fixing test

src/transformers/integrations/ggml.py

@@ -330,7 +330,7 @@ GGUF_CONFIG_MAPPING = {
         "rope.dimension_count": None,
         "rope.freq_base": "rope_theta",
         "attention.head_count": "num_attention_heads",
-        "attention.head_count_kv": "num_key_value_heads",
+        "attention.head_count_kv": "num_kv_heads",
         "attention.layer_norm_rms_epsilon": "rms_norm_eps",
         "vocab_size": "vocab_size",
     },

src/transformers/modeling_gguf_pytorch_utils.py

@@ -69,6 +69,42 @@ class TensorProcessor:
         return GGUFTensor(weights, name, {})
 
 
+class FalconTensorProcessor(TensorProcessor):
+    def __init__(self, config=None):
+        super().__init__(config=config)
+
+    def process(self, weights, name, **kwargs):
+        if "qkv" in name:
+            shape = weights.shape
+            weights_copy = weights.copy()
+            parsed_parameters = kwargs.get("parsed_parameters")
+            num_attention_heads = parsed_parameters["config"]["num_attention_heads"]
+            num_key_value_heads = parsed_parameters["config"]["num_kv_heads"]
+            hidden_size = parsed_parameters["config"]["hidden_size"]
+            head_dim = hidden_size // num_attention_heads
+
+            # Split the weights array into q, k, v
+            split_indices = [
+                num_attention_heads * head_dim,
+                num_attention_heads * head_dim + num_key_value_heads * head_dim,
+            ]
+
+            q, k, v = np.split(weights_copy, split_indices)
+
+            # Reshape q, k, and v as needed
+            q = q.reshape(num_key_value_heads, num_attention_heads // num_key_value_heads, head_dim, hidden_size)
+            k = k.reshape(num_key_value_heads, 1, head_dim, hidden_size)
+            v = v.reshape(num_key_value_heads, 1, head_dim, hidden_size)
+
+            # Concatenate q, k, and v along the second dimension
+            qkv = np.concatenate((q, k, v), axis=1)
+
+            # Reshape qkv back to the original shape
+            weights = qkv.reshape(shape)
+
+        return GGUFTensor(weights, name, {})
+
+
 class LlamaTensorProcessor(TensorProcessor):
     def __init__(self, config=None):
         super().__init__(config=config)
@@ -246,6 +282,7 @@ TENSOR_PROCESSORS = {
     "t5encoder": T5TensorProcessor,
     "gpt2": GPT2TensorProcessor,
     "mamba": MambaTensorProcessor,
+    "falcon": FalconTensorProcessor,
 }
 
 
@@ -322,6 +359,8 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
                 f"From file name, cannot determine the number of parameters for {architecture} architecture"
             )
         model_size = m.group().strip("-")  # only keeps `7b`
+        if model_size == "40b":
+            parsed_parameters["config"]["new_decoder_architecture"] = True
 
     if architecture + model_size not in GGUF_SUPPORTED_ARCHITECTURES:
         raise ValueError(f"Architecture {architecture + model_size} not supported")

tests/quantization/ggml/test_ggml.py

@@ -63,6 +63,7 @@ class GgufIntegrationTests(unittest.TestCase):
     mamba_model_id = "jpodivin/mamba-2.8b-hf-GGUF"
     nemotron_original_model_id = "nvidia/Nemotron-Mini-4B-Instruct"
     nemotron_model_id = "bartowski/Nemotron-Mini-4B-Instruct-GGUF"
+    falcon40b_model_id = "tensorblock/falcon-40b-GGUF"
 
     # standard quants
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -110,6 +111,7 @@ class GgufIntegrationTests(unittest.TestCase):
     fp16_mamba_model_id = "ggml-model-f16.gguf"
     q6_k_nemotron_model_id = "Nemotron-Mini-4B-Instruct-Q6_K.gguf"
     fp16_nemotron_model_id = "Nemotron-Mini-4B-Instruct-f16.gguf"
+    q2_falcon40b_id = "falcon-40b-Q2_K.gguf"
 
     example_text = "Hello"
 
@@ -611,7 +613,7 @@ class GgufIntegrationTests(unittest.TestCase):
         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."
+        EXPECTED_TEXT = "Hello All,\nOn ne sait plus quoi manger,"
         self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
 
     def test_falcon7b_q2_k(self):