[Bugfix][Model Loader] Restore kernel state post model loading via TensorizerLoader.

[codetalker7]

This PR targets issue #55198 about the TensorizerLoader. Particularly, the current implementation of TensorizerLoader fails to run post-loading processing on the model weights, and also skips computing any state required for the kernel backends to execute the model. As mentioned in the issue, TensorizerLoader fails to call either of process_weights_after_loading and finalize_layerwise_processing, and also fails to return the model in .eval() mode.

The reproduction code mentioned in the issue is not very helpful; it only tracks the actual function calls by constructing the call graph. For a minimal reproduction of the issue, I've only focused on the following:

• Model: Qwen/Qwen3-0.6B-FP8
• Revision: e5be08033360965ceca7b0ffd72d521a51331ce0
• Quantization: block-scaled FP8
• Linear backend: Marlin

Here are my hardware/other specs:

• GPU: NVIDIA GeForce RTX 3050, 6144MiB VRAM
• Ubuntu 22.04
• vLLM checkout: v0.28.0

Note that the issue persists in two "paths": one is the raw checkpoint path, where the TensorizerLoader is used to load raw checkpoint weights, and is supposed to run post-processing + computing the state. The second path is the "native" vLLM path: here, post-processing is already run, and the loader just needs to compute the state required by the kernels. For this PR, the focus is only on the native vLLM path, since it is trickier.

Reproduction

Here is the reproduction script that I used (with an actual model). It has two modes: load and save. Use the save mode first to load raw weights and save the model via the tensorizer. Then, the load move exposes the error:

"""
    .venv/bin/python issue-55198-lab/reproduce_native_fp8.py save
    .venv/bin/python issue-55198-lab/reproduce_native_fp8.py load

Before native Tensorizer runtime state is restored, the load phase is expected
to fail because the deserialized Marlin layers do not have a workspace.
"""

import argparse
import json
import os
import sys
from pathlib import Path

os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"

sys.path.insert(0, str(Path('/home/codetalker7/vllm')))

MODEL = "Qwen/Qwen3-0.6B-FP8"
REVISION = "e5be08033360965ceca7b0ffd72d521a51331ce0"
PROMPT = "The capital of France is"


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("phase", choices=("save", "load"), default="save")
    parser.add_argument(
        "--directory",
        type=Path,
        default=Path("issue-55198-lab/native-qwen-fp8"),
        help="Directory containing the native checkpoint and baseline tokens.",
    )
    args = parser.parse_args()

    from vllm import LLM, SamplingParams

    directory = args.directory.resolve()
    # directory = Path("issue-55198-lab/native-qwen-fp8")

    checkpoint = directory / "model.tensors"
    baseline = directory / "baseline_token_ids.json"

    options = dict(
        model=MODEL,
        revision=REVISION,
        dtype="bfloat16",
        tensor_parallel_size=1,
        linear_backend="marlin",
        enforce_eager=True,
        max_model_len=128,
        max_num_seqs=1,
        max_num_batched_tokens=128,
        gpu_memory_utilization=0.5,
        seed=0,
    )
    if args.phase == "load":
        if not checkpoint.is_file() or not baseline.is_file():
            parser.error(
                "Run the save phase first: the native checkpoint and baseline "
                f"must exist in {directory}"
            )
        options.update(
            load_format="tensorizer",
            model_loader_extra_config={
                "tensorizer_uri": str(checkpoint),
                "num_readers": 1,
            },
        )

