Add int4 weight-only QAT flow targeting tinygemm kernel

Summary: This commit adds an int4 weight-only QAT flow targeting
the efficient tinygemm kernel. This means during fine-tuning
we only simulate numerics of the kernel in bf16, but we only
actually call the kernel after quantizing the model. For more
detail, see pytorch/ao#383.

Test Plan: TODO

torchtune/training/quantization.py

@@ -6,23 +6,38 @@
 
 from typing import Callable, Optional
 
-from torchao.quantization import int8_dynamic_activation_int4_weight, quantize_
+from torchao.dtypes import TensorCoreTiledLayoutType
+from torchao.quantization import (
+    int4_weight_only,
+    int8_dynamic_activation_int4_weight,
+    quantize_,
+)
 from torchao.quantization.prototype.qat import (
+    disable_4w_fake_quant,
     disable_8da4w_fake_quant,
+    enable_4w_fake_quant,
     enable_8da4w_fake_quant,
+    Int4WeightOnlyQATQuantizer,
     Int8DynActInt4WeightQATQuantizer,
 )
 from torchao.quantization.prototype.qat._module_swap_api import (
+    disable_4w_fake_quant_module_swap,
     disable_8da4w_fake_quant_module_swap,
+    enable_4w_fake_quant_module_swap,
     enable_8da4w_fake_quant_module_swap,
+    Int4WeightOnlyQATQuantizerModuleSwap,
     Int8DynActInt4WeightQATQuantizerModuleSwap,
 )
 
 
 __all__ = [
     "get_quantizer_mode",
+    "Int4WeightOnlyQuantizer",
+    "Int4WeightOnlyQATQuantizer",
+    "Int4WeightOnlyQATQuantizerModuleSwap",
     "Int8DynActInt4WeightQuantizer",
     "Int8DynActInt4WeightQATQuantizer",
+    "Int8DynActInt4WeightQATQuantizerModuleSwap",
 ]
 
 
@@ -57,14 +72,52 @@ def quantize(self, model):
 _quantizer_mode_to_enable_fake_quant["8da4w-qat"] = enable_8da4w_fake_quant
 
 
-# ====================================================
-# int8 dynamic activations + int4 weight module swap |
-# ====================================================
+# ==================
+# int4 weight only |
+# ==================
+
+
+class Int4WeightOnlyQuantizer:
+    """
+    Quantizer for applying int4 per group weight only quantization
+    to linear layers in the model using the efficient tinygemm kernel.
+    """
+
+    def __init__(self, groupsize: int = 128, inner_k_tiles: int = 8):
+        self.groupsize = groupsize
+        self.inner_k_tiles = inner_k_tiles
+
+    def quantize(self, model):
+        layout_type = TensorCoreTiledLayoutType(self.inner_k_tiles)
+        quantize_fn = int4_weight_only(self.groupsize, layout_type)
+        quantize_(model, quantize_fn)
+        return model
+
+
+_quantizer_to_mode[Int4WeightOnlyQuantizer] = "4w"
+_quantizer_to_mode[Int4WeightOnlyQATQuantizer] = "4w-qat"
+_quantizer_mode_to_disable_fake_quant["4w-qat"] = disable_4w_fake_quant
+_quantizer_mode_to_enable_fake_quant["4w-qat"] = enable_4w_fake_quant
+
+
+# =============
+# module swap |
+# =============
 
 # Note: QAT tensor subclass implementation in torchao only works
 # with FSDP2 today. For other distribution strategies like DDP and
 # FSDP1, users will need to fall back to the old module swap flow.
-__all__.append("Int8DynActInt4WeightQATQuantizerModuleSwap")
+
+# int4 weight-only
+_quantizer_to_mode[Int4WeightOnlyQATQuantizerModuleSwap] = "4w-qat-module-swap"
+_quantizer_mode_to_disable_fake_quant[
+    "4w-qat-module-swap"
+] = disable_4w_fake_quant_module_swap
+_quantizer_mode_to_enable_fake_quant[
+    "4w-qat-module-swap"
+] = enable_4w_fake_quant_module_swap
+
+# int8 dynamic activations + int4 weight
 _quantizer_to_mode[Int8DynActInt4WeightQATQuantizerModuleSwap] = "8da4w-qat-module-swap"
 _quantizer_mode_to_disable_fake_quant[
     "8da4w-qat-module-swap"