Support RL online quantization with torchao (#23014)

Signed-off-by: Jerry Zhang <jerryzh168@gmail.com>

Author: jerryzh168

tests/quantization/test_torchao.py

@@ -20,7 +20,6 @@ def test_pre_quantized_model(vllm_runner):
         output = llm.generate_greedy(["The capital of France is"],
                                      max_tokens=32)
     assert output
-    print(output)
 
 
 @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
@@ -42,7 +41,6 @@ def test_opt_125m_int8wo_model_loading_with_params(vllm_runner,
                                      max_tokens=32)
 
         assert output
-        print(output)
 
 
 @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
@@ -57,7 +55,6 @@ def test_opt_125m_int4wo_model_per_module_quant(vllm_runner):
                                      max_tokens=32)
 
         assert output
-        print(output)
 
 
 @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
@@ -72,7 +69,6 @@ def test_qwenvl_int8wo_model_loading_with_params(vllm_runner):
                                      max_tokens=32)
 
         assert output
-        print(output)
 
 
 @pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
@@ -92,7 +88,127 @@ def test_opt_125m_awq_int4wo_model_loading_with_params(vllm_runner):
                                      max_tokens=32)
 
         assert output
-        print(output)
+
+
+@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
+def test_on_the_fly_quant_config_dict_json(vllm_runner):
+    """Testing on the fly quantization, load_weights integration point,
+    with config dict serialized to json string
+    """
+    torch._dynamo.reset()
+    model_name = "facebook/opt-125m"
+
+    import json
+
+    from torchao.core.config import config_to_dict
+    from torchao.quantization import (
+        Float8DynamicActivationFloat8WeightConfig, PerRow)
+
+    torchao_quant_config = Float8DynamicActivationFloat8WeightConfig(
+        granularity=PerRow())
+    hf_overrides = {
+        "quantization_config_dict_json":
+        json.dumps(config_to_dict(torchao_quant_config))
+    }
+    with vllm_runner(model_name=model_name,
+                     dtype="bfloat16",
+                     pt_load_map_location="cuda:0",
+                     quantization="torchao",
+                     hf_overrides=hf_overrides) as llm:
+        output = llm.generate_greedy(["The capital of France is"],
+                                     max_tokens=32)
+
+        assert output
+
+
+@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
+def test_on_the_fly_quant_config_file(vllm_runner):
+    """Testing on the fly quantization, load_weights integration point,
+    with config file
+    """
+    torch._dynamo.reset()
+    model_name = "facebook/opt-125m"
+    import json
+    from tempfile import NamedTemporaryFile
+
+    from torchao.core.config import config_to_dict
+    from torchao.quantization import (
+        Float8DynamicActivationFloat8WeightConfig, PerRow)
+
+    config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+
+    with NamedTemporaryFile(mode="w", delete=False) as f:
+        f.write(json.dumps(config_to_dict(config)))
+        # close the file to save it
+        f.close()
+        config_file_name = str(f.name)
+
+        hf_overrides = {"quantization_config_file": config_file_name}
+        with vllm_runner(model_name=model_name,
+                         dtype="bfloat16",
+                         pt_load_map_location="cuda:0",
+                         quantization="torchao",
+                         hf_overrides=hf_overrides) as llm:
+            output = llm.generate_greedy(["The capital of France is"],
+                                         max_tokens=32)
+
+            assert output
+
+
+@pytest.mark.skipif(not TORCHAO_AVAILABLE, reason="torchao is not available")
+def test_reload_weights():
+    import json
+
+    from torchao.core.config import config_to_dict
+    from torchao.quantization import (
+        Float8DynamicActivationFloat8WeightConfig, PerRow)
+
+    from vllm import LLM, SamplingParams
+
+    torchao_quant_config = Float8DynamicActivationFloat8WeightConfig(
+        granularity=PerRow())
+
+    hf_overrides = {
+        "quantization_config_dict_json":
+        json.dumps(config_to_dict(torchao_quant_config))
+    }
+
+    llm = LLM(
+        model="Qwen/Qwen3-0.6B",
+        dtype="bfloat16",
+        load_format="dummy",
+        enforce_eager=True,
+        quantization="torchao",
+        hf_overrides=hf_overrides,
+    )
+    # Update load format from `dummy` to `auto`
+    llm.collective_rpc("update_config",
+                       args=({
+                           "load_config": {
+                               "load_format": "auto"
+                           }
+                       }, ))
+    # Now reload real weights inplace
+    llm.collective_rpc("reload_weights")
+    prompts = [
+        "Hello, my name is",
+        "The president of the United States is",
+        "The capital of France is",
+        "The future of AI is",
+    ]
+    # Create a sampling params object.
+    sampling_params = SamplingParams(temperature=0, top_p=0.95)
+    outputs = llm.generate(prompts, sampling_params)
+    # make sure it runs
+    for output in outputs:
+        generated_text = output.outputs[0].text
+        assert generated_text
+        # can also uncomment locally to make sure the generated
+        # output makes sense
+        # prompt = output.prompt
+        # print(f"Prompt:    {prompt!r}")
+        # print(f"Output:    {generated_text!r}")
+        # print("-" * 60)
 
