fix memory for online fp8 quantization with streaming weight load

tests/quantization/test_fp8.py

@@ -5,7 +5,10 @@
 Run `pytest tests/quantization/test_fp8.py --forked`.
 """
 
+import logging
+
 import pytest
+import regex as re
 import torch
 
 from tests.quantization.utils import is_quant_method_supported
@@ -195,6 +198,99 @@ def check_model(model):
         print(outputs[0][1])
 
 
+@pytest.mark.skipif(
+    not is_quant_method_supported("fp8"),
+    reason="FP8 is not supported on this GPU type.",
+)
+def test_online_quant_peak_mem(
+    vllm_runner,
+    caplog_mp_spawn,
+    monkeypatch,
+) -> None:
+    # Note: `allenai/OLMoE-1B-7B-0125-Instruct` was selected because:
+    # 1. it covers both Linear and MoE paths
+    # 2. it is already used by other tests in CI, so adding it here
+    #    does not increase disk space for CI runners
+    # I really wanted to use `ibm-granite/granite-3.0-1b-a400m-base`
+    # which I think is the smallest MoE model in vLLM (2.5 GiB bf16,
+    # 1.3 GiB fp8), but could not as adding one more model makes CI
+    # run out of disk space.
+    model_name = "allenai/OLMoE-1B-7B-0125-Instruct"
+
+    # Force spawn to ensure caplog_mp_spawn works consistently
+    # (it relies on VLLM_LOGGING_CONFIG_PATH which spawn reads but fork ignores)
+    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+
+    with (
+        caplog_mp_spawn(logging.DEBUG) as log_holder,
+        vllm_runner(
+            model_name,
+            quantization="fp8",
+            enforce_eager=True,
+        ) as llm,
+    ):
+        outputs = llm.generate_greedy(["The future of AI is"], max_tokens=4)
+        print(outputs[0][1])
+
+    log_text = log_holder.text
+
+    # Parse memory usage from captured logs
+    model_memory_gib = None
+    peak_memory_gib = None
+    for line in log_text.splitlines():
+        if model_memory_gib is None:
+            match = re.search(r"Model loading took ([\d.]+) GiB memory", line)
+            if match:
+                model_memory_gib = float(match.group(1))
+        if peak_memory_gib is None:
+            match = re.search(
+                r"Peak GPU memory after loading weights: ([\d.]+) GiB", line
+            )
+            if match:
+                peak_memory_gib = float(match.group(1))
+
+    assert model_memory_gib is not None, "Could not find model loading memory log"
+    assert peak_memory_gib is not None, "Could not find peak memory log"
+    print(f"GPU memory used after loading weights: {model_memory_gib} GiB")
+    print(f"Peak GPU memory usage while loading weights: {peak_memory_gib} GiB")
+
+    # model specific, allenai/OLMoE-1B-7B-0125-Instruct fp8 online quant
+    # uses 6.65 GiB for weight loading (bf16 checkpoint is ~12.89 GiB)
+    expected_model_memory_gib = 6.7
+
+    # for allenai/OLMoE-1B-7B-0125-Instruct the number we see today is 9.06
+    # GiB, which is 1.36x above model_memory_gib. A slightly higher number is
+    # expected as when we load and quantize weights in a streaming fashion we
+    # need to have individual weights in bf16 + fp8 alive at the same time.
+    expected_peak_memory_gib = expected_model_memory_gib * 1.4
+
+    assert model_memory_gib < expected_model_memory_gib, (
+        f"{model_memory_gib=} higher than {expected_model_memory_gib}"
+    )
+    assert peak_memory_gib < expected_peak_memory_gib, (
+        f"{peak_memory_gib=} higher than {expected_peak_memory_gib}"
+    )
+
+
+@pytest.mark.skipif(
+    not is_quant_method_supported("fp8"),
+    reason="FP8 is not supported on this GPU type.",
+)
+def test_online_quant_load_format_dummy(
+    vllm_runner,
+    monkeypatch,
+    caplog,
+) -> None:
+    with vllm_runner(
+        "ibm-granite/granite-3.0-1b-a400m-base",
+        quantization="fp8",
+        enforce_eager=True,
+        load_format="dummy",
+    ) as llm:
+        outputs = llm.generate_greedy(["The future of AI is"], max_tokens=4)
+        print(outputs[0][1])
+
+
 @pytest.mark.skipif(
     not is_quant_method_supported("fp8"),
     reason="FP8 is not supported on this GPU type.",

vllm/model_executor/layers/quantization/fp8.py

@@ -86,6 +86,7 @@
     cutlass_fp8_supported,
     normalize_e4m3fn_to_e4m3fnuz,
 )
+from vllm.model_executor.model_loader.weight_utils import initialize_single_dummy_weight
 from vllm.model_executor.parameter import (
     BlockQuantScaleParameter,
     ModelWeightParameter,
@@ -293,6 +294,16 @@ def __torch_dispatch__(self, func, types, args=(), kwargs=None):
         return out
 
