Add support for LFM2 models

README.md

@@ -355,6 +355,7 @@ You can refine your search by selecting the task you're interested in (e.g., [te
 1. **JinaCLIP** (from Jina AI) released with the paper [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://huggingface.co/papers/2405.20204) by Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao.
 1. **LiteWhisper** (from University of Washington, Kotoba Technologies) released with the paper [LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation](https://huggingface.co/papers/2502.20583) by Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima, Baris Kasikci.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://huggingface.co/papers/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
+1. **[LFM2](https://huggingface.co/docs/transformers/model_doc/lfm2)** (from Liquid AI) released with the blog post [Introducing LFM2: The Fastest On-Device Foundation Models on the Market](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models) by the Liquid AI Team.
 1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://huggingface.co/papers/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
 1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
 1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://huggingface.co/papers/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.

docs/snippets/6_supported-models.snippet

@@ -69,6 +69,7 @@
 1. **JinaCLIP** (from Jina AI) released with the paper [Jina CLIP: Your CLIP Model Is Also Your Text Retriever](https://huggingface.co/papers/2405.20204) by Andreas Koukounas, Georgios Mastrapas, Michael Günther, Bo Wang, Scott Martens, Isabelle Mohr, Saba Sturua, Mohammad Kalim Akram, Joan Fontanals Martínez, Saahil Ognawala, Susana Guzman, Maximilian Werk, Nan Wang, Han Xiao.
 1. **LiteWhisper** (from University of Washington, Kotoba Technologies) released with the paper [LiteASR: Efficient Automatic Speech Recognition with Low-Rank Approximation](https://huggingface.co/papers/2502.20583) by Keisuke Kamahori, Jungo Kasai, Noriyuki Kojima, Baris Kasikci.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://huggingface.co/papers/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
+1. **[LFM2](https://huggingface.co/docs/transformers/model_doc/lfm2)** (from Liquid AI) released with the blog post [Introducing LFM2: The Fastest On-Device Foundation Models on the Market](https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models) by the Liquid AI Team.
 1. **[LLaMA](https://huggingface.co/docs/transformers/model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://huggingface.co/papers/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
 1. **[Llama2](https://huggingface.co/docs/transformers/model_doc/llama2)** (from The FAIR team of Meta AI) released with the paper [Llama2: Open Foundation and Fine-Tuned Chat Models](https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/XXX) by Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushka rMishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing EllenTan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom.
 1. **[LLaVa](https://huggingface.co/docs/transformers/model_doc/llava)** (from Microsoft Research & University of Wisconsin-Madison) released with the paper [Visual Instruction Tuning](https://huggingface.co/papers/2304.08485) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.

src/configs.js

@@ -109,6 +109,7 @@ function getNormalizedConfig(config) {
             mapping['hidden_size'] = 'hidden_size';
             break;
         case 'llama':
+        case 'lfm2':
         case 'smollm3':
         case 'olmo':
         case 'olmo2':
@@ -261,9 +262,35 @@ function getNormalizedConfig(config) {
  * @param {PretrainedConfig} config 
  * @returns {Record<string, number[]>}
  */
-export function getKeyValueShapes(config, {
+export function getCacheShapes(config, options) {
+    if (config.model_type === 'lfm2') {
+        // Custom caching mechanism for LFM2
+        /** @type {Record<string, number[]>} */
+        const cache_values = {};
+        // @ts-expect-error TS2339
+        const { layer_types, num_attention_heads, num_key_value_heads, hidden_size, conv_L_cache } = config;
+        const head_dim = hidden_size / num_attention_heads;
+        const batch_size = options?.batch_size ?? 1;
+        for (let i = 0; i < layer_types.length; ++i) {
+            if (layer_types[i] === 'full_attention') {
+                for (const kv of ['key', 'value']) {
+                    cache_values[`past_key_values.${i}.${kv}`] = [batch_size, num_key_value_heads, 0, head_dim];
+                }
+            } else if (layer_types[i] === 'conv') {
+                cache_values[`past_conv.${i}`] = [batch_size, hidden_size, conv_L_cache];
+            } else {
+                throw new Error(`Unsupported layer type: ${layer_types[i]}`);
+            }
+        }
+        return cache_values;
+    }
+    return getKeyValueShapes(config, options);
+}
+
+/** @type {typeof getKeyValueShapes} */
+function getKeyValueShapes(config, {
     prefix = 'past_key_values',
-    batch_size=1,
+    batch_size = 1,
 } = {}) {
     /** @type {Record<string, number[]>} */
     const decoderFeeds = {};

src/models.js

@@ -40,7 +40,7 @@
 
 import {
     AutoConfig,
-    getKeyValueShapes,
+    getCacheShapes,
 } from './configs.js';
 
 import {
@@ -318,7 +318,7 @@ async function getSession(pretrained_model_name_or_path, fileName, options) {
     }
 
