Add falcon gguf

docs/source/en/gguf.md

@@ -81,6 +81,7 @@ For now the supported model architectures are the architectures that have been v
 - Qwen2Moe
 - Phi3
 - Bloom
+- Falcon
 
 ## Example usage
 

src/transformers/convert_slow_tokenizer.py

@@ -345,7 +345,6 @@ def converted(self, vocab: Dict[str, int] = None, merges: List[Tuple[str, str]]
             )
         )
 
-        add_prefix_space = False
         add_prefix_space = getattr(self.original_tokenizer, "add_prefix_space", False)
         tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
         tokenizer.decoder = decoders.ByteLevel()

src/transformers/integrations/ggml.py

@@ -120,6 +120,29 @@
         "output.weight": "lm_head.weight",
         "output_norm": "transformer.ln_f",
     },
+    "falcon7b": {
+        "token_embd": "word_embeddings",
+        "blk": "h",
+        "ffn_up": "mlp.dense_h_to_4h",
+        "ffn_down": "mlp.dense_4h_to_h",
+        "attn_norm": "input_layernorm",
+        "attn_qkv": "self_attention.query_key_value",
+        "attn_output": "self_attention.dense",
+        ".output.": ".lm_head.",
+        "output_norm": "ln_f",
+    },
+    "falcon40b": {
+        "token_embd": "word_embeddings",
+        "blk": "h",
+        "ffn_up": "mlp.dense_h_to_4h",
+        "ffn_down": "mlp.dense_4h_to_h",
+        ".attn_norm.": ".ln_mlp.",
+        "attn_norm_2": "ln_attn",
+        "attn_qkv": "self_attention.query_key_value",
+        "attn_output": "self_attention.dense",
+        ".output.": ".lm_head.",
+        "output_norm": "ln_f",
+    },
 }
 
 
@@ -178,6 +201,18 @@
         "attention.layer_norm_rms_epsilon": "rms_norm_eps",
         "vocab_size": "vocab_size",
     },
+    "falcon": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        "feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": None,
+        "rope.freq_base": "rope_theta",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+        "vocab_size": "vocab_size",
+    },
     "tokenizer": {
         "ggml.bos_token_id": "bos_token_id",
         "ggml.eos_token_id": "eos_token_id",
@@ -530,6 +565,7 @@ def converted(self) -> Tokenizer:
     "qwen2_moe": GGUFQwen2Converter,
     "phi3": GGUFPhi3Converter,
     "bloom": GGUFBloomConverter,
+    "falcon": GGUFBloomConverter,
 }
 
 

src/transformers/modeling_gguf_pytorch_utils.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 from typing import Optional
 
 import numpy as np
@@ -99,8 +100,20 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
     if "qwen2moe" in architecture:
         updated_architecture = "qwen2_moe"
 
-    if architecture not in GGUF_SUPPORTED_ARCHITECTURES:
-        raise ValueError(f"Architecture {architecture} not supported")
+    model_size = ""
+    # extract the number of params from file name as architectures can differ ;
+    # eg. for falcon : `...falcon-7b-...`
+    if "falcon" in architecture:
+        gguf_file_name = gguf_checkpoint_path.split("/")[-1].lower()
+        m = re.search(r"-\d+b-", gguf_file_name)  # regex to catch `-7b-`
+        if m is None:
+            raise ValueError(
+                f"From file name, cannot determine the number of parameters for {architecture} architecture"
+            )
+        model_size = m.group().strip("-")  # only keeps `7b`
+
+    if architecture + model_size not in GGUF_SUPPORTED_ARCHITECTURES:
+        raise ValueError(f"Architecture {architecture + model_size} not supported")
 
     # List all key-value pairs in a columnized format
     for gguf_key, field in reader.fields.items():
@@ -146,17 +159,9 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
             )
 
     if return_tensors:
-        tensor_key_mapping = GGUF_TO_TRANSFORMERS_MAPPING["tensors"][architecture]
+        tensor_key_mapping = GGUF_TO_TRANSFORMERS_MAPPING["tensors"][architecture + model_size]
 
         for tensor in tqdm(reader.tensors, desc="Converting and de-quantizing GGUF tensors..."):
-            renamed_tensor_name = tensor.name
-
-            for tensor_name_mapping in GGUF_TO_TRANSFORMERS_MAPPING["tensors"]:
-                if tensor_name_mapping in renamed_tensor_name:
-                    renamed_tensor_name = renamed_tensor_name.replace(
-                        tensor_name_mapping, GGUF_TO_TRANSFORMERS_MAPPING["tensors"][tensor_name_mapping]
-                    )
-
             name = tensor.name
 
             weights = dequantize(tensor.data, tensor.tensor_type)

tests/quantization/ggml/test_ggml.py

@@ -44,6 +44,9 @@ class GgufIntegrationTests(unittest.TestCase):
     phi3_model_id = "microsoft/Phi-3-mini-4k-instruct-gguf"
     bloom_model_id = "afrideva/bloom-560m-GGUF"
     original_bloom_model_id = "bigscience/bloom-560m"
+    falcon7b_model_id = "xaviviro/falcon-7b-quantized-gguf"
+    falcon40b_model_id = "maddes8cht/tiiuae-falcon-40b-gguf"
+    original_flacon7b_model_id = "tiiuae/falcon-7b"
 
     # standard quants
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -74,6 +77,9 @@ class GgufIntegrationTests(unittest.TestCase):
     fp16_bloom_model_id = "bloom-560m.fp16.gguf"
     q8_bloom_model_id = "bloom-560m.q8_0.gguf"
     f16_tinyllama_model_id = "TinyLlama-1.1B-Chat-v1.0.FP16.gguf"
+    q2_k_falcon7b_model_id = "falcon-7b-q2_k.gguf"
+    fp16_falcon7b_model_id = "falcon-7b-fp16.gguf"
+    q2_k_falcon40b_model_id = "tiiuae-falcon-40b-Q2_K.gguf"
 
     example_text = "Hello"
 
@@ -445,6 +451,58 @@ def test_bloom_weights_conversion_fp16(self):
                 self.assertTrue(quantized_param.shape == original_param.shape)
                 torch.testing.assert_close(quantized_param, original_param)
 
+    @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)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.falcon40b_model_id,
+            gguf_file=self.q2_k_falcon40b_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello All,\nI am new to this forum."
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_falcon7b_q2_k(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.falcon7b_model_id, gguf_file=self.q2_k_falcon7b_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.falcon7b_model_id,
+            gguf_file=self.q2_k_falcon7b_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello All,\nI am new to this forum."
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_falcon7b_weights_conversion_fp16(self):
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.falcon7b_model_id,
+            gguf_file=self.fp16_falcon7b_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+        original_model = AutoModelForCausalLM.from_pretrained(
+            self.original_flacon7b_model_id,
+            device_map="auto",
+            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_tokenization_xnli(self):
         import tqdm
         from datasets import load_dataset