Allow downloading of model weights automatically

docs/examples/te_llama/te_llama.py

@@ -102,8 +102,11 @@ def from_pretrained_local(cls, pretrained_model_name_or_path, *args, config, **k
         Custom method adapted from `from_pretrained` method in HuggingFace
         Transformers repo: https://github.com/huggingface/transformers/blob/f497f564bb76697edab09184a252fc1b1a326d1e/src/transformers/modeling_utils.py#L2579
         """
-        vanilla_model = cls(config).to(kwargs["torch_dtype"])
-        is_local = os.path.isdir(pretrained_model_name_or_path)
+        # Before loading the model, set the default dtype for torch
+        torch.set_default_dtype(kwargs["torch_dtype"])
+
+        # Load the vanilla model weights
+        vanilla_model = cls(config)
         subfolder = ""
         variant = None
         if os.path.isfile(
@@ -133,7 +136,7 @@ def from_pretrained_local(cls, pretrained_model_name_or_path, *args, config, **k
         else:
             raise AssertionError("Only sharded PyTorch ckpt format supported at the moment")
 
-        resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
+        resolved_archive_file, _ = get_checkpoint_shard_files(
             pretrained_model_name_or_path,
             archive_file,
         )

docs/examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb

@@ -247,15 +247,24 @@
     "restart_jupyter_notebook()\n",
     "\n",
     "\n",
-    "# Import necessary packages and methods\n",
+    "# Import necessary packages, methods and variables\n",
     "from utils import *\n",
     "\n",
     "\n",
-    "# Default hyperparams, also defined in `utils.py` in class `Hyperparameters`\n",
-    "## !!! `model_name` attr must point to the location of the model weights !!!\n",
-    "# For Llama 2, download weights from https://huggingface.co/meta-llama/Llama-2-7b-hf (Hugging Face weight format).\n",
-    "# For Llama 3, download weights from https://huggingface.co/meta-llama/Meta-Llama-3-8B (Hugging Face weight format).\n",
-    "hyperparams.model_name = \"\" # <== Add model weight location here e.g. \"/path/to/downloaded/llama/weights\"\n",
+    "# Provide Huggingface Access Token\n",
+    "hyperparams.hf_access_token = \"\"\n",
+    "assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
+    "\n",
+    "# Provide a directory to cache weights in to avoid downloading them every time.\n",
+    "# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
+    "hyperparams.weights_cache_dir = \"\"\n",
+    "\n",
+    "# For Llama 2, uncomment this line (also set by default)\n",
+    "hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
+    "\n",
+    "# For Llama 3, uncomment this line\n",
+    "# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
+    "\n",
     "hyperparams.mixed_precision = \"bf16\"\n",
     "\n",
     "\n",
@@ -554,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "bdb34b91",
    "metadata": {},
    "outputs": [
@@ -573,15 +582,24 @@
     "restart_jupyter_notebook()\n",
     "\n",
     "\n",
-    "# Import necessary packages and methods\n",
+    "# Import necessary packages, methods and variables\n",
     "from utils import *\n",
     "\n",
     "\n",
-    "# Default hyperparams, also defined in `utils.py` in class `Hyperparameters`\n",
-    "## !!! `model_name` attr must point to the location of the model weights !!!\n",
-    "# For Llama 2, download weights from https://huggingface.co/meta-llama/Llama-2-7b-hf (Hugging Face weight format).\n",
-    "# For Llama 3, download weights from https://huggingface.co/meta-llama/Meta-Llama-3-8B (Hugging Face weight format).\n",
-    "hyperparams.model_name = \"\" # <== Add model weight location here e.g. \"/path/to/downloaded/llama/weights\"\n",
+    "# Provide Huggingface Access Token\n",
+    "hyperparams.hf_access_token = \"\"\n",
+    "assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
+    "\n",
+    "# Provide a directory to cache weights in to avoid downloading them every time.\n",
+    "# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
+    "hyperparams.weights_cache_dir = \"\"\n",
+    "\n",
+    "# For Llama 2, uncomment this line (also set by default)\n",
+    "hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
+    "\n",
+    "# For Llama 3, uncomment this line\n",
+    "# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
+    "\n",
     "hyperparams.mixed_precision = \"bf16\"\n",
     "\n",
     "\n",
@@ -653,15 +671,24 @@
     "restart_jupyter_notebook()\n",
     "\n",
     "\n",
-    "# Import necessary packages and methods\n",
+    "# Import necessary packages, methods and variables\n",
     "from utils import *\n",
     "\n",
     "\n",
-    "# Default hyperparams, also defined in `utils.py` in class `Hyperparameters`\n",
-    "## !!! `model_name` attr must point to the location of the model weights !!!\n",
-    "# For Llama 2, download weights from https://huggingface.co/meta-llama/Llama-2-7b-hf (Hugging Face weight format).\n",
-    "# For Llama 3, download weights from https://huggingface.co/meta-llama/Meta-Llama-3-8B (Hugging Face weight format).\n",
-    "hyperparams.model_name = \"\" # <== Add model weight location here e.g. \"/path/to/downloaded/llama/weights\"\n",
+    "# Provide Huggingface Access Token\n",
+    "hyperparams.hf_access_token = \"\"\n",
+    "assert hyperparams.hf_access_token, \"Provide a HF API Access Token!\"\n",
+    "\n",
+    "# Provide a directory to cache weights in to avoid downloading them every time.\n",
+    "# (By default, weights are cached in `~/.cache/huggingface/hub/models`)\n",
+    "hyperparams.weights_cache_dir = \"\"\n",
+    "\n",
+    "# For Llama 2, uncomment this line (also set by default)\n",
+    "hyperparams.model_name = \"meta-llama/Llama-2-7b-hf\"\n",
+    "\n",
+    "# For Llama 3, uncomment this line\n",
+    "# hyperparams.model_name = \"meta-llama/Meta-Llama-3-8B\"\n",
+    "\n",
     "hyperparams.mixed_precision = \"fp8\"\n",
     "\n",
     "\n",

