Add support asymmetric fake-quantization to AQTv2.

Integration of native quantization with biases will require computing the cross terms, likely in the AQT operation quantizer (`DefaultGeneralQuantizer`).

Itemized changes:

- `AqtNumerics`:
  - Rename `AqtNumerics.abs_val_mapped_to` to `AqtNumerics.get_scaled_bound` to reflect that the calibration bound may be span the whole quantization range (instead of ~half the range for a strictly linear transformation).
  - Refactor `IntNumerics` into `BaseIntNumerics`, `SymIntNumerics` and `AsymIntNumerics`.
    - `AsymIntNumerics` doesn't need `preserve_zero` or `preserve_max_val`.
- Add `MinMaxCalibration`.

I additionally tested this change by training MNIST models using `flax_e2e_model`. With symmetric quantization the model fails to converge for `config.config_v4(fwd_bits=2, dlhs_bits=None, drhs_bits=None)` (due to `NaN` losses). With asymmetric quantization the model converges even with `config.config_v4(fwd_bits=2, dlhs_bits=2, drhs_bits=4)`.

PiperOrigin-RevId: 651580879

Author: phoenix-meadowlark

README.md

@@ -169,7 +169,7 @@ from aqt.jax.v2 import utils as aqt_utils
 from aqt.jax.v2.numerics import int_numerics
 
 q = aqt_quantizer.Quantizer(
-    numerics=int_numerics.IntNumerics(
+    numerics=int_numerics.SymIntNumerics(
         bits=4,
         preserve_zero=True,
         preserve_max_val=True,

aqt/jax/v2/aqt_conv_general_test.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import functools
+
 from absl.testing import absltest
 from absl.testing import parameterized
 from aqt.jax.v2 import aqt_quantizer
@@ -28,6 +30,16 @@ def rand_unif(shape, maxval, seed, dtype=jnp.float32):
   )
 
 
+def _apply_po2_scale(quantizer):
+  calibration_cls = quantizer.calibration
+  keywords = {}
+  if isinstance(calibration_cls, functools.partial):
+    keywords = calibration_cls.keywords
+    calibration_cls = calibration_cls.func
+  keywords.update(po2_scale=True)
+  quantizer.calibration = functools.partial(calibration_cls, **keywords)
+
+
 class AqtConvGeneralTest(parameterized.TestCase):
 
   @parameterized.parameters([
@@ -48,13 +60,17 @@ def test_conv_general_dilated(
       rhs_maxval=20.0,
       seed=0,
   ):
-    dg_raw_conv = aqt_conv.conv_general_dilated_make(2, lhs_bits, rhs_bits)
-
+    dg_raw_conv = aqt_conv.conv_general_dilated_make(
+        2, lhs_bits, rhs_bits, initialize_calibration=False
+    )
+    # Power-of-2 scales allow FQ and AQT to be exactly the same.
+    dg_quantizer = dg_raw_conv.dg_quantizer
     if dg_raw_conv.lhs:
-      # Power-of-2 scales allow FQ and AQT to be exactly the same.
-      dg_raw_conv.dg_quantizer.lhs.po2_scale = True
+      _apply_po2_scale(dg_quantizer.lhs)
+      dg_quantizer.lhs.init_calibration()
     if dg_raw_conv.rhs:
-      dg_raw_conv.dg_quantizer.rhs.po2_scale = True
+      _apply_po2_scale(dg_quantizer.rhs)
+      dg_quantizer.rhs.init_calibration()
 
     batch_n = 10
     contr_n = 20
@@ -94,12 +110,17 @@ def test_conv_general_dilated_quantized(
       seed=0,
   ):
     """Check that passing quantized lhs/rhs to aqt_conv_fn works."""
-    dg_raw_conv = aqt_conv.conv_general_dilated_make(2, lhs_bits, rhs_bits)
+    dg_raw_conv = aqt_conv.conv_general_dilated_make(
+        2, lhs_bits, rhs_bits, initialize_calibration=False
+    )
+    # Power-of-2 scales allow FQ and AQT to be exactly the same.
+    dg_quantizer = dg_raw_conv.dg_quantizer
     if dg_raw_conv.lhs:
-      # Power-of-2 scales allow FQ and AQT to be exactly the same.
-      dg_raw_conv.dg_quantizer.lhs.po2_scale = True
+      _apply_po2_scale(dg_quantizer.lhs)
+      dg_quantizer.lhs.init_calibration()
     if dg_raw_conv.rhs:
-      dg_raw_conv.dg_quantizer.rhs.po2_scale = True
+      _apply_po2_scale(dg_quantizer.rhs)
+      dg_quantizer.rhs.init_calibration()
 
     batch_n = 10
     contr_n = 20