fix: Resolve Llama4 tool calling 500 errors

This commit addresses issue #2584 by:
- Implementing lazy torch imports in llama4/chat_format.py and datatypes.py to prevent ModuleNotFoundError in torch-free environments.
- Adding comprehensive unit tests to verify that text-only functionality works without torch and that vision features fail gracefully.
- Ensuring the module remains importable and functional for text-based operations, thus resolving the 500 internal server errors.

tests/unit/models/test_llama4_import_torch_free.py

@@ -0,0 +1,81 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+"""
+Test that Llama-4 modules can be imported and used for text-only operations
+even when torch is not available (addresses issue #2584).
+"""
+
+import builtins
+import importlib
+import sys
+
+import pytest
+
+
+def _block_torch(monkeypatch):
+    """Block torch imports to simulate torch-free environment."""
+    real_import = builtins.__import__
+
+    def fake_import(name, *args, **kwargs):
+        if name == "torch" or name.startswith("torch."):
+            raise ModuleNotFoundError("No module named 'torch'")
+        return real_import(name, *args, **kwargs)
+
+    sys.modules.pop("torch", None)  # forget any cached import
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+
+
+def test_llama4_chat_format_imports_without_torch(monkeypatch):
+    """Test that llama4.chat_format can be imported when torch is unavailable."""
+    _block_torch(monkeypatch)
+
+    # This should NOT raise ImportError anymore
+    chat_format_module = importlib.import_module("llama_stack.models.llama.llama4.chat_format")
+    assert chat_format_module is not None
+
+
+def test_llama4_text_decoding_works_without_torch(monkeypatch):
+    """Test that text-only tool calling decoding works without torch."""
+    _block_torch(monkeypatch)
+
+    from llama_stack.models.llama.datatypes import StopReason
+    from llama_stack.models.llama.llama4.chat_format import ChatFormat
+    from llama_stack.models.llama.llama4.tokenizer import Tokenizer
+
+    # Text-only operations should work fine
+    formatter = ChatFormat(Tokenizer.get_instance())
+    content = '[get_weather(location="SF")]<|eot|>'
+    msg = formatter.decode_assistant_message_from_content(content, StopReason.end_of_turn)
+
+    # Verify tool calling parsing works
+    assert msg.tool_calls, "Tool call should be detected"
+    tc = msg.tool_calls[0]
+    assert tc.tool_name == "get_weather"
+    assert tc.arguments == {"location": "SF"}
+
+
+def test_llama4_vision_fails_gracefully_without_torch(monkeypatch):
+    """Test that vision features raise clear error when torch unavailable."""
+    _block_torch(monkeypatch)
+
+    from llama_stack.models.llama.llama4.args import Size, VisionArgs
+    from llama_stack.models.llama.llama4.chat_format import ChatFormat
+    from llama_stack.models.llama.llama4.tokenizer import Tokenizer
+
+    # Trying to use vision features should raise helpful error
+    vision_args = VisionArgs(
+        image_size=Size(height=448, width=448),
+        patch_size=Size(height=14, width=14),
+        dim=512,
+        n_layers=6,
+        n_heads=8,
+        mlp_ratio=4.0,
+        output_dim=4096,
+        pixel_shuffle_ratio=2,
+    )
+    with pytest.raises(ImportError, match="vision features require.*torch.*Pillow"):
+        ChatFormat(Tokenizer.get_instance(), vision_args=vision_args)