Qualcomm AI Engine Direct - GA Static Smollm3 3B

backends/qualcomm/quantizer/custom_annotation.py

@@ -3,6 +3,7 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+from enum import Enum, unique
 from typing import Sequence
 
 import torch
@@ -50,6 +51,17 @@ def annotate_down_proj(
             )
 
 
+@unique
+class StaticLLMQuantConfig(Enum):
+    """
+    Layer namespace configuration for Qualcomm's static LLaMA quantization.
+    """
+
+    wq_sha = "wq_sha"  # Query weight (single head)
+    wk_sha = "wk_sha"  # Key weight (single head)
+    wv_sha = "wv_sha"  # Value weight (single head)
+
+
 def annotate_eurobert(gm: torch.fx.GraphModule):
     """
     QNN does not support int32 -> signed 16bit quant
@@ -185,11 +197,35 @@ def annotate_prefill_kv_output(gm: torch.fx.GraphModule, kv_quant_attrs: dict):
                 )
 
 
-def annotate_wv_sha(gm: torch.fx.GraphModule, quantization_config: QuantizationConfig):
+def annotate_qkv_proj_sha(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig,
+    qkv_tags: set[StaticLLMQuantConfig],
+):
+    """
+    Annotates QKV projection layers in a GraphModule for quantization,
+    specifically layers defined in StaticLLMQuantConfig.
+
+    Args:
+        qkv_tags (set[StaticLLMQuantConfig]): A set of enum tags indicating which QKV layers
+            (e.g., wq, wk, wv) should be annotated for quantization. Only tags defined in
+            StaticLLMQuantConfig are allowed.
+
+    Raises:
+        ValueError: If any tag in `qkv_tags` is not among the allowed enum members.
+    """
+
+    # Get all valid tags from the StaticLLMQuantConfig enum
+    allowed_tags = set(StaticLLMQuantConfig)
+    invalid_tags = qkv_tags - allowed_tags
+    if invalid_tags:
+        raise ValueError(
+            f"Invalid qkv tags: {invalid_tags}. Allowed tags are: {allowed_tags}"
+        )
+
     for node in gm.graph.nodes:
-        if (
-            node.target == torch.ops.aten.conv2d.default
-            and "wv_sha" in node.meta["stack_trace"]
+        if node.target == torch.ops.aten.conv2d.default and any(
+            tag.value in node.meta["stack_trace"] for tag in qkv_tags
         ):
             input_qspec_map = {}
             input_qspec_map[node.args[0]] = quantization_config.input_activation

backends/qualcomm/tests/test_qnn_delegate.py

@@ -5138,6 +5138,60 @@ def test_static_qwen3(self):
                         msg["inference_speed"], inference_speed_ref[self.model]
                     )
 
+    def test_qwen2_5(self):
+        if not self.required_envs([]):
+            self.skipTest("missing required envs")
+        prompt = "My favourite condiment is "
+        cmds = [
+            "python",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/qwen2_5/qwen2_5.py",
+            "--prompt",
+            prompt,
+            "--decoder_model",
+            "qwen2.5_0.5B",
+            "--ptq",
+            "16a8w",
+            "--enable_spinquant_r3",
+            "--max_seq_len",
+            "128",
+            "--artifact",
+            self.artifact_dir,
+            "--build_folder",
+            self.build_folder,
+            "--model",
+            self.model,
+            "--ip",
+            self.ip,
+            "--port",
+            str(self.port),
+        ]
+        if self.compile_only:
+            cmds.extend(["--compile_only"])
+        elif self.device:
+            cmds.extend(["--device", self.device])
+        if self.host:
+            cmds.extend(["--host", self.host])
+        elif self.enable_x86_64:
+            cmds.extend(["--enable_x86_64"])
+        if self.pre_gen_pte:
+            cmds.extend(["--pre_gen_pte", self.pre_gen_pte])
+
+        golden_start_with = "My favourite condiment is iced tea."
+        p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
+        with Listener((self.ip, self.port)) as listener:
+            conn = listener.accept()
+            p.communicate()
+            msg = json.loads(conn.recv())
+            if "Error" in msg:
+                self.fail(msg["Error"])
+            else:
+                if not self.compile_only:
+                    model_out = msg["result"][0]
+                    self.assertTrue(
+                        model_out.startswith(golden_start_with),
+                        f"Expected Output: '{golden_start_with}' Actual Output: '{model_out}'",
+                    )
+
     def test_static_smollm2(self):
         if not self.required_envs():
             self.skipTest("missing required envs")
@@ -5171,6 +5225,8 @@ def test_static_smollm2(self):
             "--eval_perplexity",
             "--task",
             "wikitext",
+            "--limit",
+            "1",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -5194,22 +5250,14 @@ def test_static_smollm2(self):
                 self.assertLessEqual(msg["wiki_ppl"], 25)
                 self.assertGreaterEqual(msg["inference_speed"], 200)
 
