\n")
{'score': 0.4335, 'label': 'lynx, catamount'}
{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
{'score': 0.0239, 'label': 'Egyptian cat'}
{'score': 0.0229, 'label': 'tiger cat'}
```
### Détection d'objets
La détection d'objets, à la différence de la classification d'images, identifie plusieurs objets dans une image ainsi que leurs positions, généralement définies par des boîtes englobantes (*bounding boxes* en anglais). Voici quelques exemples d'applications :
- Véhicules autonomes : détection des objets de la circulation, tels que les véhicules, piétons et feux de signalisation.
- Télédétection : surveillance des catastrophes, planification urbaine et prévisions météorologiques.
- Détection de défauts : identification des fissures ou dommages structurels dans les bâtiments, ainsi que des défauts de fabrication.
```py
>>> from transformers import pipeline
>>> detector = pipeline(task="object-detection")
>>> preds = detector(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"], "box": pred["box"]} for pred in preds]
>>> preds
[{'score': 0.9865,
'label': 'cat',
'box': {'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}]
```
### Segmentation d'images
La segmentation d'images est une tâche qui consiste à attribuer une classe à chaque pixel d'une image, ce qui la rend plus précise que la détection d'objets, qui se limite aux boîtes englobantes (*bounding boxes* en anglais). Elle permet ainsi de détecter les objets à la précision du pixel. Il existe plusieurs types de segmentation d'images :
- Segmentation d'instances : en plus de classifier un objet, elle identifie chaque instance distincte d'un même objet (par exemple, "chien-1", "chien-2").
- Segmentation panoptique : combine segmentation sémantique et segmentation d'instances, attribuant à chaque pixel une classe sémantique **et** une instance spécifique.
Ces techniques sont utiles pour les véhicules autonomes, qui doivent cartographier leur environnement pixel par pixel pour naviguer en toute sécurité autour des piétons et des véhicules. Elles sont également précieuses en imagerie médicale, où la précision au niveau des pixels permet de détecter des anomalies cellulaires ou des caractéristiques d'organes. Dans le commerce en ligne, la segmentation est utilisée pour des essayages virtuels de vêtements ou des expériences de réalité augmentée, en superposant des objets virtuels sur des images du monde réel via la caméra.
```py
>>> from transformers import pipeline
>>> segmenter = pipeline(task="image-segmentation")
>>> preds = segmenter(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> print(*preds, sep="\n")
{'score': 0.9879, 'label': 'LABEL_184'}
{'score': 0.9973, 'label': 'snow'}
{'score': 0.9972, 'label': 'cat'}
```
### Estimation de la profondeur
L'estimation de la profondeur consiste à prédire la distance de chaque pixel d'une image par rapport à la caméra. Cette tâche est cruciale pour comprendre et reconstruire des scènes réelles. Par exemple, pour les voitures autonomes, il est essentiel de déterminer la distance des objets tels que les piétons, les panneaux de signalisation et les autres véhicules pour éviter les collisions. L'estimation de la profondeur permet également de créer des modèles 3D à partir d'images 2D, ce qui est utile pour générer des représentations détaillées de structures biologiques ou de bâtiments.
Il existe deux principales approches pour estimer la profondeur :
- Stéréo : la profondeur est estimée en comparant deux images d'une même scène prises sous des angles légèrement différents.
- Monoculaire : la profondeur est estimée à partir d'une seule image.
```py
>>> from transformers import pipeline
>>> depth_estimator = pipeline(task="depth-estimation")
>>> preds = depth_estimator(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
```
## Traitement du langage naturel
Les tâches de traitement du langage naturel (*Natural Language Processing* ou *NLP* en anglais) sont courantes car le texte est une forme naturelle de communication pour nous. Pour qu'un modèle puisse traiter le texte, celui-ci doit être *tokenisé*, c'est-à-dire divisé en mots ou sous-mots appelés "*tokens*", puis converti en nombres. Ainsi, une séquence de texte peut être représentée comme une séquence de nombres, qui peut ensuite être utilisée comme données d'entrée pour un modèle afin de résoudre diverses tâches de traitement du langage naturel.
### Classification de texte
La classification de texte attribue une classe à une séquence de texte (au niveau d'une phrase, d'un paragraphe ou d'un document) à partir d'un ensemble de classes prédéfini. Voici quelques applications pratiques :
- **Analyse des sentiments** : étiqueter le texte avec une polarité telle que `positive` ou `négative`, ce qui aide à la prise de décision dans des domaines comme la politique, la finance et le marketing.
- **Classification de contenu** : organiser et filtrer les informations en attribuant des *tags* sur des sujets spécifiques, comme `météo`, `sports` ou `finance`, dans les flux d'actualités et les réseaux sociaux.
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="sentiment-analysis")
>>> preds = classifier("Hugging Face is the best thing since sliced bread!")
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.9991, 'label': 'POSITIVE'}]
```
### Classification des tokens
Dans les tâches de traitement du language naturel, le texte est d'abord prétraité en le séparant en mots ou sous-mots individuels, appelés *[tokens](glossary#token)*. La classification des tokens attribue une classe à chaque token à partir d'un ensemble de classes prédéfini.
Voici deux types courants de classification des tokens :
- **Reconnaissance d'entités nommées (*Named Entity Recognition* ou *NER* en anglais)** : étiqueter un token selon une catégorie d'entité, telle qu'organisation, personne, lieu ou date. La NER est particulièrement utilisée dans les contextes biomédicaux pour identifier des gènes, des protéines et des noms de médicaments.
- **Étiquetage des parties du discours (*Part of Speech* ou *POS* en anglais)** : étiqueter un token en fonction de sa partie du discours, comme nom, verbe ou adjectif. Le POS est utile pour les systèmes de traduction afin de comprendre comment deux mots identiques peuvent avoir des rôles grammaticaux différents (par exemple, "banque" comme nom versus "banque" comme verbe).
```py
>>> from transformers import pipeline
>>> classifier = pipeline(task="ner")
>>> preds = classifier("Hugging Face is a French company based in New York City.")
>>> preds = [
... {
... "entity": pred["entity"],
... "score": round(pred["score"], 4),
... "index": pred["index"],
... "word": pred["word"],
... "start": pred["start"],
... "end": pred["end"],
... }
... for pred in preds
... ]
>>> print(*preds, sep="\n")
{'entity': 'I-ORG', 'score': 0.9968, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
{'entity': 'I-ORG', 'score': 0.9293, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
{'entity': 'I-ORG', 'score': 0.9763, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
{'entity': 'I-MISC', 'score': 0.9983, 'index': 6, 'word': 'French', 'start': 18, 'end': 24}
{'entity': 'I-LOC', 'score': 0.999, 'index': 10, 'word': 'New', 'start': 42, 'end': 45}
{'entity': 'I-LOC', 'score': 0.9987, 'index': 11, 'word': 'York', 'start': 46, 'end': 50}
{'entity': 'I-LOC', 'score': 0.9992, 'index': 12, 'word': 'City', 'start': 51, 'end': 55}
```
### Réponse à des questions - (*Question Answering*)
La réponse à des questions (*Question Answering* ou *QA* en anglais) est une tâche de traitement du language naturel qui consiste à fournir une réponse à une question, parfois avec l'aide d'un contexte (domaine ouvert) et d'autres fois sans contexte (domaine fermé). Cette tâche intervient lorsqu'on interroge un assistant virtuel, par exemple pour savoir si un restaurant est ouvert. Elle est également utilisée pour le support client, technique, et pour aider les moteurs de recherche à fournir des informations pertinentes.
Il existe deux types courants de réponse à des questions :
- **Extractive** : pour une question donnée et un contexte fourni, la réponse est extraite directement du texte du contexte par le modèle.
- **Abstractive** : pour une question donnée et un contexte, la réponse est générée à partir du contexte. Cette approche utilise le [`Text2TextGenerationPipeline`] plutôt que le [`QuestionAnsweringPipeline`] montré ci-dessous.
```py
>>> from transformers import pipeline
>>> question_answerer = pipeline(task="question-answering")
>>> preds = question_answerer(
... question="What is the name of the repository?",
... context="The name of the repository is huggingface/transformers",
... )
>>> print(
... f"score: {round(preds['score'], 4)}, start: {preds['start']}, end: {preds['end']}, answer: {preds['answer']}"
... )
score: 0.9327, start: 30, end: 54, answer: huggingface/transformers
```
### Résumé de texte - (*Summarization*)
Le résumé de text consiste à créer une version plus courte d'un texte tout en conservant l'essentiel du sens du document original. C'est une tâche de séquence à séquence qui produit un texte plus condensé à partir du texte initial. Cette technique est utile pour aider les lecteurs à saisir rapidement les points clés de longs documents, comme les projets de loi, les documents juridiques et financiers, les brevets, et les articles scientifiques.
Il existe deux types courants de summarization :
- **Extractive** : identifier et extraire les phrases les plus importantes du texte original.
- **Abstractive** : générer un résumé qui peut inclure des mots nouveaux non présents dans le texte d'origine. Le [`SummarizationPipeline`] utilise l'approche abstractive.
```py
>>> from transformers import pipeline
>>> summarizer = pipeline(task="summarization")
>>> summarizer(
... "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles."
... )
[{'summary_text': ' The Transformer is the first sequence transduction model based entirely on attention . It replaces the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention . For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers .'}]
```
### Traduction
La traduction convertit un texte d'une langue à une autre. Elle facilite la communication entre personnes de différentes langues, permet de toucher des audiences plus larges et peut aussi servir d'outil d'apprentissage pour ceux qui apprennent une nouvelle langue. Comme le résumé de texte, la traduction est une tâche de séquence à séquence, où le modèle reçoit une séquence d'entrée (un texte est ici vu comme une séquence de mots, ou plus précisément de tokens) et produit une séquence de sortie dans la langue cible.
Initialement, les modèles de traduction étaient principalement monolingues, mais il y a eu récemment un intérêt croissant pour les modèles multilingues capables de traduire entre plusieurs paires de langues.
```py
>>> from transformers import pipeline
>>> text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning."
>>> translator = pipeline(task="translation", model="google-t5/t5-small")
>>> translator(text)
[{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}]
```
### Modélisation du langage
La modélisation du langage consiste à prédire un mot dans un texte. Cette tâche est devenue très populaire en traitement du language naturel, car un modèle de langage préentraîné sur cette tâche peut ensuite être ajusté (*finetuned*) pour accomplir de nombreuses autres tâches. Récemment, les grands modèles de langage (LLMs) ont suscité beaucoup d'intérêt pour leur capacité à apprendre avec peu ou pas de données spécifiques à une tâche, ce qui leur permet de résoudre des problèmes pour lesquels ils n'ont pas été explicitement entraînés. Ces modèles peuvent générer du texte fluide et convaincant, bien qu'il soit important de vérifier leur précision.
Il existe deux types de modélisation du langage :
- **Causale** : le modèle prédit le token suivant dans une séquence, avec les tokens futurs masqués.
```py
>>> from transformers import pipeline
>>> prompt = "Hugging Face is a community-based open-source platform for machine learning."
>>> generator = pipeline(task="text-generation")
>>> generator(prompt) # doctest: +SKIP
```
- **Masquée** : le modèle prédit un token masqué dans une séquence en ayant accès à tous les autres tokens de la séquence (passé et futur).
```py
>>> text = "Hugging Face is a community-based open-source <mask> for machine learning."
>>> fill_mask = pipeline(task="fill-mask")
>>> preds = fill_mask(text, top_k=1)
>>> preds = [
... {
... "score": round(pred["score"], 4),
... "token": pred["token"],
... "token_str": pred["token_str"],
... "sequence": pred["sequence"],
... }
... for pred in preds
... ]
>>> preds
[{'score': 0.2236,
'token': 1761,
'token_str': ' platform',
'sequence': 'Hugging Face is a community-based open-source platform for machine learning.'}]
```
## Multimodal
Les tâches multimodales nécessitent qu'un modèle traite plusieurs types de données (texte, image, audio, vidéo) pour résoudre un problème spécifique. Par exemple, la génération de légendes pour les images est une tâche multimodale où le modèle prend une image en entrée et produit une séquence de texte décrivant l'image ou ses propriétés.
Bien que les modèles multimodaux traitent divers types de données, ils convertissent toutes ces données en *embeddings* (vecteurs ou listes de nombres contenant des informations significatives). Pour des tâches comme la génération de légendes pour les images, le modèle apprend les relations entre les *embeddings* d'images et ceux de texte.
### Réponse à des questions sur des documents - (*Document Question Answering*)
La réponse à des questions sur des documents consiste à répondre à des questions en langage naturel en utilisant un document comme référence. Contrairement à la réponse à des questions au niveau des tokens, qui prend du texte en entrée, cette tâche prend une image d'un document ainsi qu'une question concernant ce document, et fournit une réponse. Elle est utile pour analyser des données structurées et extraire des informations clées. Par exemple, à partir d'un reçu, on peut extraire des informations telles que le montant total et le change dû.
```py
>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests
>>> url = "https://huggingface.co/datasets/hf-internal-testing/example-documents/resolve/main/jpeg_images/2.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> doc_question_answerer = pipeline("document-question-answering", model="magorshunov/layoutlm-invoices")
>>> preds = doc_question_answerer(
... question="What is the total amount?",
... image=image,
... )
>>> preds
[{'score': 0.8531, 'answer': '17,000', 'start': 4, 'end': 4}]
```
En espérant que cette page vous ait donné plus d'informations sur les différents types de tâches dans chaque modalité et l'importance pratique de chacune d'elles. Dans la [section suivante](tasks_explained), vous découvrirez **comment** 🤗 Transformers fonctionne pour résoudre ces tâches.
| transformers/docs/source/fr/task_summary.md/0 | {
"file_path": "transformers/docs/source/fr/task_summary.md",
"repo_id": "transformers",
"token_count": 8189
} | 411 |
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# Crea un'architettura personalizzata
Una [`AutoClass`](model_doc/auto) deduce automaticamente il modello dell'architettura e scarica la configurazione e i pesi pre-allenati. Generalmente, noi consigliamo di usare un `AutoClass` per produrre un codice indipendente dal checkpoint. Ma gli utenti che desiderano un controllo maggiore su parametri specifici del modello possono creare un modello 🤗 Transformers personalizzato da poche classi base. Questo potrebbe essere particolarmente utile per qualunque persona sia interessata nel studiare, allenare o sperimentare con un modello 🤗 Transformers. In questa guida, approfondisci la creazione di un modello personalizzato senza `AutoClass`. Impara come:
- Caricare e personalizzare una configurazione del modello.
- Creare un'architettura modello.
- Creare un tokenizer lento e veloce per il testo.
- Creare un estrattore di caratteristiche per attività riguardanti audio o immagini.
- Creare un processore per attività multimodali.
## Configurazione
Una [configurazione](main_classes/configuration) si riferisce agli attributi specifici di un modello. Ogni configurazione del modello ha attributi diversi; per esempio, tutti i modelli npl hanno questi attributi in comune `hidden_size`, `num_attention_heads`, `num_hidden_layers` e `vocab_size`. Questi attributi specificano il numero di attention heads o strati nascosti con cui costruire un modello.
Dai un'occhiata più da vicino a [DistilBERT](model_doc/distilbert) accedendo a [`DistilBertConfig`] per ispezionare i suoi attributi:
```py
>>> from transformers import DistilBertConfig
>>> config = DistilBertConfig()
>>> print(config)
DistilBertConfig {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
[`DistilBertConfig`] mostra tutti gli attributi predefiniti usati per costruire una base [`DistilBertModel`]. Tutti gli attributi sono personalizzabili, creando uno spazio per sperimentare. Per esempio, puoi configurare un modello predefinito per:
- Provare un funzione di attivazione diversa con il parametro `activation`.
- Utilizzare tasso di drop out più elevato per le probalità di attention con il parametro `attention_dropout`.
```py
>>> my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)
>>> print(my_config)
DistilBertConfig {
"activation": "relu",
"attention_dropout": 0.4,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
Nella funzione [`~PretrainedConfig.from_pretrained`] possono essere modificati gli attributi del modello pre-allenato:
```py
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
```
Quando la configurazione del modello ti soddisfa, la puoi salvare con [`~PretrainedConfig.save_pretrained`]. Il file della tua configurazione è memorizzato come file JSON nella save directory specificata:
```py
>>> my_config.save_pretrained(save_directory="./your_model_save_path")
```
Per riutilizzare la configurazione del file, caricalo con [`~PretrainedConfig.from_pretrained`]:
```py
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
```
<Tip>
Puoi anche salvare il file di configurazione come dizionario oppure come la differenza tra gli attributi della tua configurazione personalizzata e gli attributi della configurazione predefinita! Guarda la documentazione [configuration](main_classes/configuration) per più dettagli.
</Tip>
## Modello
Il prossimo passo e di creare [modello](main_classes/models). Il modello - vagamente riferito anche come architettura - definisce cosa ogni strato deve fare e quali operazioni stanno succedendo. Attributi come `num_hidden_layers` provenienti dalla configurazione sono usati per definire l'architettura. Ogni modello condivide la classe base [`PreTrainedModel`] e alcuni metodi comuni come il ridimensionamento degli input embeddings e la soppressione delle self-attention heads . Inoltre, tutti i modelli sono la sottoclasse di [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html), [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) o [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html). Cio significa che i modelli sono compatibili con l'uso di ciascun di framework.
<frameworkcontent>
<pt>
Carica gli attributi della tua configurazione personalizzata nel modello:
```py
>>> from transformers import DistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
>>> model = DistilBertModel(my_config)
```
Questo crea modelli con valori casuali invece di pesi pre-allenati. Non sarai in grado di usare questo modello per niente di utile finché non lo alleni. L'allenamento è un processo costoso e che richiede tempo . Generalmente è meglio usare un modello pre-allenato per ottenere risultati migliori velocemente, utilizzando solo una frazione delle risorse neccesarie per l'allenamento.
Crea un modello pre-allenato con [`~PreTrainedModel.from_pretrained`]:
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
Quando carichi pesi pre-allenati, la configurazione del modello predefinito è automaticamente caricata se il modello è fornito da 🤗 Transformers. Tuttavia, puoi ancora sostituire gli attributi - alcuni o tutti - di configurazione del modello predefinito con i tuoi se lo desideri:
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</pt>
<tf>
Carica gli attributi di configurazione personalizzati nel modello:
```py
>>> from transformers import TFDistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
>>> tf_model = TFDistilBertModel(my_config)
```
Questo crea modelli con valori casuali invece di pesi pre-allenati. Non sarai in grado di usare questo modello per niente di utile finché non lo alleni. L'allenamento è un processo costoso e che richiede tempo . Generalmente è meglio usare un modello pre-allenato per ottenere risultati migliori velocemente, utilizzando solo una frazione delle risorse neccesarie per l'allenamento.
Crea un modello pre-allenoto con [`~TFPreTrainedModel.from_pretrained`]:
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
Quando carichi pesi pre-allenati, la configurazione del modello predefinito è automaticamente caricato se il modello è fornito da 🤗 Transformers. Tuttavia, puoi ancora sostituire gli attributi - alcuni o tutti - di configurazione del modello predefinito con i tuoi se lo desideri:
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</tf>
</frameworkcontent>
### Model head
A questo punto, hai un modello DistilBERT base i cui output sono gli *hidden states* (in italiano stati nascosti). Gli stati nascosti sono passati come input a un model head per produrre l'output finale. 🤗 Transformers fornisce un model head diverso per ogni attività fintanto che il modello supporta l'attività (i.e., non puoi usare DistilBERT per un attività sequence-to-sequence come la traduzione).
<frameworkcontent>
<pt>
Per esempio, [`DistilBertForSequenceClassification`] è un modello DistilBERT base con una testa di classificazione per sequenze. La sequenza di classificazione head è uno strato lineare sopra gli output ragruppati.
```py
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Riutilizza facilmente questo checkpoint per un'altra attività passando ad un model head differente. Per un attività di risposta alle domande, utilizzerai il model head [`DistilBertForQuestionAnswering`]. La head per compiti di question answering è simile alla classificazione di sequenza head tranne per il fatto che è uno strato lineare sopra l'output degli stati nascosti (hidden states in inglese)
```py
>>> from transformers import DistilBertForQuestionAnswering
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</pt>
<tf>
Per esempio, [`TFDistilBertForSequenceClassification`] è un modello DistilBERT base con classificazione di sequenza head. La classificazione di sequenza head è uno strato lineare sopra gli output raggruppati.
```py
>>> from transformers import TFDistilBertForSequenceClassification
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
Riutilizza facilmente questo checkpoint per un altra attività passando ad un modello head diverso. Per un attività di risposta alle domande, utilizzerai il model head [`TFDistilBertForQuestionAnswering`]. Il head di risposta alle domande è simile alla sequenza di classificazione head tranne per il fatto che è uno strato lineare sopra l'output degli stati nascosti (hidden states in inglese)
```py
>>> from transformers import TFDistilBertForQuestionAnswering
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</tf>
</frameworkcontent>
## Tokenizer
L'ultima classe base di cui hai bisogno prima di utilizzare un modello per i dati testuali è un [tokenizer](main_classes/tokenizer) per convertire il testo grezzo in tensori. Ci sono due tipi di tokenizer che puoi usare con 🤗 Transformers:
- [`PreTrainedTokenizer`]: un'implementazione Python di un tokenizer.
- [`PreTrainedTokenizerFast`]: un tokenizer dalla nostra libreria [🤗 Tokenizer](https://huggingface.co/docs/tokenizers/python/latest/) basata su Rust. Questo tipo di tokenizer è significativamente più veloce, specialmente durante la batch tokenization, grazie alla sua implementazione Rust. Il tokenizer veloce offre anche metodi aggiuntivi come *offset mapping* che associa i token alle loro parole o caratteri originali.
Entrambi i tokenizer supportano metodi comuni come la codifica e la decodifica, l'aggiunta di nuovi token e la gestione di token speciali.
<Tip warning={true}>
Non tutti i modelli supportano un tokenizer veloce. Dai un'occhiata a questo [tabella](index#supported-frameworks) per verificare se un modello ha il supporto per tokenizer veloce.
</Tip>
Se hai addestrato il tuo tokenizer, puoi crearne uno dal tuo file *vocabolario*:
```py
>>> from transformers import DistilBertTokenizer
>>> my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")
```
È importante ricordare che il vocabolario di un tokenizer personalizzato sarà diverso dal vocabolario generato dal tokenizer di un modello preallenato. È necessario utilizzare il vocabolario di un modello preallenato se si utilizza un modello preallenato, altrimenti gli input non avranno senso. Crea un tokenizer con il vocabolario di un modello preallenato con la classe [`DistilBertTokenizer`]:
```py
>>> from transformers import DistilBertTokenizer
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
Crea un tokenizer veloce con la classe [`DistilBertTokenizerFast`]:
```py
>>> from transformers import DistilBertTokenizerFast
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip>
Per l'impostazione predefinita, [`AutoTokenizer`] proverà a caricare un tokenizer veloce. Puoi disabilitare questo comportamento impostando `use_fast=False` in `from_pretrained`.
</Tip>
## Estrattore Di Feature
Un estrattore di caratteristiche (feature in inglese) elabora input audio o immagini. Eredita dalla classe [`~feature_extraction_utils.FeatureExtractionMixin`] base e può anche ereditare dalla classe [`ImageFeatureExtractionMixin`] per l'elaborazione delle caratteristiche dell'immagine o dalla classe [`SequenceFeatureExtractor`] per l'elaborazione degli input audio.
A seconda che tu stia lavorando a un'attività audio o visiva, crea un estrattore di caratteristiche associato al modello che stai utilizzando. Ad esempio, crea un [`ViTFeatureExtractor`] predefinito se stai usando [ViT](model_doc/vit) per la classificazione delle immagini:
```py
>>> from transformers import ViTFeatureExtractor
>>> vit_extractor = ViTFeatureExtractor()
>>> print(vit_extractor)
ViTFeatureExtractor {
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "ViTFeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"size": 224
}
```
<Tip>
Se non stai cercando alcuna personalizzazione, usa il metodo `from_pretrained` per caricare i parametri di default dell'estrattore di caratteristiche di un modello.
</Tip>
Modifica uno qualsiasi dei parametri [`ViTFeatureExtractor`] per creare il tuo estrattore di caratteristiche personalizzato:
```py
>>> from transformers import ViTFeatureExtractor
>>> my_vit_extractor = ViTFeatureExtractor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3])
>>> print(my_vit_extractor)
ViTFeatureExtractor {
"do_normalize": false,
"do_resize": true,
"feature_extractor_type": "ViTFeatureExtractor",
"image_mean": [
0.3,
0.3,
0.3
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": "PIL.Image.BOX",
"size": 224
}
```
Per gli input audio, puoi creare un [`Wav2Vec2FeatureExtractor`] e personalizzare i parametri in modo simile:
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> w2v2_extractor = Wav2Vec2FeatureExtractor()
>>> print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
```
## Processore
Per modelli che supportano attività multimodali, 🤗 Transformers offre una classe di processore che racchiude comodamente un estrattore di caratteristiche e un tokenizer in un unico oggetto. Ad esempio, utilizziamo [`Wav2Vec2Processor`] per un'attività di riconoscimento vocale automatico (ASR). ASR trascrive l'audio in testo, quindi avrai bisogno di un estrattore di caratteristiche e di un tokenizer.
Crea un estrattore di feature per gestire gli input audio:
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)
```
Crea un tokenizer per gestire gli input di testo:
```py
>>> from transformers import Wav2Vec2CTCTokenizer
>>> tokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt")
```
Combinare l'estrattore di caratteristiche e il tokenizer in [`Wav2Vec2Processor`]:
```py
>>> from transformers import Wav2Vec2Processor
>>> processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
```
Con due classi di base - configurazione e modello - e una classe di preelaborazione aggiuntiva (tokenizer, estrattore di caratteristiche o processore), puoi creare qualsiasi modello supportato da 🤗 Transformers. Ognuna di queste classi base è configurabile, consentendoti di utilizzare gli attributi specifici che desideri. È possibile impostare facilmente un modello per l'addestramento o modificare un modello preallenato esistente per la messa a punto. | transformers/docs/source/it/create_a_model.md/0 | {
"file_path": "transformers/docs/source/it/create_a_model.md",
"repo_id": "transformers",
"token_count": 5882
} | 412 |
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# Processors
Transformers ライブラリでは、プロセッサは 2 つの異なる意味を持ちます。
- [Wav2Vec2](../model_doc/wav2vec2) などのマルチモーダル モデルの入力を前処理するオブジェクト (音声とテキスト)
または [CLIP](../model_doc/clip) (テキストとビジョン)
- 古いバージョンのライブラリで GLUE または SQUAD のデータを前処理するために使用されていたオブジェクトは非推奨になりました。
## Multi-modal processors
マルチモーダル モデルでは、オブジェクトが複数のモダリティ (テキスト、
視覚と音声）。これは、2 つ以上の処理オブジェクトをグループ化するプロセッサーと呼ばれるオブジェクトによって処理されます。
トークナイザー (テキスト モダリティ用)、画像プロセッサー (視覚用)、特徴抽出器 (オーディオ用) など。
これらのプロセッサは、保存およびロード機能を実装する次の基本クラスを継承します。
[[autodoc]] ProcessorMixin
## Deprecated processors
すべてのプロセッサは、同じアーキテクチャに従っています。
[`~data.processors.utils.DataProcessor`]。プロセッサは次のリストを返します。
[`~data.processors.utils.InputExample`]。これら
[`~data.processors.utils.InputExample`] は次のように変換できます。
[`~data.processors.utils.Input features`] をモデルにフィードします。
[[autodoc]] data.processors.utils.DataProcessor
[[autodoc]] data.processors.utils.InputExample
[[autodoc]] data.processors.utils.InputFeatures
## GLUE
[一般言語理解評価 (GLUE)](https://gluebenchmark.com/) は、
既存の NLU タスクの多様なセットにわたるモデルのパフォーマンス。紙と同時発売された [GLUE: A
自然言語理解のためのマルチタスクベンチマークおよび分析プラットフォーム](https://openreview.net/pdf?id=rJ4km2R5t7)
このライブラリは、MRPC、MNLI、MNLI (不一致)、CoLA、SST2、STSB、
QQP、QNLI、RTE、WNLI。
それらのプロセッサは次のとおりです。
- [`~data.processors.utils.MrpcProcessor`]
- [`~data.processors.utils.MnliProcessor`]
- [`~data.processors.utils.MnliMismatchedProcessor`]
- [`~data.processors.utils.Sst2Processor`]
- [`~data.processors.utils.StsbProcessor`]
- [`~data.processors.utils.QqpProcessor`]
- [`~data.processors.utils.QnliProcessor`]
- [`~data.processors.utils.RteProcessor`]
- [`~data.processors.utils.WnliProcessor`]
さらに、次のメソッドを使用して、データ ファイルから値をロードし、それらをリストに変換することができます。
[`~data.processors.utils.InputExample`]。
[[autodoc]] data.processors.glue.glue_convert_examples_to_features
## XNLI
[クロスリンガル NLI コーパス (XNLI)](https://www.nyu.edu/projects/bowman/xnli/) は、
言語を超えたテキスト表現の品質。 XNLI は、[*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/) に基づくクラウドソースのデータセットです。テキストのペアには、15 個のテキスト含意アノテーションがラベル付けされています。
さまざまな言語 (英語などの高リソース言語とスワヒリ語などの低リソース言語の両方を含む)。
論文 [XNLI: Evaluating Cross-lingual Sentence Representations](https://huggingface.co/papers/1809.05053) と同時にリリースされました。
このライブラリは、XNLI データをロードするプロセッサをホストします。
- [`~data.processors.utils.XnliProcessor`]
テストセットにはゴールドラベルが付いているため、評価はテストセットで行われますのでご了承ください。
これらのプロセッサを使用する例は、[run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) スクリプトに示されています。
## SQuAD
[The Stanford Question Answering Dataset (SQuAD)](https://rajpurkar.github.io/SQuAD-explorer//) は、次のベンチマークです。
質問応答に関するモデルのパフォーマンスを評価します。 v1.1 と v2.0 の 2 つのバージョンが利用可能です。最初のバージョン
(v1.1) は、論文 [SQuAD: 100,000+ question for Machine Comprehension of Text](https://huggingface.co/papers/1606.05250) とともにリリースされました。 2 番目のバージョン (v2.0) は、論文 [Know What You Don't と同時にリリースされました。
知っておくべき: SQuAD の答えられない質問](https://huggingface.co/papers/1806.03822)。
このライブラリは、次の 2 つのバージョンのそれぞれのプロセッサをホストします。
### Processors
それらのプロセッサは次のとおりです。
- [`~data.processors.utils.SquadV1Processor`]
- [`~data.processors.utils.SquadV2Processor`]
どちらも抽象クラス [`~data.processors.utils.SquadProcessor`] を継承しています。
[[autodoc]] data.processors.squad.SquadProcessor
- all
さらに、次のメソッドを使用して、SQuAD の例を次の形式に変換できます。
モデルの入力として使用できる [`~data.processors.utils.SquadFeatures`]。
[[autodoc]] data.processors.squad.squad_convert_examples_to_features
これらのプロセッサと前述の方法は、データを含むファイルだけでなく、
*tensorflow_datasets* パッケージ。以下に例を示します。
### Example usage
以下にプロセッサを使用した例と、データ ファイルを使用した変換方法を示します。
```python
# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
*tensorflow_datasets* の使用は、データ ファイルを使用するのと同じくらい簡単です。
```python
# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
これらのプロセッサを使用する別の例は、[run_squad.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py) スクリプトに示されています。
| transformers/docs/source/ja/main_classes/processors.md/0 | {
"file_path": "transformers/docs/source/ja/main_classes/processors.md",
"repo_id": "transformers",
"token_count": 3106
} | 413 |
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# BertGeneration
## Overview
BertGeneration モデルは、次を使用してシーケンス間のタスクに利用できる BERT モデルです。
[Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461) で提案されている [`EncoderDecoderModel`]
タスク、Sascha Rothe、Sishi Nagayan、Aliaksei Severyn 著。
論文の要約は次のとおりです。
*大規模なニューラル モデルの教師なし事前トレーニングは、最近、自然言語処理に革命をもたらしました。による
NLP 実践者は、公開されたチェックポイントからウォームスタートして、複数の項目で最先端の技術を推進してきました。
コンピューティング時間を大幅に節約しながらベンチマークを実行します。これまでのところ、主に自然言語に焦点を当ててきました。
タスクを理解する。この論文では、シーケンス生成のための事前トレーニングされたチェックポイントの有効性を実証します。私たちは
公開されている事前トレーニング済み BERT と互換性のある Transformer ベースのシーケンス間モデルを開発しました。
GPT-2 および RoBERTa チェックポイントを使用し、モデルの初期化の有用性について広範な実証研究を実施しました。
エンコーダとデコーダ、これらのチェックポイント。私たちのモデルは、機械翻訳に関する新しい最先端の結果をもたらします。
テキストの要約、文の分割、および文の融合。*
## Usage examples and tips
- モデルを [`EncoderDecoderModel`] と組み合わせて使用して、2 つの事前トレーニングされたモデルを活用できます。
後続の微調整のための BERT チェックポイント。
```python
>>> # leverage checkpoints for Bert2Bert model...
>>> # use BERT's cls token as BOS token and sep token as EOS token
>>> encoder = BertGenerationEncoder.from_pretrained("google-bert/bert-large-uncased", bos_token_id=101, eos_token_id=102)
>>> # add cross attention layers and use BERT's cls token as BOS token and sep token as EOS token
>>> decoder = BertGenerationDecoder.from_pretrained(
... "google-bert/bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
... )
>>> bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
>>> # create tokenizer...
>>> tokenizer = BertTokenizer.from_pretrained("google-bert/bert-large-uncased")
>>> input_ids = tokenizer(
... "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
... ).input_ids
>>> labels = tokenizer("This is a short summary", return_tensors="pt").input_ids
>>> # train...
>>> loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
>>> loss.backward()
```
- 事前トレーニングされた [`EncoderDecoderModel`] もモデル ハブで直接利用できます。
```python
>>> # instantiate sentence fusion model
>>> sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
>>> tokenizer = AutoTokenizer.from_pretrained("google/roberta2roberta_L-24_discofuse")
>>> input_ids = tokenizer(
... "This is the first sentence. This is the second sentence.", add_special_tokens=False, return_tensors="pt"
... ).input_ids
>>> outputs = sentence_fuser.generate(input_ids)
>>> print(tokenizer.decode(outputs[0]))
```
チップ：
- [`BertGenerationEncoder`] と [`BertGenerationDecoder`] は、
[`EncoderDecoder`] と組み合わせます。
- 要約、文の分割、文の融合、および翻訳の場合、入力に特別なトークンは必要ありません。
したがって、入力の末尾に EOS トークンを追加しないでください。
このモデルは、[patrickvonplaten](https://huggingface.co/patrickvonplaten) によって提供されました。元のコードは次のとおりです
[ここ](https://tfhub.dev/s?module-type=text-generation&subtype=module,placeholder) があります。
## BertGenerationConfig
[[autodoc]] BertGenerationConfig
## BertGenerationTokenizer
[[autodoc]] BertGenerationTokenizer
- save_vocabulary
## BertGenerationEncoder
[[autodoc]] BertGenerationEncoder
- forward
## BertGenerationDecoder
[[autodoc]] BertGenerationDecoder
- forward
| transformers/docs/source/ja/model_doc/bert-generation.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/bert-generation.md",
"repo_id": "transformers",
"token_count": 1975
} | 414 |
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# ByT5
## Overview
ByT5 モデルは、[ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://huggingface.co/papers/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir
Kale, Adam Roberts, Colin Raffel.
論文の要約は次のとおりです。
*最も広く使用されている事前トレーニング済み言語モデルは、単語またはサブワード単位に対応するトークンのシーケンスで動作します。
テキストをトークンのシーケンスとしてエンコードするには、トークナイザーが必要です。トークナイザーは通常、
モデル。代わりに生のテキスト (バイトまたは文字) を直接操作するトークンフリー モデルには多くの利点があります。
すぐに使用できるあらゆる言語のテキストを処理でき、ノイズに対してより堅牢であり、技術的負債を最小限に抑えます。
複雑でエラーが発生しやすいテキスト前処理パイプラインを削除します。バイトまたは文字列がトークンより長いため
トークンフリー モデルに関する過去の研究では、シーケンスのコストを償却するように設計された新しいモデル アーキテクチャが導入されることがよくありました。
生のテキストを直接操作します。この論文では、標準的な Transformer アーキテクチャが次のようなもので使用できることを示します。
バイトシーケンスを処理するための最小限の変更。パラメータ数の観点からトレードオフを注意深く特徴付けます。
FLOP のトレーニングと推論速度を調べ、バイトレベルのモデルがトークンレベルと競合できることを示します。
対応者。また、バイトレベルのモデルはノイズに対して大幅に堅牢であり、より優れたパフォーマンスを発揮することも示しています。
スペルと発音に敏感なタスク。私たちの貢献の一環として、新しいセットをリリースします。
T5 アーキテクチャに基づいた事前トレーニング済みのバイトレベルの Transformer モデルと、そこで使用されるすべてのコードとデータ
実験。*
このモデルは、[patrickvonplaten](https://huggingface.co/patrickvonplaten) によって提供されました。元のコードは次のとおりです
[ここ](https://github.com/google-research/byt5) にあります。
<Tip>
ByT5 のアーキテクチャは T5v1.1 モデルに基づいています。API リファレンスについては、[T5v1.1 のドキュメント ページ](t5v1.1) を参照してください。彼らは
モデルの入力を準備する方法が異なるだけです。以下のコード例を参照してください。
</Tip>
ByT5 は教師なしで事前トレーニングされているため、単一タスク中にタスク プレフィックスを使用する利点はありません。
微調整。マルチタスクの微調整を行う場合は、プレフィックスを使用する必要があります。
## Usage Examples
ByT5 は生の UTF-8 バイトで動作するため、トークナイザーなしで使用できます。
```python
>>> from transformers import T5ForConditionalGeneration
>>> import torch
>>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
>>> num_special_tokens = 3
>>> # Model has 3 special tokens which take up the input ids 0,1,2 of ByT5.
>>> # => Need to shift utf-8 character encodings by 3 before passing ids to model.
>>> input_ids = torch.tensor([list("Life is like a box of chocolates.".encode("utf-8"))]) + num_special_tokens
>>> labels = torch.tensor([list("La vie est comme une boîte de chocolat.".encode("utf-8"))]) + num_special_tokens
>>> loss = model(input_ids, labels=labels).loss
>>> loss.item()
2.66
```
ただし、バッチ推論とトレーニングの場合は、トークナイザーを使用することをお勧めします。
```python
>>> from transformers import T5ForConditionalGeneration, AutoTokenizer
>>> model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
>>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-small")
>>> model_inputs = tokenizer(
... ["Life is like a box of chocolates.", "Today is Monday."], padding="longest", return_tensors="pt"
... )
>>> labels_dict = tokenizer(
... ["La vie est comme une boîte de chocolat.", "Aujourd'hui c'est lundi."], padding="longest", return_tensors="pt"
... )
>>> labels = labels_dict.input_ids
>>> loss = model(**model_inputs, labels=labels).loss
>>> loss.item()
17.9
```
[T5](t5) と同様に、ByT5 はスパンマスクノイズ除去タスクでトレーニングされました。しかし、
モデルはキャラクターに直接作用するため、事前トレーニングタスクは少し複雑です
違う。のいくつかの文字を破損してみましょう
`"The dog chases a ball in the park."`という文を入力し、ByT5 に予測してもらいます。
わたしたちのため。
```python
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/byt5-base")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("google/byt5-base")
>>> input_ids_prompt = "The dog chases a ball in the park."
>>> input_ids = tokenizer(input_ids_prompt).input_ids
>>> # Note that we cannot add "{extra_id_...}" to the string directly
>>> # as the Byte tokenizer would incorrectly merge the tokens
>>> # For ByT5, we need to work directly on the character level
>>> # Contrary to T5, ByT5 does not use sentinel tokens for masking, but instead
>>> # uses final utf character ids.
>>> # UTF-8 is represented by 8 bits and ByT5 has 3 special tokens.
>>> # => There are 2**8+2 = 259 input ids and mask tokens count down from index 258.
>>> # => mask to "The dog [258]a ball [257]park."
>>> input_ids = torch.tensor([input_ids[:8] + [258] + input_ids[14:21] + [257] + input_ids[28:]])
>>> input_ids
tensor([[ 87, 107, 104, 35, 103, 114, 106, 35, 258, 35, 100, 35, 101, 100, 111, 111, 257, 35, 115, 100, 117, 110, 49, 1]])
>>> # ByT5 produces only one char at a time so we need to produce many more output characters here -> set `max_length=100`.
>>> output_ids = model.generate(input_ids, max_length=100)[0].tolist()
>>> output_ids
[0, 258, 108, 118, 35, 119, 107, 104, 35, 114, 113, 104, 35, 122, 107, 114, 35, 103, 114, 104, 118, 257, 35, 108, 113, 35, 119, 107, 104, 35, 103, 108, 118, 102, 114, 256, 108, 113, 35, 119, 107, 104, 35, 115, 100, 117, 110, 49, 35, 87, 107, 104, 35, 103, 114, 106, 35, 108, 118, 35, 119, 107, 104, 35, 114, 113, 104, 35, 122, 107, 114, 35, 103, 114, 104, 118, 35, 100, 35, 101, 100, 111, 111, 35, 108, 113, 255, 35, 108, 113, 35, 119, 107, 104, 35, 115, 100, 117, 110, 49]
>>> # ^- Note how 258 descends to 257, 256, 255
>>> # Now we need to split on the sentinel tokens, let's write a short loop for this
>>> output_ids_list = []
>>> start_token = 0
>>> sentinel_token = 258
>>> while sentinel_token in output_ids:
... split_idx = output_ids.index(sentinel_token)
... output_ids_list.append(output_ids[start_token:split_idx])
... start_token = split_idx
... sentinel_token -= 1
>>> output_ids_list.append(output_ids[start_token:])
>>> output_string = tokenizer.batch_decode(output_ids_list)
>>> output_string
['<pad>', 'is the one who does', ' in the disco', 'in the park. The dog is the one who does a ball in', ' in the park.']
```
## ByT5Tokenizer
[[autodoc]] ByT5Tokenizer
詳細については、[`ByT5Tokenizer`] を参照してください。 | transformers/docs/source/ja/model_doc/byt5.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/byt5.md",
"repo_id": "transformers",
"token_count": 3269
} | 415 |
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# CTRL
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=Salesforce/ctrl">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-ctrl-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/tiny-ctrl">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue">
</a>
</div>
## Overview
CTRL モデルは、Nitish Shirish Keskar*、Bryan McCann*、Lav R. Varshney、Caiming Xiong, Richard Socher によって [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://huggingface.co/papers/1909.05858) で提案されました。
リチャード・ソーチャー。これは、非常に大規模なコーパスの言語モデリングを使用して事前トレーニングされた因果的 (一方向) トランスフォーマーです
最初のトークンが制御コード (リンク、書籍、Wikipedia など) として予約されている、約 140 GB のテキスト データ。
論文の要約は次のとおりです。
*大規模な言語モデルは有望なテキスト生成機能を示していますが、ユーザーは特定の言語モデルを簡単に制御できません
生成されたテキストの側面。 16 億 3,000 万パラメータの条件付きトランスフォーマー言語モデルである CTRL をリリースします。
スタイル、コンテンツ、タスク固有の動作を制御する制御コードを条件付けるように訓練されています。制御コードは
生のテキストと自然に共生する構造から派生し、教師なし学習の利点を維持しながら、
テキスト生成をより明示的に制御できるようになります。これらのコードを使用すると、CTRL でどの部分が予測されるのかを予測することもできます。
トレーニング データにはシーケンスが与えられる可能性が最も高くなります。これにより、大量のデータを分析するための潜在的な方法が提供されます。
モデルベースのソース帰属を介して。*
このモデルは、[keskarnitishr](https://huggingface.co/keskarnitishr) によって提供されました。元のコードが見つかる
[こちら](https://github.com/salesforce/Salesforce/ctrl)。
## Usage tips
- CTRL は制御コードを利用してテキストを生成します。生成を特定の単語や文で開始する必要があります。
またはリンクして一貫したテキストを生成します。 [元の実装](https://github.com/salesforce/Salesforce/ctrl) を参照してください。
詳しくは。
- CTRL は絶対位置埋め込みを備えたモデルであるため、通常は入力を右側にパディングすることをお勧めします。
左。
- CTRL は因果言語モデリング (CLM) の目的でトレーニングされているため、次の予測に強力です。
シーケンス内のトークン。この機能を利用すると、CTRL は構文的に一貫したテキストを生成できるようになります。
*run_generation.py* サンプル スクリプトで確認できます。
- PyTorch モデルは、以前に計算されたキーと値のアテンション ペアである`past_key_values`を入力として受け取ることができます。
TensorFlow モデルは`past`を入力として受け入れます。 `past_key_values`値を使用すると、モデルが再計算されなくなります。
テキスト生成のコンテキストで事前に計算された値。 [`forward`](model_doc/ctrl#transformers.CTRLModel.forward) を参照してください。
この引数の使用法の詳細については、メソッドを参照してください。
## Resources
- [テキスト分類タスクガイド(英語版)](../../en/tasks/sequence_classification)
- [因果言語モデリング タスク ガイド](../tasks/language_modeling)
## CTRLConfig
[[autodoc]] CTRLConfig
## CTRLTokenizer
[[autodoc]] CTRLTokenizer
- save_vocabulary
<frameworkcontent>
<pt>
## CTRLModel
[[autodoc]] CTRLModel
- forward
## CTRLLMHeadModel
[[autodoc]] CTRLLMHeadModel
- forward
## CTRLForSequenceClassification
[[autodoc]] CTRLForSequenceClassification
- forward
</pt>
<tf>
## TFCTRLModel
[[autodoc]] TFCTRLModel
- call
## TFCTRLLMHeadModel
[[autodoc]] TFCTRLLMHeadModel
- call
## TFCTRLForSequenceClassification
[[autodoc]] TFCTRLForSequenceClassification
- call
</tf>
</frameworkcontent>
| transformers/docs/source/ja/model_doc/ctrl.md/0 | {
"file_path": "transformers/docs/source/ja/model_doc/ctrl.md",
"repo_id": "transformers",
"token_count": 2139
} | 416 |
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# 推論のための多言語モデル
[[open-in-colab]]
🤗 Transformers にはいくつかの多言語モデルがあり、それらの推論の使用方法は単一言語モデルとは異なります。ただし、多言語モデルの使用方法がすべて異なるわけではありません。 [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) などの一部のモデルは、単一言語モデルと同様に使用できます。 このガイドでは、推論のために使用方法が異なる多言語モデルをどのように使うかを示します。
## XLM
XLM には10の異なるチェックポイントがあり、そのうちの1つだけが単一言語です。 残りの9つのモデルチェックポイントは、言語埋め込みを使用するチェックポイントと使用しないチェックポイントの2つのカテゴリに分けることができます。
### 言語の埋め込みがある XLM
次の XLM モデルは、言語の埋め込みを使用して、推論で使用される言語を指定します。
- `FacebookAI/xlm-mlm-ende-1024` (マスク化された言語モデリング、英語-ドイツ語)
- `FacebookAI/xlm-mlm-enfr-1024` (マスク化された言語モデリング、英語-フランス語)
- `FacebookAI/xlm-mlm-enro-1024` (マスク化された言語モデリング、英語-ルーマニア語)
- `FacebookAI/xlm-mlm-xnli15-1024` (マスク化された言語モデリング、XNLI 言語)
- `FacebookAI/xlm-mlm-tlm-xnli15-1024` (マスク化された言語モデリング + 翻訳 + XNLI 言語)
- `FacebookAI/xlm-clm-enfr-1024` (因果言語モデリング、英語-フランス語)
- `FacebookAI/xlm-clm-ende-1024` (因果言語モデリング、英語-ドイツ語)
言語の埋め込みは、モデルに渡される `input_ids` と同じ形状のテンソルとして表されます。 これらのテンソルの値は、使用される言語に依存し、トークナイザーの `lang2id` および `id2lang` 属性によって識別されます。
この例では、`FacebookAI/xlm-clm-enfr-1024` チェックポイントをロードします (因果言語モデリング、英語-フランス語)。
```py
>>> import torch
>>> from transformers import XLMTokenizer, XLMWithLMHeadModel
>>> tokenizer = XLMTokenizer.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
>>> model = XLMWithLMHeadModel.from_pretrained("FacebookAI/xlm-clm-enfr-1024")
```
トークナイザーの `lang2id` 属性は、このモデルの言語とその ID を表示します。
```py
>>> print(tokenizer.lang2id)
{'en': 0, 'fr': 1}
```
次に、入力例を作成します。
```py
>>> input_ids = torch.tensor([tokenizer.encode("Wikipedia was used to")]) # batch size of 1
```
言語 ID を `en` に設定し、それを使用して言語の埋め込みを定義します。 言語の埋め込みは、英語の言語 ID であるため、`0` で埋められたテンソルです。 このテンソルは `input_ids` と同じサイズにする必要があります。
```py
>>> language_id = tokenizer.lang2id["en"] # 0
>>> langs = torch.tensor([language_id] * input_ids.shape[1]) # torch.tensor([0, 0, 0, ..., 0])
>>> # We reshape it to be of size (batch_size, sequence_length)
>>> langs = langs.view(1, -1) # is now of shape [1, sequence_length] (we have a batch size of 1)
```
これで、`input_ids` と言語の埋め込みをモデルに渡すことができます。
```py
>>> outputs = model(input_ids, langs=langs)
```
[run_generation.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-generation/run_generation.py) スクリプトは、`xlm-clm` チェックポイントを使用して、言語が埋め込まれたテキストを生成できます。
### 言語の埋め込みがないXLM
次の XLM モデルは、推論中に言語の埋め込みを必要としません。
- `FacebookAI/xlm-mlm-17-1280` (マスク化された言語モデリング、17の言語)
- `FacebookAI/xlm-mlm-100-1280` (マスク化された言語モデリング、100の言語)
これらのモデルは、以前の XLM チェックポイントとは異なり、一般的な文の表現に使用されます。
## BERT
以下の BERT モデルは、多言語タスクに使用できます。
- `google-bert/bert-base-multilingual-uncased` (マスク化された言語モデリング + 次の文の予測、102の言語)
- `google-bert/bert-base-multilingual-cased` (マスク化された言語モデリング + 次の文の予測、104の言語)
これらのモデルは、推論中に言語の埋め込みを必要としません。 文脈から言語を識別し、それに応じて推測する必要があります。
## XLM-RoBERTa
次の XLM-RoBERTa モデルは、多言語タスクに使用できます。
- `FacebookAI/xlm-roberta-base` (マスク化された言語モデリング、100の言語)
- `FacebookAI/xlm-roberta-large` (マスク化された言語モデリング、100の言語)
XLM-RoBERTa は、100の言語で新しく作成およびクリーニングされた2.5 TB の CommonCrawl データでトレーニングされました。 これは、分類、シーケンスのラベル付け、質問応答などのダウンストリームタスクで、mBERT や XLM などの以前にリリースされた多言語モデルを大幅に改善します。
## M2M100
次の M2M100 モデルは、多言語翻訳に使用できます。
- `facebook/m2m100_418M` (翻訳)
- `facebook/m2m100_1.2B` (翻訳)
この例では、`facebook/m2m100_418M` チェックポイントをロードして、中国語から英語に翻訳します。 トークナイザーでソース言語を設定できます。
```py
>>> from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
>>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
>>> chinese_text = "不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒."
>>> tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="zh")
>>> model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
```
テキストをトークン化します。
```py
>>> encoded_zh = tokenizer(chinese_text, return_tensors="pt")
```
M2M100 は、最初に生成されたトークンとしてターゲット言語 ID を強制的にターゲット言語に翻訳します。 英語に翻訳するには、`generate` メソッドで `forced_bos_token_id` を `en` に設定します。
```py
>>> generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id("en"))
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
'Do not interfere with the matters of the witches, because they are delicate and will soon be angry.'
```
## MBart
多言語翻訳には、次の MBart モデルを使用できます。
- `facebook/mbart-large-50-one-to-many-mmt` (One-to-many multilingual machine translation, 50 languages)
- `facebook/mbart-large-50-many-to-many-mmt` (Many-to-many multilingual machine translation, 50 languages)
- `facebook/mbart-large-50-many-to-one-mmt` (Many-to-one multilingual machine translation, 50 languages)
- `facebook/mbart-large-50` (Multilingual translation, 50 languages)
- `facebook/mbart-large-cc25`
この例では、`facebook/mbart-large-50-many-to-many-mmt` チェックポイントをロードして、フィンランド語を英語に翻訳します。トークナイザーでソース言語を設定できます。
```py
>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
>>> en_text = "Do not meddle in the affairs of wizards, for they are subtle and quick to anger."
>>> fi_text = "Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia."
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt", src_lang="fi_FI")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
```
テキストをトークン化します。
```py
>>> encoded_en = tokenizer(en_text, return_tensors="pt")
```
MBart は、最初に生成されたトークンとしてターゲット言語 ID を強制的にターゲット言語に翻訳します。 英語に翻訳するには、`generate` メソッドで `forced_bos_token_id` を `en` に設定します。
```py
>>> generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id("en_XX"))
>>> tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
"Don't interfere with the wizard's affairs, because they are subtle, will soon get angry."
```
`facebook/mbart-large-50-many-to-one-mmt` チェックポイントを使用している場合、最初に生成されたトークンとしてターゲット言語 ID を強制する必要はありません。それ以外の場合、使用方法は同じです。 | transformers/docs/source/ja/multilingual.md/0 | {
"file_path": "transformers/docs/source/ja/multilingual.md",
"repo_id": "transformers",
"token_count": 4144
} | 417 |
<!---
Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# Performance and Scalability
大規模なトランスフォーマーモデルのトレーニングおよび本番環境への展開はさまざまな課題を提起します。
トレーニング中には、モデルが利用可能なGPUメモリよりも多くを必要としたり、トレーニング速度が遅かったりする可能性があります。
デプロイフェーズでは、モデルが本番環境で必要なスループットを処理するのに苦労することがあります。
このドキュメンテーションは、これらの課題を克服し、ユースケースに最適な設定を見つけるのに役立つことを目的としています。
ガイドはトレーニングと推論のセクションに分かれており、それぞれ異なる課題と解決策が存在します。
各セクション内には、トレーニング用のシングルGPU対マルチGPU、推論用のCPU対GPUなど、異なるハードウェア構成用の別々のガイドが用意されています。
このドキュメントを出発点として、シナリオに合った方法に進むための情報源としてご利用ください。
## Training
大規模なトランスフォーマーモデルを効率的にトレーニングするには、GPUやTPUなどのアクセラレータが必要です。
最も一般的なケースは、シングルGPUがある場合です。シングルGPUでのトレーニング効率を最適化するための一般的なアプローチを学ぶには、以下を参照してください。
* [シングルGPUでの効率的なトレーニングのための方法とツール](perf_train_gpu_one): GPUメモリの効果的な利用、トレーニングの高速化などを支援する共通のアプローチを学ぶためにここから始めてください。
* [マルチGPUトレーニングセクション](perf_train_gpu_many): マルチGPU環境に適用されるデータ、テンソル、パイプライン並列性など、さらなる最適化方法について詳細に学びます。
* [CPUトレーニングセクション](perf_train_cpu): CPU上での混合精度トレーニングについて学びます。
* [複数CPUでの効率的なトレーニング](perf_train_cpu_many): 分散CPUトレーニングについて学びます。
* [TensorFlowでTPUを使用したトレーニング](perf_train_tpu_tf): TPUに慣れていない場合は、TPUでのトレーニングとXLAの使用についてのセクションを参照してください。
* [トレーニングのためのカスタムハードウェア](perf_hardware): 独自のディープラーニング環境を構築する際のヒントやトリックを見つけます。
* [Trainer APIを使用したハイパーパラメーター検索](hpo_train)
## Inference
本番環境で大規模なモデルを効率的に推論することは、それらをトレーニングすることと同じくらい難しいことがあります。
以下のセクションでは、CPUおよびシングル/マルチGPU環境で推論を実行する手順について説明します。
* [シングルCPUでの推論](perf_infer_cpu)
* [シングルGPUでの推論](perf_infer_gpu_one)
* [マルチGPU推論](perf_infer_gpu_many)
* [TensorFlowモデルのXLA統合](tf_xla)
## Training and inference
モデルをトレーニングするか、それを使用して推論を実行するかに関係なく適用されるテクニック、ヒント、トリックがここにあります。
* [大規模モデルのインスタンス化](big_models)
* [パフォーマンスの問題のトラブルシューティング](debugging)
## Contribute
このドキュメントはまだ完全ではなく、さらに追加する必要がある項目がたくさんあります。
追加や訂正が必要な場合は、遠慮せずにPRをオープンするか、詳細を議論するためにIssueを開始してください。
AがBよりも優れているという貢献を行う際には、再現可能なベンチマークやその情報の出典へのリンクを含めてみてください（あなた自身の情報である場合を除く）。
| transformers/docs/source/ja/performance.md/0 | {
"file_path": "transformers/docs/source/ja/performance.md",
"repo_id": "transformers",
"token_count": 2063
} | 418 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# Image classification
[[open-in-colab]]
<Youtube id="tjAIM7BOYhw"/>
画像分類では、画像にラベルまたはクラスを割り当てます。テキストや音声の分類とは異なり、入力は
画像を構成するピクセル値。損傷の検出など、画像分類には多くの用途があります
自然災害の後、作物の健康状態を監視したり、病気の兆候がないか医療画像をスクリーニングしたりするのに役立ちます。
このガイドでは、次の方法を説明します。
1. [Food-101](https://huggingface.co/datasets/food101) データセットの [ViT](model_doc/vit) を微調整して、画像内の食品を分類します。
2. 微調整したモデルを推論に使用します。
<Tip>
このタスクと互換性のあるすべてのアーキテクチャとチェックポイントを確認するには、[タスクページ](https://huggingface.co/tasks/image-classification) を確認することをお勧めします。
</Tip>
始める前に、必要なライブラリがすべてインストールされていることを確認してください。
```bash
pip install transformers datasets evaluate
```
Hugging Face アカウントにログインして、モデルをアップロードしてコミュニティと共有することをお勧めします。プロンプトが表示されたら、トークンを入力してログインします。
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Load Food-101 dataset
Datasets、🤗 データセット ライブラリから Food-101 データセットの小さいサブセットを読み込みます。これにより、次の機会が得られます
完全なデータセットのトレーニングにさらに時間を費やす前に、実験してすべてが機能することを確認してください。
```py
>>> from datasets import load_dataset
>>> food = load_dataset("food101", split="train[:5000]")
```
[`~datasets.Dataset.train_test_split`] メソッドを使用して、データセットの `train` 分割をトレイン セットとテスト セットに分割します。
```py
>>> food = food.train_test_split(test_size=0.2)
```
次に、例を見てみましょう。
```py
>>> food["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F52AFC8AC50>,
'label': 79}
```
データセット内の各例には 2 つのフィールドがあります。
- `image`: 食品の PIL 画像
- `label`: 食品のラベルクラス
モデルがラベル ID からラベル名を取得しやすくするために、ラベル名をマップする辞書を作成します。
整数への変換、またはその逆:
```py
>>> labels = food["train"].features["label"].names
>>> label2id, id2label = dict(), dict()
>>> for i, label in enumerate(labels):
... label2id[label] = str(i)
... id2label[str(i)] = label
```
これで、ラベル ID をラベル名に変換できるようになりました。
```py
>>> id2label[str(79)]
'prime_rib'
```
## Preprocess
次のステップでは、ViT 画像プロセッサをロードして画像をテンソルに処理します。
```py
>>> from transformers import AutoImageProcessor
>>> checkpoint = "google/vit-base-patch16-224-in21k"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```
<frameworkcontent>
<pt>
いくつかの画像変換を画像に適用して、モデルの過学習に対する堅牢性を高めます。ここでは torchvision の [`transforms`](https://pytorch.org/vision/stable/transforms.html) モジュールを使用しますが、任意の画像ライブラリを使用することもできます。
画像のランダムな部分をトリミングし、サイズを変更し、画像の平均と標準偏差で正規化します。
```py
>>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
>>> normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
>>> size = (
... image_processor.size["shortest_edge"]
... if "shortest_edge" in image_processor.size
... else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])
```
次に、変換を適用し、画像の `pixel_values` (モデルへの入力) を返す前処理関数を作成します。
```py
>>> def transforms(examples):
... examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
... del examples["image"]
... return examples
```
データセット全体に前処理関数を適用するには、🤗 Datasets [`~datasets.Dataset.with_transform`] メソッドを使用します。変換は、データセットの要素を読み込むときにオンザフライで適用されます。
```py
>>> food = food.with_transform(transforms)
```
次に、[`DefaultDataCollator`] を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合器とは異なり、`DefaultDataCollator` はパディングなどの追加の前処理を適用しません。
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator()
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
過剰適合を回避し、モデルをより堅牢にするために、データセットのトレーニング部分にデータ拡張を追加します。
ここでは、Keras 前処理レイヤーを使用してトレーニング データの変換 (データ拡張を含む) を定義します。
検証データの変換 (中央のトリミング、サイズ変更、正規化のみ)。 `tf.image` または
他のライブラリでも構いません。
```py
>>> from tensorflow import keras
>>> from tensorflow.keras import layers
>>> size = (image_processor.size["height"], image_processor.size["width"])
>>> train_data_augmentation = keras.Sequential(
... [
... layers.RandomCrop(size[0], size[1]),
... layers.Rescaling(scale=1.0 / 127.5, offset=-1),
... layers.RandomFlip("horizontal"),
... layers.RandomRotation(factor=0.02),
... layers.RandomZoom(height_factor=0.2, width_factor=0.2),
... ],
... name="train_data_augmentation",
... )
>>> val_data_augmentation = keras.Sequential(
... [
... layers.CenterCrop(size[0], size[1]),
... layers.Rescaling(scale=1.0 / 127.5, offset=-1),
... ],
... name="val_data_augmentation",
... )
```
次に、一度に 1 つの画像ではなく、画像のバッチに適切な変換を適用する関数を作成します。
```py
>>> import numpy as np
>>> import tensorflow as tf
>>> from PIL import Image
>>> def convert_to_tf_tensor(image: Image):
... np_image = np.array(image)
... tf_image = tf.convert_to_tensor(np_image)
... # `expand_dims()` is used to add a batch dimension since
... # the TF augmentation layers operates on batched inputs.
... return tf.expand_dims(tf_image, 0)
>>> def preprocess_train(example_batch):
... """Apply train_transforms across a batch."""
... images = [
... train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
... ]
... example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
... return example_batch
... def preprocess_val(example_batch):
... """Apply val_transforms across a batch."""
