Qualcomm AI Engine Direct - Static Decoder Runner Support 16bit KV IO

Author: winskuo-quic

backends/qualcomm/quantizer/custom_annotation.py

@@ -153,9 +153,7 @@ def annotate_prefill_kv_output(gm: torch.fx.GraphModule, kv_quant_attrs: dict):
                 )
 
 
-def annotate_matmul_16a8w(  # noqa: C901
-    gm: torch.fx.GraphModule, annotate_conv=True
-) -> None:
+def annotate_matmul_16a8w(gm: torch.fx.GraphModule) -> None:  # noqa: C901
     """
     This function is specific for matmul op 16a8w.
     For k, we will tag such as the below, and
@@ -319,10 +317,9 @@ def annotate_matmul_input1(node: Node):
                 # 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:
-                if annotate_conv:
-                    annotate_conv2d(
-                        node, quantization_config=quantization_config_8a4w_per_channel
-                    )
+                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]:
                 break

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4360,6 +4360,8 @@ def test_static_qwen2_5(self):
             "wikitext",
             "--limit",
             "1",
+            "--r3",
+            "--enable_masked_softmax",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -4381,7 +4383,7 @@ def test_static_qwen2_5(self):
                 self.fail(msg["Error"])
             else:
                 inference_speed_ref = {"SM8650": 110, "SM8750": 130}
-                self.assertLessEqual(msg["wiki_ppl"], 25)
+                self.assertLessEqual(msg["wiki_ppl"], 15)
                 self.assertLessEqual(msg["pte_size"], 800000000)  # 800mb
                 if self.model in inference_speed_ref:
                     self.assertGreaterEqual(

examples/qualcomm/oss_scripts/llama/README.md

@@ -57,13 +57,19 @@ At the end of this step, users should have the following files ready: `consolida
 ### Step3: Run default examples using hybrid mode.
 #### LLAMA2
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint stories110M.pt --params params.json --tokenizer_model tokenizer.model --tokenizer_bin tokenizer.bin --llama_model stories110m --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "Once upon a time"
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint stories110M.pt --params params.json --tokenizer_model tokenizer.model --tokenizer_bin tokenizer.bin --decoder_model stories110m --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "Once upon a time"
 ```
 
 #### LLAMA3.2
 Default example using hybrid mode.
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1"
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --decoder_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1"
+```
+
+#### QWEN2.5 0.5B
+Default example using hybrid mode
+```bash
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --temperature 0 --model_mode hybrid --max_seq_len 1024 --prefill_ar_len 128 --ptq 16a8w --enable_masked_softmax --r3 --decoder_model qwen2_5 --prompt "I would like to learn python, could you teach me with a simple example?"
 ```
 
 ### KV Cache update mechanism
@@ -118,21 +124,21 @@ We have two distinct mechanisms for updating the key-value (KV) cache, which can
 #### Compile Only
 If you would like to compile the model only, we have provided the flag `--compile_only`. Taking LLAMA3.2 as an example:
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --compile_only
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --decoder_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --compile_only
 ```
 
 #### Pre Generated PTE
 On the other hand, if you already have a pre-compiled .pte model, you can perform inference by providing the flag `--pre_gen_pte` and specifying the folder that contains the .pte model. Taking LLAMA3.2 as an example:
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --pre_gen_pte ${FOLDER_TO_PRE_GEN_PTE}
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --decoder_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --pre_gen_pte ${FOLDER_TO_PRE_GEN_PTE}
 ```
 
 #### KV Cache Updater
 
 You can select the KV Cache update mechanism at runtime by setting the `KV_UPDATER` variable to either "shift_pointer" or "smart_mask". By default, it is set to "smart_mask".
 `KV_UPDATER` = "shift_pointer"
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --kv_updator ${KV_UPDATER}
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --decoder_model llama3_2 --model_mode hybrid --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --kv_updator ${KV_UPDATER}
 ```
 
 #### Lookahead Decoding Mode
@@ -145,7 +151,7 @@ You can choose the lookahead mode to enhance decoding speed. To use this mode, y
 For more details, please refer to the paper ["Break the Sequential Dependency of LLM Inference Using Lookahead Decoding"](https://arxiv.org/abs/2402.02057)
 
