Add gguf support for gpt2

docs/source/en/gguf.md

@@ -83,6 +83,7 @@ For now the supported model architectures are the architectures that have been v
 - Bloom
 - Falcon
 - StableLM
+- GPT2
 
 ## Example usage
 

src/transformers/integrations/ggml.py

@@ -163,6 +163,19 @@
         "output.weight": "lm_head.weight",
         "output_norm": "model.norm",
     },
+    "gpt2": {
+        "token_embd": "transformer.wte",
+        "blk": "transformer.h",
+        "position_embd": "transformer.wpe",
+        "output_norm": "transformer.ln_f",
+        "attn_norm": "ln_1",
+        "attn_qkv": "attn.c_attn",
+        "attn_output.weight": "attn.c_proj.weight",
+        "attn_output.bias": "attn.c_proj.bias",
+        "ffn_norm": "ln_2",
+        "ffn_up": "mlp.c_fc",
+        "ffn_down": "mlp.c_proj",
+    },
 }
 
 
@@ -271,6 +284,14 @@
         "attention.layer_norm_epsilon": "layer_norm_eps",
         "vocab_size": "vocab_size",
     },
+    "gpt2": {
+        "block_count": "n_layer",
+        "context_length": "n_ctx",
+        "embedding_length": "n_embd",
+        "feed_forward_length": "feed_forward_length",
+        "attention.head_count": "n_head",
+        "attention.layer_norm_epsilon": "layer_norm_epsilon",
+    },
 }
 
 GGUF_TOKENIZER_MAPPING = {
@@ -600,6 +621,7 @@ def converted(self) -> Tokenizer:
     "bloom": GGUFGPTConverter,
     "falcon": GGUFGPTConverter,
     "stablelm": GGUFGPTConverter,
+    "gpt2": GGUFGPTConverter,
 }
 
 

src/transformers/modeling_gguf_pytorch_utils.py

@@ -191,6 +191,23 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
                 else:
                     weights = reverse_reshape_bias(weights, num_heads, n_embed)
 
+            if architecture == "gpt2":
+                if (
+                    "attn_qkv.weight" in name
+                    or "ffn_down.weight" in name
+                    or "ffn_up.weight" in name
+                    or "attn_output.weight" in name
+                ):
+                    # Original transpose implementation
+                    # https://github.com/ggerganov/llama.cpp/blob/a38b884c6c4b0c256583acfaaabdf556c62fabea/convert_hf_to_gguf.py#L2060-L2061
+                    weights = weights.T
+                if name == "output.weight":
+                    # output.weight has conflicts with attn_output.weight in name checking
+                    # we have to explicitly check that name is exactly output.weight
+                    name = "lm_head.weight"
+                    parsed_parameters["tensors"][name] = torch.from_numpy(np.copy(weights))
+                    continue
+
             for tensor_name in tensor_key_mapping:
                 if tensor_name in name:
                     name = name.replace(tensor_name, tensor_key_mapping[tensor_name])

src/transformers/models/gpt2/tokenization_gpt2_fast.py

@@ -97,8 +97,8 @@ def __init__(
         **kwargs,
     ):
         super().__init__(
-            vocab_file,
-            merges_file,
+            vocab_file=vocab_file,
+            merges_file=merges_file,
             tokenizer_file=tokenizer_file,
             unk_token=unk_token,
             bos_token=bos_token,

tests/quantization/ggml/test_ggml.py

@@ -51,6 +51,9 @@ class GgufIntegrationTests(unittest.TestCase):
     stablelm_model_id = "afrideva/stablelm-3b-4e1t-GGUF"
     stablelm2_model_id = "afrideva/stablelm-2-1_6b-GGUF"
     original_stablelm2_model_id = "stabilityai/stablelm-2-1_6b"
+    gpt2_model_id = "mradermacher/gpt2-GGUF"
+    gpt2_original_model_id = "openai-community/gpt2"
+    gpt2_xl_model_id = "RichardErkhov/openai-community_-_gpt2-xl-gguf"
 
     # standard quants
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -87,6 +90,9 @@ class GgufIntegrationTests(unittest.TestCase):
     fp16_falcon7b_model_id = "falcon-7b-fp16.gguf"
     q2_k_falcon40b_model_id = "tiiuae-falcon-40b-Q2_K.gguf"
     fp16_qwen2moe_model_id = "Qwen1.5-MoE-A2.7B.gguf"
+    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"
 
     example_text = "Hello"
 
@@ -476,6 +482,53 @@ def test_bloom_weights_conversion_fp16(self):
                 self.assertTrue(quantized_param.shape == original_param.shape)
                 torch.testing.assert_close(quantized_param, original_param)
 
+    def test_gpt2_q8(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.gpt2_model_id, gguf_file=self.q8_gpt2_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.gpt2_model_id,
+            gguf_file=self.q8_gpt2_model_id,
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt")
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, I'm sorry. I'm sorry. I"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_gpt2_weights_conversion_fp16(self):
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.gpt2_model_id,
+            gguf_file=self.fp16_gpt2_model_id,
+            torch_dtype=torch.float16,
+        )
+        original_model = AutoModelForCausalLM.from_pretrained(
+            self.gpt2_original_model_id,
+            torch_dtype=torch.float16,
+        )
+
+        quantized_state_dict = quantized_model.state_dict()
+        original_state_dict = original_model.state_dict()
+
+        for layer_name, original_params in original_state_dict.items():
+            if layer_name in quantized_state_dict:
+                self.assertTrue(original_params.shape == quantized_state_dict[layer_name].shape)
+                torch.testing.assert_close(original_params, quantized_state_dict[layer_name])
+
+    def test_gpt2_xl_Q6_K(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.gpt2_xl_model_id, gguf_file=self.q6_k_gpt2_xl_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.gpt2_xl_model_id,
+            gguf_file=self.q6_k_gpt2_xl_model_id,
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt")
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, I'm a newbie to the world of"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
     @unittest.skip(reason="Heavy memory")
     def test_falcon40b_q2_k(self):
         tokenizer = AutoTokenizer.from_pretrained(self.falcon40b_model_id, gguf_file=self.q2_k_falcon40b_model_id)