... images = [
... val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
... ]
... example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
... return example_batch
```
🤗 データセット [`~datasets.Dataset.set_transform`] を使用して、その場で変換を適用します。
```py
food["train"].set_transform(preprocess_train)
food["test"].set_transform(preprocess_val)
```
最後の前処理ステップとして、`DefaultDataCollator`を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合機能とは異なり、
`DefaultDataCollator` は、パディングなどの追加の前処理を適用しません。
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator(return_tensors="tf")
```
</tf>
</frameworkcontent>
## Evaluate
トレーニング中にメトリクスを含めると、多くの場合、モデルのパフォーマンスを評価するのに役立ちます。すぐにロードできます
🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) ライブラリを使用した評価方法。このタスクでは、ロードします
[accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) 指標 (詳細については、🤗 評価 [クイック ツアー](https://huggingface.co/docs/evaluate/a_quick_tour) を参照してくださいメトリクスをロードして計算する方法):
```py
>>> import evaluate
>>> accuracy = evaluate.load("accuracy")
```
次に、予測とラベルを [`~evaluate.EvaluationModule.compute`] に渡して精度を計算する関数を作成します。
```py
>>> import numpy as np
>>> def compute_metrics(eval_pred):
... predictions, labels = eval_pred
... predictions = np.argmax(predictions, axis=1)
... return accuracy.compute(predictions=predictions, references=labels)
```
これで `compute_metrics`関数の準備が整いました。トレーニングを設定するときにこの関数に戻ります。
## Train
<frameworkcontent>
<pt>
<Tip>
[`Trainer`] を使用したモデルの微調整に慣れていない場合は、[こちら](../training#train-with-pytorch-trainer) の基本的なチュートリアルをご覧ください。
</Tip>
これでモデルのトレーニングを開始する準備が整いました。 [`AutoModelForImageClassification`] を使用して ViT をロードします。ラベルの数と予想されるラベルの数、およびラベル マッピングを指定します。
```py
>>> from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
>>> model = AutoModelForImageClassification.from_pretrained(
... checkpoint,
... num_labels=len(labels),
... id2label=id2label,
... label2id=label2id,
... )
```
この時点で残っているステップは 3 つだけです。
1. [`TrainingArguments`] でトレーニング ハイパーパラメータを定義します。 `image` 列が削除されるため、未使用の列を削除しないことが重要です。 `image` 列がないと、`pixel_values` を作成できません。この動作を防ぐには、`remove_unused_columns=False`を設定してください。他に必要なパラメータは、モデルの保存場所を指定する `output_dir` だけです。 `push_to_hub=True`を設定して、このモデルをハブにプッシュします (モデルをアップロードするには、Hugging Face にサインインする必要があります)。各エポックの終了時に、[`Trainer`] は精度を評価し、トレーニング チェックポイントを保存します。
2. トレーニング引数を、モデル、データセット、トークナイザー、データ照合器、および `compute_metrics` 関数とともに [`Trainer`] に渡します。
3. [`~Trainer.train`] を呼び出してモデルを微調整します。
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_food_model",
... remove_unused_columns=False,
... eval_strategy="epoch",
... save_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=16,
... gradient_accumulation_steps=4,
... per_device_eval_batch_size=16,
... num_train_epochs=3,
... warmup_ratio=0.1,
... logging_steps=10,
... load_best_model_at_end=True,
... metric_for_best_model="accuracy",
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... data_collator=data_collator,
... train_dataset=food["train"],
... eval_dataset=food["test"],
... processing_class=image_processor,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
トレーニングが完了したら、 [`~transformers.Trainer.push_to_hub`] メソッドを使用してモデルをハブに共有し、誰もがモデルを使用できるようにします。
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
<Tip>
Keras を使用したモデルの微調整に慣れていない場合は、まず [基本チュートリアル](./training#train-a-tensorflow-model-with-keras) を確認してください。
</Tip>
TensorFlow でモデルを微調整するには、次の手順に従います。
1. トレーニングのハイパーパラメータを定義し、オプティマイザーと学習率スケジュールを設定します。
2. 事前トレーニングされたモデルをインスタンス化します。
3. 🤗 データセットを `tf.data.Dataset` に変換します。
4. モデルをコンパイルします。
5. コールバックを追加し、`fit()` メソッドを使用してトレーニングを実行します。
6. モデルを 🤗 Hub にアップロードしてコミュニティと共有します。
まず、ハイパーパラメーター、オプティマイザー、学習率スケジュールを定義します。
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_epochs = 5
>>> num_train_steps = len(food["train"]) * num_epochs
>>> learning_rate = 3e-5
>>> weight_decay_rate = 0.01
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=learning_rate,
... num_train_steps=num_train_steps,
... weight_decay_rate=weight_decay_rate,
... num_warmup_steps=0,
... )
```
次に、ラベル マッピングとともに [`TFAutoModelForImageClassification`] を使用して ViT を読み込みます。
```py
>>> from transformers import TFAutoModelForImageClassification
>>> model = TFAutoModelForImageClassification.from_pretrained(
... checkpoint,
... id2label=id2label,
... label2id=label2id,
... )
```
Convert your datasets to the `tf.data.Dataset` format using the [`~datasets.Dataset.to_tf_dataset`] and your `data_collator`:
```py
>>> # converting our train dataset to tf.data.Dataset
>>> tf_train_dataset = food["train"].to_tf_dataset(
... columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )
>>> # converting our test dataset to tf.data.Dataset
>>> tf_eval_dataset = food["test"].to_tf_dataset(
... columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )
```
`compile()` を使用してトレーニング用にモデルを設定します。
```py
>>> from tensorflow.keras.losses import SparseCategoricalCrossentropy
>>> loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
>>> model.compile(optimizer=optimizer, loss=loss)
```
予測から精度を計算し、モデルを 🤗 ハブにプッシュするには、[Keras callbacks](../main_classes/keras_callbacks) を使用します。
`compute_metrics` 関数を [KerasMetricCallback](../main_classes/keras_callbacks#transformers.KerasMetricCallback) に渡します。
[PushToHubCallback](../main_classes/keras_callbacks#transformers.PushToHubCallback) を使用してモデルをアップロードします。
```py
>>> from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="food_classifier",
... tokenizer=image_processor,
... save_strategy="no",
... )
>>> callbacks = [metric_callback, push_to_hub_callback]
```
ついに、モデルをトレーニングする準備が整いました。トレーニングおよび検証データセット、エポック数、
モデルを微調整するためのコールバック:
```py
>>> model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)
Epoch 1/5
250/250 [==============================] - 313s 1s/step - loss: 2.5623 - val_loss: 1.4161 - accuracy: 0.9290
Epoch 2/5
250/250 [==============================] - 265s 1s/step - loss: 0.9181 - val_loss: 0.6808 - accuracy: 0.9690
Epoch 3/5
250/250 [==============================] - 252s 1s/step - loss: 0.3910 - val_loss: 0.4303 - accuracy: 0.9820
Epoch 4/5
250/250 [==============================] - 251s 1s/step - loss: 0.2028 - val_loss: 0.3191 - accuracy: 0.9900
Epoch 5/5
250/250 [==============================] - 238s 949ms/step - loss: 0.1232 - val_loss: 0.3259 - accuracy: 0.9890
```
おめでとう！モデルを微調整し、🤗 Hub で共有しました。これで推論に使用できるようになりました。
</tf>
</frameworkcontent>
<Tip>
画像分類用のモデルを微調整する方法の詳細な例については、対応する [PyTorch ノートブック](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)
</Tip>
## Inference
モデルを微調整したので、それを推論に使用できるようになりました。
推論を実行したい画像を読み込みます。
```py
>>> ds = load_dataset("food101", split="validation[:10]")
>>> image = ds["image"][0]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png" alt="image of beignets"/>
</div>
推論用に微調整されたモデルを試す最も簡単な方法は、それを [`pipeline`] で使用することです。モデルを使用して画像分類用の`pipeline`をインスタンス化し、それに画像を渡します。
```py
>>> from transformers import pipeline
>>> classifier = pipeline("image-classification", model="my_awesome_food_model")
>>> classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
{'score': 0.015232225880026817, 'label': 'bruschetta'},
{'score': 0.01519392803311348, 'label': 'chicken_wings'},
{'score': 0.013022331520915031, 'label': 'pork_chop'},
{'score': 0.012728818692266941, 'label': 'prime_rib'}]
```
必要に応じて、`pipeline`の結果を手動で複製することもできます。
<frameworkcontent>
<pt>
画像プロセッサをロードして画像を前処理し、`input`を PyTorch テンソルとして返します。
```py
>>> from transformers import AutoImageProcessor
>>> import torch
>>> image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model")
>>> inputs = image_processor(image, return_tensors="pt")
```
入力をモデルに渡し、ロジットを返します。
```py
>>> from transformers import AutoModelForImageClassification
>>> model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
最も高い確率で予測されたラベルを取得し、モデルの `id2label` マッピングを使用してラベルに変換します。
```py
>>> predicted_label = logits.argmax(-1).item()
>>> model.config.id2label[predicted_label]
'beignets'
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
画像プロセッサをロードして画像を前処理し、`input`を TensorFlow テンソルとして返します。
```py
>>> from transformers import AutoImageProcessor
>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/food_classifier")
>>> inputs = image_processor(image, return_tensors="tf")
```
入力をモデルに渡し、ロジットを返します。
```py
>>> from transformers import TFAutoModelForImageClassification
>>> model = TFAutoModelForImageClassification.from_pretrained("MariaK/food_classifier")
>>> logits = model(**inputs).logits
```
最も高い確率で予測されたラベルを取得し、モデルの `id2label` マッピングを使用してラベルに変換します。
```py
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'beignets'
```
</tf>
</frameworkcontent>
| transformers/docs/source/ja/tasks/image_classification.md/0 | {
"file_path": "transformers/docs/source/ja/tasks/image_classification.md",
"repo_id": "transformers",
"token_count": 8613
} | 419 |
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# Visual Question Answering
[[open-in-colab]]
Visual Question Answering (VQA) は、画像に基づいて自由形式の質問に答えるタスクです。
このタスクをサポートするモデルへの入力は通常、画像と質問の組み合わせであり、出力は
自然言語で表現された答え。
VQA の注目すべき使用例には次のようなものがあります。
* 視覚障害者向けのアクセシビリティ アプリケーション。
* 教育: 講義や教科書で示されている視覚的な資料について質問を投げかけること。 VQA は、インタラクティブな博物館の展示物や史跡でも利用できます。
* カスタマー サービスと電子商取引: VQA は、ユーザーが製品について質問できるようにすることでユーザー エクスペリエンスを向上させます。
* 画像検索: VQA モデルを使用して、特定の特徴を持つ画像を検索できます。たとえば、ユーザーは「犬はいますか?」と尋ねることができます。一連の画像から犬が写っているすべての画像を検索します。
このガイドでは、次の方法を学びます。
- [`Graphcore/vqa` データセット](https://huggingface.co/datasets/Graphcore/vqa) 上で分類 VQA モデル、特に [ViLT](../model_doc/vilt) を微調整します。
- 微調整された ViLT を推論に使用します。
- BLIP-2 などの生成モデルを使用してゼロショット VQA 推論を実行します。
## Fine-tuning ViLT
ViLT モデルは、Vision Transformer (ViT) にテキスト埋め込みを組み込んでおり、最小限の設計を可能にします。
視覚と言語の事前トレーニング (VLP)。このモデルは、いくつかの下流タスクに使用できます。 VQA タスクの場合、分類子
head は最上部 (`[CLS]` トークンの最終的な非表示状態の最上部にある線形層) に配置され、ランダムに初期化されます。
したがって、視覚的質問応答は **分類問題** として扱われます。
BLIP、BLIP-2、InstructBLIP などの最近のモデルは、VQA を生成タスクとして扱います。このガイドの後半では、
ゼロショット VQA 推論にそれらを使用する方法を示します。
始める前に、必要なライブラリがすべてインストールされていることを確認してください。
```bash
pip install -q transformers datasets
```
モデルをコミュニティと共有することをお勧めします。 Hugging Face アカウントにログインして、🤗 ハブにアップロードします。
プロンプトが表示されたら、トークンを入力してログインします。
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
モデルのチェックポイントをグローバル変数として定義しましょう。
```py
>>> model_checkpoint = "dandelin/vilt-b32-mlm"
```
## Load the data
説明の目的で、このガイドでは、注釈付きの視覚的な質問に答える「Graphcore/vqa」データセットの非常に小さなサンプルを使用します。
完全なデータセットは [🤗 Hub](https://huggingface.co/datasets/Graphcore/vqa) で見つけることができます。
[`Graphcore/vqa` データセット](https://huggingface.co/datasets/Graphcore/vqa) の代わりに、
公式 [VQA データセット ページ](https://visualqa.org/download.html) から同じデータを手動で取得します。フォローしたい場合は、
カスタム データを使用したチュートリアルでは、[画像データセットを作成する](https://huggingface.co/docs/datasets/image_dataset#loading-script) 方法を確認してください。
🤗 データセットのドキュメントのガイド。
検証分割から最初の 200 個の例をロードし、データセットの機能を調べてみましょう。
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("Graphcore/vqa", split="validation[:200]")
>>> dataset
Dataset({
features: ['question', 'question_type', 'question_id', 'image_id', 'answer_type', 'label'],
num_rows: 200
})
```
データセットの特徴を理解するために例を見てみましょう。
```py
>>> dataset[0]
{'question': 'Where is he looking?',
'question_type': 'none of the above',
'question_id': 262148000,
'image_id': '/root/.cache/huggingface/datasets/downloads/extracted/ca733e0e000fb2d7a09fbcc94dbfe7b5a30750681d0e965f8e0a23b1c2f98c75/val2014/COCO_val2014_000000262148.jpg',
'answer_type': 'other',
'label': {'ids': ['at table', 'down', 'skateboard', 'table'],
'weights': [0.30000001192092896,
1.0,
0.30000001192092896,
0.30000001192092896]}}
```
このタスクに関連する機能には次のものがあります。
* `question`: 画像から回答する質問
* `image_id`: 質問が参照する画像へのパス
* `label`: 注釈
残りの機能は必要ないので削除できます。
```py
>>> dataset = dataset.remove_columns(['question_type', 'question_id', 'answer_type'])
```
ご覧のとおり、`label`機能には、さまざまなヒューマン・アノテーターによって収集された、同じ質問に対する複数の回答 (ここでは`id`と呼びます) が含まれています。
質問に対する答えは主観的なものになる可能性があるためです。この場合、問題は "彼はどこを見ているのか？"ということです。一部の人々
これには "ダウン" という注釈が付けられ、他のものには "テーブルで" という注釈が付けられ、別の注釈には "スケートボード" という注釈が付けられました。
画像を見て、どの答えを出すかを考えてください。
```python
>>> from PIL import Image
>>> image = Image.open(dataset[0]['image_id'])
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/vqa-example.png" alt="VQA Image Example"/>
</div>
質問と回答のあいまいさのため、このようなデータセットはマルチラベル分類問題として扱われます (
複数の回答が有効である可能性があります)。さらに、ワンホット エンコードされたベクトルを作成するだけではなく、
注釈内に特定の回答が出現した回数に基づくソフト エンコーディング。
たとえば、上の例では、"down"という回答が他の回答よりも頻繁に選択されるため、
スコア (データセットでは`weight`と呼ばれます) は 1.0 で、残りの回答のスコアは 1.0 未満です。
後で適切な分類ヘッドを使用してモデルをインスタンス化するために、2 つの辞書を作成しましょう。
ラベル名を整数に変換する、またはその逆:
```py
>>> import itertools
>>> labels = [item['ids'] for item in dataset['label']]
>>> flattened_labels = list(itertools.chain(*labels))
>>> unique_labels = list(set(flattened_labels))
>>> label2id = {label: idx for idx, label in enumerate(unique_labels)}
>>> id2label = {idx: label for label, idx in label2id.items()}
```
マッピングができたので、文字列の回答をその ID に置き換え、さらに前処理をより便利にするためにデータセットをフラット化することができます。
```python
>>> def replace_ids(inputs):
... inputs["label"]["ids"] = [label2id[x] for x in inputs["label"]["ids"]]
... return inputs
>>> dataset = dataset.map(replace_ids)
>>> flat_dataset = dataset.flatten()
>>> flat_dataset.features
{'question': Value(dtype='string', id=None),
'image_id': Value(dtype='string', id=None),
'label.ids': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None),
'label.weights': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None)}
```
## Preprocessing data
次のステップでは、ViLT プロセッサをロードして、モデルの画像データとテキスト データを準備します。
[`ViltProcessor`] は、BERT トークナイザーと ViLT 画像プロセッサを便利な単一プロセッサにラップします。
```py
>>> from transformers import ViltProcessor
>>> processor = ViltProcessor.from_pretrained(model_checkpoint)
```
データを前処理するには、[`ViltProcessor`] を使用して画像と質問をエンコードする必要があります。プロセッサーは使用します
[`BertTokenizerFast`] を使用してテキストをトークン化し、テキスト データの `input_ids`、`attention_mask`、および `token_type_ids` を作成します。
画像に関しては、プロセッサは [`ViltImageProcessor`] を利用して画像のサイズ変更と正規化を行い、`pixel_values` と `pixel_mask` を作成します。
これらの前処理ステップはすべて内部で行われ、`processor`を呼び出すだけで済みます。ただし、それでも必要なのは、
対象のラベルを準備します。この表現では、各要素は考えられる答え (ラベル) に対応します。正解の場合、要素は保持されます。
それぞれのスコア (重み) が設定され、残りの要素は 0 に設定されます。
次の関数は、画像と質問に `processor` を適用し、上で説明したようにラベルをフォーマットします。
```py
>>> import torch
>>> def preprocess_data(examples):
... image_paths = examples['image_id']
... images = [Image.open(image_path) for image_path in image_paths]
... texts = examples['question']
... encoding = processor(images, texts, padding="max_length", truncation=True, return_tensors="pt")
... for k, v in encoding.items():
... encoding[k] = v.squeeze()
... targets = []
... for labels, scores in zip(examples['label.ids'], examples['label.weights']):
... target = torch.zeros(len(id2label))
... for label, score in zip(labels, scores):
... target[label] = score
... targets.append(target)
... encoding["labels"] = targets
... return encoding
```
データセット全体に前処理関数を適用するには、🤗 Datasets [`~datasets.map`] 関数を使用します。 `map` を高速化するには、次のようにします。
データセットの複数の要素を一度に処理するには、`batched=True` を設定します。この時点で、不要な列は自由に削除してください。
```py
>>> processed_dataset = flat_dataset.map(preprocess_data, batched=True, remove_columns=['question','question_type', 'question_id', 'image_id', 'answer_type', 'label.ids', 'label.weights'])
>>> processed_dataset
Dataset({
features: ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values', 'pixel_mask', 'labels'],
num_rows: 200
})
```
最後のステップとして、[`DefaultDataCollator`] を使用してサンプルのバッチを作成します。
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator()
```
## Train the model
これでモデルのトレーニングを開始する準備が整いました。 [`ViltForQuestionAnswering`] で ViLT をロードします。ラベルの数を指定します
ラベルマッピングとともに:
```py
>>> from transformers import ViltForQuestionAnswering
>>> model = ViltForQuestionAnswering.from_pretrained(model_checkpoint, num_labels=len(id2label), id2label=id2label, label2id=label2id)
```
この時点で残っているステップは 3 つだけです。
1. [`TrainingArguments`] でトレーニング ハイパーパラメータを定義します。
```py
>>> from transformers import TrainingArguments
>>> repo_id = "MariaK/vilt_finetuned_200"
>>> training_args = TrainingArguments(
... output_dir=repo_id,
... per_device_train_batch_size=4,
... num_train_epochs=20,
... save_steps=200,
... logging_steps=50,
... learning_rate=5e-5,
... save_total_limit=2,
... remove_unused_columns=False,
... push_to_hub=True,
... )
```
2. トレーニング引数をモデル、データセット、プロセッサー、データ照合器とともに [`Trainer`] に渡します。
```py
>>> from transformers import Trainer
>>> trainer = Trainer(
... model=model,
... args=training_args,
... data_collator=data_collator,
... train_dataset=processed_dataset,
... processing_class=processor,
... )
```
3. [`~Trainer.train`] を呼び出してモデルを微調整します。
```py
>>> trainer.train()
```
トレーニングが完了したら、 [`~Trainer.push_to_hub`] メソッドを使用してモデルをハブに共有し、🤗 ハブで最終モデルを共有します。
```py
>>> trainer.push_to_hub()
```
## Inference
ViLT モデルを微調整し、🤗 Hub にアップロードしたので、それを推論に使用できます。もっとも単純な
推論用に微調整されたモデルを試す方法は、それを [`pipeline`] で使用することです。
```py
>>> from transformers import pipeline
>>> pipe = pipeline("visual-question-answering", model="MariaK/vilt_finetuned_200")
```
このガイドのモデルは 200 の例でのみトレーニングされているため、多くを期待しないでください。少なくともそれがあるかどうか見てみましょう
データから何かを学習し、推論を説明するためにデータセットから最初の例を取り出します。
```py
>>> example = dataset[0]
>>> image = Image.open(example['image_id'])
>>> question = example['question']
>>> print(question)
>>> pipe(image, question, top_k=1)
"Where is he looking?"
[{'score': 0.5498199462890625, 'answer': 'down'}]
```
あまり自信がありませんが、モデルは確かに何かを学習しました。より多くの例とより長いトレーニングを行うと、はるかに良い結果が得られます。
必要に応じて、パイプラインの結果を手動で複製することもできます。
1. 画像と質問を取得し、モデルのプロセッサを使用してモデル用に準備します。
2. モデルを通じて結果または前処理を転送します。
3. ロジットから、最も可能性の高い回答の ID を取得し、`id2label` で実際の回答を見つけます。
```py
>>> processor = ViltProcessor.from_pretrained("MariaK/vilt_finetuned_200")
>>> image = Image.open(example['image_id'])
>>> question = example['question']
>>> # prepare inputs
>>> inputs = processor(image, question, return_tensors="pt")
>>> model = ViltForQuestionAnswering.from_pretrained("MariaK/vilt_finetuned_200")
>>> # forward pass
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> logits = outputs.logits
>>> idx = logits.argmax(-1).item()
>>> print("Predicted answer:", model.config.id2label[idx])
Predicted answer: down
```
## Zero-shot VQA
以前のモデルでは、VQA を分類タスクとして扱いました。 BLIP、BLIP-2、InstructBLIP アプローチなどの一部の最近のモデル
生成タスクとしての VQA。 [BLIP-2](../model_doc/blip-2) を例として考えてみましょう。新しいビジュアル言語の事前トレーニングを導入しました
事前にトレーニングされたビジョン エンコーダーと LLM を任意に組み合わせて使用できるパラダイム (詳細については、[BLIP-2 ブログ投稿](https://huggingface.co/blog/blip-2) を参照)。
これにより、視覚的な質問応答を含む複数の視覚言語タスクで最先端の結果を達成することができます。
このモデルを VQA に使用する方法を説明しましょう。まず、モデルをロードしましょう。ここではモデルを明示的に送信します。
GPU (利用可能な場合)。これは [`Trainer`] が自動的に処理するため、トレーニング時に事前に行う必要はありませんでした。
```py
>>> from transformers import AutoProcessor, Blip2ForConditionalGeneration
>>> import torch
>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
>>> model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", dtype=torch.float16)
>>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model.to(device)
```
モデルは画像とテキストを入力として受け取るため、VQA データセットの最初の例とまったく同じ画像と質問のペアを使用してみましょう。
```py
>>> example = dataset[0]
>>> image = Image.open(example['image_id'])
>>> question = example['question']
```
視覚的な質問応答タスクに BLIP-2 を使用するには、テキスト プロンプトが特定の形式 (`Question: {} Answer:`) に従う必要があります。
```py
>>> prompt = f"Question: {question} Answer:"
```
次に、モデルのプロセッサで画像/プロンプトを前処理し、処理された入力をモデルに渡し、出力をデコードする必要があります。
```py
>>> inputs = processor(image, text=prompt, return_tensors="pt").to(device, torch.float16)
>>> generated_ids = model.generate(**inputs, max_new_tokens=10)
>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
>>> print(generated_text)
"He is looking at the crowd"
```
ご覧のとおり、モデルは群衆と顔の向き (下を向いている) を認識しましたが、見逃しているようです。
観客がスケーターの後ろにいるという事実。それでも、人間が注釈を付けたデータセットを取得することが不可能な場合には、これは
このアプローチにより、有用な結果がすぐに得られます。
| transformers/docs/source/ja/tasks/visual_question_answering.md/0 | {
"file_path": "transformers/docs/source/ja/tasks/visual_question_answering.md",
"repo_id": "transformers",
"token_count": 7926
} | 420 |
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# 어떻게 사용자 정의 파이프라인을 생성하나요? [[how-to-create-a-custom-pipeline]]
이 가이드에서는 사용자 정의 파이프라인을 어떻게 생성하고 [허브](https://hf.co/models)에 공유하거나 🤗 Transformers 라이브러리에 추가하는 방법을 살펴보겠습니다.
먼저 파이프라인이 수용할 수 있는 원시 입력을 결정해야 합니다.
문자열, 원시 바이트, 딕셔너리 또는 가장 원하는 입력일 가능성이 높은 것이면 무엇이든 가능합니다.
이 입력을 가능한 한 순수한 Python 형식으로 유지해야 (JSON을 통해 다른 언어와도) 호환성이 좋아집니다.
이것이 전처리(`preprocess`) 파이프라인의 입력(`inputs`)이 될 것입니다.
그런 다음 `outputs`를 정의하세요.
`inputs`와 같은 정책을 따르고, 간단할수록 좋습니다.
이것이 후처리(`postprocess`) 메소드의 출력이 될 것입니다.
먼저 4개의 메소드(`preprocess`, `_forward`, `postprocess` 및 `_sanitize_parameters`)를 구현하기 위해 기본 클래스 `Pipeline`을 상속하여 시작합니다.
```python
from transformers import Pipeline
class MyPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
return preprocess_kwargs, {}, {}
def preprocess(self, inputs, maybe_arg=2):
model_input = Tensor(inputs["input_ids"])
return {"model_input": model_input}
def _forward(self, model_inputs):
# model_inputs == {"model_input": model_input}
outputs = self.model(**model_inputs)
# Maybe {"logits": Tensor(...)}
return outputs
def postprocess(self, model_outputs):
best_class = model_outputs["logits"].softmax(-1)
return best_class
```
이 분할 구조는 CPU/GPU에 대한 비교적 원활한 지원을 제공하는 동시에, 다른 스레드에서 CPU에 대한 사전/사후 처리를 수행할 수 있게 지원하는 것입니다.
`preprocess`는 원래 정의된 입력을 가져와 모델에 공급할 수 있는 형식으로 변환합니다.
더 많은 정보를 포함할 수 있으며 일반적으로 `Dict` 형태입니다.
`_forward`는 구현 세부 사항이며 직접 호출할 수 없습니다.
`forward`는 예상 장치에서 모든 것이 작동하는지 확인하기 위한 안전장치가 포함되어 있어 선호되는 호출 메소드입니다.
실제 모델과 관련된 것은 `_forward` 메소드에 속하며, 나머지는 전처리/후처리 과정에 있습니다.
`postprocess` 메소드는 `_forward`의 출력을 가져와 이전에 결정한 최종 출력 형식으로 변환합니다.
`_sanitize_parameters`는 초기화 시간에 `pipeline(...., maybe_arg=4)`이나 호출 시간에 `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`과 같이, 사용자가 원하는 경우 언제든지 매개변수를 전달할 수 있도록 허용합니다.
`_sanitize_parameters`의 반환 값은 `preprocess`, `_forward`, `postprocess`에 직접 전달되는 3개의 kwargs 딕셔너리입니다.
호출자가 추가 매개변수로 호출하지 않았다면 아무것도 채우지 마십시오.
이렇게 하면 항상 더 "자연스러운" 함수 정의의 기본 인수를 유지할 수 있습니다.
분류 작업에서 `top_k` 매개변수가 대표적인 예입니다.
```python
>>> pipe = pipeline("my-new-task")
>>> pipe("This is a test")
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
>>> pipe("This is a test", top_k=2)
[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
```
이를 달성하기 위해 우리는 `postprocess` 메소드를 기본 매개변수인 `5`로 업데이트하고 `_sanitize_parameters`를 수정하여 이 새 매개변수를 허용합니다.
```python
def postprocess(self, model_outputs, top_k=5):
best_class = model_outputs["logits"].softmax(-1)
# top_k를 처리하는 로직 추가
return best_class
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "maybe_arg" in kwargs:
preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
postprocess_kwargs = {}
if "top_k" in kwargs:
postprocess_kwargs["top_k"] = kwargs["top_k"]
return preprocess_kwargs, {}, postprocess_kwargs
```
입/출력을 가능한 한 간단하고 완전히 JSON 직렬화 가능한 형식으로 유지하려고 노력하십시오.
이렇게 하면 사용자가 새로운 종류의 개체를 이해하지 않고도 파이프라인을 쉽게 사용할 수 있습니다.
또한 사용 용이성을 위해 여러 가지 유형의 인수(오디오 파일은 파일 이름, URL 또는 순수한 바이트일 수 있음)를 지원하는 것이 비교적 일반적입니다.
## 지원되는 작업 목록에 추가하기 [[adding-it-to-the-list-of-supported-tasks]]
`new-task`를 지원되는 작업 목록에 등록하려면 `PIPELINE_REGISTRY`에 추가해야 합니다:
```python
from transformers.pipelines import PIPELINE_REGISTRY
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
)
```
원하는 경우 기본 모델을 지정할 수 있으며, 이 경우 특정 개정(분기 이름 또는 커밋 해시일 수 있음, 여기서는 "abcdef")과 타입을 함께 가져와야 합니다:
```python
PIPELINE_REGISTRY.register_pipeline(
"new-task",
pipeline_class=MyPipeline,
pt_model=AutoModelForSequenceClassification,
default={"pt": ("user/awesome_model", "abcdef")},
type="text", # 현재 지원 유형: text, audio, image, multimodal
)
```
## Hub에 파이프라인 공유하기 [[share-your-pipeline-on-the-hub]]
Hub에 사용자 정의 파이프라인을 공유하려면 `Pipeline` 하위 클래스의 사용자 정의 코드를 Python 파일에 저장하기만 하면 됩니다.
예를 들어, 다음과 같이 문장 쌍 분류를 위한 사용자 정의 파이프라인을 사용한다고 가정해 보겠습니다:
```py
import numpy as np
from transformers import Pipeline
def softmax(outputs):
maxes = np.max(outputs, axis=-1, keepdims=True)
shifted_exp = np.exp(outputs - maxes)
return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
class PairClassificationPipeline(Pipeline):
def _sanitize_parameters(self, **kwargs):
preprocess_kwargs = {}
if "second_text" in kwargs:
preprocess_kwargs["second_text"] = kwargs["second_text"]
return preprocess_kwargs, {}, {}
def preprocess(self, text, second_text=None):
return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)
def _forward(self, model_inputs):
return self.model(**model_inputs)
def postprocess(self, model_outputs):
logits = model_outputs.logits[0].numpy()
probabilities = softmax(logits)
best_class = np.argmax(probabilities)
label = self.model.config.id2label[best_class]
score = probabilities[best_class].item()
logits = logits.tolist()
return {"label": label, "score": score, "logits": logits}
```
구현은 프레임워크에 구애받지 않으며, PyTorch와 TensorFlow 모델에 대해 작동합니다.
이를 `pair_classification.py`라는 파일에 저장한 경우, 다음과 같이 가져오고 등록할 수 있습니다:
```py
from pair_classification import PairClassificationPipeline
from transformers.pipelines import PIPELINE_REGISTRY
from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
PIPELINE_REGISTRY.register_pipeline(
"pair-classification",
pipeline_class=PairClassificationPipeline,
pt_model=AutoModelForSequenceClassification,
tf_model=TFAutoModelForSequenceClassification,
)
```
이 작업이 완료되면 사전훈련된 모델과 함께 사용할 수 있습니다.
예를 들어, `sgugger/finetuned-bert-mrpc`은 MRPC 데이터 세트에서 미세 조정되어 문장 쌍을 패러프레이즈인지 아닌지를 분류합니다.
```py
from transformers import pipeline
classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
```
그런 다음 `push_to_hub` 메소드를 사용하여 허브에 공유할 수 있습니다:
```py
classifier.push_to_hub("test-dynamic-pipeline")
```
이렇게 하면 "test-dynamic-pipeline" 폴더 내에 `PairClassificationPipeline`을 정의한 파일이 복사되며, 파이프라인의 모델과 토크나이저도 저장한 후, `{your_username}/test-dynamic-pipeline` 저장소에 있는 모든 것을 푸시합니다.
이후에는 `trust_remote_code=True` 옵션만 제공하면 누구나 사용할 수 있습니다.
```py
from transformers import pipeline
classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)
```
## 🤗 Transformers에 파이프라인 추가하기 [[add-the-pipeline-to-transformers]]
🤗 Transformers에 사용자 정의 파이프라인을 기여하려면, `pipelines` 하위 모듈에 사용자 정의 파이프라인 코드와 함께 새 모듈을 추가한 다음, `pipelines/__init__.py`에서 정의된 작업 목록에 추가해야 합니다.
그런 다음 테스트를 추가해야 합니다.
`tests/test_pipelines_MY_PIPELINE.py`라는 새 파일을 만들고 다른 테스트와 예제를 함께 작성합니다.
`run_pipeline_test` 함수는 매우 일반적이며, `model_mapping` 및 `tf_model_mapping`에서 정의된 가능한 모든 아키텍처의 작은 무작위 모델에서 실행됩니다.
이는 향후 호환성을 테스트하는 데 매우 중요하며, 누군가 `XXXForQuestionAnswering`을 위한 새 모델을 추가하면 파이프라인 테스트가 해당 모델에서 실행을 시도한다는 의미입니다.
모델이 무작위이기 때문에 실제 값을 확인하는 것은 불가능하므로, 단순히 파이프라인 출력 `TYPE`과 일치시키기 위한 도우미 `ANY`가 있습니다.
또한 2개(이상적으로는 4개)의 테스트를 구현해야 합니다.
- `test_small_model_pt`: 이 파이프라인에 대한 작은 모델 1개를 정의(결과가 의미 없어도 상관없음)하고 파이프라인 출력을 테스트합니다.
결과는 `test_small_model_tf`와 동일해야 합니다.
- `test_small_model_tf`: 이 파이프라인에 대한 작은 모델 1개를 정의(결과가 의미 없어도 상관없음)하고 파이프라인 출력을 테스트합니다.
결과는 `test_small_model_pt`와 동일해야 합니다.
- `test_large_model_pt`(`선택사항`): 결과가 의미 있을 것으로 예상되는 실제 파이프라인에서 파이프라인을 테스트합니다.
이러한 테스트는 속도가 느리므로 이를 표시해야 합니다.
여기서의 목표는 파이프라인을 보여주고 향후 릴리즈에서의 변화가 없는지 확인하는 것입니다.
- `test_large_model_tf`(`선택사항`): 결과가 의미 있을 것으로 예상되는 실제 파이프라인에서 파이프라인을 테스트합니다.
이러한 테스트는 속도가 느리므로 이를 표시해야 합니다.
여기서의 목표는 파이프라인을 보여주고 향후 릴리즈에서의 변화가 없는지 확인하는 것입니다.
| transformers/docs/source/ko/add_new_pipeline.md/0 | {
"file_path": "transformers/docs/source/ko/add_new_pipeline.md",
"repo_id": "transformers",
"token_count": 7731
} | 421 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# Trainer API를 사용한 하이퍼파라미터 탐색 [[hyperparameter-search-using-trainer-api]]
🤗 Transformers에서는 🤗 Transformers 모델을 학습시키는데 최적화된 [`Trainer`] 클래스를 제공하기 때문에, 사용자는 직접 훈련 루프를 작성할 필요 없이 더욱 간편하게 학습을 시킬 수 있습니다. 또한, [`Trainer`]는 하이퍼파라미터 탐색을 위한 API를 제공합니다. 이 문서에서 이 API를 활용하는 방법을 예시와 함께 보여드리겠습니다.
## 하이퍼파라미터 탐색 백엔드 [[hyperparameter-search-backend]]
[`Trainer`]는 현재 아래 4가지 하이퍼파라미터 탐색 백엔드를 지원합니다:
[optuna](https://optuna.org/)와 [sigopt](https://sigopt.com/), [raytune](https://docs.ray.io/en/latest/tune/index.html), [wandb](https://wandb.ai/site/sweeps) 입니다.
하이퍼파라미터 탐색 백엔드로 사용하기 전에 아래의 명령어를 사용하여 라이브러리들을 설치하세요.
```bash
pip install optuna/sigopt/wandb/ray[tune]
```
## 예제에서 하이퍼파라미터 탐색을 활성화하는 방법 [[how-to-enable-hyperparameter-search-in-example]]
하이퍼파라미터 탐색 공간을 정의하세요. 하이퍼파라미터 탐색 백엔드마다 서로 다른 형식이 필요합니다.
sigopt의 경우, 해당 [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter) 문서를 참조하여 아래와 같이 작성하세요:
```py
>>> def sigopt_hp_space(trial):
... return [
... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
... {
... "categorical_values": ["16", "32", "64", "128"],
... "name": "per_device_train_batch_size",
... "type": "categorical",
... },
... ]
```
optuna의 경우, 해당 [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py) 문서를 참조하여 아래와 같이 작성하세요:
```py
>>> def optuna_hp_space(trial):
... return {
... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
... }
```
raytune의 경우, 해당 [object_parameter](https://docs.ray.io/en/latest/tune/api/search_space.html) 문서를 참조하여 아래와 같이 작성하세요:
```py
>>> def ray_hp_space(trial):
... return {
... "learning_rate": tune.loguniform(1e-6, 1e-4),
... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
... }
```
wandb의 경우, 해당 [object_parameter](https://docs.wandb.ai/guides/sweeps/configuration) 문서를 참조하여 아래와 같이 작성하세요:
```py
>>> def wandb_hp_space(trial):
... return {
... "method": "random",
... "metric": {"name": "objective", "goal": "minimize"},
... "parameters": {
... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
... "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
... },
... }
```
`model_init` 함수를 정의하고 이를 [`Trainer`]에 전달하세요. 아래는 그 예시입니다.
```py
>>> def model_init(trial):
... return AutoModelForSequenceClassification.from_pretrained(
... model_args.model_name_or_path,
... from_tf=bool(".ckpt" in model_args.model_name_or_path),
... config=config,
... cache_dir=model_args.cache_dir,
... revision=model_args.model_revision,
... token=True if model_args.use_auth_token else None,
... )
```
아래와 같이 `model_init` 함수, 훈련 인수, 훈련 및 테스트 데이터셋, 그리고 평가 함수를 사용하여 [`Trainer`]를 생성하세요:
```py
>>> trainer = Trainer(
... model=None,
... args=training_args,
... train_dataset=small_train_dataset,
... eval_dataset=small_eval_dataset,
... compute_metrics=compute_metrics,
... processing_class=tokenizer,
... model_init=model_init,
... data_collator=data_collator,
... )
```
하이퍼파라미터 탐색을 호출하고, 최적의 시험 매개변수를 가져오세요. 백엔드는 `"optuna"`/`"sigopt"`/`"wandb"`/`"ray"` 중에서 선택할 수 있습니다. 방향은 `"minimize"` 또는 `"maximize"` 중 선택하며, 목표를 최소화할 것인지 최대화할 것인지를 결정합니다.
자신만의 compute_objective 함수를 정의할 수 있습니다. 만약 이 함수를 정의하지 않으면, 기본 compute_objective가 호출되고, f1과 같은 평가 지표의 합이 목푯값으로 반환됩니다.
```py
>>> best_trial = trainer.hyperparameter_search(
... direction="maximize",
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )
```
## DDP 미세 조정을 위한 하이퍼파라미터 탐색 [[hyperparameter-search-for-ddp-finetune]]
현재, DDP(Distributed Data Parallelism; 분산 데이터 병렬처리)를 위한 하이퍼파라미터 탐색은 optuna와 sigopt에서 가능합니다. 최상위 프로세스가 하이퍼파라미터 탐색 과정을 시작하고 그 결과를 다른 프로세스에 전달합니다.
| transformers/docs/source/ko/hpo_train.md/0 | {
"file_path": "transformers/docs/source/ko/hpo_train.md",
"repo_id": "transformers",
"token_count": 3520
} | 422 |
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# 콜백 [[callbacks]]
콜백은 PyTorch [`Trainer`]의 반복 학습 동작을 사용자 정의할 수 있는 객체입니다
(이 기능은 TensorFlow에서는 아직 구현되지 않았습니다). 콜백은 반복 학습의 상태를
검사하여 (진행 상황 보고, TensorBoard 또는 기타 머신 러닝 플랫폼에 로그 남기기 등)
결정(예: 조기 종료)을 내릴 수 있습니다.
콜백은 [`TrainerControl`] 객체를 반환하는 것 외에는 반복 학습에서 어떤 것도 변경할 수 없는
"읽기 전용" 코드 조각입니다. 반복 학습에 변경이 필요한 사용자 정의 작업이 필요한 경우,
[`Trainer`]를 서브클래스로 만들어 필요한 메소드들을 오버라이드해야 합니다 (예시는 [trainer](trainer)를 참조하세요).
기본적으로 `TrainingArguments.report_to`는 `"all"`로 설정되어 있으므로, [`Trainer`]는 다음 콜백을 사용합니다.
- [`DefaultFlowCallback`]는 로그, 저장, 평가에 대한 기본 동작을 처리합니다.
- [`PrinterCallback`] 또는 [`ProgressCallback`]는 진행 상황을 표시하고 로그를 출력합니다
([`TrainingArguments`]를 통해 tqdm을 비활성화하면 첫 번째 콜백이 사용되고, 그렇지 않으면 두 번째가 사용됩니다).
- [`~integrations.TensorBoardCallback`]는 TensorBoard가 (PyTorch >= 1.4
또는 tensorboardX를 통해) 접근 가능하면 사용됩니다.
- [`~integrations.WandbCallback`]는 [wandb](https://www.wandb.com/)가 설치되어 있으면
사용됩니다.
- [`~integrations.CometCallback`]는 [comet_ml](https://www.comet.com/site/)이 설치되어 있으면 사용됩니다.
- [`~integrations.MLflowCallback`]는 [mlflow](https://www.mlflow.org/)가 설치되어 있으면 사용됩니다.
- [`~integrations.NeptuneCallback`]는 [neptune](https://neptune.ai/)이 설치되어 있으면 사용됩니다.
- [`~integrations.AzureMLCallback`]는 [azureml-sdk](https://pypi.org/project/azureml-sdk/)가 설치되어
있으면 사용됩니다.
- [`~integrations.CodeCarbonCallback`]는 [codecarbon](https://pypi.org/project/codecarbon/)이 설치되어
있으면 사용됩니다.
- [`~integrations.ClearMLCallback`]는 [clearml](https://github.com/allegroai/clearml)이 설치되어 있으면 사용됩니다.
- [`~integrations.DagsHubCallback`]는 [dagshub](https://dagshub.com/)이 설치되어 있으면 사용됩니다.
- [`~integrations.FlyteCallback`]는 [flyte](https://flyte.org/)가 설치되어 있으면 사용됩니다.
- [`~integrations.DVCLiveCallback`]는 [dvclive](https://dvc.org/doc/dvclive)가 설치되어 있으면 사용됩니다.
- [`~integrations.SwanLabCallback`]는 [swanlab](https://swanlab.cn)가 설치되어 있으면 사용됩니다.
패키지가 설치되어 있지만 해당 통합 기능을 사용하고 싶지 않다면, `TrainingArguments.report_to`를 사용하고자 하는 통합 기능 목록으로 변경할 수 있습니다 (예: `["azure_ml", "wandb"]`).
콜백을 구현하는 주요 클래스는 [`TrainerCallback`]입니다. 이 클래스는 [`Trainer`]를
인스턴스화하는 데 사용된 [`TrainingArguments`]를 가져오고, 해당 Trainer의 내부 상태를
[`TrainerState`]를 통해 접근할 수 있으며, [`TrainerControl`]을 통해 반복 학습에서 일부
작업을 수행할 수 있습니다.
## 사용 가능한 콜백 [[available-callbacks]]
라이브러리에서 사용 가능한 [`TrainerCallback`] 목록은 다음과 같습니다:
[[autodoc]] integrations.CometCallback
- setup
[[autodoc]] DefaultFlowCallback
[[autodoc]] PrinterCallback
[[autodoc]] ProgressCallback
[[autodoc]] EarlyStoppingCallback
[[autodoc]] integrations.TensorBoardCallback
[[autodoc]] integrations.WandbCallback
- setup
[[autodoc]] integrations.MLflowCallback
- setup
[[autodoc]] integrations.AzureMLCallback
[[autodoc]] integrations.CodeCarbonCallback
[[autodoc]] integrations.NeptuneCallback
[[autodoc]] integrations.ClearMLCallback
[[autodoc]] integrations.DagsHubCallback
[[autodoc]] integrations.FlyteCallback
[[autodoc]] integrations.DVCLiveCallback
- setup
[[autodoc]] integrations.SwanLabCallback
- setup
## TrainerCallback [[trainercallback]]
[[autodoc]] TrainerCallback
여기 PyTorch [`Trainer`]와 함께 사용자 정의 콜백을 등록하는 예시가 있습니다:
```python
class MyCallback(TrainerCallback):
"A callback that prints a message at the beginning of training"
def on_train_begin(self, args, state, control, **kwargs):
print("Starting training")
trainer = Trainer(
model,
args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
callbacks=[MyCallback], # 우리는 콜백 클래스를 이 방식으로 전달하거나 그것의 인스턴스(MyCallback())를 전달할 수 있습니다
)
```
또 다른 콜백을 등록하는 방법은 `trainer.add_callback()`을 호출하는 것입니다:
```python
trainer = Trainer(...)
trainer.add_callback(MyCallback)
# 다른 방법으로는 콜백 클래스의 인스턴스를 전달할 수 있습니다
trainer.add_callback(MyCallback())
```
## TrainerState [[trainerstate]]
[[autodoc]] TrainerState
## TrainerControl [[trainercontrol]]
[[autodoc]] TrainerControl
| transformers/docs/source/ko/main_classes/callback.md/0 | {
"file_path": "transformers/docs/source/ko/main_classes/callback.md",
"repo_id": "transformers",
"token_count": 3382
} | 423 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Trainer [[trainer]]
[`Trainer`] 클래스는 PyTorch에서 완전한 기능(feature-complete)의 훈련을 위한 API를 제공하며, 다중 GPU/TPU에서의 분산 훈련, [NVIDIA GPU](https://nvidia.github.io/apex/), [AMD GPU](https://rocm.docs.amd.com/en/latest/rocm.html)를 위한 혼합 정밀도, 그리고 PyTorch의 [`torch.amp`](https://pytorch.org/docs/stable/amp.html)를 지원합니다. [`Trainer`]는 모델의 훈련 방식을 커스터마이즈할 수 있는 다양한 옵션을 제공하는 [`TrainingArguments`] 클래스와 함께 사용됩니다. 이 두 클래스는 함께 완전한 훈련 API를 제공합니다.
[`Seq2SeqTrainer`]와 [`Seq2SeqTrainingArguments`]는 [`Trainer`]와 [`TrainingArguments`] 클래스를 상속하며, 요약이나 번역과 같은 시퀀스-투-시퀀스 작업을 위한 모델 훈련에 적합하게 조정되어 있습니다.
<Tip warning={true}>
[`Trainer`] 클래스는 🤗 Transformers 모델에 최적화되어 있으며, 다른 모델과 함께 사용될 때 예상치 못한 동작을 하게 될 수 있습니다. 자신만의 모델을 사용할 때는 다음을 확인하세요:
- 모델은 항상 튜플이나 [`~utils.ModelOutput`]의 서브클래스를 반환해야 합니다.
- 모델은 `labels` 인자가 제공되면 손실을 계산할 수 있고, 모델이 튜플을 반환하는 경우 그 손실이 튜플의 첫 번째 요소로 반환되어야 합니다.
- 모델은 여러 개의 레이블 인자를 수용할 수 있어야 하며, [`Trainer`]에게 이름을 알리기 위해 [`TrainingArguments`]에서 `label_names`를 사용하지만, 그 중 어느 것도 `"label"`로 명명되어서는 안 됩니다.
</Tip>
## Trainer [[transformers.Trainer]]
[[autodoc]] Trainer
- all
## Seq2SeqTrainer [[transformers.Seq2SeqTrainer]]
[[autodoc]] Seq2SeqTrainer
- evaluate
- predict
## TrainingArguments [[transformers.TrainingArguments]]
[[autodoc]] TrainingArguments
- all
## Seq2SeqTrainingArguments [[transformers.Seq2SeqTrainingArguments]]
[[autodoc]] Seq2SeqTrainingArguments
- all
| transformers/docs/source/ko/main_classes/trainer.md/0 | {
"file_path": "transformers/docs/source/ko/main_classes/trainer.md",
"repo_id": "transformers",
"token_count": 1572
} | 424 |
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the License. You may obtain a copy of the License at
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# CodeGen[[Codegen]]
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## 개요[[Overview]]
CodeGen 모델은 Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong이 작성한 논문 [A Conversational Paradigm for Program Synthesis](https://huggingface.co/papers/2203.13474)에서 제안되었습니다.
CodeGen 모델은 프로그램 합성(program synthesis)을 위한 자기회귀(autoregressive) 언어 모델로, [The Pile](https://pile.eleuther.ai/), BigQuery, BigPython 데이터로 순차적으로 학습되었습니다.
논문의 초록은 다음과 같습니다:
*프로그램 합성(program synthesis)은 주어진 문제 명세에 대한 해답으로 프로그램을 생성하는 것을 목표로 합니다. 이 논문에서는 대규모 언어 모델(LLM)을 활용한 대화형 프로그램 합성(conversational program synthesis) 접근법을 제안하여, 기존 접근법에서의 방대한 프로그램 탐색 공간과 사용자의 의도를 명세화하는 과정에서의 어려움을 해결합니다. 제안된 방식에서는 프로그램 명세 작성과 실제 프로그램 작성을 사용자와 시스템 간 다회 대화(multi-turn conversation)로 바라봅니다. 즉, 프로그램 합성 과정 명세를 자연어로 표현하고, 기대하는 프로그램 합성을 조건부로 예측하여 생성하는 일종의 순차적 예측 문제(sequence prediction problem)로 접근했습니다. 이를 위해 자연어와 프로그래밍 언어 데이터를 기반으로 CodeGen이라는 대규모 언어 모델 그룹을 학습시켰으며, 데이터로부터 약한 지도(weak supervision)와 데이터 및 모델 규모의 확장만으로도 모델이 자연스럽게 대화 능력을 갖추게 된다는 점을 확인하였습니다. 더해서 모델의 대화형 프로그램 합성 능력을 평가하기 위해 다회 대화 기반 프로그래밍 벤치마크(MTPB)를 개발했습니다. 이 벤치마크는 각 문제를 해결하기 위해 사용자와 모델 간 여러 단계의 대화를 거쳐 프로그램이 점진적으로 합성되는 과정을 요구합니다. 연구 결과, CodeGen 모델은 대화형 능력을 성공적으로 발휘했으며 본 논문에서 제안한 대화형 합성 패러다임의 우수성과 효율성을 입증했습니다. 특히 16B 파라미터 규모로 TPU-v4에서 학습된 CodeGen 모델은 HumanEval 벤치마크에서 OpenAI의 Codex를 뛰어넘는 성능을 기록했습니다. 학습된 사용된 라이브러리인 JaxFormer와 모델 체크포인트는 오픈소스로 공개되었습니다: [이 https URL에서 확인하세요](https://github.com/salesforce/codegen).*
이 모델은[Hiroaki Hayashi](https://huggingface.co/rooa)가 기여했습니다.
모델의 원본 코드는 [여기](https://github.com/salesforce/codegen)에 있습니다.
## 체크포인트 명명 규칙[[Checkpoint Naming]]
* CodeGen 모델의 [체크포인트](https://huggingface.co/models?other=codegen)는 서로 다른 사전 학습 데이터와 다양한 크기로 제공됩니다.
* 체크포인트의 형식은 다음과 같습니다: `Salesforce/codegen-{size}-{data}`
* `size`: `350M`, `2B`, `6B`, `16B`
* `data`:
* `nl`: The Pile 데이터로 사전학습된 모델
* `multi`: `nl` 모델에서 시작하여 다양한 프로그래밍 언어를 추가적으로 학습한 모델
* `mono`: `multi` 모델에서 시작하여 추가로 Python 데이터에 대해 학습된 모델
* 예를 들어, `Salesforce/codegen-350M-mono`는 3억 5천만(350M) 개의 파라미터를 모델로, The Pile, 다양한 프로그래밍 언어, Python 데이터의 순서로 단계적으로 학습한 체크포인트를 의미합니다.
## 사용 예시[[Usage example]]
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> checkpoint = "Salesforce/codegen-350M-mono"
>>> model = AutoModelForCausalLM.from_pretrained(checkpoint)
>>> tokenizer = AutoTokenizer.from_pretrained(checkpoint)
>>> text = "def hello_world():"
>>> completion = model.generate(**tokenizer(text, return_tensors="pt"))
>>> print(tokenizer.decode(completion[0]))
def hello_world():
print("Hello World")
hello_world()
```
## 자료[[Resources]]
- [Causal language modeling task guide](../tasks/language_modeling)
## CodeGenConfig
[[autodoc]] CodeGenConfig
- all
## CodeGenTokenizer
[[autodoc]] CodeGenTokenizer
- create_token_type_ids_from_sequences
- save_vocabulary
## CodeGenTokenizerFast
[[autodoc]] CodeGenTokenizerFast
## CodeGenModel
[[autodoc]] CodeGenModel
- forward
## CodeGenForCausalLM
[[autodoc]] CodeGenForCausalLM
- forward
| transformers/docs/source/ko/model_doc/codegen.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/codegen.md",
"repo_id": "transformers",
"token_count": 3377
} | 425 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Grounding DINO[[grounding-dino]]
<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>
## 개요[[overview]]
Grounding DINO 모델은 Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, Lei Zhang이 [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://huggingface.co/papers/2303.05499)에서 제안한 모델입니다. Grounding DINO는 폐쇄형 객체 탐지 모델을 텍스트 인코더로 확장하여 개방형 객체 탐지를 가능하게 합니다. 이 모델은 COCO 제로샷에서 52.5 AP와 같은 놀라운 결과를 달성합니다.
논문의 초록은 다음과 같습니다:
*본 논문에서는 트랜스포머 기반 탐지기 DINO를 기반 사전 학습과 결합하여 Grounding DINO라는 개방형 객체 탐지기를 제시합니다. 이는 카테고리 이름이나 참조 표현 등의 사용자 입력으로 임의의 객체를 탐지할 수 있습니다. 개방형 객체 탐지의 핵심 해결책은 개방형 개념 일반화를 위해 폐쇄형 탐지기에 언어를 도입하는 것입니다. 언어와 비전 모달리티를 효과적으로 융합하기 위해, 폐쇄형 탐지기를 개념적으로 세 단계로 나누어 특성 강화기, 언어 기반 쿼리 선택, 교차 모달리티 융합을 위한 교차 모달리티 디코더를 포함하는 긴밀한 융합 솔루션을 제안합니다. 이전 연구들이 주로 새로운 카테고리에 대한 개방형 객체 탐지를 평가한 반면, 우리는 속성으로 지정된 객체에 대한 참조 표현 이해에 대한 평가도 수행할 것을 제안합니다. Grounding DINO는 COCO, LVIS, ODinW, RefCOCO/+/g 벤치마크를 포함한 세 가지 설정 모두에서 놀라운 성능을 보입니다. Grounding DINO는 COCO 탐지 제로샷 전이 벤치마크에서 52.5 AP(Average Precision, 평균 정밀도)를 달성했습니다. 즉, COCO의 학습 데이터 없이도 이러한 성과를 얻었습니다. 평균 26.1 AP로 ODinW 제로샷 벤치마크에서 새로운 기록을 세웠습니다.*
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grouding_dino_architecture.png"
alt="drawing" width="600"/>
<small> Grounding DINO 개요. <a href="https://huggingface.co/papers/2303.05499">원본 논문</a>에서 가져왔습니다. </small>
이 모델은 [EduardoPacheco](https://huggingface.co/EduardoPacheco)와 [nielsr](https://huggingface.co/nielsr)에 의해 기여되었습니다.
원본 코드는 [여기](https://github.com/IDEA-Research/GroundingDINO)에서 찾을 수 있습니다.
## 사용 팁[[usage-tips]]
- [`GroundingDinoProcessor`]를 사용하여 모델을 위한 이미지-텍스트 쌍을 준비할 수 있습니다.
- 텍스트에서 클래스를 구분할 때는 마침표를 사용하세요. 예: "a cat. a dog."
- 여러 클래스를 사용할 때(예: `"a cat. a dog."`), [`GroundingDinoProcessor`]의 `post_process_grounded_object_detection`을 사용해 출력을 후처리해야 합니다. `post_process_object_detection`에서 반환되는 레이블은 prob > threshold인 모델 차원의 인덱스를 나타내기 때문입니다.
다음은 제로샷 객체 탐지에 모델을 사용하는 방법입니다:
```python
>>> import requests
>>> import torch
>>> from PIL import Image
>>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
>>> model_id = "IDEA-Research/grounding-dino-tiny"
>>> device = "cuda"
>>> processor = AutoProcessor.from_pretrained(model_id)
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
>>> image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(image_url, stream=True).raw)
>>> # 고양이와 리모컨 확인
>>> text_labels = [["a cat", "a remote control"]]
>>> inputs = processor(images=image, text=text_labels, return_tensors="pt").to(device)
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> results = processor.post_process_grounded_object_detection(
... outputs,
... inputs.input_ids,
... box_threshold=0.4,
... text_threshold=0.3,
... target_sizes=[image.size[::-1]]
... )
# 첫 번째 이미지 결과 가져오기
>>> result = results[0]
>>> for box, score, labels in zip(result["boxes"], result["scores"], result["labels"]):
... box = [round(x, 2) for x in box.tolist()]
... print(f"Detected {labels} with confidence {round(score.item(), 3)} at location {box}")
Detected a cat with confidence 0.468 at location [344.78, 22.9, 637.3, 373.62]
Detected a cat with confidence 0.426 at location [11.74, 51.55, 316.51, 473.22]
```
## Grounded SAM[[grounded-sam]]
[Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://huggingface.co/papers/2401.14159)에서 소개된 대로 Grounding DINO를 [Segment Anything](sam) 모델과 결합하여 텍스트 기반 마스크 생성을 할 수 있습니다. 자세한 내용은 이 [데모 노트북](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Grounding%20DINO/GroundingDINO_with_Segment_Anything.ipynb) 🌍을 참조하세요.
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/grounded_sam.png"
alt="drawing" width="900"/>
<small> Grounded SAM 개요. <a href="https://github.com/IDEA-Research/Grounded-Segment-Anything">원본 저장소</a>에서 가져왔습니다. </small>
## 리소스[[resources]]
Grounding DINO를 시작하는 데 도움이 되는 공식 Hugging Face 및 커뮤니티(🌎로 표시) 리소스 목록입니다. 여기에 포함될 리소스를 제출하고 싶다면 Pull Request를 자유롭게 열어주세요. 검토해드리겠습니다! 리소스는 기존 리소스를 복제하는 대신 새로운 것을 보여주는 것이 이상적입니다.
- Grounding DINO로 추론하고 [SAM](sam)과 결합하는 데모 노트북은 [여기](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Grounding%20DINO)에서 찾을 수 있습니다. 🌎
## GroundingDinoImageProcessor
[[autodoc]] GroundingDinoImageProcessor
- preprocess
## GroundingDinoImageProcessorFast
[[autodoc]] GroundingDinoImageProcessorFast
- preprocess
- post_process_object_detection
## GroundingDinoProcessor
[[autodoc]] GroundingDinoProcessor
- post_process_grounded_object_detection
## GroundingDinoConfig
[[autodoc]] GroundingDinoConfig
## GroundingDinoModel
[[autodoc]] GroundingDinoModel
- forward
## GroundingDinoForObjectDetection
[[autodoc]] GroundingDinoForObjectDetection
- forward | transformers/docs/source/ko/model_doc/grounding-dino.md/0 | {
"file_path": "transformers/docs/source/ko/model_doc/grounding-dino.md",
"repo_id": "transformers",
"token_count": 4175
} | 426 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# 패딩과 잘라내기[[padding-and-truncation]]
배치 입력은 길이가 다른 경우가 많아서 고정 크기 텐서로 변환할 수 없습니다. 패딩과 잘라내기는 다양한 길이의 배치에서 직사각형 텐서를 생성할 수 있도록 이 문제를 해결하는 전략입니다. 패딩은 특수한 **패딩 토큰**을 추가하여 짧은 시퀀스가 배치에서 가장 긴 시퀀스 또는 모델에서 허용하는 최대 길이와 동일한 길이를 갖도록 합니다. 잘라내기는 긴 시퀀스를 잘라내어 패딩과 다른 방식으로 시퀀스의 길이를 동일하게 합니다.
대부분의 경우 배치에 가장 긴 시퀀스의 길이로 패딩하고 모델이 허용할 수 있는 최대 길이로 잘라내는 것이 잘 작동합니다. 그러나 필요하다면 API가 지원하는 더 많은 전략을 사용할 수 있습니다. 필요한 인수는 `padding`, `truncation`, `max_length` 세 가지입니다.
`padding` 인수는 패딩을 제어합니다. 불리언 또는 문자열일 수 있습니다:
- `True` 또는 `'longest'`: 배치에서 가장 긴 시퀀스로 패딩합니다(단일 시퀀스만 제공하는 경우 패딩이 적용되지 않습니다).
- `'max_length'`: `max_length` 인수가 지정한 길이로 패딩하거나, `max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 패딩합니다. 단일 시퀀스만 제공하는 경우에도 패딩이 적용됩니다.
- `False` 또는 `'do_not_pad'`: 패딩이 적용되지 않습니다. 이것이 기본 동작입니다.
`truncation` 인수는 잘라낼 방법을 정합니다. 불리언 또는 문자열일 수 있습니다:
- `True` 또는 `longest_first`: `max_length` 인수가 지정한 최대 길이로 잘라내거나,
`max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다.
시퀀스 쌍에서 가장 긴 시퀀스의 토큰을 적절한 길이에 도달할 때까지 하나씩 제거합니다.
- `'only_second'`: `max_length` 인수가 지정한 최대 길이로 잘라내거나,
`max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다.
시퀀스 쌍(또는 시퀀스 쌍의 배치)가 제공된 경우 쌍의 두 번째 문장만 잘라냅니다.
- `'only_first'`: `max_length` 인수가 지정한 최대 길이로 잘라내거나,
`max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다.
시퀀스 쌍(또는 시퀀스 쌍의 배치)가 제공된 경우 쌍의 첫 번째 문장만 잘라냅니다.
- `False` 또는 `'do_not_truncate'`: 잘라내기를 적용하지 않습니다. 이것이 기본 동작입니다.
`max_length` 인수는 패딩 및 잘라내기를 적용할 길이를 제어합니다. 이 인수는 정수 또는 `None`일 수 있으며, `None`일 경우 모델이 허용할 수 있는 최대 길이로 기본값이 설정됩니다. 모델에 특정한 최대 입력 길이가 없는 경우 `max_length`에 대한 잘라내기 또는 패딩이 비활성화됩니다.
다음 표에는 패딩 및 잘라내기를 설정하는 권장 방법이 요약되어 있습니다.
입력으로 시퀀스 쌍을 사용하는 경우, 다음 예제에서 `truncation=True`를 `['only_first', 'only_second', 'longest_first']`에서 선택한 `STRATEGY`, 즉 `truncation='only_second'` 또는 `truncation='longest_first'`로 바꾸면 앞서 설명한 대로 쌍의 두 시퀀스가 잘리는 방식을 제어할 수 있습니다.
| 잘라내기 | 패딩 | 사용 방법 |
|--------------------------------------|-----------------------------------|------------------------------------------------------------------------------------------|
| 잘라내기 없음 | 패딩 없음 | `tokenizer(batch_sentences)` |
| | 배치 내 최대 길이로 패딩 | `tokenizer(batch_sentences, padding=True)` 또는 |
| | | `tokenizer(batch_sentences, padding='longest')` |
| | 모델의 최대 입력 길이로 패딩 | `tokenizer(batch_sentences, padding='max_length')` |
| | 특정 길이로 패딩 | `tokenizer(batch_sentences, padding='max_length', max_length=42)` |
| | 다양한 길이로 패딩 | `tokenizer(batch_sentences, padding=True, pad_to_multiple_of=8)` |
| 모델의 최대 입력 길이로 잘라내기 | 패딩 없음 | `tokenizer(batch_sentences, truncation=True)` 또는 |
| | | `tokenizer(batch_sentences, truncation=STRATEGY)` |
| | 배치 내 최대 길이로 패딩 | `tokenizer(batch_sentences, padding=True, truncation=True)` 또는 |
| | | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY)` |
| | 모델의 최대 입력 길이로 패딩 | `tokenizer(batch_sentences, padding='max_length', truncation=True)` 또는 |
| | | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)` |
| | 특정 길이로 패딩 | 사용 불가 |
| 특정 길이로 잘라내기 | 패딩 없음 | `tokenizer(batch_sentences, truncation=True, max_length=42)` 또는 |
| | | `tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)` |
| | 배치 내 최대 길이로 패딩 | `tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` 또는 |
| | | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)` |
| | 모델의 최대 입력 길이로 패딩 | 사용 불가 |
| | 특정 길이로 패딩 | `tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` 또는 |
| | | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)` |
| transformers/docs/source/ko/pad_truncation.md/0 | {
"file_path": "transformers/docs/source/ko/pad_truncation.md",
"repo_id": "transformers",
"token_count": 5964
} | 427 |
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Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Pull Request에 대한 검사 [[checks-on-a-pull-request]]
🤗 Transformers에서 Pull Request를 열 때, 기존에 있는 것을 망가뜨리지 않는지 확인하기 위해 상당한 수의 검사가 실행됩니다. 이러한 검사는 다음과 같은 네 가지 유형으로 구성됩니다:
- 일반적인 테스트
- 문서 빌드
- 코드 및 문서 스타일
- 일반 저장소 일관성
이 문서에서는 이러한 다양한 검사와 그 이유를 설명하고, PR에서 하나 이상의 검사가 실패한 경우 로컬에서 어떻게 디버그하는지 알아보겠습니다.
참고로, 이러한 검사를 사용하려면 개발 설치가 필요합니다:
```bash
pip install transformers[dev]
```
또는 Transformers 저장소 내에 편집 가능한 설치가 필요합니다:
```bash
pip install -e .[dev]
```
Transformers의 선택적 종속성 수가 많이 늘어났기 때문에 개발 설치를 실패할 수도 있습니다. 개발 설치가 실패하는 경우, 작업 중인 Deep Learning 프레임워크 (PyTorch, TensorFlow 및/또는 Flax)를 설치하고 다음 명령을 실행하세요.
```bash
pip install transformers[quality]
```
편집 가능한 설치의 경우는 다음 명령을 실행하세요.
```bash
pip install -e .[quality]
```
## 테스트 [[tests]]
`ci/circleci: run_tests_`로 시작하는 모든 작업은 Transformers 테스트 모음의 일부를 실행합니다. 이러한 작업은 특정 환경에서 일부 라이브러리에 중점을 둡니다. 예를 들어 `ci/circleci: run_tests_pipelines_tf`는 TensorFlow만 설치된 환경에서 파이프라인 테스트를 실행합니다.
테스트 모듈에서 실제로 변경 사항이 없을 때 테스트를 실행하지 않기 위해, 테스트 모음의 일부만 실행됩니다. 라이브러리의 변경 전후에 대한 차이를 확인하기 위해 유틸리티가 실행되고, 해당 차이에 영향을 받는 테스트가 선택됩니다. 이 유틸리티는 로컬에서 다음과 같이 실행할 수 있습니다:
```bash
python utils/tests_fetcher.py
```
Transformers 저장소의 최상단에서 실행합니다. 이 유틸리티는 다음과 같은 작업을 수행합니다:
1. 변경 사항이 있는 파일마다 변경 사항이 코드인지 주석 또는 문서 문자열인지 확인합니다. 실제 코드 변경이 있는 파일만 유지됩니다.
2. 소스 코드 파일의 각 파일에 대해 재귀적으로 영향을 주는 모든 파일을 제공하는 내부 맵을 작성합니다. 모듈 B가 모듈 A를 가져오면 모듈 A는 모듈 B에 영향을 줍니다. 재귀적인 영향에는 각 모듈이 이전 모듈을 가져오는 모듈 체인이 필요합니다.
3. 단계 1에서 수집한 파일에 이 맵을 적용하여 PR에 영향을 받는 모델 파일 목록을 얻습니다.
4. 각 파일을 해당하는 테스트 파일에 매핑하고 실행할 테스트 목록을 가져옵니다.
로컬에서 스크립트를 실행하면 단계 1, 3 및 4의 결과를 출력하여 실행되는 테스트를 알 수 있습니다. 스크립트는 또한 `test_list.txt`라는 파일을 생성하여 실행할 테스트 목록을 포함하며, 다음 명령으로 해당 테스트를 로컬에서 실행할 수 있습니다:
```bash
python -m pytest -n 8 --dist=loadfile -rA -s $(cat test_list.txt)
```
잘못된 사항이 누락되었을 경우, 전체 테스트 모음도 매일 실행됩니다.
## 문서 빌드 [[documentation-build]]
`build_pr_documentation` 작업은 문서를 빌드하고 미리 보기를 생성하여 PR이 병합된 후 모든 것이 제대로 보이는지 확인합니다. 로봇은 PR에 문서 미리보기 링크를 추가합니다. PR에서 만든 변경 사항은 자동으로 미리보기에 업데이트됩니다. 문서 빌드에 실패한 경우 **세부 정보**를 클릭하여 어디에서 문제가 발생했는지 확인할 수 있습니다. 오류는 주로 `toctree`에 누락된 파일과 같이 간단한 오류입니다.
로컬에서 문서를 빌드하거나 미리 볼 경우, docs 폴더의 [`README.md`](https://github.com/huggingface/transformers/tree/main/docs)를 참조하세요.
## 코드 및 문서 스타일 [[code-and-documentation-style]]
`black`과 `ruff`를 사용하여 모든 소스 파일, 예제 및 테스트에 코드 형식을 적용합니다. 또한, `utils/style_doc.py`에서 문서 문자열과 `rst` 파일의 형식, 그리고 Transformers의 `__init__.py` 파일에서 실행되는 지연된 임포트의 순서에 대한 사용자 정의 도구가 있습니다. 이 모든 것은 다음을 실행함으로써 실행할 수 있습니다:
```bash
make style
```
CI는 이러한 사항이 `ci/circleci: check_code_quality` 검사 내에서 적용되었는지 확인합니다. 또한 `ruff`도 실행되며, 정의되지 않은 변수나 사용되지 않은 변수를 발견하면 경고합니다. 이 검사를 로컬에서 실행하려면 다음을 사용하세요:
```bash
make quality
```
이 작업은 많은 시간이 소요될 수 있으므로 현재 브랜치에서 수정한 파일에 대해서만 동일한 작업을 실행하려면 다음을 실행하세요.
```bash
make fixup
```
이 명령은 현재 브랜치에서 수정한 파일에 대한 모든 추가적인 검사도 실행합니다. 이제 이들을 살펴보겠습니다.
## 저장소 일관성 [[repository-consistency]]
이는 PR이 저장소를 정상적인 상태로 유지하는지 확인하는 모든 테스트를 모은 것이며, `ci/circleci: check_repository_consistency` 검사에서 수행됩니다. 다음을 실행함으로써 로컬에서 이 검사를 실행할 수 있습니다.
```bash
make repo-consistency
```
이 검사는 다음을 확인합니다.
- init에 추가된 모든 객체가 문서화되었는지 (`utils/check_repo.py`에서 수행)
- `__init__.py` 파일의 두 섹션에 동일한 내용이 있는지 (`utils/check_inits.py`에서 수행)
- 다른 모듈에서 복사된 코드가 원본과 일치하는지 (`utils/check_copies.py`에서 수행)
- 모든 구성 클래스에 docstring에 언급된 유효한 체크포인트가 적어도 하나 있는지 (`utils/check_config_docstrings.py`에서 수행)
- 모든 구성 클래스가 해당하는 모델링 파일에서 사용되는 속성만 포함하고 있는지 (`utils/check_config_attributes.py`에서 수행)
- README와 문서 인덱스의 번역이 메인 README와 동일한 모델 목록을 가지고 있는지 (`utils/check_copies.py`에서 수행)
- 문서의 자동 생성된 테이블이 최신 상태인지 (`utils/check_table.py`에서 수행)
- 라이브러리에는 선택적 종속성이 설치되지 않았더라도 모든 객체가 사용 가능한지 (`utils/check_dummies.py`에서 수행)
이러한 검사가 실패하는 경우, 처음 두 가지 항목은 수동으로 수정해야 하며, 나머지 네 가지 항목은 다음 명령을 실행하여 자동으로 수정할 수 있습니다.
```bash
make fix-copies
```
추가적인 검사는 새로운 모델을 추가하는 PR에 대한 것으로, 주로 다음과 같습니다:
- 추가된 모든 모델이 Auto-mapping에 있는지 (`utils/check_repo.py`에서 수행)
<!-- TODO Sylvain, add a check that makes sure the common tests are implemented.-->
- 모든 모델이 올바르게 테스트되었는지 (`utils/check_repo.py`에서 수행)
<!-- TODO Sylvain, add the following
- 모든 모델이 메인 README, 주요 문서에 추가되었는지
- 사용된 모든 체크포인트가 실제로 Hub에 존재하는지
-->
### 복사본 확인 [[check-copies]]
Transformers 라이브러리는 모델 코드에 대해 매우 완고하며, 각 모델은 다른 모델에 의존하지 않고 완전히 단일 파일로 구현되어야 합니다. 이렇게 하기 위해 특정 모델의 코드 복사본이 원본과 일관된 상태로 유지되는지 확인하는 메커니즘을 추가했습니다. 따라서 버그 수정이 필요한 경우 다른 모델에 영향을 주는 모든 모델을 볼 수 있으며 수정을 적용할지 수정된 사본을 삭제할지 선택할 수 있습니다.
<Tip>
파일이 다른 파일의 완전한 사본인 경우 해당 파일을 `utils/check_copies.py`의 `FULL_COPIES` 상수에 등록해야 합니다.
</Tip>
이 메커니즘은 `# Copied from xxx` 형식의 주석을 기반으로 합니다. `xxx`에는 아래에 복사되는 클래스 또는 함수의 전체 경로가 포함되어야 합니다. 예를 들어 `RobertaSelfOutput`은 `BertSelfOutput` 클래스의 복사본입니다. 따라서 [여기](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L289)에서 주석이 있습니다:
```py
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput
```
클래스 전체에 수정을 적용하는 대신에 복사본과 관련있는 메서드에 적용할 수도 있습니다. 예를 들어 [여기](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L598)에서 `RobertaPreTrainedModel._init_weights`가 `BertPreTrainedModel`의 동일한 메서드에서 복사된 것을 볼 수 있으며 해당 주석이 있습니다:
```py
# Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
```
복사본이 이름만 다른 경우가 있습니다: 예를 들어 `RobertaAttention`에서 `BertSelfAttention` 대신 `RobertaSelfAttention`을 사용하지만 그 외에는 코드가 완전히 동일합니다: 이 때 `# Copied from`은 `Copied from xxx with foo->bar`와 같은 간단한 문자열 대체를 지원합니다. 이는 모든 `foo` 인스턴스를 `bar`로 바꿔서 코드를 복사합니다. [여기](https://github.com/huggingface/transformers/blob/2bd7a27a671fd1d98059124024f580f8f5c0f3b5/src/transformers/models/roberta/modeling_roberta.py#L304C1-L304C86)에서 어떻게 사용되는지 볼 수 있습니다:
```py
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->Roberta
```
화살표 주변에는 공백이 없어야 합니다(공백이 대체 패턴의 일부인 경우는 예외입니다).
대체 패턴을 쉼표로 구분하여 여러 패턴을 추가할 수 있습니다. 예를 들어 `CamemberForMaskedLM`은 두 가지 대체 사항을 가진 `RobertaForMaskedLM`의 복사본입니다: `Roberta`를 `Camembert`로 대체하고 `ROBERTA`를 `CAMEMBERT`로 대체합니다. [여기](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/camembert/modeling_camembert.py#L929)에서 이것이 주석으로 어떻게 구현되었는지 확인할 수 있습니다:
```py
# Copied from transformers.models.roberta.modeling_roberta.RobertaForMaskedLM with Roberta->Camembert, ROBERTA->CAMEMBERT
```
순서가 중요한 경우(이전 수정과 충돌할 수 있는 경우) 수정은 왼쪽에서 오른쪽으로 실행됩니다.
