Improve QAT int4 weight-only numerics

**Summary:** Similar to #2937, this commit improves the prepare
vs convert SQNR of int4 weight-only QAT from 12 to 45. This is
achieved by mimicking the numerics of the target FBGEMM bf16-int4
kernel more closely. In particular, the FBGEMM kernel:

1. Performs asymmetric [0, 15] quant first then recenters to 8
2. Uses smaller scale eps of 1e-6 instead of bf16's eps (0.0078125)
3. Quantizes the weights using min val instead of zero points

**Test Plan:**
```
python test/quantization/test_qat.py -k test_quantize_api_int4
python test/quantization/test_qat.py -k test_fbgemm_int4_weight_only_primitives
```

End-to-end tests TBD

Author: andrewor14

test/quantization/test_qat.py

@@ -83,6 +83,7 @@
     dequantize_affine,
     quantize_affine,
 )
+from torchao.quantization.quantize_.workflows import Int4PackingFormat
 from torchao.quantization.unified import (
     TwoStepQuantizer,
 )
@@ -1942,15 +1943,18 @@ def test_quantize_api_fp8_int4(self):
     )
     @unittest.skipIf(is_fbcode(), "cutlass cannot initialize")
     @parametrize("version", [1, 2])
-    def test_quantize_api_int4(self, version: int):
+    @parametrize(
+        "packing_format", [Int4PackingFormat.PLAIN, Int4PackingFormat.PRESHUFFLED]
+    )
+    def test_quantize_api_int4(self, version: int, packing_format: Int4PackingFormat):
         """
         Test the following:
             quantize_(model, QATConfig(Int4WeightOnlyConfig(), step="prepare"))
             quantize_(model, QATConfig(Int4WeightOnlyConfig(), step="convert"))
         """
         self._test_quantize_api_against_ptq(
-            Int4WeightOnlyConfig(version=version),
-            target_prepare_sqnr=12,
+            Int4WeightOnlyConfig(version=version, int4_packing_format=packing_format),
+            target_prepare_sqnr=45 if version == 2 else 12,
             target_convert_sqnr=float("inf"),
         )
 
@@ -2004,9 +2008,9 @@ def test_infer_int4_weight_only_config(self):
         base_config = Int4WeightOnlyConfig(version=2)
         (act_config, weight_config) = _infer_fake_quantize_configs(base_config)
         self.assertIsNone(act_config)
-        self.assertEqual(weight_config.dtype, torch.int4)
+        self.assertIsInstance(weight_config, Int4WeightPreshuffledFakeQuantizeConfig)
         self.assertEqual(weight_config.group_size, 128)
-        self.assertTrue(weight_config.is_symmetric)
+        self.assertEqual(weight_config.activation_dtype, torch.bfloat16)
 
     @unittest.skipIf(not is_sm_at_least_89(), "Need sm89+")
     def test_quantize_api_nvfp4(self):
@@ -2094,7 +2098,7 @@ def test_fbgemm_fp8_primitives(self):
         not _is_fbgemm_genai_gpu_available(), "Requires fbgemm-gpu-genai >= 1.2.0"
     )
     @unittest.skipIf(is_fbcode(), "triton compilation error")
-    def test_fbgemm_int4_preshuffled_primitives(self):
+    def test_fbgemm_fp8_int4_preshuffled_primitives(self):
         """
         Compare numerics between:
             (1) fbgemm_gpu.experimental.gen_ai.quantize.quantize_int4_preshuffle
@@ -2171,6 +2175,71 @@ def shuffle_and_pack(t: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
         )
         self.assertGreater(sqnr_q1_q3_preshuffle, 32)
 