 
+def _copy_missing_attrs(old: torch.Tensor, new: torch.Tensor) -> None:
+    """Copies any attrs present in `old` but not in `new` to `new`"""
+    new_attrs = set(dir(new))
+    attrs_to_set = {}
+    for attr in dir(old):
+        if attr not in new_attrs:
+            attrs_to_set[attr] = getattr(old, attr)
+    set_weight_attrs(new, attrs_to_set)
+
+
 class Fp8LinearMethod(LinearMethodBase):
     """Linear method for FP8.
     Supports loading FP8 checkpoints with static weight scale and
@@ -578,6 +589,22 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
             if not hasattr(layer, "_loaded_numel"):
                 layer._loaded_numel = 0
 
+                # when the first `loaded_weight` is about to be
+                # loaded to `param`, materialize `param` just-in-time
+                weight = ModelWeightParameter(
+                    data=torch.empty_like(layer.weight, device=layer._load_device),
+                    input_dim=1,
+                    output_dim=0,
+                    weight_loader=patched_weight_loader,
+                )
+                _copy_missing_attrs(layer.weight, weight)
+                layer.register_parameter("weight", weight)
+                del layer._load_device
+
+            # refresh the reference to `param` to reflect just-in-time
+            # materialization
+            param = layer.weight
+
             # load the current weight chunk
             copy_numel_counter = CopyNumelCounter()
             with copy_numel_counter:
@@ -590,30 +617,50 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
             if layer._loaded_numel == target_loaded_numel:
                 self.process_weights_after_loading(layer)
 
-                # Delete the bookkeeping
-                del layer._loaded_numel
                 # Prevent the usual `process_weights_after_loading` call from doing
                 # anything
                 layer._already_called_process_weights_after_loading = True
 
+                # Note that we keep `layer._loaded_numel` around just in case
+                # there is logic added to vllm in the future which calls a
+                # weight loader twice - we do not want to re-initialize in
+                # that case.
+
             return res
 
         weight = ModelWeightParameter(
             data=torch.empty(
                 output_size_per_partition,
                 input_size_per_partition,
+                # materialized just-in-time in `patched_weight_loader`
+                device="meta",
                 dtype=params_dtype,
             ),
             input_dim=1,
             output_dim=0,
             weight_loader=patched_weight_loader,
         )
+        # stash the correct device for `patched_weight_loader`
+        layer._load_device = torch.get_default_device()
         layer.register_parameter("weight", weight)
 
     def process_weights_after_loading(self, layer: Module) -> None:
         if getattr(layer, "_already_called_process_weights_after_loading", False):
             return
 
+        # deferred initialization of randomly initialized weights for the
+        # `--load_format dummy` feature
+        if layer.weight.device == torch.device("meta"):
+            weight = ModelWeightParameter(
+                data=torch.empty_like(layer.weight, device=layer._load_device),
+                input_dim=1,
+                output_dim=0,
+                weight_loader=layer.weight.weight_loader,
+            )
+            _copy_missing_attrs(layer.weight, weight)
+            layer.register_parameter("weight", weight)
+            initialize_single_dummy_weight(layer.weight)
+
         # TODO(future): support block_quant in online quant path
         assert not self.block_quant
 
@@ -1069,6 +1116,39 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
             if not hasattr(layer, "_loaded_numel"):
                 layer._loaded_numel = 0
 
+                # save the ids of original w13 and w2 so that we can
+                # distinguish which one `param` should map to further
+                # down in this file
+                layer._w13_weight_orig_id = id(layer.w13_weight)
+                layer._w2_weight_orig_id = id(layer.w2_weight)
+
+                # when the first `loaded_weight` is about to be
+                # loaded to `param`, materialize `param` just-in-time
+
+                w13_weight = torch.nn.Parameter(
+                    torch.empty_like(layer.w13_weight, device=layer._load_device),
+                    requires_grad=False,
+                )
+                set_weight_attrs(w13_weight, extra_weight_attrs)
+                _copy_missing_attrs(layer.w13_weight, w13_weight)
+                layer.register_parameter("w13_weight", w13_weight)
+
+                w2_weight = torch.nn.Parameter(
+                    torch.empty_like(layer.w2_weight, device=layer._load_device),
+                    requires_grad=False,
+                )
+                set_weight_attrs(w2_weight, extra_weight_attrs)
+                _copy_missing_attrs(layer.w2_weight, w2_weight)
+                layer.register_parameter("w2_weight", w2_weight)
+                del layer._load_device
+
+            # refresh the reference to `param` to reflect just-in-time
+            # materialization
+            if id(param) == layer._w13_weight_orig_id:
+                param = layer.w13_weight
+            elif id(param) == layer._w2_weight_orig_id:
+                param = layer.w2_weight
+
             # load the current weight chunk
             copy_numel_counter = CopyNumelCounter()
             with copy_numel_counter:
@@ -1081,12 +1161,16 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
             if layer._loaded_numel == target_loaded_numel:
                 self.process_weights_after_loading(layer)
 