<Tip>
새 변경이 서식을 변경하는 경우(짧은 이름을 매우 긴 이름으로 바꾸는 경우) 자동 서식 지정기를 적용한 후 복사본이 검사됩니다.
</Tip>
패턴의 대소문자가 다른 경우(대문자와 소문자가 혼용된 대체 양식) `all-casing` 옵션을 추가하는 방법도 있습니다. [여기](https://github.com/huggingface/transformers/blob/15082a9dc6950ecae63a0d3e5060b2fc7f15050a/src/transformers/models/mobilebert/modeling_mobilebert.py#L1237)에서 `MobileBertForSequenceClassification`에서 사용된 예시를 볼 수 있습니다:
```py
# Copied from transformers.models.bert.modeling_bert.BertForSequenceClassification with Bert->MobileBert all-casing
```
이 경우, 코드는 다음과 같이 복사됩니다:
- `MobileBert`에서 `Bert`로(예: `MobileBertModel`을 init에서 사용할 때)
- `mobilebert`에서 `bert`로(예: `self.mobilebert`를 정의할 때)
- `MOBILEBERT`에서 `BERT`로(`MOBILEBERT_INPUTS_DOCSTRING` 상수에서)
| transformers/docs/source/ko/pr_checks.md/0 | {
"file_path": "transformers/docs/source/ko/pr_checks.md",
"repo_id": "transformers",
"token_count": 9042
} | 428 |
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the License. You may obtain a copy of the License at
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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.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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-->
# 이미지 분류[[image-classification]]
[[open-in-colab]]
<Youtube id="tjAIM7BOYhw"/>
이미지 분류는 이미지에 레이블 또는 클래스를 할당합니다. 텍스트 또는 오디오 분류와 달리 입력은
이미지를 구성하는 픽셀 값입니다. 이미지 분류에는 자연재해 후 피해 감지, 농작물 건강 모니터링, 의료 이미지에서 질병의 징후 검사 지원 등
다양한 응용 사례가 있습니다.
이 가이드에서는 다음을 설명합니다:
1. [Food-101](https://huggingface.co/datasets/food101) 데이터 세트에서 [ViT](model_doc/vit)를 미세 조정하여 이미지에서 식품 항목을 분류합니다.
2. 추론을 위해 미세 조정 모델을 사용합니다.
<Tip>
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/image-classification)를 확인하는 것이 좋습니다.
</Tip>
시작하기 전에, 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
```bash
pip install transformers datasets evaluate
```
Hugging Face 계정에 로그인하여 모델을 업로드하고 커뮤니티에 공유하는 것을 권장합니다. 메시지가 표시되면, 토큰을 입력하여 로그인하세요:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## Food-101 데이터 세트 가져오기[[load-food101-dataset]]
🤗 Datasets 라이브러리에서 Food-101 데이터 세트의 더 작은 부분 집합을 가져오는 것으로 시작합니다. 이렇게 하면 전체 데이터 세트에 대한
훈련에 많은 시간을 할애하기 전에 실험을 통해 모든 것이 제대로 작동하는지 확인할 수 있습니다.
```py
>>> from datasets import load_dataset
>>> food = load_dataset("food101", split="train[:5000]")
```
데이터 세트의 `train`을 [`~datasets.Dataset.train_test_split`] 메소드를 사용하여 훈련 및 테스트 세트로 분할하세요:
```py
>>> food = food.train_test_split(test_size=0.2)
```
그리고 예시를 살펴보세요:
```py
>>> food["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512 at 0x7F52AFC8AC50>,
'label': 79}
```
데이터 세트의 각 예제에는 두 개의 필드가 있습니다:
- `image`: 식품 항목의 PIL 이미지
- `label`: 식품 항목의 레이블 클래스
모델이 레이블 ID에서 레이블 이름을 쉽게 가져올 수 있도록
레이블 이름을 정수로 매핑하고, 정수를 레이블 이름으로 매핑하는 사전을 만드세요:
```py
>>> labels = food["train"].features["label"].names
>>> label2id, id2label = dict(), dict()
>>> for i, label in enumerate(labels):
... label2id[label] = str(i)
... id2label[str(i)] = label
```
이제 레이블 ID를 레이블 이름으로 변환할 수 있습니다:
```py
>>> id2label[str(79)]
'prime_rib'
```
## 전처리[[preprocess]]
다음 단계는 이미지를 텐서로 처리하기 위해 ViT 이미지 프로세서를 가져오는 것입니다:
```py
>>> from transformers import AutoImageProcessor
>>> checkpoint = "google/vit-base-patch16-224-in21k"
>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
```
<frameworkcontent>
<pt>
이미지에 몇 가지 이미지 변환을 적용하여 과적합에 대해 모델을 더 견고하게 만듭니다. 여기서 Torchvision의 [`transforms`](https://pytorch.org/vision/stable/transforms.html) 모듈을 사용하지만, 원하는 이미지 라이브러리를 사용할 수도 있습니다.
이미지의 임의 부분을 크롭하고 크기를 조정한 다음, 이미지 평균과 표준 편차로 정규화하세요:
```py
>>> from torchvision.transforms import RandomResizedCrop, Compose, Normalize, ToTensor
>>> normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
>>> size = (
... image_processor.size["shortest_edge"]
... if "shortest_edge" in image_processor.size
... else (image_processor.size["height"], image_processor.size["width"])
... )
>>> _transforms = Compose([RandomResizedCrop(size), ToTensor(), normalize])
```
그런 다음 전처리 함수를 만들어 변환을 적용하고 이미지의 `pixel_values`(모델에 대한 입력)를 반환하세요:
```py
>>> def transforms(examples):
... examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
... del examples["image"]
... return examples
```
전체 데이터 세트에 전처리 기능을 적용하려면 🤗 Datasets [`~datasets.Dataset.with_transform`]을 사용합니다. 데이터 세트의 요소를 가져올 때 변환이 즉시 적용됩니다:
```py
>>> food = food.with_transform(transforms)
```
이제 [`DefaultDataCollator`]를 사용하여 예제 배치를 만듭니다. 🤗 Transformers의 다른 데이터 콜레이터와 달리, `DefaultDataCollator`는 패딩과 같은 추가적인 전처리를 적용하지 않습니다.
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator()
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
과적합을 방지하고 모델을 보다 견고하게 만들기 위해 데이터 세트의 훈련 부분에 데이터 증강을 추가합니다.
여기서 Keras 전처리 레이어로 훈련 데이터에 대한 변환(데이터 증강 포함)과
검증 데이터에 대한 변환(중앙 크로핑, 크기 조정, 정규화만)을 정의합니다.
`tf.image` 또는 다른 원하는 라이브러리를 사용할 수 있습니다.
```py
>>> from tensorflow import keras
>>> from tensorflow.keras import layers
>>> size = (image_processor.size["height"], image_processor.size["width"])
>>> train_data_augmentation = keras.Sequential(
... [
... layers.RandomCrop(size[0], size[1]),
... layers.Rescaling(scale=1.0 / 127.5, offset=-1),
... layers.RandomFlip("horizontal"),
... layers.RandomRotation(factor=0.02),
... layers.RandomZoom(height_factor=0.2, width_factor=0.2),
... ],
... name="train_data_augmentation",
... )
>>> val_data_augmentation = keras.Sequential(
... [
... layers.CenterCrop(size[0], size[1]),
... layers.Rescaling(scale=1.0 / 127.5, offset=-1),
... ],
... name="val_data_augmentation",
... )
```
다음으로 한 번에 하나의 이미지가 아니라 이미지 배치에 적절한 변환을 적용하는 함수를 만듭니다.
```py
>>> import numpy as np
>>> import tensorflow as tf
>>> from PIL import Image
>>> def convert_to_tf_tensor(image: Image):
... np_image = np.array(image)
... tf_image = tf.convert_to_tensor(np_image)
... # `expand_dims()` is used to add a batch dimension since
... # the TF augmentation layers operates on batched inputs.
... return tf.expand_dims(tf_image, 0)
>>> def preprocess_train(example_batch):
... """Apply train_transforms across a batch."""
... images = [
... train_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
... ]
... example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
... return example_batch
... def preprocess_val(example_batch):
... """Apply val_transforms across a batch."""
... images = [
... val_data_augmentation(convert_to_tf_tensor(image.convert("RGB"))) for image in example_batch["image"]
... ]
... example_batch["pixel_values"] = [tf.transpose(tf.squeeze(image)) for image in images]
... return example_batch
```
🤗 Datasets [`~datasets.Dataset.set_transform`]를 사용하여 즉시 변환을 적용하세요:
```py
food["train"].set_transform(preprocess_train)
food["test"].set_transform(preprocess_val)
```
최종 전처리 단계로 `DefaultDataCollator`를 사용하여 예제 배치를 만듭니다. 🤗 Transformers의 다른 데이터 콜레이터와 달리
`DefaultDataCollator`는 패딩과 같은 추가 전처리를 적용하지 않습니다.
```py
>>> from transformers import DefaultDataCollator
>>> data_collator = DefaultDataCollator(return_tensors="tf")
```
</tf>
</frameworkcontent>
## 평가[[evaluate]]
훈련 중에 평가 지표를 포함하면 모델의 성능을 평가하는 데 도움이 되는 경우가 많습니다.
🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 라이브러리로 평가 방법을 빠르게 가져올 수 있습니다. 이 작업에서는
[accuracy](https://huggingface.co/spaces/evaluate-metric/accuracy) 평가 지표를 가져옵니다. (🤗 Evaluate [빠른 둘러보기](https://huggingface.co/docs/evaluate/a_quick_tour)를 참조하여 평가 지표를 가져오고 계산하는 방법에 대해 자세히 알아보세요):
```py
>>> import evaluate
>>> accuracy = evaluate.load("accuracy")
```
그런 다음 예측과 레이블을 [`~evaluate.EvaluationModule.compute`]에 전달하여 정확도를 계산하는 함수를 만듭니다:
```py
>>> import numpy as np
>>> def compute_metrics(eval_pred):
... predictions, labels = eval_pred
... predictions = np.argmax(predictions, axis=1)
... return accuracy.compute(predictions=predictions, references=labels)
```
이제 `compute_metrics` 함수를 사용할 준비가 되었으며, 훈련을 설정하면 이 함수로 되돌아올 것입니다.
## 훈련[[train]]
<frameworkcontent>
<pt>
<Tip>
[`Trainer`]를 사용하여 모델을 미세 조정하는 방법에 익숙하지 않은 경우, [여기](../training#train-with-pytorch-trainer)에서 기본 튜토리얼을 확인하세요!
</Tip>
이제 모델을 훈련시킬 준비가 되었습니다! [`AutoModelForImageClassification`]로 ViT를 가져옵니다. 예상되는 레이블 수, 레이블 매핑 및 레이블 수를 지정하세요:
```py
>>> from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
>>> model = AutoModelForImageClassification.from_pretrained(
... checkpoint,
... num_labels=len(labels),
... id2label=id2label,
... label2id=label2id,
... )
```
이제 세 단계만 거치면 끝입니다:
1. [`TrainingArguments`]에서 훈련 하이퍼파라미터를 정의하세요. `image` 열이 삭제되기 때문에 미사용 열을 제거하지 않는 것이 중요합니다. `image` 열이 없으면 `pixel_values`을 생성할 수 없습니다. 이 동작을 방지하려면 `remove_unused_columns=False`로 설정하세요! 다른 유일한 필수 매개변수는 모델 저장 위치를 지정하는 `output_dir`입니다. `push_to_hub=True`로 설정하면 이 모델을 허브에 푸시합니다(모델을 업로드하려면 Hugging Face에 로그인해야 합니다). 각 에폭이 끝날 때마다, [`Trainer`]가 정확도를 평가하고 훈련 체크포인트를 저장합니다.
2. [`Trainer`]에 모델, 데이터 세트, 토크나이저, 데이터 콜레이터 및 `compute_metrics` 함수와 함께 훈련 인수를 전달하세요.
3. [`~Trainer.train`]을 호출하여 모델을 미세 조정하세요.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_food_model",
... remove_unused_columns=False,
... eval_strategy="epoch",
... save_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=16,
... gradient_accumulation_steps=4,
... per_device_eval_batch_size=16,
... num_train_epochs=3,
... warmup_ratio=0.1,
... logging_steps=10,
... load_best_model_at_end=True,
... metric_for_best_model="accuracy",
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... data_collator=data_collator,
... train_dataset=food["train"],
... eval_dataset=food["test"],
... processing_class=image_processor,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
훈련이 완료되면, 모든 사람이 모델을 사용할 수 있도록 [`~transformers.Trainer.push_to_hub`] 메소드로 모델을 허브에 공유하세요:
```py
>>> trainer.push_to_hub()
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
<Tip>
Keras를 사용하여 모델을 미세 조정하는 방법에 익숙하지 않은 경우, 먼저 [기본 튜토리얼](./training#train-a-tensorflow-model-with-keras)을 확인하세요!
</Tip>
TensorFlow에서 모델을 미세 조정하려면 다음 단계를 따르세요:
1. 훈련 하이퍼파라미터를 정의하고 옵티마이저와 학습률 스케쥴을 설정합니다.
2. 사전 훈련된 모델을 인스턴스화합니다.
3. 🤗 Dataset을 `tf.data.Dataset`으로 변환합니다.
4. 모델을 컴파일합니다.
5. 콜백을 추가하고 훈련을 수행하기 위해 `fit()` 메소드를 사용합니다.
6. 커뮤니티와 공유하기 위해 모델을 🤗 Hub에 업로드합니다.
하이퍼파라미터, 옵티마이저 및 학습률 스케쥴을 정의하는 것으로 시작합니다:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_epochs = 5
>>> num_train_steps = len(food["train"]) * num_epochs
>>> learning_rate = 3e-5
>>> weight_decay_rate = 0.01
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=learning_rate,
... num_train_steps=num_train_steps,
... weight_decay_rate=weight_decay_rate,
... num_warmup_steps=0,
... )
```
그런 다음 레이블 매핑과 함께 [`TFAuto ModelForImageClassification`]으로 ViT를 가져옵니다:
```py
>>> from transformers import TFAutoModelForImageClassification
>>> model = TFAutoModelForImageClassification.from_pretrained(
... checkpoint,
... id2label=id2label,
... label2id=label2id,
... )
```
데이터 세트를 [`~datasets.Dataset.to_tf_dataset`]와 `data_collator`를 사용하여 `tf.data.Dataset` 형식으로 변환하세요:
```py
>>> # converting our train dataset to tf.data.Dataset
>>> tf_train_dataset = food["train"].to_tf_dataset(
... columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )
>>> # converting our test dataset to tf.data.Dataset
>>> tf_eval_dataset = food["test"].to_tf_dataset(
... columns="pixel_values", label_cols="label", shuffle=True, batch_size=batch_size, collate_fn=data_collator
... )
```
`compile()`를 사용하여 훈련 모델을 구성하세요:
```py
>>> from tensorflow.keras.losses import SparseCategoricalCrossentropy
>>> loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
>>> model.compile(optimizer=optimizer, loss=loss)
```
예측에서 정확도를 계산하고 모델을 🤗 Hub로 푸시하려면 [Keras callbacks](../main_classes/keras_callbacks)를 사용하세요.
`compute_metrics` 함수를 [KerasMetricCallback](../main_classes/keras_callbacks#transformers.KerasMetricCallback)에 전달하고,
[PushToHubCallback](../main_classes/keras_callbacks#transformers.PushToHubCallback)을 사용하여 모델을 업로드합니다:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="food_classifier",
... tokenizer=image_processor,
... save_strategy="no",
... )
>>> callbacks = [metric_callback, push_to_hub_callback]
```
이제 모델을 훈련할 준비가 되었습니다! 훈련 및 검증 데이터 세트, 에폭 수와 함께 `fit()`을 호출하고,
콜백을 사용하여 모델을 미세 조정합니다:
```py
>>> model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)
Epoch 1/5
250/250 [==============================] - 313s 1s/step - loss: 2.5623 - val_loss: 1.4161 - accuracy: 0.9290
Epoch 2/5
250/250 [==============================] - 265s 1s/step - loss: 0.9181 - val_loss: 0.6808 - accuracy: 0.9690
Epoch 3/5
250/250 [==============================] - 252s 1s/step - loss: 0.3910 - val_loss: 0.4303 - accuracy: 0.9820
Epoch 4/5
250/250 [==============================] - 251s 1s/step - loss: 0.2028 - val_loss: 0.3191 - accuracy: 0.9900
Epoch 5/5
250/250 [==============================] - 238s 949ms/step - loss: 0.1232 - val_loss: 0.3259 - accuracy: 0.9890
```
축하합니다! 모델을 미세 조정하고 🤗 Hub에 공유했습니다. 이제 추론에 사용할 수 있습니다!
</tf>
</frameworkcontent>
<Tip>
이미지 분류를 위한 모델을 미세 조정하는 자세한 예제는 다음 [PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb)을 참조하세요.
</Tip>
## 추론[[inference]]
좋아요, 이제 모델을 미세 조정했으니 추론에 사용할 수 있습니다!
추론을 수행하고자 하는 이미지를 가져와봅시다:
```py
>>> ds = load_dataset("food101", split="validation[:10]")
>>> image = ds["image"][0]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png" alt="image of beignets"/>
</div>
미세 조정 모델로 추론을 시도하는 가장 간단한 방법은 [`pipeline`]을 사용하는 것입니다. 모델로 이미지 분류를 위한 `pipeline`을 인스턴스화하고 이미지를 전달합니다:
```py
>>> from transformers import pipeline
>>> classifier = pipeline("image-classification", model="my_awesome_food_model")
>>> classifier(image)
[{'score': 0.31856709718704224, 'label': 'beignets'},
{'score': 0.015232225880026817, 'label': 'bruschetta'},
{'score': 0.01519392803311348, 'label': 'chicken_wings'},
{'score': 0.013022331520915031, 'label': 'pork_chop'},
{'score': 0.012728818692266941, 'label': 'prime_rib'}]
```
원한다면, `pipeline`의 결과를 수동으로 복제할 수도 있습니다:
<frameworkcontent>
<pt>
이미지를 전처리하기 위해 이미지 프로세서를 가져오고 `input`을 PyTorch 텐서로 반환합니다:
```py
>>> from transformers import AutoImageProcessor
>>> import torch
>>> image_processor = AutoImageProcessor.from_pretrained("my_awesome_food_model")
>>> inputs = image_processor(image, return_tensors="pt")
```
입력을 모델에 전달하고 logits을 반환합니다:
```py
>>> from transformers import AutoModelForImageClassification
>>> model = AutoModelForImageClassification.from_pretrained("my_awesome_food_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
확률이 가장 높은 예측 레이블을 가져오고, 모델의 `id2label` 매핑을 사용하여 레이블로 변환합니다:
```py
>>> predicted_label = logits.argmax(-1).item()
>>> model.config.id2label[predicted_label]
'beignets'
```
</pt>
</frameworkcontent>
<frameworkcontent>
<tf>
이미지를 전처리하기 위해 이미지 프로세서를 가져오고 `input`을 TensorFlow 텐서로 반환합니다:
```py
>>> from transformers import AutoImageProcessor
>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/food_classifier")
>>> inputs = image_processor(image, return_tensors="tf")
```
입력을 모델에 전달하고 logits을 반환합니다:
```py
>>> from transformers import TFAutoModelForImageClassification
>>> model = TFAutoModelForImageClassification.from_pretrained("MariaK/food_classifier")
>>> logits = model(**inputs).logits
```
확률이 가장 높은 예측 레이블을 가져오고, 모델의 `id2label` 매핑을 사용하여 레이블로 변환합니다:
```py
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'beignets'
```
</tf>
</frameworkcontent>
| transformers/docs/source/ko/tasks/image_classification.md/0 | {
"file_path": "transformers/docs/source/ko/tasks/image_classification.md",
"repo_id": "transformers",
"token_count": 11495
} | 429 |
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# 토큰 분류[[token-classification]]
[[open-in-colab]]
<Youtube id="wVHdVlPScxA"/>
토큰 분류는 문장의 개별 토큰에 레이블을 할당합니다. 가장 일반적인 토큰 분류 작업 중 하나는 개체명 인식(Named Entity Recognition, NER)입니다. 개체명 인식은 문장에서 사람, 위치 또는 조직과 같은 각 개체의 레이블을 찾으려고 시도합니다.
이 가이드에서 학습할 내용은:
1. [WNUT 17](https://huggingface.co/datasets/wnut_17) 데이터 세트에서 [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased)를 파인 튜닝하여 새로운 개체를 탐지합니다.
2. 추론을 위해 파인 튜닝 모델을 사용합니다.
<Tip>
이 작업과 호환되는 모든 아키텍처와 체크포인트를 보려면 [작업 페이지](https://huggingface.co/tasks/token-classification)를 확인하는 것이 좋습니다.
</Tip>
시작하기 전에, 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
```bash
pip install transformers datasets evaluate seqeval
```
Hugging Face 계정에 로그인하여 모델을 업로드하고 커뮤니티에 공유하는 것을 권장합니다. 메시지가 표시되면, 토큰을 입력하여 로그인하세요:
```py
>>> from huggingface_hub import notebook_login
>>> notebook_login()
```
## WNUT 17 데이터 세트 가져오기[[load-wnut-17-dataset]]
먼저 🤗 Datasets 라이브러리에서 WNUT 17 데이터 세트를 가져옵니다:
```py
>>> from datasets import load_dataset
>>> wnut = load_dataset("wnut_17")
```
다음 예제를 살펴보세요:
```py
>>> wnut["train"][0]
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']
}
```
`ner_tags`의 각 숫자는 개체를 나타냅니다. 숫자를 레이블 이름으로 변환하여 개체가 무엇인지 확인합니다:
```py
>>> label_list = wnut["train"].features[f"ner_tags"].feature.names
>>> label_list
[
"O",
"B-corporation",
"I-corporation",
"B-creative-work",
"I-creative-work",
"B-group",
"I-group",
"B-location",
"I-location",
"B-person",
"I-person",
"B-product",
"I-product",
]
```
각 `ner_tag`의 앞에 붙은 문자는 개체의 토큰 위치를 나타냅니다:
- `B-`는 개체의 시작을 나타냅니다.
- `I-`는 토큰이 동일한 개체 내부에 포함되어 있음을 나타냅니다(예를 들어 `State` 토큰은 `Empire State Building`와 같은 개체의 일부입니다).
- `0`는 토큰이 어떤 개체에도 해당하지 않음을 나타냅니다.
## 전처리[[preprocess]]
<Youtube id="iY2AZYdZAr0"/>
다음으로 `tokens` 필드를 전처리하기 위해 DistilBERT 토크나이저를 가져옵니다:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
위의 예제 `tokens` 필드를 보면 입력이 이미 토큰화된 것처럼 보입니다. 그러나 실제로 입력은 아직 토큰화되지 않았으므로 단어를 하위 단어로 토큰화하기 위해 `is_split_into_words=True`를 설정해야 합니다. 예제로 확인합니다:
```py
>>> example = wnut["train"][0]
>>> tokenized_input = tokenizer(example["tokens"], is_split_into_words=True)
>>> tokens = tokenizer.convert_ids_to_tokens(tokenized_input["input_ids"])
>>> tokens
['[CLS]', '@', 'paul', '##walk', 'it', "'", 's', 'the', 'view', 'from', 'where', 'i', "'", 'm', 'living', 'for', 'two', 'weeks', '.', 'empire', 'state', 'building', '=', 'es', '##b', '.', 'pretty', 'bad', 'storm', 'here', 'last', 'evening', '.', '[SEP]']
```
그러나 이로 인해 `[CLS]`과 `[SEP]`라는 특수 토큰이 추가되고, 하위 단어 토큰화로 인해 입력과 레이블 간에 불일치가 발생합니다. 하나의 레이블에 해당하는 단일 단어는 이제 두 개의 하위 단어로 분할될 수 있습니다. 토큰과 레이블을 다음과 같이 재정렬해야 합니다:
1. [`word_ids`](https://huggingface.co/docs/transformers/main_classes/tokenizer#transformers.BatchEncoding.word_ids) 메소드로 모든 토큰을 해당 단어에 매핑합니다.
2. 특수 토큰 `[CLS]`와 `[SEP]`에 `-100` 레이블을 할당하여, PyTorch 손실 함수가 해당 토큰을 무시하도록 합니다.
3. 주어진 단어의 첫 번째 토큰에만 레이블을 지정합니다. 같은 단어의 다른 하위 토큰에 `-100`을 할당합니다.
다음은 토큰과 레이블을 재정렬하고 DistilBERT의 최대 입력 길이보다 길지 않도록 시퀀스를 잘라내는 함수를 만드는 방법입니다:
```py
>>> def tokenize_and_align_labels(examples):
... tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)
... labels = []
... for i, label in enumerate(examples[f"ner_tags"]):
... word_ids = tokenized_inputs.word_ids(batch_index=i) # Map tokens to their respective word.
... previous_word_idx = None
... label_ids = []
... for word_idx in word_ids: # Set the special tokens to -100.
... if word_idx is None:
... label_ids.append(-100)
... elif word_idx != previous_word_idx: # Only label the first token of a given word.
... label_ids.append(label[word_idx])
... else:
... label_ids.append(-100)
... previous_word_idx = word_idx
... labels.append(label_ids)
... tokenized_inputs["labels"] = labels
... return tokenized_inputs
```
전체 데이터 세트에 전처리 함수를 적용하려면, 🤗 Datasets [`~datasets.Dataset.map`] 함수를 사용하세요. `batched=True`로 설정하여 데이터 세트의 여러 요소를 한 번에 처리하면 `map` 함수의 속도를 높일 수 있습니다:
```py
>>> tokenized_wnut = wnut.map(tokenize_and_align_labels, batched=True)
```
이제 [`DataCollatorWithPadding`]를 사용하여 예제 배치를 만들어봅시다. 데이터 세트 전체를 최대 길이로 패딩하는 대신, *동적 패딩*을 사용하여 배치에서 가장 긴 길이에 맞게 문장을 패딩하는 것이 효율적입니다.
<frameworkcontent>
<pt>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer)
```
</pt>
<tf>
```py
>>> from transformers import DataCollatorForTokenClassification
>>> data_collator = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="tf")
```
</tf>
</frameworkcontent>
## 평가[[evaluation]]
훈련 중 모델의 성능을 평가하기 위해 평가 지표를 포함하는 것이 유용합니다. 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 라이브러리를 사용하여 빠르게 평가 방법을 가져올 수 있습니다. 이 작업에서는 [seqeval](https://huggingface.co/spaces/evaluate-metric/seqeval) 평가 지표를 가져옵니다. (평가 지표를 가져오고 계산하는 방법에 대해서는 🤗 Evaluate [빠른 둘러보기](https://huggingface.co/docs/evaluate/a_quick_tour)를 참조하세요). Seqeval은 실제로 정밀도, 재현률, F1 및 정확도와 같은 여러 점수를 산출합니다.
```py
>>> import evaluate
>>> seqeval = evaluate.load("seqeval")
```
먼저 NER 레이블을 가져온 다음, [`~evaluate.EvaluationModule.compute`]에 실제 예측과 실제 레이블을 전달하여 점수를 계산하는 함수를 만듭니다:
```py
>>> import numpy as np
>>> labels = [label_list[i] for i in example[f"ner_tags"]]
>>> def compute_metrics(p):
... predictions, labels = p
... predictions = np.argmax(predictions, axis=2)
... true_predictions = [
... [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... true_labels = [
... [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
... for prediction, label in zip(predictions, labels)
... ]
... results = seqeval.compute(predictions=true_predictions, references=true_labels)
... return {
... "precision": results["overall_precision"],
... "recall": results["overall_recall"],
... "f1": results["overall_f1"],
... "accuracy": results["overall_accuracy"],
... }
```
이제 `compute_metrics` 함수를 사용할 준비가 되었으며, 훈련을 설정하면 이 함수로 되돌아올 것입니다.
## 훈련[[train]]
모델을 훈련하기 전에, `id2label`와 `label2id`를 사용하여 예상되는 id와 레이블의 맵을 생성하세요:
```py
>>> id2label = {
... 0: "O",
... 1: "B-corporation",
... 2: "I-corporation",
... 3: "B-creative-work",
... 4: "I-creative-work",
... 5: "B-group",
... 6: "I-group",
... 7: "B-location",
... 8: "I-location",
... 9: "B-person",
... 10: "I-person",
... 11: "B-product",
... 12: "I-product",
... }
>>> label2id = {
... "O": 0,
... "B-corporation": 1,
... "I-corporation": 2,
... "B-creative-work": 3,
... "I-creative-work": 4,
... "B-group": 5,
... "I-group": 6,
... "B-location": 7,
... "I-location": 8,
... "B-person": 9,
... "I-person": 10,
... "B-product": 11,
... "I-product": 12,
... }
```
<frameworkcontent>
<pt>
<Tip>
[`Trainer`]를 사용하여 모델을 파인 튜닝하는 방법에 익숙하지 않은 경우, [여기](../training#train-with-pytorch-trainer)에서 기본 튜토리얼을 확인하세요!
</Tip>
이제 모델을 훈련시킬 준비가 되었습니다! [`AutoModelForSequenceClassification`]로 DistilBERT를 가져오고 예상되는 레이블 수와 레이블 매핑을 지정하세요:
```py
>>> from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
>>> model = AutoModelForTokenClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
이제 세 단계만 거치면 끝입니다:
1. [`TrainingArguments`]에서 하이퍼파라미터를 정의하세요. `output_dir`는 모델을 저장할 위치를 지정하는 유일한 매개변수입니다. 이 모델을 허브에 업로드하기 위해 `push_to_hub=True`를 설정합니다(모델을 업로드하기 위해 Hugging Face에 로그인해야합니다.) 각 에폭이 끝날 때마다, [`Trainer`]는 seqeval 점수를 평가하고 훈련 체크포인트를 저장합니다.
2. [`Trainer`]에 훈련 인수와 모델, 데이터 세트, 토크나이저, 데이터 콜레이터 및 `compute_metrics` 함수를 전달하세요.
3. [`~Trainer.train`]를 호출하여 모델을 파인 튜닝하세요.
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_wnut_model",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,
... num_train_epochs=2,
... weight_decay=0.01,
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,
... )
>>> trainer = Trainer(
... model=model,
... args=training_args,
... train_dataset=tokenized_wnut["train"],
... eval_dataset=tokenized_wnut["test"],
... processing_class=tokenizer,
... data_collator=data_collator,
... compute_metrics=compute_metrics,
... )
>>> trainer.train()
```
훈련이 완료되면, [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 모델을 허브에 공유할 수 있습니다.
```py
>>> trainer.push_to_hub()
```
</pt>
<tf>
<Tip>
Keras를 사용하여 모델을 파인 튜닝하는 방법에 익숙하지 않은 경우, [여기](../training#train-a-tensorflow-model-with-keras)의 기본 튜토리얼을 확인하세요!
</Tip>
TensorFlow에서 모델을 파인 튜닝하려면, 먼저 옵티마이저 함수와 학습률 스케쥴, 그리고 일부 훈련 하이퍼파라미터를 설정해야 합니다:
```py
>>> from transformers import create_optimizer
>>> batch_size = 16
>>> num_train_epochs = 3
>>> num_train_steps = (len(tokenized_wnut["train"]) // batch_size) * num_train_epochs
>>> optimizer, lr_schedule = create_optimizer(
... init_lr=2e-5,
... num_train_steps=num_train_steps,
... weight_decay_rate=0.01,
... num_warmup_steps=0,
... )
```
그런 다음 [`TFAutoModelForSequenceClassification`]을 사용하여 DistilBERT를 가져오고, 예상되는 레이블 수와 레이블 매핑을 지정합니다:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained(
... "distilbert/distilbert-base-uncased", num_labels=13, id2label=id2label, label2id=label2id
... )
```
[`~transformers.TFPreTrainedModel.prepare_tf_dataset`]을 사용하여 데이터 세트를 `tf.data.Dataset` 형식으로 변환합니다:
```py
>>> tf_train_set = model.prepare_tf_dataset(
... tokenized_wnut["train"],
... shuffle=True,
... batch_size=16,
... collate_fn=data_collator,
... )
>>> tf_validation_set = model.prepare_tf_dataset(
... tokenized_wnut["validation"],
... shuffle=False,
... batch_size=16,
... collate_fn=data_collator,
... )
```
[`compile`](https://keras.io/api/models/model_training_apis/#compile-method)를 사용하여 훈련할 모델을 구성합니다:
```py
>>> import tensorflow as tf
>>> model.compile(optimizer=optimizer)
```
훈련을 시작하기 전에 설정해야할 마지막 두 가지는 예측에서 seqeval 점수를 계산하고, 모델을 허브에 업로드할 방법을 제공하는 것입니다. 모두 [Keras callbacks](../main_classes/keras_callbacks)를 사용하여 수행됩니다.
[`~transformers.KerasMetricCallback`]에 `compute_metrics` 함수를 전달하세요:
```py
>>> from transformers.keras_callbacks import KerasMetricCallback
>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
```
[`~transformers.PushToHubCallback`]에서 모델과 토크나이저를 업로드할 위치를 지정합니다:
```py
>>> from transformers.keras_callbacks import PushToHubCallback
>>> push_to_hub_callback = PushToHubCallback(
... output_dir="my_awesome_wnut_model",
... tokenizer=tokenizer,
... )
```
그런 다음 콜백을 함께 묶습니다:
```py
>>> callbacks = [metric_callback, push_to_hub_callback]
```
드디어, 모델 훈련을 시작할 준비가 되었습니다! [`fit`](https://keras.io/api/models/model_training_apis/#fit-method)에 훈련 데이터 세트, 검증 데이터 세트, 에폭의 수 및 콜백을 전달하여 파인 튜닝합니다:
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3, callbacks=callbacks)
```
훈련이 완료되면, 모델이 자동으로 허브에 업로드되어 누구나 사용할 수 있습니다!
</tf>
</frameworkcontent>
<Tip>
토큰 분류를 위한 모델을 파인 튜닝하는 자세한 예제는 다음
[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb)
또는 [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb)를 참조하세요.
</Tip>
## 추론[[inference]]
좋아요, 이제 모델을 파인 튜닝했으니 추론에 사용할 수 있습니다!
추론을 수행하고자 하는 텍스트를 가져와봅시다:
```py
>>> text = "The Golden State Warriors are an American professional basketball team based in San Francisco."
```
파인 튜닝된 모델로 추론을 시도하는 가장 간단한 방법은 [`pipeline`]를 사용하는 것입니다. 모델로 NER의 `pipeline`을 인스턴스화하고, 텍스트를 전달해보세요:
```py
>>> from transformers import pipeline
>>> classifier = pipeline("ner", model="stevhliu/my_awesome_wnut_model")
>>> classifier(text)
[{'entity': 'B-location',
'score': 0.42658573,
'index': 2,
'word': 'golden',
'start': 4,
'end': 10},
{'entity': 'I-location',
'score': 0.35856336,
'index': 3,
'word': 'state',
'start': 11,
'end': 16},
{'entity': 'B-group',
'score': 0.3064001,
'index': 4,
'word': 'warriors',
'start': 17,
'end': 25},
{'entity': 'B-location',
'score': 0.65523505,
'index': 13,
'word': 'san',
'start': 80,
'end': 83},
{'entity': 'B-location',
'score': 0.4668663,
'index': 14,
'word': 'francisco',
'start': 84,
'end': 93}]
```
원한다면, `pipeline`의 결과를 수동으로 복제할 수도 있습니다:
<frameworkcontent>
<pt>
텍스트를 토큰화하고 PyTorch 텐서를 반환합니다:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="pt")
```
입력을 모델에 전달하고 `logits`을 반환합니다:
```py
>>> from transformers import AutoModelForTokenClassification
>>> model = AutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> with torch.no_grad():
... logits = model(**inputs).logits
```
가장 높은 확률을 가진 클래스를 모델의 `id2label` 매핑을 사용하여 텍스트 레이블로 변환합니다:
```py
>>> predictions = torch.argmax(logits, dim=2)
>>> predicted_token_class = [model.config.id2label[t.item()] for t in predictions[0]]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</pt>
<tf>
텍스트를 토큰화하고 TensorFlow 텐서를 반환합니다:
```py
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> inputs = tokenizer(text, return_tensors="tf")
```
입력값을 모델에 전달하고 `logits`을 반환합니다:
```py
>>> from transformers import TFAutoModelForTokenClassification
>>> model = TFAutoModelForTokenClassification.from_pretrained("stevhliu/my_awesome_wnut_model")
>>> logits = model(**inputs).logits
```
가장 높은 확률을 가진 클래스를 모델의 `id2label` 매핑을 사용하여 텍스트 레이블로 변환합니다:
```py
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>> predicted_token_class = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_token_class
['O',
'O',
'B-location',
'I-location',
'B-group',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'O',
'B-location',
'B-location',
'O',
'O']
```
</tf>
</frameworkcontent>
| transformers/docs/source/ko/tasks/token_classification.md/0 | {
"file_path": "transformers/docs/source/ko/tasks/token_classification.md",
"repo_id": "transformers",
"token_count": 11315
} | 430 |
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-->
# 创建自定义架构
[`AutoClass`](model_doc/auto) 自动推断模型架构并下载预训练的配置和权重。一般来说，我们建议使用 `AutoClass` 生成与检查点（checkpoint）无关的代码。希望对特定模型参数有更多控制的用户，可以仅从几个基类创建自定义的 🤗 Transformers 模型。这对于任何有兴趣学习、训练或试验 🤗 Transformers 模型的人可能特别有用。通过本指南，深入了解如何不通过 `AutoClass` 创建自定义模型。了解如何：
- 加载并自定义模型配置。
- 创建模型架构。
- 为文本创建慢速和快速分词器。
- 为视觉任务创建图像处理器。
- 为音频任务创建特征提取器。
- 为多模态任务创建处理器。
## 配置
[配置](main_classes/configuration) 涉及到模型的具体属性。每个模型配置都有不同的属性；例如，所有 NLP 模型都共享 `hidden_size`、`num_attention_heads`、 `num_hidden_layers` 和 `vocab_size` 属性。这些属性用于指定构建模型时的注意力头数量或隐藏层层数。
访问 [`DistilBertConfig`] 以更近一步了解 [DistilBERT](model_doc/distilbert)，检查它的属性：
```py
>>> from transformers import DistilBertConfig
>>> config = DistilBertConfig()
>>> print(config)
DistilBertConfig {
"activation": "gelu",
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
[`DistilBertConfig`] 显示了构建基础 [`DistilBertModel`] 所使用的所有默认属性。所有属性都可以进行自定义，为实验创造了空间。例如，您可以将默认模型自定义为：
- 使用 `activation` 参数尝试不同的激活函数。
- 使用 `attention_dropout` 参数为 attention probabilities 使用更高的 dropout ratio。
```py
>>> my_config = DistilBertConfig(activation="relu", attention_dropout=0.4)
>>> print(my_config)
DistilBertConfig {
"activation": "relu",
"attention_dropout": 0.4,
"dim": 768,
"dropout": 0.1,
"hidden_dim": 3072,
"initializer_range": 0.02,
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"transformers_version": "4.16.2",
"vocab_size": 30522
}
```
预训练模型的属性可以在 [`~PretrainedConfig.from_pretrained`] 函数中进行修改：
```py
>>> my_config = DistilBertConfig.from_pretrained("distilbert/distilbert-base-uncased", activation="relu", attention_dropout=0.4)
```
当你对模型配置满意时，可以使用 [`~PretrainedConfig.save_pretrained`] 来保存配置。你的配置文件将以 JSON 文件的形式存储在指定的保存目录中：
```py
>>> my_config.save_pretrained(save_directory="./your_model_save_path")
```
要重用配置文件，请使用 [`~PretrainedConfig.from_pretrained`] 进行加载：
```py
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
```
<Tip>
你还可以将配置文件保存为字典，甚至只保存自定义配置属性与默认配置属性之间的差异！有关更多详细信息，请参阅 [配置](main_classes/configuration) 文档。
</Tip>
## 模型
接下来，创建一个[模型](main_classes/models)。模型，也可泛指架构，定义了每一层网络的行为以及进行的操作。配置中的 `num_hidden_layers` 等属性用于定义架构。每个模型都共享基类 [`PreTrainedModel`] 和一些常用方法，例如调整输入嵌入的大小和修剪自注意力头。此外，所有模型都是 [`torch.nn.Module`](https://pytorch.org/docs/stable/generated/torch.nn.Module.html)、[`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) 或 [`flax.linen.Module`](https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html) 的子类。这意味着模型与各自框架的用法兼容。
<frameworkcontent>
<pt>
将自定义配置属性加载到模型中：
```py
>>> from transformers import DistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/config.json")
>>> model = DistilBertModel(my_config)
```
这段代码创建了一个具有随机参数而不是预训练权重的模型。在训练该模型之前，您还无法将该模型用于任何用途。训练是一项昂贵且耗时的过程。通常来说，最好使用预训练模型来更快地获得更好的结果，同时仅使用训练所需资源的一小部分。
使用 [`~PreTrainedModel.from_pretrained`] 创建预训练模型：
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
当加载预训练权重时，如果模型是由 🤗 Transformers 提供的，将自动加载默认模型配置。然而，如果你愿意，仍然可以将默认模型配置的某些或者所有属性替换成你自己的配置：
```py
>>> model = DistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</pt>
<tf>
将自定义配置属性加载到模型中：
```py
>>> from transformers import TFDistilBertModel
>>> my_config = DistilBertConfig.from_pretrained("./your_model_save_path/my_config.json")
>>> tf_model = TFDistilBertModel(my_config)
```
这段代码创建了一个具有随机参数而不是预训练权重的模型。在训练该模型之前，您还无法将该模型用于任何用途。训练是一项昂贵且耗时的过程。通常来说，最好使用预训练模型来更快地获得更好的结果，同时仅使用训练所需资源的一小部分。
使用 [`~TFPreTrainedModel.from_pretrained`] 创建预训练模型：
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased")
```
当加载预训练权重时，如果模型是由 🤗 Transformers 提供的，将自动加载默认模型配置。然而，如果你愿意，仍然可以将默认模型配置的某些或者所有属性替换成自己的配置：
```py
>>> tf_model = TFDistilBertModel.from_pretrained("distilbert/distilbert-base-uncased", config=my_config)
```
</tf>
</frameworkcontent>
### 模型头（Model heads）
此时，你已经有了一个输出*隐藏状态*的基础 DistilBERT 模型。隐藏状态作为输入传递到模型头以生成最终输出。🤗 Transformers 为每个任务提供不同的模型头，只要模型支持该任务（即，您不能使用 DistilBERT 来执行像翻译这样的序列到序列任务）。
<frameworkcontent>
<pt>
例如，[`DistilBertForSequenceClassification`] 是一个带有序列分类头（sequence classification head）的基础 DistilBERT 模型。序列分类头是池化输出之上的线性层。
```py
>>> from transformers import DistilBertForSequenceClassification
>>> model = DistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
通过切换到不同的模型头，可以轻松地将此检查点重复用于其他任务。对于问答任务，你可以使用 [`DistilBertForQuestionAnswering`] 模型头。问答头（question answering head）与序列分类头类似，不同点在于它是隐藏状态输出之上的线性层。
```py
>>> from transformers import DistilBertForQuestionAnswering
>>> model = DistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</pt>
<tf>
例如，[`TFDistilBertForSequenceClassification`] 是一个带有序列分类头（sequence classification head）的基础 DistilBERT 模型。序列分类头是池化输出之上的线性层。
```py
>>> from transformers import TFDistilBertForSequenceClassification
>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("distilbert/distilbert-base-uncased")
```
通过切换到不同的模型头,可以轻松地将此检查点重复用于其他任务。对于问答任务，你可以使用 [`TFDistilBertForQuestionAnswering`] 模型头。问答头（question answering head）与序列分类头类似，不同点在于它是隐藏状态输出之上的线性层。
```py
>>> from transformers import TFDistilBertForQuestionAnswering
>>> tf_model = TFDistilBertForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")
```
</tf>
</frameworkcontent>
## 分词器
在将模型用于文本数据之前，你需要的最后一个基类是 [tokenizer](main_classes/tokenizer)，它用于将原始文本转换为张量。🤗 Transformers 支持两种类型的分词器：
- [`PreTrainedTokenizer`]：分词器的Python实现
- [`PreTrainedTokenizerFast`]：来自我们基于 Rust 的 [🤗 Tokenizer](https://huggingface.co/docs/tokenizers/python/latest/) 库的分词器。因为其使用了 Rust 实现，这种分词器类型的速度要快得多，尤其是在批量分词（batch tokenization）的时候。快速分词器还提供其他的方法，例如*偏移映射（offset mapping）*，它将标记（token）映射到其原始单词或字符。
这两种分词器都支持常用的方法，如编码和解码、添加新标记以及管理特殊标记。
<Tip warning={true}>
并非每个模型都支持快速分词器。参照这张 [表格](index#supported-frameworks) 查看模型是否支持快速分词器。
</Tip>
如果您训练了自己的分词器，则可以从*词表*文件创建一个分词器：
```py
>>> from transformers import DistilBertTokenizer
>>> my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")
```
请务必记住，自定义分词器生成的词表与预训练模型分词器生成的词表是不同的。如果使用预训练模型，则需要使用预训练模型的词表，否则输入将没有意义。 使用 [`DistilBertTokenizer`] 类创建具有预训练模型词表的分词器：
```py
>>> from transformers import DistilBertTokenizer
>>> slow_tokenizer = DistilBertTokenizer.from_pretrained("distilbert/distilbert-base-uncased")
```
使用 [`DistilBertTokenizerFast`] 类创建快速分词器：
```py
>>> from transformers import DistilBertTokenizerFast
>>> fast_tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert/distilbert-base-uncased")
```
<Tip>
默认情况下，[`AutoTokenizer`] 将尝试加载快速标记生成器。你可以通过在 `from_pretrained` 中设置 `use_fast=False` 以禁用此行为。
</Tip>
## 图像处理器
图像处理器用于处理视觉输入。它继承自 [`~image_processing_utils.ImageProcessingMixin`] 基类。
要使用它，需要创建一个与你使用的模型关联的图像处理器。例如，如果你使用 [ViT](model_doc/vit) 进行图像分类，可以创建一个默认的 [`ViTImageProcessor`]：
```py
>>> from transformers import ViTImageProcessor
>>> vit_extractor = ViTImageProcessor()
>>> print(vit_extractor)
ViTImageProcessor {
"do_normalize": true,
"do_resize": true,
"image_processor_type": "ViTImageProcessor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"size": 224
}
```
<Tip>
如果您不需要进行任何自定义，只需使用 `from_pretrained` 方法加载模型的默认图像处理器参数。
</Tip>
修改任何 [`ViTImageProcessor`] 参数以创建自定义图像处理器：
```py
>>> from transformers import ViTImageProcessor
>>> my_vit_extractor = ViTImageProcessor(resample="PIL.Image.BOX", do_normalize=False, image_mean=[0.3, 0.3, 0.3])
>>> print(my_vit_extractor)
ViTImageProcessor {
"do_normalize": false,
"do_resize": true,
"image_processor_type": "ViTImageProcessor",
"image_mean": [
0.3,
0.3,
0.3
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": "PIL.Image.BOX",
"size": 224
}
```
## 特征提取器
特征提取器用于处理音频输入。它继承自 [`~feature_extraction_utils.FeatureExtractionMixin`] 基类，亦可继承 [`SequenceFeatureExtractor`] 类来处理音频输入。
要使用它，创建一个与你使用的模型关联的特征提取器。例如，如果你使用 [Wav2Vec2](model_doc/wav2vec2) 进行音频分类，可以创建一个默认的 [`Wav2Vec2FeatureExtractor`]：
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> w2v2_extractor = Wav2Vec2FeatureExtractor()
>>> print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 16000
}
```
<Tip>
如果您不需要进行任何自定义，只需使用 `from_pretrained` 方法加载模型的默认特征提取器参数。
</Tip>
修改任何 [`Wav2Vec2FeatureExtractor`] 参数以创建自定义特征提取器：
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> w2v2_extractor = Wav2Vec2FeatureExtractor(sampling_rate=8000, do_normalize=False)
>>> print(w2v2_extractor)
Wav2Vec2FeatureExtractor {
"do_normalize": false,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": false,
"sampling_rate": 8000
}
```
## 处理器
对于支持多模式任务的模型，🤗 Transformers 提供了一个处理器类，可以方便地将特征提取器和分词器等处理类包装到单个对象中。例如，让我们使用 [`Wav2Vec2Processor`] 来执行自动语音识别任务 (ASR)。 ASR 将音频转录为文本，因此您将需要一个特征提取器和一个分词器。
创建一个特征提取器来处理音频输入：
```py
>>> from transformers import Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor(padding_value=1.0, do_normalize=True)
```
创建一个分词器来处理文本输入：
```py
>>> from transformers import Wav2Vec2CTCTokenizer
>>> tokenizer = Wav2Vec2CTCTokenizer(vocab_file="my_vocab_file.txt")
```
将特征提取器和分词器合并到 [`Wav2Vec2Processor`] 中：
```py
>>> from transformers import Wav2Vec2Processor
>>> processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)
```
通过两个基类 - 配置类和模型类 - 以及一个附加的预处理类（分词器、图像处理器、特征提取器或处理器），你可以创建 🤗 Transformers 支持的任何模型。 每个基类都是可配置的，允许你使用所需的特定属性。 你可以轻松设置模型进行训练或修改现有的预训练模型进行微调。
| transformers/docs/source/zh/create_a_model.md/0 | {
"file_path": "transformers/docs/source/zh/create_a_model.md",
"repo_id": "transformers",
"token_count": 8520
} | 431 |
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# Processors
在 Transformers 库中，processors可以有两种不同的含义：
- 为多模态模型，例如[Wav2Vec2](../model_doc/wav2vec2)（语音和文本）或[CLIP](../model_doc/clip)（文本和视觉）预处理输入的对象
- 在库的旧版本中用于预处理GLUE或SQUAD数据的已弃用对象。
## 多模态processors
任何多模态模型都需要一个对象来编码或解码将多个模态（包括文本、视觉和音频）组合在一起的数据。这由称为processors的对象处理，这些processors将两个或多个处理对象组合在一起，例如tokenizers（用于文本模态），image processors（用于视觉）和feature extractors（用于音频）。
这些processors继承自以下实现保存和加载功能的基类：
[[autodoc]] ProcessorMixin
## 已弃用的processors
所有processor都遵循与 [`~data.processors.utils.DataProcessor`] 相同的架构。processor返回一个 [`~data.processors.utils.InputExample`] 列表。这些 [`~data.processors.utils.InputExample`] 可以转换为 [`~data.processors.utils.InputFeatures`] 以供输送到模型。
[[autodoc]] data.processors.utils.DataProcessor
[[autodoc]] data.processors.utils.InputExample
[[autodoc]] data.processors.utils.InputFeatures
## GLUE
[General Language Understanding Evaluation (GLUE)](https://gluebenchmark.com/) 是一个基准测试，评估模型在各种现有的自然语言理解任务上的性能。它与论文 [GLUE: A multi-task benchmark and analysis platform for natural language understanding](https://openreview.net/pdf?id=rJ4km2R5t7) 一同发布。
该库为以下任务提供了总共10个processor：MRPC、MNLI、MNLI（mismatched）、CoLA、SST2、STSB、QQP、QNLI、RTE 和 WNLI。
这些processor是：
- [`~data.processors.utils.MrpcProcessor`]
- [`~data.processors.utils.MnliProcessor`]
- [`~data.processors.utils.MnliMismatchedProcessor`]
- [`~data.processors.utils.Sst2Processor`]
- [`~data.processors.utils.StsbProcessor`]
- [`~data.processors.utils.QqpProcessor`]
- [`~data.processors.utils.QnliProcessor`]
- [`~data.processors.utils.RteProcessor`]
- [`~data.processors.utils.WnliProcessor`]
此外，还可以使用以下方法从数据文件加载值并将其转换为 [`~data.processors.utils.InputExample`] 列表。
[[autodoc]] data.processors.glue.glue_convert_examples_to_features
## XNLI
[跨语言NLI语料库（XNLI）](https://www.nyu.edu/projects/bowman/xnli/) 是一个评估跨语言文本表示质量的基准测试。XNLI是一个基于[*MultiNLI*](http://www.nyu.edu/projects/bowman/multinli/)的众包数据集：”文本对“被标记为包含15种不同语言（包括英语等高资源语言和斯瓦希里语等低资源语言）的文本蕴涵注释。
它与论文 [XNLI: Evaluating Cross-lingual Sentence Representations](https://huggingface.co/papers/1809.05053) 一同发布。
该库提供了加载XNLI数据的processor：
- [`~data.processors.utils.XnliProcessor`]
请注意，由于测试集上有“gold”标签，因此评估是在测试集上进行的。
使用这些processor的示例在 [run_xnli.py](https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification/run_xnli.py) 脚本中提供。
## SQuAD
[斯坦福问答数据集（SQuAD）](https://rajpurkar.github.io/SQuAD-explorer//) 是一个评估模型在问答上性能的基准测试。有两个版本，v1.1 和 v2.0。第一个版本（v1.1）与论文 [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://huggingface.co/papers/1606.05250) 一同发布。第二个版本（v2.0）与论文 [Know What You Don't Know: Unanswerable Questions for SQuAD](https://huggingface.co/papers/1806.03822) 一同发布。
该库为两个版本各自提供了一个processor：
### Processors
这两个processor是：
- [`~data.processors.utils.SquadV1Processor`]
- [`~data.processors.utils.SquadV2Processor`]
它们都继承自抽象类 [`~data.processors.utils.SquadProcessor`]。
[[autodoc]] data.processors.squad.SquadProcessor
- all
此外，可以使用以下方法将 SQuAD 示例转换为可用作模型输入的 [`~data.processors.utils.SquadFeatures`]。
[[autodoc]] data.processors.squad.squad_convert_examples_to_features
这些processor以及前面提到的方法可以与包含数据的文件以及tensorflow_datasets包一起使用。下面给出了示例。
### Example使用
以下是使用processor以及使用数据文件的转换方法的示例：
```python
# Loading a V2 processor
processor = SquadV2Processor()
examples = processor.get_dev_examples(squad_v2_data_dir)
# Loading a V1 processor
processor = SquadV1Processor()
examples = processor.get_dev_examples(squad_v1_data_dir)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
使用 *tensorflow_datasets* 就像使用数据文件一样简单：
```python
# tensorflow_datasets only handle Squad V1.
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
features = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=args.doc_stride,
max_query_length=max_query_length,
is_training=not evaluate,
)
```
另一个使用这些processor的示例在 [run_squad.py](https://github.com/huggingface/transformers/tree/main/examples/legacy/question-answering/run_squad.py) 脚本中提供。 | transformers/docs/source/zh/main_classes/processors.md/0 | {
"file_path": "transformers/docs/source/zh/main_classes/processors.md",
"repo_id": "transformers",
"token_count": 3057
} | 432 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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# 推理pipeline
[`pipeline`] 让使用[Hub](https://huggingface.co/models)上的任何模型进行任何语言、计算机视觉、语音以及多模态任务的推理变得非常简单。即使您对特定的模态没有经验，或者不熟悉模型的源码，您仍然可以使用[`pipeline`]进行推理！本教程将教您：
- 如何使用[`pipeline`] 进行推理。
- 如何使用特定的`tokenizer`(分词器)或模型。
- 如何使用[`pipeline`] 进行音频、视觉和多模态任务的推理。
<Tip>
请查看[`pipeline`]文档以获取已支持的任务和可用参数的完整列表。
</Tip>
## Pipeline使用
虽然每个任务都有一个关联的[`pipeline`]，但使用通用的抽象的[`pipeline`]更加简单，其中包含所有特定任务的`pipelines`。[`pipeline`]会自动加载一个默认模型和一个能够进行任务推理的预处理类。让我们以使用[`pipeline`]进行自动语音识别（ASR）或语音转文本为例。
1. 首先，创建一个[`pipeline`]并指定推理任务：
```py
>>> from transformers import pipeline
>>> transcriber = pipeline(task="automatic-speech-recognition")
```
2. 将您的输入传递给[`pipeline`]。对于语音识别，这通常是一个音频输入文件：
```py
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP LIVE UP THE TRUE MEANING OF ITS TREES'}
```
您没有得到您期望的结果？可以在Hub上查看一些[最受欢迎的自动语音识别模型](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition&sort=trending)
，看看是否可以获得更好的转录。
让我们尝试来自 OpenAI 的[Whisper large-v2](https://huggingface.co/openai/whisper-large) 模型。Whisperb比Wav2Vec2晚2年发布，使用接近10倍的数据进行了训练。因此，它在大多数下游基准测试上击败了Wav2Vec2。
它还具有预测标点和大小写的附加优势，而Wav2Vec2则无法实现这些功能。
让我们在这里尝试一下，看看它的表现如何：
```py
>>> transcriber = pipeline(model="openai/whisper-large-v2")
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
```
现在这个结果看起来更准确了！要进行深入的Wav2Vec2与Whisper比较，请参阅[音频变换器课程](https://huggingface.co/learn/audio-course/chapter5/asr_models)。
我们鼓励您在 Hub 上查看不同语言的模型，以及专业领域的模型等。您可以在Hub上直接查看并比较模型的结果，以确定是否适合或处理边缘情况是否比其他模型更好。如果您没有找到适用于您的用例的模型，您始终可以[训练](training)自己的模型！
如果您有多个输入，您可以将输入作为列表传递：
```py
transcriber(
[
"https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac",
"https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac",
]
)
```
`Pipelines`非常适合用于测试，因为从一个模型切换到另一个模型非常琐碎；但是，还有一些方法可以将它们优化后用于大型工作负载而不仅仅是测试。请查看以下指南，深入探讨如何迭代整个数据集或在Web服务器中使用`Pipelines`：
* [在数据集上使用流水线](#using-pipelines-on-a-dataset)
* [在Web服务器中使用流水线](./pipeline_webserver)
## 参数
[`pipeline`] 支持许多参数；有些是适用于特定任务的，而有些适用于所有`pipeline`。通常情况下，您可以在任何地方指定对应参数：
```py
transcriber = pipeline(model="openai/whisper-large-v2", my_parameter=1)
out = transcriber(...) # This will use `my_parameter=1`.
out = transcriber(..., my_parameter=2) # This will override and use `my_parameter=2`.
out = transcriber(...) # This will go back to using `my_parameter=1`.
```
让我们查看其中的三个重要参数：
### 设备
如果您使用 `device=n`，`pipeline`会自动将模型放在指定的设备上。无论您使用PyTorch还是Tensorflow，这都可以工作。
```py
transcriber = pipeline(model="openai/whisper-large-v2", device=0)
```
如果模型对于单个GPU来说过于庞大，并且您正在使用PyTorch，您可以设置 `device_map="auto"` 以自动确定如何加载和存储模型权重。使用 `device_map` 参数需要安装🤗 [Accelerate](https://huggingface.co/docs/accelerate) 软件包：
```bash
pip install --upgrade accelerate
```
以下代码会自动在各个设备上加载和存储模型权重：
```py
transcriber = pipeline(model="openai/whisper-large-v2", device_map="auto")
```
请注意，如果传递了 `device_map="auto"`，在实例化您的 `pipeline` 时不需要添加 `device=device` 参数，否则可能会遇到一些意外的状况！
### 批量大小
默认情况下，`pipelines`不会进行批量推理，原因在[这里](https://huggingface.co/docs/transformers/main_classes/pipelines#pipeline-batching)详细解释。因为批处理不一定更快，实际上在某些情况下可能会更慢。
但如果在您的用例中起作用，您可以使用：
```py
transcriber = pipeline(model="openai/whisper-large-v2", device=0, batch_size=2)
audio_filenames = [f"https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/{i}.flac" for i in range(1, 5)]
texts = transcriber(audio_filenames)
```
以上代码会在提供的4个音频文件上运行`pipeline`，它会将它们以2个一组的批次传递给模型（模型在GPU上，此时批处理更有可能有所帮助），而您无需编写额外的代码。输出应始终与没有批处理时收到的结果相一致。它只是一种帮助您更快地使用`pipeline`的方式。
`pipeline`也可以减轻一些批处理的复杂性，因为对于某些`pipeline`，需要将单个项目（如长音频文件）分成多个部分以供模型处理。`pipeline`为您执行这种[*chunk batching*](./main_classes/pipelines#pipeline-chunk-batching)。
### 任务特定参数
所有任务都提供了特定于任务的参数，这些参数提供额外的灵活性和选择，以帮助您完成工作。
例如，[`transformers.AutomaticSpeechRecognitionPipeline.__call__`] 方法具有一个 `return_timestamps` 参数，对于字幕视频似乎很有帮助：
```py
>>> transcriber = pipeline(model="openai/whisper-large-v2", return_timestamps=True)
>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.', 'chunks': [{'timestamp': (0.0, 11.88), 'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its'}, {'timestamp': (11.88, 12.38), 'text': ' creed.'}]}
```
正如您所看到的，模型推断出了文本，还输出了各个句子发音的**时间**。
每个任务都有许多可用的参数，因此请查看每个任务的API参考，以了解您可以进行哪些调整！例如，[`~transformers.AutomaticSpeechRecognitionPipeline`] 具有 `chunk_length_s` 参数，对于处理非常长的音频文件（例如，为整部电影或长达一小时的视频配字幕）非常有帮助，这通常是模型无法单独处理的：
```python
>>> transcriber = pipeline(model="openai/whisper-large-v2", chunk_length_s=30, return_timestamps=True)
>>> transcriber("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav")
{'text': " Chapter 16. I might have told you of the beginning of this liaison in a few lines, but I wanted you to see every step by which we came. I, too, agree to whatever Marguerite wished, Marguerite to be unable to live apart from me. It was the day after the evening...
```
如果您找不到一个真正有帮助的参数，欢迎[提出请求](https://github.com/huggingface/transformers/issues/new?assignees=&labels=feature&template=feature-request.yml)！
## 在数据集上使用pipelines
`pipelines`也可以对大型数据集进行推理。我们建议使用迭代器来完成这一任务，这是最简单的方法：
```py
def data():
for i in range(1000):
yield f"My example {i}"
pipe = pipeline(model="openai-community/gpt2", device=0)
generated_characters = 0
for out in pipe(data()):
generated_characters += len(out[0]["generated_text"])
```
迭代器 `data()` 会产生每个结果，`pipelines`会自动识别输入为可迭代对象，并在GPU上处理数据的同时开始获取数据（在底层使用[DataLoader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)）。这一点非常重要，因为您不必为整个数据集分配内存，可以尽可能快地将数据传送到GPU。
由于批处理可以加速处理，因此在这里尝试调整 `batch_size` 参数可能会很有用。
迭代数据集的最简单方法就是从🤗 [Datasets](https://github.com/huggingface/datasets/) 中加载数据集：
```py
# KeyDataset is a util that will just output the item we're interested in.
from transformers.pipelines.pt_utils import KeyDataset
from datasets import load_dataset
pipe = pipeline(model="hf-internal-testing/tiny-random-wav2vec2", device=0)
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:10]")
for out in pipe(KeyDataset(dataset, "audio")):
print(out)
```
## 在Web服务器上使用pipelines
<Tip>
创建推理引擎是一个复杂的主题，值得有自己的页面。
</Tip>
[链接](./pipeline_webserver)
## 视觉流水线
对于视觉任务，使用[`pipeline`] 几乎是相同的。
指定您的任务并将图像传递给分类器。图像可以是链接、本地路径或base64编码的图像。例如，下面显示的是哪种品种的猫？
```py
>>> from transformers import pipeline
>>> vision_classifier = pipeline(model="google/vit-base-patch16-224")
>>> preds = vision_classifier(
... images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
... )
>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
>>> preds
[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
```
## 文本流水线
对于NLP任务，使用[`pipeline`] 几乎是相同的。
```py
>>> from transformers import pipeline
>>> # This model is a `zero-shot-classification` model.
>>> # It will classify text, except you are free to choose any label you might imagine
>>> classifier = pipeline(model="facebook/bart-large-mnli")
>>> classifier(
... "I have a problem with my iphone that needs to be resolved asap!!",
... candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
... )
{'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}
```
## 多模态流水线
[`pipeline`] 支持多个模态。例如，视觉问题回答（VQA）任务结合了文本和图像。请随意使用您喜欢的任何图像链接和您想要问关于该图像的问题。图像可以是URL或图像的本地路径。
例如，如果您使用这个[invoice image](https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png)：
```py
>>> from transformers import pipeline
>>> vqa = pipeline(model="impira/layoutlm-document-qa")
>>> output = vqa(
... image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
... question="What is the invoice number?",
... )
>>> output[0]["score"] = round(output[0]["score"], 3)
>>> output
[{'score': 0.425, 'answer': 'us-001', 'start': 16, 'end': 16}]
```
<Tip>
要运行上面的示例，除了🤗 Transformers之外，您需要安装[`pytesseract`](https://pypi.org/project/pytesseract/)。
```bash
sudo apt install -y tesseract-ocr
pip install pytesseract
```
</Tip>
## 在大模型上使用🤗 `accelerate`和`pipeline`：
您可以轻松地使用🤗 `accelerate`在大模型上运行 `pipeline`！首先确保您已经使用 `pip install accelerate` 安装了 `accelerate`。
首先使用 `device_map="auto"` 加载您的模型！我们将在示例中使用 `facebook/opt-1.3b`。
```py
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline(model="facebook/opt-1.3b", dtype=torch.bfloat16, device_map="auto")
output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
```
如果安装 `bitsandbytes` 并添加参数 `load_in_8bit=True`，您还可以传递8位加载的模型。
```py
# pip install accelerate bitsandbytes
import torch
from transformers import pipeline
pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"load_in_8bit": True})
output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
```
请注意，您可以将`checkpoint `替换为任何支持大模型加载的Hugging Face模型，比如BLOOM！
| transformers/docs/source/zh/pipeline_tutorial.md/0 | {
"file_path": "transformers/docs/source/zh/pipeline_tutorial.md",
"repo_id": "transformers",
"token_count": 7469
} | 433 |
#!/usr/bin/env python
# Copyright 2021 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Pre-training/Fine-tuning ViT for image classification .
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=vit
"""
import logging
import os
import sys
import time
from dataclasses import asdict, dataclass, field
from enum import Enum
from pathlib import Path
from typing import Callable, Optional
import jax
import jax.numpy as jnp
import optax
# for dataset and preprocessing
import torch
import torchvision
from flax import jax_utils
from flax.jax_utils import pad_shard_unpad, unreplicate
from flax.training import train_state
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
from huggingface_hub import HfApi
from torchvision import transforms
from tqdm import tqdm
import transformers
from transformers import (
CONFIG_MAPPING,
FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
AutoConfig,
FlaxAutoModelForImageClassification,
HfArgumentParser,
is_tensorboard_available,
set_seed,
)
from transformers.utils import send_example_telemetry
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class TrainingArguments:
output_dir: str = field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."},
)
overwrite_output_dir: bool = field(
default=False,
metadata={
"help": (
"Overwrite the content of the output directory. "
"Use this to continue training if output_dir points to a checkpoint directory."
)
},
)
do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
per_device_train_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."}
)
per_device_eval_batch_size: int = field(
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}
)
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."})
weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."})
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"})
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"})
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."})
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."})
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."})
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."})
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."})
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."})
eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."})
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."})
push_to_hub: bool = field(
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."}
)
hub_model_id: str = field(
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."}
)
hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."})
def __post_init__(self):
if self.output_dir is not None:
self.output_dir = os.path.expanduser(self.output_dir)
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates
the token values by removing their value.
"""
d = asdict(self)
for k, v in d.items():
if isinstance(v, Enum):
d[k] = v.value
if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum):
d[k] = [x.value for x in v]
if k.endswith("_token"):
d[k] = f"<{k.upper()}>"
return d
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
dtype: Optional[str] = field(
default="float32",
metadata={
"help": (
"Floating-point format in which the model weights should be initialized and trained. Choose one of"
" `[float32, float16, bfloat16]`."
)
},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `hf auth login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
"execute code present on the Hub on your local machine."
