Qualcomm AI Engine Direct - Update mix-precision analyzer unitest (#20688)

### Summary
- Update test_analyzer_to_file_generation to the new
  analyze(decoder_inference, text_dataloader) signature
- Replace SimpleModel with SimpleLLMDecoder so the SQNR analyzer
captures a realistic
per-layer structure; add it to tests/models.py

### Test plan
``` bash
python -m backends.qualcomm.tests.test_qnn_delegate TestUtilsScript.test_analyzer_to_file_generation --device $DEVICE --soc_model ${soc} --build_folder build-android --executorch_root .
```

Author: DannyYuyang-quic

backends/qualcomm/tests/models.py

@@ -2397,6 +2397,77 @@ def forward(self, x, y):
         return z5
 
 
+class SimpleLLMDecoder(torch.nn.Module):
+    """
+    Minimal transformer decoder mirroring how QNN LLM decoders are built:
+    a token embedding feeds a stack of decoder blocks whose linear projections
+    are expressed as 1x1 conv2d (see static_llama.py), grouped under a
+    ``layers.N`` ModuleList. Takes token ids and an additive attention mask.
+    """
+
+    class ConvAttention(torch.nn.Module):
+        def __init__(self, dim, n_heads):
+            super().__init__()
+            self.n_heads = n_heads
+            self.head_dim = dim // n_heads
+            self.scale = self.head_dim**-0.5
+            self.wq_conv = torch.nn.Conv2d(dim, dim, 1, bias=False)
+            self.wk_conv = torch.nn.Conv2d(dim, dim, 1, bias=False)
+            self.wv_conv = torch.nn.Conv2d(dim, dim, 1, bias=False)
+            self.wo_conv = torch.nn.Conv2d(dim, dim, 1, bias=False)
+
+        def forward(self, x, atten_mask):  # x: (b, dim, 1, seq)
+            b, dim, _, seq = x.shape
+            q = self.wq_conv(x).view(b, self.n_heads, self.head_dim, seq)
+            k = self.wk_conv(x).view(b, self.n_heads, self.head_dim, seq)
+            v = self.wv_conv(x).view(b, self.n_heads, self.head_dim, seq)
+            attn = torch.matmul(q.transpose(-2, -1), k) * self.scale
+            attn = torch.softmax(attn + atten_mask, dim=-1)
+            ctx = torch.matmul(v, attn.transpose(-2, -1))
+            ctx = ctx.reshape(b, dim, 1, seq)
+            return self.wo_conv(ctx)
+
+    class ConvFeedForward(torch.nn.Module):
+        def __init__(self, dim, hidden_dim):
+            super().__init__()
+            self.w1_conv = torch.nn.Conv2d(dim, hidden_dim, 1, bias=False)
+            self.w2_conv = torch.nn.Conv2d(hidden_dim, dim, 1, bias=False)
+            self.w3_conv = torch.nn.Conv2d(dim, hidden_dim, 1, bias=False)
+            self.act_fn = torch.nn.SiLU()
+
+        def forward(self, x):
+            return self.w2_conv(self.act_fn(self.w1_conv(x)) * self.w3_conv(x))
+
+    class DecoderLayer(torch.nn.Module):
+        def __init__(self, dim, hidden_dim, n_heads):
+            super().__init__()
+            self.attention = SimpleLLMDecoder.ConvAttention(dim, n_heads)
+            self.feed_forward = SimpleLLMDecoder.ConvFeedForward(dim, hidden_dim)
+
+        def forward(self, x, atten_mask):
+            x = x + self.attention(x, atten_mask)
+            x = x + self.feed_forward(x)
+            return x
+
+    def __init__(self, vocab_size=128, dim=32, hidden_dim=64, n_heads=4, n_layers=1):
+        super().__init__()
+        self.tok_embeddings = torch.nn.Embedding(vocab_size, dim)
+        self.layers = torch.nn.ModuleList(
+            [self.DecoderLayer(dim, hidden_dim, n_heads) for _ in range(n_layers)]
+        )
+        self.output_conv = torch.nn.Conv2d(dim, dim, 1, bias=False)
+        self.eval()
+
+    def forward(self, input_ids, atten_mask):  # input_ids: (b, seq)
+        x = self.tok_embeddings(input_ids)  # (b, seq, dim)
+        b, seq, dim = x.shape
+        x = x.reshape(b, seq, 1, dim).transpose(1, 3)  # (b, dim, 1, seq)
+        for layer in self.layers:
+            x = layer(x, atten_mask)
+        x = self.output_conv(x)
+        return x.transpose(1, 3).reshape(b, seq, dim)
+
+
 class SkipBackToBack(torch.nn.Module):
 