 ```bash
-python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --llama_model llama3_2 --model_mode lookahead --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --ngram 3 --window 2 --gcap 2
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --ptq 16a4w --checkpoint consolidated.00.pth --params params.json --tokenizer_model tokenizer.model --decoder_model llama3_2 --model_mode lookahead --prefill_ar_len 32 --max_seq_len 128 --prompt "what is 1+1" --ngram 3 --window 2 --gcap 2
 ```
 
 #### Masked Softmax

examples/qualcomm/oss_scripts/llama/llama.py

@@ -265,8 +265,8 @@ def quantize(
 
         self.llama_graph_module = convert_pt2e(fx_graph_module)
 
-        logging.info("Verifying the QDQ model...")
         if args.eval_perplexity:
+            logging.info("Verifying the QDQ model...")
             # Check qdq cpu results
             graph_module_inference(
                 args=args,
@@ -375,6 +375,7 @@ def compile(args, pte_filename, tokenizer):
     kv_config.use_kv_cache = True
     kv_config.enable_masked_softmax = args.enable_masked_softmax
     kv_config.enable_r3 = args.r3
+    kv_config.kv_io_bit_width = 16 if args.ptq == "16a8w" else 8
 
     prefill_config = copy.copy(kv_config)
     prefill_config.use_kv_cache = (
@@ -551,11 +552,15 @@ def permute(w, heads):
     fixed_point_type = {"kv_type": torch.float32, "io_type": torch.float32}
     if args.ptq:
         use_fp16 = False
-        fixed_point_type["kv_type"] = torch.uint8
         if args.ptq == "8a8w":
             fixed_point_type["io_type"] = torch.uint8
-        elif args.ptq in ("16a4w", "16a4w_block", "16a8w"):
+            fixed_point_type["kv_type"] = torch.uint8
+        elif args.ptq in ("16a4w", "16a4w_block"):
             fixed_point_type["io_type"] = torch.uint16
+            fixed_point_type["kv_type"] = torch.uint8
+        elif args.ptq == "16a8w":
+            fixed_point_type["io_type"] = torch.uint16
+            fixed_point_type["kv_type"] = torch.uint16
         else:
             assert args.ptq in [
                 "8a8w",
@@ -588,14 +593,11 @@ def permute(w, heads):
 
     if args.ptq:
         start_quantize_ts = time.time()
-        custom_annotations = (
-            # For qwen2.5, skip annotate_conv can improve result.
-            partial(
-                annotate_matmul_16a8w,
-                annotate_conv=args.ptq != "16a8w",
-            ),
-        )
-        if args.decoder_model == {"stories110m", "stories260k"}:
+        custom_annotations = ()
+        if args.ptq != "16a8w":
+            # 16a8w use 16bit kv io, so skip this custom annotation
+            custom_annotations = custom_annotations + (annotate_matmul_16a8w,)
+        if args.decoder_model in {"stories110m", "stories260k"}:
             custom_annotations = custom_annotations + (
                 annotate_linear_16a8w_in_affine_layer,
             )

examples/qualcomm/oss_scripts/llama/model/static_llama.py

@@ -393,6 +393,7 @@ def __init__(
         self.output_new_cache_only = output_new_cache_only
         self.use_i64_token = use_i64_token
         self.output_cache = output_cache
+        self.kv_io_bit_width = config.kv_io_bit_width
 
         self.layers = nn.ModuleList(
             [
@@ -546,4 +547,5 @@ def get_metadata(self):
             "get_n_layers": self.n_layers,
             "get_vocab_size": self.vocab_size,
             "get_use_kv_cache": self.use_kv_cache,
+            "get_kv_io_bit_width": self.kv_io_bit_width,
         }

examples/qualcomm/oss_scripts/llama/qnn_llama_runner.cpp

@@ -123,24 +123,16 @@ std::string get_formatted_prompt(
   return formatted_prompt;
 }
 
-int main(int argc, char** argv) {
-  std::vector<std::string> prompts = CollectPrompts(argc, argv);
-  gflags::ParseCommandLineFlags(&argc, &argv, true);
-  if (!gflags::GetCommandLineFlagInfoOrDie("prompt").is_default &&
-      !gflags::GetCommandLineFlagInfoOrDie("tokenized_prompt").is_default) {
-    ET_CHECK_MSG(false, "Only provide prompt or tokenized_input but not both.");
-  }
-  if (!gflags::GetCommandLineFlagInfoOrDie("dump_logits_path").is_default &&
-      FLAGS_eval_mode != 0) {
-    ET_CHECK_MSG(
-        false, "Only TokenGenerator(kv) mode is supported to dump all logits.");
-  }
-
+template <typename T>
+void start_runner(
+    std::unique_ptr<executorch::extension::Module> module,
+    std::vector<std::string>& prompts) {
   bool use_tokenized_prompt =
       gflags::GetCommandLineFlagInfoOrDie("tokenized_prompt").is_default ? false
                                                                          : true;
   // create llama runner
-  example::Runner runner(
+  example::Runner<T> runner(
+      std::move(module),
       FLAGS_decoder_model_version.c_str(),
       FLAGS_model_path.c_str(),
       FLAGS_tokenizer_path.c_str(),
@@ -186,5 +178,43 @@ int main(int argc, char** argv) {
 
   fout.write(buf.data(), buf.size());
   fout.close();
+}
+
+int main(int argc, char** argv) {
+  std::vector<std::string> prompts = CollectPrompts(argc, argv);
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+  if (!gflags::GetCommandLineFlagInfoOrDie("prompt").is_default &&
+      !gflags::GetCommandLineFlagInfoOrDie("tokenized_prompt").is_default) {
+    ET_CHECK_MSG(false, "Only provide prompt or tokenized_input but not both.");
+  }
+  if (!gflags::GetCommandLineFlagInfoOrDie("dump_logits_path").is_default &&
+      FLAGS_eval_mode != 0) {
+    ET_CHECK_MSG(
+        false, "Only TokenGenerator(kv) mode is supported to dump all logits.");
+  }
+
+  std::unique_ptr<executorch::extension::Module> module =
+      std::make_unique<executorch::extension::Module>(
+          FLAGS_model_path.c_str(),
+          executorch::extension::Module::LoadMode::MmapUseMlockIgnoreErrors);
+  // Using 8bit as default since this meta is introduced with 16bit kv io
+  // support and older models only have 8bit kv io.
+  example::KvBitWidth kv_bitwidth = example::KvBitWidth::kWidth8;
+  if (module->method_names()->count("get_kv_io_bit_width") > 0) {
+    kv_bitwidth = static_cast<example::KvBitWidth>(
+        module->get("get_kv_io_bit_width").get().toScalar().to<int64_t>());
+  }
+
+  if (kv_bitwidth == example::KvBitWidth::kWidth8) {
+    start_runner<uint8_t>(std::move(module), prompts);
+  } else if (kv_bitwidth == example::KvBitWidth::kWidth16) {
+    start_runner<uint16_t>(std::move(module), prompts);
+  } else {
+    ET_CHECK_MSG(
+        false,
+        "Unsupported kv bitwidth: %ld",
+        static_cast<int64_t>(kv_bitwidth));
+  }
+
   return 0;
 }