+    @unittest.skipIf(not _CUDA_IS_AVAILABLE, "skipping when cuda is not available")
+    @unittest.skipIf(
+        not _is_fbgemm_genai_gpu_available(), "Requires fbgemm-gpu-genai >= 1.2.0"
+    )
+    @unittest.skipIf(is_fbcode(), "triton compilation error")
+    def test_fbgemm_int4_weight_only_primitives(self):
+        """
+        Compare numerics between:
+            (1) fbgemm_gpu.experimental.gen_ai.quantize.int4_row_quantize_zp
+            (2) Our reference QAT version in `Int4WeightPreshuffledFakeQuantizer`
+        """
+        from fbgemm_gpu.experimental.gen_ai.quantize import (
+            int4_row_quantize_zp,
+            pack_int4,
+            quantize_int4_preshuffle,
+        )
+
+        group_size = 128
+        x1 = torch.randn([128, 256], dtype=torch.bfloat16).cuda()
+        x2 = copy.deepcopy(x1)
+        x3 = copy.deepcopy(x1)
+
+        # (1) Just call `quantize_int4_preshuffle` with dtype="bf16"
+        (q1, (scale1, _)) = quantize_int4_preshuffle(x1, group_size, dtype="bf16")
+
+        # (2) Call `int4_row_quantize_zp`, which should be the same as (1)
+        # but without the packing and shuffling
+        (q2, scale2, _) = int4_row_quantize_zp(x2, group_size)
+
+        # (3) Reference implementation for QAT without the dequantize
+        eps = 1e-6
+        qmin, qmax = 0, 15
+        fbgemm_symmetric_qmax = 8
+        w_grouped = x3.to(torch.float32).view(x3.shape[0], -1, group_size)
+        max_val = torch.amax(w_grouped, dim=-1, keepdim=True)
+        min_val = torch.amin(w_grouped, dim=-1, keepdim=True)
+        scale3 = torch.clamp(max_val - min_val, min=eps) / qmax
+        q3 = (w_grouped.sub(min_val).div(scale3)).round().clamp_(qmin, qmax)
+        q3 = q3 - fbgemm_symmetric_qmax
+        q3 = q3.view(x3.shape)
+
+        def shuffle_and_pack(t: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
+            t = pack_int4(t.to(torch.int8))
+            return torch.ops.fbgemm.preshuffle_i4(t, scale.to(torch.bfloat16))[0]
+
+        # First, sanity check that shuffle_and_pack(q2) == q1
+        torch.testing.assert_close(q1, shuffle_and_pack(q2, scale2), atol=0, rtol=0)
+
+        # Now check q2 vs q3 with and without shuffle
+        torch.testing.assert_close(q2.to(torch.float32), q3, atol=0, rtol=0)
+        torch.testing.assert_close(
+            shuffle_and_pack(q2, scale2).to(torch.float32),
+            shuffle_and_pack(q3, scale3).to(torch.float32),
+            atol=0,
+            rtol=0,
+        )
+
+        # Now check shuffle_and_pack(q3) vs q1
+        torch.testing.assert_close(
+            q1.to(torch.float32),
+            shuffle_and_pack(q3, scale3).to(torch.float32),
+            atol=0,
+            rtol=0,
+        )
+
 
 instantiate_parametrized_tests(TestQAT)
 

torchao/quantization/qat/fake_quantize_config.py

@@ -31,6 +31,7 @@
     TorchAODType,
     ZeroPointDomain,
 )
+from torchao.quantization.quantize_.workflows import Int4PackingFormat
 from torchao.utils import _is_float8_type
 
 from .utils import _log_deprecation_warning
@@ -77,11 +78,14 @@ def __post_init__(self):
             )
 
 
+# TODO: rename this config, it actually works for both plain and preshuffled
 @dataclass
 class Int4WeightPreshuffledFakeQuantizeConfig(FakeQuantizeConfigBase):
     """
     Config for pint4 weight fake quantization that targets the numerics in the following preshuffled kernel:
         torch.ops.fbgemm.f8i4bf16_shuffled
+        torch.ops.fbgemm.bf16i4bf16_shuffled
+        torch.ops.fbgemm.bf16i4bf16_rowwise
 
     Currently this only supports float8 input activations. It is expected to be used in conjunction with
     :class:`~torchao.quantization.Float8DynamicActivationInt4WeightConfig`. In the future, we may extend
@@ -92,8 +96,10 @@ class Int4WeightPreshuffledFakeQuantizeConfig(FakeQuantizeConfigBase):
     activation_dtype: torch.dtype = e4m3_dtype
 
     def __post_init__(self):
-        if self.activation_dtype != e4m3_dtype:
-            raise ValueError(f"Only {e4m3_dtype} activation is supported currently")
+        if self.activation_dtype not in [e4m3_dtype, torch.bfloat16]:
+            raise ValueError(
+                f"Only {e4m3_dtype} or torch.bfloat16 activation are supported"
+            )
 