)
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
train_dir: str = field(
metadata={"help": "Path to the root training directory which contains one subdirectory per class."}
)
validation_dir: str = field(
metadata={"help": "Path to the root validation directory which contains one subdirectory per class."},
)
image_size: Optional[int] = field(default=224, metadata={"help": " The size (resolution) of each image."})
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
class TrainState(train_state.TrainState):
dropout_rng: jnp.ndarray
def replicate(self):
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
def write_metric(summary_writer, train_metrics, eval_metrics, train_time, step):
summary_writer.scalar("train_time", train_time, step)
train_metrics = get_metrics(train_metrics)
for key, vals in train_metrics.items():
tag = f"train_{key}"
for i, val in enumerate(vals):
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
for metric_name, value in eval_metrics.items():
summary_writer.scalar(f"eval_{metric_name}", value, step)
def create_learning_rate_fn(
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
) -> Callable[[int], jnp.ndarray]:
"""Returns a linear warmup, linear_decay learning rate function."""
steps_per_epoch = train_ds_size // train_batch_size
num_train_steps = steps_per_epoch * num_train_epochs
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
decay_fn = optax.linear_schedule(
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
)
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
return schedule_fn
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_image_classification", model_args, data_args, framework="flax")
if (
os.path.exists(training_args.output_dir)
and os.listdir(training_args.output_dir)
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
# Setup logging, we only want one process per machine to log things on the screen.
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
if jax.process_index() == 0:
transformers.utils.logging.set_verbosity_info()
else:
transformers.utils.logging.set_verbosity_error()
# Set the verbosity to info of the Transformers logger (on main process only):
logger.info(f"Training/evaluation parameters {training_args}")
# set seed for random transforms and torch dataloaders
set_seed(training_args.seed)
# Handle the repository creation
if training_args.push_to_hub:
# Retrieve of infer repo_name
repo_name = training_args.hub_model_id
if repo_name is None:
repo_name = Path(training_args.output_dir).absolute().name
# Create repo and retrieve repo_id
api = HfApi()
repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
# Initialize datasets and pre-processing transforms
# We use torchvision here for faster pre-processing
# Note that here we are using some default pre-processing, for maximum accuracy
# one should tune this part and carefully select what transformations to use.
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_dataset = torchvision.datasets.ImageFolder(
data_args.train_dir,
transforms.Compose(
[
transforms.RandomResizedCrop(data_args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]
),
)
eval_dataset = torchvision.datasets.ImageFolder(
data_args.validation_dir,
transforms.Compose(
[
transforms.Resize(data_args.image_size),
transforms.CenterCrop(data_args.image_size),
transforms.ToTensor(),
normalize,
]
),
)
# Load pretrained model and tokenizer
if model_args.config_name:
config = AutoConfig.from_pretrained(
model_args.config_name,
num_labels=len(train_dataset.classes),
image_size=data_args.image_size,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(
model_args.model_name_or_path,
num_labels=len(train_dataset.classes),
image_size=data_args.image_size,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.model_name_or_path:
model = FlaxAutoModelForImageClassification.from_pretrained(
model_args.model_name_or_path,
config=config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
model = FlaxAutoModelForImageClassification.from_config(
config,
seed=training_args.seed,
dtype=getattr(jnp, model_args.dtype),
trust_remote_code=model_args.trust_remote_code,
)
# Store some constant
num_epochs = int(training_args.num_train_epochs)
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size)
eval_batch_size = per_device_eval_batch_size * jax.device_count()
steps_per_epoch = len(train_dataset) // train_batch_size
total_train_steps = steps_per_epoch * num_epochs
def collate_fn(examples):
pixel_values = torch.stack([example[0] for example in examples])
labels = torch.tensor([example[1] for example in examples])
batch = {"pixel_values": pixel_values, "labels": labels}
batch = {k: v.numpy() for k, v in batch.items()}
return batch
# Create data loaders
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=True,
collate_fn=collate_fn,
)
eval_loader = torch.utils.data.DataLoader(
eval_dataset,
batch_size=eval_batch_size,
shuffle=False,
num_workers=data_args.preprocessing_num_workers,
persistent_workers=True,
drop_last=False,
collate_fn=collate_fn,
)
# Enable tensorboard only on the master node
has_tensorboard = is_tensorboard_available()
if has_tensorboard and jax.process_index() == 0:
try:
from flax.metrics.tensorboard import SummaryWriter
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
except ImportError as ie:
has_tensorboard = False
logger.warning(
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
)
else:
logger.warning(
"Unable to display metrics through TensorBoard because the package is not installed: "
"Please run pip install tensorboard to enable."
)
# Initialize our training
rng = jax.random.PRNGKey(training_args.seed)
rng, dropout_rng = jax.random.split(rng)
# Create learning rate schedule
linear_decay_lr_schedule_fn = create_learning_rate_fn(
len(train_dataset),
train_batch_size,
training_args.num_train_epochs,
training_args.warmup_steps,
training_args.learning_rate,
)
# create adam optimizer
adamw = optax.adamw(
learning_rate=linear_decay_lr_schedule_fn,
b1=training_args.adam_beta1,
b2=training_args.adam_beta2,
eps=training_args.adam_epsilon,
weight_decay=training_args.weight_decay,
)
# Setup train state
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=adamw, dropout_rng=dropout_rng)
def loss_fn(logits, labels):
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
return loss.mean()
# Define gradient update step fn
def train_step(state, batch):
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
def compute_loss(params):
labels = batch.pop("labels")
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
loss = loss_fn(logits, labels)
return loss
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(state.params)
grad = jax.lax.pmean(grad, "batch")
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return new_state, metrics
# Define eval fn
def eval_step(params, batch):
labels = batch.pop("labels")
logits = model(**batch, params=params, train=False)[0]
loss = loss_fn(logits, labels)
# summarize metrics
accuracy = (jnp.argmax(logits, axis=-1) == labels).mean()
metrics = {"loss": loss, "accuracy": accuracy}
metrics = jax.lax.pmean(metrics, axis_name="batch")
return metrics
# Create parallel version of the train and eval step
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
p_eval_step = jax.pmap(eval_step, "batch")
# Replicate the train state on each device
state = state.replicate()
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_epochs}")
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
logger.info(f" Total optimization steps = {total_train_steps}")
train_time = 0
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
for epoch in epochs:
# ======================== Training ================================
train_start = time.time()
# Create sampling rng
rng, input_rng = jax.random.split(rng)
train_metrics = []
steps_per_epoch = len(train_dataset) // train_batch_size
train_step_progress_bar = tqdm(total=steps_per_epoch, desc="Training...", position=1, leave=False)
# train
for batch in train_loader:
batch = shard(batch)
state, train_metric = p_train_step(state, batch)
train_metrics.append(train_metric)
train_step_progress_bar.update(1)
train_time += time.time() - train_start
train_metric = unreplicate(train_metric)
train_step_progress_bar.close()
epochs.write(
f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metric['loss']}, Learning Rate:"
f" {train_metric['learning_rate']})"
)
# ======================== Evaluating ==============================
eval_metrics = []
eval_steps = len(eval_dataset) // eval_batch_size
eval_step_progress_bar = tqdm(total=eval_steps, desc="Evaluating...", position=2, leave=False)
for batch in eval_loader:
# Model forward
metrics = pad_shard_unpad(p_eval_step, static_return=True)(
state.params, batch, min_device_batch=per_device_eval_batch_size
)
eval_metrics.append(metrics)
eval_step_progress_bar.update(1)
# normalize eval metrics
eval_metrics = get_metrics(eval_metrics)
eval_metrics = jax.tree_util.tree_map(jnp.mean, eval_metrics)
# Print metrics and update progress bar
eval_step_progress_bar.close()
desc = (
f"Epoch... ({epoch + 1}/{num_epochs} | Eval Loss: {round(eval_metrics['loss'].item(), 4)} | "
f"Eval Accuracy: {round(eval_metrics['accuracy'].item(), 4)})"
)
epochs.write(desc)
epochs.desc = desc
# Save metrics
if has_tensorboard and jax.process_index() == 0:
cur_step = epoch * (len(train_dataset) // train_batch_size)
write_metric(summary_writer, train_metrics, eval_metrics, train_time, cur_step)
# save checkpoint after each epoch and push checkpoint to the hub
if jax.process_index() == 0:
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
model.save_pretrained(training_args.output_dir, params=params)
if training_args.push_to_hub:
api.upload_folder(
commit_message=f"Saving weights and logs of epoch {epoch}",
folder_path=training_args.output_dir,
repo_id=repo_id,
repo_type="model",
token=training_args.hub_token,
)
if __name__ == "__main__":
main()
| transformers/examples/flax/vision/run_image_classification.py/0 | {
"file_path": "transformers/examples/flax/vision/run_image_classification.py",
"repo_id": "transformers",
"token_count": 9563
} | 434 |
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning the library models for question-answering on SQuAD (DistilBERT, Bert, XLM, XLNet)."""
import argparse
import glob
import logging
import os
import random
import timeit
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
import transformers
from transformers import (
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
WEIGHTS_NAME,
AutoConfig,
AutoModelForQuestionAnswering,
AutoTokenizer,
get_linear_schedule_with_warmup,
squad_convert_examples_to_features,
)
from transformers.data.metrics.squad_metrics import (
compute_predictions_log_probs,
compute_predictions_logits,
squad_evaluate,
)
from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
from transformers.trainer_utils import is_main_process
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def to_list(tensor):
return tensor.tolist()
def train(args, train_dataset, model, tokenizer):
"""Train the model"""
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": args.weight_decay,
},
{"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
]
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total
)
# Check if saved optimizer or scheduler states exist
if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile(
os.path.join(args.model_name_or_path, "scheduler.pt")
):
# Load in optimizer and scheduler states
optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"), weights_only=True))
scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"), weights_only=True))
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
# multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True
)
# Train!
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size
* args.gradient_accumulation_steps
* (torch.distributed.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 1
epochs_trained = 0
steps_trained_in_current_epoch = 0
# Check if continuing training from a checkpoint
if os.path.exists(args.model_name_or_path):
try:
# set global_step to global_step of last saved checkpoint from model path
checkpoint_suffix = args.model_name_or_path.split("-")[-1].split("/")[0]
global_step = int(checkpoint_suffix)
epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)
steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)
logger.info(" Continuing training from checkpoint, will skip to saved global_step")
logger.info(" Continuing training from epoch %d", epochs_trained)
logger.info(" Continuing training from global step %d", global_step)
logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)
except ValueError:
logger.info(" Starting fine-tuning.")
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(
epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0]
)
# Added here for reproducibility
set_seed(args)
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
# Skip past any already trained steps if resuming training
if steps_trained_in_current_epoch > 0:
steps_trained_in_current_epoch -= 1
continue
model.train()
batch = tuple(t.to(args.device) for t in batch)
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
"start_positions": batch[3],
"end_positions": batch[4],
}
if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart", "longformer"]:
del inputs["token_type_ids"]
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[5], "p_mask": batch[6]})
if args.version_2_with_negative:
inputs.update({"is_impossible": batch[7]})
if hasattr(model, "config") and hasattr(model.config, "lang2id"):
inputs.update(
{"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
)
outputs = model(**inputs)
# model outputs are always tuple in transformers (see doc)
loss = outputs[0]
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1
# Log metrics
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Only evaluate when single GPU otherwise metrics may not average well
if args.local_rank == -1 and args.evaluate_during_training:
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar(f"eval_{key}", value, global_step)
tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step)
tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step)
logging_loss = tr_loss
# Save model checkpoint
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
output_dir = os.path.join(args.output_dir, f"checkpoint-{global_step}")
# Take care of distributed/parallel training
model_to_save = model.module if hasattr(model, "module") else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
torch.save(args, os.path.join(output_dir, "training_args.bin"))
logger.info("Saving model checkpoint to %s", output_dir)
torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt"))
torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
logger.info("Saving optimizer and scheduler states to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def evaluate(args, model, tokenizer, prefix=""):
dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
# multi-gpu evaluate
if args.n_gpu > 1 and not isinstance(model, torch.nn.DataParallel):
model = torch.nn.DataParallel(model)
# Eval!
logger.info(f"***** Running evaluation {prefix} *****")
logger.info(" Num examples = %d", len(dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
all_results = []
start_time = timeit.default_timer()
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad():
inputs = {
"input_ids": batch[0],
"attention_mask": batch[1],
"token_type_ids": batch[2],
}
if args.model_type in ["xlm", "roberta", "distilbert", "camembert", "bart", "longformer"]:
del inputs["token_type_ids"]
feature_indices = batch[3]
# XLNet and XLM use more arguments for their predictions
if args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": batch[4], "p_mask": batch[5]})
# for lang_id-sensitive xlm models
if hasattr(model, "config") and hasattr(model.config, "lang2id"):
inputs.update(
{"langs": (torch.ones(batch[0].shape, dtype=torch.int64) * args.lang_id).to(args.device)}
)
outputs = model(**inputs)
for i, feature_index in enumerate(feature_indices):
eval_feature = features[feature_index.item()]
unique_id = int(eval_feature.unique_id)
output = [to_list(output[i]) for output in outputs.to_tuple()]
# Some models (XLNet, XLM) use 5 arguments for their predictions, while the other "simpler"
# models only use two.
if len(output) >= 5:
start_logits = output[0]
start_top_index = output[1]
end_logits = output[2]
end_top_index = output[3]
cls_logits = output[4]
result = SquadResult(
unique_id,
start_logits,
end_logits,
start_top_index=start_top_index,
end_top_index=end_top_index,
cls_logits=cls_logits,
)
else:
start_logits, end_logits = output
result = SquadResult(unique_id, start_logits, end_logits)
all_results.append(result)
evalTime = timeit.default_timer() - start_time
logger.info(" Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(dataset))
# Compute predictions
output_prediction_file = os.path.join(args.output_dir, f"predictions_{prefix}.json")
output_nbest_file = os.path.join(args.output_dir, f"nbest_predictions_{prefix}.json")
if args.version_2_with_negative:
output_null_log_odds_file = os.path.join(args.output_dir, f"null_odds_{prefix}.json")
else:
output_null_log_odds_file = None
# XLNet and XLM use a more complex post-processing procedure
if args.model_type in ["xlnet", "xlm"]:
start_n_top = model.config.start_n_top if hasattr(model, "config") else model.module.config.start_n_top
end_n_top = model.config.end_n_top if hasattr(model, "config") else model.module.config.end_n_top
predictions = compute_predictions_log_probs(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
start_n_top,
end_n_top,
args.version_2_with_negative,
tokenizer,
args.verbose_logging,
)
else:
predictions = compute_predictions_logits(
examples,
features,
all_results,
args.n_best_size,
args.max_answer_length,
args.do_lower_case,
output_prediction_file,
output_nbest_file,
output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold,
tokenizer,
)
# Compute the F1 and exact scores.
results = squad_evaluate(examples, predictions)
return results
def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
if args.local_rank not in [-1, 0] and not evaluate:
# Make sure only the first process in distributed training process the dataset, and the others will use the cache
torch.distributed.barrier()
# Load data features from cache or dataset file
input_dir = args.data_dir if args.data_dir else "."
cached_features_file = os.path.join(
input_dir,
"cached_{}_{}_{}".format(
"dev" if evaluate else "train",
list(filter(None, args.model_name_or_path.split("/"))).pop(),
str(args.max_seq_length),
),
)
# Init features and dataset from cache if it exists
if os.path.exists(cached_features_file) and not args.overwrite_cache:
logger.info("Loading features from cached file %s", cached_features_file)
features_and_dataset = torch.load(cached_features_file, weights_only=True)
features, dataset, examples = (
features_and_dataset["features"],
features_and_dataset["dataset"],
features_and_dataset["examples"],
)
else:
logger.info("Creating features from dataset file at %s", input_dir)
if not args.data_dir and ((evaluate and not args.predict_file) or (not evaluate and not args.train_file)):
try:
import tensorflow_datasets as tfds
except ImportError:
raise ImportError("If not data_dir is specified, tensorflow_datasets needs to be installed.")
if args.version_2_with_negative:
logger.warning("tensorflow_datasets does not handle version 2 of SQuAD.")
tfds_examples = tfds.load("squad")
examples = SquadV1Processor().get_examples_from_dataset(tfds_examples, evaluate=evaluate)
else:
processor = SquadV2Processor() if args.version_2_with_negative else SquadV1Processor()
if evaluate:
examples = processor.get_dev_examples(args.data_dir, filename=args.predict_file)
else:
examples = processor.get_train_examples(args.data_dir, filename=args.train_file)
features, dataset = squad_convert_examples_to_features(
examples=examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=not evaluate,
return_dataset="pt",
threads=args.threads,
)
if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save({"features": features, "dataset": dataset, "examples": examples}, cached_features_file)
if args.local_rank == 0 and not evaluate:
# Make sure only the first process in distributed training process the dataset, and the others will use the cache
torch.distributed.barrier()
if output_examples:
return dataset, examples, features
return dataset
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: " + ", ".join(MODEL_TYPES),
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pretrained model or model identifier from huggingface.co/models",
)
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints and predictions will be written.",
)
# Other parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
help="The input data dir. Should contain the .json files for the task."
+ "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
)
parser.add_argument(
"--train_file",
default=None,
type=str,
help="The input training file. If a data dir is specified, will look for the file there"
+ "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
)
parser.add_argument(
"--predict_file",
default=None,
type=str,
help="The input evaluation file. If a data dir is specified, will look for the file there"
+ "If no data dir or train/predict files are specified, will run with tensorflow_datasets.",
)
parser.add_argument(
"--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
)
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name",
)
parser.add_argument(
"--cache_dir",
default="",
type=str,
help="Where do you want to store the pre-trained models downloaded from huggingface.co",
)
parser.add_argument(
"--version_2_with_negative",
action="store_true",
help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
"--null_score_diff_threshold",
type=float,
default=0.0,
help="If null_score - best_non_null is greater than the threshold predict null.",
)
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=(
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
),
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help="When splitting up a long document into chunks, how much stride to take between chunks.",
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=(
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length."
),
)
parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.")
parser.add_argument(
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
)
parser.add_argument(
"--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model."
)
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument(
"--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation."
)
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument(
"--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform."
)
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help="If > 0: set total number of training steps to perform. Override num_train_epochs.",
)
parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument(
"--n_best_size",
default=20,
type=int,
help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=(
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another."
),
)
parser.add_argument(
"--verbose_logging",
action="store_true",
help=(
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation."
),
)
parser.add_argument(
"--lang_id",
default=0,
type=int,
help=(
"language id of input for language-specific xlm models (see"
" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"
),
)
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number",
)
parser.add_argument("--no_cuda", action="store_true", help="Whether not to use CUDA when available")
parser.add_argument(
"--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory"
)
parser.add_argument(
"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
)
parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")
parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
parser.add_argument(
"--fp16",
action="store_true",
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. "
"See details at https://nvidia.github.io/apex/amp.html"
),
)
parser.add_argument("--server_ip", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--server_port", type=str, default="", help="Can be used for distant debugging.")
parser.add_argument("--threads", type=int, default=1, help="multiple threads for converting example to features")
args = parser.parse_args()
if args.doc_stride >= args.max_seq_length - args.max_query_length:
logger.warning(
"WARNING - You've set a doc stride which may be superior to the document length in some "
"examples. This could result in errors when building features from the examples. Please reduce the doc "
"stride or increase the maximum length to ensure the features are correctly built."
)
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and args.do_train
and not args.overwrite_output_dir
):
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(
args.output_dir
)
)
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend="nccl")
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank,
device,
args.n_gpu,
bool(args.local_rank != -1),
args.fp16,
)
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(args.local_rank):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set seed
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
# Make sure only the first process in distributed training will download model & vocab
torch.distributed.barrier()
args.model_type = args.model_type.lower()
config = AutoConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
cache_dir=args.cache_dir if args.cache_dir else None,
)
tokenizer = AutoTokenizer.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,
do_lower_case=args.do_lower_case,
cache_dir=args.cache_dir if args.cache_dir else None,
use_fast=False, # SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handling
)
model = AutoModelForQuestionAnswering.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir if args.cache_dir else None,
)
if args.local_rank == 0:
# Make sure only the first process in distributed training will download model & vocab
torch.distributed.barrier()
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Before we do anything with models, we want to ensure that we get fp16 execution of torch.einsum if args.fp16 is set.
# Otherwise it'll default to "promote" mode, and we'll get fp32 operations. Note that running `--fp16_opt_level="O2"` will
# remove the need for this code, but it is still valid.
if args.fp16:
try:
import apex
apex.amp.register_half_function(torch, "einsum")
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Save the trained model and the tokenizer
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
# Take care of distributed/parallel training
model_to_save = model.module if hasattr(model, "module") else model
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, "training_args.bin"))
# Load a trained model and vocabulary that you have fine-tuned
model = AutoModelForQuestionAnswering.from_pretrained(args.output_dir) # , force_download=True)
# SquadDataset is not compatible with Fast tokenizers which have a smarter overflow handling
# So we use use_fast=False here for now until Fast-tokenizer-compatible-examples are out
tokenizer = AutoTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case, use_fast=False)
model.to(args.device)
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
if args.do_train:
logger.info("Loading checkpoints saved during training for evaluation")
checkpoints = [args.output_dir]
if args.eval_all_checkpoints:
checkpoints = [
os.path.dirname(c)
for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True))
]
else:
logger.info("Loading checkpoint %s for evaluation", args.model_name_or_path)
checkpoints = [args.model_name_or_path]
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else ""
model = AutoModelForQuestionAnswering.from_pretrained(checkpoint) # , force_download=True)
model.to(args.device)
# Evaluate
result = evaluate(args, model, tokenizer, prefix=global_step)
result = {k + (f"_{global_step}" if global_step else ""): v for k, v in result.items()}
results.update(result)
logger.info(f"Results: {results}")
return results
if __name__ == "__main__":
main()
| transformers/examples/legacy/question-answering/run_squad.py/0 | {
"file_path": "transformers/examples/legacy/question-answering/run_squad.py",
"repo_id": "transformers",
"token_count": 14747
} | 435 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
PRED = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of the'
' final seconds on board Flight 9525. The Germanwings co-pilot says he had a "previous episode of severe'
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
TGT = [
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .'
' Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz'
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def test_disaggregated_scores_are_determinstic():
no_aggregation = calculate_rouge(PRED, TGT, bootstrap_aggregation=False, rouge_keys=["rouge2", "rougeL"])
assert isinstance(no_aggregation, defaultdict)
no_aggregation_just_r2 = calculate_rouge(PRED, TGT, bootstrap_aggregation=False, rouge_keys=["rouge2"])
assert (
pd.DataFrame(no_aggregation["rouge2"]).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_r2["rouge2"]).fmeasure.mean()
)
def test_newline_cnn_improvement():
k = "rougeLsum"
score = calculate_rouge(PRED, TGT, newline_sep=True, rouge_keys=[k])[k]
score_no_sep = calculate_rouge(PRED, TGT, newline_sep=False, rouge_keys=[k])[k]
assert score > score_no_sep
def test_newline_irrelevant_for_other_metrics():
k = ["rouge1", "rouge2", "rougeL"]
score_sep = calculate_rouge(PRED, TGT, newline_sep=True, rouge_keys=k)
score_no_sep = calculate_rouge(PRED, TGT, newline_sep=False, rouge_keys=k)
assert score_sep == score_no_sep
def test_single_sent_scores_dont_depend_on_newline_sep():
pred = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
]
tgt = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(pred, tgt, newline_sep=True) == calculate_rouge(pred, tgt, newline_sep=False)
def test_pegasus_newline():
pred = [
"""" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" """
]
tgt = [
""" Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
]
prev_score = calculate_rouge(pred, tgt, rouge_keys=["rougeLsum"], newline_sep=False)["rougeLsum"]
new_score = calculate_rouge(pred, tgt, rouge_keys=["rougeLsum"])["rougeLsum"]
assert new_score > prev_score
def test_rouge_cli():
data_dir = Path("examples/seq2seq/test_data/wmt_en_ro")
metrics = calculate_rouge_path(data_dir.joinpath("test.source"), data_dir.joinpath("test.target"))
assert isinstance(metrics, dict)
metrics_default_dict = calculate_rouge_path(
data_dir.joinpath("test.source"), data_dir.joinpath("test.target"), bootstrap_aggregation=False
)
assert isinstance(metrics_default_dict, defaultdict)
| transformers/examples/legacy/seq2seq/old_test_calculate_rouge.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/old_test_calculate_rouge.py",
"repo_id": "transformers",
"token_count": 1793
} | 436 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Optional, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_xla_available
logger = logging.get_logger(__name__)
arg_to_scheduler = {
"linear": get_linear_schedule_with_warmup,
"cosine": get_cosine_schedule_with_warmup,
"cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
"polynomial": get_polynomial_decay_schedule_with_warmup,
"constant": get_constant_schedule,
"constant_w_warmup": get_constant_schedule_with_warmup,
}
class Seq2SeqTrainer(Trainer):
def __init__(self, config=None, data_args=None, *args, **kwargs):
super().__init__(*args, **kwargs)
if config is None:
assert isinstance(self.model, PreTrainedModel), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f" {self.model.__class__}"
)
self.config = self.model.config
else:
self.config = config
self.data_args = data_args
self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correctly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
" padding.."
)
if self.args.label_smoothing == 0:
self.loss_fn = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
self.loss_fn = label_smoothed_nll_loss
def create_optimizer_and_scheduler(self, num_training_steps: int):
"""
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
"""
if self.optimizer is None:
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
if self.args.adafactor:
optimizer_cls = Adafactor
optimizer_kwargs = {"scale_parameter": False, "relative_step": False}
else:
optimizer_cls = torch.optim.AdamW
optimizer_kwargs = {
"betas": (self.args.adam_beta1, self.args.adam_beta2),
"eps": self.args.adam_epsilon,
}
optimizer_kwargs["lr"] = self.args.learning_rate
self.optimizer = optimizer_cls(optimizer_grouped_parameters, **optimizer_kwargs)
if self.lr_scheduler is None:
self.lr_scheduler = self._get_lr_scheduler(num_training_steps)
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")
def _get_lr_scheduler(self, num_training_steps):
schedule_func = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
scheduler = schedule_func(self.optimizer)
elif self.args.lr_scheduler == "constant_w_warmup":
scheduler = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps)
else:
scheduler = schedule_func(
self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
)
return scheduler
def _get_train_sampler(self) -> Optional[torch.utils.data.Sampler]:
if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
return None
elif is_torch_xla_available():
return get_tpu_sampler(self.train_dataset)
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size,
distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
)
return (
RandomSampler(self.train_dataset)
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset)
)
def _compute_loss(self, model, inputs, labels):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
logits = model(**inputs, use_cache=False)[0]
loss = self.loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
else:
# compute usual loss via models
loss, logits = model(**inputs, labels=labels, use_cache=False)[:2]
else:
# compute label smoothed loss
logits = model(**inputs, use_cache=False)[0]
lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
loss, _ = self.loss_fn(lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id)
return loss, logits
def compute_loss(self, model, inputs):
labels = inputs.pop("labels")
loss, _ = self._compute_loss(model, inputs, labels)
return loss
def prediction_step(
self,
model: nn.Module,
inputs: dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[list[str]] = None,
) -> tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an evaluation step on :obj:`model` using obj:`inputs`.
Subclass and override to inject custom behavior.
Args:
model (:obj:`nn.Module`):
The model to evaluate.
inputs (:obj:`dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument :obj:`labels`. Check your model's documentation for all accepted arguments.
prediction_loss_only (:obj:`bool`):
Whether or not to return the loss only.
Return:
tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
A tuple with the loss, logits and labels (each being optional).
"""
inputs = self._prepare_inputs(inputs)
gen_kwargs = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
generated_tokens = self.model.generate(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
**gen_kwargs,
)
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])
labels = inputs.pop("labels")
with torch.no_grad():
# compute loss on predict data
loss, logits = self._compute_loss(model, inputs, labels)
loss = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
logits = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])
return (loss, logits, labels)
def _pad_tensors_to_max_len(self, tensor, max_length):
# If PAD token is not defined at least EOS token has to be defined
pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
f" padded to `max_length`={max_length}"
)
padded_tensor = pad_token_id * torch.ones(
(tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
)
padded_tensor[:, : tensor.shape[-1]] = tensor
return padded_tensor
| transformers/examples/legacy/seq2seq/seq2seq_trainer.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/seq2seq_trainer.py",
"repo_id": "transformers",
"token_count": 4827
} | 437 |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import json
import linecache
import math
import os
import pickle
import socket
from collections.abc import Iterable
from logging import getLogger
from pathlib import Path
from typing import Callable, Union
import git
import numpy as np
import torch
import torch.distributed as dist
from rouge_score import rouge_scorer, scoring
from sacrebleu import corpus_bleu
from sentence_splitter import add_newline_to_end_of_each_sentence
from torch import nn
from torch.utils.data import Dataset, Sampler
from transformers import BartTokenizer, EvalPrediction, PreTrainedTokenizer, T5Tokenizer
from transformers.models.bart.modeling_bart import shift_tokens_right
from transformers.utils import cached_property
try:
from fairseq.data.data_utils import batch_by_size
FAIRSEQ_AVAILABLE = True
except (ImportError, ModuleNotFoundError):
FAIRSEQ_AVAILABLE = False
def label_smoothed_nll_loss(lprobs, target, epsilon, ignore_index=-100):
"""From fairseq"""
if target.dim() == lprobs.dim() - 1:
target = target.unsqueeze(-1)
nll_loss = -lprobs.gather(dim=-1, index=target)
smooth_loss = -lprobs.sum(dim=-1, keepdim=True)
if ignore_index is not None:
pad_mask = target.eq(ignore_index)
nll_loss.masked_fill_(pad_mask, 0.0)
smooth_loss.masked_fill_(pad_mask, 0.0)
else:
nll_loss = nll_loss.squeeze(-1)
smooth_loss = smooth_loss.squeeze(-1)
nll_loss = nll_loss.sum() # mean()? Scared to break other math.
smooth_loss = smooth_loss.sum()
eps_i = epsilon / lprobs.size(-1)
loss = (1.0 - epsilon) * nll_loss + eps_i * smooth_loss
return loss, nll_loss
def lmap(f: Callable, x: Iterable) -> list:
"""list(map(f, x))"""
return list(map(f, x))
def calculate_bleu(output_lns, refs_lns, **kwargs) -> dict:
"""Uses sacrebleu's corpus_bleu implementation."""
return {"bleu": round(corpus_bleu(output_lns, [refs_lns], **kwargs).score, 4)}
def build_compute_metrics_fn(task_name: str, tokenizer: PreTrainedTokenizer) -> Callable[[EvalPrediction], dict]:
def non_pad_len(tokens: np.ndarray) -> int:
return np.count_nonzero(tokens != tokenizer.pad_token_id)
def decode_pred(pred: EvalPrediction) -> tuple[list[str], list[str]]:
pred_ids = pred.predictions
label_ids = pred.label_ids
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
label_ids[label_ids == -100] = tokenizer.pad_token_id
label_str = tokenizer.batch_decode(label_ids, skip_special_tokens=True)
pred_str = lmap(str.strip, pred_str)
label_str = lmap(str.strip, label_str)
return pred_str, label_str
def summarization_metrics(pred: EvalPrediction) -> dict:
pred_str, label_str = decode_pred(pred)
rouge: dict = calculate_rouge(pred_str, label_str)
summ_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
rouge.update({"gen_len": summ_len})
return rouge
def translation_metrics(pred: EvalPrediction) -> dict:
pred_str, label_str = decode_pred(pred)
bleu: dict = calculate_bleu(pred_str, label_str)
gen_len = np.round(np.mean(lmap(non_pad_len, pred.predictions)), 1)
bleu.update({"gen_len": gen_len})
return bleu
compute_metrics_fn = summarization_metrics if "summarization" in task_name else translation_metrics
return compute_metrics_fn
def trim_batch(
input_ids,
pad_token_id,
attention_mask=None,
):
"""Remove columns that are populated exclusively by pad_token_id"""
keep_column_mask = input_ids.ne(pad_token_id).any(dim=0)
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class AbstractSeq2SeqDataset(Dataset):
def __init__(
self,
tokenizer,
data_dir,
max_source_length,
max_target_length,
type_path="train",
n_obs=None,
prefix="",
**dataset_kwargs,
):
super().__init__()
self.src_file = Path(data_dir).joinpath(type_path + ".source")
self.tgt_file = Path(data_dir).joinpath(type_path + ".target")
self.len_file = Path(data_dir).joinpath(type_path + ".len")
if os.path.exists(self.len_file):
self.src_lens = pickle_load(self.len_file)
self.used_char_len = False
else:
self.src_lens = self.get_char_lens(self.src_file)
self.used_char_len = True
self.max_source_length = max_source_length
self.max_target_length = max_target_length
assert min(self.src_lens) > 0, f"found empty line in {self.src_file}"
self.tokenizer = tokenizer
self.prefix = prefix if prefix is not None else ""
if n_obs is not None:
self.src_lens = self.src_lens[:n_obs]
self.pad_token_id = self.tokenizer.pad_token_id
self.dataset_kwargs = dataset_kwargs
dataset_kwargs.update({"add_prefix_space": True} if isinstance(self.tokenizer, BartTokenizer) else {})
def __len__(self):
return len(self.src_lens)
@staticmethod
def get_char_lens(data_file):
return [len(x) for x in Path(data_file).open().readlines()]
@cached_property
def tgt_lens(self):
"""Length in characters of target documents"""
return self.get_char_lens(self.tgt_file)
def make_sortish_sampler(self, batch_size, distributed=False, shuffle=True, **kwargs):
if distributed:
return DistributedSortishSampler(self, batch_size, shuffle=shuffle, **kwargs)
else:
return SortishSampler(self.src_lens, batch_size, shuffle=shuffle)
def make_dynamic_sampler(self, max_tokens_per_batch=1024, **kwargs):
assert FAIRSEQ_AVAILABLE, "Dynamic batch size requires `pip install fairseq`"
assert not self.used_char_len, "You must call python make_len_file.py before calling make_dynamic_sampler"
sorted_indices = list(self.make_sortish_sampler(1024, shuffle=False))
def num_tokens_in_example(i):
return min(self.src_lens[i], self.max_target_length)
# call fairseq cython function
batch_sampler: list[list[int]] = batch_by_size(
sorted_indices,
num_tokens_fn=num_tokens_in_example,
max_tokens=max_tokens_per_batch,
required_batch_size_multiple=64,
)
shuffled_batches = [batch_sampler[i] for i in np.random.permutation(range(len(batch_sampler)))]
# move the largest batch to the front to OOM quickly (uses an approximation for padding)
approximate_toks_per_batch = [max(self.src_lens[i] for i in batch) * len(batch) for batch in shuffled_batches]
largest_batch_idx = np.argmax(approximate_toks_per_batch)
shuffled_batches[0], shuffled_batches[largest_batch_idx] = (
shuffled_batches[largest_batch_idx],
shuffled_batches[0],
)
return shuffled_batches
def __getitem__(self, item):
raise NotImplementedError("You must implement this")
def collate_fn(self, batch):
raise NotImplementedError("You must implement this")
class LegacySeq2SeqDataset(AbstractSeq2SeqDataset):
def __getitem__(self, index) -> dict[str, torch.Tensor]:
"""Call tokenizer on src and tgt_lines"""
index = index + 1 # linecache starts at 1
source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
source_inputs = self.encode_line(self.tokenizer, source_line, self.max_source_length)
target_inputs = self.encode_line(self.tokenizer, tgt_line, self.max_target_length)
source_ids = source_inputs["input_ids"].squeeze()
target_ids = target_inputs["input_ids"].squeeze()
src_mask = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"labels": target_ids,
}
def encode_line(self, tokenizer, line, max_length, pad_to_max_length=True, return_tensors="pt"):
"""Only used by LegacyDataset"""
return tokenizer(
[line],
max_length=max_length,
padding="max_length" if pad_to_max_length else None,
truncation=True,
return_tensors=return_tensors,
**self.dataset_kwargs,
)
def collate_fn(self, batch) -> dict[str, torch.Tensor]:
input_ids = torch.stack([x["input_ids"] for x in batch])
masks = torch.stack([x["attention_mask"] for x in batch])
target_ids = torch.stack([x["labels"] for x in batch])
pad_token_id = self.pad_token_id
y = trim_batch(target_ids, pad_token_id)
source_ids, source_mask = trim_batch(input_ids, pad_token_id, attention_mask=masks)
batch = {
"input_ids": source_ids,
"attention_mask": source_mask,
"labels": y,
}
return batch
class Seq2SeqDataset(AbstractSeq2SeqDataset):
"""A dataset that calls prepare_seq2seq_batch."""
def __getitem__(self, index) -> dict[str, str]:
index = index + 1 # linecache starts at 1
source_line = self.prefix + linecache.getline(str(self.src_file), index).rstrip("\n")
tgt_line = linecache.getline(str(self.tgt_file), index).rstrip("\n")
assert source_line, f"empty source line for index {index}"
assert tgt_line, f"empty tgt line for index {index}"
return {"tgt_texts": tgt_line, "src_texts": source_line, "id": index - 1}
def collate_fn(self, batch) -> dict[str, torch.Tensor]:
"""Call prepare_seq2seq_batch."""
batch_encoding: dict[str, torch.Tensor] = self.tokenizer.prepare_seq2seq_batch(
[x["src_texts"] for x in batch],
tgt_texts=[x["tgt_texts"] for x in batch],
max_length=self.max_source_length,
max_target_length=self.max_target_length,
return_tensors="pt",
**self.dataset_kwargs,
).data
batch_encoding["ids"] = torch.tensor([x["id"] for x in batch])
return batch_encoding
class Seq2SeqDataCollator:
def __init__(self, tokenizer, data_args, decoder_start_token_id, tpu_num_cores=None):
self.tokenizer = tokenizer
self.pad_token_id = tokenizer.pad_token_id
self.decoder_start_token_id = decoder_start_token_id
assert self.pad_token_id is not None, (
f"pad_token_id is not defined for ({self.tokenizer.__class__.__name__}), it must be defined."
)
self.data_args = data_args
self.tpu_num_cores = tpu_num_cores
self.dataset_kwargs = {"add_prefix_space": True} if isinstance(tokenizer, BartTokenizer) else {}
if data_args.src_lang is not None:
self.dataset_kwargs["src_lang"] = data_args.src_lang
if data_args.tgt_lang is not None:
self.dataset_kwargs["tgt_lang"] = data_args.tgt_lang
def __call__(self, batch) -> dict[str, torch.Tensor]:
if hasattr(self.tokenizer, "prepare_seq2seq_batch"):
batch = self._encode(batch)
input_ids, attention_mask, labels = (
batch["input_ids"],
batch["attention_mask"],
batch["labels"],
)
else:
input_ids = torch.stack([x["input_ids"] for x in batch])
attention_mask = torch.stack([x["attention_mask"] for x in batch])
labels = torch.stack([x["labels"] for x in batch])
labels = trim_batch(labels, self.pad_token_id)
input_ids, attention_mask = trim_batch(input_ids, self.pad_token_id, attention_mask=attention_mask)
if isinstance(self.tokenizer, T5Tokenizer):
decoder_input_ids = self._shift_right_t5(labels)
else:
decoder_input_ids = shift_tokens_right(labels, self.pad_token_id, self.decoder_start_token_id)
batch = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
"labels": labels,
}
return batch
def _shift_right_t5(self, input_ids):
# shift inputs to the right
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
shifted_input_ids[..., 0] = self.pad_token_id
return shifted_input_ids
def _encode(self, batch) -> dict[str, torch.Tensor]:
batch_encoding = self.tokenizer.prepare_seq2seq_batch(
[x["src_texts"] for x in batch],
tgt_texts=[x["tgt_texts"] for x in batch],
max_length=self.data_args.max_source_length,
max_target_length=self.data_args.max_target_length,
padding="max_length" if self.tpu_num_cores is not None else "longest", # TPU hack
return_tensors="pt",
**self.dataset_kwargs,
)
return batch_encoding.data
class SortishSampler(Sampler):
"Go through the text data by order of src length with a bit of randomness. From fastai repo."
def __init__(self, data, batch_size, shuffle=True):
self.data, self.bs, self.shuffle = data, batch_size, shuffle
def __len__(self) -> int:
return len(self.data)
def __iter__(self):
return iter(sortish_sampler_indices(self.data, self.bs, shuffle=self.shuffle))
def sortish_sampler_indices(data: list, bs: int, shuffle=True) -> np.array:
"Go through the text data by order of src length with a bit of randomness. From fastai repo."
if not shuffle:
return np.argsort(np.array(data) * -1)
def key_fn(i):
return data[i]
idxs = np.random.permutation(len(data))
sz = bs * 50
ck_idx = [idxs[i : i + sz] for i in range(0, len(idxs), sz)]
sort_idx = np.concatenate([sorted(s, key=key_fn, reverse=True) for s in ck_idx])
sz = bs
ck_idx = [sort_idx[i : i + sz] for i in range(0, len(sort_idx), sz)]
max_ck = np.argmax([key_fn(ck[0]) for ck in ck_idx]) # find the chunk with the largest key,
ck_idx[0], ck_idx[max_ck] = ck_idx[max_ck], ck_idx[0] # then make sure it goes first.
sort_idx = np.concatenate(np.random.permutation(ck_idx[1:])) if len(ck_idx) > 1 else np.array([], dtype=int)
sort_idx = np.concatenate((ck_idx[0], sort_idx))
return sort_idx
class DistributedSortishSampler(Sampler):
"""Copied from torch DistributedSampler"""
def __init__(self, dataset, batch_size, num_replicas=None, rank=None, add_extra_examples=True, shuffle=True):
if num_replicas is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
num_replicas = dist.get_world_size()
if rank is None:
if not dist.is_available():
raise RuntimeError("Requires distributed package to be available")
rank = dist.get_rank()
self.dataset = dataset
self.num_replicas = num_replicas
self.rank = rank
self.epoch = 0
if add_extra_examples:
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
self.total_size = self.num_samples * self.num_replicas
else:
self.total_size = len(dataset)
self.num_samples = len(self.available_indices)
self.batch_size = batch_size
self.add_extra_examples = add_extra_examples
self.shuffle = shuffle
def __iter__(self) -> Iterable:
g = torch.Generator()
g.manual_seed(self.epoch)
sortish_data = [self.dataset.src_lens[i] for i in self.available_indices]
sortish_indices = sortish_sampler_indices(sortish_data, self.batch_size, shuffle=self.shuffle)
indices = [self.available_indices[i] for i in sortish_indices]
assert len(indices) == self.num_samples
return iter(indices)
@cached_property
def available_indices(self) -> np.array:
indices = list(range(len(self.dataset)))
# add extra samples to make it evenly divisible
indices += indices[: (self.total_size - len(indices))]
assert len(indices) == self.total_size
# subsample
available_indices = indices[self.rank : self.total_size : self.num_replicas]
return available_indices
def __len__(self):
return self.num_samples
def set_epoch(self, epoch):
self.epoch = epoch
logger = getLogger(__name__)
def use_task_specific_params(model, task):
"""Update config with summarization specific params."""
task_specific_params = model.config.task_specific_params
if task_specific_params is not None:
pars = task_specific_params.get(task, {})
logger.info(f"setting model.config to task specific params for {task}:\n {pars}")
logger.info("note: command line args may override some of these")
model.config.update(pars)
def pickle_load(path):
"""pickle.load(path)"""
with open(path, "rb") as f:
return pickle.load(f)
def pickle_save(obj, path):
"""pickle.dump(obj, path)"""
with open(path, "wb") as f:
return pickle.dump(obj, f)
def flatten_list(summary_ids: list[list]):
return list(itertools.chain.from_iterable(summary_ids))
def save_git_info(folder_path: str) -> None:
"""Save git information to output_dir/git_log.json"""
repo_infos = get_git_info()
save_json(repo_infos, os.path.join(folder_path, "git_log.json"))
def save_json(content, path, indent=4, **json_dump_kwargs):
with open(path, "w") as f:
json.dump(content, f, indent=indent, sort_keys=True, **json_dump_kwargs)
def load_json(path):
with open(path) as f:
return json.load(f)
def get_git_info():
try:
repo = git.Repo(search_parent_directories=True)
repo_infos = {
"repo_id": str(repo),
"repo_sha": str(repo.head.object.hexsha),
"repo_branch": str(repo.active_branch),
"hostname": str(socket.gethostname()),
}
return repo_infos
except TypeError:
return {
"repo_id": None,
"repo_sha": None,
"repo_branch": None,
"hostname": None,
}
ROUGE_KEYS = ["rouge1", "rouge2", "rougeL", "rougeLsum"]
def extract_rouge_mid_statistics(dct):
new_dict = {}
for k1, v1 in dct.items():
mid = v1.mid
new_dict[k1] = {stat: round(getattr(mid, stat), 4) for stat in ["precision", "recall", "fmeasure"]}
return new_dict
def calculate_rouge(
pred_lns: list[str],
tgt_lns: list[str],
use_stemmer=True,
rouge_keys=ROUGE_KEYS,
return_precision_and_recall=False,
bootstrap_aggregation=True,
newline_sep=True,
) -> dict:
"""Calculate rouge using rouge_scorer package.
Args:
pred_lns: list of summaries generated by model
tgt_lns: list of groundtruth summaries (e.g. contents of val.target)
use_stemmer: Bool indicating whether Porter stemmer should be used to
strip word suffixes to improve matching.
rouge_keys: which metrics to compute, defaults to rouge1, rouge2, rougeL, rougeLsum
return_precision_and_recall: (False) whether to also return precision and recall.
bootstrap_aggregation: whether to do the typical bootstrap resampling of scores. Defaults to True, if False
this function returns a collections.defaultdict[metric: list of values for each observation for each subscore]``
newline_sep:(default=True) whether to add newline between sentences. This is essential for calculation rougeL
on multi sentence summaries (CNN/DM dataset).
Returns:
dict[score: value] if aggregate else defaultdict(list) keyed by rouge_keys
"""
scorer = rouge_scorer.RougeScorer(rouge_keys, use_stemmer=use_stemmer)
aggregator = scoring.BootstrapAggregator()
for pred, tgt in zip(tgt_lns, pred_lns):
# rougeLsum expects "\n" separated sentences within a summary
if newline_sep:
pred = add_newline_to_end_of_each_sentence(pred)
tgt = add_newline_to_end_of_each_sentence(tgt)
scores = scorer.score(pred, tgt)
aggregator.add_scores(scores)
if bootstrap_aggregation:
result = aggregator.aggregate()
if return_precision_and_recall:
return extract_rouge_mid_statistics(result) # here we return dict
else:
return {k: round(v.mid.fmeasure * 100, 4) for k, v in result.items()}
else:
return aggregator._scores # here we return defaultdict(list)
# Utilities for freezing parameters and checking whether they are frozen
def freeze_params(model: nn.Module):
"""Set requires_grad=False for each of model.parameters()"""
for par in model.parameters():
par.requires_grad = False
def freeze_embeds(model):
"""Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
model_type = model.config.model_type
if model_type in ["t5", "mt5"]:
freeze_params(model.shared)
for d in [model.encoder, model.decoder]:
freeze_params(d.embed_tokens)
elif model_type == "fsmt":
for d in [model.model.encoder, model.model.decoder]:
freeze_params(d.embed_positions)
freeze_params(d.embed_tokens)
else:
freeze_params(model.model.shared)
for d in [model.model.encoder, model.model.decoder]:
freeze_params(d.embed_positions)
freeze_params(d.embed_tokens)
def grad_status(model: nn.Module) -> Iterable:
return (par.requires_grad for par in model.parameters())
def any_requires_grad(model: nn.Module) -> bool:
return any(grad_status(model))
def assert_all_frozen(model):
model_grads: list[bool] = list(grad_status(model))
n_require_grad = sum(lmap(int, model_grads))
npars = len(model_grads)
assert not any(model_grads), f"{n_require_grad / npars:.1%} of {npars} weights require grad"
def assert_not_all_frozen(model):
model_grads: list[bool] = list(grad_status(model))
npars = len(model_grads)
assert any(model_grads), f"none of {npars} weights require grad"
def parse_numeric_n_bool_cl_kwargs(unparsed_args: list[str]) -> dict[str, Union[int, float, bool]]:
"""
Parse an argv list of unspecified command line args to a dict.
Assumes all values are either numeric or boolean in the form of true/false.
"""
result = {}
assert len(unparsed_args) % 2 == 0, f"got odd number of unparsed args: {unparsed_args}"
num_pairs = len(unparsed_args) // 2
for pair_num in range(num_pairs):
i = 2 * pair_num
assert unparsed_args[i].startswith("--")
if unparsed_args[i + 1].lower() == "true":
value = True
elif unparsed_args[i + 1].lower() == "false":
value = False
else:
try:
value = int(unparsed_args[i + 1])
except ValueError:
value = float(unparsed_args[i + 1]) # this can raise another informative ValueError
result[unparsed_args[i][2:]] = value
return result
def write_txt_file(ordered_tgt, path):
f = Path(path).open("w")
for ln in ordered_tgt:
f.write(ln + "\n")
f.flush()
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i : i + n]
def check_output_dir(args, expected_items=0):
"""
Checks whether to bail out if output_dir already exists and has more than expected_items in it
`args`: needs to have the following attributes of `args`:
- output_dir
- do_train
- overwrite_output_dir
`expected_items`: normally 0 (default) - i.e. empty dir, but in some cases a few files are expected (e.g. recovery from OOM)
"""
if (
os.path.exists(args.output_dir)
and len(os.listdir(args.output_dir)) > expected_items
and args.do_train
and not args.overwrite_output_dir
):
raise ValueError(
f"Output directory ({args.output_dir}) already exists and "
f"has {len(os.listdir(args.output_dir))} items in it (expected {expected_items} items). "
"Use --overwrite_output_dir to overcome."
)
| transformers/examples/legacy/seq2seq/utils.py/0 | {
"file_path": "transformers/examples/legacy/seq2seq/utils.py",
"repo_id": "transformers",
"token_count": 10902
} | 438 |
global:
scrape_interval: 15s
| transformers/examples/metrics-monitoring/prometheus.yml/0 | {
"file_path": "transformers/examples/metrics-monitoring/prometheus.yml",
"repo_id": "transformers",
"token_count": 12
} | 439 |
from typing import ClassVar, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
from ...cache_utils import Cache
class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
def __init__(self, config):
super().__init__(config=config)
self.embedding_dim = self.config.embedding_dim
self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
if self.language_model._tied_weights_keys is not None:
self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = None,
token_type_ids: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
num_logits_to_keep: int = 0,
):
r"""
Returns:
"""
vlm_outputs = super().forward(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
token_type_ids=token_type_ids,
cache_position=cache_position,
inputs_embeds=inputs_embeds,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=True,
num_logits_to_keep=num_logits_to_keep,
)
last_hidden_states = vlm_outputs.hidden_states[-1] # (batch_size, sequence_length, hidden_size)
proj = self.custom_text_proj(last_hidden_states) # (batch_size, sequence_length, dim)
# L2 normalization
embeddings = proj / proj.norm(dim=-1, keepdim=True) # (batch_size, sequence_length, dim)
if attention_mask is not None:
embeddings = embeddings * attention_mask.unsqueeze(-1) # (batch_size, sequence_length, dim)
return (embeddings,) + vlm_outputs
def resize_token_embeddings(
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
) -> nn.Embedding:
model_embeds = self.language_model.resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
# Update vocab size
self.config.text_config.vocab_size = model_embeds.num_embeddings
self.config.vocab_size = model_embeds.num_embeddings
self.vocab_size = model_embeds.num_embeddings
return model_embeds
| transformers/examples/modular-transformers/modular_new_task_model.py/0 | {
"file_path": "transformers/examples/modular-transformers/modular_new_task_model.py",
"repo_id": "transformers",
"token_count": 1442
} | 440 |
#!/usr/bin/env python
# Copyright 2022 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "transformers @ git+https://github.com/huggingface/transformers.git",
# "torch>=1.5.0",
# "torchvision>=0.6.0",
# "datasets>=1.8.0",
# ]
# ///
"""
Training a CLIP like dual encoder models using text and vision encoders in the library.
The script can be used to train CLIP like models for languages other than English by using
a text encoder pre-trained in the desired language. Currently this script supports the following vision
and text models:
Vision models: ViT(https://huggingface.co/models?filter=vit), CLIP (https://huggingface.co/models?filter=clip)
Text models: BERT, ROBERTa (https://huggingface.co/models?filter=fill-mask)
"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.io import ImageReadMode, read_image
from torchvision.transforms import CenterCrop, ConvertImageDtype, Normalize, Resize
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
AutoImageProcessor,
AutoModel,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
logger = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.56.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
image_processor_name: str = field(default=None, metadata={"help": "Name or path of preprocessor config."})
cache_dir: Optional[str] = field(
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `hf auth login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
freeze_vision_model: bool = field(
default=False, metadata={"help": "Whether to freeze the vision model parameters or not."}
)
freeze_text_model: bool = field(
default=False, metadata={"help": "Whether to freeze the text model parameters or not."}
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
data_dir: Optional[str] = field(default=None, metadata={"help": "The data directory containing input files."})
image_column: Optional[str] = field(
default="image_path",
metadata={"help": "The name of the column in the datasets containing the full image file paths."},
)
caption_column: Optional[str] = field(
default="caption",
metadata={"help": "The name of the column in the datasets containing the image captions."},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "The input training data file (a jsonlines file)."}
)
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file (a jsonlines file)."},
)
max_seq_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
dataset_name_mapping = {
"image_caption_dataset.py": ("image_path", "caption"),
}
# We use torchvision for faster image pre-processing. The transforms are implemented as nn.Module,
# so we jit it to be faster.
class Transform(torch.nn.Module):
def __init__(self, image_size, mean, std):
super().__init__()
self.transforms = torch.nn.Sequential(
Resize([image_size], interpolation=InterpolationMode.BICUBIC),
CenterCrop(image_size),
ConvertImageDtype(torch.float),
Normalize(mean, std),
)
def forward(self, x) -> torch.Tensor:
"""`x` should be an instance of `PIL.Image.Image`"""
with torch.no_grad():
x = self.transforms(x)
return x
def collate_fn(examples):
pixel_values = torch.stack([example["pixel_values"] for example in examples])
input_ids = torch.tensor([example["input_ids"] for example in examples], dtype=torch.long)
attention_mask = torch.tensor([example["attention_mask"] for example in examples], dtype=torch.long)
return {
"pixel_values": pixel_values,
"input_ids": input_ids,
"attention_mask": attention_mask,
"return_loss": True,
}
def main():
# 1. Parse input arguments
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clip", model_args, data_args)
# 2. Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# 3. Detecting last checkpoint and eventually continue from last checkpoint
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# 4. Load dataset
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files this script will use the first column for the full image path and the second column for the
# captions (unless you specify column names for this with the `image_column` and `caption_column` arguments).
#
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
dataset = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
keep_in_memory=False,
data_dir=data_args.data_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
dataset = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# 5. Load pretrained model, tokenizer, and image processor
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
# Load image_processor, in this script we only use this to get the mean and std for normalization.
image_processor = AutoImageProcessor.from_pretrained(
model_args.image_processor_name or model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
model = AutoModel.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
config = model.config
def _freeze_params(module):
for param in module.parameters():
param.requires_grad = False
if model_args.freeze_vision_model:
_freeze_params(model.vision_model)
if model_args.freeze_text_model:
_freeze_params(model.text_model)
# set seed for torch dataloaders
set_seed(training_args.seed)
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
if training_args.do_train:
column_names = dataset["train"].column_names
elif training_args.do_eval:
column_names = dataset["validation"].column_names
else:
logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
return
# 6. Get the column names for input/target.
dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None)
if data_args.image_column is None:
image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
image_column = data_args.image_column
if image_column not in column_names:
raise ValueError(
f"--image_column' value '{data_args.image_column}' needs to be one of: {', '.join(column_names)}"
)
if data_args.caption_column is None:
caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
caption_column = data_args.caption_column
if caption_column not in column_names:
raise ValueError(
f"--caption_column' value '{data_args.caption_column}' needs to be one of: {', '.join(column_names)}"
)
# 7. Preprocessing the datasets.
# Initialize torchvision transforms and jit it for faster processing.
image_transformations = Transform(
config.vision_config.image_size, image_processor.image_mean, image_processor.image_std
)
image_transformations = torch.jit.script(image_transformations)
# Preprocessing the datasets.
# We need to tokenize input captions and transform the images.
def tokenize_captions(examples):
captions = list(examples[caption_column])
text_inputs = tokenizer(captions, max_length=data_args.max_seq_length, padding="max_length", truncation=True)
examples["input_ids"] = text_inputs.input_ids
examples["attention_mask"] = text_inputs.attention_mask
return examples
def transform_images(examples):
images = [read_image(image_file, mode=ImageReadMode.RGB) for image_file in examples[image_column]]
examples["pixel_values"] = [image_transformations(image) for image in images]
return examples
def filter_corrupt_images(examples):
"""remove problematic images"""
valid_images = []
for image_file in examples[image_column]:
try:
Image.open(image_file)
valid_images.append(True)
except Exception:
valid_images.append(False)
return valid_images
if training_args.do_train:
if "train" not in dataset:
raise ValueError("--do_train requires a train dataset")
train_dataset = dataset["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
train_dataset = train_dataset.filter(
filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
)
train_dataset = train_dataset.map(
function=tokenize_captions,
batched=True,
remove_columns=[col for col in column_names if col != image_column],
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
# Transform images on the fly as doing it on the whole dataset takes too much time.
train_dataset.set_transform(transform_images)
if training_args.do_eval:
if "validation" not in dataset:
raise ValueError("--do_eval requires a train validation")
eval_dataset = dataset["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
eval_dataset = eval_dataset.filter(
filter_corrupt_images, batched=True, num_proc=data_args.preprocessing_num_workers
)
eval_dataset = eval_dataset.map(
function=tokenize_captions,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=[col for col in column_names if col != image_column],
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
# Transform images on the fly as doing it on the whole dataset takes too much time.
eval_dataset.set_transform(transform_images)
# 8. Initialize our trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
data_collator=collate_fn,
)
# 9. Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir)
image_processor.save_pretrained(training_args.output_dir)
trainer.log_metrics("train", train_result.metrics)
trainer.save_metrics("train", train_result.metrics)
trainer.save_state()
# 10. Evaluation
if training_args.do_eval:
metrics = trainer.evaluate()
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
# 11. Write Training Stats and push to hub.
finetuned_from = model_args.model_name_or_path
# If from a local directory, don't set `finetuned_from` as this is required to be a valid repo. id on the Hub.
if os.path.isdir(finetuned_from):
finetuned_from = None
kwargs = {"finetuned_from": finetuned_from, "tasks": "contrastive-image-text-modeling"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
if __name__ == "__main__":
main()
| transformers/examples/pytorch/contrastive-image-text/run_clip.py/0 | {
"file_path": "transformers/examples/pytorch/contrastive-image-text/run_clip.py",
"repo_id": "transformers",
"token_count": 8871
} | 441 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "transformers @ git+https://github.com/huggingface/transformers.git",
# "albumentations >= 1.4.16",
# "accelerate >= 0.12.0",
# "torch >= 1.3",
# "datasets >= 2.14.0",
# "sentencepiece != 0.1.92",
# "protobuf",
# "evaluate",
# "scikit-learn",
# ]
# ///
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional
import datasets
import evaluate
import torch
from datasets import IterableDataset, IterableDatasetDict, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
is_torch_xla_available,
set_seed,
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.56.0.dev0")
require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `hf auth login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[int] = field(
default=5,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def split_streaming_dataset(
full_streaming_dataset,
validation_percentage: int = 5,
) -> IterableDatasetDict:
"""
Splits a streaming dataset into
training and validation IterableDatasets, and supports methods like .map(), .filter(),
.take() and properties like .features on the resulting streams.
Args:
full_streaming_dataset (Dataset): The name of the dataset to load (e.g., "HuggingFaceFW/fineweb").
validation_percentage (int): The proportion of the dataset to be used for validation split.
Returns:
IterableDatasetDict: An IterableDatasetDict containing two IterableDataset objects: (train_stream, validation_stream).
"""
if not (0 < validation_percentage < 100):
raise ValueError(
f"validation_percentage must be between 0 and 100 (exclusive). Passed: {validation_percentage}"
)
def split_generator(is_train: bool):
for i, example in enumerate(full_streaming_dataset):
if is_train:
if i % 100 > validation_percentage:
yield example
else:
if i % 100 < validation_percentage:
yield example
features = full_streaming_dataset.features
train_stream = IterableDataset.from_generator(split_generator, gen_kwargs={"is_train": True}, features=features)
validation_stream = IterableDataset.from_generator(
split_generator, gen_kwargs={"is_train": False}, features=features
)
return IterableDatasetDict({"train": train_stream, "validation": validation_stream})
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
if "validation" not in raw_datasets:
if data_args.streaming:
dataset_stream = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split="train",
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
raw_datasets = split_streaming_dataset(dataset_stream, data_args.validation_split_percentage)
else:
raw_datasets["validation"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
raw_datasets["train"] = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
token=model_args.token,
streaming=data_args.streaming,
trust_remote_code=model_args.trust_remote_code,
)
else:
data_files = {}
dataset_args = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = (
data_args.train_file.split(".")[-1]
if data_args.train_file is not None
else data_args.validation_file.split(".")[-1]
)
if extension == "txt":
extension = "text"
dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
# If no validation data is there, validation_split_percentage will be used to divide the dataset.
if "validation" not in raw_datasets:
if data_args.streaming:
dataset_stream = load_dataset(
extension,
data_files=data_files,
split="train",
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
raw_datasets = split_streaming_dataset(dataset_stream, data_args.validation_split_percentage)
else:
raw_datasets["validation"] = load_dataset(
extension,
data_files=data_files,
split=f"train[:{data_args.validation_split_percentage}%]",
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
raw_datasets["train"] = load_dataset(
extension,
data_files=data_files,
split=f"train[{data_args.validation_split_percentage}%:]",
cache_dir=model_args.cache_dir,
token=model_args.token,
**dataset_args,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"token": model_args.token,
"trust_remote_code": model_args.trust_remote_code,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"token": model_args.token,
"trust_remote_code": model_args.trust_remote_code,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.model_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.model_name_or_path:
dtype = model_args.dtype if model_args.dtype in ["auto", None] else getattr(torch, model_args.dtype)
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
dtype=dtype,
)
else:
model = AutoModelForCausalLM.from_config(config, trust_remote_code=model_args.trust_remote_code)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params / 2**20:.2f}M params")
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
# on a small vocab and want a smaller embedding size, remove this test.
embedding_size = model.get_input_embeddings().weight.shape[0]
if len(tokenizer) > embedding_size:
model.resize_token_embeddings(len(tokenizer))
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
column_names = list(raw_datasets["train"].features)
else:
column_names = list(raw_datasets["validation"].features)
text_column_name = "text" if "text" in column_names else column_names[0]
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples[text_column_name])
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
with training_args.main_process_first(desc="dataset map tokenization"):
if not data_args.streaming:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset",
)
else:
tokenized_datasets = raw_datasets.map(
tokenize_function,
batched=True,
remove_columns=column_names,
)
if hasattr(config, "max_position_embeddings"):
max_pos_embeddings = config.max_position_embeddings
else:
# Define a default value if the attribute is missing in the config.
max_pos_embeddings = 1024
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > max_pos_embeddings:
logger.warning(
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
f"Using block_size={min(1024, max_pos_embeddings)} instead. You can change that default value by passing --block_size xxx."
)
if max_pos_embeddings > 0:
block_size = min(1024, max_pos_embeddings)
else:
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model "
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict.
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
# to preprocess.
#
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
# https://huggingface.co/docs/datasets/process#map
with training_args.main_process_first(desc="grouping texts together"):
if not data_args.streaming:
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=not data_args.overwrite_cache,
desc=f"Grouping texts in chunks of {block_size}",
)
else:
lm_datasets = tokenized_datasets.map(
group_texts,
batched=True,
)
if training_args.do_train:
if "train" not in tokenized_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = lm_datasets["train"]
if data_args.max_train_samples is not None:
if data_args.streaming:
train_dataset = train_dataset.take(data_args.max_train_samples)
else:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
if training_args.do_eval:
if "validation" not in tokenized_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = lm_datasets["validation"]
if data_args.max_eval_samples is not None:
if data_args.streaming:
eval_dataset = eval_dataset.take(data_args.max_eval_samples)
else:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)
metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
def compute_metrics(eval_preds):
preds, labels = eval_preds
# preds have the same shape as the labels, after the argmax(-1) has been calculated
# by preprocess_logits_for_metrics but we need to shift the labels
labels = labels[:, 1:].reshape(-1)
preds = preds[:, :-1].reshape(-1)
return metric.compute(predictions=preds, references=labels)
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
processing_class=tokenizer,
# Data collator will default to DataCollatorWithPadding, so we change it.
data_collator=default_data_collator,
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_xla_available() else None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics
if training_args.do_eval and not is_torch_xla_available()
else None,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
if data_args.streaming:
metrics["train_samples"] = max_train_samples
else:
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
if data_args.streaming:
metrics["eval_samples"] = max_eval_samples
else:
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
if data_args.dataset_name is not None:
kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
kwargs["dataset_args"] = data_args.dataset_config_name
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/pytorch/language-modeling/run_clm.py/0 | {
"file_path": "transformers/examples/pytorch/language-modeling/run_clm.py",
"repo_id": "transformers",
"token_count": 13380
} | 442 |
# Copyright 2018 HuggingFace Inc..
#
# 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.
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_xla
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_results(output_dir):
results = {}
path = os.path.join(output_dir, "all_results.json")
if os.path.exists(path):
with open(path) as f:
results = json.load(f)
else:
raise ValueError(f"can't find {path}")
return results
stream_handler = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_xla
class TorchXLAExamplesTests(TestCasePlus):
def test_run_glue(self):
import xla_spawn
tmp_dir = self.get_auto_remove_tmp_dir()
testargs = f"""
./examples/pytorch/text-classification/run_glue.py
--num_cores=8
./examples/pytorch/text-classification/run_glue.py
--model_name_or_path distilbert/distilbert-base-uncased
--output_dir {tmp_dir}
--overwrite_output_dir
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--do_train
--do_eval
--debug tpu_metrics_debug
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--max_steps=10
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(sys, "argv", testargs):
start = time()
xla_spawn.main()
end = time()
result = get_results(tmp_dir)
self.assertGreaterEqual(result["eval_accuracy"], 0.75)
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start, 500)
def test_trainer_tpu(self):
import xla_spawn
testargs = """
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
""".split()
with patch.object(sys, "argv", testargs):
xla_spawn.main()
| transformers/examples/pytorch/old_test_xla_examples.py/0 | {
"file_path": "transformers/examples/pytorch/old_test_xla_examples.py",
"repo_id": "transformers",
"token_count": 1239
} | 443 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "transformers @ git+https://github.com/huggingface/transformers.git",
# "accelerate >= 0.12.0",
# "datasets >= 1.8.0",
# "sentencepiece != 0.1.92",
# "scipy",
# "scikit-learn",
# "protobuf",
# "torch >= 1.3",
# "evaluate",
# ]
# ///
"""Finetuning the library models for sequence classification on GLUE."""
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
import logging
import os
import random
import sys
from collections import Counter
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
PretrainedConfig,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.56.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
logger = logging.getLogger(__name__)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.
"""
task_name: Optional[str] = field(
default=None,
metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
)
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the training data."}
)
validation_file: Optional[str] = field(
default=None, metadata={"help": "A csv or a json file containing the validation data."}
)
test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
def __post_init__(self):
if self.task_name is not None:
self.task_name = self.task_name.lower()
if self.task_name not in task_to_keys:
raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
elif self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
else:
train_extension = self.train_file.split(".")[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
validation_extension = self.validation_file.split(".")[-1]
assert validation_extension == train_extension, (
"`validation_file` should have the same extension (csv or json) as `train_file`."
)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `hf auth login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
ignore_mismatched_sizes: bool = field(
default=False,
metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_glue", model_args, data_args)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
# sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
# label if at least two columns are provided.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.task_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(
"nyu-mll/glue",
data_args.task_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
elif data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
train_extension = data_args.train_file.split(".")[-1]
test_extension = data_args.test_file.split(".")[-1]
assert test_extension == train_extension, (
"`test_file` should have the same extension (csv or json) as `train_file`."
)
data_files["test"] = data_args.test_file
else:
raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
for key in data_files:
logger.info(f"load a local file for {key}: {data_files[key]}")
if data_args.train_file.endswith(".csv"):
# Loading a dataset from local csv files
raw_datasets = load_dataset(
"csv",
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
else:
# Loading a dataset from local json files
raw_datasets = load_dataset(
"json",
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.
# Labels
if data_args.task_name is not None:
is_regression = data_args.task_name == "stsb"
if not is_regression:
label_list = raw_datasets["train"].features["label"].names
num_labels = len(label_list)
else:
num_labels = 1
else:
# Trying to have good defaults here, don't hesitate to tweak to your needs.
is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
if is_regression:
num_labels = 1
else:
# A useful fast method:
# https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset.unique
label_list = raw_datasets["train"].unique("label")
label_list.sort() # Let's sort it for determinism
num_labels = len(label_list)
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
num_labels=num_labels,
finetuning_task=data_args.task_name,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
)
# Preprocessing the raw_datasets
if data_args.task_name is not None:
sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
else:
# Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
sentence1_key, sentence2_key = "sentence1", "sentence2"
else:
if len(non_label_column_names) >= 2:
sentence1_key, sentence2_key = non_label_column_names[:2]
else:
sentence1_key, sentence2_key = non_label_column_names[0], None
# Padding strategy
if data_args.pad_to_max_length:
padding = "max_length"
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
padding = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
label_to_id = None
if (
model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
and data_args.task_name is not None
and not is_regression
):
# Some have all caps in their config, some don't.
label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
if sorted(label_name_to_id.keys()) == sorted(label_list):
label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
else:
logger.warning(
"Your model seems to have been trained with labels, but they don't match the dataset: "
f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}."
