Mistral Nemo (#782)

* Update __init__.py

* dynamic RoPE

* Update mistral.py

* Update llama.py

* Update tokenizer_utils.py

* Update mistral.py

* Update llama.py

* Update __init__.py

* Update flex_attention.py

* Update llama.py

* Update llama.py

* Mistral Nemo

unsloth/models/_utils.py

@@ -65,8 +65,26 @@ logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.CRITI
 
 # =============================================
 # Edits all Config files to enable RoPE Scaling for all models
-from transformers import PretrainedConfig
 
+# Transformers had to update for Mistral Nemo 12b since Attention is (5120, 4096) now.
+def patch_mistral_nemo_config(config):
+    if "head_dim (" not in config:
+        add_head_dim = "If it is not specified, will default to `8`.\n"\
+            "        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):\n"\
+            "            The attention head dimension."
+        config = config.replace("If it is not specified, will default to `8`.", add_head_dim)
+
+        add_head_dim = "num_key_value_heads=8,\n        head_dim=None,"
+        config = config.replace("num_key_value_heads=8,", add_head_dim)
+
+        add_head_dim = "self.sliding_window = sliding_window\n        self.head_dim = head_dim or hidden_size // num_attention_heads\n"
+        config = config.replace("self.sliding_window = sliding_window", add_head_dim)
+    pass
+    return config
+pass
+
+from transformers import __version__ as transformers_version
+from transformers import PretrainedConfig
 model_architectures = ["llama", "mistral", "gemma", "gemma2", "qwen2",]
 
 for model_name in model_architectures:
@@ -87,8 +105,14 @@ for model_name in model_architectures:
         r"\n        self.rope_scaling = rope_scaling\n",
         config,
     )
-    exec(config, globals())
 
+    # Just for Mistral Nemo
+    if model_name == "mistral":
+        if Version(transformers_version) <= Version("4.42.4"):
+            config = patch_mistral_nemo_config(config)
+    pass
+
+    exec(config, globals())
     exec(f"import {config_filepath}", globals())
     exec(f"{config_filepath}.{config_filename} = {config_filename}", globals())
 pass
@@ -97,7 +121,6 @@ pass
 # =============================================
 # torch.cuda.amp.custom_fwd is deprecated >= 2.4
 import torch
-from packaging.version import Version
 if Version(torch.__version__) < Version("2.4.0"):
     torch_amp_custom_fwd = torch.cuda.amp.custom_fwd
     torch_amp_custom_bwd = torch.cuda.amp.custom_bwd
@@ -748,7 +771,7 @@ def patch_linear_scaling(
         "self.rotary_emb = .+?\)", function,
         flags = re.DOTALL | re.MULTILINE,
     )
-    if len(rotary_emb) == 0: return
+    if len(rotary_emb) == 0: return None, function
     rotary_emb = rotary_emb[0]
     function = function.replace(rotary_emb, fix_rope_function, 1)
     function = exec_code + "\n\n" + function

unsloth/models/llama.py

@@ -1162,9 +1162,12 @@ class FastLlamaModel:
         print(statistics)
 
         # Warn about fast transfers
+        old_hf_transfer = os.environ.get("HF_HUB_ENABLE_HF_TRANSFER", "0")
         if os.environ.get("HF_HUB_ENABLE_HF_TRANSFER", "0") == "1":
-            logger.warning_once("Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!")
+            print("Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!")
         pass
+        # Return old flag
+        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
 
         model_patcher.pre_patch()
         get_statistics() # For debugging - we use a download counter to see if environments are not breaking 
@@ -1247,6 +1250,8 @@ class FastLlamaModel:
             attn_implementation     = "eager",
             **kwargs,
         )
+        # Return old flag
+        os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
         # We currently only support NVIDIA GPUs - AMD / Intel is a work in progress!
         post_check = check_nvidia()
 

unsloth/models/mistral.py

@@ -270,6 +270,24 @@ def MistralForCausalLM_fast_forward(
 pass
 
 
+# Transformers had to update for Mistral Nemo 12b since Attention is (5120, 4096) now.
+def patch_mistral_nemo_attention(function):
+    function = function.replace(
+        "(self.head_dim * self.num_heads) != self.hidden_size",
+        "False",
+    )
+    function = function.replace(
+        "self.head_dim = self.hidden_size // self.num_heads",
+        "self.head_dim = config.head_dim",
+    )
+    function = function.replace(
+        "self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)",
+        "self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)",
+    )
+    return function
+pass
+
+
 class FastMistralModel(FastLlamaModel):
 
     @staticmethod
@@ -280,7 +298,9 @@ class FastMistralModel(FastLlamaModel):
             scaled_rope_module = LlamaLinearScalingRotaryEmbedding,
             attention_module   = MistralAttention,
         )
-        if init_name is not None:
+        # Just for Mistral Nemo models!
+        function = patch_mistral_nemo_attention(function)
+        if True:#init_name is not None:
             exec(function, globals())
             MistralAttention.__init__  = eval(init_name)
         pass