     def __init__(self):

backends/qualcomm/tests/test_qnn_delegate.py

@@ -10881,19 +10881,40 @@ def test_analyzer_to_file_generation(self):
             save_suggest_recipes,
         )
 
-        module = SimpleModel()  # noqa: F405
-        sample_input = (torch.ones(1, 32, 28, 28), torch.ones(1, 32, 28, 28))
+        torch.manual_seed(8)
+        n_layers = 20
+        vocab_size, seq_len, n_heads = 128, 8, 4
+        module = SimpleLLMDecoder(  # noqa: F405
+            vocab_size=vocab_size, n_heads=n_heads, n_layers=n_layers
+        )
+        input_ids = torch.randint(0, vocab_size, (1, seq_len), dtype=torch.int32)
+        atten_mask = torch.triu(
+            torch.full((1, 1, seq_len, seq_len), float("-inf")), diagonal=1
+        )
+        sample_input = (input_ids, atten_mask)
         fp32_gm = torch.export.export(module, sample_input, strict=True).module()
         qdq_gm = self.get_qdq_module(
             module, sample_input, quant_dtype=QuantDtype.use_8a4w
         )
 
+        class DecoderInference:
+            def get_inputs(self, input_ids, attn_mask):
+                return (input_ids, attn_mask)
+
+        text_dataloader = [
+            {
+                "input_ids": input_ids,
+                "attention_mask": atten_mask,
+            }
+        ]
+
+        num_sharding = 5
         report = PerLayerSqnrAnalyzer(
-            model_name="simple_conv",
-            num_layers=4,
+            model_name="simple_llm_decoder",
+            num_layers=n_layers,
             fp32_gm=fp32_gm,
             qdq_gm=qdq_gm,
-        ).analyze([sample_input], num_sharding=4)
+        ).analyze(DecoderInference(), text_dataloader, num_sharding=num_sharding)
 
         overrides = report.suggest_recipe_overrides(sqnr_threshold=22.0)
 
@@ -10902,10 +10923,13 @@ def test_analyzer_to_file_generation(self):
             save_suggest_recipes(report, overrides, output_dir=tmp_dir)
 
             # --- save_analysis_summary csv file ---
-            with open(f"{tmp_dir}/simple_conv_quantization_error.csv") as f:
+            with open(f"{tmp_dir}/simple_llm_decoder_quantization_error.csv") as f:
                 csv_content = f.read()
             rows = list(csv.reader(csv_content.splitlines()))
-            self.assertEqual(len(rows), 5)  # 1 header + 4 group rows
+            # 1 header + per-shard conv groups (7 projections each: wq/wk/wv/wo,
+            # w1/w2/w3) + the model-level output_conv. Layers are bucketed into
+            # num_sharding contiguous shards (n_layers >= num_sharding).
+            self.assertEqual(len(rows), 1 + num_sharding * 7 + 1)
             self.assertEqual(
                 rows[0],
                 [
@@ -10921,11 +10945,11 @@ def test_analyzer_to_file_generation(self):
 
             # --- save_suggest_recipes .py file (only written when sensitive layers exist) ---
             if overrides:
-                with open(f"{tmp_dir}/simple_conv_suggest_recipe.py") as f:
+                with open(f"{tmp_dir}/simple_llm_decoder_suggest_recipe.py") as f:
                     py_content = f.read()
                 # generated file must be valid Python
                 try:
-                    compile(py_content, "simple_conv_suggest_recipe.py", "exec")
+                    compile(py_content, "simple_llm_decoder_suggest_recipe.py", "exec")
                 except SyntaxError as e:
                     self.fail(
                         f"Generated recipe file has syntax error: {e}\n{py_content}"