     if (selectedDevice === 'webgpu') {
-        const shapes = getKeyValueShapes(options.config, {
+        const shapes = getCacheShapes(options.config, {
             prefix: 'present',
         });
         if (Object.keys(shapes).length > 0 && !isONNXProxy()) {
@@ -1960,7 +1960,9 @@ export class PreTrainedModel extends Callable {
 
         for (const name in decoderResults) {
             if (name.startsWith('present')) {
-                const newName = name.replace('present', 'past_key_values');
+                const newName = name
+                    .replace('present_conv', 'past_conv') // Hybrid cache architecture (e.g., LFM2)
+                    .replace('present', 'past_key_values');
                 const is_encoder_pkv = name.includes('encoder');
                 if (is_encoder_pkv && pastKeyValues) {
                     // Optimization introduced by optimum to reuse past key values.
@@ -2017,14 +2019,14 @@ export class PreTrainedModel extends Callable {
             Object.assign(decoderFeeds, pastKeyValues)
         } else {
             const session = this.sessions['decoder_model_merged'] ?? this.sessions['model'];
-            const dtype = session?.config?.kv_cache_dtype ?? 'float32';
-            const empty = (dtype === 'float16') ? new DataTypeMap.float16() : [];
-
             const batch_size = (decoderFeeds[this.main_input_name] ?? decoderFeeds.attention_mask)?.dims?.[0] ?? 1;
-            const shapes = getKeyValueShapes(this.config, { batch_size });
 
+            const dtype = session?.config?.kv_cache_dtype ?? 'float32';
+            const cls = (dtype === 'float16') ? DataTypeMap.float16 : DataTypeMap.float32;
+            const shapes = getCacheShapes(this.config, { batch_size });
             for (const name in shapes) {
-                decoderFeeds[name] = new Tensor(dtype, empty, shapes[name]);
+                const size = shapes[name].reduce((a, b) => a * b, 1);
+                decoderFeeds[name] = new Tensor(dtype, new cls(size), shapes[name]);
             }
         }
     }
@@ -4586,6 +4588,13 @@ export class LlamaModel extends LlamaPreTrainedModel { }
 export class LlamaForCausalLM extends LlamaPreTrainedModel { }
 //////////////////////////////////////////////////
 
+//////////////////////////////////////////////////
+// LFM2 models
+export class Lfm2PreTrainedModel extends PreTrainedModel { }
+export class Lfm2Model extends Lfm2PreTrainedModel { }
+export class Lfm2ForCausalLM extends Lfm2PreTrainedModel { }
+//////////////////////////////////////////////////
+
 //////////////////////////////////////////////////
 // SmolLM3 models
 export class SmolLM3PreTrainedModel extends PreTrainedModel { }
@@ -7803,6 +7812,7 @@ const MODEL_MAPPING_NAMES_DECODER_ONLY = new Map([
     ['gpt_neox', ['GPTNeoXModel', GPTNeoXModel]],
     ['codegen', ['CodeGenModel', CodeGenModel]],
     ['llama', ['LlamaModel', LlamaModel]],
+    ['lfm2', ['Lfm2Model', Lfm2Model]],
     ['smollm3', ['SmolLM3Model', SmolLM3Model]],
     ['exaone', ['ExaoneModel', ExaoneModel]],
     ['olmo', ['OlmoModel', OlmoModel]],
@@ -7908,6 +7918,7 @@ const MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = new Map([
     ['gpt_neox', ['GPTNeoXForCausalLM', GPTNeoXForCausalLM]],
     ['codegen', ['CodeGenForCausalLM', CodeGenForCausalLM]],
     ['llama', ['LlamaForCausalLM', LlamaForCausalLM]],
+    ['lfm2', ['Lfm2ForCausalLM', Lfm2ForCausalLM]],
     ['smollm3', ['SmolLM3ForCausalLM', SmolLM3ForCausalLM]],
     ['exaone', ['ExaoneForCausalLM', ExaoneForCausalLM]],
     ['olmo', ['OlmoForCausalLM', OlmoForCausalLM]],

tests/models/lfm2/test_modeling_lfm2.js

@@ -0,0 +1,51 @@
+import { PreTrainedTokenizer, Lfm2ForCausalLM } 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("Lfm2ForCausalLM", () => {
+    const model_id = "onnx-internal-testing/tiny-random-Lfm2ForCausalLM";
+    /** @type {Lfm2ForCausalLM} */
+    let model;
+    /** @type {PreTrainedTokenizer} */
+    let tokenizer;
+    beforeAll(async () => {
+      model = await Lfm2ForCausalLM.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, 52572n, 38892n, 6902n, 53329n, 33092n, 13656n, 49822n, 6902n, 52520n]]);
+      },
+      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, 1n, 52572n, 60239n, 57205n, 6790n, 58292n, 30935n, 5959n, 6902n],
+          [1n, 52572n, 2031n, 59572n, 43345n, 42427n, 31142n, 41100n, 5321n, 5816n],
+        ]);
+      },
+      MAX_TEST_EXECUTION_TIME,
+    );
+
+    afterAll(async () => {
+      await model?.dispose();
+    }, MAX_MODEL_DISPOSE_TIME);
+  });
+};