CodeLinaro
diff --git a/‎backends/qualcomm/quantizer/custom_annotation.py‎
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diff --git a/‎backends/qualcomm/serialization/qnn_compile_spec_schema.py‎
Lines changed: 2 additions & 0 deletions b/‎backends/qualcomm/serialization/qnn_compile_spec_schema.py‎
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diff --git a/‎backends/qualcomm/serialization/schema.fbs‎
Lines changed: 2 additions & 1 deletion b/‎backends/qualcomm/serialization/schema.fbs‎
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diff --git a/‎backends/qualcomm/tests/utils.py‎
Lines changed: 1 addition & 0 deletions b/‎backends/qualcomm/tests/utils.py‎
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diff --git a/‎examples/models/llama2/export_llama_lib.py‎
Lines changed: 27 additions & 28 deletions b/‎examples/models/llama2/export_llama_lib.py‎
Lines changed: 27 additions & 28 deletions
@@ -118,3 +118,29 @@ def annotate_matmul_input1(node: Node, quantization_config: QuantizationConfig):
                 if "SDPA" in full_qualified_name:
                     annotate_matmul(node, quantization_config_16a8w)
                     annotate_matmul_input1(node.args[1], quantization_config_8a8w)
+
+
+def custom_annotate_matmul_16a8w(gm: torch.fx.GraphModule):
+    """
+    Annotate matmul op with 16a8w quantization config
+    """
+
+    def annotate_matmul(node: Node, quantization_config: QuantizationConfig):
+        input_qspec_map = {}
+        input_act = node.args[0]
+        input_spec = quantization_config.input_activation
+        input_qspec_map[input_act] = input_spec
+        input_act1 = node.args[1]
+        input_spec1 = quantization_config.weight
+        input_qspec_map[input_act1] = input_spec1
+        node.meta[QUANT_ANNOTATION_KEY] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=quantization_config.output_activation,
+            _annotated=True,
+        )
+
+    # Annotate 16a8w for matmul op to get better performance
+    quantization_config_16a8w = get_16a8w_qnn_ptq_config()
+    for node in gm.graph.nodes:
+        if node.op == "call_function" and node.target == torch.ops.aten.matmul.default:
+            annotate_matmul(node, quantization_config_16a8w)
@@ -34,6 +34,7 @@ class QcomChipset(IntEnum):
     SM8475 = 42  # v69
     SM8550 = 43  # v73
     SM8650 = 57  # v75
+    SSG2115P = 46  # v73... I wish I can know where the number comes from...
 
 
 @dataclass
@@ -47,6 +48,7 @@ class SocInfo:
     QcomChipset.SM8475: SocInfo(QcomChipset.SM8475, HtpInfo(HtpArch.V69, 8)),
     QcomChipset.SM8550: SocInfo(QcomChipset.SM8550, HtpInfo(HtpArch.V73, 8)),
     QcomChipset.SM8650: SocInfo(QcomChipset.SM8650, HtpInfo(HtpArch.V75, 8)),
+    QcomChipset.SSG2115P: SocInfo(QcomChipset.SSG2115P, HtpInfo(HtpArch.V73, 2)),
 }
 
 
 
@@ -32,6 +32,7 @@ enum QcomChipset: int {
   SM8450 = 36,
   SM8475 = 42,
   SM8550 = 43,
+  SSG2115P = 46,
   SM8650 = 57,
 }
 
@@ -170,7 +171,7 @@ table QnnExecuTorchOptions {
 
   /// Profiling level of the delegate and the backend. Default is off.
   profile_level:QnnExecuTorchProfileLevel;
-  
+
   /// Enables usage of shared buffer between application and backend for graph I/O.
   shared_buffer:bool;
 
 
@@ -118,6 +118,7 @@ class TestQNN(unittest.TestCase):
     model: QcomChipset = None
     compiler_specs: List[CompileSpec] = None
     arch_table = {
+        "SSG2115P": QcomChipset.SSG2115P,
         "SM8650": QcomChipset.SM8650,
         "SM8550": QcomChipset.SM8550,
         "SM8475": QcomChipset.SM8475,
 
@@ -53,21 +53,23 @@
     get_quant_embedding_transform,
     get_quant_weight_transform,
 )
-from .source_transformation.quantized_kv_cache import (
-    replace_kv_cache_with_quantized_kv_cache,
-)
+
+# from .source_transformation.quantized_kv_cache import (
+#     replace_kv_cache_with_quantized_kv_cache,
+# )
 from .source_transformation.rms_norm import replace_rms_norm_with_native_rms_norm
 
 from .source_transformation.rope import materialze_broadcast_of_rope_freq_cis
-from .source_transformation.sdpa import (
-    replace_causal_mask,
-    replace_kv_cache_with_coreml_kv_cache,
-    replace_kv_cache_with_simple_kv_cache,
-    replace_sdpa_with_coreml_sdpa,
-    replace_sdpa_with_custom_op,
-    replace_sdpa_with_flex_sdpa,
-    replace_sdpa_with_simple_sdpa,
-)
+
+# from .source_transformation.sdpa import (
+#     replace_causal_mask,
+#     replace_kv_cache_with_coreml_kv_cache,
+#     replace_kv_cache_with_simple_kv_cache,
+#     replace_sdpa_with_coreml_sdpa,
+#     replace_sdpa_with_custom_op,
+#     replace_sdpa_with_flex_sdpa,
+#     replace_sdpa_with_simple_sdpa,
+# )
 
 IS_FBCODE = True  #  os.environ.get("FBCODE_PLATFORM", False)
 FORMAT = "[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s"
@@ -910,23 +912,20 @@ def _get_source_transforms(  # noqa
         assert args.use_kv_cache, "quantize_kv_cache requires use_kv_cache=True"
         transforms.append(replace_kv_cache_with_quantized_kv_cache)
 
+    if args.qnn:
+        # pyre-ignore: Undefined import [21]: Could not find a module corresponding to import `executorch.backends.qualcomm.utils.utils`
+        from executorch.backends.qualcomm.utils.utils import convert_linear_to_conv2d
+
+        # transforms.append(replace_kv_cache_with_simple_kv_cache)
+        # transforms.append(replace_sdpa_with_flex_sdpa)
+        # transforms.append(replace_causal_mask)
+        transforms.append(replace_rms_norm_with_native_rms_norm)
+        if args.optimized_rotation_path:
+            transforms.append(fuse_layer_norms)
+            transforms.append(get_model_with_r1_r2(args.optimized_rotation_path))
+        transforms.append(convert_linear_to_conv2d)
     if args.use_kv_cache:
-        if args.qnn:
-            # pyre-ignore: Undefined import [21]: Could not find a module corresponding to import `executorch.backends.qualcomm.utils.utils`
-            from executorch.backends.qualcomm.utils.utils import (
-                convert_linear_to_conv2d,
-            )
-
-            transforms.append(replace_kv_cache_with_simple_kv_cache)
-            transforms.append(replace_sdpa_with_flex_sdpa)
-            transforms.append(replace_causal_mask)
-            transforms.append(replace_rms_norm_with_native_rms_norm)
-            if args.optimized_rotation_path:
-                transforms.append(fuse_layer_norms)
-                transforms.append(get_model_with_r1_r2(args.optimized_rotation_path))
-            transforms.append(convert_linear_to_conv2d)
-
-        elif args.mps:
+        if args.mps:
             # Currently mps doesn't support sdpa op, use the simpler decomposition
             # to get free perf gain.
             transforms.append(replace_sdpa_with_simple_sdpa)