 
 if __name__ == "__main__":

vllm/model_executor/layers/quantization/torchao.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import json
 from typing import Any, Optional
 
 import torch
@@ -40,7 +41,8 @@ class TorchAOConfig(QuantizationConfig):
 
     def __init__(self,
                  torchao_config,
-                 skip_modules: Optional[list[str]] = None) -> None:
+                 skip_modules: Optional[list[str]] = None,
+                 is_checkpoint_torchao_serialized: bool = False) -> None:
         """
         # TorchAO quantization relies on tensor subclasses. In order,
         # to enable proper caching this needs standalone compile
@@ -58,9 +60,11 @@ def __init__(self,
         super().__init__()
         self.torchao_config = torchao_config
         self.skip_modules = skip_modules or []
+        self.is_checkpoint_torchao_serialized = is_checkpoint_torchao_serialized
 
     def __repr__(self) -> str:
-        return f"TorchAOConfig({self.torchao_config})"
+        return f"TorchAOConfig({self.torchao_config=}, {self.skip_modules=}, " \
+            f"{self.is_checkpoint_torchao_serialized=})"
 
     def get_name(self) -> QuantizationMethods:
         return "torchao"
@@ -74,7 +78,10 @@ def get_min_capability(cls) -> int:
 
     @staticmethod
     def get_config_filenames() -> list[str]:
-        return ["config.json"]
+        """torchao doesn't require additional config files, we use
+        `config.json` from huggingface: `model_config.hf_config`
+        """
+        return []
 
     @classmethod
     def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig":
@@ -87,6 +94,10 @@ def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig":
                 "`pip install torchao>=0.10.0` to use torchao quantization."
             ) from err
 
+        quant_method = cls.get_from_keys_or(config, ["quant_method"], None)
+        is_checkpoint_torchao_serialized = (quant_method is not None
+                                            and "torchao" in quant_method)
+
         hf_config = cls.get_from_keys_or(config, ["quant_type"], None)
         assert hf_config is not None, "quant_type must be specified"
         assert len(hf_config) == 1 and "default" in hf_config, (
@@ -110,7 +121,38 @@ def from_config(cls, config: dict[str, Any]) -> "TorchAOConfig":
             if layer_cfg is None:
                 skip_modules.append(layer)
 
