Add GGUF for starcoder2

docs/source/en/gguf.md

@@ -84,6 +84,7 @@ For now the supported model architectures are the architectures that have been v
 - Falcon
 - StableLM
 - GPT2
+- Starcoder2
 
 ## Example usage
 

src/transformers/integrations/ggml.py

@@ -176,6 +176,20 @@
         "ffn_up": "mlp.c_fc",
         "ffn_down": "mlp.c_proj",
     },
+    "starcoder2": {
+        "token_embd": "model.embed_tokens",
+        "blk": "model.layers",
+        "ffn_up": "mlp.c_fc",
+        "ffn_down": "mlp.c_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "attn_norm": "input_layernorm",
+        "attn_q": "self_attn.q_proj",
+        "attn_v": "self_attn.v_proj",
+        "attn_k": "self_attn.k_proj",
+        "attn_output": "self_attn.o_proj",
+        "output.weight": "lm_head.weight",
+        "output_norm": "model.norm",
+    },
 }
 
 
@@ -292,6 +306,15 @@
         "attention.head_count": "n_head",
         "attention.layer_norm_epsilon": "layer_norm_epsilon",
     },
+    "starcoder2": {
+        "block_count": "num_hidden_layers",
+        "context_length": "max_position_embeddings",
+        "embedding_length": "hidden_size",
+        "feed_forward_length": "intermediate_size",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_epsilon": "norm_epsilon",
+    },
 }
 
 GGUF_TOKENIZER_MAPPING = {
@@ -622,6 +645,7 @@ def converted(self) -> Tokenizer:
     "falcon": GGUFGPTConverter,
     "stablelm": GGUFGPTConverter,
     "gpt2": GGUFGPTConverter,
+    "starcoder2": GGUFGPTConverter,
 }
 
 

tests/quantization/ggml/test_ggml.py

@@ -54,6 +54,9 @@ class GgufIntegrationTests(unittest.TestCase):
     gpt2_model_id = "mradermacher/gpt2-GGUF"
     gpt2_original_model_id = "openai-community/gpt2"
     gpt2_xl_model_id = "RichardErkhov/openai-community_-_gpt2-xl-gguf"
+    starcoder2_model_id = "QuantFactory/starcoder2-3b-GGUF"
+    starcoder2_fp16_model_id = "brittlewis12/starcoder2-3b-GGUF"
+    starcoder2_original_model_id = "bigcode/starcoder2-3b"
 
     # standard quants
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -93,6 +96,8 @@ class GgufIntegrationTests(unittest.TestCase):
     fp16_gpt2_model_id = "gpt2.f16.gguf"
     q8_gpt2_model_id = "gpt2.Q8_0.gguf"
     q6_k_gpt2_xl_model_id = "gpt2-xl.Q6_K.gguf"
+    q6_k_starcoder2_model_id = "starcoder2-3b.Q6_K.gguf"
+    fp16_starcoder2_gguf_model_id = "starcoder2-3b.fp16.gguf"
 
     example_text = "Hello"
 
@@ -650,6 +655,45 @@ def test_stablelm_weights_conversion_fp16(self):
                 self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
                 torch.testing.assert_close(original_params, converted_state_dict[layer_name])
 
+    def test_starcoder2_weights_conversion_fp16(self):
+        original_model = AutoModelForCausalLM.from_pretrained(
+            self.starcoder2_original_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        converted_model = AutoModelForCausalLM.from_pretrained(
+            self.starcoder2_fp16_model_id,
+            gguf_file=self.fp16_starcoder2_gguf_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        converted_state_dict = converted_model.state_dict()
+        original_state_dict = original_model.state_dict()
+
+        for layer_name, original_params in original_state_dict.items():
+            if layer_name in converted_state_dict and layer_name != "lm_head.weight":
+                # quantized models do not contain "lm_head.weight" layer
+                self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
+                torch.testing.assert_close(original_params, converted_state_dict[layer_name])
+
+    def test_starcoder2_q6_k(self):
+        example_function_text = "def print_hello_world():"
+        model = AutoModelForCausalLM.from_pretrained(
+            self.starcoder2_model_id,
+            gguf_file=self.q6_k_starcoder2_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(self.starcoder2_model_id, gguf_file=self.q6_k_starcoder2_model_id)
+        text = tokenizer(example_function_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = 'def print_hello_world():\n    print("Hello World")\n\ndef print'
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
     def test_tokenization_xnli(self):
         import tqdm
         from datasets import load_dataset