    # Native reload can fail during engine startup profiling, before LLM(...)
    # returns. Preserve that traceback because it identifies missing state.
    llm = LLM(**options)
    outputs = llm.generate(
        PROMPT,
        SamplingParams(temperature=0, max_tokens=8),
        use_tqdm=False,
    )
    output = outputs[0].outputs[0]
    token_ids = list(output.token_ids)
    print(f"{args.phase} generation: {output.text!r}", flush=True)

    if args.phase == "save":
        directory.mkdir(parents=True, exist_ok=True)
        llm.collective_rpc(
            "save_tensorized_model",
            kwargs={
                "tensorizer_config": {
                    "tensorizer_uri": str(checkpoint),
                }
            },
        )
        baseline.write_text(json.dumps(token_ids) + "\n")
        print(f"Native checkpoint saved: {checkpoint}", flush=True)
        print(f"Baseline token IDs saved: {baseline}", flush=True)
    else:
        expected = json.loads(baseline.read_text())
        assert token_ids == expected, (token_ids, expected)
        print("Native round trip passed: generated token IDs match", flush=True)


if __name__ == "__main__":
    main()

"""
%autoindent
%env VLLM_ENABLE_V1_MULTIPROCESSING=0
# for normal mode, DefaultModelLoader is the model loader
# for tensorized mode, TensorLoader is the model loader

%debug llm = (...)
"""

Upon running the load phase, here is the error that I get:

