Support dequantizing GGUF FP16 format (#31783)

* support gguf fp16

* support gguf bf16 with pytorch

* add gguf f16 test

* remove bf16

src/transformers/integrations/ggml.py

@@ -36,6 +36,7 @@ logger = logging.get_logger(__name__)
 # Listed here: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md
 GGML_TYPES = {
     "F32": 0,
+    "F16": 1,
     "Q4_0": 2,
     "Q8_0": 8,
     "Q2_K": 10,
@@ -489,6 +490,8 @@ def dequantize_q5_k(data):
 def load_dequant_gguf_tensor(shape, ggml_type, data):
     if ggml_type == GGML_TYPES["F32"]:
         values = data
+    elif ggml_type == GGML_TYPES["F16"]:
+        values = data
     elif ggml_type == GGML_TYPES["Q8_0"]:
         values = dequantize_q8_0(data)
     elif ggml_type == GGML_TYPES["Q4_0"]:

tests/quantization/ggml/test_ggml.py

@@ -33,6 +33,7 @@ class GgufIntegrationTests(unittest.TestCase):
     mistral_model_id = "TheBloke/Mistral-7B-Instruct-v0.2-GGUF"
     qwen2_model_id = "Qwen/Qwen1.5-0.5B-Chat-GGUF"
     llama3_model_id = "NousResearch/Meta-Llama-3-8B-GGUF"
+    tinyllama_model_id = "PenutChen/TinyLlama-1.1B-Chat-v1.0-GGUF"
 
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
     q4_k_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf"
@@ -45,6 +46,7 @@ class GgufIntegrationTests(unittest.TestCase):
     q4_0_mistral_model_id = "mistral-7b-instruct-v0.2.Q4_0.gguf"
     q4_0_qwen2_model_id = "qwen1_5-0_5b-chat-q4_0.gguf"
     q4_llama3_model_id = "Meta-Llama-3-8B-Q4_K_M.gguf"
+    f16_tinyllama_model_id = "TinyLlama-1.1B-Chat-v1.0.FP16.gguf"
 
     example_text = "Hello"
 
@@ -149,6 +151,18 @@ class GgufIntegrationTests(unittest.TestCase):
         EXPECTED_TEXT = "Hello, World!\n\n5. Use a library"
         self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
 
+    def test_f16(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.tinyllama_model_id, gguf_file=self.f16_tinyllama_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.tinyllama_model_id, gguf_file=self.f16_tinyllama_model_id
+        ).to(torch_device)
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, World!\n\n5. Node.js"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
     def test_mistral_q4_0(self):
         tokenizer = AutoTokenizer.from_pretrained(self.mistral_model_id, gguf_file=self.q4_0_mistral_model_id)
         model = AutoModelForCausalLM.from_pretrained(