-    def test_qwen2_5(self):
-        if not self.required_envs([]):
+    def test_static_smollm3(self):
+        if not self.required_envs():
             self.skipTest("missing required envs")
+
         prompt = "My favourite condiment is "
         cmds = [
             "python",
-            f"{self.executorch_root}/examples/qualcomm/oss_scripts/qwen2_5/qwen2_5.py",
-            "--prompt",
-            prompt,
-            "--decoder_model",
-            "qwen2.5_0.5B",
-            "--ptq",
-            "16a8w",
-            "--enable_spinquant_r3",
-            "--max_seq_len",
-            "128",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/llama/llama.py",
             "--artifact",
             self.artifact_dir,
             "--build_folder",
@@ -5220,6 +5268,21 @@ def test_qwen2_5(self):
             self.ip,
             "--port",
             str(self.port),
+            "--prompt",
+            f"{prompt}",
+            "--decoder_model",
+            "smollm3-3b",
+            "--model_mode",
+            "kv",
+            "--temperature",
+            "0",
+            "--max_seq_len",
+            "1024",
+            "--eval_perplexity",
+            "--task",
+            "wikitext",
+            "--limit",
+            "1",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -5232,7 +5295,6 @@ def test_qwen2_5(self):
         if self.pre_gen_pte:
             cmds.extend(["--pre_gen_pte", self.pre_gen_pte])
 
-        golden_start_with = "My favourite condiment is iced tea."
         p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
         with Listener((self.ip, self.port)) as listener:
             conn = listener.accept()
@@ -5241,11 +5303,12 @@ def test_qwen2_5(self):
             if "Error" in msg:
                 self.fail(msg["Error"])
             else:
-                if not self.compile_only:
-                    model_out = msg["result"][0]
-                    self.assertTrue(
-                        model_out.startswith(golden_start_with),
-                        f"Expected Output: '{golden_start_with}' Actual Output: '{model_out}'",
+                inference_speed_ref = {"SM8650": 23, "SM8750": 28}
+                self.assertLessEqual(msg["wiki_ppl"], 10)
+                self.assertLessEqual(msg["pte_size"], 2_600_000_000)  # 2.6GB
+                if self.model in inference_speed_ref:
+                    self.assertGreaterEqual(
+                        msg["inference_speed"], inference_speed_ref[self.model]
                     )
 
 

examples/models/llama/model_args.py

@@ -78,6 +78,9 @@ class ModelArgs:
     use_qk_norm: bool = False  # apply normalization to q and k in the attention
     qk_norm_before_rope: bool = False  # when to apply qk norm
     use_hf_rope: bool = False  # Use HuggingFace's RoPE implementation
+    no_rope_layer_interval: Optional[int] = (
+        None  # Interval at which to skip RoPE. From Rope to Nope and Back Again: A New Hybrid Attention Strategy (https://huggingface.co/papers/2501.18795).
+    )
     partial_rotary_factor: float = 1.0
     rope_theta: Optional[float] = (
         None  # The official name to override self.rope_freq_base.

examples/models/smollm3/3b_config.json

@@ -0,0 +1,15 @@
+{
+    "dim": 2048,
+    "ffn_dim_multiplier": 1,
+    "hidden_dim": 11008,
+    "n_heads": 16,
+    "n_kv_heads": 4,
+    "n_layers": 36,
+    "norm_eps": 1e-06,
+    "rope_theta": 5000000.0,
+    "use_scaled_rope": false,
+    "vocab_size": 128256,
+    "use_hf_rope": false,
+    "no_rope_layer_interval": 4,
+    "attention_qkv_bias": false
+  }

examples/models/smollm3/__init__.py

@@ -0,0 +1,16 @@
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from executorch.examples.models.llama.model import Llama2Model
+from executorch.examples.models.smollm3.convert_weights import convert_weights
+
+
+class SmolLM3Model(Llama2Model):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+
+__all__ = [
+    "SmolLM3Model",
+    "convert_weights",
+]