INFO 09-25 20:28:20 [api_utils.py:272] non-default args: {'load_format': 'tensorizer', 'dtype': 'bfloat16', 'max_model_len': 128, 'linear_backend': 'marlin', 'gpu_memory_utilization': 0.5, 'max_num_batched_tokens': 128, 'max_num_seqs': 1, 'disable_log_stats': True, 'revision': 'e5be08033360965ceca7b0ffd72d521a51331ce0', 'enforce_eager': True, 'model_loader_extra_config': {'tensorizer_uri': '/home/codetalker7/vllm/issue-55198-lab/native-qwen-fp8/model.tensors', 'num_readers': 1}, 'model': 'Qwen/Qwen3-0.6B-FP8'}
WARNING 09-25 20:28:20 [arg_utils.py:1719] The global random seed is set to 0. Since VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may affect the random state of the Python process that launched vLLM.
INFO 09-25 20:28:21 [model.py:672] Resolved architecture: Qwen3ForCausalLM
INFO 09-25 20:28:21 [model.py:1965] Using max model len 128
INFO 09-25 20:28:21 [scheduler.py:242] Chunked prefill is enabled with max_num_batched_tokens=128.
WARNING 09-25 20:28:21 [vllm.py:1285] Enforce eager set, disabling torch.compile and CUDAGraphs. This is equivalent to setting -cc.mode=none -cc.cudagraph_mode=none
WARNING 09-25 20:28:21 [vllm.py:1354] Inductor compilation was disabled by user settings, optimizations settings that are only active during inductor compilation will be ignored.
INFO 09-25 20:28:21 [kernel.py:308] Final IR op priority after setting platform defaults: IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native'])
INFO 09-25 20:28:21 [vllm.py:1533] Cudagraph is disabled under eager mode
INFO 09-25 20:28:21 [compilation.py:329] Enabled custom fusions: norm_quant, act_quant
INFO 09-25 20:28:23 [core.py:122] Initializing a V1 LLM engine (v0.28.1.dev0+g2cf0a6915.d20260915) with config: model='Qwen/Qwen3-0.6B-FP8', speculative_config=None, tokenizer='Qwen/Qwen3-0.6B-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=e5be08033360965ceca7b0ffd72d521a51331ce0, tokenizer_revision=e5be08033360965ceca7b0ffd72d521a51331ce0, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=128, download_dir=None, load_format=tensorizer, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=fp8, quantization_config=None, enforce_eager=True, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False, jit_monitor_mode='warn', jit_monitor_verbose=False), seed=0, served_model_name=Qwen/Qwen3-0.6B-FP8, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.NONE: 0>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['+quant_fp8', 'all', '+quant_fp8'], 'ir_enable_torch_wrap': False, 'splitting_ops': [], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_vision_items_per_batch': 0, 'encoder_cudagraph_max_frames_per_batch': None, 'compile_sizes': [], 'compile_ranges_endpoints': [128], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.NONE: 0>, 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': True, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False, 'enable_qk_norm_rope_fusion': False, 'fuse_rope_kvcache_cat_mla': False, 'fuse_act_padding': False, 'fuse_qk_norm_rope_kvcache': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['vllm_c', 'native'], fused_add_rms_norm=['vllm_c', 'native']), enable_flashinfer_autotune=True, enable_cutedsl_warmup=True, enable_jit_warmup=True, enable_bf16x3_router_gemm=False, moe_backend='auto', linear_backend='marlin')
INFO 09-25 20:28:23 [parallel_state.py:1638] world_size=1 rank=0 local_rank=0 distributed_init_method=file:///tmp/vllm_dist_d207fb52539a42808977a3f4be8c6981 backend=nccl
INFO 09-25 20:28:23 [parallel_state.py:1982] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A
INFO 09-25 20:28:23 [gpu_worker.py:396] Using V2 Model Runner
WARNING 09-25 20:28:24 [tensorizer.py:242] Provided both tensorizer_dir and tensorizer_uri. Inferring tensorizer_dir from tensorizer_uri as the latter takes precedence.
INFO 09-25 20:28:24 [model_runner.py:358] Loading model from scratch...
WARNING 09-25 20:28:24 [tensorizer.py:322] Loading a model using Tensorizer with quantization on vLLM is unstable and may lead to errors.
INFO 09-25 20:28:24 [__init__.py:689] Selected MarlinFP8ScaledMMLinearKernel for Fp8LinearMethod
INFO 09-25 20:28:24 [cuda.py:486] Using FLASH_ATTN attention backend out of potential backends: ['FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION'].
INFO 09-25 20:28:24 [flash_attn.py:866] Using FlashAttention version 2
INFO 09-25 20:28:25 [tensorizer.py:564] Deserialized 1.1 GB in 0.53s, 2.0 GB/s
INFO 09-25 20:28:25 [tensorizer.py:567] Memory usage before: CPU: (maxrss: 3,234MiB F: 1,791MiB) GPU: (U: 1,194MiB F: 4,610MiB T: 5,804MiB) TORCH: (R: 1,058MiB/1,058MiB, A: 729MiB/1,027MiB)
INFO 09-25 20:28:25 [tensorizer.py:568] Memory usage after: CPU: (maxrss: 3,836MiB F: 1,188MiB) GPU: (U: 2,026MiB F: 3,778MiB T: 5,804MiB) TORCH: (R: 1,890MiB/1,890MiB, A: 826MiB/1,556MiB)
INFO 09-25 20:28:26 [model_runner.py:380] Model loading took 0.81 GiB memory and 1.850716 seconds
INFO 09-25 20:28:26 [topk_topp_sampler.py:62] Using FlashInfer for top-p & top-k sampling.
[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/codetalker7/vllm/issue-55198-lab/reproduce_native_fp8.py", line 106, in <module>
[rank0]:     main()
[rank0]:   File "/home/codetalker7/vllm/issue-55198-lab/reproduce_native_fp8.py", line 76, in main
[rank0]:     llm = LLM(**options)
[rank0]:           ^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/entrypoints/llm.py", line 343, in __init__
[rank0]:     self.llm_engine = LLMEngine.from_engine_args(
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/llm_engine.py", line 179, in from_engine_args
[rank0]:     return cls(
[rank0]:            ^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/llm_engine.py", line 105, in __init__
[rank0]:     self.engine_core = EngineCoreClient.make_client(
