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Add support for Helium and Glm (huggingface#1156)
* Add support for Helium and Glm * Add unit tests * Remove duplicate definitions
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,51 @@ | ||
| import { PreTrainedTokenizer, GlmForCausalLM } from "../../../src/transformers.js"; | ||
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|
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| import { MAX_MODEL_LOAD_TIME, MAX_TEST_EXECUTION_TIME, MAX_MODEL_DISPOSE_TIME, DEFAULT_MODEL_OPTIONS } from "../../init.js"; | ||
|
|
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| export default () => { | ||
| describe("GlmForCausalLM", () => { | ||
| const model_id = "hf-internal-testing/tiny-random-GlmForCausalLM"; | ||
| /** @type {GlmForCausalLM} */ | ||
| let model; | ||
| /** @type {PreTrainedTokenizer} */ | ||
| let tokenizer; | ||
| beforeAll(async () => { | ||
| model = await GlmForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS); | ||
| tokenizer = await PreTrainedTokenizer.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([[23582n, 5797n, 38238n, 24486n, 36539n, 34489n, 6948n, 34489n, 6948n, 16014n]]); | ||
| }, | ||
| 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([ | ||
| [59246n, 23582n, 5797n, 38238n, 24486n, 36539n, 34489n, 6948n, 34489n, 6948n], | ||
| [23582n, 2901n, 39936n, 25036n, 55411n, 10337n, 3424n, 39183n, 30430n, 37285n], | ||
| ]); | ||
| }, | ||
| MAX_TEST_EXECUTION_TIME, | ||
| ); | ||
|
|
||
| afterAll(async () => { | ||
| await model?.dispose(); | ||
| }, MAX_MODEL_DISPOSE_TIME); | ||
| }); | ||
| }; |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,51 @@ | ||
| import { PreTrainedTokenizer, HeliumForCausalLM } 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("HeliumForCausalLM", () => { | ||
| const model_id = "hf-internal-testing/tiny-random-HeliumForCausalLM"; | ||
| /** @type {HeliumForCausalLM} */ | ||
| let model; | ||
| /** @type {PreTrainedTokenizer} */ | ||
| let tokenizer; | ||
| beforeAll(async () => { | ||
| model = await HeliumForCausalLM.from_pretrained(model_id, DEFAULT_MODEL_OPTIONS); | ||
| tokenizer = await PreTrainedTokenizer.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([[1n, 456n, 5660n, 1700n, 1486n, 37744n, 35669n, 39396n, 12024n, 32253n]]); | ||
| }, | ||
| 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([ | ||
| [3n, 1n, 456n, 5660n, 1700n, 1486n, 37744n, 35669n, 39396n, 12024n], | ||
| [1n, 456n, 5660n, 998n, 6136n, 2080n, 172n, 8843n, 40579n, 23953n], | ||
| ]); | ||
| }, | ||
| MAX_TEST_EXECUTION_TIME, | ||
| ); | ||
|
|
||
| afterAll(async () => { | ||
| await model?.dispose(); | ||
| }, MAX_MODEL_DISPOSE_TIME); | ||
| }); | ||
| }; |