"\nIgnoring the model labels as a result.",
)
elif data_args.task_name is None and not is_regression:
label_to_id = {v: i for i, v in enumerate(label_list)}
if label_to_id is not None:
model.config.label2id = label_to_id
model.config.id2label = {id: label for label, id in config.label2id.items()}
elif data_args.task_name is not None and not is_regression:
model.config.label2id = {l: i for i, l in enumerate(label_list)}
model.config.id2label = {id: label for label, id in config.label2id.items()}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the "
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
)
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
def preprocess_function(examples):
# Tokenize the texts
args = (
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
)
result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
# Map labels to IDs (not necessary for GLUE tasks)
if label_to_id is not None and "label" in examples:
result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
return result
with training_args.main_process_first(desc="dataset map pre-processing"):
raw_datasets = raw_datasets.map(
preprocess_function,
batched=True,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on dataset",
)
def print_class_distribution(dataset, split_name):
label_counts = Counter(dataset["label"])
total = sum(label_counts.values())
logger.info(f"Class distribution in {split_name} set:")
for label, count in label_counts.items():
logger.info(f" Label {label}: {count} ({count / total:.2%})")
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
print_class_distribution(train_dataset, "train")
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
print_class_distribution(eval_dataset, "validation")
if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("--do_predict requires a test dataset")
predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
if data_args.max_predict_samples is not None:
max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
predict_dataset = predict_dataset.select(range(max_predict_samples))
print_class_distribution(predict_dataset, "test")
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(train_dataset)), 3):
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
# Get the metric function
if data_args.task_name is not None:
metric = evaluate.load("glue", data_args.task_name, cache_dir=model_args.cache_dir)
elif is_regression:
metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
else:
metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(p: EvalPrediction):
preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
labels = p.label_ids
if not training_args.eval_do_concat_batches:
preds = np.concatenate(preds, axis=0)
labels = np.concatenate(p.label_ids, axis=0)
preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
result = metric.compute(predictions=preds, references=labels)
if len(result) > 1:
result["combined_score"] = np.mean(list(result.values())).item()
return result
# Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
# we already did the padding.
if data_args.pad_to_max_length:
data_collator = default_data_collator
elif training_args.fp16:
data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
else:
data_collator = None
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
compute_metrics=compute_metrics,
processing_class=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = [data_args.task_name]
eval_datasets = [eval_dataset]
if data_args.task_name == "mnli":
tasks.append("mnli-mm")
valid_mm_dataset = raw_datasets["validation_mismatched"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(valid_mm_dataset), data_args.max_eval_samples)
valid_mm_dataset = valid_mm_dataset.select(range(max_eval_samples))
eval_datasets.append(valid_mm_dataset)
combined = {}
for eval_dataset, task in zip(eval_datasets, tasks):
metrics = trainer.evaluate(eval_dataset=eval_dataset)
max_eval_samples = (
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
if task == "mnli-mm":
metrics = {k + "_mm": v for k, v in metrics.items()}
if task is not None and "mnli" in task:
combined.update(metrics)
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
if training_args.do_predict:
logger.info("*** Predict ***")
# Loop to handle MNLI double evaluation (matched, mis-matched)
tasks = [data_args.task_name]
predict_datasets = [predict_dataset]
if data_args.task_name == "mnli":
tasks.append("mnli-mm")
predict_datasets.append(raw_datasets["test_mismatched"])
for predict_dataset, task in zip(predict_datasets, tasks):
# Removing the `label` columns because it contains -1 and Trainer won't like that.
predict_dataset = predict_dataset.remove_columns("label")
predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
if trainer.is_world_process_zero():
with open(output_predict_file, "w") as writer:
logger.info(f"***** Predict results {task} *****")
writer.write("index\tprediction\n")
for index, item in enumerate(predictions):
if is_regression:
writer.write(f"{index}\t{item:3.3f}\n")
else:
item = label_list[item]
writer.write(f"{index}\t{item}\n")
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
if data_args.task_name is not None:
kwargs["language"] = "en"
kwargs["dataset_tags"] = "glue"
kwargs["dataset_args"] = data_args.task_name
kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
def _mp_fn(index):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| transformers/examples/pytorch/text-classification/run_glue.py/0 | {
"file_path": "transformers/examples/pytorch/text-classification/run_glue.py",
"repo_id": "transformers",
"token_count": 11950
} | 444 |
#!/usr/bin/env python
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for summarization.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
import json
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import nltk # Here to have a nice missing dependency error message early on
import numpy as np
import tensorflow as tf
from datasets import load_dataset
from filelock import FileLock
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
KerasMetricCallback,
PushToHubCallback,
TFAutoModelForSeq2SeqLM,
TFTrainingArguments,
create_optimizer,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, is_offline_mode, send_example_telemetry
from transformers.utils.versions import require_version
# region Checking dependencies
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.56.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
logger = logging.getLogger(__name__)
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
# endregion
# region Arguments
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
token: str = field(
default=None,
metadata={
"help": (
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
"generated when running `hf auth login` (stored in `~/.huggingface`)."
)
},
)
trust_remote_code: bool = field(
default=False,
metadata={
"help": (
"Whether to trust the execution of code from datasets/models defined on the Hub."
" This option should only be set to `True` for repositories you trust and in which you have read the"
" code, as it will execute code present on the Hub on your local machine."
)
},
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_name: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
text_column: Optional[str] = field(
default=None,
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
)
summary_column: Optional[str] = field(
default=None,
metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
)
train_file: Optional[str] = field(
default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
)
validation_file: Optional[str] = field(
default=None,
metadata={
"help": (
"An optional input evaluation data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
)
},
)
test_file: Optional[str] = field(
default=None,
metadata={
"help": "An optional input test data file to evaluate the metrics (rouge) on (a jsonlines or csv file)."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
max_source_length: Optional[int] = field(
default=1024,
metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
max_target_length: Optional[int] = field(
default=128,
metadata={
"help": (
"The maximum total sequence length for target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
)
},
)
val_max_target_length: Optional[int] = field(
default=None,
metadata={
"help": (
"The maximum total sequence length for validation target text after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`. "
"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
"during ``evaluate`` and ``predict``."
)
},
)
pad_to_max_length: bool = field(
default=False,
metadata={
"help": (
"Whether to pad all samples to model maximum sentence length. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
"efficient on GPU but very bad for TPU."
)
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of prediction examples to this "
"value if set."
)
},
)
num_beams: Optional[int] = field(
default=1,
metadata={
"help": (
"Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
"which is used during ``evaluate`` and ``predict``."
)
},
)
ignore_pad_token_for_loss: bool = field(
default=True,
metadata={
"help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
},
)
source_prefix: Optional[str] = field(
default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
)
def __post_init__(self):
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
raise ValueError("Need either a dataset name or a training/validation file.")
else:
if self.train_file is not None:
extension = self.train_file.split(".")[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
extension = self.validation_file.split(".")[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
if self.val_max_target_length is None:
self.val_max_target_length = self.max_target_length
# endregion
# region Dataset name mappings
summarization_name_mapping = {
"amazon_reviews_multi": ("review_body", "review_title"),
"big_patent": ("description", "abstract"),
"cnn_dailymail": ("article", "highlights"),
"orange_sum": ("text", "summary"),
"pn_summary": ("article", "summary"),
"psc": ("extract_text", "summary_text"),
"samsum": ("dialogue", "summary"),
"thaisum": ("body", "summary"),
"xglue": ("news_body", "news_title"),
"xsum": ("document", "summary"),
"wiki_summary": ("article", "highlights"),
"multi_news": ("document", "summary"),
}
# endregion
def main():
# region Argument parsing
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_summarization", model_args, data_args, framework="tensorflow")
# endregion
# region Logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
handlers=[logging.StreamHandler(sys.stdout)],
)
logger.setLevel(logging.INFO)
datasets.utils.logging.set_verbosity(logging.INFO)
transformers.utils.logging.set_verbosity(logging.INFO)
# Log on each process the small summary:
logger.info(f"Training/evaluation parameters {training_args}")
# endregion
# region T5 special-casing
if data_args.source_prefix is None and model_args.model_name_or_path in [
"google-t5/t5-small",
"google-t5/t5-base",
"google-t5/t5-large",
"google-t5/t5-3b",
"google-t5/t5-11b",
]:
logger.warning(
"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
"`--source_prefix 'summarize: ' `"
)
# endregion
# region Detecting last checkpoint
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# endregion
# Set seed before initializing model.
set_seed(training_args.seed)
# region Load datasets
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files this script will use the first column for the full texts and the second column for the
# summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
raw_datasets = load_dataset(
data_args.dataset_name,
data_args.dataset_config_name,
cache_dir=model_args.cache_dir,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
else:
data_files = {}
if data_args.train_file is not None:
data_files["train"] = data_args.train_file
extension = data_args.train_file.split(".")[-1]
if data_args.validation_file is not None:
data_files["validation"] = data_args.validation_file
extension = data_args.validation_file.split(".")[-1]
if data_args.test_file is not None:
data_files["test"] = data_args.test_file
extension = data_args.test_file.split(".")[-1]
raw_datasets = load_dataset(
extension,
data_files=data_files,
cache_dir=model_args.cache_dir,
token=model_args.token,
)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.
# endregion
# region Load model config and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
config = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
tokenizer = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
prefix = data_args.source_prefix if data_args.source_prefix is not None else ""
# endregion
# region Dataset preprocessing
# We need to tokenize inputs and targets.
if training_args.do_train:
column_names = raw_datasets["train"].column_names
elif training_args.do_eval:
column_names = raw_datasets["validation"].column_names
else:
logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.")
return
# Get the column names for input/target.
dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
if data_args.text_column is None:
text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
else:
text_column = data_args.text_column
if text_column not in column_names:
raise ValueError(
f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
)
if data_args.summary_column is None:
summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
else:
summary_column = data_args.summary_column
if summary_column not in column_names:
raise ValueError(
f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
)
# Temporarily set max_target_length for training.
max_target_length = data_args.max_target_length
padding = "max_length" if data_args.pad_to_max_length else False
def preprocess_function(examples):
inputs = examples[text_column]
targets = examples[summary_column]
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
# Tokenize targets with the `text_target` keyword argument
labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == "max_length" and data_args.ignore_pad_token_for_loss:
labels["input_ids"] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
]
model_inputs["labels"] = labels["input_ids"]
return model_inputs
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("--do_train requires a train dataset")
train_dataset = raw_datasets["train"]
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
train_dataset = train_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
else:
train_dataset = None
if training_args.do_eval:
max_target_length = data_args.val_max_target_length
if "validation" not in raw_datasets:
raise ValueError("--do_eval requires a validation dataset")
eval_dataset = raw_datasets["validation"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
eval_dataset = eval_dataset.map(
preprocess_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
else:
eval_dataset = None
# endregion
# region Text preprocessing
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
# rougeLSum expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
return preds, labels
# endregion
with training_args.strategy.scope():
# region Prepare model
model = TFAutoModelForSeq2SeqLM.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
token=model_args.token,
trust_remote_code=model_args.trust_remote_code,
)
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
# on a small vocab and want a smaller embedding size, remove this test.
embeddings = model.get_input_embeddings()
# Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings.
# As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and
# the weights will always be in embeddings.embeddings.
if hasattr(embeddings, "embeddings"):
embedding_size = embeddings.embeddings.shape[0]
else:
embedding_size = embeddings.weight.shape[0]
if len(tokenizer) > embedding_size:
model.resize_token_embeddings(len(tokenizer))
# endregion
# region Prepare TF Dataset objects
if model.config.decoder_start_token_id is None:
raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
data_collator = DataCollatorForSeq2Seq(
tokenizer,
model=model,
label_pad_token_id=label_pad_token_id,
pad_to_multiple_of=128, # Reduce the number of unique shapes for XLA, especially for generation
return_tensors="np",
)
dataset_options = tf.data.Options()
dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
num_replicas = training_args.strategy.num_replicas_in_sync
total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
# model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
# training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
# use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
# yourself if you use this method, whereas they are automatically inferred from the model input names when
# using model.prepare_tf_dataset()
# For more info see the docs:
# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
# https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset
tf_train_dataset = model.prepare_tf_dataset(
train_dataset,
collate_fn=data_collator,
batch_size=total_train_batch_size,
shuffle=True,
).with_options(dataset_options)
tf_eval_dataset = model.prepare_tf_dataset(
eval_dataset,
collate_fn=data_collator,
batch_size=total_eval_batch_size,
shuffle=False,
).with_options(dataset_options)
# endregion
# region Optimizer, loss and LR scheduling
num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs)
if training_args.warmup_steps > 0:
num_warmup_steps = training_args.warmup_steps
elif training_args.warmup_ratio > 0:
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
else:
num_warmup_steps = 0
if training_args.do_train:
optimizer, lr_schedule = create_optimizer(
init_lr=training_args.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
adam_beta1=training_args.adam_beta1,
adam_beta2=training_args.adam_beta2,
adam_epsilon=training_args.adam_epsilon,
weight_decay_rate=training_args.weight_decay,
adam_global_clipnorm=training_args.max_grad_norm,
)
else:
optimizer = "sgd" # Just write anything because we won't be using it
# endregion
# region Metric and KerasMetricCallback
if training_args.do_eval:
metric = evaluate.load("rouge", cache_dir=model_args.cache_dir)
if data_args.val_max_target_length is None:
data_args.val_max_target_length = data_args.max_target_length
gen_kwargs = {
"max_length": data_args.val_max_target_length if data_args is not None else config.max_length,
"num_beams": data_args.num_beams,
"no_repeat_ngram_size": 0, # Not supported under XLA right now, and some models set it by default
}
def compute_metrics(preds):
predictions, labels = preds
if isinstance(predictions, tuple):
predictions = predictions[0]
decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
metrics = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
# Only print the mid f-measures, but there are a lot of other statistics in there too!
metrics = {key: round(val.mid.fmeasure * 100, 4) for key, val in metrics.items()}
return metrics
# The KerasMetricCallback allows metrics that are too complex to write as standard Keras metrics
# to be computed each epoch. Any Python code can be included in the metric_fn. This is especially
# useful for metrics like BLEU and ROUGE that perform string comparisons on decoded model outputs.
# For more information, see the docs at
# https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.KerasMetricCallback
metric_callback = KerasMetricCallback(
metric_fn=compute_metrics,
eval_dataset=tf_eval_dataset,
predict_with_generate=True,
use_xla_generation=True,
generate_kwargs=gen_kwargs,
)
callbacks = [metric_callback]
else:
callbacks = []
# endregion
# region Preparing push_to_hub and model card
push_to_hub_model_id = training_args.push_to_hub_model_id
model_name = model_args.model_name_or_path.split("/")[-1]
if not push_to_hub_model_id:
if data_args.dataset_name is not None:
push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
else:
push_to_hub_model_id = f"{model_name}-finetuned-summarization"
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
if data_args.dataset_name is not None:
model_card_kwargs["dataset_tags"] = data_args.dataset_name
if data_args.dataset_config_name is not None:
model_card_kwargs["dataset_args"] = data_args.dataset_config_name
model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
else:
model_card_kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub:
# Because this training can be quite long, we save once per epoch.
callbacks.append(
PushToHubCallback(
output_dir=training_args.output_dir,
hub_model_id=push_to_hub_model_id,
hub_token=training_args.push_to_hub_token,
tokenizer=tokenizer,
**model_card_kwargs,
)
)
# endregion
# region Training
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=optimizer, jit_compile=training_args.xla)
eval_metrics = None
if training_args.do_train:
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
logger.info(f" Total train batch size = {total_train_batch_size}")
logger.info(f" Total optimization steps = {num_train_steps}")
if training_args.xla and not data_args.pad_to_max_length:
logger.warning(
"XLA training may be slow at first when --pad_to_max_length is not set "
"until all possible shapes have been compiled."
)
history = model.fit(tf_train_dataset, epochs=int(training_args.num_train_epochs), callbacks=callbacks)
eval_metrics = {key: val[-1] for key, val in history.history.items()}
# endregion
# region Validation
if training_args.do_eval and not training_args.do_train:
# Do a standalone evaluation run
logger.info("Evaluation...")
# Compiling generation with XLA yields enormous speedups, see https://huggingface.co/blog/tf-xla-generate
@tf.function(jit_compile=True)
def generate(**kwargs):
return model.generate(**kwargs)
for batch, labels in tf_eval_dataset:
batch.update(gen_kwargs)
generated_tokens = generate(**batch)
if isinstance(generated_tokens, tuple):
generated_tokens = generated_tokens[0]
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
metric.add_batch(predictions=decoded_preds, references=decoded_labels)
eval_metrics = metric.compute(use_stemmer=True)
result = {key: round(val.mid.fmeasure * 100, 4) for key, val in eval_metrics.items()}
logger.info(result)
# endregion
if training_args.output_dir is not None and eval_metrics is not None:
output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
with open(output_eval_file, "w") as writer:
writer.write(json.dumps(eval_metrics))
if training_args.output_dir is not None and not training_args.push_to_hub:
# If we're not pushing to hub, at least save a local copy when we're done
model.save_pretrained(training_args.output_dir)
if __name__ == "__main__":
main()
| transformers/examples/tensorflow/summarization/run_summarization.py/0 | {
"file_path": "transformers/examples/tensorflow/summarization/run_summarization.py",
"repo_id": "transformers",
"token_count": 13623
} | 445 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py
To create the package for pypi.
1. Create the release branch named: v<RELEASE>-release, for example v4.19-release. For a patch release checkout the
current release branch.
If releasing on a special branch, copy the updated README.md on the main branch for the commit you will make
for the post-release and run `make fix-copies` on the main branch as well.
2. Run `make pre-release` (or `make pre-patch` for a patch release) and commit these changes with the message:
"Release: <VERSION>" and push.
3. Go back to the main branch and run `make post-release` then `make fix-copies`. Commit these changes with the
message "v<NEXT_VERSION>.dev.0" and push to main.
# If you were just cutting the branch in preparation for a release, you can stop here for now.
4. Wait for the tests on the release branch to be completed and be green (otherwise revert and fix bugs)
5. On the release branch, add a tag in git to mark the release: "git tag v<VERSION> -m 'Adds tag v<VERSION> for pypi' "
Push the tag to git: git push --tags origin v<RELEASE>-release
6. Build both the sources and the wheel. Do not change anything in setup.py between
creating the wheel and the source distribution (obviously).
Run `make build-release`. This will build the release and do some sanity checks for you. If this ends with an error
message, you need to fix things before going further.
You should now have a /dist directory with both .whl and .tar.gz source versions.
7. Check that everything looks correct by uploading the package to the pypi test server:
twine upload dist/* -r testpypi
(pypi suggest using twine as other methods upload files via plaintext.)
You may have to specify the repository url, use the following command then:
twine upload dist/* -r testpypi --repository-url=https://test.pypi.org/legacy/
Check that you can install it in a virtualenv by running:
pip install -i https://test.pypi.org/simple/ transformers
Check you can run the following commands:
python -c "from transformers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))"
python -c "from transformers import *"
python utils/check_build.py --check_lib
If making a patch release, double check the bug you are patching is indeed resolved.
8. Upload the final version to actual pypi:
twine upload dist/* -r pypi
9. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.
"""
import os
import re
import shutil
from pathlib import Path
from setuptools import Command, find_packages, setup
# Remove stale transformers.egg-info directory to avoid https://github.com/pypa/pip/issues/5466
stale_egg_info = Path(__file__).parent / "transformers.egg-info"
if stale_egg_info.exists():
print(
(
"Warning: {} exists.\n\n"
"If you recently updated transformers to 3.0 or later, this is expected,\n"
"but it may prevent transformers from installing in editable mode.\n\n"
"This directory is automatically generated by Python's packaging tools.\n"
"I will remove it now.\n\n"
"See https://github.com/pypa/pip/issues/5466 for details.\n"
).format(stale_egg_info)
)
shutil.rmtree(stale_egg_info)
# IMPORTANT:
# 1. all dependencies should be listed here with their version requirements if any
# 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
_deps = [
"Pillow>=10.0.1,<=15.0",
"accelerate>=0.26.0",
"av",
"beautifulsoup4",
"blobfile",
"codecarbon>=2.8.1",
"cookiecutter==1.7.3",
"dataclasses",
"datasets>=2.15.0", # We need either this pin or pyarrow<21.0.0
"deepspeed>=0.9.3",
"diffusers",
"dill<0.3.5",
"evaluate>=0.2.0",
"faiss-cpu",
"fastapi",
"filelock",
"flax>=0.4.1,<=0.7.0",
"ftfy",
"fugashi>=1.0",
"GitPython<3.1.19",
"hf-doc-builder>=0.3.0",
"hf_xet",
"huggingface-hub>=0.34.0,<1.0",
"importlib_metadata",
"ipadic>=1.0.0,<2.0",
"jax>=0.4.1,<=0.4.13",
"jaxlib>=0.4.1,<=0.4.13",
"jieba",
"jinja2>=3.1.0",
"kenlm",
# Keras pin - this is to make sure Keras 3 doesn't destroy us. Remove or change when we have proper support.
"keras>2.9,<2.16",
"keras-nlp>=0.3.1,<0.14.0", # keras-nlp 0.14 doesn't support keras 2, see pin on keras.
"kernels>=0.6.1,<=0.9",
"librosa",
"natten>=0.14.6,<0.15.0",
"nltk<=3.8.1",
"num2words",
"numpy>=1.17",
"onnxconverter-common",
"onnxruntime-tools>=1.4.2",
"onnxruntime>=1.4.0",
"openai>=1.98.0",
"opencv-python",
"optimum-benchmark>=0.3.0",
"optuna",
"optax>=0.0.8,<=0.1.4",
"pandas<2.3.0", # `datasets` requires `pandas` while `pandas==2.3.0` has issues with CircleCI on 2025/06/05
"packaging>=20.0",
"parameterized>=0.9", # older version of parameterized cause pytest collection to fail on .expand
"phonemizer",
"protobuf",
"psutil",
"pyyaml>=5.1",
"pydantic>=2",
"pytest>=7.2.0",
"pytest-asyncio",
"pytest-rerunfailures",
"pytest-timeout",
"pytest-xdist",
"pytest-order",
"python>=3.9.0",
"ray[tune]>=2.7.0",
"regex!=2019.12.17",
"requests",
"rhoknp>=1.1.0,<1.3.1",
"rjieba",
"rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
"ruff==0.11.2",
# `sacrebleu` not used in `transformers`. However, it is needed in several tests, when a test calls
# `evaluate.load("sacrebleu")`. This metric is used in the examples that we use to test the `Trainer` with, in the
# `Trainer` tests (see references to `run_translation.py`).
"sacrebleu>=1.4.12,<2.0.0",
"sacremoses",
"safetensors>=0.4.3",
"sagemaker>=2.31.0",
"schedulefree>=1.2.6",
"scikit-learn",
"scipy<1.13.0", # SciPy >= 1.13.0 is not supported with the current jax pin (`jax>=0.4.1,<=0.4.13`)
"sentencepiece>=0.1.91,!=0.1.92",
"sigopt",
"starlette",
"sudachipy>=0.6.6",
"sudachidict_core>=20220729",
"tensorboard",
# TensorFlow pin. When changing this value, update examples/tensorflow/_tests_requirements.txt accordingly
"tensorflow-cpu>2.9,<2.16",
"tensorflow>2.9,<2.16",
"tensorflow-text<2.16",
"tensorflow-probability<0.24",
"tf2onnx",
"timeout-decorator",
"tiktoken",
"timm<=1.0.19,!=1.0.18",
"tokenizers>=0.21,<0.22",
"torch>=2.2",
"torchaudio",
"torchvision",
"pyctcdecode>=0.4.0",
"tqdm>=4.27",
"unidic>=1.0.2",
"unidic_lite>=1.0.7",
"urllib3<2.0.0",
"uvicorn",
"pytest-rich",
"libcst",
"rich",
"opentelemetry-api",
"mistral-common[opencv]>=1.6.3",
]
# this is a lookup table with items like:
#
# tokenizers: "tokenizers==0.9.4"
# packaging: "packaging"
#
# some of the values are versioned whereas others aren't.
deps = {b: a for a, b in (re.findall(r"^(([^!=<>~ ]+)(?:[!=<>~ ].*)?$)", x)[0] for x in _deps)}
# since we save this data in src/transformers/dependency_versions_table.py it can be easily accessed from
# anywhere. If you need to quickly access the data from this table in a shell, you can do so easily with:
#
# python -c 'import sys; from transformers.dependency_versions_table import deps; \
# print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets
#
# Just pass the desired package names to that script as it's shown with 2 packages above.
#
# If transformers is not yet installed and the work is done from the cloned repo remember to add `PYTHONPATH=src` to the script above
#
# You can then feed this for example to `pip`:
#
# pip install -U $(python -c 'import sys; from transformers.dependency_versions_table import deps; \
# print(" ".join([deps[x] for x in sys.argv[1:]]))' tokenizers datasets)
#
def deps_list(*pkgs):
return [deps[pkg] for pkg in pkgs]
class DepsTableUpdateCommand(Command):
"""
A custom distutils command that updates the dependency table.
usage: python setup.py deps_table_update
"""
description = "build runtime dependency table"
user_options = [
# format: (long option, short option, description).
("dep-table-update", None, "updates src/transformers/dependency_versions_table.py"),
]
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
entries = "\n".join([f' "{k}": "{v}",' for k, v in deps.items()])
content = [
"# THIS FILE HAS BEEN AUTOGENERATED. To update:",
"# 1. modify the `_deps` dict in setup.py",
"# 2. run `make deps_table_update``",
"deps = {",
entries,
"}",
"",
]
target = "src/transformers/dependency_versions_table.py"
print(f"updating {target}")
with open(target, "w", encoding="utf-8", newline="\n") as f:
f.write("\n".join(content))
extras = {}
extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic", "sudachipy", "sudachidict_core", "rhoknp")
extras["sklearn"] = deps_list("scikit-learn")
extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "tf2onnx", "tensorflow-text", "keras-nlp")
extras["tf-cpu"] = deps_list(
"keras",
"tensorflow-cpu",
"onnxconverter-common",
"tf2onnx",
"tensorflow-text",
"keras-nlp",
"tensorflow-probability",
)
extras["torch"] = deps_list("torch", "accelerate")
extras["accelerate"] = deps_list("accelerate")
extras["hf_xet"] = deps_list("hf_xet")
if os.name == "nt": # windows
extras["retrieval"] = deps_list("datasets") # faiss is not supported on windows
extras["flax"] = [] # jax is not supported on windows
else:
extras["retrieval"] = deps_list("faiss-cpu", "datasets")
extras["flax"] = deps_list("jax", "jaxlib", "flax", "optax", "scipy")
extras["tokenizers"] = deps_list("tokenizers")
extras["ftfy"] = deps_list("ftfy")
extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")
extras["onnx"] = deps_list("onnxconverter-common", "tf2onnx") + extras["onnxruntime"]
extras["modelcreation"] = deps_list("cookiecutter")
extras["sagemaker"] = deps_list("sagemaker")
extras["deepspeed"] = deps_list("deepspeed") + extras["accelerate"]
extras["optuna"] = deps_list("optuna")
extras["ray"] = deps_list("ray[tune]")
extras["sigopt"] = deps_list("sigopt")
extras["hub-kernels"] = deps_list("kernels")
extras["integrations"] = extras["hub-kernels"] + extras["optuna"] + extras["ray"] + extras["sigopt"]
extras["serving"] = deps_list("openai", "pydantic", "uvicorn", "fastapi", "starlette") + extras["torch"]
extras["audio"] = deps_list(
"librosa",
"pyctcdecode",
"phonemizer",
"kenlm",
)
# `pip install ".[speech]"` is deprecated and `pip install ".[torch-speech]"` should be used instead
extras["speech"] = deps_list("torchaudio") + extras["audio"]
extras["torch-speech"] = deps_list("torchaudio") + extras["audio"]
extras["tf-speech"] = extras["audio"]
extras["flax-speech"] = extras["audio"]
extras["vision"] = deps_list("Pillow")
extras["timm"] = deps_list("timm")
extras["torch-vision"] = deps_list("torchvision") + extras["vision"]
extras["natten"] = deps_list("natten")
extras["codecarbon"] = deps_list("codecarbon")
extras["video"] = deps_list("av")
extras["num2words"] = deps_list("num2words")
extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
extras["tiktoken"] = deps_list("tiktoken", "blobfile")
extras["mistral-common"] = deps_list("mistral-common[opencv]")
extras["chat_template"] = deps_list("jinja2")
extras["testing"] = (
deps_list(
"pytest",
"pytest-asyncio",
"pytest-rich",
"pytest-xdist",
"pytest-order",
"pytest-rerunfailures",
"timeout-decorator",
"parameterized",
"psutil",
"datasets",
"dill",
"evaluate",
"pytest-timeout",
"ruff",
"rouge-score",
"nltk",
"GitPython",
"sacremoses",
"rjieba",
"beautifulsoup4",
"tensorboard",
"pydantic",
"sentencepiece",
"sacrebleu", # needed in trainer tests, see references to `run_translation.py`
"libcst",
)
+ extras["retrieval"]
+ extras["modelcreation"]
+ extras["mistral-common"]
)
extras["deepspeed-testing"] = extras["deepspeed"] + extras["testing"] + extras["optuna"] + extras["sentencepiece"]
extras["ruff"] = deps_list("ruff")
extras["quality"] = deps_list("datasets", "ruff", "GitPython", "urllib3", "libcst", "rich", "pandas")
extras["all"] = (
extras["tf"]
+ extras["torch"]
+ extras["flax"]
+ extras["sentencepiece"]
+ extras["tokenizers"]
+ extras["torch-speech"]
+ extras["vision"]
+ extras["integrations"]
+ extras["timm"]
+ extras["torch-vision"]
+ extras["codecarbon"]
+ extras["accelerate"]
+ extras["video"]
+ extras["num2words"]
+ extras["mistral-common"]
+ extras["chat_template"]
)
extras["dev-torch"] = (
extras["testing"]
+ extras["torch"]
+ extras["sentencepiece"]
+ extras["tokenizers"]
+ extras["torch-speech"]
+ extras["vision"]
+ extras["integrations"]
+ extras["timm"]
+ extras["torch-vision"]
+ extras["codecarbon"]
+ extras["quality"]
+ extras["ja"]
+ extras["sklearn"]
+ extras["modelcreation"]
+ extras["onnxruntime"]
+ extras["num2words"]
)
extras["dev-tensorflow"] = (
extras["testing"]
+ extras["tf"]
+ extras["sentencepiece"]
+ extras["tokenizers"]
+ extras["vision"]
+ extras["quality"]
+ extras["sklearn"]
+ extras["modelcreation"]
+ extras["onnx"]
+ extras["tf-speech"]
)
extras["dev"] = (
extras["all"] + extras["testing"] + extras["quality"] + extras["ja"] + extras["sklearn"] + extras["modelcreation"]
)
extras["torchhub"] = deps_list(
"filelock",
"huggingface-hub",
"importlib_metadata",
"numpy",
"packaging",
"protobuf",
"regex",
"requests",
"sentencepiece",
"torch",
"tokenizers",
"tqdm",
)
extras["benchmark"] = deps_list("optimum-benchmark")
# OpenTelemetry dependencies for metrics collection in continuous batching
extras["open-telemetry"] = deps_list("opentelemetry-api")
# when modifying the following list, make sure to update src/transformers/dependency_versions_check.py
install_requires = [
deps["filelock"], # filesystem locks, e.g., to prevent parallel downloads
deps["huggingface-hub"],
deps["numpy"],
deps["packaging"], # utilities from PyPA to e.g., compare versions
deps["pyyaml"], # used for the model cards metadata
deps["regex"], # for OpenAI GPT
deps["requests"], # for downloading models over HTTPS
deps["tokenizers"],
deps["safetensors"],
deps["tqdm"], # progress bars in model download and training scripts
]
setup(
name="transformers",
version="4.56.0.dev0", # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
author_email="transformers@huggingface.co",
description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",
long_description=open("README.md", "r", encoding="utf-8").read(),
long_description_content_type="text/markdown",
keywords="NLP vision speech deep learning transformer pytorch tensorflow jax BERT GPT-2 Wav2Vec2 ViT",
license="Apache 2.0 License",
url="https://github.com/huggingface/transformers",
package_dir={"": "src"},
packages=find_packages("src"),
include_package_data=True,
package_data={"": ["**/*.cu", "**/*.cpp", "**/*.cuh", "**/*.h", "**/*.pyx", "py.typed"]},
zip_safe=False,
extras_require=extras,
entry_points={
"console_scripts": [
"transformers=transformers.commands.transformers_cli:main",
"transformers-cli=transformers.commands.transformers_cli:main_cli",
]
},
python_requires=">=3.9.0",
install_requires=list(install_requires),
classifiers=[
"Development Status :: 5 - Production/Stable",
"Intended Audience :: Developers",
"Intended Audience :: Education",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
],
cmdclass={"deps_table_update": DepsTableUpdateCommand},
)
extras["tests_torch"] = deps_list()
extras["tests_tf"] = deps_list()
extras["tests_flax"] = deps_list()
extras["tests_hub"] = deps_list()
extras["tests_pipelines_torch"] = deps_list()
extras["tests_pipelines_tf"] = deps_list()
extras["tests_onnx"] = deps_list()
extras["tests_examples_torch"] = deps_list()
extras["tests_examples_tf"] = deps_list()
extras["tests_custom_tokenizers"] = deps_list()
extras["tests_exotic_models"] = deps_list()
extras["consistency"] = deps_list()
| transformers/setup.py/0 | {
"file_path": "transformers/setup.py",
"repo_id": "transformers",
"token_count": 7382
} | 446 |
#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from transformers import HfArgumentParser
from transformers.commands.add_fast_image_processor import AddFastImageProcessorCommand
from transformers.commands.add_new_model_like import AddNewModelLikeCommand
from transformers.commands.chat import ChatCommand
from transformers.commands.convert import ConvertCommand
from transformers.commands.download import DownloadCommand
from transformers.commands.env import EnvironmentCommand
from transformers.commands.run import RunCommand
from transformers.commands.serving import ServeCommand
def main_cli():
warnings.warn(
"`transformers-cli` is deprecated in favour of `transformers` directly and will be removed in v5.",
DeprecationWarning,
)
main()
def main():
parser = HfArgumentParser(prog="Transformers CLI tool", usage="transformers <command> [<args>]")
commands_parser = parser.add_subparsers(help="transformers command helpers")
# Register commands
ChatCommand.register_subcommand(commands_parser)
ConvertCommand.register_subcommand(commands_parser)
DownloadCommand.register_subcommand(commands_parser)
EnvironmentCommand.register_subcommand(commands_parser)
RunCommand.register_subcommand(commands_parser)
ServeCommand.register_subcommand(commands_parser)
AddNewModelLikeCommand.register_subcommand(commands_parser)
AddFastImageProcessorCommand.register_subcommand(commands_parser)
# Let's go
args = parser.parse_args()
if not hasattr(args, "func"):
parser.print_help()
exit(1)
# Run
service = args.func(args)
service.run()
if __name__ == "__main__":
main()
| transformers/src/transformers/commands/transformers_cli.py/0 | {
"file_path": "transformers/src/transformers/commands/transformers_cli.py",
"repo_id": "transformers",
"token_count": 683
} | 447 |
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""GLUE processors and helpers"""
import os
import warnings
from dataclasses import asdict
from enum import Enum
from typing import Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_tf_available, logging
from .utils import DataProcessor, InputExample, InputFeatures
if is_tf_available():
import tensorflow as tf
logger = logging.get_logger(__name__)
DEPRECATION_WARNING = (
"This {0} will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
"library. You can have a look at this example script for pointers: "
"https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)
def glue_convert_examples_to_features(
examples: Union[list[InputExample], "tf.data.Dataset"],
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
task=None,
label_list=None,
output_mode=None,
):
"""
Loads a data file into a list of `InputFeatures`
Args:
examples: List of `InputExamples` or `tf.data.Dataset` containing the examples.
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length. Defaults to the tokenizer's max_len
task: GLUE task
label_list: List of labels. Can be obtained from the processor using the `processor.get_labels()` method
output_mode: String indicating the output mode. Either `regression` or `classification`
Returns:
If the `examples` input is a `tf.data.Dataset`, will return a `tf.data.Dataset` containing the task-specific
features. If the input is a list of `InputExamples`, will return a list of task-specific `InputFeatures` which
can be fed to the model.
"""
warnings.warn(DEPRECATION_WARNING.format("function"), FutureWarning)
if is_tf_available() and isinstance(examples, tf.data.Dataset):
if task is None:
raise ValueError("When calling glue_convert_examples_to_features from TF, the task parameter is required.")
return _tf_glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
return _glue_convert_examples_to_features(
examples, tokenizer, max_length=max_length, task=task, label_list=label_list, output_mode=output_mode
)
if is_tf_available():
def _tf_glue_convert_examples_to_features(
examples: tf.data.Dataset,
tokenizer: PreTrainedTokenizer,
task=str,
max_length: Optional[int] = None,
) -> tf.data.Dataset:
"""
Returns:
A `tf.data.Dataset` containing the task-specific features.
"""
processor = glue_processors[task]()
examples = [processor.tfds_map(processor.get_example_from_tensor_dict(example)) for example in examples]
features = glue_convert_examples_to_features(examples, tokenizer, max_length=max_length, task=task)
label_type = tf.float32 if task == "sts-b" else tf.int64
def gen():
for ex in features:
d = {k: v for k, v in asdict(ex).items() if v is not None}
label = d.pop("label")
yield (d, label)
input_names = tokenizer.model_input_names
return tf.data.Dataset.from_generator(
gen,
(dict.fromkeys(input_names, tf.int32), label_type),
({k: tf.TensorShape([None]) for k in input_names}, tf.TensorShape([])),
)
def _glue_convert_examples_to_features(
examples: list[InputExample],
tokenizer: PreTrainedTokenizer,
max_length: Optional[int] = None,
task=None,
label_list=None,
output_mode=None,
):
if max_length is None:
max_length = tokenizer.model_max_length
if task is not None:
processor = glue_processors[task]()
if label_list is None:
label_list = processor.get_labels()
logger.info(f"Using label list {label_list} for task {task}")
if output_mode is None:
output_mode = glue_output_modes[task]
logger.info(f"Using output mode {output_mode} for task {task}")
label_map = {label: i for i, label in enumerate(label_list)}
def label_from_example(example: InputExample) -> Union[int, float, None]:
if example.label is None:
return None
if output_mode == "classification":
return label_map[example.label]
elif output_mode == "regression":
return float(example.label)
raise KeyError(output_mode)
labels = [label_from_example(example) for example in examples]
batch_encoding = tokenizer(
[(example.text_a, example.text_b) for example in examples],
max_length=max_length,
padding="max_length",
truncation=True,
)
features = []
for i in range(len(examples)):
inputs = {k: batch_encoding[k][i] for k in batch_encoding}
feature = InputFeatures(**inputs, label=labels[i])
features.append(feature)
for i, example in enumerate(examples[:5]):
logger.info("*** Example ***")
logger.info(f"guid: {example.guid}")
logger.info(f"features: {features[i]}")
return features
class OutputMode(Enum):
classification = "classification"
regression = "regression"
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
logger.info(f"LOOKING AT {os.path.join(data_dir, 'train.tsv')}")
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{i}"
text_a = line[3]
text_b = line[4]
label = None if set_type == "test" else line[0]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["premise"].numpy().decode("utf-8"),
tensor_dict["hypothesis"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")), "dev_matched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test_matched")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[8]
text_b = line[9]
label = None if set_type.startswith("test") else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliMismatchedProcessor(MnliProcessor):
"""Processor for the MultiNLI Mismatched data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev_mismatched.tsv")), "dev_mismatched")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test_mismatched.tsv")), "test_mismatched")
class ColaProcessor(DataProcessor):
"""Processor for the CoLA data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence"].numpy().decode("utf-8"),
None,
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
test_mode = set_type == "test"
if test_mode:
lines = lines[1:]
text_index = 1 if test_mode else 3
examples = []
for i, line in enumerate(lines):
guid = f"{set_type}-{i}"
text_a = line[text_index]
label = None if test_mode else line[1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence"].numpy().decode("utf-8"),
None,
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
text_index = 1 if set_type == "test" else 0
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{i}"
text_a = line[text_index]
label = None if set_type == "test" else line[1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class StsbProcessor(DataProcessor):
"""Processor for the STS-B data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return [None]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[7]
text_b = line[8]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QqpProcessor(DataProcessor):
"""Processor for the QQP data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["question1"].numpy().decode("utf-8"),
tensor_dict["question2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
test_mode = set_type == "test"
q1_index = 1 if test_mode else 3
q2_index = 2 if test_mode else 4
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
try:
text_a = line[q1_index]
text_b = line[q2_index]
label = None if test_mode else line[5]
except IndexError:
continue
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class QnliProcessor(DataProcessor):
"""Processor for the QNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["question"].numpy().decode("utf-8"),
tensor_dict["sentence"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class RteProcessor(DataProcessor):
"""Processor for the RTE data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["entailment", "not_entailment"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class WnliProcessor(DataProcessor):
"""Processor for the WNLI data set (GLUE version)."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format("processor"), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
"""See base class."""
return InputExample(
tensor_dict["idx"].numpy(),
tensor_dict["sentence1"].numpy().decode("utf-8"),
tensor_dict["sentence2"].numpy().decode("utf-8"),
str(tensor_dict["label"].numpy()),
)
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
return self._create_examples(self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training, dev and test sets."""
examples = []
for i, line in enumerate(lines):
if i == 0:
continue
guid = f"{set_type}-{line[0]}"
text_a = line[1]
text_b = line[2]
label = None if set_type == "test" else line[-1]
examples.append(InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
glue_tasks_num_labels = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"sst-2": 2,
"sts-b": 1,
"qqp": 2,
"qnli": 2,
"rte": 2,
"wnli": 2,
}
glue_processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mnli-mm": MnliMismatchedProcessor,
"mrpc": MrpcProcessor,
"sst-2": Sst2Processor,
"sts-b": StsbProcessor,
"qqp": QqpProcessor,
"qnli": QnliProcessor,
"rte": RteProcessor,
"wnli": WnliProcessor,
}
glue_output_modes = {
"cola": "classification",
"mnli": "classification",
"mnli-mm": "classification",
"mrpc": "classification",
"sst-2": "classification",
"sts-b": "regression",
"qqp": "classification",
"qnli": "classification",
"rte": "classification",
"wnli": "classification",
}
| transformers/src/transformers/data/processors/glue.py/0 | {
"file_path": "transformers/src/transformers/data/processors/glue.py",
"repo_id": "transformers",
"token_count": 10213
} | 448 |
# 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.
import copy
import weakref
from typing import TYPE_CHECKING, Any, Optional
import numpy as np
import torch
import torch.nn as nn
from ..pytorch_utils import prune_linear_layer
from ..utils import is_sklearn_available
if is_sklearn_available():
from sklearn.metrics import roc_curve
from ..pytorch_utils import isin_mps_friendly
from .logits_process import LogitsProcessorList, MinLengthLogitsProcessor, SuppressTokensLogitsProcessor
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..tokenization_utils_base import PreTrainedTokenizerBase
from .configuration_utils import GenerationConfig
class CandidateGenerator:
"""Abstract base class for all candidate generators that can be applied during assisted generation."""
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Fetches the candidates to be tried for the current input.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
Return:
`torch.LongTensor` of shape `(batch_size, candidate_length)` containing the candidate sequences to be
assessed by the model and, optionally, a `torch.FloatTensor` of shape `(batch_size, candidate_length,
vocabulary_size)` containing the logits associated to each candidate.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can call `get_candidates`."
)
def update_candidate_strategy(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, num_matches: int):
"""
Updates the candidate generation strategy based on the outcomes.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, candidate_length, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
beam search or log softmax for each vocabulary token when using beam search
num_matches (`int`):
The number of matches between the candidate sequences and the model predictions.
"""
raise NotImplementedError(
f"{self.__class__} is an abstract class. Only classes inheriting this class can call "
"`update_candidate_strategy`."
)
class AssistedCandidateGenerator(CandidateGenerator):
"""
`CandidateGenerator` class to be used for assisted generation and speculative decoding. This class generates
candidates through the use of a smaller model. Read the following blog post for more information:
https://huggingface.co/blog/assisted-generation
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
assistant_model (`PreTrainedModel`):
The model to be used for generating candidates. This model should be smaller than the main model.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call.
logits_processor (`LogitsProcessorList`):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
model_kwargs (`Dict`):
The keyword arguments that will be passed to the main model, and are used as base inputs for the assistant
model as well.
inputs_tensor (`torch.Tensor`, *optional*):
The model input tensor. In encoder-decoder models, this is the encoder input.
"""
def __init__(
self,
input_ids: torch.LongTensor,
assistant_model: "PreTrainedModel",
generation_config: "GenerationConfig",
model_kwargs: dict,
inputs_tensor: Optional[torch.Tensor] = None,
logits_processor: "LogitsProcessorList" = None,
):
# Make sure all data at the same device as assistant model
device = assistant_model.device
input_ids = input_ids.to(device)
if inputs_tensor is not None:
inputs_tensor = inputs_tensor.to(device)
# Prepare the assistant and the starting number of candidate tokens
self.assistant_model = assistant_model
self.num_assistant_tokens = assistant_model.generation_config.num_assistant_tokens
self.assistant_confidence_threshold = assistant_model.generation_config.assistant_confidence_threshold
# Set eos in assistant same as in target model
self.assistant_model.generation_config.eos_token_id = generation_config.eos_token_id
# Prepare the kwargs for the assistant model
assistant_kwargs = {}
for key, value in model_kwargs.items(): # deepcopy crashes if we attempt to copy encoder outputs with grads
if key not in ("encoder_outputs", "past_key_values"):
assistant_kwargs[key] = (
value.detach().to(device) if isinstance(value, torch.Tensor) else copy.deepcopy(value)
)
# Remove potential default "logits_to_keep" key
if "logits_to_keep" in assistant_kwargs and not assistant_model._supports_logits_to_keep():
del assistant_kwargs["logits_to_keep"]
# If the assistant is an encoder-decoder model, assume the encoder is different on the assistant.
if assistant_model.config.is_encoder_decoder:
inputs_tensor, model_input_name, assistant_kwargs = assistant_model._prepare_model_inputs(
inputs_tensor, assistant_model.generation_config.bos_token_id, assistant_kwargs
)
assistant_kwargs = assistant_model._prepare_encoder_decoder_kwargs_for_generation(
inputs_tensor, assistant_kwargs, model_input_name, assistant_model.generation_config
)
elif "encoder_outputs" in model_kwargs:
assistant_kwargs["encoder_outputs"] = model_kwargs["encoder_outputs"]
self.assistant_kwargs = assistant_kwargs
# Prepare assistant model's keys of inputs
if assistant_model.config.is_encoder_decoder:
# both are encoder-decoder
self.input_ids_key = "decoder_input_ids"
elif "encoder_outputs" in assistant_kwargs:
# special case for encoder-decoder with decoder-only assistant (like DistilWhisper)
self.input_ids_key = "input_ids"
self.assistant_kwargs["attention_mask"] = self.assistant_kwargs.get(
"decoder_attention_mask",
torch.ones((input_ids.shape[0], 1), device=input_ids.device, dtype=torch.long),
)
else:
# both are decoder-only
self.input_ids_key = "input_ids"
# Prepare generation-related options.
self.logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
self.generation_config = copy.deepcopy(generation_config)
self.generation_config.return_dict_in_generate = True
self.generation_config.output_scores = True
self.generation_config.assistant_confidence_threshold = self.assistant_confidence_threshold
# this flag allow us set the confidence stopping criteria for assistant model generation.
self.generation_config.is_assistant = True
# avoid unnecessary warnings that min_length is larger than max_new_tokens
# remove the `MinLengthLogitsProcessor` if exists (NOTE: no need to check for `MinNewTokensLogitsProcessor`)
self.main_model_min_length = self.generation_config.min_length
self.generation_config.min_length = 0
self.generation_config.min_new_tokens = None
for processor in self.logits_processor:
if isinstance(processor, MinLengthLogitsProcessor):
raise ValueError(
"Passing `MinLengthLogitsProcessor` when using `assisted_generation is disabled. "
"Please pass in `min_length` into `.generate()` instead"
)
# We need to roll back the cache in assisted generation, only DynamicCache is supported
self.generation_config.cache_implementation = None
if (
is_sklearn_available()
and self.assistant_model.generation_config.assistant_confidence_threshold
and type(self) is AssistedCandidateGenerator
):
self.probs = []
self.matches = []
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Fetches the candidates to be tried for the current input.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
Return:
`torch.LongTensor` of shape `(batch_size, candidate_length)` containing the candidate sequences to be
assessed by the model and a `torch.FloatTensor` of shape `(batch_size, candidate_length,
vocabulary_size)` containing the logits associated to each candidate.
"""
input_ids = input_ids.to(self.assistant_model.device)
# Calculate new tokens to generate
min_new_tokens, max_new_tokens = self._calculate_new_tokens(input_ids)
if max_new_tokens == 0:
return input_ids, None
# Update past key values and masks
self._update_past_and_masks(input_ids)
# Generate candidates
generation_args = self._prepare_generation_args(input_ids, min_new_tokens, max_new_tokens)
candidate_ids, candidate_logits = self._generate_candidates(generation_args)
return candidate_ids, candidate_logits
def update_candidate_strategy(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, num_matches: int):
"""
Updates the candidate generation strategy based on the outcomes.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, candidate_length, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
beam search or log softmax for each vocabulary token when using beam search
num_matches (`int`):
The number of matches between the candidate sequences and the model predictions.
"""
# Adjust the max number of assistant tokens to use in the next iteration. This is a simple heuristic,
# probably can be improved -- we want to balance the benefits of getting assistant tokens correct with the
# cost of forecasting incorrect assistant tokens.
if self.assistant_model.generation_config.num_assistant_tokens_schedule in {
"heuristic",
"heuristic_transient",
}:
# len(scores[0])-1 is the number of candidates according to the target tokenizer.
if num_matches == len(scores[0]) - 1:
self.num_assistant_tokens += 2.0
else:
self.num_assistant_tokens = max(1.0, self.num_assistant_tokens - 1.0)
# The assistant's confidence threshold is adjusted throughout the speculative iterations to reduce the number of unnecessary draft and target forward passes. The costs are estimated based on the ROC curve, which considers the probability of the draft token and its match with the target. A cost of 25% is assigned to false positives and 75% to false negatives.
# This adaptation is not compatible with UAG, as it relies on the number of matched tokens based on the draft vocabulary, which is unavailable in UAG.
if (
is_sklearn_available()
and self.assistant_model.generation_config.assistant_confidence_threshold
and type(self) is AssistedCandidateGenerator
):
# update self.matches
self.matches.extend([1] * num_matches)
if len(self.probs) > len(self.matches):
self.matches.append(0)
# update self.probs
excess_length = len(self.probs) - len(self.matches)
if excess_length > 0:
del self.probs[-excess_length:]
if (
len(self.probs) > 5 and {0, 1}.issubset(self.matches)
): # require at least 5 samples to calculate the ROC curve and at least one positive and one negative sample
fpr, tpr, thresholds = roc_curve(self.matches, self.probs)
fnr = 1 - tpr
# Calculate the cost for each threshold
costs = fpr + 3 * fnr
# Find the threshold that minimizes the cost
optimal_threshold_index = np.argmin(costs)
best_threshold = thresholds[optimal_threshold_index]
self.assistant_model.generation_config.assistant_confidence_threshold = best_threshold
def _calculate_new_tokens(self, input_ids: torch.LongTensor) -> tuple[int, int]:
"""Calculate the minimum and maximum number of new tokens to generate."""
new_cur_len = input_ids.shape[-1]
max_new_tokens = min(int(self.num_assistant_tokens), self.generation_config.max_length - new_cur_len - 1)
min_new_tokens = max(min(max_new_tokens, self.main_model_min_length - new_cur_len), 0)
return min_new_tokens, max_new_tokens
def _update_past_and_masks(
self, input_ids: torch.LongTensor, remove_from_pkv: int = 0, num_added_tokens: int = 1
) -> bool:
"""Update past key values and attention masks for subsequent generation rounds."""
has_past_key_values = self.assistant_kwargs.get("past_key_values", None) is not None
if has_past_key_values:
new_cache_size = input_ids.shape[-1] - 1 - remove_from_pkv
self.assistant_kwargs["past_key_values"].crop(new_cache_size - num_added_tokens)
self.assistant_kwargs = _prepare_attention_mask(
self.assistant_kwargs, input_ids.shape[-1], self.assistant_model.config.is_encoder_decoder
)
self.assistant_kwargs = _prepare_token_type_ids(self.assistant_kwargs, input_ids.shape[-1])
return has_past_key_values
def _prepare_generation_args(self, input_ids: torch.LongTensor, min_new_tokens: int, max_new_tokens: int) -> dict:
"""Prepare arguments for the generation call."""
return {
self.input_ids_key: input_ids,
"min_new_tokens": min_new_tokens,
"max_new_tokens": max_new_tokens,
"generation_config": self.generation_config,
"logits_processor": self.logits_processor,
}
def _generate_candidates(self, generation_args: dict) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""Generate candidate sequences using the assistant model."""
assistant_output = self.assistant_model.generate(**generation_args, **self.assistant_kwargs)
self.assistant_kwargs["past_key_values"] = assistant_output.past_key_values
if (
is_sklearn_available()
and self.assistant_model.generation_config.assistant_confidence_threshold
and type(self) is AssistedCandidateGenerator
):
scores_tensor = torch.cat(assistant_output.scores, dim=0)
scores_softmax = torch.softmax(scores_tensor, dim=-1)
ids = assistant_output.sequences[-1, -len(assistant_output.scores) :]
p = scores_softmax[range(len(ids)), ids]
self.probs.extend(p.tolist())
candidate_logits = torch.stack(assistant_output.scores, dim=1)
candidate_ids = assistant_output.sequences
return candidate_ids, candidate_logits
class AssistedCandidateGeneratorDifferentTokenizers(AssistedCandidateGenerator):
"""
`CandidateGenerator` class to be used for Universal Assisted Generation (UAD): assisted generation with different tokenizers
for the assistant and main models. This class generates candidates through the use of a smaller
model.
The main model input tokens are re-encoded into assistant model tokens, then candidate tokens are generated in the assistant encoding, which are
in turn re-encoded into main model candidate tokens. Validation then proceeds as explained above.
The re-encoding steps involve decoding token ids into text and then encoding the text using a different tokenizer.
Since re-encoding the tokens may result in tokenization discrepancies, UAD finds the longest common subsequence between the source and target encodings,
to ensure the new tokens include the correct prompt suffix.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
assistant_model (`PreTrainedModel`):
The model to be used for generating candidates. This model should be smaller than the main model.
target_tokenizer (`PreTrainedTokenizerBase`):
The tokenizer used for the target model.
assistant_tokenizer (`PreTrainedTokenizerBase`):
The tokenizer used for the assistant model.
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call.
logits_processor (`LogitsProcessorList`):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
model_kwargs (`Dict`):
The keyword arguments that will be passed to the main model, and are used as base inputs for the assistant
model as well.
inputs_tensor (`torch.Tensor`, *optional*):
The model input tensor. In encoder-decoder models, this is the encoder input.
"""
def __init__(
self,
input_ids: torch.LongTensor,
assistant_model: "PreTrainedModel",
target_tokenizer: "PreTrainedTokenizerBase",
assistant_tokenizer: "PreTrainedTokenizerBase",
generation_config: "GenerationConfig",
model_kwargs: dict,
inputs_tensor: Optional[torch.Tensor] = None,
logits_processor: "LogitsProcessorList" = None,
):
super().__init__(input_ids, assistant_model, generation_config, model_kwargs, inputs_tensor, logits_processor)
self.target_tokenizer = target_tokenizer
self.assistant_tokenizer = assistant_tokenizer
self.prev_target_ids_len: Optional[int] = None
self.prev_assistant_ids = None
self.target_lookbehind = assistant_model.generation_config.target_lookbehind
self.assistant_lookbehind = assistant_model.generation_config.assistant_lookbehind
@staticmethod
def _get_longest_diag_dict(input_matrix, nonzero_idx):
"""
Calculates the length of the longest diagonal sequence in a given matrix.
Args:
input_matrix (torch.Tensor): The input matrix.
nonzero_idx (torch.Tensor): The indices of the non-zero elements in the matrix.
Returns:
dict: A dictionary where the keys are the indices of the non-zero elements and the values are the lengths of the longest diagonal sequences starting from those indices.
"""
visited = set()
diags = {}
for idx in nonzero_idx:
start_idx = torch.clone(idx)
tuple_start_idx = tuple(start_idx.tolist())
if tuple_start_idx in visited:
continue
visited.add(tuple_start_idx)
cur_diag_len = 1
start_idx += 1
while start_idx[0] < input_matrix.shape[0] and start_idx[1] < input_matrix.shape[1]:
tuple_start_idx = tuple(start_idx.tolist())
visited.add(tuple_start_idx)
if input_matrix[start_idx[0], start_idx[1]] == 1:
cur_diag_len += 1
start_idx += 1
else:
break
diags[idx] = cur_diag_len
return diags
@staticmethod
def _get_longest_diag_index(input_matrix):
"""
Returns the start index and length of the longest diagonal in the given input.
Args:
input_matrix (numpy.ndarray): The input matrix.
Returns:
tuple: A tuple containing the start index and length of the longest diagonal.
"""
diags = AssistedCandidateGeneratorDifferentTokenizers._get_longest_diag_dict(
input_matrix, input_matrix.nonzero()
)
diags_values = list(diags.values())
diags_keys = list(diags.keys())
best_diag = np.argmax(diags_values)
diag_start_index = diags_keys[best_diag]
diag_start_length = diags_values[best_diag]
return diag_start_index, diag_start_length
@staticmethod
def _get_tokens_diag(prompt, prompt_plus_new_tokens):
"""
Input:
prompt: 2D array of shape (batch_size, prompt_length), represents the original prompt tokens
prompt_plus_new_tokens: 2D array of shape (batch_size, prompt_length), represents the suffix of the original prompt, with additional new tokens.
Output:
discrepancy_length: int, represents the number of tokens that need to be replaced from prompt
new_tokens_only: 2D array of shape (batch_size, new_token_length), represents the new tokens that are not in prompt
discrepancy_only: 2D array of shape (batch_size, discrepancy_length), represents the new tokens that are in prompt but not in prompt_plus_new_tokens
"""
compare_mat = prompt_plus_new_tokens.T == prompt
if not torch.is_tensor(compare_mat):
compare_mat = torch.tensor(compare_mat)
compare_mat_int = compare_mat.to(int)
if not compare_mat_int.any().item():
# empty intersection between prompt and prompt_plus_new_tokens
return None, None, None
longest_location, longest_diag_length = AssistedCandidateGeneratorDifferentTokenizers._get_longest_diag_index(
compare_mat_int
)
new_token_start_index = longest_location[0] + longest_diag_length
discrepancy_with_old = longest_location[1] + longest_diag_length
discrepancy_length = (prompt.shape[1] - discrepancy_with_old).item()
new_tokens_only = prompt_plus_new_tokens[:, new_token_start_index + discrepancy_length :]
discrepancy_only = prompt_plus_new_tokens[
:, new_token_start_index : new_token_start_index + discrepancy_length
]
return discrepancy_length, new_tokens_only, discrepancy_only
def convert_source_tokens_to_target_tokens(
self,
input_ids,
source_tokenizer,
destination_tokenizer,
):
"""
Convert token IDs from one tokenizer to another.
Args:
input_ids: The input token IDs.
source_tokenizer: The source tokenizer.
destination_tokenizer: The destination tokenizer.
Returns:
The converted token IDs.
"""
text = source_tokenizer.batch_decode(input_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
dest_ids = destination_tokenizer(text, add_special_tokens=True, return_tensors="pt")["input_ids"]
return dest_ids.to(input_ids.device)
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Fetches the candidates to be tried for the current input.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
Return:
`torch.LongTensor` of shape `(batch_size, candidate_length)` containing the candidate sequences to be
assessed by the model and a `torch.FloatTensor` of shape `(batch_size, candidate_length,
vocabulary_size)` containing the logits associated to each candidate.
"""
max_new_tokens = int(self.num_assistant_tokens)
if max_new_tokens == 0:
return input_ids, None
input_ids = input_ids.to(self.assistant_model.device)
remove_from_pkv = 0
assistant_input_ids, remove_from_pkv = self._prepare_assistant_input_ids(input_ids)
self.prev_assistant_ids = assistant_input_ids
min_new_tokens = max(min(max_new_tokens, self.main_model_min_length - assistant_input_ids.shape[-1]), 0)
self._update_past_and_masks(assistant_input_ids, remove_from_pkv)
generation_args = self._prepare_generation_args(assistant_input_ids, min_new_tokens, max_new_tokens)
self.assistant_kwargs.pop("attention_mask", None)
assistant_output = self.assistant_model.generate(**generation_args, **self.assistant_kwargs)
new_target_ids = self._process_assistant_outputs(input_ids, assistant_output.sequences, assistant_input_ids)
# Update state
self.prev_target_ids_len = input_ids.shape[1]
self.assistant_kwargs["past_key_values"] = assistant_output.past_key_values
self.prev_assistant_ids = assistant_output.sequences
if self.prev_target_ids_len >= new_target_ids.shape[1]:
return input_ids, None
return new_target_ids, None
def _prepare_assistant_input_ids(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, int]:
"""Converts target input IDs to assistant input IDs, handling discrepancies."""
convert_kwargs = {
"source_tokenizer": self.target_tokenizer,
"destination_tokenizer": self.assistant_tokenizer,
}
remove_from_pkv = 0
if self.prev_assistant_ids is not None and self.prev_target_ids_len > self.target_lookbehind:
# input_ids contains all target prompt input ids and some new target input ids
start_index_in_target_window = self.prev_target_ids_len - self.target_lookbehind
new_assistant_ids = self.convert_source_tokens_to_target_tokens(
input_ids[:, start_index_in_target_window:], **convert_kwargs
)
prompt_use_length = new_assistant_ids.shape[1]
prompt_use = self.prev_assistant_ids[:, -prompt_use_length:]
discrepancy_length, new_tokens_only, discrepancy_only = self._get_tokens_diag(
prompt_use, new_assistant_ids
)
assistant_input_ids = self.prev_assistant_ids
if new_tokens_only is not None:
if discrepancy_length > 0 and discrepancy_only.shape[1] > 0:
if discrepancy_length == discrepancy_only.shape[1]:
assistant_input_ids[:, -discrepancy_length:] = discrepancy_only
elif discrepancy_length > discrepancy_only.shape[1]:
discrepancy_length_diff = discrepancy_length - discrepancy_only.shape[1]
assistant_input_ids = assistant_input_ids[:, :-discrepancy_length_diff]
assistant_input_ids[:, -discrepancy_only.shape[1] :] = discrepancy_only
remove_from_pkv = discrepancy_length
if new_tokens_only.shape[1] > 0:
assistant_input_ids = torch.cat([assistant_input_ids, new_tokens_only], dim=-1)
else:
# edge case: in case of no intersection between prompt and new_assistant_ids
assistant_input_ids = torch.cat([assistant_input_ids, new_assistant_ids], dim=-1)
else:
assistant_input_ids = self.convert_source_tokens_to_target_tokens(input_ids, **convert_kwargs)
self.prev_target_ids_len = input_ids.shape[1]
return assistant_input_ids, remove_from_pkv
def _process_assistant_outputs(
self, input_ids: torch.LongTensor, assistant_sequences: torch.LongTensor, assistant_input_ids: torch.LongTensor
) -> torch.LongTensor:
"""Processes assistant outputs to obtain target input IDs."""
num_prev_assistant = self.prev_assistant_ids.shape[1]
start_assistant_look_index = num_prev_assistant - self.assistant_lookbehind
new_target_ids_from_window = self.convert_source_tokens_to_target_tokens(
assistant_sequences[:, start_assistant_look_index:],
source_tokenizer=self.assistant_tokenizer,
destination_tokenizer=self.target_tokenizer,
)
target_prompt_use_length = new_target_ids_from_window.shape[1]
target_prompt_use = input_ids[:, -target_prompt_use_length:]
_, target_new_tokens_only, _ = self._get_tokens_diag(target_prompt_use, new_target_ids_from_window)
new_target_ids = input_ids
if target_new_tokens_only is not None:
if target_new_tokens_only.shape[1] > 0:
new_target_ids = torch.cat([new_target_ids, target_new_tokens_only], dim=-1)
else:
# edge case: in case of no intersection between prompt and new_target_ids
new_target_ids = torch.cat([new_target_ids, new_target_ids_from_window], dim=-1)
if hasattr(self.generation_config, "max_length"):
new_target_ids = new_target_ids[:, : self.generation_config.max_length]
return new_target_ids
class _PruneReindexingLMHead(nn.Module):
"""
A class to prune and reindex the language model head.
This class prunes the language model head to only include the specified token IDs and reindexes the logits
to map back to the original vocabulary.
Args:
original_lm_head (nn.Module): The original language model head.
token_ids (list[int]): The list of token IDs to keep.
"""
def __init__(self, original_lm_head, assistant_overlap_token_ids):
super().__init__()
self.pruned_lm_head = prune_linear_layer(original_lm_head, assistant_overlap_token_ids).to(
original_lm_head.weight.dtype
)
def forward(self, hidden_states):
pruned_logits = self.pruned_lm_head(hidden_states)
return pruned_logits
class _MapInputEmbedding(nn.Module):
def __init__(self, original_embedding: nn.Embedding, assistant_overlap_token_ids):
"""
Wraps an existing embedding layer and remaps token IDs before lookup.
Args:
original_embedding (nn.Embedding): Pre-trained or existing embedding layer.
assistant_overlap_token_ids (dict): Mapping from original token IDs to new token IDs.
Example: {old_id: new_id}
"""
super().__init__()
self.original_embedding = original_embedding
self.weight = original_embedding.weight
self.assistant_overlap_token_ids = assistant_overlap_token_ids
self.map = False
def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
"""
Args:
input_ids (torch.LongTensor): Tensor of token IDs (batch_size, seq_len).
Returns:
torch.FloatTensor: Corresponding input embeddings.
"""
if self.map:
# Get the last item from input_ids
my_input_ids = self.assistant_overlap_token_ids[input_ids[0, -1]].unsqueeze(0).unsqueeze(0)
else:
self.map = True
my_input_ids = input_ids
return self.original_embedding(my_input_ids)
class AssistantToTargetTranslator:
"""
Translates token ids and logits between assistant and target model vocabularies. This class is used to handle
vocabulary mismatches when using different tokenizers for the assistant and target models in speculative decoding,
as introduced in the paper "Lossless Speculative Decoding Algorithms for Heterogeneous Vocabularies"
(https://huggingface.co/papers/2502.05202).
It maintains mappings between the two vocabularies and handles token/logit conversion.
Args:
target_tokenizer (`PreTrainedTokenizerBase`):
The tokenizer used by the target (main) model.
assistant_tokenizer (`PreTrainedTokenizerBase`):
The tokenizer used by the assistant model.
target_vocab_size (`int`):
The size of the target model's vocabulary. If not provided, will be inferred from the target tokenizer.
assistant_model (Optional[PreTrainedModel], optional): The assistant model to be used. Defaults to None for backward compatibility.
assistant_prune_lm_head (bool): Whether to prune the assistant model's language model
head to match the target vocabulary. This is only applicable if `assistant_model` is provided.
Defaults to False for backward compatibility.
"""
FILTER_VALUE: float = -float("Inf") # The value used to filter out unmapped tokens in the logits.
SUPPRESS_TOKEN_ID: int = -1 # The ID used to mark suppressed tokens in the mapping.
def __init__(
self,
target_tokenizer: "PreTrainedTokenizerBase",
assistant_tokenizer: "PreTrainedTokenizerBase",
target_vocab_size: int, # required since target_vocab_size can be different from the length of target_tokenizer.get_vocab()
assistant_model: Optional["PreTrainedModel"] = None,
assistant_prune_lm_head: bool = False,
):
self._target_tokenizer: PreTrainedTokenizerBase = target_tokenizer
self._assistant_tokenizer: PreTrainedTokenizerBase = assistant_tokenizer
self._assistant_model_device = assistant_model.device if assistant_model is not None else "cpu"
self.target_vocab_size: int = target_vocab_size
self._assistant_to_target_input_ids, self.target_to_assistant_input_ids = (
self._get_assistant_to_target_input_ids()
)
self._suppress_input_ids: list[int] = self._get_suppress_input_ids()
self.logits_processors: Optional[LogitsProcessorList] = None
self.assistant_prune_lm_head = assistant_prune_lm_head and assistant_model is not None
if len(self._suppress_input_ids) > 0:
# the assistant vocab is not a subset of the target vocab
if self.assistant_prune_lm_head:
self.assistant_overlap_token_ids = torch.tensor(
list(self.target_to_assistant_input_ids.values()),
dtype=torch.long,
device=self._assistant_model_device,
)
original_lm_head = assistant_model.get_output_embeddings()
pruned_lm_head = _PruneReindexingLMHead(original_lm_head, self.assistant_overlap_token_ids)
del original_lm_head
assistant_model.set_output_embeddings(pruned_lm_head)
original_input_embeddings = assistant_model.get_input_embeddings()
map_input_embeddings = _MapInputEmbedding(original_input_embeddings, self.assistant_overlap_token_ids)
del original_input_embeddings
assistant_model.set_input_embeddings(map_input_embeddings)
self.map_input_embeddings = map_input_embeddings
else:
self.logits_processors = LogitsProcessorList(
[SuppressTokensLogitsProcessor(self._get_suppress_input_ids(), self._assistant_model_device)]
)
def unmap_input_ids(self):
"""
Disables the mapping of input ids despite the assistant pruning for the language model head being enabled.
This method is required for the first forward pass of `_MapInputEmbedding` where input ids are already in the assistant vocabulary space. By disabling the mapping, it ensures that the input ids are processed correctly without remapping.
"""
if self.assistant_prune_lm_head:
self.map_input_embeddings.map = False
def _get_assistant_to_target_input_ids(self):
target_vocab = self._target_tokenizer.get_vocab()
assistant_vocab = self._assistant_tokenizer.get_vocab()
space_str = " "
target_space_ids = self._target_tokenizer(space_str, add_special_tokens=False)["input_ids"]
if len(target_space_ids) > 0:
target_space_sign = self._target_tokenizer.convert_ids_to_tokens(target_space_ids)[0][0]
assistant_space_ids = self._assistant_tokenizer(space_str, add_special_tokens=False)["input_ids"]
if len(assistant_space_ids) > 0:
assistant_space_sign = self._assistant_tokenizer.convert_ids_to_tokens(assistant_space_ids)[0][0]
if target_space_sign != assistant_space_sign:
# If the assistant tokenizer has a different space sign than the target tokenizer,
# we need to replace the assistant space sign with the target space sign in the assistant_vocab.
assistant_vocab = {
(
tok.replace(assistant_space_sign, target_space_sign, 1)
if tok.startswith(assistant_space_sign)
else tok
): idx
for tok, idx in assistant_vocab.items()
}
max_assistant_index = max(assistant_vocab.values())
assistant_to_target_input_ids = torch.full((max_assistant_index + 1,), self.SUPPRESS_TOKEN_ID, dtype=int)
target_to_assistant_input_ids: dict[int, int] = {}
for tok, assistant_id in assistant_vocab.items():
target_id = target_vocab.get(tok)
if target_id is not None:
assistant_to_target_input_ids[assistant_id] = target_id
target_to_assistant_input_ids[target_id] = assistant_id
return assistant_to_target_input_ids.to(self._assistant_model_device), target_to_assistant_input_ids
def _get_suppress_input_ids(self) -> list[int]:
"""
Get the input ids that are in the assistant vocab but not in the target vocab.
