Qualcomm AI Engine Direct - Static LLM Refactor & Qwen3 1.7B Improvement (#13755)

### Summary

- Refactor llama.py. The current script has some limitations when it
gets to customizing configs, especially quantization configs. As there
are more models enabled, the script is a little messy, consisting of
multiple `if`-`else` statement deciding what model should go into
specific optimization. We want to move it all the model specs under
`__init__.py`.
- Hiding scale/offset into model's metadata, so args.quant_attrs_path is
no longer required when evaluating ppl score.
- Enable Qwen3 1.7B with 16a4w_block quant. Before is using 16a8w, which
is much slower. Targeting maximizing token rate while ensuring ppl
remains within a 20% margin compared to the FP CPU baseline

#### Stats
token rate = 37tok/sec
ppl = 14.79

### Test plan
Tested all scripts ensuring no regression

cc: @haowhsu-quic

Author: winskuo-quic

backends/qualcomm/quantizer/custom_annotation.py

@@ -92,9 +92,12 @@ def annotate_mimi_decoder(gm: torch.fx.GraphModule):
             break
 
 
-def annotate_linear_16a8w_in_affine_layer(
-    gm: torch.fx.GraphModule, is_qat: bool = False
-) -> None:
+def annotate_output_16a8w(gm: torch.fx.GraphModule, is_qat: bool = False) -> None:
+    """
+    This function is for static LLM models.
+    This function will annotate the last conv(linear), which is the lm_head, as 16a8w.
+    """
+
     def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None:
         input_qspec_map = {}
         input_act = node.args[0]
@@ -163,11 +166,30 @@ def annotate_prefill_kv_output(gm: torch.fx.GraphModule, kv_quant_attrs: dict):
                 )
 
 
-def annotate_matmul_16a8w(  # noqa: C901
+def annotate_wv_sha(gm: torch.fx.GraphModule, quantization_config: QuantizationConfig):
+    for node in gm.graph.nodes:
+        if (
+            node.target == torch.ops.aten.conv2d.default
+            and "wv_sha" in node.meta["stack_trace"]
+        ):
+            input_qspec_map = {}
+            input_qspec_map[node.args[0]] = quantization_config.input_activation
+            input_qspec_map[node.args[1]] = quantization_config.weight
+            if len(node.args) > 2 and isinstance(node.args[2], Node):
+                input_qspec_map[node.args[2]] = quantization_config.bias(node)
+            node.meta[Q_ANNOTATION_KEY] = QuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                output_qspec=quantization_config.output_activation,
+                _annotated=True,
+            )
+
+
+def annotate_kv_8bit(  # noqa: C901
     gm: torch.fx.GraphModule,
     is_qat=False,
 ) -> None:
     """
+    This function is for static LLM models.
     This function is specific for matmul op 16a8w.
     For k, we will tag such as the below, and
     for v, we will tag 8a until conv op.
@@ -213,25 +235,6 @@ def annotate_cat(node: Node, quantization_config: QuantizationConfig):
             _annotated=True,
         )
 
-    def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None:
-        input_qspec_map = {}
-        input_act = node.args[0]
-        input_spec = quantization_config.input_activation
-        input_qspec_map[input_act] = input_spec
-
-        weight = node.args[1]
-        input_qspec_map[weight] = quantization_config.weight
-
-        if len(node.args) > 2 and isinstance(node.args[2], Node):
-            bias = node.args[2]
-            input_qspec_map[bias] = quantization_config.bias(node)
-
-        node.meta[Q_ANNOTATION_KEY] = QuantizationAnnotation(
-            input_qspec_map=input_qspec_map,
-            output_qspec=quantization_config.output_activation,
-            _annotated=True,
-        )
-
     def annotate_rms_norm(node: Node, quantization_config: QuantizationConfig) -> None:
         act_node = node.args[0]
         weight_node = node.args[2]
@@ -301,22 +304,10 @@ def annotate_matmul_input1(node: Node, is_qat: str):
             quantization_config_8a8w = get_8a8w_qnn_qat_config(
                 act_symmetric=True, act_observer=MinMaxObserver
             )
-            quantization_config_8a4w_per_channel = get_qat_per_channel_quant_config(
-                act_dtype=torch.uint8,
-                weight_dtype=torch.int4,
-                act_observer=MinMaxObserver,
-                act_symmetric=True,
-            )
         else:
             quantization_config_8a8w = get_8a8w_qnn_ptq_config(
                 act_symmetric=True, act_observer=MinMaxObserver
             )
-            quantization_config_8a4w_per_channel = get_ptq_per_channel_quant_config(
-                act_dtype=torch.uint8,
-                weight_dtype=torch.int4,
-                act_observer=MinMaxObserver,
-                act_symmetric=True,
-            )
         while isinstance(node, Node) and node.op == "call_function":
             if node.target in [
                 torch.ops.aten.permute.default,
@@ -343,15 +334,11 @@ def annotate_matmul_input1(node: Node, is_qat: str):
                 # For k, we tag 8a until add or sub op (rotatary embedding).
                 # The arguments of cat op: (the past kv cache, the new kv cache)
                 node = node.args[0][1]
-            elif node.target == torch.ops.aten.conv2d.default:
-                annotate_conv2d(
-                    node, quantization_config=quantization_config_8a4w_per_channel
-                )
-                break
             elif node.target in [
                 torch.ops.aten.add.Tensor,
                 torch.ops.aten.sub.Tensor,
                 torch.ops.aten.matmul.default,
+                torch.ops.aten.conv2d.default,
             ]:
                 break
             else:

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4582,8 +4582,6 @@ def test_llama3_2_1b(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a4w",
             "--temperature",
             "0",
             "--decoder_model",
@@ -4594,8 +4592,6 @@ def test_llama3_2_1b(self):
             "32",
             "--max_seq_len",
             "512",
-            "--num_sharding",
-            "4",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -4662,8 +4658,6 @@ def test_llama_stories_260k(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a4w",
             "--temperature",
             "0",
             "--decoder_model",
@@ -4740,8 +4734,6 @@ def test_llama_stories_110m(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a4w",
             "--temperature",
             "0",
             "--decoder_model",
@@ -4806,18 +4798,12 @@ def test_static_phi4(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a4w_block",
-            "--group_size",
-            "16",
             "--decoder_model",
             "phi_4_mini",
             "--model_mode",
             "kv",
             "--max_seq_len",
             "1024",
-            "--num_sharding",
-            "8",
             "--eval_perplexity",
             "--tasks",
             "wikitext",
@@ -4877,8 +4863,6 @@ def test_static_qwen2_5(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a8w",
             "--decoder_model",
             "qwen2_5-0_5b",
             "--model_mode",
@@ -4890,8 +4874,6 @@ def test_static_qwen2_5(self):
             "wikitext",
             "--limit",
             "1",
-            "--r3",
-            "--enable_masked_softmax",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -4940,8 +4922,6 @@ def test_static_qwen3(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a8w",
             "--decoder_model",
             "qwen3-0_6b",
             "--model_mode",
@@ -4953,8 +4933,6 @@ def test_static_qwen3(self):
             "wikitext",
             "--limit",
             "1",
-            "--r3",
-            "--enable_masked_softmax",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -5003,8 +4981,6 @@ def test_static_smollm2(self):
             str(self.port),
             "--prompt",
             f"{prompt}",
-            "--ptq",
-            "16a8w",
             "--decoder_model",
             "smollm2_135m",
             "--model_mode",