Update quantization to use tensor subclasses

**Summary:** In torchao, we are migrating our quantization flows
from module swap to tensor subclasses. The existing
`Int8DynActInt4WeightQuantizer` will be deprecated in the near
future in favor of quantizing using the `quantize_` API,
so we should do the same in torchtune. This quantizer is
currently only used by QAT, which also recently migrated to
a tensor subclass implementation.

This also changes the eval script slightly since models
quantized through the torchao tensor subclasses are expected
to be loaded with `assign=True`: https://github.com/pytorch/ao/blob/9a56e80cb6070599701b8f5f587bd8187c8dccb4/test/quantization/test_quant_api.py#L610.
We should load the model similarly in torchtune.

**Test Plan:**

Quantized and evaluated the base Llama3-8B model on 1 A100 GPU:

```
CUDA_VISIBLE_DEVICES=1 tune run quantize --config recipes/configs/my_quantization.yaml \
    model._component_=torchtune.models.llama3.llama3_8b \
    checkpointer.checkpoint_dir=/tmp/Meta-Llama-3-8B-Instruct/original \
    checkpointer.output_dir=/tmp/Meta-Llama-3-8B-Instruct/original \
    checkpointer.checkpoint_files=[consolidated.00.pth] \
    checkpointer.model_type=LLAMA3

CUDA_VISIBLE_DEVICES=1 tune run eleuther_eval --config eleuther_evaluation \
    model._component_=torchtune.models.llama3.llama3_8b \
    checkpointer._component_=torchtune.utils.FullModelTorchTuneCheckpointer \
    checkpointer.checkpoint_dir=/tmp/Meta-Llama-3-8B-Instruct/original \
    checkpointer.output_dir=/tmp/Meta-Llama-3-8B-Instruct/original \
    checkpointer.checkpoint_files=[consolidated-8da4w.pt] \
    checkpointer.model_type=LLAMA3 \
    tokenizer._component_=torchtune.models.llama3.llama3_tokenizer \
    tokenizer.path=/tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model \
    quantizer._component_=torchtune.utils.quantization.Int8DynActInt4WeightQuantizer
```

Reviewers: ebsmothers, kartikayk, RdoubleA

Subscribers: ebsmothers, kartikayk, RdoubleA

Subscribers:

recipes/eleuther_eval.py

@@ -219,11 +219,15 @@ def _setup_model(
     ) -> nn.Module:
         with utils.set_default_dtype(self._dtype), self._device:
             model = config.instantiate(model_cfg)
+
         if self._quantization_mode is not None:
             model = self._quantizer.quantize(model)
             model = model.to(device=self._device, dtype=self._dtype)
-
-        model.load_state_dict(model_state_dict)
+            for k, v in model_state_dict.items():
+                model_state_dict[k] = v.to(self._device)
+            model.load_state_dict(model_state_dict, assign=True)
+        else:
+            model.load_state_dict(model_state_dict)
 
         # Put model in eval mode.
         # Note: This will not disable the dropout applied in SDPA,

torchtune/utils/quantization.py

@@ -22,7 +22,19 @@
 
 
 if TORCH_VERSION_AFTER_2_3:
-    from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer
+    from torchao.quantization.quant_api import (
+        int8_dynamic_activation_int4_weight,
+        quantize_,
+    )
+
+    class Int8DynActInt4WeightQuantizer:
+        def __init__(self, groupsize: int = 256):
+            self.groupsize = groupsize
+
+        def quantize(self, model):
+            quantize_fn = int8_dynamic_activation_int4_weight(self.groupsize)
+            quantize_(model, quantize_fn)
+            return model
 
     __all__.append("Int8DynActInt4WeightQuantizer")
     _quantizer_to_mode[Int8DynActInt4WeightQuantizer] = "8da4w"