docs/examples/te_llama/utils.py

@@ -25,7 +25,10 @@
 class HyperParameters:
     def __init__(self):
         self.mixed_precision = "bf16"
-        # self.model_name = "" # <== Add model weight location here
+
+        # Set to Meta Llama 2 by default.
+        self.model_name = "meta-llama/Llama-2-7b-hf"
+
         self.dataset_name = "timdettmers/openassistant-guanaco"
         self.dataset_text_field = "text"
         self.learning_rate = 1.41e-5
@@ -35,6 +38,10 @@ def __init__(self):
         self.num_warmup_steps = 5
         self.num_training_steps = 10
 
+        # This is either provided by the user or it will be set when the
+        # model weights are downloaded.
+        self.weights_cache_dir = ""
+
 
 hyperparams = HyperParameters()
 
@@ -76,13 +83,49 @@ def tokenize(element):
     return train_dataloader
 
 
+def ensure_model_is_downloaded(hyperparams):
+    assert hyperparams.model_name in [
+        "meta-llama/Meta-Llama-3-8B",
+        "meta-llama/Llama-2-7b-hf",
+    ], "Only Meta Llama 2 7B and Meta Llama 3 8B models are supported!"
+
+    # Login using Huggingface Hub API
+    from huggingface_hub import login
+
+    try:
+        login(hyperparams.hf_access_token)
+    except Exception as e:
+        if "Invalid token passed!" in str(e):
+            print(
+                "Please pass a valid HF Access Token! More info at"
+                " https://huggingface.co/docs/hub/en/security-tokens."
+            )
+        else:
+            print(f"Exception is {e}")
+
+    # Download the model if it doesn't exist
+    from huggingface_hub import snapshot_download
+
+    supplied_cache_dir = (
+        hyperparams.weights_cache_dir if hyperparams.weights_cache_dir != "" else None
+    )
+    hyperparams.weights_cache_dir = snapshot_download(
+        repo_id=hyperparams.model_name, cache_dir=supplied_cache_dir
+    )
+
+    print(f"Model cache directory : {hyperparams.weights_cache_dir}")
+
+
 def init_baseline_model(hyperparams):
+    # Download and cache the weights
+    ensure_model_is_downloaded(hyperparams)
+
     # Init the model
-    config = AutoConfig.from_pretrained(hyperparams.model_name)
+    config = AutoConfig.from_pretrained(hyperparams.weights_cache_dir)
     # make sure to use flash_attention to do iso comparison with TELlamaModel
     config._attn_implementation = "flash_attention_2"
     model = AutoModelForCausalLM.from_pretrained(
-        hyperparams.model_name,
+        hyperparams.weights_cache_dir,
         config=config,
         torch_dtype=torch.bfloat16,
     )
@@ -94,13 +137,16 @@ def init_baseline_model(hyperparams):
 
 
 def init_te_llama_model(hyperparams):
+    # Download and cache the weights
+    ensure_model_is_downloaded(hyperparams)
+
     # Init the model
     from te_llama import TELlamaForCausalLM
 
-    config = AutoConfig.from_pretrained(hyperparams.model_name)
+    config = AutoConfig.from_pretrained(hyperparams.weights_cache_dir)
     config._attn_implementation = "flash_attention_2"
     model = TELlamaForCausalLM.from_pretrained_local(
-        hyperparams.model_name,
+        hyperparams.weights_cache_dir,
         config=config,
         torch_dtype=torch.bfloat16,
     )