-                # Delete the bookkeeping
-                del layer._loaded_numel
                 # Prevent the usual `process_weights_after_loading` call
                 # from doing anything
                 layer._already_called_process_weights_after_loading = True
 
+                # Note that we keep `layer._loaded_numel`,
+                # `layer._w13_weight_orig_id` and `layer._w2_weight_orig_id`
+                # around because if EP is on, weight loaders for non-local
+                # experts will run but not actually copy any elements, and we
+                # need to not re-initialize in that case.
+
             return res
 
         new_extra_weight_attrs["weight_loader"] = patched_weight_loader
@@ -1098,6 +1182,8 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
                 num_experts,
                 2 * intermediate_size_per_partition,
                 hidden_size,
+                # materialized just-in-time in `patched_weight_loader`
+                device="meta",
                 dtype=params_dtype,
             ),
             requires_grad=False,
@@ -1110,12 +1196,16 @@ def patched_weight_loader(param, loaded_weight, *args, **kwargs):
                 num_experts,
                 hidden_size,
                 intermediate_size_per_partition,
+                # materialized just-in-time in `patched_weight_loader`
+                device="meta",
                 dtype=params_dtype,
             ),
             requires_grad=False,
         )
         layer.register_parameter("w2_weight", w2_weight)
         set_weight_attrs(w2_weight, extra_weight_attrs)
+        # stash the correct device for `patched_weight_loader`
+        layer._load_device = torch.get_default_device()
 
         # WEIGHT_SCALES
         # Allocate 2 scales for w1 and w3 respectively.
@@ -1138,6 +1228,31 @@ def process_weights_after_loading(self, layer: Module) -> None:
         if getattr(layer, "_already_called_process_weights_after_loading", False):
             return
 
+        # deferred initialization of randomly initialized weights for the
+        # `--load_format dummy` feature
+        if layer.w13_weight.device == torch.device("meta"):
+            w13_weight = torch.nn.Parameter(
+                torch.empty_like(layer.w13_weight, device=layer._load_device),
+                requires_grad=False,
+            )
+            set_weight_attrs(
+                w13_weight, {"weight_loader": layer.w13_weight.weight_loader}
+            )
+            _copy_missing_attrs(layer.w13_weight, w13_weight)
+            layer.register_parameter("w13_weight", w13_weight)
+            initialize_single_dummy_weight(layer.w13_weight)
+        if layer.w2_weight.device == torch.device("meta"):
+            w2_weight = torch.nn.Parameter(
+                torch.empty_like(layer.w2_weight, device=layer._load_device),
+                requires_grad=False,
+            )
+            set_weight_attrs(
+                w2_weight, {"weight_loader": layer.w2_weight.weight_loader}
+            )
+            _copy_missing_attrs(layer.w2_weight, w2_weight)
+            layer.register_parameter("w2_weight", w2_weight)
+            initialize_single_dummy_weight(layer.w2_weight)
+
         # If checkpoint is fp16, quantize in place.
         fp8_dtype = current_platform.fp8_dtype()
         w13 = torch.empty_like(layer.w13_weight, dtype=fp8_dtype)

vllm/model_executor/model_loader/base_loader.py

@@ -13,6 +13,8 @@
     initialize_model,
     process_weights_after_loading,
 )
+from vllm.platforms import current_platform
+from vllm.utils.mem_utils import format_gib
 from vllm.utils.torch_utils import set_default_torch_dtype
 
 logger = init_logger(__name__)
@@ -56,6 +58,17 @@ def load_model(
             logger.debug("Loading weights on %s ...", load_device)
             # Quantization does not happen in `load_weights` but after it
             self.load_weights(model, model_config)
+
+            # Log peak GPU memory after loading weights. This is needed
+            # to have test coverage on peak memory for online quantization.
+            if current_platform.is_cuda():
+                peak_memory = torch.cuda.max_memory_allocated()
+                logger.debug_once(
+                    "Peak GPU memory after loading weights: %s GiB",
+                    format_gib(peak_memory),
+                    scope="local",
+                )
+
             process_weights_after_loading(model, model_config, target_device)
 
         return model.eval()

vllm/model_executor/model_loader/dummy_loader.py

@@ -25,4 +25,4 @@ def download_model(self, model_config: ModelConfig) -> None:
     def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
         # NOTE(woosuk): For accurate performance evaluation, we assign
         # random values to the weights.
-        initialize_dummy_weights(model)
+        initialize_dummy_weights(model, model_config)