 
 @dataclass
@@ -379,10 +385,17 @@ def _infer_fake_quantize_configs(
     elif isinstance(base_config, Int4WeightOnlyConfig):
         act_config = None
         if base_config.version == 2:
-            weight_config = IntxFakeQuantizeConfig(
-                dtype=torch.int4,
-                group_size=base_config.group_size,
-                is_symmetric=True,
+            supported_packing_formats = [
+                Int4PackingFormat.PLAIN,
+                Int4PackingFormat.PRESHUFFLED,
+            ]
+            if base_config.int4_packing_format not in supported_packing_formats:
+                raise ValueError(
+                    f"Packing format must be one of {supported_packing_formats}"
+                )
+            weight_config = Int4WeightPreshuffledFakeQuantizeConfig(
+                group_size=128,
+                activation_dtype=torch.bfloat16,
             )
         elif base_config.version == 1:
             # For BC

torchao/quantization/qat/fake_quantizer.py

@@ -103,11 +103,14 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         return dq
 
 
+# TODO: rename this, it also works for plain Int4Tensor
 class Int4WeightPreshuffledFakeQuantizer(FakeQuantizerBase):
     """
     Generic module for applying int4 fake quantization to a weight tensor,
-    targeting the following FBGEMM kernel:
+    targeting the following FBGEMM kernels:
         torch.ops.fbgemm.f8i4bf16_shuffled
+        torch.ops.fbgemm.bf16i4bf16_shuffled
+        torch.ops.fbgemm.bf16i4bf16_rowwise
     """
 
     def __init__(self, config: Int4WeightPreshuffledFakeQuantizeConfig):
@@ -118,11 +121,18 @@ def __init__(self, config: Int4WeightPreshuffledFakeQuantizeConfig):
         )
 
     def forward(self, w: torch.Tensor) -> torch.Tensor:
-        """
-        Apply int4 fake quantization to the weight tensor, using the following as a reference:
-        https://github.com/pytorch/FBGEMM/blob/80cc48c4b2b7fcc579e53211fc8715a8592cbd2c/fbgemm_gpu/experimental/gen_ai/gen_ai/quantize.py#L112
+        if self.config.activation_dtype == torch.float8_e4m3fn:
+            return self._fp8_activations_forward(w)
+        elif self.config.activation_dtype == torch.bfloat16:
+            return self._bf16_activations_forward(w)
+        else:
+            raise ValueError(f"Unknown activation dtype {self.config.activation_dtype}")
 
-        Currently, we expect the activations to always be rowwise float8.
+    def _fp8_activations_forward(self, w: torch.Tensor) -> torch.Tensor:
+        """
+        Apply int4 fake quantization to the weight tensor where the input activations
+        are expected to be rowwise fp8, using the following as a reference:
+        https://github.com/pytorch/FBGEMM/blob/80cc48c4b2b7fcc579e53211fc8715a8592cbd2c/fbgemm_gpu/experimental/gen_ai/gen_ai/quantize.py#L136
         """
         assert w.dim() == 2
         assert self.config.activation_dtype == torch.float8_e4m3fn
@@ -159,6 +169,28 @@ def forward(self, w: torch.Tensor) -> torch.Tensor:
         )
         return fq.to(w.dtype)
 
+    def _bf16_activations_forward(self, w: torch.Tensor) -> torch.Tensor:
+        """
+        Apply int4 fake quantization to the weight tensor where the input activations
+        are expected to be bf16, using the following as a reference:
+        https://github.com/pytorch/FBGEMM/blob/80cc48c4b2b7fcc579e53211fc8715a8592cbd2c/fbgemm_gpu/experimental/gen_ai/gen_ai/quantize.py#L152
+        """
+        assert w.dim() == 2
+        assert self.config.activation_dtype == torch.bfloat16
+
+        eps = 1e-6
+        qmin, qmax = 0, 15
+        fbgemm_symmetric_qmax = 8
+        w_grouped = w.to(torch.float32).view(w.shape[0], -1, self.config.group_size)
+        max_val = torch.amax(w_grouped, dim=-1, keepdim=True)
+        min_val = torch.amin(w_grouped, dim=-1, keepdim=True)
+        scale = torch.clamp(max_val - min_val, min=eps) / qmax
+        zero_point = min_val + scale * fbgemm_symmetric_qmax
+        fq = _Round.apply((w_grouped - min_val) / scale).clamp(qmin, qmax)
+        fq = fq - fbgemm_symmetric_qmax
+        fq = fq * scale + zero_point
+        return fq.view(w.shape).to(w.dtype)
+
 
 class IntxFakeQuantizer(FakeQuantizerBase):
     """