"""
return torch.where(self._assistant_to_target_input_ids == self.SUPPRESS_TOKEN_ID)[0]
def get_target_ids(
self, assistant_input_ids, target_input_ids, assistant_candidate_ids: torch.LongTensor
) -> torch.LongTensor:
"""
Return the target candidate ids that correspond to the assistant candidate ids.
Note that we have already the target ids for the prompt and we only need to find the target ids for the new tokens.
Moreover, assistant ids of the original prompt does not necessarily appear in _assistant_to_target_input_ids.
"""
num_new_tokens = len(assistant_candidate_ids[0]) - assistant_input_ids.shape[1]
if num_new_tokens == 0:
return target_input_ids
else:
# Get last `num_new_tokens` candidate IDs
last_candidate_ids = assistant_candidate_ids[0, -num_new_tokens:]
if self.assistant_prune_lm_head:
# Map assistant IDs -> target input IDs
last_candidate_ids = self.assistant_overlap_token_ids[last_candidate_ids]
transformed_slice = self._assistant_to_target_input_ids[last_candidate_ids]
return torch.cat((target_input_ids, transformed_slice.unsqueeze(0)), dim=1)
def get_target_logits(self, assistant_logits: torch.FloatTensor) -> torch.FloatTensor:
"""
Return the target logits that correspond to the assistant logits.
"""
target_shape: tuple[int, ...] = (*assistant_logits.shape[:-1], self.target_vocab_size)
target_logits: torch.FloatTensor = torch.full(
target_shape, self.FILTER_VALUE, device=self._assistant_model_device
)
# Mask for valid indices
assistant_indices_mask = self._assistant_to_target_input_ids != self.SUPPRESS_TOKEN_ID
# Exclude invalid indices
target_logits_supported_indices = self._assistant_to_target_input_ids[assistant_indices_mask]
if self.assistant_prune_lm_head:
target_logits[..., target_logits_supported_indices] = assistant_logits
else:
valid_assistant_logits = assistant_logits[..., : self._assistant_to_target_input_ids.shape[0]]
target_logits[..., target_logits_supported_indices] = valid_assistant_logits[..., assistant_indices_mask]
return target_logits
class AssistantVocabTranslatorCache:
"""
Cache for `AssistantToTargetTranslator` instances. The instances are computed at
pre-processing time, and this cache allows us to avoid recomputing them.
"""
_cache = weakref.WeakKeyDictionary()
@classmethod
def get_translator(
cls,
target_tokenizer: "PreTrainedTokenizerBase",
assistant_tokenizer: "PreTrainedTokenizerBase",
target_vocab_size: int,
assistant_model: Optional["PreTrainedModel"] = None,
assistant_prune_lm_head: bool = False,
) -> AssistantToTargetTranslator:
assistant_dict = cls._cache.get(target_tokenizer)
if assistant_dict is None:
assistant_dict = weakref.WeakKeyDictionary()
cls._cache[target_tokenizer] = assistant_dict
mapping = assistant_dict.get(assistant_tokenizer)
if mapping is None:
mapping = AssistantToTargetTranslator(
target_tokenizer,
assistant_tokenizer,
target_vocab_size,
assistant_model,
assistant_prune_lm_head,
)
assistant_dict[assistant_tokenizer] = mapping
return mapping
@classmethod
def cleanup(cls):
"""
Clean up dead references in the cache.
This removes entries where either the target_tokenizer or assistant_tokenizer
has been garbage collected.
"""
# Remove entries from the outer cache where the target_tokenizer is no longer alive
dead_keys = [key for key in cls._cache if key is None]
for key in dead_keys:
del cls._cache[key]
# For each assistant_dict, remove entries where assistant_tokenizer is no longer alive
for assistant_dict in cls._cache.values():
dead_keys = [key for key in assistant_dict if key is None]
for key in dead_keys:
del assistant_dict[key]
class UniversalSpeculativeDecodingGenerator(AssistedCandidateGeneratorDifferentTokenizers):
"""
`CandidateGenerator` class to be used for Universal Speculative Decoding (USD): speculative decoding with different tokenizers
for the assistant and main models. This class generates candidates through the use of a smaller model.
"""
def __init__(
self,
input_ids: torch.LongTensor,
assistant_model: "PreTrainedModel",
target_tokenizer: "PreTrainedTokenizerBase",
assistant_tokenizer: "PreTrainedTokenizerBase",
generation_config: "GenerationConfig",
model_kwargs: dict,
atm_translator: AssistantToTargetTranslator,
inputs_tensor: Optional[torch.Tensor] = None,
logits_processor: "LogitsProcessorList" = None,
):
# Initialize translator before parent class
self._atm_translator = atm_translator
super().__init__(
input_ids,
assistant_model,
target_tokenizer,
assistant_tokenizer,
generation_config,
model_kwargs,
inputs_tensor,
logits_processor,
)
# Track sequence lengths and previous assistant IDs
self._target_seq_len_with_candidates: int = 0
self._prev_assistant_ids: Optional[torch.LongTensor] = None
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Simplified version of get_candidates that uses the translator cache for token conversion.
"""
target_input_ids = input_ids.to(self.assistant_model.device)
assistant_input_ids, num_added_tokens = self._prepare_assistant_input_ids(target_input_ids)
min_new_tokens, max_new_tokens = self._calculate_new_tokens(target_input_ids)
if max_new_tokens == 0:
return input_ids, None
self._update_past_and_masks(assistant_input_ids, num_added_tokens=num_added_tokens)
generation_args = self._prepare_generation_args(assistant_input_ids, min_new_tokens, max_new_tokens)
# Ensure scores are returned
generation_args["generation_config"].output_scores = True
generation_args["generation_config"].return_dict_in_generate = True
# Generate and process outputs using translator
if self._atm_translator.logits_processors is not None:
generation_args["logits_processor"] = self._atm_translator.logits_processors
self._prev_assistant_ids, assistant_candidate_logits = self._generate_candidates(generation_args)
# Use translator to convert tokens and logits
target_candidate_ids = self._atm_translator.get_target_ids(
assistant_input_ids, target_input_ids, self._prev_assistant_ids
)
self._target_seq_len_with_candidates = target_candidate_ids.shape[-1]
target_candidate_logits = self._atm_translator.get_target_logits(assistant_candidate_logits)
return target_candidate_ids, target_candidate_logits
def _update_past_and_masks(self, assistant_input_ids: torch.LongTensor, num_added_tokens: int = 1) -> bool:
if self._prev_assistant_ids is None:
# Prepare attention mask for the first generation.
# For subsequent generations, the attention mask is updated in super()_update_past_and_masks.
self.assistant_kwargs = _prepare_attention_mask(
self.assistant_kwargs, assistant_input_ids.shape[-1], self.assistant_model.config.is_encoder_decoder
)
return super()._update_past_and_masks(assistant_input_ids, num_added_tokens=num_added_tokens)
def _prepare_assistant_input_ids(self, target_input_ids: torch.LongTensor) -> torch.LongTensor:
"""
Simplified token conversion that only processes new tokens.
"""
# Calculate new tokens since last call
target_seq_len = target_input_ids.shape[-1]
if self._target_seq_len_with_candidates == 0:
new_token_count = target_seq_len
else:
new_token_count = 1
target_new_ids = target_input_ids[:, -new_token_count:]
# Convert the new tokens
assistant_new_ids = None
if self._target_seq_len_with_candidates > 0:
# we have only one new token and we can directly convert it
assistant_new_ids = self._atm_translator.target_to_assistant_input_ids.get(target_new_ids[0].item())
if assistant_new_ids is None:
target_new_text = self.target_tokenizer.batch_decode(
target_new_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
)
assistant_new_ids = self.assistant_tokenizer(
target_new_text, add_special_tokens=False, return_tensors="pt"
)["input_ids"].to(self.assistant_model.device)
else:
assistant_new_ids = torch.tensor([[assistant_new_ids]], device=self.assistant_model.device)
# Update or initialize assistant IDs
if self._prev_assistant_ids is None:
assistant_input_ids = assistant_new_ids
else:
tokens_to_remove = self._target_seq_len_with_candidates + 1 - target_seq_len
# If the number of new tokens is greater than zero, truncate the previous assistant IDs
if tokens_to_remove > 0:
self._prev_assistant_ids = self._prev_assistant_ids[:, :-tokens_to_remove]
assistant_input_ids = torch.cat([self._prev_assistant_ids, assistant_new_ids], dim=-1)
assistant_input_ids = assistant_input_ids.to(dtype=torch.long)
self._atm_translator.unmap_input_ids()
return assistant_input_ids, len(assistant_new_ids[0])
class PromptLookupCandidateGenerator(CandidateGenerator):
"""
`CandidateGenerator` class to be used for prompt lookup generation. This class generates candidates by looking up
likely continuations in the provided prompt (input_ids) itself.
Read the following blog post for more information: https://github.com/apoorvumang/prompt-lookup-decoding
Args:
max_matching_ngram_size (`int`):
The maximum ngram size to be considered for matching in the prompt
num_output_tokens (`int`):
The number of tokens to be output as candidate tokens.
max_length (`int`):
The number of total maximum tokens that can be generated. For decoder-only models that includes the prompt length.
Defaults to 20, which is the max length used as default in generation config.
"""
def __init__(
self,
eos_token_id: Optional[torch.Tensor] = None,
num_output_tokens: int = 10,
max_matching_ngram_size: Optional[int] = None,
max_length: int = 20,
):
self.num_output_tokens = num_output_tokens
self.max_matching_ngram_size = max_matching_ngram_size if max_matching_ngram_size else 2
self.max_length = max_length
self.eos_token_id = eos_token_id
if self.max_matching_ngram_size <= 0 or self.num_output_tokens <= 0:
raise ValueError("Invalid max_matching_ngram_size or num_output_tokens")
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
"""
Fetches the candidates to be tried for the current input.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
Return:
`torch.LongTensor` of shape `(num_candidates, candidate_length)`: The candidate sequences to be tried.
"""
input_length = input_ids.size(1)
# Don't generate more than `max_length - 1` candidates since the target model generates one extra token.
if self.max_length == input_length + 1:
return input_ids, None
chosen_ids = None
match_found = False
for ngram_size in range(min(self.max_matching_ngram_size, input_length - 1), 0, -1):
# Create sliding windows of size ngram_size
windows = input_ids.unfold(dimension=1, size=ngram_size, step=1)
# Convert ngram to a tensor for comparison
ngram_tensor = input_ids[0, -ngram_size:]
# Find where the windows match the ngram
matches = (windows == ngram_tensor).all(dim=2)
# Get the indices of matches
match_indices = matches.nonzero(as_tuple=True)[1]
# Iterate through match indices to find a valid continuation
for idx in match_indices:
start_idx = idx + ngram_size
end_idx = start_idx + self.num_output_tokens
end_idx = min(end_idx, input_length, self.max_length)
if start_idx < end_idx:
chosen_ids = input_ids[0, start_idx:end_idx]
match_found = True
# remove remaining candidate ids if an "eos" token is found, otherwise the target model may
# accept eos and the rest as valid, thus not stopping generation after "eos"
# NOTE: below code is written based on the fact that assisted decoding supports only bs=1
mask = isin_mps_friendly(chosen_ids, self.eos_token_id)
match_indices_eos = torch.nonzero(mask)
if match_indices_eos.numel() > 0:
first_eos_index = match_indices_eos[0].item()
chosen_ids = chosen_ids[:first_eos_index]
break
if match_found:
break
if chosen_ids is None or len(chosen_ids) == 0:
# In case we didn't find a match return the input sequence unchanged, reverts back to autoregressive decoding
return input_ids, None
# Now need extend input_ids with chosen_ids
chosen_ids = chosen_ids.unsqueeze(0)
candidate_input_ids = torch.cat((input_ids, chosen_ids), dim=1)
# assisted_generation expects logits as well, but we don't have those here, so returning None
return candidate_input_ids, None
def update_candidate_strategy(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, num_matches: int):
"""
Updates the candidate generation strategy based on the outcomes.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, candidate_length, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using
beam search or log softmax for each vocabulary token when using beam search
num_matches (`int`):
The number of matches between the candidate sequences and the model predictions.
"""
# Currently does nothing
return
class EarlyExitCandidateGenerator(AssistedCandidateGenerator):
"""
`CandidateGenerator` class to be used for assisted generation and speculative decoding. This class generates
candidates through the use of **the model itself**, exiting early. Can only be used with models that support early
exit, e.g., `facebook/layerskip-llama3.2-1B`.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. [What are input IDs?](../glossary#input-ids)
assistant_model (`PreTrainedModel`):
The original model. This model must support early exit (i.e. is trained to compute logits in earlier
layers).
generation_config (`~generation.GenerationConfig`, *optional*):
The generation configuration to be used as base parametrization for the generation call.
logits_processor (`LogitsProcessorList`):
An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
used to modify the prediction scores of the language modeling head applied at each generation step.
model_kwargs (`Dict`):
The keyword arguments that will be passed to the main model, and are used as base inputs for the assistant
model as well.
inputs_tensor (`torch.Tensor`, *optional*):
The model input tensor. In encoder-decoder models, this is the encoder input.
"""
def __init__(
self,
input_ids: torch.LongTensor,
assistant_model: "PreTrainedModel",
generation_config: "GenerationConfig",
model_kwargs: dict,
inputs_tensor: Optional[torch.Tensor] = None,
logits_processor: "LogitsProcessorList" = None,
):
super().__init__(
input_ids=input_ids,
assistant_model=assistant_model,
generation_config=generation_config,
model_kwargs=model_kwargs,
inputs_tensor=inputs_tensor,
logits_processor=logits_processor,
)
# We have to move early exit out of the generation config, otherwise the assistant will also call `generate`
# with early exit
self.assistant_early_exit = self.generation_config.assistant_early_exit
self.generation_config.assistant_early_exit = None
def get_candidates(self, input_ids: torch.LongTensor) -> tuple[torch.LongTensor, Optional[torch.FloatTensor]]:
# Temporarily sets the number of hidden layers to the early exit value
base_model = getattr(self.assistant_model, self.assistant_model.base_model_prefix)
original_num_hidden_layers = base_model.config.num_hidden_layers
base_model.config.num_hidden_layers = self.assistant_early_exit
candidate_ids, candidate_logits = super().get_candidates(input_ids)
base_model.config.num_hidden_layers = original_num_hidden_layers
return candidate_ids, candidate_logits
def _prepare_attention_mask(model_kwargs: dict[str, Any], new_length: int, is_encoder_decoder: bool) -> dict[str, Any]:
"""Expands or crops the model's mask for decoding purposes, to the defined length"""
mask_key = "decoder_attention_mask" if is_encoder_decoder else "attention_mask"
if mask_key not in model_kwargs:
return model_kwargs
mask = model_kwargs[mask_key]
mask_length_diff = new_length - mask.shape[1]
if mask_length_diff < 0:
model_kwargs[mask_key] = mask[:, :mask_length_diff]
elif mask_length_diff > 0:
model_kwargs[mask_key] = torch.cat([mask, mask.new_ones((mask.shape[0], mask_length_diff))], dim=-1)
# Handle cross attention models
if "cross_attention_mask" in model_kwargs:
# Mllama case
cross_mask = model_kwargs["cross_attention_mask"]
if mask_length_diff < 0:
model_kwargs["cross_attention_mask"] = cross_mask[:, :mask_length_diff]
elif mask_length_diff > 0:
new_mask = cross_mask[:, -1:, :, :].repeat(1, mask_length_diff, 1, 1)
model_kwargs["cross_attention_mask"] = torch.cat([cross_mask, new_mask], dim=1)
elif "image_attention_mask" in model_kwargs:
# IDEFICS case
cross_mask = model_kwargs["image_attention_mask"]
if mask_length_diff < 0:
model_kwargs["image_attention_mask"] = cross_mask[:, :mask_length_diff]
elif mask_length_diff > 0:
new_mask = cross_mask[:, -1:, :].repeat(1, mask_length_diff, 1)
model_kwargs["image_attention_mask"] = torch.cat([cross_mask, new_mask], dim=1)
return model_kwargs
def _prepare_token_type_ids(model_kwargs: dict[str, Any], new_length: int) -> dict[str, Any]:
"""Expands or crops the model's token_type_ids for decoding purposes, to the defined length"""
if "token_type_ids" not in model_kwargs or model_kwargs["token_type_ids"] is None:
return model_kwargs
token_type_ids = model_kwargs["token_type_ids"]
final_token_type = token_type_ids[:, -1].unsqueeze(-1)
type_length_diff = new_length - token_type_ids.shape[1]
if type_length_diff < 0:
token_type_ids = token_type_ids[:, :type_length_diff]
elif type_length_diff > 0:
token_type_copies = final_token_type.repeat(1, type_length_diff)
model_kwargs["token_type_ids"] = torch.cat([model_kwargs["token_type_ids"], token_type_copies], dim=-1)
return model_kwargs
| transformers/src/transformers/generation/candidate_generator.py/0 | {
"file_path": "transformers/src/transformers/generation/candidate_generator.py",
"repo_id": "transformers",
"token_count": 24773
} | 449 |
# Copyright 2024 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.
from collections.abc import Iterable
from copy import deepcopy
from functools import lru_cache, partial
from typing import Any, Optional, TypedDict, Union
import numpy as np
from .image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from .image_transforms import (
convert_to_rgb,
get_resize_output_image_size,
get_size_with_aspect_ratio,
group_images_by_shape,
reorder_images,
)
from .image_utils import (
ChannelDimension,
ImageInput,
ImageType,
SizeDict,
get_image_size,
get_image_size_for_max_height_width,
get_image_type,
infer_channel_dimension_format,
make_flat_list_of_images,
validate_kwargs,
validate_preprocess_arguments,
)
from .processing_utils import Unpack
from .utils import (
TensorType,
auto_docstring,
is_torch_available,
is_torchvision_available,
is_torchvision_v2_available,
is_vision_available,
logging,
)
from .utils.import_utils import is_rocm_platform
if is_vision_available():
from .image_utils import PILImageResampling
if is_torch_available():
import torch
if is_torchvision_available():
from .image_utils import pil_torch_interpolation_mapping
if is_torchvision_v2_available():
from torchvision.transforms.v2 import functional as F
else:
from torchvision.transforms import functional as F
else:
pil_torch_interpolation_mapping = None
logger = logging.get_logger(__name__)
@lru_cache(maxsize=10)
def validate_fast_preprocess_arguments(
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_pad: Optional[bool] = None,
size_divisibility: Optional[int] = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[SizeDict] = None,
do_resize: Optional[bool] = None,
size: Optional[SizeDict] = None,
resample: Optional["PILImageResampling"] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
):
"""
Checks validity of typically used arguments in an `ImageProcessorFast` `preprocess` method.
Raises `ValueError` if arguments incompatibility is caught.
"""
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_pad=do_pad,
size_divisibility=size_divisibility,
do_center_crop=do_center_crop,
crop_size=crop_size,
do_resize=do_resize,
size=size,
resample=resample,
)
# Extra checks for ImageProcessorFast
if return_tensors is not None and return_tensors != "pt":
raise ValueError("Only returning PyTorch tensors is currently supported.")
if data_format != ChannelDimension.FIRST:
raise ValueError("Only channel first data format is currently supported.")
def safe_squeeze(tensor: "torch.Tensor", axis: Optional[int] = None) -> "torch.Tensor":
"""
Squeezes a tensor, but only if the axis specified has dim 1.
"""
if axis is None:
return tensor.squeeze()
try:
return tensor.squeeze(axis=axis)
except ValueError:
return tensor
def max_across_indices(values: Iterable[Any]) -> list[Any]:
"""
Return the maximum value across all indices of an iterable of values.
"""
return [max(values_i) for values_i in zip(*values)]
def get_max_height_width(images: list["torch.Tensor"]) -> tuple[int]:
"""
Get the maximum height and width across all images in a batch.
"""
_, max_height, max_width = max_across_indices([img.shape for img in images])
return (max_height, max_width)
def divide_to_patches(
image: Union[np.array, "torch.Tensor"], patch_size: int
) -> list[Union[np.array, "torch.Tensor"]]:
"""
Divides an image into patches of a specified size.
Args:
image (`Union[np.array, "torch.Tensor"]`):
The input image.
patch_size (`int`):
The size of each patch.
Returns:
list: A list of Union[np.array, "torch.Tensor"] representing the patches.
"""
patches = []
height, width = get_image_size(image, channel_dim=ChannelDimension.FIRST)
for i in range(0, height, patch_size):
for j in range(0, width, patch_size):
patch = image[:, i : i + patch_size, j : j + patch_size]
patches.append(patch)
return patches
class DefaultFastImageProcessorKwargs(TypedDict, total=False):
do_resize: Optional[bool]
size: Optional[dict[str, int]]
default_to_square: Optional[bool]
resample: Optional[Union["PILImageResampling", "F.InterpolationMode"]]
do_center_crop: Optional[bool]
crop_size: Optional[dict[str, int]]
do_rescale: Optional[bool]
rescale_factor: Optional[Union[int, float]]
do_normalize: Optional[bool]
image_mean: Optional[Union[float, list[float]]]
image_std: Optional[Union[float, list[float]]]
do_convert_rgb: Optional[bool]
return_tensors: Optional[Union[str, TensorType]]
data_format: Optional[ChannelDimension]
input_data_format: Optional[Union[str, ChannelDimension]]
device: Optional["torch.device"]
disable_grouping: Optional[bool]
@auto_docstring
class BaseImageProcessorFast(BaseImageProcessor):
resample = None
image_mean = None
image_std = None
size = None
default_to_square = True
crop_size = None
do_resize = None
do_center_crop = None
do_rescale = None
rescale_factor = 1 / 255
do_normalize = None
do_convert_rgb = None
return_tensors = None
data_format = ChannelDimension.FIRST
input_data_format = None
device = None
model_input_names = ["pixel_values"]
valid_kwargs = DefaultFastImageProcessorKwargs
unused_kwargs = None
def __init__(self, **kwargs: Unpack[DefaultFastImageProcessorKwargs]):
super().__init__(**kwargs)
kwargs = self.filter_out_unused_kwargs(kwargs)
size = kwargs.pop("size", self.size)
self.size = (
get_size_dict(size=size, default_to_square=kwargs.pop("default_to_square", self.default_to_square))
if size is not None
else None
)
crop_size = kwargs.pop("crop_size", self.crop_size)
self.crop_size = get_size_dict(crop_size, param_name="crop_size") if crop_size is not None else None
for key in self.valid_kwargs.__annotations__:
kwarg = kwargs.pop(key, None)
if kwarg is not None:
setattr(self, key, kwarg)
else:
setattr(self, key, deepcopy(getattr(self, key, None)))
# get valid kwargs names
self._valid_kwargs_names = list(self.valid_kwargs.__annotations__.keys())
@property
def is_fast(self) -> bool:
"""
`bool`: Whether or not this image processor is a fast processor (backed by PyTorch and TorchVision).
"""
return True
def resize(
self,
image: "torch.Tensor",
size: SizeDict,
interpolation: "F.InterpolationMode" = None,
antialias: bool = True,
**kwargs,
) -> "torch.Tensor":
"""
Resize an image to `(size["height"], size["width"])`.
Args:
image (`torch.Tensor`):
Image to resize.
size (`SizeDict`):
Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
interpolation (`InterpolationMode`, *optional*, defaults to `InterpolationMode.BILINEAR`):
`InterpolationMode` filter to use when resizing the image e.g. `InterpolationMode.BICUBIC`.
Returns:
`torch.Tensor`: The resized image.
"""
interpolation = interpolation if interpolation is not None else F.InterpolationMode.BILINEAR
if size.shortest_edge and size.longest_edge:
# Resize the image so that the shortest edge or the longest edge is of the given size
# while maintaining the aspect ratio of the original image.
new_size = get_size_with_aspect_ratio(
image.size()[-2:],
size.shortest_edge,
size.longest_edge,
)
elif size.shortest_edge:
new_size = get_resize_output_image_size(
image,
size=size.shortest_edge,
default_to_square=False,
input_data_format=ChannelDimension.FIRST,
)
elif size.max_height and size.max_width:
new_size = get_image_size_for_max_height_width(image.size()[-2:], size.max_height, size.max_width)
elif size.height and size.width:
new_size = (size.height, size.width)
else:
raise ValueError(
"Size must contain 'height' and 'width' keys, or 'max_height' and 'max_width', or 'shortest_edge' key. Got"
f" {size}."
)
# This is a workaround to avoid a bug in torch.compile when dealing with uint8 on AMD MI3XX GPUs
# Tracked in PyTorch issue: https://github.com/pytorch/pytorch/issues/155209
# TODO: remove this once the bug is fixed (detected with torch==2.7.0+git1fee196, torchvision==0.22.0+9eb57cd)
if torch.compiler.is_compiling() and is_rocm_platform():
return self.compile_friendly_resize(image, new_size, interpolation, antialias)
return F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
@staticmethod
def compile_friendly_resize(
image: "torch.Tensor",
new_size: tuple[int, int],
interpolation: Optional["F.InterpolationMode"] = None,
antialias: bool = True,
) -> "torch.Tensor":
"""
A wrapper around `F.resize` so that it is compatible with torch.compile when the image is a uint8 tensor.
"""
if image.dtype == torch.uint8:
image = image.float() / 256
image = F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
image = image * 256
image = torch.where(image > 255, 255, image)
image = torch.where(image < 0, 0, image)
image = image.round().to(torch.uint8)
else:
image = F.resize(image, new_size, interpolation=interpolation, antialias=antialias)
return image
def rescale(
self,
image: "torch.Tensor",
scale: float,
**kwargs,
) -> "torch.Tensor":
"""
Rescale an image by a scale factor. image = image * scale.
Args:
image (`torch.Tensor`):
Image to rescale.
scale (`float`):
The scaling factor to rescale pixel values by.
Returns:
`torch.Tensor`: The rescaled image.
"""
return image * scale
def normalize(
self,
image: "torch.Tensor",
mean: Union[float, Iterable[float]],
std: Union[float, Iterable[float]],
**kwargs,
) -> "torch.Tensor":
"""
Normalize an image. image = (image - image_mean) / image_std.
Args:
image (`torch.Tensor`):
Image to normalize.
mean (`torch.Tensor`, `float` or `Iterable[float]`):
Image mean to use for normalization.
std (`torch.Tensor`, `float` or `Iterable[float]`):
Image standard deviation to use for normalization.
Returns:
`torch.Tensor`: The normalized image.
"""
return F.normalize(image, mean, std)
@lru_cache(maxsize=10)
def _fuse_mean_std_and_rescale_factor(
self,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
device: Optional["torch.device"] = None,
) -> tuple:
if do_rescale and do_normalize:
# Fused rescale and normalize
image_mean = torch.tensor(image_mean, device=device) * (1.0 / rescale_factor)
image_std = torch.tensor(image_std, device=device) * (1.0 / rescale_factor)
do_rescale = False
return image_mean, image_std, do_rescale
def rescale_and_normalize(
self,
images: "torch.Tensor",
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Union[float, list[float]],
image_std: Union[float, list[float]],
) -> "torch.Tensor":
"""
Rescale and normalize images.
"""
image_mean, image_std, do_rescale = self._fuse_mean_std_and_rescale_factor(
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
device=images.device,
)
# if/elif as we use fused rescale and normalize if both are set to True
if do_normalize:
images = self.normalize(images.to(dtype=torch.float32), image_mean, image_std)
elif do_rescale:
images = self.rescale(images, rescale_factor)
return images
def center_crop(
self,
image: "torch.Tensor",
size: dict[str, int],
**kwargs,
) -> "torch.Tensor":
"""
Center crop an image to `(size["height"], size["width"])`. If the input size is smaller than `crop_size` along
any edge, the image is padded with 0's and then center cropped.
Args:
image (`"torch.Tensor"`):
Image to center crop.
size (`dict[str, int]`):
Size of the output image.
Returns:
`torch.Tensor`: The center cropped image.
"""
if size.height is None or size.width is None:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
return F.center_crop(image, (size["height"], size["width"]))
def convert_to_rgb(
self,
image: ImageInput,
) -> ImageInput:
"""
Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image
as is.
Args:
image (ImageInput):
The image to convert.
Returns:
ImageInput: The converted image.
"""
return convert_to_rgb(image)
def filter_out_unused_kwargs(self, kwargs: dict):
"""
Filter out the unused kwargs from the kwargs dictionary.
"""
if self.unused_kwargs is None:
return kwargs
for kwarg_name in self.unused_kwargs:
if kwarg_name in kwargs:
logger.warning_once(f"This processor does not use the `{kwarg_name}` parameter. It will be ignored.")
kwargs.pop(kwarg_name)
return kwargs
def _prepare_images_structure(
self,
images: ImageInput,
expected_ndims: int = 3,
) -> ImageInput:
"""
Prepare the images structure for processing.
Args:
images (`ImageInput`):
The input images to process.
Returns:
`ImageInput`: The images with a valid nesting.
"""
return make_flat_list_of_images(images, expected_ndims=expected_ndims)
def _process_image(
self,
image: ImageInput,
do_convert_rgb: Optional[bool] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
device: Optional["torch.device"] = None,
) -> "torch.Tensor":
image_type = get_image_type(image)
if image_type not in [ImageType.PIL, ImageType.TORCH, ImageType.NUMPY]:
raise ValueError(f"Unsupported input image type {image_type}")
if do_convert_rgb:
image = self.convert_to_rgb(image)
if image_type == ImageType.PIL:
image = F.pil_to_tensor(image)
elif image_type == ImageType.NUMPY:
# not using F.to_tensor as it doesn't handle (C, H, W) numpy arrays
image = torch.from_numpy(image).contiguous()
# If the image is 2D, we need to unsqueeze it to add a channel dimension for processing
if image.ndim == 2:
image = image.unsqueeze(0)
# Infer the channel dimension format if not provided
if input_data_format is None:
input_data_format = infer_channel_dimension_format(image)
if input_data_format == ChannelDimension.LAST:
# We force the channel dimension to be first for torch tensors as this is what torchvision expects.
image = image.permute(2, 0, 1).contiguous()
# Now that we have torch tensors, we can move them to the right device
if device is not None:
image = image.to(device)
return image
def _prepare_image_like_inputs(
self,
images: ImageInput,
do_convert_rgb: Optional[bool] = None,
input_data_format: Optional[Union[str, ChannelDimension]] = None,
device: Optional["torch.device"] = None,
expected_ndims: int = 3,
) -> list["torch.Tensor"]:
"""
Prepare image-like inputs for processing.
Args:
images (`ImageInput`):
The image-like inputs to process.
do_convert_rgb (`bool`, *optional*):
Whether to convert the images to RGB.
input_data_format (`str` or `ChannelDimension`, *optional*):
The input data format of the images.
device (`torch.device`, *optional*):
The device to put the processed images on.
expected_ndims (`int`, *optional*):
The expected number of dimensions for the images. (can be 2 for segmentation maps etc.)
Returns:
List[`torch.Tensor`]: The processed images.
"""
# Get structured images (potentially nested)
images = self._prepare_images_structure(images, expected_ndims=expected_ndims)
process_image_partial = partial(
self._process_image, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
)
# Check if we have nested structure, assuming the nesting is consistent
has_nested_structure = len(images) > 0 and isinstance(images[0], (list, tuple))
if has_nested_structure:
processed_images = [[process_image_partial(img) for img in nested_list] for nested_list in images]
else:
processed_images = [process_image_partial(img) for img in images]
return processed_images
def _further_process_kwargs(
self,
size: Optional[SizeDict] = None,
crop_size: Optional[SizeDict] = None,
default_to_square: Optional[bool] = None,
image_mean: Optional[Union[float, list[float]]] = None,
image_std: Optional[Union[float, list[float]]] = None,
data_format: Optional[ChannelDimension] = None,
**kwargs,
) -> dict:
"""
Update kwargs that need further processing before being validated
Can be overridden by subclasses to customize the processing of kwargs.
"""
if kwargs is None:
kwargs = {}
if size is not None:
size = SizeDict(**get_size_dict(size=size, default_to_square=default_to_square))
if crop_size is not None:
crop_size = SizeDict(**get_size_dict(crop_size, param_name="crop_size"))
if isinstance(image_mean, list):
image_mean = tuple(image_mean)
if isinstance(image_std, list):
image_std = tuple(image_std)
if data_format is None:
data_format = ChannelDimension.FIRST
kwargs["size"] = size
kwargs["crop_size"] = crop_size
kwargs["default_to_square"] = default_to_square
kwargs["image_mean"] = image_mean
kwargs["image_std"] = image_std
kwargs["data_format"] = data_format
return kwargs
def _validate_preprocess_kwargs(
self,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, tuple[float]]] = None,
image_std: Optional[Union[float, tuple[float]]] = None,
do_resize: Optional[bool] = None,
size: Optional[SizeDict] = None,
do_center_crop: Optional[bool] = None,
crop_size: Optional[SizeDict] = None,
resample: Optional[Union["PILImageResampling", "F.InterpolationMode"]] = None,
return_tensors: Optional[Union[str, TensorType]] = None,
data_format: Optional[ChannelDimension] = None,
**kwargs,
):
"""
validate the kwargs for the preprocess method.
"""
validate_fast_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
do_center_crop=do_center_crop,
crop_size=crop_size,
resample=resample,
return_tensors=return_tensors,
data_format=data_format,
)
def __call__(self, images: ImageInput, *args, **kwargs: Unpack[DefaultFastImageProcessorKwargs]) -> BatchFeature:
return self.preprocess(images, *args, **kwargs)
@auto_docstring
def preprocess(self, images: ImageInput, *args, **kwargs: Unpack[DefaultFastImageProcessorKwargs]) -> BatchFeature:
# args are not validated, but their order in the `preprocess` and `_preprocess` signatures must be the same
validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_kwargs_names)
# Set default kwargs from self. This ensures that if a kwarg is not provided
# by the user, it gets its default value from the instance, or is set to None.
for kwarg_name in self._valid_kwargs_names:
kwargs.setdefault(kwarg_name, getattr(self, kwarg_name, None))
# Extract parameters that are only used for preparing the input images
do_convert_rgb = kwargs.pop("do_convert_rgb")
input_data_format = kwargs.pop("input_data_format")
device = kwargs.pop("device")
# Update kwargs that need further processing before being validated
kwargs = self._further_process_kwargs(**kwargs)
# Validate kwargs
self._validate_preprocess_kwargs(**kwargs)
# torch resize uses interpolation instead of resample
resample = kwargs.pop("resample")
# Check if resample is an int before checking if it's an instance of PILImageResampling
# because if pillow < 9.1.0, resample is an int and PILImageResampling is a module.
# Checking PILImageResampling will fail with error `TypeError: isinstance() arg 2 must be a type or tuple of types`.
kwargs["interpolation"] = (
pil_torch_interpolation_mapping[resample] if isinstance(resample, (int, PILImageResampling)) else resample
)
# Pop kwargs that are not needed in _preprocess
kwargs.pop("default_to_square")
kwargs.pop("data_format")
return self._preprocess_image_like_inputs(
images, *args, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device, **kwargs
)
def _preprocess_image_like_inputs(
self,
images: ImageInput,
*args,
do_convert_rgb: bool,
input_data_format: ChannelDimension,
device: Optional[Union[str, "torch.device"]] = None,
**kwargs: Unpack[DefaultFastImageProcessorKwargs],
) -> BatchFeature:
"""
Preprocess image-like inputs.
To be overridden by subclasses when image-like inputs other than images should be processed.
It can be used for segmentation maps, depth maps, etc.
"""
# Prepare input images
images = self._prepare_image_like_inputs(
images=images, do_convert_rgb=do_convert_rgb, input_data_format=input_data_format, device=device
)
return self._preprocess(images, *args, **kwargs)
def _preprocess(
self,
images: list["torch.Tensor"],
do_resize: bool,
size: SizeDict,
interpolation: Optional["F.InterpolationMode"],
do_center_crop: bool,
crop_size: SizeDict,
do_rescale: bool,
rescale_factor: float,
do_normalize: bool,
image_mean: Optional[Union[float, list[float]]],
image_std: Optional[Union[float, list[float]]],
disable_grouping: Optional[bool],
return_tensors: Optional[Union[str, TensorType]],
**kwargs,
) -> BatchFeature:
# Group images by size for batched resizing
grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
resized_images_grouped = {}
for shape, stacked_images in grouped_images.items():
if do_resize:
stacked_images = self.resize(image=stacked_images, size=size, interpolation=interpolation)
resized_images_grouped[shape] = stacked_images
resized_images = reorder_images(resized_images_grouped, grouped_images_index)
# Group images by size for further processing
# Needed in case do_resize is False, or resize returns images with different sizes
grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
processed_images_grouped = {}
for shape, stacked_images in grouped_images.items():
if do_center_crop:
stacked_images = self.center_crop(stacked_images, crop_size)
# Fused rescale and normalize
stacked_images = self.rescale_and_normalize(
stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
)
processed_images_grouped[shape] = stacked_images
processed_images = reorder_images(processed_images_grouped, grouped_images_index)
processed_images = torch.stack(processed_images, dim=0) if return_tensors else processed_images
return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
def to_dict(self):
encoder_dict = super().to_dict()
encoder_dict.pop("_valid_processor_keys", None)
encoder_dict.pop("_valid_kwargs_names", None)
return encoder_dict
| transformers/src/transformers/image_processing_utils_fast.py/0 | {
"file_path": "transformers/src/transformers/image_processing_utils_fast.py",
"repo_id": "transformers",
"token_count": 11935
} | 450 |
import torch
from ..generation.continuous_batching import PagedAttentionCache
from ..utils import is_flash_attn_2_available
try:
if is_flash_attn_2_available():
from flash_attn import flash_attn_varlen_func # noqa: F401
except Exception:
pass
def paged_attention_forward(
module: torch.nn.Module,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
attention_mask: torch.Tensor = None,
cache: PagedAttentionCache = None,
cumulative_seqlens_q=None,
cumulative_seqlens_k=None,
max_seqlen_q=None,
max_seqlen_k=None,
block_tables=None,
implementation=None,
**kwargs,
) -> torch.Tensor:
r"""Perform the forward pass of attention with paged key-value cache.
This function handles the cache updates and performs the attention computation
using the flash_attn_varlen_func for efficient processing.
Args:
q: (total_q, nheads, headdim), where total_q = total number of query tokens in the batch.
k: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch. but if there is a block table it can be the full k
v: (total_k, nheads_k, headdim), where total_k = total number of key tokens in the batch. but if there is a block table it can be the full v
cumulative_seqlens_q: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into q.
cumulative_seqlens_k: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
of the sequences in the batch, used to index into kv.
max_seqlen_q: int. Maximum query sequence length in the batch.
max_seqlen_k: int. Maximum key sequence length in the batch.
dropout_p: float. Dropout probability.
softmax_scale: float. The scaling of QK^T before applying softmax.
Default to 1 / sqrt(headdim).
causal: bool. Whether to apply causal attention mask (e.g., for auto-regressive modeling).
window_size: (left, right). If not (-1, -1), implements sliding window local attention.
softcap: float. Anything > 0 activates softcapping attention.
"""
k, v = cache.update(k, v, module.layer_idx, cumulative_seqlens_k=cumulative_seqlens_k, **kwargs)
sliding_window = (-1, -1) if not getattr(module, "sliding_window", False) else (module.sliding_window, 0)
if implementation is not None:
flash_attn_varlen_func = implementation.flash_attn_varlen_func
custom_kwargs = {"s_aux": kwargs.get("s_aux")}
attn_output = flash_attn_varlen_func(
q.transpose(1, 2).squeeze(0).contiguous(),
k.transpose(1, 2).squeeze(0).contiguous(),
v.transpose(1, 2).squeeze(0).contiguous(),
cumulative_seqlens_q.to(torch.int32),
cumulative_seqlens_k.to(torch.int32).clone(),
max_seqlen_q,
max_seqlen_k,
softmax_scale=module.scaling,
causal=True, # kind of a must, it automatically aligns the mask for q < k
window_size=sliding_window, # -1 means infinite context window
# block_table=block_tables, -> torch.Tensor
**custom_kwargs,
)
if isinstance(attn_output, tuple):
attn_output = attn_output[0]
return attn_output, None
| transformers/src/transformers/integrations/flash_paged.py/0 | {
"file_path": "transformers/src/transformers/integrations/flash_paged.py",
"repo_id": "transformers",
"token_count": 1282
} | 451 |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"SpQR (Sparse-Quantized Representation) integration file"
from ..utils import is_accelerate_available, is_spqr_available, is_torch_available
if is_torch_available():
import torch.nn as nn
def replace_with_spqr_linear(
model,
quantization_config=None,
modules_to_not_convert=None,
current_key_name=None,
has_been_replaced=False,
):
"""
Public method that recursively replaces the Linear layers of the given model with SpQR quantized layers.
`accelerate` is needed to use this method. Returns the converted model and a boolean that indicates if the
conversion has been successful or not.
Args:
model (`torch.nn.Module`):
The model to convert, can be any `torch.nn.Module` instance.
quantization_config (`SpQRConfig`):
The quantization config object that contains the quantization parameters.
modules_to_not_convert (`list[str]`, *optional*):
A list of nn.Linear weights to not convert. If a parameter path is in the list (e.g. `lm_head.weight`), the corresponding module will not be
converted.
current_key_name (`list`, *optional*):
A list that contains the current key name. This is used for recursion and should not be passed by the user.
has_been_replaced (`bool`, *optional*):
A boolean that indicates if the conversion has been successful or not. This is used for recursion and
should not be passed by the user.
"""
if modules_to_not_convert is None:
modules_to_not_convert = []
if is_accelerate_available():
from accelerate import init_empty_weights
if is_spqr_available():
from spqr_quant import QuantizedLinear
for name, module in model.named_children():
if current_key_name is None:
current_key_name = []
current_key_name.append(name)
if isinstance(module, nn.Linear):
# Check if the current key is not in the `modules_to_not_convert`
if ".".join(current_key_name) + ".weight" not in modules_to_not_convert:
with init_empty_weights():
tensor_name = ".".join(current_key_name)
shapes = quantization_config.shapes
shapes_keys = shapes.keys()
shapes_valid = (
f"{tensor_name}.dense_weights.shape" in shapes_keys
and f"{tensor_name}.row_offsets.shape" in shapes_keys
and f"{tensor_name}.col_vals.shape" in shapes_keys
and f"{tensor_name}.in_perm.shape" in shapes_keys
)
if not shapes_valid:
raise ValueError(
f"The SpQR quantization config does not contain the shape "
f"configuration for {tensor_name}. This indicates that the "
f"configuration is either invalid or corrupted."
)
dense_weights_shape = shapes[f"{tensor_name}.dense_weights.shape"]
row_offsets_shape = shapes[f"{tensor_name}.row_offsets.shape"]
col_vals_shape = shapes[f"{tensor_name}.col_vals.shape"]
in_perm_shape = shapes[f"{tensor_name}.in_perm.shape"]
in_features = module.in_features
out_features = module.out_features
model._modules[name] = QuantizedLinear.create_placehodler(
rows=out_features,
cols=in_features,
bits=quantization_config.bits,
beta1=quantization_config.beta1,
beta2=quantization_config.beta2,
dense_weights_shape=dense_weights_shape,
row_offsets_shape=row_offsets_shape,
col_vals_shape=col_vals_shape,
in_perm_shape=in_perm_shape,
)
has_been_replaced = True
# Store the module class in case we need to transpose the weight later
model._modules[name].source_cls = type(module)
# Force requires grad to False to avoid unexpected errors
model._modules[name].requires_grad_(False)
else:
pass
if len(list(module.children())) > 0:
_, has_been_replaced = replace_with_spqr_linear(
module,
quantization_config=quantization_config,
modules_to_not_convert=modules_to_not_convert,
current_key_name=current_key_name,
has_been_replaced=has_been_replaced,
)
# Remove the last key for recursion
current_key_name.pop(-1)
return model, has_been_replaced
| transformers/src/transformers/integrations/spqr.py/0 | {
"file_path": "transformers/src/transformers/integrations/spqr.py",
"repo_id": "transformers",
"token_count": 2525
} | 452 |
# coding=utf-8
# Copyright 2024 Tri Dao, Albert Gu, Technological Innovation Institute and 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.
# Original code from: https://github.com/state-spaces/mamba/blob/main/mamba_ssm/ops/selective_scan_interface.py
import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import causal_conv1d_cuda
except ImportError:
causal_conv1d_cuda = None
import mamba_ssm
import selective_scan_cuda
# For BC for old mamba-ssm versions: https://github.com/huggingface/transformers/pull/33195#discussion_r1736401127
if hasattr(mamba_ssm.ops.triton, "layernorm"):
from mamba_ssm.ops.triton.layernorm import _layer_norm_fwd
else:
from mamba_ssm.ops.triton.layer_norm import _layer_norm_fwd
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(
ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
if u.stride(-1) != 1:
u = u.contiguous()
if delta.stride(-1) != 1:
delta = delta.contiguous()
if D is not None:
D = D.contiguous()
if B.stride(-1) != 1:
B = B.contiguous()
if C.stride(-1) != 1:
C = C.contiguous()
if z is not None and z.stride(-1) != 1:
z = z.contiguous()
if B.dim() == 3:
B = rearrange(B, "b dstate l -> b 1 dstate l")
ctx.squeeze_B = True
if C.dim() == 3:
C = rearrange(C, "b dstate l -> b 1 dstate l")
ctx.squeeze_C = True
out, x, *rest = selective_scan_cuda.fwd(u, delta, A, B, C, D, z, delta_bias, delta_softplus)
ctx.delta_softplus = delta_softplus
ctx.has_z = z is not None
last_state = x[:, :, -1, 1::2] # (batch, dim, dstate)
if not ctx.has_z:
ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
return out if not return_last_state else (out, last_state)
else:
ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
out_z = rest[0]
return out_z if not return_last_state else (out_z, last_state)
@staticmethod
def backward(ctx, dout, *args):
if not ctx.has_z:
u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
z = None
out = None
else:
u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensors
if dout.stride(-1) != 1:
dout = dout.contiguous()
# The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
# backward of selective_scan_cuda with the backward of chunk).
# Here we just pass in None and dz will be allocated in the C++ code.
du, ddelta, dA, dB, dC, dD, ddelta_bias, *rest = selective_scan_cuda.bwd(
u,
delta,
A,
B,
C,
D,
z,
delta_bias,
dout,
x,
out,
None,
ctx.delta_softplus,
False, # option to recompute out_z, not used here
)
dz = rest[0] if ctx.has_z else None
dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
return (
du,
ddelta,
dA,
dB,
dC,
dD if D is not None else None,
dz,
ddelta_bias if delta_bias is not None else None,
None,
None,
)
def rms_norm_forward(
x,
weight,
bias,
eps=1e-6,
is_rms_norm=True,
):
# x (b l) d
if x.stride(-1) != 1:
x = x.contiguous()
weight = weight.contiguous()
if bias is not None:
bias = bias.contiguous()
y = _layer_norm_fwd(x, weight, bias, eps, None, residual_dtype=None, is_rms_norm=is_rms_norm)[0]
# y (b l) d
return y
def selective_scan_fn(
u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
"""if return_last_state is True, returns (out, last_state)
last_state has shape (batch, dim, dstate). Note that the gradient of the last state is
not considered in the backward pass.
"""
return SelectiveScanFn.apply(u, delta, A, B, C, D, z, delta_bias, delta_softplus, return_last_state)
def selective_scan_ref(
u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
"""
u: r(B D L)
delta: r(B D L)
A: c(D N) or r(D N)
B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
C: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
D: r(D)
z: r(B D L)
delta_bias: r(D), fp32
out: r(B D L)
last_state (optional): r(B D dstate) or c(B D dstate)
"""
dtype_in = u.dtype
u = u.float()
delta = delta.float()
if delta_bias is not None:
delta = delta + delta_bias[..., None].float()
if delta_softplus:
delta = F.softplus(delta)
batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
is_variable_B = B.dim() >= 3
is_variable_C = C.dim() >= 3
if A.is_complex():
if is_variable_B:
B = torch.view_as_complex(rearrange(B.float(), "... (L two) -> ... L two", two=2))
if is_variable_C:
C = torch.view_as_complex(rearrange(C.float(), "... (L two) -> ... L two", two=2))
else:
B = B.float()
C = C.float()
x = A.new_zeros((batch, dim, dstate))
ys = []
deltaA = torch.exp(torch.einsum("bdl,dn->bdln", delta, A))
if not is_variable_B:
deltaB_u = torch.einsum("bdl,dn,bdl->bdln", delta, B, u)
else:
if B.dim() == 3:
deltaB_u = torch.einsum("bdl,bnl,bdl->bdln", delta, B, u)
else:
B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
deltaB_u = torch.einsum("bdl,bdnl,bdl->bdln", delta, B, u)
if is_variable_C and C.dim() == 4:
C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
last_state = None
for i in range(u.shape[2]):
x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
if not is_variable_C:
y = torch.einsum("bdn,dn->bd", x, C)
else:
if C.dim() == 3:
y = torch.einsum("bdn,bn->bd", x, C[:, :, i])
else:
y = torch.einsum("bdn,bdn->bd", x, C[:, :, :, i])
if i == u.shape[2] - 1:
last_state = x
if y.is_complex():
y = y.real * 2
ys.append(y)
y = torch.stack(ys, dim=2) # (batch dim L)
out = y if D is None else y + u * rearrange(D, "d -> d 1")
if z is not None:
out = out * F.silu(z)
out = out.to(dtype=dtype_in)
return out if not return_last_state else (out, last_state)
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx,
xz,
conv1d_weight,
conv1d_bias,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
out_proj_bias,
A,
B=None,
C=None,
D=None,
delta_bias=None,
B_proj_bias=None,
C_proj_bias=None,
delta_softplus=True,
checkpoint_lvl=1,
b_rms_weight=None,
c_rms_weight=None,
dt_rms_weight=None,
b_c_dt_rms_eps=1e-6,
):
"""
xz: (batch, dim, seqlen)
"""
assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
assert checkpoint_lvl in [0, 1]
L = xz.shape[-1]
delta_rank = delta_proj_weight.shape[1]
d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
if torch.is_autocast_enabled():
x_proj_weight = x_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
delta_proj_weight = delta_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_weight = out_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_bias = (
out_proj_bias.to(dtype=torch.get_autocast_gpu_dtype()) if out_proj_bias is not None else None
)
if xz.stride(-1) != 1:
xz = xz.contiguous()
conv1d_weight = rearrange(conv1d_weight, "d 1 w -> d w")
x, z = xz.chunk(2, dim=1)
conv1d_bias = conv1d_bias.contiguous() if conv1d_bias is not None else None
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, None, None, True)
# We're being very careful here about the layout, to avoid extra transposes.
# We want delta to have d as the slowest moving dimension
# and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
x_dbl = F.linear(rearrange(conv1d_out, "b d l -> (b l) d"), x_proj_weight) # (bl d)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
ctx.is_variable_B = B is None
ctx.is_variable_C = C is None
ctx.B_proj_bias_is_None = B_proj_bias is None
ctx.C_proj_bias_is_None = C_proj_bias is None
if B is None: # variable B
B = x_dbl[:, delta_rank : delta_rank + d_state] # (bl dstate)
if B_proj_bias is not None:
B = B + B_proj_bias.to(dtype=B.dtype)
if not A.is_complex():
# B = rearrange(B, "(b l) dstate -> b dstate l", l=L).contiguous()
B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
else:
B = rearrange(B, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
else:
if B.stride(-1) != 1:
B = B.contiguous()
if C is None: # variable C
C = x_dbl[:, -d_state:] # (bl dstate)
if C_proj_bias is not None:
C = C + C_proj_bias.to(dtype=C.dtype)
if not A.is_complex():
# C = rearrange(C, "(b l) dstate -> b dstate l", l=L).contiguous()
C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
else:
C = rearrange(C, "(b l) (dstate two) -> b 1 dstate (l two)", l=L, two=2).contiguous()
else:
if C.stride(-1) != 1:
C = C.contiguous()
if D is not None:
D = D.contiguous()
if b_rms_weight is not None:
B = rearrange(B, "b 1 dstate l -> (b l) dstate", l=L).contiguous()
B = rms_norm_forward(B, b_rms_weight, bias=None, eps=b_c_dt_rms_eps)
B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
if c_rms_weight is not None:
C = rearrange(C, "b 1 dstate l -> (b l) dstate", l=L).contiguous()
C = rms_norm_forward(C, c_rms_weight, bias=None, eps=b_c_dt_rms_eps)
C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
if dt_rms_weight is not None:
delta = rearrange(delta, "b d l -> (b l) d", l=L).contiguous()
delta = rms_norm_forward(delta, dt_rms_weight, bias=None, eps=b_c_dt_rms_eps)
delta = rearrange(delta, "(b l) d -> b d l", l=L).contiguous()
out, scan_intermediates, out_z = selective_scan_cuda.fwd(
conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus
)
ctx.delta_softplus = delta_softplus
ctx.out_proj_bias_is_None = out_proj_bias is None
ctx.checkpoint_lvl = checkpoint_lvl
ctx.b_rms_weight = b_rms_weight
ctx.c_rms_weight = c_rms_weight
ctx.dt_rms_weight = dt_rms_weight
ctx.b_c_dt_rms_eps = b_c_dt_rms_eps
if checkpoint_lvl >= 1: # Will recompute conv1d_out and delta in the backward pass
conv1d_out, delta = None, None
ctx.save_for_backward(
xz,
conv1d_weight,
conv1d_bias,
x_dbl,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
conv1d_out,
delta,
A,
B,
C,
D,
delta_bias,
scan_intermediates,
b_rms_weight,
c_rms_weight,
dt_rms_weight,
out,
)
return F.linear(rearrange(out_z, "b d l -> b l d"), out_proj_weight, out_proj_bias)
@staticmethod
@custom_bwd
def backward(ctx, dout):
# dout: (batch, seqlen, dim)
assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
(
xz,
conv1d_weight,
conv1d_bias,
x_dbl,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
conv1d_out,
delta,
A,
B,
C,
D,
delta_bias,
scan_intermediates,
b_rms_weight,
c_rms_weight,
dt_rms_weight,
out,
) = ctx.saved_tensors
L = xz.shape[-1]
delta_rank = delta_proj_weight.shape[1]
d_state = A.shape[-1] * (1 if not A.is_complex() else 2)
x, z = xz.chunk(2, dim=1)
if dout.stride(-1) != 1:
dout = dout.contiguous()
if ctx.checkpoint_lvl == 1:
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, None, None, True)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
if dt_rms_weight is not None:
delta = rearrange(delta, "b d l -> (b l) d", l=L).contiguous()
delta = rms_norm_forward(delta, ctx.dt_rms_weight, None, ctx.b_c_dt_rms_eps)
delta = rearrange(delta, "(b l) d -> b d l", l=L).contiguous()
if b_rms_weight is not None:
# Recompute & RMSNorm B
B = rearrange(B, "b 1 dstate l -> (b l) dstate", l=L).contiguous()
B = rms_norm_forward(B, ctx.b_rms_weight, None, ctx.b_c_dt_rms_eps)
B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
if c_rms_weight is not None:
# Recompute & RMSNorm C
C = rearrange(C, "b 1 dstate l -> (b l) dstate", l=L).contiguous()
C = rms_norm_forward(C, ctx.c_rms_weight, None, ctx.b_c_dt_rms_eps)
C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
# The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
# backward of selective_scan_cuda with the backward of chunk).
dxz = torch.empty_like(xz) # (batch, dim, seqlen)
dx, dz = dxz.chunk(2, dim=1)
dout = rearrange(dout, "b l e -> e (b l)")
dout_y = rearrange(out_proj_weight.t() @ dout, "d (b l) -> b d l", l=L)
dconv1d_out, ddelta, dA, dB, dC, dD, ddelta_bias, dz, out_z = selective_scan_cuda.bwd(
conv1d_out,
delta,
A,
B,
C,
D,
z,
delta_bias,
dout_y,
scan_intermediates,
out,
dz,
ctx.delta_softplus,
True, # option to recompute out_z
)
dout_proj_weight = torch.einsum("eB,dB->ed", dout, rearrange(out_z, "b d l -> d (b l)"))
dout_proj_bias = dout.sum(dim=(0, 1)) if not ctx.out_proj_bias_is_None else None
dD = dD if D is not None else None
dx_dbl = torch.empty_like(x_dbl)
dB_proj_bias = None
if ctx.is_variable_B:
if not A.is_complex():
dB = rearrange(dB, "b 1 dstate l -> (b l) dstate").contiguous()
else:
dB = rearrange(dB, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
dB_proj_bias = dB.sum(0) if not ctx.B_proj_bias_is_None else None
dx_dbl[:, delta_rank : delta_rank + d_state] = dB # (bl d)
dB = None
dC_proj_bias = None
if ctx.is_variable_C:
if not A.is_complex():
dC = rearrange(dC, "b 1 dstate l -> (b l) dstate").contiguous()
else:
dC = rearrange(dC, "b 1 dstate (l two) -> (b l) (dstate two)", two=2).contiguous()
dC_proj_bias = dC.sum(0) if not ctx.C_proj_bias_is_None else None
dx_dbl[:, -d_state:] = dC # (bl d)
dC = None
ddelta = rearrange(ddelta, "b d l -> d (b l)")
ddelta_proj_weight = torch.einsum("dB,Br->dr", ddelta, x_dbl[:, :delta_rank])
dx_dbl[:, :delta_rank] = torch.einsum("dB,dr->Br", ddelta, delta_proj_weight)
dconv1d_out = rearrange(dconv1d_out, "b d l -> d (b l)")
dx_proj_weight = torch.einsum("Br,Bd->rd", dx_dbl, rearrange(conv1d_out, "b d l -> (b l) d"))
dconv1d_out = torch.addmm(dconv1d_out, x_proj_weight.t(), dx_dbl.t(), out=dconv1d_out)
dconv1d_out = rearrange(dconv1d_out, "d (b l) -> b d l", b=x.shape[0], l=x.shape[-1])
# The kernel supports passing in a pre-allocated dx (e.g., in case we want to fuse the
# backward of conv1d with the backward of chunk).
dx, dconv1d_weight, dconv1d_bias, *_ = causal_conv1d_cuda.causal_conv1d_bwd(
x, conv1d_weight, conv1d_bias, dconv1d_out, None, None, None, dx, False, True
)
dconv1d_bias = dconv1d_bias if conv1d_bias is not None else None
dconv1d_weight = rearrange(dconv1d_weight, "d w -> d 1 w")
return (
dxz,
dconv1d_weight,
dconv1d_bias,
dx_proj_weight,
ddelta_proj_weight,
dout_proj_weight,
dout_proj_bias,
dA,
dB,
dC,
dD,
ddelta_bias if delta_bias is not None else None,
# 6-None are delta_softplus, checkpoint_lvl, b_rms_weight, c_rms_weight, dt_rms_weight, b_c_dt_rms_eps
dB_proj_bias,
dC_proj_bias,
None,
None,
None,
None,
None,
None,
)
def mamba_inner_fn(
xz,
conv1d_weight,
conv1d_bias,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
out_proj_bias,
A,
B=None,
C=None,
D=None,
delta_bias=None,
B_proj_bias=None,
C_proj_bias=None,
delta_softplus=True,
checkpoint_lvl=1,
b_rms_weight=None,
c_rms_weight=None,
dt_rms_weight=None,
b_c_dt_rms_eps=1e-6,
):
return MambaInnerFn.apply(
xz,
conv1d_weight,
conv1d_bias,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
out_proj_bias,
A,
B,
C,
D,
delta_bias,
B_proj_bias,
C_proj_bias,
delta_softplus,
checkpoint_lvl,
b_rms_weight,
c_rms_weight,
dt_rms_weight,
b_c_dt_rms_eps,
)
| transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py/0 | {
"file_path": "transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py",
"repo_id": "transformers",
"token_count": 10691
} | 453 |
#include <torch/extension.h>
#include <ATen/ATen.h>
#include "fast_lsh_cumulation.h"
#include "common_cuda.h"
#include <vector>
std::vector<at::Tensor> fast_hash(
at::Tensor query_mask,
at::Tensor query_vector,
at::Tensor key_mask,
at::Tensor key_vector,
int num_hash_f,
int hash_code_len,
bool use_cuda,
int version
) {
return fast_hash_ver1_kernel(
query_mask,
query_vector,
key_mask,
key_vector,
num_hash_f,
hash_code_len,
use_cuda
);
}
at::Tensor lsh_cumulation(
at::Tensor query_mask, // [batch_size, num_query]
at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f]
at::Tensor key_mask, // [batch_size, num_key]
at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f]
at::Tensor value, // [batch_size, num_key, value_dim]
int hashtable_capacity,
bool use_cuda,
int version
) {
return lsh_cumulation_ver1_kernel(
query_mask,
query_hash_code,
key_mask,
key_hash_code,
value,
hashtable_capacity,
use_cuda
);
}
at::Tensor lsh_weighted_cumulation(
at::Tensor query_mask, // [batch_size, num_query]
at::Tensor query_hash_code, // [batch_size, num_query, num_hash_f]
at::Tensor query_weight, // [batch_size, num_query, weight_dim]
at::Tensor key_mask, // [batch_size, num_key]
at::Tensor key_hash_code, // [batch_size, num_key, num_hash_f]
at::Tensor key_weight, // [batch_size, num_key, weight_dim]
at::Tensor value, // [batch_size, num_key, value_dim]
int hashtable_capacity,
bool use_cuda,
int version
) {
if (version == 1) {
return lsh_weighted_cumulation_ver1_kernel(
query_mask,
query_hash_code,
query_weight,
key_mask,
key_hash_code,
key_weight,
value,
hashtable_capacity,
use_cuda
);
} else if (version == 2) {
return lsh_weighted_cumulation_ver2_kernel(
query_mask,
query_hash_code,
query_weight,
key_mask,
key_hash_code,
key_weight,
value,
hashtable_capacity,
use_cuda
);
} else if (version == 3) {
return lsh_weighted_cumulation_ver3_kernel(
query_mask,
query_hash_code,
query_weight,
key_mask,
key_hash_code,
key_weight,
value,
hashtable_capacity,
use_cuda
);
} else if (version == 4) {
return lsh_weighted_cumulation_ver4_kernel(
query_mask,
query_hash_code,
query_weight,
key_mask,
key_hash_code,
key_weight,
value,
hashtable_capacity,
use_cuda
);
} else {
return lsh_weighted_cumulation_ver3_kernel(
query_mask,
query_hash_code,
query_weight,
key_mask,
key_hash_code,
key_weight,
value,
hashtable_capacity,
use_cuda
);
}
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("fast_hash", &fast_hash, "Fast Hash (CUDA)");
m.def("lsh_cumulation", &lsh_cumulation, "LSH Cumulation (CUDA)");
m.def("lsh_weighted_cumulation", &lsh_weighted_cumulation, "LSH Weighted Cumulation (CUDA)");
}
| transformers/src/transformers/kernels/yoso/fast_lsh_cumulation_torch.cpp/0 | {
"file_path": "transformers/src/transformers/kernels/yoso/fast_lsh_cumulation_torch.cpp",
"repo_id": "transformers",
"token_count": 1498
} | 454 |
# Copyright 2024 The ggml.ai team and The HuggingFace Inc. team. and pygguf author (github.com/99991)
# https://github.com/99991/pygguf
#
# 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.
import re
from typing import NamedTuple, Optional
import numpy as np
from tqdm.auto import tqdm
from .integrations import (
GGUF_CONFIG_MAPPING,
GGUF_TOKENIZER_MAPPING,
_gguf_parse_value,
)
from .utils import is_torch_available
from .utils.import_utils import is_gguf_available
from .utils.logging import get_logger
if is_torch_available():
import torch
logger = get_logger(__name__)
GGUF_TO_TRANSFORMERS_MAPPING = {
"ignore": {
"GGUF": {
"version": "version",
"tensor_count": "tensor_count",
"kv_count": "kv_count",
},
"general": {"file_type": "file_type", "quantization_version": "quantization_version"},
},
"config": GGUF_CONFIG_MAPPING,
"tokenizer": {"tokenizer": GGUF_TOKENIZER_MAPPING["tokenizer"]},
"tokenizer_config": {"tokenizer": GGUF_TOKENIZER_MAPPING["tokenizer_config"]},
}
GGUF_SUPPORTED_ARCHITECTURES = list(GGUF_TO_TRANSFORMERS_MAPPING["config"].keys())
class GGUFTensor(NamedTuple):
weights: np.ndarray
name: str
metadata: dict
class TensorProcessor:
def __init__(self, config=None):
self.config = config or {}
def process(self, weights, name, **kwargs):
return GGUFTensor(weights, name, {})
class LlamaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if ".attn_k." in name or ".attn_q." in name:
num_heads = self.config.get("num_attention_heads")
num_kv_heads = self.config.get("num_key_value_heads")
if None in (num_heads, num_kv_heads):
return GGUFTensor(weights, name, {})
if ".attn_q." in name:
weights = self._reverse_permute_weights(weights, num_heads, num_heads)
elif ".attn_k." in name:
weights = self._reverse_permute_weights(weights, num_heads, num_kv_heads)
return GGUFTensor(weights, name, {})
def _reverse_permute_weights(
self, weights: np.ndarray, n_head: int, num_kv_heads: Optional[int] = None
) -> np.ndarray:
# Original permutation implementation
# https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_to_gguf.py#L1402-L1408
if num_kv_heads is not None and n_head != num_kv_heads:
n_head = num_kv_heads
dim = weights.shape[0] // n_head // 2
w = weights.reshape(n_head, dim, 2, *weights.shape[1:])
return w.swapaxes(2, 1).reshape(weights.shape)
class Qwen2MoeTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "_exp" in name:
tensor_key_mapping = kwargs.get("tensor_key_mapping")
parsed_parameters = kwargs.get("parsed_parameters")
if tensor_key_mapping:
self._split_moe_expert_tensor(weights, parsed_parameters, name, tensor_key_mapping)
return GGUFTensor(weights, None, {})
if "ffn_gate_inp_shexp" in name:
# for compatibility tensor shared_expert_gate must be (1, 2048) dim,
# quantized one is (2048)
weights = np.expand_dims(weights, axis=0)
return GGUFTensor(weights, name, {})
def _split_moe_expert_tensor(
self, weights: np.ndarray, parsed_parameters: dict[str, dict], name: str, tensor_key_mapping: dict
):
# Original merge implementation
# https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L1994-L2022
name = tensor_key_mapping[name]
w_counter = self.config.get("num_experts", 60)
for i in range(0, w_counter):
temp_name = name.replace("mlp.experts.", f"mlp.experts.{i}.")
exp_weight = weights[i]
parsed_parameters["tensors"][temp_name] = torch.from_numpy(np.copy(exp_weight))
class BloomTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "attn_qkv" in name:
num_heads = self.config["n_head"]
n_embed = self.config["hidden_size"]
if "weight" in name:
weights = self._reverse_reshape_weights(weights, num_heads, n_embed)
else:
weights = self._reverse_reshape_bias(weights, num_heads, n_embed)
return GGUFTensor(weights, name, {})
def _reverse_reshape_weights(self, weights: np.ndarray, n_head: int, n_embed: int):
# Original reshape implementation
# https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L972-L985
q, k, v = np.array_split(weights, 3, axis=0)
q = q.reshape(n_head, n_embed // n_head, n_embed)
k = k.reshape(n_head, n_embed // n_head, n_embed)
v = v.reshape(n_head, n_embed // n_head, n_embed)
qkv_weights = np.stack([q, k, v], axis=1)
return qkv_weights.reshape(n_head * 3 * (n_embed // n_head), n_embed)
def _reverse_reshape_bias(self, weights: np.ndarray, n_head: int, n_embed: int):
# Original reshape implementation
# https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L986-L998
q_bias, k_bias, v_bias = np.array_split(weights, 3)
q_bias = q_bias.reshape(n_head, n_embed // n_head)
k_bias = k_bias.reshape(n_head, n_embed // n_head)
v_bias = v_bias.reshape(n_head, n_embed // n_head)
qkv_bias = np.stack([q_bias, k_bias, v_bias], axis=1).flatten()
return qkv_bias
class T5TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
bid = None
for chunk in name.split("."):
if chunk.isdigit():
bid = int(chunk)
break
return GGUFTensor(weights, name, {"bid": bid})
class GPT2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
# Original transpose implementation
# https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_to_gguf.py#L2060-L2061
if (
"attn_qkv.weight" in name
or "ffn_down.weight" in name
or "ffn_up.weight" in name
or "attn_output.weight" in name
):
weights = weights.T
# Handle special case for output.weight
if name == "output.weight":
# output.weight has conflicts with attn_output.weight in name checking
# Store the tensor directly and signal to skip further processing
name = "lm_head.weight"
parsed_parameters = kwargs.get("parsed_parameters", {})
parsed_parameters["tensors"][name] = torch.from_numpy(np.copy(weights))
name = None # Signal to skip further processing
return GGUFTensor(weights, name, {})
class MambaTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
def process(self, weights, name, **kwargs):
if "ssm_conv1d.weight" in name:
# for compatibility tensor ssm_conv1d must be (5120, 1, 4]) dim,
# quantized one is (5120, 4)
weights = np.expand_dims(weights, axis=1)
if "ssm_a" in name:
# Original exponential implementation
# https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L2975-L2977
weights = np.log(-weights)
return GGUFTensor(weights, name, {})
class NemotronTensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref : https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L4666
def process(self, weights, name, **kwargs):
if "norm.weight" in name:
weights = weights - 1
return GGUFTensor(weights, name, {})
class Gemma2TensorProcessor(TensorProcessor):
def __init__(self, config=None):
super().__init__(config=config)
# ref: https://github.com/ggerganov/llama.cpp/blob/d79d8f39b4da6deca4aea8bf130c6034c482b320/convert_hf_to_gguf.py#L3191
# ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
def process(self, weights, name, **kwargs):
if "norm.weight" in name:
weights = weights - 1
return GGUFTensor(weights, name, {})
TENSOR_PROCESSORS = {
"llama": LlamaTensorProcessor,
"qwen2moe": Qwen2MoeTensorProcessor,
"qwen3moe": Qwen2MoeTensorProcessor,
"bloom": BloomTensorProcessor,
"t5": T5TensorProcessor,
"t5encoder": T5TensorProcessor,
"gpt2": GPT2TensorProcessor,
"mamba": MambaTensorProcessor,
"nemotron": NemotronTensorProcessor,
"gemma2": Gemma2TensorProcessor,
"gemma3": Gemma2TensorProcessor,
}
def read_field(reader, field):
if field not in reader.fields:
return []
value = reader.fields[field]
return [_gguf_parse_value(value.parts[_data_index], value.types) for _data_index in value.data]
# modified from https://github.com/vllm-project/vllm/blob/v0.6.4.post1/vllm/model_executor/model_loader/loader.py#L1115-L1147
def get_gguf_hf_weights_map(
hf_model,
model_type: Optional[str] = None,
num_layers: Optional[int] = None,
qual_name: str = "",
):
"""
GGUF uses this naming convention for their tensors from HF checkpoint:
`blk.N.BB.weight` and `blk.N.BB.bias`
where N signifies the block number of a layer, and BB signifies the
attention/mlp layer components.
