[torchao] fix safetensors for sharding (#28169)

Signed-off-by: Angel Li <liangel@meta.com>

tests/quantization/test_torchao.py

@@ -225,13 +225,12 @@ def test_reload_weights():
 @pytest.mark.skip(
     reason="since torchao nightly is only compatible with torch nightly"
     "currently https://github.com/pytorch/ao/issues/2919, we'll have to skip "
-    "torchao tests that requires newer versions (0.14.0.dev+) for now"
+    "torchao tests that requires newer versions (0.15.0.dev+) for now"
 )
-def test_opt_125m_float8_weight_only_safetensors_model_loading_with_params(vllm_runner):
+def test_safetensors_model_loading_with_params(vllm_runner):
     torch._dynamo.reset()
-    model_name = (
-        "torchao-testing/opt-125m-Float8WeightOnlyConfig-v2-0.14.0.dev-safetensors"
-    )
+    # using this model to test safetensors loading with file sharding
+    model_name = "torchao-testing/Qwen3-8B-INT4-0.15.0dev-safetensors"
     with vllm_runner(model_name=model_name, dtype="bfloat16") as llm:
         output = llm.generate_greedy(["The capital of France is"], max_tokens=4)
 

vllm/model_executor/model_loader/default_loader.py

@@ -279,7 +279,7 @@ class DefaultModelLoader(BaseModelLoader):
             if (
                 hasattr(quant_config, "is_checkpoint_torchao_serialized")
                 and quant_config.is_checkpoint_torchao_serialized
-                and torchao_version_at_least("0.14.0")
+                and torchao_version_at_least("0.15.0")
             ):
                 self.load_config.safetensors_load_strategy = "torchao"
 

vllm/model_executor/model_loader/weight_utils.py

@@ -595,6 +595,9 @@ def safetensors_weights_iterator(
     if safetensors_load_strategy == "eager":
         loading_desc += " (eager)"
 
+    state_dict = {}
+    leftover_state_dict: dict[str, torch.Tensor] = {}
+
     for st_file in tqdm(
         hf_weights_files,
         desc=loading_desc,
@@ -606,9 +609,11 @@ def safetensors_weights_iterator(
                 state_dict = load(f.read())
             yield from state_dict.items()
         elif safetensors_load_strategy == "torchao":
-            if not torchao_version_at_least("0.14.0"):
+            # we can't load flattened torchao tensor subclasses directly into the model
+            # instead we reconstruct the subclasses here before returning
+            if not torchao_version_at_least("0.15.0"):
                 raise ValueError(
-                    "Please use torchao version >= 0.14.0 \
+                    "Please use torchao version >= 0.15.0 \
                         to load torchao safetensors checkpoint"
                 )
             from torchao.prototype.safetensors.safetensors_support import (
@@ -616,12 +621,20 @@ def safetensors_weights_iterator(
             )
 
             with safe_open(st_file, framework="pt") as f:
-                state_dict = {}
                 for name in f.keys():  # noqa: SIM118
                     state_dict[name] = f.get_tensor(name)
+
+                # update with leftover tensor data from previous iteration, if any
+                state_dict.update(leftover_state_dict)
                 metadata = f.metadata()
-                updated_state_dict = unflatten_tensor_state_dict(state_dict, metadata)
-            yield from updated_state_dict.items()
+                # due to sharded checkpoints, we are not guaranteed that we have all
+                # tensor subclass data on one file
+                # state_dict has the leftover data from this step and we wait for
+                # missing information to be provided in a future iteration
+                unflattened_state_dict, leftover_state_dict = (
+                    unflatten_tensor_state_dict(state_dict, metadata)
+                )
+            yield from unflattened_state_dict.items()
         else:
             with safe_open(st_file, framework="pt") as f:
                 for name in f.keys():  # noqa: SIM118