[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/core_client.py", line 112, in make_client
[rank0]:     return InprocClient(vllm_config, executor_class, log_stats)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/core_client.py", line 317, in __init__
[rank0]:     self.engine_core = EngineCore(*args, **kwargs)
[rank0]:                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/core.py", line 144, in __init__
[rank0]:     kv_cache_config = self._initialize_kv_caches(vllm_config)
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/tracing/otel.py", line 178, in sync_wrapper
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/engine/core.py", line 296, in _initialize_kv_caches
[rank0]:     available_gpu_memory = self.model_executor.determine_available_memory()
[rank0]:                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/executor/abstract.py", line 149, in determine_available_memory
[rank0]:     return self.collective_rpc("determine_available_memory")
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/executor/uniproc_executor.py", line 103, in collective_rpc
[rank0]:     result = run_method(self.driver_worker, method, args, kwargs)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/serial_utils.py", line 510, in run_method
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/worker/gpu_worker.py", line 519, in determine_available_memory
[rank0]:     self.model_runner.profile_run()
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/worker/gpu/model_runner.py", line 813, in profile_run
[rank0]:     hidden_states, sample_hidden_states = self._dummy_run(
[rank0]:                                           ^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/worker/gpu/eplb_utils.py", line 27, in wrapper
[rank0]:     result = fn(self, *args, **kwargs)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/worker/gpu/model_runner.py", line 695, in _dummy_run
[rank0]:     self.execute_model(
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 124, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/v1/worker/gpu/model_runner.py", line 1666, in execute_model
[rank0]:     model_output = self.model(**model_inputs)
[rank0]:                    ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/models/qwen3.py", line 325, in forward
[rank0]:     hidden_states = self.model(
[rank0]:                     ^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/compilation/decorators.py", line 507, in __call__
[rank0]:     return self.forward(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/models/qwen2.py", line 419, in forward
[rank0]:     hidden_states, residual = layer(positions, hidden_states, residual)
[rank0]:                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/models/qwen3.py", line 238, in forward
[rank0]:     hidden_states = self.self_attn(
[rank0]:                     ^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/models/qwen3.py", line 158, in forward
[rank0]:     qkv, _ = self.qkv_proj(hidden_states)
[rank0]:              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1778, in _wrapped_call_impl
[rank0]:     return self._call_impl(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1789, in _call_impl
[rank0]:     return forward_call(*args, **kwargs)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/layers/linear.py", line 576, in forward
[rank0]:     output_parallel = self.quant_method.apply(self, input_, bias)
[rank0]:                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/layers/quantization/fp8.py", line 461, in apply
[rank0]:     return self.fp8_linear.apply_weights(layer, x, bias)
[rank0]:            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/vllm/model_executor/kernels/linear/scaled_mm/marlin.py", line 100, in apply_weights
[rank0]:     workspace=layer.workspace,
[rank0]:               ^^^^^^^^^^^^^^^
[rank0]:   File "/home/codetalker7/vllm/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1967, in __getattr__
[rank0]:     raise AttributeError(
[rank0]: AttributeError: 'QKVParallelLinear' object has no attribute 'workspace'
[rank0]:[W925 20:28:27.004132960 ProcessGroupNCCL.cpp:1624] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

As it can be seen, the TensorizerLoader never constructs the workspace object needed by the Marlin kernel. This is because process_weights_after_loading was never called.

Purpose

The PR aims to fix the above error. There is something to note, however: process_weights_after_loading is not idempotent for the native path. Particularly, if a checkpoint has already been saved via the native path, then the tensors in the file are already processed. Running process_weights_after_loading on those tensors will result in errors.

Instead of calling process_weights_after_loading, our solution introduces a new function to the class design: initialize_runtime_state_after_loading. This function does a subset of what process_weights_after_loading does; particularly, it only computes the state needed for the backend execution. For the case of the Marlin kernel, this function will compute the linear workspace (see the diff in the class MarlinFP8ScaledMMLinearKernel). Similar to the design for the process_weights_after_loading, we use dynamic dispatch for the introduced function.

However, I'd love to discuss with the maintainers if this is a good design choice (or if they'd like something else to be done). I can also potentially extend the scope of this PR (to involve more linear backends) if the maintainers agree that the proposed solution is good.

Test Plan

This PR will be updated with the relevant tests.

Test Result

---

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