See "Standardized tensor names" in
https://github.com/ggerganov/ggml/blob/master/docs/gguf.md for details.
"""
if is_gguf_available() and is_torch_available():
from gguf import MODEL_ARCH_NAMES, get_tensor_name_map
else:
logger.error(
"Loading a GGUF checkpoint in PyTorch, requires both PyTorch and GGUF>=0.10.0 to be installed. Please see "
"https://pytorch.org/ and https://github.com/ggerganov/llama.cpp/tree/master/gguf-py for installation instructions."
)
raise ImportError("Please install torch and gguf>=0.10.0 to load a GGUF checkpoint in PyTorch.")
model_type = hf_model.config.model_type if model_type is None else model_type
num_layers = hf_model.config.num_hidden_layers if num_layers is None else num_layers
# hack: ggufs have a different name for cohere
if model_type == "cohere":
model_type = "command-r"
elif model_type == "qwen2_moe":
model_type = "qwen2moe"
elif model_type == "qwen3_moe":
model_type = "qwen3moe"
elif model_type == "gemma3_text":
model_type = "gemma3"
arch = None
for key, value in MODEL_ARCH_NAMES.items():
if value == model_type:
arch = key
break
if arch is None:
raise NotImplementedError(
f"Unknown gguf model_type: {model_type} in gguf-py. "
"This might because you're using an outdated version of gguf-py package, "
"you can install `gguf` package from source refer to "
"https://github.com/ggerganov/llama.cpp/tree/master/gguf-py#development"
)
name_map = get_tensor_name_map(arch, num_layers)
# Use a dummy conversion to get the mapping, because
# hf => gguf and gguf => hf mappings are reversed
gguf_to_hf_name_map = {}
state_dict = hf_model.state_dict()
for hf_name in state_dict:
# An exception for qwen2moe/qwen3moe model, where the expert layers are packed
if model_type in ("qwen2moe", "qwen3moe") and "mlp.experts." in hf_name:
hf_name = re.sub(r"mlp.experts.\d+.", "mlp.experts.", hf_name)
name, suffix = hf_name, ""
if hf_name.endswith(".weight") or hf_name.endswith(".bias"):
name, suffix = hf_name.rsplit(".", 1)
suffix = "." + suffix
gguf_name = name_map.get_name(name)
if gguf_name is None:
continue
gguf_to_hf_name_map[gguf_name + suffix] = qual_name + hf_name
# Some model like Bloom converted from BloomModel instead of BloomForCausalLM
# Therefore, we need to check submodule as well to get a correct mapping
if named_children := hf_model.named_children():
for name, child in named_children:
sub_map = get_gguf_hf_weights_map(child, model_type, num_layers, qual_name=f"{qual_name}{name}.")
# Ignore the keys that are already in the main map to avoid overwriting
sub_map = {k: v for k, v in sub_map.items() if k not in gguf_to_hf_name_map}
gguf_to_hf_name_map.update(sub_map)
return gguf_to_hf_name_map
def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False, model_to_load=None):
"""
Load a GGUF file and return a dictionary of parsed parameters containing tensors, the parsed
tokenizer and config attributes.
Args:
gguf_checkpoint_path (`str`):
The path the to GGUF file to load
return_tensors (`bool`, defaults to `False`):
Whether to read the tensors from the file and return them. Not doing so is faster
and only loads the metadata in memory.
"""
if is_gguf_available() and is_torch_available():
from gguf import GGUFReader, dequantize
else:
logger.error(
"Loading a GGUF checkpoint in PyTorch, requires both PyTorch and GGUF>=0.10.0 to be installed. Please see "
"https://pytorch.org/ and https://github.com/ggerganov/llama.cpp/tree/master/gguf-py for installation instructions."
)
raise ImportError("Please install torch and gguf>=0.10.0 to load a GGUF checkpoint in PyTorch.")
reader = GGUFReader(gguf_checkpoint_path)
fields = reader.fields
reader_keys = list(fields.keys())
parsed_parameters = {k: {} for k in GGUF_TO_TRANSFORMERS_MAPPING}
architecture = read_field(reader, "general.architecture")[0]
# NOTE: Some GGUF checkpoints may miss `general.name` field in metadata
model_name = read_field(reader, "general.name")
updated_architecture = None
# in llama.cpp mistral models use the same architecture as llama. We need
# to add this patch to ensure things work correctly on our side.
if "llama" in architecture and "mistral" in model_name:
updated_architecture = "mistral"
# FIXME: Currently this implementation is only for flan-t5 architecture.
# It needs to be developed for supporting legacy t5.
elif "t5" in architecture or "t5encoder" in architecture:
parsed_parameters["config"]["is_gated_act"] = True
if "t5encoder" in architecture:
parsed_parameters["config"]["architectures"] = ["T5EncoderModel"]
updated_architecture = "t5"
else:
updated_architecture = architecture
if "qwen2moe" in architecture:
updated_architecture = "qwen2_moe"
elif "qwen3moe" in architecture:
updated_architecture = "qwen3_moe"
# For stablelm architecture, we need to set qkv_bias and use_parallel_residual from tensors
# If `qkv_bias=True`, qkv_proj with bias will be present in the tensors
# If `use_parallel_residual=False`, ffn_norm will be present in the tensors
if "stablelm" in architecture:
attn_bias_name = {"attn_q.bias", "attn_k.bias", "attn_v.bias"}
ffn_norm_name = "ffn_norm"
qkv_bias = any(bias_name in tensor.name for tensor in reader.tensors for bias_name in attn_bias_name)
use_parallel_residual = any(ffn_norm_name in tensor.name for tensor in reader.tensors)
parsed_parameters["config"]["use_qkv_bias"] = qkv_bias
parsed_parameters["config"]["use_parallel_residual"] = not use_parallel_residual
if architecture not in GGUF_SUPPORTED_ARCHITECTURES and updated_architecture not in GGUF_SUPPORTED_ARCHITECTURES:
raise ValueError(f"GGUF model with architecture {architecture} is not supported yet.")
# Handle tie_word_embeddings, if lm_head.weight is not present in tensors,
# tie_word_embeddings is true otherwise false
exceptions = ["falcon", "bloom"]
parsed_parameters["config"]["tie_word_embeddings"] = (
all("output.weight" != tensor.name for tensor in reader.tensors) or architecture in exceptions
)
# List all key-value pairs in a columnized format
for gguf_key, field in reader.fields.items():
gguf_key = gguf_key.replace(architecture, updated_architecture)
split = gguf_key.split(".")
prefix = split[0]
config_key = ".".join(split[1:])
value = [_gguf_parse_value(field.parts[_data_index], field.types) for _data_index in field.data]
if len(value) == 1:
value = value[0]
if isinstance(value, str) and architecture in value:
value = value.replace(architecture, updated_architecture)
for parameter, parameter_renames in GGUF_TO_TRANSFORMERS_MAPPING.items():
if prefix in parameter_renames and config_key in parameter_renames[prefix]:
renamed_config_key = parameter_renames[prefix][config_key]
if renamed_config_key == -1:
continue
if renamed_config_key is not None:
parsed_parameters[parameter][renamed_config_key] = value
if gguf_key in reader_keys:
reader_keys.remove(gguf_key)
if gguf_key in reader_keys:
logger.info(f"Some keys were not parsed and added into account {gguf_key} | {value}")
# Gemma3 GGUF checkpoint only contains weights of text backbone
if parsed_parameters["config"]["model_type"] == "gemma3":
parsed_parameters["config"]["model_type"] = "gemma3_text"
# retrieve config vocab_size from tokenizer
# Please refer to https://github.com/huggingface/transformers/issues/32526 for more details
if "vocab_size" not in parsed_parameters["config"]:
tokenizer_parameters = parsed_parameters["tokenizer"]
if "tokens" in tokenizer_parameters:
parsed_parameters["config"]["vocab_size"] = len(tokenizer_parameters["tokens"])
else:
logger.warning(
"Can't find a way to retrieve missing config vocab_size from tokenizer parameters. "
"This will use default value from model config class and cause unexpected behavior."
)
if return_tensors:
parsed_parameters["tensors"] = {}
tensor_key_mapping = get_gguf_hf_weights_map(model_to_load)
config = parsed_parameters.get("config", {})
ProcessorClass = TENSOR_PROCESSORS.get(architecture, TensorProcessor)
processor = ProcessorClass(config=config)
for tensor in tqdm(reader.tensors, desc="Converting and de-quantizing GGUF tensors..."):
name = tensor.name
weights = dequantize(tensor.data, tensor.tensor_type)
result = processor.process(
weights=weights,
name=name,
tensor_key_mapping=tensor_key_mapping,
parsed_parameters=parsed_parameters,
)
weights = result.weights
name = result.name
if name not in tensor_key_mapping:
continue
name = tensor_key_mapping[name]
parsed_parameters["tensors"][name] = torch.from_numpy(np.copy(weights))
if len(reader_keys) > 0:
logger.info(f"Some keys of the GGUF file were not considered: {reader_keys}")
return parsed_parameters
| transformers/src/transformers/modeling_gguf_pytorch_utils.py/0 | {
"file_path": "transformers/src/transformers/modeling_gguf_pytorch_utils.py",
"repo_id": "transformers",
"token_count": 8892
} | 455 |
# coding=utf-8
# Copyright 2018 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.
"""Convert ALBERT checkpoint."""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, albert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = AlbertConfig.from_json_file(albert_config_file)
print(f"Building PyTorch model from configuration: {config}")
model = AlbertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_albert(model, config, tf_checkpoint_path)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
torch.save(model.state_dict(), pytorch_dump_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--albert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained ALBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
args = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| transformers/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py/0 | {
"file_path": "transformers/src/transformers/models/albert/convert_albert_original_tf_checkpoint_to_pytorch.py",
"repo_id": "transformers",
"token_count": 755
} | 456 |
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/arcee/modular_arcee.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_arcee.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# coding=utf-8
# Copyright 2025 Arcee AI and the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ...configuration_utils import PretrainedConfig
from ...modeling_rope_utils import rope_config_validation
class ArceeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ArceeModel`]. It is used to instantiate an Arcee
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the AFM-4.5B-Base.
Pre-trained weights are available at
[arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B)
and were used to build the examples below.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Arcee model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ArceeModel`]
hidden_size (`int`, *optional*, defaults to 2560):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 18432):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 4096):
The maximum sequence length that this model might ever be used with. AFM-4.5B-Base supports up to 16384 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 128000):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 128001):
End of stream token id.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'yarn'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'yarn'. The original max position embeddings used during pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn'. The scaling factor to be applied on the attention computation. If unspecified,
it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
head_dim (`int`, *optional*):
The attention head dimension. If None, it will default to hidden_size // num_attention_heads
```python
>>> from transformers import ArceeModel, ArceeConfig
>>> # Initializing an Arcee AFM-4.5B-Base style configuration
>>> configuration = ArceeConfig()
>>> # Initializing a model from the AFM-4.5B-Base style configuration
>>> model = ArceeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "arcee"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=32000,
hidden_size=2560,
intermediate_size=18432,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="relu2",
max_position_embeddings=4096,
initializer_range=0.02,
rms_norm_eps=1e-5,
use_cache=True,
pad_token_id=None,
bos_token_id=128000,
eos_token_id=128001,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
mlp_bias=False,
head_dim=None,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
__all__ = ["ArceeConfig"]
| transformers/src/transformers/models/arcee/configuration_arcee.py/0 | {
"file_path": "transformers/src/transformers/models/arcee/configuration_arcee.py",
"repo_id": "transformers",
"token_count": 4446
} | 457 |
# coding=utf-8
# Copyright 2021 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.
"""Factory function to build auto-model classes."""
import copy
import importlib
import json
import os
import warnings
from collections import OrderedDict
from collections.abc import Iterator
from typing import Any, TypeVar, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...utils import (
CONFIG_NAME,
cached_file,
copy_func,
extract_commit_hash,
find_adapter_config_file,
is_peft_available,
is_torch_available,
logging,
requires_backends,
)
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
if is_torch_available():
from ...generation import GenerationMixin
logger = logging.get_logger(__name__)
_T = TypeVar("_T")
# Tokenizers will depend on packages installed, too much variance and there are no common base or Protocol
_LazyAutoMappingValue = tuple[Union[type[Any], None], Union[type[Any], None]]
CLASS_DOCSTRING = """
This is a generic model class that will be instantiated as one of the model classes of the library when created
with the [`~BaseAutoModelClass.from_pretrained`] class method or the [`~BaseAutoModelClass.from_config`] class
method.
This class cannot be instantiated directly using `__init__()` (throws an error).
"""
FROM_CONFIG_DOCSTRING = """
Instantiates one of the model classes of the library from a configuration.
Note:
Loading a model from its configuration file does **not** load the model weights. It only affects the
model's configuration. Use [`~BaseAutoModelClass.from_pretrained`] to load the model weights.
Args:
config ([`PretrainedConfig`]):
The model class to instantiate is selected based on the configuration class:
List options
attn_implementation (`str`, *optional*):
The attention implementation to use in the model (if relevant). Can be any of `"eager"` (manual implementation of the attention), `"sdpa"` (using [`F.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html)), or `"flash_attention_2"` (using [Dao-AILab/flash-attention](https://github.com/Dao-AILab/flash-attention)). By default, if available, SDPA will be used for torch>=2.1.1. The default is otherwise the manual `"eager"` implementation.
Examples:
```python
>>> from transformers import AutoConfig, BaseAutoModelClass
>>> # Download configuration from huggingface.co and cache.
>>> config = AutoConfig.from_pretrained("checkpoint_placeholder")
>>> model = BaseAutoModelClass.from_config(config)
```
"""
FROM_PRETRAINED_TORCH_DOCSTRING = """
Instantiate one of the model classes of the library from a pretrained model.
The model class to instantiate is selected based on the `model_type` property of the config object (either
passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
falling back to using pattern matching on `pretrained_model_name_or_path`:
List options
The model is set in evaluation mode by default using `model.eval()` (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with `model.train()`
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *tensorflow index checkpoint file* (e.g, `./tf_model/model.ckpt.index`). In
this case, `from_tf` should be set to `True` and a configuration object should be provided as
`config` argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
model_args (additional positional arguments, *optional*):
Will be passed along to the underlying model `__init__()` method.
config ([`PretrainedConfig`], *optional*):
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the *model id* string of a pretrained
model).
- The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
save directory.
- The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
configuration JSON file named *config.json* is found in the directory.
state_dict (*dict[str, torch.Tensor]*, *optional*):
A state dictionary to use instead of a state dictionary loaded from saved weights file.
This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using [`~PreTrainedModel.save_pretrained`] and
[`~PreTrainedModel.from_pretrained`] is not a simpler option.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
from_tf (`bool`, *optional*, defaults to `False`):
Load the model weights from a TensorFlow checkpoint save file (see docstring of
`pretrained_model_name_or_path` argument).
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (e.g., not try downloading the model).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to `True` for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.
code_revision (`str`, *optional*, defaults to `"main"`):
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier
allowed by git.
kwargs (additional keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`). Behaves differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with `config`, `**kwargs` will be directly passed to the
underlying model's `__init__` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, `kwargs` will be first passed to the configuration class
initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
corresponds to a configuration attribute will be used to override said attribute with the
supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model's `__init__` function.
Examples:
```python
>>> from transformers import AutoConfig, BaseAutoModelClass
>>> # Download model and configuration from huggingface.co and cache.
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
>>> # Update configuration during loading
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a TF checkpoint file instead of a PyTorch model (slower)
>>> config = AutoConfig.from_pretrained("./tf_model/shortcut_placeholder_tf_model_config.json")
>>> model = BaseAutoModelClass.from_pretrained(
... "./tf_model/shortcut_placeholder_tf_checkpoint.ckpt.index", from_tf=True, config=config
... )
```
"""
FROM_PRETRAINED_TF_DOCSTRING = """
Instantiate one of the model classes of the library from a pretrained model.
The model class to instantiate is selected based on the `model_type` property of the config object (either
passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
falling back to using pattern matching on `pretrained_model_name_or_path`:
List options
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.
model_args (additional positional arguments, *optional*):
Will be passed along to the underlying model `__init__()` method.
config ([`PretrainedConfig`], *optional*):
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the *model id* string of a pretrained
model).
- The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
save directory.
- The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
configuration JSON file named *config.json* is found in the directory.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
from_pt (`bool`, *optional*, defaults to `False`):
Load the model weights from a PyTorch checkpoint save file (see docstring of
`pretrained_model_name_or_path` argument).
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (e.g., not try downloading the model).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to `True` for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.
code_revision (`str`, *optional*, defaults to `"main"`):
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier
allowed by git.
kwargs (additional keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`). Behaves differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with `config`, `**kwargs` will be directly passed to the
underlying model's `__init__` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, `kwargs` will be first passed to the configuration class
initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
corresponds to a configuration attribute will be used to override said attribute with the
supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model's `__init__` function.
Examples:
```python
>>> from transformers import AutoConfig, BaseAutoModelClass
>>> # Download model and configuration from huggingface.co and cache.
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
>>> # Update configuration during loading
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
>>> model = BaseAutoModelClass.from_pretrained(
... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
... )
```
"""
FROM_PRETRAINED_FLAX_DOCSTRING = """
Instantiate one of the model classes of the library from a pretrained model.
The model class to instantiate is selected based on the `model_type` property of the config object (either
passed as an argument or loaded from `pretrained_model_name_or_path` if possible), or when it's missing, by
falling back to using pattern matching on `pretrained_model_name_or_path`:
List options
Args:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTorch state_dict save file* (e.g, `./pt_model/pytorch_model.bin`). In this
case, `from_pt` should be set to `True` and a configuration object should be provided as `config`
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.
model_args (additional positional arguments, *optional*):
Will be passed along to the underlying model `__init__()` method.
config ([`PretrainedConfig`], *optional*):
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:
- The model is a model provided by the library (loaded with the *model id* string of a pretrained
model).
- The model was saved using [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
save directory.
- The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
configuration JSON file named *config.json* is found in the directory.
cache_dir (`str` or `os.PathLike`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.
from_pt (`bool`, *optional*, defaults to `False`):
Load the model weights from a PyTorch checkpoint save file (see docstring of
`pretrained_model_name_or_path` argument).
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download:
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`):
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (e.g., not try downloading the model).
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git.
trust_remote_code (`bool`, *optional*, defaults to `False`):
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to `True` for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.
code_revision (`str`, *optional*, defaults to `"main"`):
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so `revision` can be any identifier
allowed by git.
kwargs (additional keyword arguments, *optional*):
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
`output_attentions=True`). Behaves differently depending on whether a `config` is provided or
automatically loaded:
- If a configuration is provided with `config`, `**kwargs` will be directly passed to the
underlying model's `__init__` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, `kwargs` will be first passed to the configuration class
initialization function ([`~PretrainedConfig.from_pretrained`]). Each key of `kwargs` that
corresponds to a configuration attribute will be used to override said attribute with the
supplied `kwargs` value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model's `__init__` function.
Examples:
```python
>>> from transformers import AutoConfig, BaseAutoModelClass
>>> # Download model and configuration from huggingface.co and cache.
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder")
>>> # Update configuration during loading
>>> model = BaseAutoModelClass.from_pretrained("checkpoint_placeholder", output_attentions=True)
>>> model.config.output_attentions
True
>>> # Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)
>>> config = AutoConfig.from_pretrained("./pt_model/shortcut_placeholder_pt_model_config.json")
>>> model = BaseAutoModelClass.from_pretrained(
... "./pt_model/shortcut_placeholder_pytorch_model.bin", from_pt=True, config=config
... )
```
"""
def _get_model_class(config, model_mapping):
supported_models = model_mapping[type(config)]
if not isinstance(supported_models, (list, tuple)):
return supported_models
name_to_model = {model.__name__: model for model in supported_models}
architectures = getattr(config, "architectures", [])
for arch in architectures:
if arch in name_to_model:
return name_to_model[arch]
elif f"TF{arch}" in name_to_model:
return name_to_model[f"TF{arch}"]
elif f"Flax{arch}" in name_to_model:
return name_to_model[f"Flax{arch}"]
# If not architecture is set in the config or match the supported models, the first element of the tuple is the
# defaults.
return supported_models[0]
class _BaseAutoModelClass:
# Base class for auto models.
_model_mapping = None
def __init__(self, *args, **kwargs) -> None:
raise OSError(
f"{self.__class__.__name__} is designed to be instantiated "
f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or "
f"`{self.__class__.__name__}.from_config(config)` methods."
)
@classmethod
def from_config(cls, config, **kwargs):
trust_remote_code = kwargs.pop("trust_remote_code", None)
has_remote_code = hasattr(config, "auto_map") and cls.__name__ in config.auto_map
has_local_code = type(config) in cls._model_mapping
if has_remote_code:
class_ref = config.auto_map[cls.__name__]
if "--" in class_ref:
upstream_repo = class_ref.split("--")[0]
else:
upstream_repo = None
trust_remote_code = resolve_trust_remote_code(
trust_remote_code, config._name_or_path, has_local_code, has_remote_code, upstream_repo=upstream_repo
)
if has_remote_code and trust_remote_code:
if "--" in class_ref:
repo_id, class_ref = class_ref.split("--")
else:
repo_id = config.name_or_path
model_class = get_class_from_dynamic_module(class_ref, repo_id, **kwargs)
# This block handles the case where the user is loading a model with `trust_remote_code=True`
# but a library model exists with the same name. We don't want to override the autoclass
# mappings in this case, or all future loads of that model will be the remote code model.
if not has_local_code:
cls.register(config.__class__, model_class, exist_ok=True)
model_class.register_for_auto_class(auto_class=cls)
_ = kwargs.pop("code_revision", None)
model_class = add_generation_mixin_to_remote_model(model_class)
return model_class._from_config(config, **kwargs)
elif type(config) in cls._model_mapping:
model_class = _get_model_class(config, cls._model_mapping)
return model_class._from_config(config, **kwargs)
raise ValueError(
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping)}."
)
@classmethod
def _prepare_config_for_auto_class(cls, config: PretrainedConfig) -> PretrainedConfig:
"""Additional autoclass-specific config post-loading manipulation. May be overridden in subclasses."""
return config
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike[str]], *model_args, **kwargs):
config = kwargs.pop("config", None)
trust_remote_code = kwargs.get("trust_remote_code")
kwargs["_from_auto"] = True
hub_kwargs_names = [
"cache_dir",
"force_download",
"local_files_only",
"proxies",
"resume_download",
"revision",
"subfolder",
"use_auth_token",
"token",
]
hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
code_revision = kwargs.pop("code_revision", None)
commit_hash = kwargs.pop("_commit_hash", None)
adapter_kwargs = kwargs.pop("adapter_kwargs", None)
token = hub_kwargs.pop("token", None)
use_auth_token = hub_kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
"`token` and `use_auth_token` are both specified. Please set only the argument `token`."
)
token = use_auth_token
if token is not None:
hub_kwargs["token"] = token
if commit_hash is None:
if not isinstance(config, PretrainedConfig):
# We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
resolved_config_file = cached_file(
pretrained_model_name_or_path,
CONFIG_NAME,
_raise_exceptions_for_gated_repo=False,
_raise_exceptions_for_missing_entries=False,
_raise_exceptions_for_connection_errors=False,
**hub_kwargs,
)
commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
else:
commit_hash = getattr(config, "_commit_hash", None)
if is_peft_available():
if adapter_kwargs is None:
adapter_kwargs = {}
if token is not None:
adapter_kwargs["token"] = token
maybe_adapter_path = find_adapter_config_file(
pretrained_model_name_or_path, _commit_hash=commit_hash, **adapter_kwargs
)
if maybe_adapter_path is not None:
with open(maybe_adapter_path, "r", encoding="utf-8") as f:
adapter_config = json.load(f)
adapter_kwargs["_adapter_model_path"] = pretrained_model_name_or_path
pretrained_model_name_or_path = adapter_config["base_model_name_or_path"]
if not isinstance(config, PretrainedConfig):
kwargs_orig = copy.deepcopy(kwargs)
# ensure not to pollute the config object with dtype="auto" - since it's
# meaningless in the context of the config object - torch.dtype values are acceptable
if kwargs.get("torch_dtype") == "auto":
_ = kwargs.pop("torch_dtype")
if kwargs.get("dtype") == "auto":
_ = kwargs.pop("dtype")
# to not overwrite the quantization_config if config has a quantization_config
if kwargs.get("quantization_config") is not None:
_ = kwargs.pop("quantization_config")
config, kwargs = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
return_unused_kwargs=True,
code_revision=code_revision,
_commit_hash=commit_hash,
**hub_kwargs,
**kwargs,
)
# if torch_dtype=auto was passed here, ensure to pass it on
if kwargs_orig.get("torch_dtype", None) == "auto":
kwargs["torch_dtype"] = "auto"
if kwargs_orig.get("dtype", None) == "auto":
kwargs["dtype"] = "auto"
if kwargs_orig.get("quantization_config", None) is not None:
kwargs["quantization_config"] = kwargs_orig["quantization_config"]
has_remote_code = hasattr(config, "auto_map") and cls.__name__ in config.auto_map
has_local_code = type(config) in cls._model_mapping
upstream_repo = None
if has_remote_code:
class_ref = config.auto_map[cls.__name__]
if "--" in class_ref:
upstream_repo = class_ref.split("--")[0]
trust_remote_code = resolve_trust_remote_code(
trust_remote_code,
pretrained_model_name_or_path,
has_local_code,
has_remote_code,
upstream_repo=upstream_repo,
)
kwargs["trust_remote_code"] = trust_remote_code
# Set the adapter kwargs
kwargs["adapter_kwargs"] = adapter_kwargs
if has_remote_code and trust_remote_code:
model_class = get_class_from_dynamic_module(
class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs
)
_ = hub_kwargs.pop("code_revision", None)
# This block handles the case where the user is loading a model with `trust_remote_code=True`
# but a library model exists with the same name. We don't want to override the autoclass
# mappings in this case, or all future loads of that model will be the remote code model.
if not has_local_code:
cls.register(config.__class__, model_class, exist_ok=True)
model_class.register_for_auto_class(auto_class=cls)
model_class = add_generation_mixin_to_remote_model(model_class)
return model_class.from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
)
elif type(config) in cls._model_mapping:
model_class = _get_model_class(config, cls._model_mapping)
if model_class.config_class == config.sub_configs.get("text_config", None):
config = config.get_text_config()
return model_class.from_pretrained(
pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
)
raise ValueError(
f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping)}."
)
@classmethod
def register(cls, config_class, model_class, exist_ok=False) -> None:
"""
Register a new model for this class.
Args:
config_class ([`PretrainedConfig`]):
The configuration corresponding to the model to register.
model_class ([`PreTrainedModel`]):
The model to register.
"""
if hasattr(model_class, "config_class") and model_class.config_class.__name__ != config_class.__name__:
raise ValueError(
"The model class you are passing has a `config_class` attribute that is not consistent with the "
f"config class you passed (model has {model_class.config_class} and you passed {config_class}. Fix "
"one of those so they match!"
)
cls._model_mapping.register(config_class, model_class, exist_ok=exist_ok)
class _BaseAutoBackboneClass(_BaseAutoModelClass):
# Base class for auto backbone models.
_model_mapping = None
@classmethod
def _load_timm_backbone_from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
requires_backends(cls, ["vision", "timm"])
from ...models.timm_backbone import TimmBackboneConfig
config = kwargs.pop("config", TimmBackboneConfig())
if kwargs.get("out_features") is not None:
raise ValueError("Cannot specify `out_features` for timm backbones")
if kwargs.get("output_loading_info", False):
raise ValueError("Cannot specify `output_loading_info=True` when loading from timm")
num_channels = kwargs.pop("num_channels", config.num_channels)
features_only = kwargs.pop("features_only", config.features_only)
use_pretrained_backbone = kwargs.pop("use_pretrained_backbone", config.use_pretrained_backbone)
out_indices = kwargs.pop("out_indices", config.out_indices)
config = TimmBackboneConfig(
backbone=pretrained_model_name_or_path,
num_channels=num_channels,
features_only=features_only,
use_pretrained_backbone=use_pretrained_backbone,
out_indices=out_indices,
)
return super().from_config(config, **kwargs)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
use_timm_backbone = kwargs.pop("use_timm_backbone", False)
if use_timm_backbone:
return cls._load_timm_backbone_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
def insert_head_doc(docstring, head_doc: str = ""):
if len(head_doc) > 0:
return docstring.replace(
"one of the model classes of the library ",
f"one of the model classes of the library (with a {head_doc} head) ",
)
return docstring.replace(
"one of the model classes of the library ", "one of the base model classes of the library "
)
def auto_class_update(cls, checkpoint_for_example: str = "google-bert/bert-base-cased", head_doc: str = ""):
# Create a new class with the right name from the base class
model_mapping = cls._model_mapping
name = cls.__name__
class_docstring = insert_head_doc(CLASS_DOCSTRING, head_doc=head_doc)
cls.__doc__ = class_docstring.replace("BaseAutoModelClass", name)
# Now we need to copy and re-register `from_config` and `from_pretrained` as class methods otherwise we can't
# have a specific docstrings for them.
from_config = copy_func(_BaseAutoModelClass.from_config)
from_config_docstring = insert_head_doc(FROM_CONFIG_DOCSTRING, head_doc=head_doc)
from_config_docstring = from_config_docstring.replace("BaseAutoModelClass", name)
from_config_docstring = from_config_docstring.replace("checkpoint_placeholder", checkpoint_for_example)
from_config.__doc__ = from_config_docstring
from_config = replace_list_option_in_docstrings(model_mapping._model_mapping, use_model_types=False)(from_config)
cls.from_config = classmethod(from_config)
if name.startswith("TF"):
from_pretrained_docstring = FROM_PRETRAINED_TF_DOCSTRING
elif name.startswith("Flax"):
from_pretrained_docstring = FROM_PRETRAINED_FLAX_DOCSTRING
else:
from_pretrained_docstring = FROM_PRETRAINED_TORCH_DOCSTRING
from_pretrained = copy_func(_BaseAutoModelClass.from_pretrained)
from_pretrained_docstring = insert_head_doc(from_pretrained_docstring, head_doc=head_doc)
from_pretrained_docstring = from_pretrained_docstring.replace("BaseAutoModelClass", name)
from_pretrained_docstring = from_pretrained_docstring.replace("checkpoint_placeholder", checkpoint_for_example)
shortcut = checkpoint_for_example.split("/")[-1].split("-")[0]
from_pretrained_docstring = from_pretrained_docstring.replace("shortcut_placeholder", shortcut)
from_pretrained.__doc__ = from_pretrained_docstring
from_pretrained = replace_list_option_in_docstrings(model_mapping._model_mapping)(from_pretrained)
cls.from_pretrained = classmethod(from_pretrained)
return cls
def get_values(model_mapping):
result = []
for model in model_mapping.values():
if isinstance(model, (list, tuple)):
result += list(model)
else:
result.append(model)
return result
def getattribute_from_module(module, attr):
if attr is None:
return None
if isinstance(attr, tuple):
return tuple(getattribute_from_module(module, a) for a in attr)
if hasattr(module, attr):
return getattr(module, attr)
# Some of the mappings have entries model_type -> object of another model type. In that case we try to grab the
# object at the top level.
transformers_module = importlib.import_module("transformers")
if module != transformers_module:
try:
return getattribute_from_module(transformers_module, attr)
except ValueError:
raise ValueError(f"Could not find {attr} neither in {module} nor in {transformers_module}!")
else:
raise ValueError(f"Could not find {attr} in {transformers_module}!")
def add_generation_mixin_to_remote_model(model_class):
"""
Adds `GenerationMixin` to the inheritance of `model_class`, if `model_class` is a PyTorch model.
This function is used for backwards compatibility purposes: in v4.45, we've started a deprecation cycle to make
`PreTrainedModel` stop inheriting from `GenerationMixin`. Without this function, older models dynamically loaded
from the Hub may not have the `generate` method after we remove the inheritance.
"""
# 1. If it is not a PT model (i.e. doesn't inherit Module), do nothing
if "torch.nn.modules.module.Module" not in str(model_class.__mro__):
return model_class
# 2. If it already **directly** inherits from GenerationMixin, do nothing
if "GenerationMixin" in str(model_class.__bases__):
return model_class
# 3. Prior to v4.45, we could detect whether a model was `generate`-compatible if it had its own `generate` and/or
# `prepare_inputs_for_generation` method.
has_custom_generate_in_class = hasattr(model_class, "generate") and "GenerationMixin" not in str(
getattr(model_class, "generate")
)
has_custom_prepare_inputs = hasattr(model_class, "prepare_inputs_for_generation") and "GenerationMixin" not in str(
getattr(model_class, "prepare_inputs_for_generation")
)
if has_custom_generate_in_class or has_custom_prepare_inputs:
model_class_with_generation_mixin = type(
model_class.__name__, (model_class, GenerationMixin), {**model_class.__dict__}
)
return model_class_with_generation_mixin
return model_class
class _LazyAutoMapping(OrderedDict[type[PretrainedConfig], _LazyAutoMappingValue]):
"""
" A mapping config to object (model or tokenizer for instance) that will load keys and values when it is accessed.
Args:
- config_mapping: The map model type to config class
- model_mapping: The map model type to model (or tokenizer) class
"""
def __init__(self, config_mapping, model_mapping) -> None:
self._config_mapping = config_mapping
self._reverse_config_mapping = {v: k for k, v in config_mapping.items()}
self._model_mapping = model_mapping
self._model_mapping._model_mapping = self
self._extra_content = {}
self._modules = {}
def __len__(self) -> int:
common_keys = set(self._config_mapping.keys()).intersection(self._model_mapping.keys())
return len(common_keys) + len(self._extra_content)
def __getitem__(self, key: type[PretrainedConfig]) -> _LazyAutoMappingValue:
if key in self._extra_content:
return self._extra_content[key]
model_type = self._reverse_config_mapping[key.__name__]
if model_type in self._model_mapping:
model_name = self._model_mapping[model_type]
return self._load_attr_from_module(model_type, model_name)
# Maybe there was several model types associated with this config.
model_types = [k for k, v in self._config_mapping.items() if v == key.__name__]
for mtype in model_types:
if mtype in self._model_mapping:
model_name = self._model_mapping[mtype]
return self._load_attr_from_module(mtype, model_name)
raise KeyError(key)
def _load_attr_from_module(self, model_type, attr):
module_name = model_type_to_module_name(model_type)
if module_name not in self._modules:
self._modules[module_name] = importlib.import_module(f".{module_name}", "transformers.models")
return getattribute_from_module(self._modules[module_name], attr)
def keys(self) -> list[type[PretrainedConfig]]:
mapping_keys = [
self._load_attr_from_module(key, name)
for key, name in self._config_mapping.items()
if key in self._model_mapping
]
return mapping_keys + list(self._extra_content.keys())
def get(self, key: type[PretrainedConfig], default: _T) -> Union[_LazyAutoMappingValue, _T]:
try:
return self.__getitem__(key)
except KeyError:
return default
def __bool__(self) -> bool:
return bool(self.keys())
def values(self) -> list[_LazyAutoMappingValue]:
mapping_values = [
self._load_attr_from_module(key, name)
for key, name in self._model_mapping.items()
if key in self._config_mapping
]
return mapping_values + list(self._extra_content.values())
def items(self) -> list[tuple[type[PretrainedConfig], _LazyAutoMappingValue]]:
mapping_items = [
(
self._load_attr_from_module(key, self._config_mapping[key]),
self._load_attr_from_module(key, self._model_mapping[key]),
)
for key in self._model_mapping
if key in self._config_mapping
]
return mapping_items + list(self._extra_content.items())
def __iter__(self) -> Iterator[type[PretrainedConfig]]:
return iter(self.keys())
def __contains__(self, item: type) -> bool:
if item in self._extra_content:
return True
if not hasattr(item, "__name__") or item.__name__ not in self._reverse_config_mapping:
return False
model_type = self._reverse_config_mapping[item.__name__]
return model_type in self._model_mapping
def register(self, key: type[PretrainedConfig], value: _LazyAutoMappingValue, exist_ok=False) -> None:
"""
Register a new model in this mapping.
"""
if hasattr(key, "__name__") and key.__name__ in self._reverse_config_mapping:
model_type = self._reverse_config_mapping[key.__name__]
if model_type in self._model_mapping and not exist_ok:
raise ValueError(f"'{key}' is already used by a Transformers model.")
self._extra_content[key] = value
__all__ = ["get_values"]
| transformers/src/transformers/models/auto/auto_factory.py/0 | {
"file_path": "transformers/src/transformers/models/auto/auto_factory.py",
"repo_id": "transformers",
"token_count": 18987
} | 458 |
# coding=utf-8
# Copyright 2025 the Cohere Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch AyaVision model."""
from typing import Optional, Union
import torch
from torch import nn
from transformers.models.llava.modeling_llava import (
LlavaCausalLMOutputWithPast,
LlavaForConditionalGeneration,
LlavaModel,
LlavaModelOutputWithPast,
LlavaPreTrainedModel,
TransformersKwargs,
)
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...processing_utils import Unpack
from ...utils import auto_docstring, logging
from ...utils.generic import check_model_inputs
from .configuration_aya_vision import AyaVisionConfig
logger = logging.get_logger(__name__)
class AyaVisionMultiModalProjector(nn.Module):
def __init__(self, config: AyaVisionConfig):
super().__init__()
self.config = config
self.downsample_factor = config.downsample_factor
self.alignment_intermediate_size = getattr(
config, "alignment_intermediate_size", config.text_config.hidden_size
)
self.layernorm = nn.LayerNorm(
config.vision_config.hidden_size * (config.downsample_factor**2), eps=config.adapter_layer_norm_eps
)
self.linear_1 = nn.Linear(
config.vision_config.hidden_size * (config.downsample_factor**2),
self.alignment_intermediate_size,
bias=True,
)
self.act = ACT2FN["silu"] # SwiGLU uses SiLU activation
# For SwiGLU, project down to half size since we split intermediate dim
self.linear_2 = nn.Linear(self.alignment_intermediate_size // 2, config.text_config.hidden_size, bias=True)
def forward(self, image_features):
image_features = self.pixel_shuffle(image_features)
image_features = self.layernorm(image_features)
hidden_states = self.linear_1(image_features)
# Split along last dimension and apply SwiGLU
x, gate = hidden_states.chunk(2, dim=-1)
hidden_states = self.act(gate) * x
hidden_states = self.linear_2(hidden_states)
return hidden_states
def pixel_shuffle(self, image_features): # B, S, D
batch_size, seq_length, feature_dim = image_features.shape
height = width = int(seq_length**0.5)
image_features = image_features.reshape(image_features.shape[0], width, height, -1)
channels = image_features.shape[-1]
image_features = image_features.reshape(
batch_size, width, int(height / self.downsample_factor), int(channels * self.downsample_factor)
)
image_features = image_features.permute(0, 2, 1, 3)
image_features = image_features.reshape(
batch_size, int(height / self.downsample_factor), int(width / self.downsample_factor), -1
)
image_features = image_features.permute(0, 2, 1, 3)
return image_features
class AyaVisionPreTrainedModel(LlavaPreTrainedModel):
_can_compile_fullgraph = False
_can_record_outputs = {
"hidden_states": "DecoderLayer",
"attentions": "Attention",
}
class AyaVisionCausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
pass
class AyaVisionModelOutputWithPast(LlavaModelOutputWithPast):
pass
class AyaVisionModel(LlavaModel):
# Unlike LLaVA, the model doesn't have to deal with Pixtral-style image states
def get_image_features(
self,
pixel_values: torch.FloatTensor,
vision_feature_layer: Optional[Union[int, list[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
**kwargs,
):
"""
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
The tensors corresponding to the input images.
vision_feature_layer (`Union[int, list[int]]`, *optional*):
The index of the layer to select the vision feature. If multiple indices are provided,
the vision feature of the corresponding indices will be concatenated to form the
vision features.
vision_feature_select_strategy (`str`, *optional*):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`
Returns:
image_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`).
"""
vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
)
vision_feature_select_strategy = (
vision_feature_select_strategy
if vision_feature_select_strategy is not None
else self.config.vision_feature_select_strategy
)
if vision_feature_select_strategy not in ["default", "full"]:
raise ValueError(f"Unexpected select feature strategy: {self.config.vision_feature_select_strategy}")
kwargs = {k: v for k, v in kwargs.items() if v is not None}
# this is not memory efficient at all (output_hidden_states=True) will save all the hidden states.
image_outputs = self.vision_tower(pixel_values, output_hidden_states=True, **kwargs)
# If we have one vision feature layer, return the corresponding hidden states,
# otherwise, select the hidden states of each feature layer and concatenate them
if isinstance(vision_feature_layer, int):
selected_image_feature = image_outputs.hidden_states[vision_feature_layer]
if vision_feature_select_strategy == "default":
selected_image_feature = selected_image_feature[:, 1:]
else:
hs_pool = [image_outputs.hidden_states[layer_idx] for layer_idx in vision_feature_layer]
# For default; crop CLS from each hidden state in the hidden state pool
if vision_feature_select_strategy == "default":
hs_pool = [hs[:, 1:] for hs in hs_pool]
selected_image_feature = torch.cat(hs_pool, dim=-1)
image_features = self.multi_modal_projector(selected_image_feature)
return image_features
@check_model_inputs
@auto_docstring
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layer: Optional[Union[int, list[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttentionKwargs],
) -> Union[tuple, AyaVisionModelOutputWithPast]:
vision_feature_layer = (
vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
)
vision_feature_select_strategy = (
vision_feature_select_strategy
if vision_feature_select_strategy is not None
else self.config.vision_feature_select_strategy
)
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
if pixel_values is not None:
image_features = self.get_image_features(
pixel_values=pixel_values,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
)
image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
special_image_mask = self.get_placeholder_mask(
input_ids, inputs_embeds=inputs_embeds, image_features=image_features
)
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
cache_position=cache_position,
**kwargs,
)
return AyaVisionModelOutputWithPast(
last_hidden_state=outputs.last_hidden_state,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
image_hidden_states=image_features if pixel_values is not None else None,
)
class AyaVisionForConditionalGeneration(LlavaForConditionalGeneration):
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
vision_feature_layer: Optional[Union[int, list[int]]] = None,
vision_feature_select_strategy: Optional[str] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
image_sizes: Optional[torch.Tensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> Union[tuple, AyaVisionCausalLMOutputWithPast]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoProcessor, AyaVisionForConditionalGeneration
>>> import torch
>>> torch_device = "cuda:0"
>>> processor = AutoProcessor.from_pretrained("CohereForAI/aya-vision-8b", use_fast=True)
>>> model = AyaVisionForConditionalGeneration.from_pretrained("CohereForAI/aya-vision-8b", device_map=torch_device)
>>> messages = [
... {
... "role": "user",
... "content": [
... {
... "type": "image",
... "url": "https://pbs.twimg.com/media/Fx7YvfQWYAIp6rZ?format=jpg&name=medium",
... },
... {"type": "text", "text": "चित्र में लिखा पाठ क्या कहता है?"},
... ],
... }
... ]
>>> inputs = processor.apply_chat_template(
... messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", device=torch_device
... ).to(model.device)
>>> gen_tokens = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.3)
>>> processor.tokenizer.decode(gen_tokens[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
```"""
super().forward(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
labels=labels,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
logits_to_keep=logits_to_keep,
image_sizes=image_sizes,
**kwargs,
)
__all__ = ["AyaVisionForConditionalGeneration", "AyaVisionPreTrainedModel", "AyaVisionModel"]
| transformers/src/transformers/models/aya_vision/modular_aya_vision.py/0 | {
"file_path": "transformers/src/transformers/models/aya_vision/modular_aya_vision.py",
"repo_id": "transformers",
"token_count": 5556
} | 459 |
# coding=utf-8
# Copyright 2021 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BEiT model."""
import collections.abc
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutput,
BaseModelOutputWithPooling,
ImageClassifierOutput,
MaskedLMOutput,
SemanticSegmenterOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import compile_compatible_method_lru_cache, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging, torch_int
from ...utils.backbone_utils import BackboneMixin
from .configuration_beit import BeitConfig
logger = logging.get_logger(__name__)
@dataclass
@auto_docstring(
custom_intro="""
Class for outputs of [`BeitModel`].
"""
)
class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
r"""
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
*config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
will be returned.
"""
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
"""
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
argument.
"""
if drop_prob == 0.0 or not training:
return input
keep_prob = 1 - drop_prob
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
random_tensor.floor_() # binarize
output = input.div(keep_prob) * random_tensor
return output
class BeitDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
return drop_path(hidden_states, self.drop_prob, self.training)
def extra_repr(self) -> str:
return f"p={self.drop_prob}"
# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class BeitEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
if config.use_mask_token:
self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
else:
self.mask_token = None
self.patch_embeddings = BeitPatchEmbeddings(config)
self.patch_size = config.patch_size
self.image_size = (
config.image_size
if isinstance(config.image_size, collections.abc.Iterable)
else (config.image_size, config.image_size)
)
num_patches = self.patch_embeddings.num_patches
if config.use_absolute_position_embeddings:
self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
else:
self.position_embeddings = None
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# Copied from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
Adapted from:
- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
"""
num_patches = embeddings.shape[1] - 1
num_positions = self.position_embeddings.shape[1] - 1
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.position_embeddings[:, 1:]
dim = embeddings.shape[-1]
new_height = height // self.patch_size
new_width = width // self.patch_size
sqrt_num_positions = torch_int(num_positions**0.5)
patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
patch_pos_embed = nn.functional.interpolate(
patch_pos_embed,
size=(new_height, new_width),
mode="bicubic",
align_corners=False,
)
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
interpolate_pos_encoding: Optional[bool] = None,
) -> torch.Tensor:
if self.position_embeddings is not None and interpolate_pos_encoding is not None:
warnings.warn(
"`interpolate_pos_encoding` argument has no effect for BEiTEmbeddings, embeddings are always "
"interpolated to the input image size. The argument will be removed in transformers v4.51.0."
)
_, _, height, width = pixel_values.shape
embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
batch_size, seq_len, _ = embeddings.size()
if bool_masked_pos is not None:
mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
# replace the masked visual tokens by mask_tokens
w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
embeddings = embeddings * (1 - w) + mask_tokens * w
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
if self.position_embeddings is not None:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
embeddings = self.dropout(embeddings)
return embeddings, (patch_height, patch_width)
class BeitPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.num_patches = num_patches
self.patch_shape = patch_shape
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
batch_size, num_channels, height, width = pixel_values.shape
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
)
embeddings = self.projection(pixel_values)
patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
embeddings = embeddings.flatten(2).transpose(1, 2)
return embeddings, (patch_height, patch_width)
class BeitSelfAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.has_relative_position_bias = bool(window_size)
if self.has_relative_position_bias:
self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relative_position_bias: Optional[torch.Tensor] = None,
interpolate_pos_encoding: bool = False,
resolution: Optional[tuple[int]] = None,
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
batch_size, seq_length, _ = hidden_states.shape
query_layer = (
self.query(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
key_layer = (
self.key(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
value_layer = (
self.value(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# Add relative position bias if present.
if self.has_relative_position_bias:
height, width = resolution
window_size = (height // self.config.patch_size, width // self.config.patch_size)
attention_scores = attention_scores + self.relative_position_bias(
window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
)
# Add shared relative position bias if provided.
if relative_position_bias is not None:
attention_scores = attention_scores + relative_position_bias
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class BeitSdpaSelfAttention(BeitSelfAttention):
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relative_position_bias: Optional[torch.Tensor] = None,
interpolate_pos_encoding: bool = False,
resolution: Optional[tuple[int]] = None,
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
if output_attentions or head_mask is not None:
logger.warning_once(
"`BeitSdpaSelfAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not "
"support `output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, "
"but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. "
'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
head_mask=head_mask,
output_attentions=output_attentions,
relative_position_bias=relative_position_bias,
interpolate_pos_encoding=interpolate_pos_encoding,
resolution=resolution,
)
batch_size, seq_length, _ = hidden_states.shape
query_layer = (
self.query(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
key_layer = (
self.key(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
value_layer = (
self.value(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
attn_bias = None
if self.has_relative_position_bias:
height, width = resolution
window_size = (height // self.config.patch_size, width // self.config.patch_size)
attn_bias = self.relative_position_bias(
window_size, interpolate_pos_encoding, dim_size=hidden_states.shape[1]
)
# Add shared relative position bias if provided.
if relative_position_bias is not None:
if attn_bias is None:
attn_bias = relative_position_bias
else:
attn_bias += relative_position_bias
scaling = 1 / math.sqrt(self.attention_head_size)
context_layer = torch.nn.functional.scaled_dot_product_attention(
query_layer,
key_layer,
value_layer,
attn_mask=attn_bias,
dropout_p=self.config.attention_probs_dropout_prob if self.training else 0.0,
is_causal=False,
scale=scaling,
)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer, None
class BeitSelfOutput(nn.Module):
"""
The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor, gamma=None) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
BEIT_SELF_ATTENTION_CLASSES = {
"eager": BeitSelfAttention,
"sdpa": BeitSdpaSelfAttention,
}
class BeitAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.attention = BEIT_SELF_ATTENTION_CLASSES[config._attn_implementation](config, window_size=window_size)
self.output = BeitSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.attention.query = prune_linear_layer(self.attention.query, index)
self.attention.key = prune_linear_layer(self.attention.key, index)
self.attention.value = prune_linear_layer(self.attention.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relative_position_bias: Optional[torch.Tensor] = None,
interpolate_pos_encoding: bool = False,
resolution: Optional[tuple[int]] = None,
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
self_outputs = self.attention(
hidden_states, head_mask, output_attentions, relative_position_bias, interpolate_pos_encoding, resolution
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class BeitIntermediate(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class BeitOutput(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
return hidden_states
class BeitLayer(GradientCheckpointingLayer):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None, drop_path_rate: float = 0.0) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BeitAttention(config, window_size=window_size)
self.intermediate = BeitIntermediate(config)
self.output = BeitOutput(config)
self.layernorm_before = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.drop_path = BeitDropPath(drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
self.layernorm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
init_values = config.layer_scale_init_value
if init_values > 0:
self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
else:
self.lambda_1, self.lambda_2 = None, None
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relative_position_bias: Optional[torch.Tensor] = None,
interpolate_pos_encoding: bool = False,
resolution: Optional[tuple[int, int]] = None,
) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
self_attention_outputs = self.attention(
self.layernorm_before(hidden_states), # in BEiT, layernorm is applied before self-attention
head_mask,
output_attentions=output_attentions,
relative_position_bias=relative_position_bias,
interpolate_pos_encoding=interpolate_pos_encoding,
resolution=resolution,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
# apply lambda_1 if present
if self.lambda_1 is not None:
attention_output = self.lambda_1 * attention_output
# first residual connection
hidden_states = self.drop_path(attention_output) + hidden_states
# in BEiT, layernorm is also applied after self-attention
layer_output = self.layernorm_after(hidden_states)
layer_output = self.intermediate(layer_output)
layer_output = self.output(layer_output)
if self.lambda_2 is not None:
layer_output = self.lambda_2 * layer_output
# second residual connection
layer_output = self.drop_path(layer_output) + hidden_states
outputs = (layer_output,) + outputs
return outputs
class BeitRelativePositionBias(nn.Module):
def __init__(self, config: BeitConfig, window_size: tuple) -> None:
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn.Parameter(
torch.zeros(self.num_relative_distance, config.num_attention_heads)
) # 2*Wh-1 * 2*Ww-1, nH
# cls to token & token 2 cls & cls to cls
@compile_compatible_method_lru_cache(maxsize=10)
def generate_relative_position_index(self, window_size: tuple[int, int]) -> torch.Tensor:
"""
This method creates the relative position index, modified to support arbitrary window sizes,
as introduced in [MiDaS v3.1](https://huggingface.co/papers/2307.14460).
"""
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
# cls to token & token 2 cls & cls to cls
# get pair-wise relative position index for each token inside the window
window_area = window_size[0] * window_size[1]
grid = torch.meshgrid(torch.arange(window_size[0]), torch.arange(window_size[1]), indexing="ij")
coords = torch.stack(grid) # 2, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += window_size[1] - 1
relative_coords[:, :, 0] *= 2 * window_size[1] - 1
relative_position_index = torch.zeros(size=(window_area + 1,) * 2, dtype=relative_coords.dtype)
relative_position_index[1:, 1:] = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
relative_position_index[0, 0:] = num_relative_distance - 3
relative_position_index[0:, 0] = num_relative_distance - 2
relative_position_index[0, 0] = num_relative_distance - 1
return relative_position_index
def forward(self, window_size, interpolate_pos_encoding: bool = False, dim_size=None) -> torch.Tensor:
"""
Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
"""
old_height = 2 * self.window_size[0] - 1
old_width = 2 * self.window_size[1] - 1
new_height = 2 * window_size[0] - 1
new_width = 2 * window_size[1] - 1
old_relative_position_bias_table = self.relative_position_bias_table
old_num_relative_distance = self.num_relative_distance
new_num_relative_distance = new_height * new_width + 3
old_sub_table = old_relative_position_bias_table[: old_num_relative_distance - 3]
old_sub_table = old_sub_table.reshape(1, old_width, old_height, -1).permute(0, 3, 1, 2)
new_sub_table = nn.functional.interpolate(
old_sub_table, size=(torch_int(new_height), torch_int(new_width)), mode="bilinear"
)
new_sub_table = new_sub_table.permute(0, 2, 3, 1).reshape(new_num_relative_distance - 3, -1)
new_relative_position_bias_table = torch.cat(
[new_sub_table, old_relative_position_bias_table[old_num_relative_distance - 3 :]]
)
relative_position_index = self.generate_relative_position_index(window_size)
relative_position_bias = new_relative_position_bias_table[relative_position_index.view(-1)]
# patch_size*num_patches_height, patch_size*num_patches_width, num_attention_heads
relative_position_bias = relative_position_bias.view(
window_size[0] * window_size[1] + 1, window_size[0] * window_size[1] + 1, -1
)
# num_attention_heads, patch_size*num_patches_width, patch_size*num_patches_height
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
if interpolate_pos_encoding:
relative_position_bias = nn.functional.interpolate(
relative_position_bias.unsqueeze(1),
size=(dim_size, dim_size),
mode="bilinear",
align_corners=False,
).squeeze(1)
return relative_position_bias.unsqueeze(0)
class BeitEncoder(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.config = config
self.has_relative_position_bias = config.use_shared_relative_position_bias
if self.has_relative_position_bias:
self.relative_position_bias = BeitRelativePositionBias(config, window_size=window_size)
# stochastic depth decay rule
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, config.num_hidden_layers, device="cpu")]
self.layer = nn.ModuleList(
[
BeitLayer(
config,
window_size=window_size if config.use_relative_position_bias else None,
drop_path_rate=dpr[i],
)
for i in range(config.num_hidden_layers)
]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
interpolate_pos_encoding: bool = False,
resolution: Optional[tuple[int, int]] = None,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.has_relative_position_bias:
height, width = resolution
window_size = (height // self.config.patch_size, width // self.config.patch_size)
relative_position_bias = self.relative_position_bias(
window_size, interpolate_pos_encoding=interpolate_pos_encoding, dim_size=hidden_states.shape[1]
)
else:
relative_position_bias = None
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
head_mask=layer_head_mask,
output_attentions=output_attentions,
relative_position_bias=relative_position_bias,
interpolate_pos_encoding=interpolate_pos_encoding,
resolution=resolution,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
@auto_docstring
class BeitPreTrainedModel(PreTrainedModel):
config: BeitConfig
base_model_prefix = "beit"
main_input_name = "pixel_values"
supports_gradient_checkpointing = True
_no_split_modules = ["BeitLayer"]
_keys_to_ignore_on_load_unexpected = [r".*relative_position_index.*"]
_supports_sdpa = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d, nn.ConvTranspose2d)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, BeitEmbeddings):
module.cls_token.data.zero_()
if module.mask_token is not None:
module.mask_token.data.zero_()
if module.position_embeddings is not None:
module.position_embeddings.data.zero_()
elif isinstance(module, BeitRelativePositionBias):
module.relative_position_bias_table.data.zero_()
elif isinstance(module, BeitLayer):
if module.lambda_1 is not None:
module.lambda_1.data.fill_(self.config.layer_scale_init_value)
module.lambda_2.data.fill_(self.config.layer_scale_init_value)
@auto_docstring
class BeitModel(BeitPreTrainedModel):
def __init__(self, config: BeitConfig, add_pooling_layer: bool = True) -> None:
r"""
add_pooling_layer (bool, *optional*, defaults to `True`):
Whether to add a pooling layer
"""
super().__init__(config)
self.config = config
self.embeddings = BeitEmbeddings(config)
self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)
self.layernorm = (
nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
)
self.pooler = BeitPooler(config) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@auto_docstring
def forward(
self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[tuple, BeitModelOutputWithPooling]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output, _ = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
resolution = pixel_values.shape[2:]
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
resolution=resolution,
return_dict=return_dict,
interpolate_pos_encoding=interpolate_pos_encoding,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
if not return_dict:
head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
return head_outputs + encoder_outputs[1:]
return BeitModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class BeitPooler(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.layernorm = (
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
if self.layernorm is not None:
# Mean pool the final hidden states of the patch tokens
patch_tokens = hidden_states[:, 1:, :]
pooled_output = self.layernorm(patch_tokens.mean(1))
else:
# Pool by simply taking the final hidden state of the [CLS] token
pooled_output = hidden_states[:, 0]
return pooled_output
@auto_docstring(
custom_intro="""
Beit Model transformer with a 'language' modeling head on top. BEiT does masked image modeling by predicting
visual tokens of a Vector-Quantize Variational Autoencoder (VQ-VAE), whereas other vision models like ViT and DeiT
predict RGB pixel values. As a result, this class is incompatible with [`AutoModelForMaskedImageModeling`], so you
will need to use [`BeitForMaskedImageModeling`] directly if you wish to do masked image modeling with BEiT.
"""
)
class BeitForMaskedImageModeling(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=False)
# Classifier head
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
# NOTE: get_output_embeddings() must return None to prevent accidental weight tying.
# Vision models like BEiT use a Conv2d patch embed layer (no `.weight`) so calling tie_weights() would fail.
# See e.g. https://github.com/huggingface/transformers/pull/39339#discussion_r2219126400
return None
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[tuple, MaskedLMOutput]:
r"""
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
Examples:
```python
>>> from transformers import AutoImageProcessor, BeitForMaskedImageModeling
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
>>> model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k")
>>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
>>> pixel_values = image_processor(images=image, return_tensors="pt").pixel_values
>>> # create random boolean mask of shape (batch_size, num_patches)
>>> bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool()
>>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
>>> loss, logits = outputs.loss, outputs.logits
>>> list(logits.shape)
[1, 196, 8192]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.beit(
pixel_values,
bool_masked_pos=bool_masked_pos,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.layernorm(sequence_output)
prediction_scores = self.lm_head(sequence_output[:, 1:])
masked_lm_loss = None
if labels is not None:
loss_fct = CrossEntropyLoss() # -100 index = padding token
masked_lm_loss = loss_fct(prediction_scores[bool_masked_pos], labels)
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return MaskedLMOutput(
loss=masked_lm_loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
@auto_docstring(
custom_intro="""
Beit Model transformer with an image classification head on top (a linear layer on top of the average of the final
hidden states of the patch tokens) e.g. for ImageNet.
"""
)
class BeitForImageClassification(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=True)
# Classifier head
self.classifier = nn.Linear(config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.beit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class BeitConvModule(nn.Module):
"""
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: Union[int, tuple[int, int]],
padding: Union[int, tuple[int, int], str] = 0,
bias: bool = False,
dilation: Union[int, tuple[int, int]] = 1,
) -> None:
super().__init__()
self.conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=padding,
bias=bias,
dilation=dilation,
)
self.bn = nn.BatchNorm2d(out_channels)
self.activation = nn.ReLU()
def forward(self, input: torch.Tensor) -> torch.Tensor:
output = self.conv(input)
output = self.bn(output)
output = self.activation(output)
return output
class BeitPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
super().__init__()
self.layers = [
nn.AdaptiveAvgPool2d(pool_scale),
BeitConvModule(in_channels, channels, kernel_size=1),
]
for i, layer in enumerate(self.layers):
self.add_module(str(i), layer)
def forward(self, input: torch.Tensor) -> torch.Tensor:
hidden_state = input
for layer in self.layers:
hidden_state = layer(hidden_state)
return hidden_state
class BeitPyramidPoolingModule(nn.Module):
"""
Pyramid Pooling Module (PPM) used in PSPNet.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
in_channels (int): Input channels.
channels (int): Channels after modules, before conv_seg.
align_corners (bool): align_corners argument of F.interpolate.
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
"""
def __init__(self, pool_scales: tuple[int, ...], in_channels: int, channels: int, align_corners: bool) -> None:
super().__init__()
self.pool_scales = pool_scales
self.align_corners = align_corners
self.in_channels = in_channels
self.channels = channels
self.blocks = []
for i, pool_scale in enumerate(pool_scales):
block = BeitPyramidPoolingBlock(pool_scale=pool_scale, in_channels=in_channels, channels=channels)
self.blocks.append(block)
self.add_module(str(i), block)
def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
ppm_outs = []
for ppm in self.blocks:
ppm_out = ppm(x)
upsampled_ppm_out = nn.functional.interpolate(
ppm_out, size=x.size()[2:], mode="bilinear", align_corners=self.align_corners
)
ppm_outs.append(upsampled_ppm_out)
return ppm_outs
class BeitUperHead(nn.Module):
"""
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://huggingface.co/papers/1807.10221).
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
"""
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.pool_scales = config.pool_scales # e.g. (1, 2, 3, 6)
self.in_channels = [config.hidden_size] * 4 # e.g. [768, 768, 768, 768]
self.channels = config.hidden_size
self.align_corners = False
self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
# PSP Module
self.psp_modules = BeitPyramidPoolingModule(
self.pool_scales,
self.in_channels[-1],
self.channels,
align_corners=self.align_corners,
)
self.bottleneck = BeitConvModule(
self.in_channels[-1] + len(self.pool_scales) * self.channels,
self.channels,
kernel_size=3,
padding=1,
)
# FPN Module
self.lateral_convs = nn.ModuleList()
self.fpn_convs = nn.ModuleList()
for in_channels in self.in_channels[:-1]: # skip the top layer
l_conv = BeitConvModule(in_channels, self.channels, kernel_size=1)
fpn_conv = BeitConvModule(self.channels, self.channels, kernel_size=3, padding=1)
self.lateral_convs.append(l_conv)
self.fpn_convs.append(fpn_conv)
self.fpn_bottleneck = BeitConvModule(
len(self.in_channels) * self.channels,
self.channels,
kernel_size=3,
padding=1,
)
def psp_forward(self, inputs):
x = inputs[-1]
psp_outs = [x]
psp_outs.extend(self.psp_modules(x))
psp_outs = torch.cat(psp_outs, dim=1)
output = self.bottleneck(psp_outs)
return output
def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
# build laterals
laterals = [lateral_conv(encoder_hidden_states[i]) for i, lateral_conv in enumerate(self.lateral_convs)]
laterals.append(self.psp_forward(encoder_hidden_states))
# build top-down path
used_backbone_levels = len(laterals)
for i in range(used_backbone_levels - 1, 0, -1):
prev_shape = laterals[i - 1].shape[2:]
laterals[i - 1] = laterals[i - 1] + nn.functional.interpolate(
laterals[i], size=prev_shape, mode="bilinear", align_corners=self.align_corners
)
# build outputs
fpn_outs = [self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels - 1)]
# append psp feature
fpn_outs.append(laterals[-1])
for i in range(used_backbone_levels - 1, 0, -1):
fpn_outs[i] = nn.functional.interpolate(
fpn_outs[i], size=fpn_outs[0].shape[2:], mode="bilinear", align_corners=self.align_corners
)
fpn_outs = torch.cat(fpn_outs, dim=1)
output = self.fpn_bottleneck(fpn_outs)
output = self.classifier(output)
return output
class BeitFCNHead(nn.Module):
"""
Fully Convolution Networks for Semantic Segmentation. This head is implemented of
[FCNNet](https://huggingface.co/papers/1411.4038>).
Args:
config (BeitConfig): Configuration.
in_channels
kernel_size (int): The kernel size for convs in the head. Default: 3.
dilation (int): The dilation rate for convs in the head. Default: 1.
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
"""
def __init__(
self, config: BeitConfig, in_index: int = 2, kernel_size: int = 3, dilation: Union[int, tuple[int, int]] = 1
) -> None:
super().__init__()
self.in_channels = config.hidden_size
self.channels = config.auxiliary_channels
self.num_convs = config.auxiliary_num_convs
self.concat_input = config.auxiliary_concat_input
self.in_index = in_index
conv_padding = (kernel_size // 2) * dilation
convs = []
convs.append(
BeitConvModule(
self.in_channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
)
)
for i in range(self.num_convs - 1):
convs.append(
BeitConvModule(
self.channels, self.channels, kernel_size=kernel_size, padding=conv_padding, dilation=dilation
)
)
if self.num_convs == 0:
self.convs = nn.Identity()
else:
self.convs = nn.Sequential(*convs)
if self.concat_input:
self.conv_cat = BeitConvModule(
self.in_channels + self.channels, self.channels, kernel_size=kernel_size, padding=kernel_size // 2
)
self.classifier = nn.Conv2d(self.channels, config.num_labels, kernel_size=1)
def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
# just take the relevant feature maps
hidden_states = encoder_hidden_states[self.in_index]
output = self.convs(hidden_states)
if self.concat_input:
output = self.conv_cat(torch.cat([hidden_states, output], dim=1))
output = self.classifier(output)
return output
@auto_docstring
class BeitForSemanticSegmentation(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=False)
# FPNs
if len(self.config.out_indices) != 4:
raise ValueError(
"BeitForSemanticSegmentation requires config.out_indices to be a list of 4 integers, "
"specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
"a base-sized architecture."
)
self.fpn1 = nn.Sequential(
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
nn.BatchNorm2d(config.hidden_size),
nn.GELU(),
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
)
self.fpn2 = nn.Sequential(
nn.ConvTranspose2d(config.hidden_size, config.hidden_size, kernel_size=2, stride=2),
)
self.fpn3 = nn.Identity()
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
# Semantic segmentation head(s)
self.decode_head = BeitUperHead(config)
self.auxiliary_head = BeitFCNHead(config) if config.use_auxiliary_head else None
# Initialize weights and apply final processing
self.post_init()
def compute_loss(self, logits, auxiliary_logits, labels):
# upsample logits to the images' original size
upsampled_logits = nn.functional.interpolate(
logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
)
if auxiliary_logits is not None:
upsampled_auxiliary_logits = nn.functional.interpolate(
auxiliary_logits, size=labels.shape[-2:], mode="bilinear", align_corners=False
)
# compute weighted loss
loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
main_loss = loss_fct(upsampled_logits, labels)
loss = main_loss
if auxiliary_logits is not None:
auxiliary_loss = loss_fct(upsampled_auxiliary_logits, labels)
loss += self.config.auxiliary_loss_weight * auxiliary_loss
return loss
@auto_docstring
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
interpolate_pos_encoding: bool = False,
return_dict: Optional[bool] = None,
) -> Union[tuple, SemanticSegmenterOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
Examples:
```python
>>> from transformers import AutoImageProcessor, BeitForSemanticSegmentation
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image_processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
>>> model = BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640")
>>> inputs = image_processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> # logits are of shape (batch_size, num_labels, height, width)
>>> logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
if labels is not None and self.config.num_labels == 1:
raise ValueError("The number of labels should be greater than one")
outputs = self.beit(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=True, # we need the intermediate hidden states
interpolate_pos_encoding=interpolate_pos_encoding,
return_dict=return_dict,
)
encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
# only keep certain features, and reshape
# note that we do +1 as the encoder_hidden_states also includes the initial embeddings
features = [feature for idx, feature in enumerate(encoder_hidden_states) if idx + 1 in self.config.out_indices]
batch_size = pixel_values.shape[0]
patch_resolution = self.config.image_size // self.config.patch_size
features = [
x[:, 1:, :].permute(0, 2, 1).reshape(batch_size, -1, patch_resolution, patch_resolution) for x in features
]
# apply FPNs
ops = [self.fpn1, self.fpn2, self.fpn3, self.fpn4]
for i in range(len(features)):
features[i] = ops[i](features[i])
logits = self.decode_head(features)
auxiliary_logits = None
if self.auxiliary_head is not None:
auxiliary_logits = self.auxiliary_head(features)
loss = None
if labels is not None:
loss = self.compute_loss(logits, auxiliary_logits, labels)
if not return_dict:
if output_hidden_states:
output = (logits,) + outputs[1:]
else:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SemanticSegmenterOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
)
@auto_docstring(
custom_intro="""
BEiT backbone, to be used with frameworks like DETR and MaskFormer.