-        return cls(ao_config, skip_modules)
+        return cls(ao_config, skip_modules, is_checkpoint_torchao_serialized)
+
+    @classmethod
+    def from_config_file(cls, config_file: str) -> "TorchAOConfig":
+        """Initialize class from a config file. Example:
+        ```
+        config = (
+           Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())
+        )
+        fn = "torchao_config.json"
+
+        with open(fn, "w") as f:
+            f.write(json.dumps(config_to_dict(config)))
+        ```
+        """
+        with open(config_file) as f:
+            f.seek(0)
+            f_read = f.read()
+            config_dict = json.loads(f_read)
+
+        hf_config = {"quant_type": {"default": config_dict}}
+        return cls.from_config(hf_config)
+
+    @classmethod
+    def from_config_dict_json(cls, config_dict_json: str) -> "TorchAOConfig":
+        """Iniitalize class from a config_dict json string, got from
+        torchao_config_object = some AOBaseConfig object
+        json.dumps(config_to_dict(torchao_config_object))
+        """
+        config_dict = json.loads(config_dict_json)
+        hf_config = {"quant_type": {"default": config_dict}}
+        return cls.from_config(hf_config)
 
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
@@ -128,7 +170,9 @@ def get_quant_method(self, layer: torch.nn.Module,
             c = module_fqn_to_config.get(
                 module_fqn) or module_fqn_to_config.get("_default", None)
             if c is not None:
-                current_torchao_config = TorchAOConfig(c, self.skip_modules)
+                current_torchao_config = TorchAOConfig(
+                    c, self.skip_modules,
+                    self.is_checkpoint_torchao_serialized)
                 return TorchAOLinearMethod(current_torchao_config)
             else:
                 return UnquantizedLinearMethod()
@@ -172,7 +216,7 @@ class TorchAOLinearMethod(LinearMethodBase):
     """Linear method for torchao.
 
     Args:
-        quant_config: The torchao quantization config, a string that encodes 
+        quant_config: The torchao quantization config, a string that encodes
             the type of quantization and all relevant arguments.
     """
 
@@ -197,8 +241,9 @@ def create_weights(
             ),
             requires_grad=False,
         )
-        weight = torchao_quantize_param_data(weight,
-                                             self.quant_config.torchao_config)
+        if self.quant_config.is_checkpoint_torchao_serialized:
+            weight = torchao_quantize_param_data(
+                weight, self.quant_config.torchao_config)
 
         set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
 
@@ -212,3 +257,14 @@ def apply(
         bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         return F.linear(x, layer.weight, bias)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        if self.quant_config.is_checkpoint_torchao_serialized:
+            return
+
+        # quantize the weight on the fly if the checkpoint is not already
+        # quantized by torchao
+        weight = torchao_quantize_param_data(layer.weight,
+                                             self.quant_config.torchao_config)
+        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
+        layer.register_parameter("weight", weight)

vllm/model_executor/model_loader/default_loader.py

@@ -261,8 +261,35 @@ def download_model(self, model_config: ModelConfig) -> None:
     def load_weights(self, model: nn.Module,
                      model_config: ModelConfig) -> None:
         weights_to_load = {name for name, _ in model.named_parameters()}
-        loaded_weights = model.load_weights(
-            self.get_all_weights(model_config, model))
+
+        # if we don't have `model.weight_metadata_and_attr_saved` defined and
+        # set to True, it means that this is either offline quantization case
+        # or the first run of online quantization
+        # see online_quantization.py for detailed notes
+        offline_quantization_or_first_run_of_online_quantization = not getattr(
+            model, "weight_metadata_and_attr_saved", False)
+
+        if model_config.quantization is None:
+            # model is not quantized
+            loaded_weights = model.load_weights(
+                self.get_all_weights(model_config, model))
+        elif offline_quantization_or_first_run_of_online_quantization:
+            # case 1: offline quantized checkpoint
+            # case 2: Step I1 first run of weight loading with
+            # online quantization
+            # see online_quantization.py for detailed notes
+            loaded_weights = model.load_weights(
+                self.get_all_weights(model_config, model))
+        else:
+            # to avoid circular dependency
+            from vllm.model_executor.model_loader.online_quantization import (
+                load_weights_and_online_quantize)
+
+            # subsequent runs of weight loading with online
+            # quantization
+            loaded_weights = load_weights_and_online_quantize(
+                self, model, model_config)
+
         self.counter_after_loading_weights = time.perf_counter()
         logger.info(
             "Loading weights took %.2f seconds",