examples/models/smollm3/convert_weights.py

@@ -0,0 +1,112 @@
+import argparse
+import json
+import os
+from typing import Dict
+
+import torch
+
+from safetensors.torch import load_file
+
+from torchtune.models.convert_weights import get_mapped_key
+
+
+_SMOLLM_TO_META = {
+    "model.embed_tokens.weight": "tok_embeddings.weight",
+    "model.norm.weight": "norm.weight",
+    "model.layers.{}.self_attn.k_proj.weight": "layers.{}.attention.wk.weight",
+    "model.layers.{}.self_attn.q_proj.weight": "layers.{}.attention.wq.weight",
+    "model.layers.{}.self_attn.v_proj.weight": "layers.{}.attention.wv.weight",
+    "model.layers.{}.self_attn.o_proj.weight": "layers.{}.attention.wo.weight",
+    "model.layers.{}.input_layernorm.weight": "layers.{}.attention_norm.weight",
+    "model.layers.{}.post_attention_layernorm.weight": "layers.{}.ffn_norm.weight",
+    "model.layers.{}.mlp.gate_proj.weight": "layers.{}.feed_forward.w1.weight",
+    "model.layers.{}.mlp.down_proj.weight": "layers.{}.feed_forward.w2.weight",
+    "model.layers.{}.mlp.up_proj.weight": "layers.{}.feed_forward.w3.weight",
+}
+
+
+def smollm_to_meta(state_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+    """
+    Convert a state dict from torchtune's format to Meta's format. This function
+    doesn't handle any sharding or splitting of state dicts. It follows the
+    state_dict IN -> state_dict OUT pattern.
+
+    Args:
+        state_dict (Dict[str, torch.Tensor]): State dict in torchtune's format.
+
+    Returns:
+        Dict[str, torch.Tensor]: State dict in Meta's format.
+    """
+    converted_state_dict = {}
+    for key, value in state_dict.items():
+        new_key = get_mapped_key(key, _SMOLLM_TO_META)
+        converted_state_dict[new_key] = value
+    converted_state_dict["output.weight"] = converted_state_dict[
+        "tok_embeddings.weight"
+    ]
+
+    return converted_state_dict
+
+
+def load_checkpoint_from_safetensors(input_dir: str) -> Dict:
+    index_path = os.path.join(input_dir, "model.safetensors.index.json")
+    if os.path.exists(index_path):
+        # Sharded checkpoint.
+        with open(index_path, "r") as f:
+            index = json.load(f)
+        weight_map = index["weight_map"]
+        checkpoint_shards = sorted(set(weight_map.values()))
+
+        # Load all the shards into memory
+        shard_to_weights = {}
+        for shard in checkpoint_shards:
+            shard_to_weights[shard] = load_file(os.path.join(input_dir, shard))
+
+        # Merge tensors into consolidated state dict.
+        merged_state_dict = {}
+        for weight_name, shard in weight_map.items():
+            tensor = shard_to_weights[shard][weight_name]
+            merged_state_dict[weight_name] = tensor
+        return merged_state_dict
+    else:
+        # Single checkpoint.
+        state_dict = load_file(os.path.join(input_dir, "model.safetensors"))
+        return state_dict
+
+
+def load_checkpoint(input_dir: str) -> Dict:
+    pytorch_path = os.path.join(input_dir, "pytorch_model.bin")
+    if os.path.exists(pytorch_path):
+        print("Loading checkpoint from PyTorch .bin file")
+        return torch.load(pytorch_path, map_location="cpu", weights_only=True)
+    print("Loading checkpoint from safetensors directory")
+    return load_checkpoint_from_safetensors(input_dir)
+
+
+def convert_weights(input_dir: str, output_file: str) -> None:
+    print("Loading checkpoint...")
+    sd = load_checkpoint(input_dir)
+    print("Converting checkpoint...")
+    sd = smollm_to_meta(sd)
+    print("Saving checkpoint...")
+    torch.save(sd, output_file)
+    print("Done.")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Convert SmolLM weights to Meta format."
+    )
+    parser.add_argument(
+        "input_dir",
+        type=str,
+        help="Path to directory containing checkpoint files",
+    )
+    parser.add_argument("output", type=str, help="Path to the output checkpoint")
+
+    args = parser.parse_args()
+    convert_weights(args.input_dir, args.output)
+
+
+if __name__ == "__main__":
+    main()

examples/qualcomm/oss_scripts/llama/README.md

@@ -9,7 +9,8 @@ This file provides you the instructions to run LLM Decoder model with different
  5. Phi4-mini-instruct
  6. QWEN2.5 0.5B / 1.5B
  7. QWEN3 0.6B / 1.7B
- 8. SMOLLM2 135M
+ 8. SmolLM2 135M
+ 9. SmolLM3 3B
 
 
 We offer the following modes to execute the model:
@@ -113,10 +114,16 @@ Default example using hybrid mode
 python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --temperature 0 --model_mode kv --max_seq_len 1024 --decoder_model qwen3-1_7b --prompt "I would like to learn python, could you teach me with a simple example?" --tasks wikitext --limit 1
 ```
 
-#### SMOLLM2
+#### SmolLM2
 Default example using hybrid mode.
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -H mlgtw-linux -s ${SERIAL_NUM} -m ${SOC_MODEL} --decoder_model smollm2_135m --model_mode hybrid --prefill_ar_len 128 --max_seq_len 1024 --prompt "I would like to learn python, could you teach me with a simple example?" --tasks wikitext --limit 1
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --decoder_model smollm2_135m --model_mode hybrid --prefill_ar_len 128 --max_seq_len 1024 --prompt "I would like to learn python, could you teach me with a simple example?" --tasks wikitext --limit 1
+```
+
+#### SmolLM3
+Default example using kv mode.
+```bash
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --decoder_model smollm3-3b --model_mode kv --max_seq_len 1024 --prompt "I would like to learn python, could you teach me with a simple example?" --tasks wikitext --limit 1
 ```