"""
)
class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = BeitEmbeddings(config)
self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embeddings.patch_shape)
if config.add_fpn:
if len(self.config.out_indices) != 4:
raise ValueError(
"BeitBackbone requires config.out_indices to be a list of 4 integers, "
"specifying which features to use from the backbone. One can use [3, 5, 7, 11] in case of "
"a base-sized architecture."
)
hidden_size = config.hidden_size
self.fpn1 = nn.Sequential(
nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
nn.BatchNorm2d(hidden_size, eps=config.batch_norm_eps),
nn.GELU(),
nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2),
)
self.fpn2 = nn.Sequential(nn.ConvTranspose2d(hidden_size, hidden_size, kernel_size=2, stride=2))
self.fpn3 = nn.Identity()
self.fpn4 = nn.MaxPool2d(kernel_size=2, stride=2)
# initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.patch_embeddings
@auto_docstring
def forward(
self,
pixel_values: Tensor,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> BackboneOutput:
r"""
Examples:
```python
>>> from transformers import AutoImageProcessor, AutoBackbone
>>> import torch
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224")
>>> model = AutoBackbone.from_pretrained(
... "microsoft/beit-base-patch16-224", out_features=["stage1", "stage2", "stage3", "stage4"]
... )
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> feature_maps = outputs.feature_maps
>>> list(feature_maps[-1].shape)
[1, 768, 14, 14]
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
batch_size = pixel_values.shape[0]
embedding_output, (patch_height, patch_width) = self.embeddings(pixel_values)
resolution = pixel_values.shape[2:]
outputs = self.encoder(
embedding_output,
output_hidden_states=True,
output_attentions=output_attentions,
resolution=resolution,
return_dict=return_dict,
)
hidden_states = outputs.hidden_states if return_dict else outputs[1]
feature_maps = ()
for stage, hidden_state in zip(self.stage_names, hidden_states):
if stage in self.out_features:
if self.config.reshape_hidden_states:
hidden_state = hidden_state[:, 1:, :]
hidden_state = hidden_state.permute(0, 2, 1)
hidden_state = hidden_state.reshape(batch_size, -1, patch_height, patch_width)
feature_maps += (hidden_state,)
if self.config.add_fpn:
feature_maps = [
self.fpn1(feature_maps[0]),
self.fpn2(feature_maps[1]),
self.fpn3(feature_maps[2]),
self.fpn4(feature_maps[3]),
]
feature_maps = tuple(feature_maps)
if not return_dict:
if output_hidden_states:
output = (feature_maps,) + outputs[1:]
else:
output = (feature_maps,) + outputs[2:]
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
)
__all__ = [
"BeitForImageClassification",
"BeitForMaskedImageModeling",
"BeitForSemanticSegmentation",
"BeitModel",
"BeitPreTrainedModel",
"BeitBackbone",
]
| transformers/src/transformers/models/beit/modeling_beit.py/0 | {
"file_path": "transformers/src/transformers/models/beit/modeling_beit.py",
"repo_id": "transformers",
"token_count": 28625
} | 460 |
# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors and 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.
"""PyTorch BERT model specific for generation."""
import math
from typing import Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import auto_docstring, logging
from ...utils.deprecation import deprecate_kwarg
from .configuration_bert_generation import BertGenerationConfig
logger = logging.get_logger(__name__)
# Copied from transformers.models.bert.modeling_bert.BertSelfOutput with Bert->BertGeneration
class BertGenerationSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertSelfAttention with Bert->BertGeneration
class BertGenerationSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, layer_idx=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size)
self.key = nn.Linear(config.hidden_size, self.all_head_size)
self.value = nn.Linear(config.hidden_size, self.all_head_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
self.max_position_embeddings = config.max_position_embeddings
self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
self.is_decoder = config.is_decoder
self.layer_idx = layer_idx
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor]:
batch_size, seq_length, _ = hidden_states.shape
query_layer = self.query(hidden_states)
query_layer = query_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
1, 2
)
is_cross_attention = encoder_hidden_states is not None
if past_key_values is not None:
if isinstance(past_key_values, EncoderDecoderCache):
is_updated = past_key_values.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_layer from cache
curr_past_key_value = past_key_values.cross_attention_cache
else:
curr_past_key_value = past_key_values.self_attention_cache
else:
curr_past_key_value = past_key_values
current_states = encoder_hidden_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_values is not None and is_updated:
# reuse k,v, cross_attentions
key_layer = curr_past_key_value.layers[self.layer_idx].keys
value_layer = curr_past_key_value.layers[self.layer_idx].values
else:
key_layer = self.key(current_states)
key_layer = key_layer.view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(
1, 2
)
value_layer = self.value(current_states)
value_layer = value_layer.view(
batch_size, -1, self.num_attention_heads, self.attention_head_size
).transpose(1, 2)
if past_key_values is not None:
# save all key/value_layer to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_layer, value_layer = curr_past_key_value.update(
key_layer, value_layer, self.layer_idx, {"cache_position": cache_position}
)
# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
if is_cross_attention:
past_key_values.is_updated[self.layer_idx] = True
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if past_key_values is not None:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hidden_states.device).view(
-1, 1
)
else:
position_ids_l = torch.arange(query_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
position_ids_r = torch.arange(key_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
distance = position_ids_l - position_ids_r
positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BertGenerationModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(new_context_layer_shape)
return context_layer, attention_probs
BERT_GENERATION_SELF_ATTENTION_CLASSES = {
"eager": BertGenerationSelfAttention,
}
# Copied from transformers.models.bert.modeling_bert.BertAttention with Bert->BertGeneration,BERT->BERT_GENERATION
class BertGenerationAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, layer_idx=None):
super().__init__()
self.self = BERT_GENERATION_SELF_ATTENTION_CLASSES[config._attn_implementation](
config,
position_embedding_type=position_embedding_type,
layer_idx=layer_idx,
)
self.output = BertGenerationSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
)
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Update hyper params and store pruned heads
self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor]:
self_outputs = self.self(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
past_key_values=past_key_values,
output_attentions=output_attentions,
cache_position=cache_position,
)
attention_output = self.output(self_outputs[0], hidden_states)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->BertGeneration
class BertGenerationIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertOutput with Bert->BertGeneration
class BertGenerationOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
# Copied from transformers.models.bert.modeling_bert.BertLayer with Bert->BertGeneration
class BertGenerationLayer(GradientCheckpointingLayer):
def __init__(self, config, layer_idx=None):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = BertGenerationAttention(config, layer_idx=layer_idx)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add_cross_attention
if self.add_cross_attention:
if not self.is_decoder:
raise ValueError(f"{self} should be used as a decoder model if cross attention is added")
self.crossattention = BertGenerationAttention(
config, position_embedding_type="absolute", layer_idx=layer_idx
)
self.intermediate = BertGenerationIntermediate(config)
self.output = BertGenerationOutput(config)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
cache_position: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor]:
self_attention_outputs = self.attention(
hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
output_attentions=output_attentions,
past_key_values=past_key_values,
cache_position=cache_position,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
if self.is_decoder and encoder_hidden_states is not None:
if not hasattr(self, "crossattention"):
raise ValueError(
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers"
" by setting `config.add_cross_attention=True`"
)
cross_attention_outputs = self.crossattention(
attention_output,
attention_mask=encoder_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
past_key_values=past_key_values,
output_attentions=output_attentions,
cache_position=cache_position,
)
attention_output = cross_attention_outputs[0]
outputs = outputs + cross_attention_outputs[1:] # add cross attentions if we output attention weights
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
return outputs
def feed_forward_chunk(self, attention_output):
intermediate_output = self.intermediate(attention_output)
layer_output = self.output(intermediate_output, attention_output)
return layer_output
class BertEncoder(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
self.layer = nn.ModuleList([BertGenerationLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
cache_position: Optional[torch.Tensor] = None,
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
if use_cache and past_key_values is None:
past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
if use_cache and isinstance(past_key_values, tuple):
logger.warning_once(
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
)
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
layer_outputs = layer_module(
hidden_states,
attention_mask,
layer_head_mask,
encoder_hidden_states, # as a positional argument for gradient checkpointing
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if self.config.add_cross_attention:
all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
past_key_values,
all_hidden_states,
all_self_attentions,
all_cross_attentions,
]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
cross_attentions=all_cross_attentions,
)
def load_tf_weights_in_bert_generation(
model, tf_hub_path, model_class, is_encoder_named_decoder=False, is_encoder=False
):
try:
import numpy as np
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
import tensorflow_text # noqa: F401
tf.disable_eager_execution()
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_model = hub.Module(tf_hub_path)
init = tf.global_variables_initializer()
with tf.Session() as sess:
init.run()
all_variables = tf_model.variable_map
keep_track_variables = all_variables.copy()
for key in list(all_variables.keys()):
if "global" in key:
logger.info(f"Skipping {key}...")
continue
if not is_encoder:
model_pointer = getattr(model, model_class)
else:
model_pointer = model
is_embedding = False
logger.info(f"Trying to match {key}...")
# remove start_string = "module/bert/"
sub_layers = key.split("/")[2:]
if is_encoder_named_decoder and sub_layers[0] == "encoder":
logger.info(f"Skipping encoder layer {key} for decoder")
continue
if is_encoder and sub_layers[0] == "decoder":
logger.info(f"Skipping decoder layer {key} for encoder")
continue
for i, sub_layer in enumerate(sub_layers):
if sub_layer == "embeddings":
is_embedding = True
elif sub_layer == "LayerNorm":
is_embedding = False
if "layer" in sub_layer:
model_pointer = model_pointer.layer[int(sub_layer.split("_")[-1])]
elif sub_layer in ["kernel", "gamma"]:
model_pointer = model_pointer.weight
elif sub_layer == "beta":
model_pointer = model_pointer.bias
elif sub_layer == "encdec":
model_pointer = model_pointer.crossattention.self
elif sub_layer == "encdec_output":
model_pointer = model_pointer.crossattention.output
elif is_encoder_named_decoder and sub_layer == "decoder":
model_pointer = model_pointer.encoder
else:
if sub_layer == "attention" and "encdec" in sub_layers[i + 1]:
continue
try:
model_pointer = getattr(model_pointer, sub_layer)
except AttributeError:
logger.info(f"Skipping to initialize {key} at {sub_layer}...")
raise AttributeError
array = np.asarray(sess.run(all_variables[key]))
if not is_embedding:
logger.info(f"Transposing numpy weight of shape {array.shape} for {key}")
array = np.transpose(array)
else:
model_pointer = model_pointer.weight
if model_pointer.shape != array.shape:
raise ValueError(f"Pointer shape {model_pointer.shape} and array shape {array.shape} mismatched")
logger.info(f"Initialize PyTorch weight {key}")
model_pointer.data = torch.from_numpy(array.astype(np.float32))
keep_track_variables.pop(key, None)
logger.info(f"Weights not copied to PyTorch model: {', '.join(keep_track_variables.keys())}")
return model
class BertGenerationEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False
)
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0):
if input_ids is not None:
input_shape = input_ids.size()
else:
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
@auto_docstring
class BertGenerationPreTrainedModel(PreTrainedModel):
config: BertGenerationConfig
base_model_prefix = "bert"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
elif isinstance(module, BertGenerationOnlyLMHead):
module.bias.data.zero_()
@auto_docstring(
custom_intro="""
The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.
"""
)
class BertGenerationEncoder(BertGenerationPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://huggingface.co/papers/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
This model should be used when leveraging Bert or Roberta checkpoints for the [`EncoderDecoderModel`] class as
described in [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://huggingface.co/papers/1907.12461)
by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn.
To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
`add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = BertGenerationEmbeddings(config)
self.encoder = BertEncoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(heads)
@auto_docstring
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs, # NOOP kwargs, for now
) -> Union[tuple, BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.config.is_decoder:
use_cache = use_cache if use_cache is not None else self.config.use_cache
else:
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
batch_size, seq_length = input_shape
device = input_ids.device if input_ids is not None else inputs_embeds.device
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = (
past_key_values[0][0].shape[-2]
if not isinstance(past_key_values, Cache)
else past_key_values.get_seq_length()
)
if attention_mask is None:
attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
else:
encoder_extended_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
encoder_outputs = self.encoder(
embedding_output,
attention_mask=extended_attention_mask,
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=sequence_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
class BertGenerationOnlyLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
self.decoder.bias = self.bias
def forward(self, hidden_states):
logits = self.decoder(hidden_states)
return logits
def _tie_weights(self):
# For accelerate compatibility and to not break backward compatibility
if self.decoder.bias.device.type == "meta":
self.decoder.bias = self.bias
else:
# To tie those two weights if they get disconnected (on TPU or when the bias is resized)
self.bias = self.decoder.bias
@auto_docstring(
custom_intro="""
BertGeneration Model with a `language modeling` head on top for CLM fine-tuning.
"""
)
class BertGenerationDecoder(BertGenerationPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.decoder.weight", "lm_head.decoder.bias"]
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `BertGenerationDecoder` as a standalone, add `is_decoder=True.`")
self.bert = BertGenerationEncoder(config)
self.lm_head = BertGenerationOnlyLMHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.lm_head.decoder
def set_output_embeddings(self, new_embeddings):
self.lm_head.decoder = new_embeddings
self.lm_head.bias = new_embeddings.bias
@auto_docstring
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
past_key_values: Optional[tuple[tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
Example:
```python
>>> from transformers import AutoTokenizer, BertGenerationDecoder, BertGenerationConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
>>> config = BertGenerationConfig.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder")
>>> config.is_decoder = True
>>> model = BertGenerationDecoder.from_pretrained(
... "google/bert_for_seq_generation_L-24_bbc_encoder", config=config
... )
>>> inputs = tokenizer("Hello, my dog is cute", return_token_type_ids=False, return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
**kwargs,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
lm_loss = None
if labels is not None:
lm_loss = self.loss_function(
prediction_scores,
labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
if not return_dict:
output = (prediction_scores,) + outputs[1:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=lm_loss,
logits=prediction_scores,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
__all__ = [
"BertGenerationDecoder",
"BertGenerationEncoder",
"BertGenerationPreTrainedModel",
"load_tf_weights_in_bert_generation",
]
| transformers/src/transformers/models/bert_generation/modeling_bert_generation.py/0 | {
"file_path": "transformers/src/transformers/models/bert_generation/modeling_bert_generation.py",
"repo_id": "transformers",
"token_count": 17226
} | 461 |
# coding=utf-8
# Copyright 2021 Google Research The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""PyTorch BigBirdPegasus model."""
import math
from typing import Callable, Optional, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
from ...generation import GenerationMixin
from ...modeling_attn_mask_utils import (
AttentionMaskConverter,
_prepare_4d_attention_mask,
_prepare_4d_attention_mask_for_sdpa,
)
from ...modeling_flash_attention_utils import FlashAttentionKwargs
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
CausalLMOutputWithCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
Seq2SeqQuestionAnsweringModelOutput,
Seq2SeqSequenceClassifierOutput,
)
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import auto_docstring, is_torch_flex_attn_available, is_torchdynamo_compiling, logging
from ...utils.deprecation import deprecate_kwarg
from .configuration_bigbird_pegasus import BigBirdPegasusConfig
if is_torch_flex_attn_available():
from ...integrations.flex_attention import BlockMask, make_flex_block_causal_mask
logger = logging.get_logger(__name__)
_EXPECTED_OUTPUT_SHAPE = [1, 7, 1024]
def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int):
"""
Shift input ids one token to the right.
"""
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shifted_input_ids[:, 1:] = input_ids[:, :-1].clone()
shifted_input_ids[:, 0] = decoder_start_token_id
if pad_token_id is None:
raise ValueError("self.model.config.pad_token_id has to be defined.")
# replace possible -100 values in labels by `pad_token_id`
shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
return shifted_input_ids
class BigBirdPegasusLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
super().__init__(num_embeddings, embedding_dim)
def forward(self, input_ids_shape: torch.Size, past_key_values_length: int = 0, position_ids: torch.Tensor = None):
"""`input_ids' shape is expected to be [bsz x seqlen]."""
if position_ids is None:
bsz, seq_len = input_ids_shape[:2]
position_ids = torch.arange(
past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
)
return super().forward(position_ids)
# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->BigBirdPegasus
class BigBirdPegasusScaledWordEmbedding(nn.Embedding):
"""
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
super().__init__(num_embeddings, embedding_dim, padding_idx)
self.embed_scale = embed_scale
def forward(self, input_ids: torch.Tensor):
return super().forward(input_ids) * self.embed_scale
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdSelfAttention with BigBird->BigBirdPegasus
class BigBirdPegasusSelfAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.is_decoder = config.is_decoder
self.layer_idx = layer_idx
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
output_attentions=False,
cache_position=None,
):
batch_size, seq_length, _ = hidden_states.shape
query_layer = (
self.query(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
is_cross_attention = encoder_hidden_states is not None
current_states = encoder_hidden_states if is_cross_attention else hidden_states
attention_mask = encoder_attention_mask if is_cross_attention else attention_mask
if is_cross_attention and past_key_values is not None and past_key_values.get_seq_length(self.layer_idx) > 0:
# reuse k,v, cross_attentions
key_layer = past_key_values.layers[self.layer_idx].keys
value_layer = past_key_values.layers[self.layer_idx].values
else:
key_layer = (
self.key(current_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
value_layer = (
self.value(current_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
if past_key_values is not None:
# save all key/value_layer to cache to be re-used for fast auto-regressive generation
key_layer, value_layer = past_key_values.update(
key_layer,
value_layer,
self.layer_idx,
)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in BigBirdPegasusModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer, attention_probs
# Copied from transformers.models.big_bird.modeling_big_bird.BigBirdBlockSparseAttention with BigBird->BigBirdPegasus
class BigBirdPegasusBlockSparseAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.max_seqlen = config.max_position_embeddings
self.seed = seed
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
f"heads {config.num_attention_heads}."
)
self.num_attention_heads = config.num_attention_heads
self.num_random_blocks = config.num_random_blocks
self.block_size = config.block_size
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.use_bias)
def forward(
self,
hidden_states,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions=None,
):
# Currently this `class` can't be used in decoder.
batch_size, seqlen, _ = hidden_states.size()
to_seq_length = from_seq_length = seqlen
from_block_size = to_block_size = self.block_size
if from_seq_length % from_block_size != 0:
raise ValueError("Query sided sequence length must be multiple of block size")
if to_seq_length % to_block_size != 0:
raise ValueError("Key/Value sided sequence length must be multiple of block size")
query_layer = (
self.query(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
key_layer = (
self.key(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
value_layer = (
self.value(hidden_states)
.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
.transpose(1, 2)
)
context_layer, attention_probs = self.bigbird_block_sparse_attention(
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
self.num_attention_heads,
self.num_random_blocks,
self.attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_length,
to_seq_length,
seed=self.seed,
plan_from_length=None,
plan_num_rand_blocks=None,
output_attentions=output_attentions,
)
context_layer = context_layer.contiguous().view(batch_size, from_seq_length, -1)
return context_layer, attention_probs
@staticmethod
def torch_bmm_nd(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication"""
# faster replacement of torch.einsum ("bhqk,bhkd->bhqd")
return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(
inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1])
)
@staticmethod
def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None):
"""Fast nd matrix multiplication with transpose"""
# faster replacement of torch.einsum (bhqd,bhkd->bhqk)
return torch.bmm(
inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)
).view(inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 2]))
def bigbird_block_sparse_attention(
self,
query_layer,
key_layer,
value_layer,
band_mask,
from_mask,
to_mask,
from_blocked_mask,
to_blocked_mask,
n_heads,
n_rand_blocks,
attention_head_size,
from_block_size,
to_block_size,
batch_size,
from_seq_len,
to_seq_len,
seed,
plan_from_length,
plan_num_rand_blocks,
output_attentions,
):
# BigBirdPegasus block-sparse attention as suggested in paper
# ITC:
# global tokens: 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# ETC:
# global tokens: extra_globals_tokens + 2 x block_size
# window tokens: 3 x block_size
# random tokens: num_rand_tokens x block_size
# Note:
# 1) Currently, ETC is not supported.
# 2) Window size is fixed to 3 blocks & it can be changed only by
# changing `block_size`.
# 3) Number of global blocks are fixed (2 blocks here) & global tokens can be
# controlled only by `block_size`.
# attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of shifting tokens (for calculating sliding attention)
# hence following code can be divided into 5 parts.
if from_seq_len // from_block_size != to_seq_len // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rsqrt_d = 1 / math.sqrt(attention_head_size)
bsz = batch_size
attn_mask_penalty = -10000.0
# generate random attention and corresponding masks
np.random.seed(seed)
if from_seq_len in [1024, 3072, 4096]: # old plans used in paper
rand_attn = [
self._bigbird_block_rand_mask(
self.max_seqlen, self.max_seqlen, from_block_size, to_block_size, n_rand_blocks, last_idx=1024
)[: (from_seq_len // from_block_size - 2)]
for _ in range(n_heads)
]
else:
if plan_from_length is None:
plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
from_seq_len, from_block_size, n_rand_blocks
)
rand_attn = self._bigbird_block_rand_mask_with_head(
from_seq_length=from_seq_len,
to_seq_length=to_seq_len,
from_block_size=from_block_size,
to_block_size=to_block_size,
num_heads=n_heads,
plan_from_length=plan_from_length,
plan_num_rand_blocks=plan_num_rand_blocks,
)
rand_attn = np.stack(rand_attn, axis=0)
rand_attn = torch.tensor(rand_attn, device=query_layer.device, dtype=torch.long)
rand_attn.unsqueeze_(0)
rand_attn = torch.cat([rand_attn for _ in range(batch_size)], dim=0)
rand_mask = self._create_rand_mask_from_inputs(
from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
)
blocked_query_matrix = query_layer.view(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
blocked_key_matrix = key_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
blocked_value_matrix = value_layer.view(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
# preparing block for randn attn
gathered_key = self.torch_gather_b2(blocked_key_matrix, rand_attn)
gathered_key = gathered_key.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
gathered_value = self.torch_gather_b2(blocked_value_matrix, rand_attn)
gathered_value = gathered_value.view(
bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1
) # [bsz, n_heads, to_seq_len//to_block_size-2, n_rand_blocks, to_block_size, -1]
# 1st PART
# 1st block (global block) attention scores
# q[0] x (k[0], k[1], k[2], k[3], k[4] .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
first_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 0], key_layer, ndim=4)
first_product = first_product * rsqrt_d
first_product += (1.0 - to_mask) * attn_mask_penalty
first_attn_weights = nn.functional.softmax(
first_product, dim=-1
) # [bsz, n_heads, from_block_size, to_seq_len]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
first_context_layer = self.torch_bmm_nd(first_attn_weights, value_layer, ndim=4)
first_context_layer.unsqueeze_(2)
# 2nd PART
# 2nd block attention scores
# q[1] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> 2nd, 3rd blocks
# global key blocks -> 1st block
second_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, 1],
blocked_key_matrix[:, :, 2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
second_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, 1],
blocked_value_matrix[:, :, 2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, 0],
],
dim=2,
) # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, 1], second_key_mat, ndim=4)
second_seq_pad = torch.cat(
[
to_mask[:, :, :, : 3 * to_block_size],
to_mask[:, :, :, -to_block_size:],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, 0],
],
dim=3,
)
second_product = second_product * rsqrt_d
second_product += (1.0 - torch.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
second_attn_weights = nn.functional.softmax(
second_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_context_layer = self.torch_bmm_nd(second_attn_weights, second_value_mat, ndim=4)
second_context_layer.unsqueeze_(2)
# 3rd PART
# Middle blocks attention scores
# q[-2:2] x (sliding_keys, random_keys, global_keys)
# sliding attn is calculated using special trick of shifting tokens as discussed in paper
# random keys are generated by taking random indices as per `rand_attn`
# global keys -> 1st & last block
exp_blocked_key_matrix = torch.cat(
[blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], dim=3
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
exp_blocked_value_matrix = torch.cat(
[blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
dim=3,
) # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
middle_query_matrix = blocked_query_matrix[:, :, 2:-2]
# sliding attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
inner_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, exp_blocked_key_matrix, ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
inner_band_product = inner_band_product * rsqrt_d
# randn attention scores for q[-2:2]
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
rand_band_product = self.torch_bmm_nd_transpose(middle_query_matrix, gathered_key[:, :, 1:-1], ndim=5)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
rand_band_product = rand_band_product * rsqrt_d
# Including 1st block (since it's global)
first_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
first_band_product = first_band_product * rsqrt_d
# Including last block (since it's global)
last_band_product = torch.einsum(
"bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
last_band_product = last_band_product * rsqrt_d
# masking padded tokens
inner_band_product += (1.0 - band_mask) * attn_mask_penalty
first_band_product += (1.0 - to_mask[:, :, :, :to_block_size].unsqueeze(3)) * attn_mask_penalty
last_band_product += (1.0 - to_mask[:, :, :, -to_block_size:].unsqueeze(3)) * attn_mask_penalty
rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty
# completing attention scores matrix for all q[-2:2]
band_product = torch.cat(
[first_band_product, inner_band_product, rand_band_product, last_band_product], dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# safely doing softmax since attention matrix is completed
attn_weights = nn.functional.softmax(
band_product, dim=-1
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]
# contribution of sliding keys
# [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
context_layer = self.torch_bmm_nd(
attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix, ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of random keys
# [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size] x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
context_layer += self.torch_bmm_nd(
attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size], gathered_value[:, :, 1:-1], ndim=5
)
# ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# adding contribution of global keys
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
context_layer += torch.einsum(
"bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
) # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1] ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
# 4th PART
# last 2nd token attention scores
# q[-2] x (sliding_keys, random_keys, global_keys)
# sliding key blocks -> last 3 blocks
# global key block -> 1st block
# random key block -> based on indices stored in `randn_attn`
second_last_key_mat = torch.cat(
[
blocked_key_matrix[:, :, 0],
blocked_key_matrix[:, :, -3],
blocked_key_matrix[:, :, -2],
blocked_key_matrix[:, :, -1],
gathered_key[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
second_last_value_mat = torch.cat(
[
blocked_value_matrix[:, :, 0],
blocked_value_matrix[:, :, -3],
blocked_value_matrix[:, :, -2],
blocked_value_matrix[:, :, -1],
gathered_value[:, :, -1],
],
dim=2,
) # [bsz, n_heads, (4+r)*to_block_size, -1]
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
second_last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -2], second_last_key_mat, ndim=4)
second_last_seq_pad = torch.cat(
[
to_mask[:, :, :, :to_block_size],
to_mask[:, :, :, -3 * to_block_size :],
to_mask.new_ones([bsz, 1, 1, n_rand_blocks * to_block_size]),
],
dim=3,
)
second_last_rand_pad = torch.cat(
[
rand_mask.new_ones([bsz, n_heads, from_block_size, 4 * to_block_size]),
rand_mask[:, :, -1],
],
dim=3,
)
second_last_product = second_last_product * rsqrt_d
second_last_product += (1.0 - torch.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
second_last_attn_weights = nn.functional.softmax(
second_last_product, dim=-1
) # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
# [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1] ==> [bsz, n_heads, from_block_size, -1]
second_last_context_layer = self.torch_bmm_nd(second_last_attn_weights, second_last_value_mat, ndim=4)
second_last_context_layer.unsqueeze_(2)
# 5th PART
# last block (global) attention scores
# q[-1] x (k[0], k[1], k[2], k[3], .... )
# [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
last_product = self.torch_bmm_nd_transpose(blocked_query_matrix[:, :, -1], key_layer, ndim=4)
last_product = last_product * rsqrt_d
last_product += (1.0 - to_mask) * attn_mask_penalty
last_attn_weights = nn.functional.softmax(last_product, dim=-1) # [bsz, n_heads, from_block_size, n]
# [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
last_context_layer = self.torch_bmm_nd(last_attn_weights, value_layer, ndim=4)
last_context_layer.unsqueeze_(2)
# combining representations of all tokens
context_layer = torch.cat(
[first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
dim=2,
)
context_layer = context_layer.view((bsz, n_heads, from_seq_len, -1)) * from_mask
context_layer = torch.transpose(context_layer, 1, 2)
# this is just for visualizing; forward pass doesn't depend on following code
if output_attentions:
# TODO(PVP): need to verify if below code is correct
attention_probs = torch.zeros(
bsz, n_heads, from_seq_len, to_seq_len, dtype=torch.float, device=context_layer.device
)
# 1st query block
# corresponding to `first_context_layer`
attention_probs[:, :, :from_block_size, :] = first_attn_weights # all keys global
# 2nd query block
# corresponding to `second_context_layer`
attention_probs[:, :, from_block_size : 2 * from_block_size, : 3 * to_block_size] = second_attn_weights[
:, :, :, : 3 * to_block_size
] # 1st three key blocks (global + sliding)
attention_probs[:, :, from_block_size : 2 * from_block_size, -to_block_size:] = second_attn_weights[
:, :, :, 3 * to_block_size : 4 * to_block_size
] # last key block (global)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, 1, :, i2[0]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Middle query blocks
# corresponding to `context_layer`
# sliding keys
for q_idx in range(from_seq_len // from_block_size - 4):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)[:, :, 2:-2, :, 1:-1, :]
right_slice = attn_weights[:, :, q_idx, :, to_block_size : 4 * to_block_size]
attn_probs_view[:, :, q_idx, :, q_idx : q_idx + 3, :] = right_slice.view(
bsz, n_heads, from_block_size, 3, to_block_size
) # inner_band_product
# global keys (corresponding to 1st key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, :to_block_size] = attn_weights[
:, :, :, :, :to_block_size
].view(bsz, n_heads, -1, to_block_size) # first_band_product
# global keys (corresponding to last key block)
attention_probs[:, :, 2 * from_block_size : -2 * from_block_size, -to_block_size:] = attn_weights[
:, :, :, :, -to_block_size:
].view(bsz, n_heads, -1, to_block_size) # last_band_product
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
for q_idx in range(1, len(i2) - 1):
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[q_idx - 1, :, 4 * to_block_size : -to_block_size]
attn_probs_view[p1, p2, q_idx + 1, :, i2[q_idx]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# Second-last query block
# corresponding to `second_last_context_layer`
attention_probs[:, :, -2 * from_block_size : -from_block_size, :to_block_size] = second_last_attn_weights[
:, :, :, :to_block_size
] # 1st key block (global)
attention_probs[:, :, -2 * from_block_size : -from_block_size, -3 * to_block_size :] = (
second_last_attn_weights[:, :, :, to_block_size : 4 * to_block_size]
) # last three blocks (global + sliding)
# random keys
for p1, i1, w1 in zip(range(bsz), rand_attn, second_last_attn_weights):
# p1, i1, w1 corresponds to batch_dim i.e. following operation is done for each sequence in batch
for p2, i2, w2 in zip(range(n_heads), i1, w1):
# p2, i2, w2 corresponds to head_dim i.e. following operation is done for each heads
attn_probs_view = attention_probs.view(
bsz,
n_heads,
from_seq_len // from_block_size,
from_block_size,
to_seq_len // to_block_size,
to_block_size,
)
right_slice = w2[:, 4 * to_block_size :]
attn_probs_view[p1, p2, -2, :, i2[-1]] = right_slice.view(
from_block_size, n_rand_blocks, to_block_size
)
# last query block
# corresponding to `last_context_layer`
attention_probs[:, :, -from_block_size:, :] = last_attn_weights # all keys global
else:
attention_probs = None
return context_layer, attention_probs
@staticmethod
def torch_gather_b2(params, indices):
# this operation is equivalent to tf.gather when batch_dims=2
if params.shape[:2] != indices.shape[:2]:
raise ValueError(
"Make sure that the first two dimensions of params and indices are identical, but"
f" they are params: {params.shape[:2]} vs. indices: {indices.shape[:2]}"
)
num_indices_to_gather = indices.shape[-2] * indices.shape[-1]
num_indices_to_pick_from = params.shape[2]
shift = torch.arange(indices.shape[0] * indices.shape[1] * num_indices_to_gather, device=indices.device)
indices_shift = torch.div(shift, num_indices_to_gather, rounding_mode="floor") * num_indices_to_pick_from
flattened_indices = indices.view(-1) + indices_shift
flattened_params = params.reshape(-1, params.shape[-2], params.shape[-1])
out_flattened = flattened_params.index_select(0, flattened_indices)
out = out_flattened.reshape(params.shape[:2] + (num_indices_to_gather,) + params.shape[3:])
return out
@staticmethod
def _create_rand_mask_from_inputs(
from_blocked_mask,
to_blocked_mask,
rand_attn,
num_attention_heads,
num_rand_blocks,
batch_size,
from_seq_length,
from_block_size,
):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
rand_attn: [batch_size, num_attention_heads,
from_seq_length//from_block_size-2, num_rand_blocks]
num_attention_heads: int. Number of attention heads.
num_rand_blocks: int. Number of random chunks per row.
batch_size: int. Batch size for computation.
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
Returns:
float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
from_block_size, num_rand_blocks*to_block_size].
"""
num_windows = from_seq_length // from_block_size - 2
rand_mask = torch.stack([p1[i1.flatten()] for p1, i1 in zip(to_blocked_mask, rand_attn)])
rand_mask = rand_mask.view(batch_size, num_attention_heads, num_windows, num_rand_blocks * from_block_size)
rand_mask = torch.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
return rand_mask
@staticmethod
def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
"""
Gives the plan of where to put random attention.
Args:
from_seq_length: int. length of from sequence.
from_block_size: int. size of block in from sequence.
num_rand_blocks: int. Number of random chunks per row.
Returns:
plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
each block
"""
plan_from_length = []
plan_num_rand_blocks = []
if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(0)
elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
plan_num_rand_blocks.append(num_rand_blocks // 2)
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
else:
plan_from_length.append(from_seq_length)
plan_num_rand_blocks.append(num_rand_blocks)
return plan_from_length, plan_num_rand_blocks
def _bigbird_block_rand_mask(
self, from_seq_length, to_seq_length, from_block_size, to_block_size, num_rand_blocks, last_idx=-1
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_rand_blocks: int. Number of random chunks per row.
last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
if positive then num_rand_blocks blocks chosen only up to last_idx.
Returns:
adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
"""
# using this method when from_seq_length in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
rand_attn = np.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=np.int32)
# During inference (eval) no randomness
if not self.training:
return rand_attn
middle_seq = np.arange(1, to_seq_length // to_block_size - 1, dtype=np.int32)
last = to_seq_length // to_block_size - 1
if last_idx > (2 * to_block_size):
last = (last_idx // to_block_size) - 1
r = num_rand_blocks # shorthand
for i in range(1, from_seq_length // from_block_size - 1):
start = i - 2
end = i
if i == 1:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[2:last])[:r]
elif i == 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[3:last])[:r]
elif i == from_seq_length // from_block_size - 3:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -3: should have been sliced till last-3
elif i == from_seq_length // from_block_size - 2:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:last])[:r]
# Missing -4: should have been sliced till last-4
else:
if start > last:
start = last
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
elif (end + 1) == last:
rand_attn[i - 1, :] = np.random.permutation(middle_seq[:start])[:r]
else:
rand_attn[i - 1, :] = np.random.permutation(
np.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
)[:r]
return rand_attn
def _bigbird_block_rand_mask_with_head(
self,
from_seq_length,
to_seq_length,
from_block_size,
to_block_size,
num_heads,
plan_from_length,
plan_num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_top=1,
global_block_bottom=1,
global_block_left=1,
global_block_right=1,
):
"""
Create adjacency list of random attention.
Args:
from_seq_length: int. length of from sequence.
to_seq_length: int. length of to sequence.
from_block_size: int. size of block in from sequence.
to_block_size: int. size of block in to sequence.
num_heads: int. total number of heads.
plan_from_length: list. plan from length where num_random_blocks are chosen from.
plan_num_rand_blocks: list. number of rand blocks within the plan.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_top: int. number of blocks at the top.
global_block_bottom: int. number of blocks at the bottom.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
num_rand_blocks
"""
# using this method when from_seq_length not in [1024, 3072, 4096]
if from_seq_length // from_block_size != to_seq_length // to_block_size:
raise ValueError("Error the number of blocks needs to be same!")
if from_seq_length not in plan_from_length:
raise ValueError("Error from sequence length not in plan!")
# Total number of blocks in the mmask
num_blocks = from_seq_length // from_block_size
# Number of blocks per plan
plan_block_length = np.array(plan_from_length) // from_block_size
# till when to follow plan
max_plan_idx = plan_from_length.index(from_seq_length)
# Random Attention adjacency list
rand_attn = [
np.zeros((num_blocks, np.sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=np.int32)
for i in range(num_heads)
]
# During inference (eval) no randomness
if not self.training:
for nh in range(num_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
# We will go iteratively over the plan blocks and pick random number of
# Attention blocks from the legally allowed blocks
for plan_idx in range(max_plan_idx + 1):
rnd_r_cnt = 0
if plan_idx > 0:
# set the row for all from_blocks starting from 0 to
# plan_block_length[plan_idx-1]
# column indx start from plan_block_length[plan_idx-1] and ends at
# plan_block_length[plan_idx]
if plan_num_rand_blocks[plan_idx] > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=plan_block_length[plan_idx - 1],
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for pl_id in range(plan_idx):
if plan_num_rand_blocks[pl_id] == 0:
continue
for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
rnd_r_cnt = 0
to_start_block_id = 0
if pl_id > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:pl_id]))
to_start_block_id = plan_block_length[pl_id - 1]
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: pl_id + 1]))
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[pl_id],
num_rand_blocks=plan_num_rand_blocks[pl_id],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
if plan_num_rand_blocks[plan_idx] == 0:
continue
curr_r_cnt = int(np.sum(plan_num_rand_blocks[: plan_idx + 1]))
from_start_block_id = global_block_top
to_start_block_id = 0
if plan_idx > 0:
rnd_r_cnt = int(np.sum(plan_num_rand_blocks[:plan_idx]))
from_start_block_id = plan_block_length[plan_idx - 1]
to_start_block_id = plan_block_length[plan_idx - 1]
for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
for h in range(num_heads):
rand_attn[h][blk_rw_idx, rnd_r_cnt:curr_r_cnt] = self._get_single_block_row_attention(
block_id=blk_rw_idx,
to_start_block_id=to_start_block_id,
to_end_block_id=plan_block_length[plan_idx],
num_rand_blocks=plan_num_rand_blocks[plan_idx],
window_block_left=window_block_left,
window_block_right=window_block_right,
global_block_left=global_block_left,
global_block_right=global_block_right,
)
for nh in range(num_heads):
rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
return rand_attn
@staticmethod
def _get_single_block_row_attention(
block_id,
to_start_block_id,
to_end_block_id,
num_rand_blocks,
window_block_left=1,
window_block_right=1,
global_block_left=1,
global_block_right=1,
):
"""
For a single row block get random row attention.
Args:
block_id: int. block id of row.
to_start_block_id: int. random attention column start id.
to_end_block_id: int. random attention column end id.
num_rand_blocks: int. number of random blocks to be selected.
window_block_left: int. number of blocks of window to left of a block.
window_block_right: int. number of blocks of window to right of a block.
global_block_left: int. Number of blocks globally used to the left.
global_block_right: int. Number of blocks globally used to the right.
Returns:
row containing the random attention vector of size num_rand_blocks.
"""
# list of to_blocks from which to choose random attention
to_block_list = np.arange(to_start_block_id, to_end_block_id, dtype=np.int32)
# permute the blocks
perm_block = np.random.permutation(to_block_list)
# illegal blocks for the current block id, using window
illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))
# Add blocks at the start and at the end
illegal_blocks.extend(list(range(global_block_left)))
illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))
# The second from_block cannot choose random attention on second last to_block
if block_id == 1:
illegal_blocks.append(to_end_block_id - 2)
# The second last from_block cannot choose random attention on second to_block
if block_id == to_end_block_id - 2:
illegal_blocks.append(1)
selected_random_blokcs = []
for i in range(to_end_block_id - to_start_block_id):
if perm_block[i] not in illegal_blocks:
selected_random_blokcs.append(perm_block[i])
if len(selected_random_blokcs) == num_rand_blocks:
break
return np.array(selected_random_blokcs, dtype=np.int32)
class BigBirdPegasusEncoderAttention(nn.Module):
def __init__(self, config, seed=None):
super().__init__()
self.config = config
self.seed = seed
self.attention_type = config.attention_type
if self.attention_type == "original_full":
self.self = BigBirdPegasusSelfAttention(config)
elif self.attention_type == "block_sparse":
self.self = BigBirdPegasusBlockSparseAttention(config, seed)
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.config.attention_type}"
)
self.output = nn.Linear(config.hidden_size, config.hidden_size, bias=config.use_bias)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
if value == "original_full":
# copy all weights to new full attention class
attn_weights = BigBirdPegasusSelfAttention(self.config)
else:
# copy all weights to new sparse attention class
attn_weights = BigBirdPegasusBlockSparseAttention(self.config, self.seed)
attn_weights.query = self.self.query
attn_weights.value = self.self.value
attn_weights.key = self.self.key
self.self = attn_weights
self.attention_type = value
if not self.training:
self.self.eval()
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
output_attentions=False,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
):
# Expand dims to enable multiplication in the self-attention module
head_mask = head_mask.reshape(1, -1, 1, 1) if head_mask is not None else None
if self.attention_type == "original_full":
self_outputs = self.self(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
else:
self_outputs = self.self(
hidden_states, band_mask, from_mask, to_mask, from_blocked_mask, to_blocked_mask, output_attentions
)
attention_output = self.output(self_outputs[0])
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
# Copied from transformers.models.bart.modeling_bart.eager_attention_forward
def eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: Optional[float] = None,
dropout: float = 0.0,
head_mask: Optional[torch.Tensor] = None,
**kwargs,
):
if scaling is None:
scaling = query.size(-1) ** -0.5
attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
if head_mask is not None:
attn_weights = attn_weights * head_mask.view(1, -1, 1, 1)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
# Copied from transformers.models.bart.modeling_bart.BartAttention with BartConfig->BigBirdPegasusConfig, Bart->BigBirdPegasusDecoder
class BigBirdPegasusDecoderAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
config: Optional[BigBirdPegasusConfig] = None,
layer_idx: Optional[int] = None,
):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = dropout
self.head_dim = embed_dim // num_heads
self.config = config
if (self.head_dim * num_heads) != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
f" and `num_heads`: {num_heads})."
)
self.scaling = self.head_dim**-0.5
self.is_decoder = is_decoder
self.is_causal = is_causal
self.layer_idx = layer_idx
if layer_idx is None and self.is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
)
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_values: Optional[Cache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
cache_position: Optional[torch.Tensor] = None,
# TODO: we need a refactor so that the different attention modules can get their specific kwargs
# ATM, we have mixed things encoder, decoder, and encoder-decoder attn
**kwargs: Unpack[FlashAttentionKwargs],
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
"""Input shape: Batch x Time x Channel"""
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
# determine input shapes
bsz, tgt_len = hidden_states.shape[:-1]
src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
q_input_shape = (bsz, tgt_len, -1, self.head_dim)
kv_input_shape = (bsz, src_len, -1, self.head_dim)
# get query proj
query_states = self.q_proj(hidden_states).view(*q_input_shape).transpose(1, 2)
if past_key_values is not None:
if isinstance(past_key_values, EncoderDecoderCache):
is_updated = past_key_values.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
curr_past_key_value = past_key_values.cross_attention_cache
else:
curr_past_key_value = past_key_values.self_attention_cache
else:
curr_past_key_value = past_key_values
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_values is not None and is_updated:
# reuse k,v, cross_attentions
key_states = curr_past_key_value.layers[self.layer_idx].keys
value_states = curr_past_key_value.layers[self.layer_idx].values
else:
key_states = self.k_proj(current_states)
value_states = self.v_proj(current_states)
key_states = key_states.view(*kv_input_shape).transpose(1, 2)
value_states = value_states.view(*kv_input_shape).transpose(1, 2)
if past_key_values is not None:
# save all key/value_states to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(
key_states, value_states, self.layer_idx, {"cache_position": cache_position}
)
# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
if is_cross_attention:
past_key_values.is_updated[self.layer_idx] = True
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.dropout,
scaling=self.scaling,
output_attentions=output_attentions,
head_mask=layer_head_mask,
**kwargs,
)
attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
class BigBirdPegasusEncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BigBirdPegasusConfig, seed=None):
super().__init__()
self.attention_type = config.attention_type
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusEncoderAttention(config, seed=seed)
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
band_mask=None,
from_mask=None,
to_mask=None,
from_blocked_mask=None,
to_blocked_mask=None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
self_attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=layer_head_mask,
output_attentions=output_attentions,
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=from_blocked_mask,
to_blocked_mask=to_blocked_mask,
)
hidden_states = self_attention_outputs[0]
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
if hidden_states.dtype == torch.float16 and (
torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
):
clamp_value = torch.finfo(hidden_states.dtype).max - 1000
hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attention_outputs[1],)
return outputs
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
self.self_attn.set_attention_type(value)
class BigBirdPegasusDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: BigBirdPegasusConfig, layer_idx: Optional[int] = None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = BigBirdPegasusDecoderAttention(
embed_dim=self.embed_dim,
num_heads=config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
config=config,
layer_idx=layer_idx,
)
self.dropout = config.dropout
self.activation_fn = ACT2FN[config.activation_function]
self.activation_dropout = config.activation_dropout
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.encoder_attn = BigBirdPegasusDecoderAttention(
self.embed_dim,
config.decoder_attention_heads,
dropout=config.attention_dropout,
is_decoder=True,
bias=config.use_bias,
config=config,
layer_idx=layer_idx,
)
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = True,
cache_position: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
encoder_hidden_states (`torch.FloatTensor`):
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size
`(encoder_attention_heads,)`.
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
size `(decoder_attention_heads,)`.
past_key_values (`Tuple(torch.FloatTensor)`): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
residual = hidden_states
hidden_states = self.self_attn_layer_norm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
past_key_values=past_key_values,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
cache_position=cache_position,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states)
hidden_states, cross_attn_weights = self.encoder_attn(
hidden_states=hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
layer_head_mask=cross_attn_layer_head_mask,
past_key_values=past_key_values,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self.fc2(hidden_states)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
return outputs
# Copied from transformers.models.bart.modeling_bart.BartClassificationHead with Bart->BigBirdPegasus
class BigBirdPegasusClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(
self,
input_dim: int,
inner_dim: int,
num_classes: int,
pooler_dropout: float,
):
super().__init__()
self.dense = nn.Linear(input_dim, inner_dim)
self.dropout = nn.Dropout(p=pooler_dropout)
self.out_proj = nn.Linear(inner_dim, num_classes)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dropout(hidden_states)
hidden_states = self.dense(hidden_states)
hidden_states = torch.tanh(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.out_proj(hidden_states)
return hidden_states
@auto_docstring
class BigBirdPegasusPreTrainedModel(PreTrainedModel):
config: BigBirdPegasusConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["BigBirdPegasusEncoderLayer", "BigBirdPegasusDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_param_buffer_assignment = False
_can_compile_fullgraph = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding_idx is not None:
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.weight.data.fill_(1.0)
module.bias.data.zero_()
@property
def dummy_inputs(self):
pad_token = self.config.pad_token_id
input_ids = torch.tensor([[0, 6, 10, 4, 2], [0, 8, 12, 2, pad_token]], device=self.device)
dummy_inputs = {
"attention_mask": input_ids.ne(pad_token),
"input_ids": input_ids,
}
return dummy_inputs
# Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: Optional[Union[torch.Tensor, "BlockMask"]],
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
):
if self.config._attn_implementation == "flex_attention":
if isinstance(attention_mask, torch.Tensor):
attention_mask = make_flex_block_causal_mask(attention_mask)
# Other attention flavors support in-built causal (when `mask is None`)
# while we need to create our specific block mask regardless
elif attention_mask is None:
attention_mask = make_flex_block_causal_mask(
torch.ones(
size=(input_tensor.shape[0], input_tensor.shape[1]),
device=attention_mask.device,
)
)
return attention_mask
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and (attention_mask == 0.0).any():
return attention_mask
return None
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_compilable_cache:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
inputs_embeds=input_tensor,
past_key_values_length=past_seen_tokens,
is_training=self.training,
):
return None
dtype = input_tensor.dtype
sequence_length = input_tensor.shape[1]
if using_compilable_cache:
target_length = past_key_values.get_max_cache_shape()
else:
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
)
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
cache_position=cache_position,
batch_size=input_tensor.shape[0],
)
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type in ["cuda", "xpu", "npu"]
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
# Details: https://github.com/pytorch/pytorch/issues/110213
min_dtype = torch.finfo(dtype).min
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
return causal_mask
@staticmethod
# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
"""
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target_length (`int`):
The target length: when generating with static cache, the mask should be as long as the static cache,
to account for the 0 padding, the part of the cache that is not filled yet.
dtype (`torch.dtype`):
The dtype to use for the 4D attention mask.
cache_position (`torch.Tensor`):
Indices depicting the position of the input sequence tokens in the sequence.
batch_size (`torch.Tensor`):
Batch size.
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
)
if sequence_length != 1:
causal_mask = torch.triu(causal_mask, diagonal=1)
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask
# Copied from transformers.models.bart.modeling_bart.BartPreTrainedModel._update_cross_attn_mask
def _update_cross_attn_mask(
self,
encoder_hidden_states: Union[torch.Tensor, None],
encoder_attention_mask: Union[torch.Tensor, None],
input_shape: torch.Size,
inputs_embeds: torch.Tensor,
):
# expand encoder attention mask
if encoder_hidden_states is not None and encoder_attention_mask is not None:
if self.config._attn_implementation == "flash_attention_2":
encoder_attention_mask = encoder_attention_mask if 0 in encoder_attention_mask else None
elif self.config._attn_implementation == "sdpa":
# output_attentions=True & cross_attn_head_mask can not be supported when using SDPA, and we fall back on
# the manual implementation that requires a 4D causal mask in all cases.
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask_for_sdpa(
encoder_attention_mask,
inputs_embeds.dtype,
tgt_len=input_shape[-1],
)
elif self.config._attn_implementation == "flex_attention":
if isinstance(encoder_attention_mask, torch.Tensor):
encoder_attention_mask = make_flex_block_causal_mask(
encoder_attention_mask,
query_length=input_shape[-1],
is_causal=False,
)
else:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
encoder_attention_mask = _prepare_4d_attention_mask(
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
)
return encoder_attention_mask
class BigBirdPegasusEncoder(BigBirdPegasusPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`BigBirdPegasusEncoderLayer`].
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.attention_type = config.attention_type
self.block_size = config.block_size
self.dropout = config.dropout
self.layerdrop = config.encoder_layerdrop
embed_dim = config.d_model
self.padding_idx = config.pad_token_id
self.max_source_positions = config.max_position_embeddings
embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
self.embed_tokens = BigBirdPegasusScaledWordEmbedding(
config.vocab_size, embed_dim, self.padding_idx, embed_scale=embed_scale
)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
embed_dim,
)
self.layers = nn.ModuleList([BigBirdPegasusEncoderLayer(config, seed=i) for i in range(config.encoder_layers)])
self.layernorm_embedding = nn.LayerNorm(embed_dim)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
embed_pos = self.embed_positions(input_shape)
hidden_states = inputs_embeds + embed_pos
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
if attention_mask is None:
attention_mask = torch.ones(input_shape, device=hidden_states.device)
attention_mask = attention_mask.long()
# in order to use block_sparse attention, sequence_length has to be at least
# bigger than all global attentions: 2 * block_size
# + sliding tokens: 3 * block_size
# + random tokens: 2 * num_random_blocks * block_size
max_tokens_to_attend = (5 + 2 * self.config.num_random_blocks) * self.config.block_size
if self.attention_type == "block_sparse" and input_shape[1] <= max_tokens_to_attend:
# change attention_type from block_sparse to original_full
sequence_length = input_shape[1]
logger.warning(
"Attention type 'block_sparse' is not possible if sequence_length: "
f"{sequence_length} <= num global tokens: 2 * config.block_size "
"+ min. num sliding tokens: 3 * config.block_size "
"+ config.num_random_blocks * config.block_size "
"+ additional buffer: config.num_random_blocks * config.block_size "
f"= {max_tokens_to_attend} with config.block_size "
f"= {self.config.block_size}, config.num_random_blocks "
f"= {self.config.num_random_blocks}. "
"Changing attention type to 'original_full'..."
)
self.set_attention_type("original_full")
if self.attention_type == "block_sparse":
padding_len, hidden_states, attention_mask = self._pad_to_block_size(hidden_states, attention_mask)
else:
padding_len = 0
# expand attention_mask
if self.attention_type == "original_full":
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype)
blocked_encoder_mask = band_mask = from_mask = to_mask = None
elif self.attention_type == "block_sparse":
blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
attention_mask, self.block_size
)
attention_mask = None
else:
raise ValueError(
f"attention_type can either be original_full or block_sparse, but is {self.attention_type}"
)
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
# check if head_mask has a correct number of layers specified if desired
if head_mask is not None:
if head_mask.size()[0] != len(self.layers):
raise ValueError(
f"The head_mask should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
to_drop = False
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop: # skip the layer
to_drop = True
if to_drop:
layer_outputs = (None, None)
else:
layer_outputs = encoder_layer(
hidden_states,
attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
band_mask=band_mask,
from_mask=from_mask,
to_mask=to_mask,
from_blocked_mask=blocked_encoder_mask,
to_blocked_mask=blocked_encoder_mask,
output_attentions=output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
hidden_states = self.layernorm_embedding(hidden_states)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if padding_len > 0:
# unpad `sequence_output` because the calling function is expecting a length == input_ids.size(1)
hidden_states = hidden_states[:, :-padding_len]
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
)
def set_attention_type(self, value: str):
if value not in ["original_full", "block_sparse"]:
raise ValueError(
f"attention_type can only be set to either 'original_full' or 'block_sparse', but is {value}"
)
# attention type is already correctly set
if value == self.attention_type:
return
self.attention_type = value
for layer in self.layers:
layer.set_attention_type(value)
@staticmethod # Copied from transformers.models.big_bird.modeling_big_bird.BigBirdModel.create_masks_for_block_sparse_attn
def create_masks_for_block_sparse_attn(attention_mask: torch.Tensor, block_size: int):
batch_size, seq_length = attention_mask.size()
if seq_length % block_size != 0:
raise ValueError(
f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
f" size is {block_size}."
)
def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
"""
Create 3D attention mask from a 2D tensor mask.
Args:
from_blocked_mask: 2D Tensor of shape [batch_size,
from_seq_length//from_block_size, from_block_size].
to_blocked_mask: int32 Tensor of shape [batch_size,
to_seq_length//to_block_size, to_block_size].
Returns:
float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
3*to_block_size].
"""
exp_blocked_to_pad = torch.cat(
[to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], dim=2
)
band_mask = torch.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
band_mask.unsqueeze_(1)
return band_mask
blocked_encoder_mask = attention_mask.view(batch_size, seq_length // block_size, block_size)
band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)
from_mask = attention_mask.view(batch_size, 1, seq_length, 1)
to_mask = attention_mask.view(batch_size, 1, 1, seq_length)
return blocked_encoder_mask, band_mask, from_mask, to_mask
def _pad_to_block_size(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor):
"""A helper function to pad tokens and mask to work with implementation of BigBird block-sparse attention."""
# padding
block_size = self.config.block_size
batch_size, seq_len = hidden_states.shape[:2]
padding_len = (block_size - seq_len % block_size) % block_size
if padding_len > 0:
logger.warning_once(
f"Input ids are automatically padded from {seq_len} to {seq_len + padding_len} to be a multiple of "
f"`config.block_size`: {block_size}"
)
pad_id = self.config.pad_token_id
device = hidden_states.device
input_ids_padding = torch.ones((batch_size, padding_len), dtype=torch.long, device=device) * pad_id
inputs_embeds_padding = self.embed_tokens(input_ids_padding)
hidden_states = torch.cat([hidden_states, inputs_embeds_padding], dim=-2)
attention_mask = nn.functional.pad(
attention_mask, (0, padding_len), value=0
) # no attention on the padding tokens
return padding_len, hidden_states, attention_mask
class BigBirdPegasusDecoder(BigBirdPegasusPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BigBirdPegasusDecoderLayer`]
Args:
config: BigBirdPegasusConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: BigBirdPegasusConfig, embed_tokens: Optional[nn.Embedding] = None):
super().__init__(config)
self.dropout = config.dropout
self.layerdrop = config.decoder_layerdrop
self.padding_idx = config.pad_token_id
self.max_target_positions = config.max_position_embeddings
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.embed_tokens = BigBirdPegasusScaledWordEmbedding(
config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
)
if embed_tokens is not None:
self.embed_tokens.weight = embed_tokens.weight
self.embed_positions = BigBirdPegasusLearnedPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
)
self.layers = nn.ModuleList(
[BigBirdPegasusDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]
)
self.layernorm_embedding = nn.LayerNorm(config.d_model)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.Tensor] = None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
of the decoder.
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules in decoder to avoid performing
cross-attention on hidden heads. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
for more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
Indices depicting the position of the input sequence tokens in the sequence. It is used to update the
cache in the correct position and to infer the complete sequence length.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
# retrieve input_ids and inputs_embeds
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.embed_tokens(input_ids)
# initialize `past_key_values`
if use_cache and past_key_values is None:
past_key_values = (
EncoderDecoderCache(DynamicCache(), DynamicCache())
if encoder_hidden_states is not None
else DynamicCache()
)
if use_cache and isinstance(past_key_values, tuple):
logger.warning_once(
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
)
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
batch_size, seq_length = inputs_embeds.size()[:-1]
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
if cache_position is None:
cache_position = torch.arange(
past_key_values_length, past_key_values_length + seq_length, device=inputs_embeds.device
)
if attention_mask is None and not is_torchdynamo_compiling():
# required mask seq length can be calculated via length of past cache
mask_seq_length = past_key_values_length + seq_length
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
self_attn_cache = (
past_key_values.self_attention_cache
if isinstance(past_key_values, EncoderDecoderCache)
else past_key_values
)
attention_mask = self._update_causal_mask(
attention_mask,
inputs_embeds,
cache_position,
self_attn_cache,
)
encoder_attention_mask = self._update_cross_attn_mask(
encoder_hidden_states,
encoder_attention_mask,
input_shape,
inputs_embeds,
)
# embed positions
positions = self.embed_positions(input, past_key_values_length, position_ids=cache_position)
positions = positions.to(inputs_embeds.device)
hidden_states = inputs_embeds + positions
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
raise ValueError(
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
f" {head_mask.size()[0]}."
)
for idx, decoder_layer in enumerate(self.layers):
# add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
if output_hidden_states:
all_hidden_states += (hidden_states,)
if self.training:
dropout_probability = torch.rand([])
if dropout_probability < self.layerdrop:
continue
layer_outputs = decoder_layer(
hidden_states,
attention_mask,
encoder_hidden_states, # as a positional argument for gradient checkpointing
encoder_attention_mask=encoder_attention_mask,
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None),
past_key_values=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=cache_position,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hidden_states = self.layernorm_embedding(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
if not return_dict:
return tuple(
v
for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
@auto_docstring
class BigBirdPegasusModel(BigBirdPegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
padding_idx, vocab_size = config.pad_token_id, config.vocab_size
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
self.shared = BigBirdPegasusScaledWordEmbedding(
vocab_size, config.d_model, padding_idx, embed_scale=embed_scale
)
self.encoder = BigBirdPegasusEncoder(config, self.shared)
self.decoder = BigBirdPegasusDecoder(config, self.shared)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, value):
self.shared = value
self.encoder.embed_tokens = self.shared
self.decoder.embed_tokens = self.shared
def _tie_weights(self):
if self.config.tie_word_embeddings:
self._tie_or_clone_weights(self.encoder.embed_tokens, self.shared)
self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
def get_encoder(self):
return self.encoder
def get_decoder(self):
return self.decoder
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[list[torch.FloatTensor]] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, Seq2SeqModelOutput]:
r"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
"""
# different to other models, BigBirdPegasus automatically creates decoder_input_ids from
# input_ids if no decoder_input_ids are provided
if decoder_input_ids is None and decoder_inputs_embeds is None:
if input_ids is None:
raise ValueError(
"If no `decoder_input_ids` or `decoder_inputs_embeds` are "
"passed, `input_ids` cannot be `None`. Please pass either "
"`input_ids` or `decoder_input_ids` or `decoder_inputs_embeds`."
)
decoder_input_ids = shift_tokens_right(
input_ids, self.config.pad_token_id, self.config.decoder_start_token_id
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if encoder_outputs is None:
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0],
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
)
# decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
encoder_hidden_states=encoder_outputs[0],
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return Seq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
@auto_docstring(
custom_intro="""
The BigBirdPegasus Model with a language modeling head. Can be used for summarization.
"""
)
# Copied from transformers.models.bart.modeling_bart.BartForConditionalGeneration with Bart->BigBirdPegasus, BART->BIGBIRD_PEGASUS
class BigBirdPegasusForConditionalGeneration(BigBirdPegasusPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight", "lm_head.weight"]
_keys_to_ignore_on_load_missing = ["final_logits_bias"]
def __init__(self, config: BigBirdPegasusConfig):
super().__init__(config)
self.model = BigBirdPegasusModel(config)
self.register_buffer("final_logits_bias", torch.zeros((1, self.model.shared.num_embeddings)))
self.lm_head = nn.Linear(config.d_model, self.model.shared.num_embeddings, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_encoder(self):
return self.model.get_encoder()
def get_decoder(self):
return self.model.get_decoder()
def resize_token_embeddings(
self, new_num_tokens: int, pad_to_multiple_of: Optional[int] = None, mean_resizing: bool = True
) -> nn.Embedding:
new_embeddings = super().resize_token_embeddings(new_num_tokens, pad_to_multiple_of, mean_resizing)
self._resize_final_logits_bias(new_embeddings.weight.shape[0])
return new_embeddings
def _resize_final_logits_bias(self, new_num_tokens: int) -> None:
old_num_tokens = self.final_logits_bias.shape[-1]
if new_num_tokens <= old_num_tokens:
new_bias = self.final_logits_bias[:, :new_num_tokens]
else:
extra_bias = torch.zeros((1, new_num_tokens - old_num_tokens), device=self.final_logits_bias.device)
new_bias = torch.cat([self.final_logits_bias, extra_bias], dim=1)
self.register_buffer("final_logits_bias", new_bias)
def _tie_weights(self):
if self.config.tie_word_embeddings:
self.model._tie_weights()
self._tie_or_clone_weights(self.lm_head, self.model.shared)
@auto_docstring
# Ignore copy
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[list[torch.FloatTensor]] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, Seq2SeqLMOutput]:
r"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example summarization:
```python
>>> from transformers import AutoTokenizer, BigBirdPegasusForConditionalGeneration
>>> model = BigBirdPegasusForConditionalGeneration.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> ARTICLE_TO_SUMMARIZE = (
... "The dominant sequence transduction models are based on complex recurrent or convolutional neural "
... "networks in an encoder-decoder configuration. The best performing models also connect the encoder "
... "and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, "
... "based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. "
... "Experiments on two machine translation tasks show these models to be superior in quality "
... "while being more parallelizable and requiring significantly less time to train."
... )
>>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=4096, return_tensors="pt", truncation=True)
>>> # Generate Summary
>>> summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=15)
>>> tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
'dominant sequence models are based on recurrent or convolutional neural networks .'
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
if use_cache:
logger.warning("The `use_cache` argument is changed to `False` since `labels` is provided.")
use_cache = False
if decoder_input_ids is None and decoder_inputs_embeds is None:
decoder_input_ids = shift_tokens_right(
labels, self.config.pad_token_id, self.config.decoder_start_token_id
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
encoder_outputs=encoder_outputs,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
lm_logits = self.lm_head(outputs[0])
lm_logits = lm_logits + self.final_logits_bias.to(lm_logits.device)
masked_lm_loss = None
if labels is not None:
labels = labels.to(lm_logits.device)
loss_fct = CrossEntropyLoss()
masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (lm_logits,) + outputs[1:]
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
return Seq2SeqLMOutput(
loss=masked_lm_loss,
logits=lm_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
@auto_docstring(
custom_intro="""
BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
"""
)
class BigBirdPegasusForSequenceClassification(BigBirdPegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config: BigBirdPegasusConfig, **kwargs):
super().__init__(config, **kwargs)
self.model = BigBirdPegasusModel(config)
self.classification_head = BigBirdPegasusClassificationHead(
config.d_model,
config.d_model,
config.num_labels,
config.classifier_dropout,
)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[list[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, Seq2SeqSequenceClassifierOutput]:
r"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if labels is not None:
use_cache = False
if input_ids is None and inputs_embeds is not None:
raise NotImplementedError(
f"Passing input embeddings is currently not supported for {self.__class__.__name__}"
)
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
hidden_states = outputs[0] # last hidden state
eos_mask = input_ids.eq(self.config.eos_token_id).to(hidden_states.device)
if len(torch.unique_consecutive(eos_mask.sum(1))) > 1:
raise ValueError("All examples must have the same number of <eos> tokens.")
sentence_representation = hidden_states[eos_mask, :].view(hidden_states.size(0), -1, hidden_states.size(-1))[
:, -1, :
]
logits = self.classification_head(sentence_representation)
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.config.num_labels == 1:
self.config.problem_type = "regression"
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.config.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return Seq2SeqSequenceClassifierOutput(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@auto_docstring
class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
_tied_weights_keys = ["encoder.embed_tokens.weight", "decoder.embed_tokens.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.model = BigBirdPegasusModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
@auto_docstring
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[list[torch.FloatTensor]] = None,
start_positions: Optional[torch.LongTensor] = None,
end_positions: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, Seq2SeqQuestionAnsweringModelOutput]:
r"""
decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Provide for translation and summarization training. By default, the model will create this tensor by
shifting the `input_ids` to the right, following the paper.
decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default.
If you want to change padding behavior, you should read
[`modeling_bigbird_pegasus._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in
[the paper](https://huggingface.co/papers/1910.13461) for more information on the default strategy.
decoder_head_mask (`torch.Tensor` of shape `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if start_positions is not None and end_positions is not None:
use_cache = False
outputs = self.model(
input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
head_mask=head_mask,
decoder_head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
encoder_outputs=encoder_outputs,
inputs_embeds=inputs_embeds,
decoder_inputs_embeds=decoder_inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = logits.split(1, dim=-1)
start_logits = start_logits.squeeze(-1).contiguous()
end_logits = end_logits.squeeze(-1).contiguous()
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.size(1)
start_positions = start_positions.clamp(0, ignored_index)
end_positions = end_positions.clamp(0, ignored_index)
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (
start_logits,
end_logits,
) + outputs[1:]
return ((total_loss,) + output) if total_loss is not None else output
return Seq2SeqQuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
past_key_values=outputs.past_key_values,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
# Copied from transformers.models.pegasus.modeling_pegasus.PegasusDecoderWrapper with Pegasus->BigBirdPegasus
class BigBirdPegasusDecoderWrapper(BigBirdPegasusPreTrainedModel):
"""
This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is
used in combination with the [`EncoderDecoderModel`] framework.
"""
def __init__(self, config):
super().__init__(config)
self.decoder = BigBirdPegasusDecoder(config)
def forward(self, *args, **kwargs):
return self.decoder(*args, **kwargs)
class BigBirdPegasusForCausalLM(BigBirdPegasusPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
config.is_decoder = True
config.is_encoder_decoder = False
super().__init__(config)
self.model = BigBirdPegasusDecoderWrapper(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.model.decoder.embed_tokens
def set_input_embeddings(self, value):
self.model.decoder.embed_tokens = value
def set_decoder(self, decoder):
self.model.decoder = decoder
def get_decoder(self):
return self.model.decoder
@auto_docstring
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[tuple[tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[tuple, CausalLMOutputWithCrossAttentions]:
r"""
cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*):
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, BigBirdPegasusForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")
>>> model = BigBirdPegasusForCausalLM.from_pretrained(
... "google/bigbird-pegasus-large-arxiv", add_cross_attention=False
... )
>>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder."
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> logits = outputs.logits
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model.decoder(
input_ids=input_ids,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
head_mask=head_mask,
cross_attn_head_mask=cross_attn_head_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
)
logits = self.lm_head(outputs[0])
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithCrossAttentions(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
__all__ = [
"BigBirdPegasusForCausalLM",
"BigBirdPegasusForConditionalGeneration",
"BigBirdPegasusForQuestionAnswering",
"BigBirdPegasusForSequenceClassification",
"BigBirdPegasusModel",
"BigBirdPegasusPreTrainedModel",
]
| transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py/0 | {
"file_path": "transformers/src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py",
"repo_id": "transformers",
"token_count": 65430
} | 462 |
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