Embedding quant unification

.ci/scripts/test_llama.sh

@@ -237,7 +237,7 @@ if [[ "${CUSTOM}" == "ON" ]]; then
   EXPORT_ARGS="${EXPORT_ARGS} model.use_sdpa_with_kv_cache=true"
 fi
 if [[ "${QE}" == "ON" ]]; then
-  EXPORT_ARGS="${EXPORT_ARGS} quantization.embedding_quantize=\"8,1024\""
+  EXPORT_ARGS="${EXPORT_ARGS} quantization.embedding_quantize=\"8,768\""
 fi
 if [[ "${MPS}" == "ON" ]]; then
   EXPORT_ARGS="${EXPORT_ARGS} backend.mps.enabled=true model.enable_dynamic_shape=false debug.verbose=true"

examples/apple/coreml/llama/export.py

@@ -23,7 +23,6 @@
 from executorch.exir.backend.utils import format_delegated_graph
 from executorch.exir.capture._config import ExecutorchBackendConfig
 from executorch.exir.passes import MemoryPlanningPass
-from executorch.exir.passes.quant_fusion_pass import QuantFusionPass
 from executorch.exir.passes.sym_shape_eval_pass import ConstraintBasedSymShapeEvalPass
 from executorch.extension.export_util.utils import save_pte_program
 
@@ -211,9 +210,7 @@ def main() -> None:
     executorch_program = edge_manager.to_executorch(
         ExecutorchBackendConfig(
             extract_delegate_segments=True,
-            passes=[
-                QuantFusionPass(),
-            ],
+            do_quant_fusion_and_const_prop=True,
             memory_planning_pass=MemoryPlanningPass(alloc_graph_input=False),
             sym_shape_eval_pass=ConstraintBasedSymShapeEvalPass(),
         )

examples/models/llama/source_transformation/quantize.py

@@ -595,19 +595,16 @@ def __init__(
 
     @torch.no_grad()
     def create_quantized_state_dict(self, packed=False) -> Dict:
+        from torchao.quantization.granularity import PerAxis, PerGroup
+        from torchao.quantization.quant_api import (
+            IntxWeightOnlyConfig,
+            MappingType,
+            quantize_,
+        )
+
         cur_state_dict = self.mod.state_dict()
 
-        if self.bitwidth == 2:
-            range_min = -2
-            range_max = 1
-        elif self.bitwidth == 4:
-            range_min = -8
-            range_max = 7
-        elif self.bitwidth == 8:
-            range_min = -128
-            range_max = 127
-        else:
-            raise ValueError(f"Unsupported bitwidth {self.bitwidth}")
+        assert self.bitwidth in [2, 4, 8], f"Unsupported bitwidth {self.bitwidth}"
 
         for fqn, mod in self.mod.named_modules():
             if isinstance(mod, nn.Embedding):
@@ -619,18 +616,22 @@ def create_quantized_state_dict(self, packed=False) -> Dict:
                 print(
                     f"quantize {fqn, mod} with group_size {self.group_size}, bitwidth {self.bitwidth}"
                 )
-                weight, scales, _ = dynamically_quantize_per_channel(
-                    (
-                        mod.weight.to(dtype=self.precision)
-                        if self.precision
-                        else mod.weight
+                tmp_model = nn.Embedding(mod.weight.shape[0], mod.weight.shape[1])
+                if self.precision:
+                    tmp_model = tmp_model.to(dtype=self.precision)
+                tmp_model.weight = nn.Parameter(mod.weight)
+                config = IntxWeightOnlyConfig(
+                    weight_dtype=getattr(torch, f"int{self.bitwidth}"),
+                    granularity=(
+                        PerAxis(0)
+                        if (self.group_size is None or self.group_size == 0)
+                        else PerGroup(self.group_size)
                     ),
-                    range_min,
-                    range_max,
-                    torch.int8,
-                    self.group_size,
-                    scales_dtype=mod.weight.dtype,
+                    mapping_type=MappingType.SYMMETRIC,
                 )
+                quantize_(tmp_model, config, lambda m, fqn: isinstance(m, nn.Embedding))
+                weight = tmp_model.weight.qdata  # pyre-ignore[16]
+                scales = tmp_model.weight.scale  # pyre-ignore[16]
 
                 if packed:
                     if self.bitwidth == 2: