Removed unused MCore < 0.29 checkpointing support

Signed-off-by: realAsma <[email protected]>

Author: realAsma

modelopt/torch/quantization/conversion.py

@@ -198,7 +198,6 @@ def _replace_quant_module(model: nn.Module, version=None, registry=QuantModuleRe
             # REPLACE on the parent (model), not on child
             quantized = registry.convert(child)
             setattr(model, name, quantized)
-            quantized.mopt_ckpt_versn = version
 
         # now recurse into whichever module is now at `model.name`
         _replace_quant_module(getattr(model, name), version=version, registry=registry)

modelopt/torch/quantization/nn/modules/quant_module.py

@@ -37,24 +37,6 @@
 class QuantModule(DynamicModule):
     """A base class for quantized modules."""
 
-    @property
-    def mopt_ckpt_versn(self):
-        """Checkpoint version of the modelopt."""
-        for module in self.modules():
-            if isinstance(module, TensorQuantizer):
-                return module.mopt_ckpt_versn
-        return None
-
-    @mopt_ckpt_versn.setter
-    def mopt_ckpt_versn(self, version: str):
-        """Set the checkpoint version for the TensorQuantizer states."""
-
-        def _set_ckpt_version(module):
-            if isinstance(module, TensorQuantizer):
-                module.mopt_ckpt_versn = version
-
-        self.apply(_set_ckpt_version)
-
     def modelopt_post_restore(self, prefix: str = ""):
         """Post-restore to correctly configure the TensorQuantizer states.
 

modelopt/torch/quantization/nn/modules/tensor_quantizer.py

@@ -29,7 +29,6 @@
     DTensor = None
 
 import torch.nn.functional as F
-from packaging.version import Version
 from torch import nn
 from torch.onnx._globals import GLOBALS
 
@@ -1023,48 +1022,6 @@ def extra_repr(self):
         s += " calib" if (self._if_calib) else ""
         return s
 
-    @property
-    def mopt_ckpt_versn(self):
-        """Version of the checkpoint if it is restored from a checkpoint."""
-        return getattr(self, "_mopt_ckpt_versn", None)
-
-    @mopt_ckpt_versn.setter
-    def mopt_ckpt_versn(self, version: str):
-        self._mopt_ckpt_versn = str(version)
-
-    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
-        """Special handling for loading older checkpoints.
-
-        This implementation is for backward compatibility and can be deprecated in future versions.
-
-        Args:
-            state_dict: A dict containing the state of the top level module
-            prefix: A string that prefixes all of this modules state in state_dict, e.g. 'model.conv1.'
-        """
-        if self.mopt_ckpt_versn is None or Version(self.mopt_ckpt_versn) >= Version("0.29"):
-            # Warnings below are raised if users use partial state dictionary intentionally (eg:- HF ckpts)
-            # For ModelOpt >= 0.29, the buffers will be correctly created, So lets skip the warnings
-            return super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
-        _attrs = ["_amax", "_pre_quant_scale", "_svdquant_lora_a", "_svdquant_lora_b"]
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-        for attr in _attrs:
-            has_dst = attr in self._buffers
-            has_src = prefix + attr in state_dict
-
-            if not has_src and has_dst:
-                warnings.warn(f"{prefix[:-1]}: No {attr} in state_dict.")
-            elif has_src and not has_dst:
-                warnings.warn(
-                    f"{prefix[:-1]}: No '{attr}' buffer to load {attr} into."
-                    f" '{attr}` is created as a buffer for now. Please move the model to the correct device and "
-                    "dtype after this by calling `model.to(device, dtype)`."
-                )
-                self.register_buffer(attr, state_dict[prefix + attr].clone().detach().to(device))
-
-        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
-
     def _get_properties_for_modelopt_state(self):
         return (
             self.__dict__.keys()

modelopt/torch/quantization/plugins/custom.py

@@ -21,9 +21,7 @@
 from types import ModuleType
 
 import torch
-from packaging.version import Version
 
-from modelopt import __version__
 from modelopt.torch.utils.distributed import ParallelState
 
 from ..nn import QuantModule, SequentialQuantizer, TensorQuantizer
@@ -174,19 +172,3 @@ def _has_state(quantizer, name):
             max_calibrate(self.output_quantizer, lambda oq: oq(dummy_input), distributed_sync=False)
         # If there are any other states, lets move them to the correct device
         super().modelopt_post_restore(prefix=prefix)
-
-    def is_version_less_than(self, version: str) -> bool:
-        self_version = (
-            Version(self.mopt_ckpt_versn)
-            if self.mopt_ckpt_versn is not None
-            else Version(__version__)
-        )
-
-        # version in NeMo container is 0.0.0 if installed from source without history
-        if self_version < Version(version) and self_version != Version("0.0.0"):
-            warnings.warn(
-                f"Checkpoint version {self_version} is less than {version}. "
-                "Please re-save model to avoid this warning."
-            )
-            return True
-        return False

modelopt/torch/quantization/plugins/vllm.py

@@ -185,15 +185,3 @@ def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
         output = super().forward(hidden_states, router_logits)
         self.invoke_fused_moe_quantized = self._invoke_fused_moe_quantized
         return output
-
-    @property
-    def mopt_ckpt_versn(self):
-        """Checkpoint version of the modelopt."""
-        return None
-
-    @mopt_ckpt_versn.setter
-    def mopt_ckpt_versn(self, version: str):
-        """Set the checkpoint version for the TensorQuantizer states."""
-        # vLLM defined an apply method that overwrites nn.Module.apply
-        # To avoid conflicting, disable the apply call here
-        # self.apply(_set_ckpt_version)

tests/_test_utils/torch_quantization/checkpointing.py

@@ -26,8 +26,6 @@
 from modelopt.torch.quantization.utils import is_quantized_linear
 from modelopt.torch.utils import torch_to
 
-from .checkpointing import format_modelopt_checkpoint_by_version
-
 INT4_AWQ_FULL_CFG = mtq.INT4_AWQ_CFG.copy()
 
 INT4_AWQ_FULL_CFG["algorithm"] = "awq_full"
@@ -84,9 +82,6 @@ def save_restore_test(model_cls, device, quant_config, compress=False, version=N
 
     state_dict = mto.modelopt_state(model_quant)
 
-    if version is not None:
-        state_dict = format_modelopt_checkpoint_by_version(state_dict, version)
-
     mto.restore_from_modelopt_state(model_ref, state_dict)
     model_ref.load_state_dict(model_quant.state_dict())
     assert torch.allclose(model_quant(calib_data[0]), model_ref(calib_data[0]))

tests/_test_utils/torch_quantization/quantize_common.py

@@ -92,8 +92,3 @@ def test_quantize(model_cls, config):
 )
 def test_save_restore(model_cls, quant_config):
     save_restore_test(model_cls, "cuda", quant_config)
-
-
-@pytest.mark.parametrize("version", [None, "0.29", "0.28"])
-def test_save_restore_all_versions(version):
-    save_restore_test(SimpleLinear, "cuda", mtq.INT8_DEFAULT_CFG, version=version)