Qualcomm AI Engine Direct - Support simple_eval in calibration, perpl…

backends/qualcomm/tests/test_qnn_delegate.py

@@ -4313,7 +4313,7 @@ def test_llama_stories_110m(self):
                 if not self.compile_only and not self.enable_x86_64:
                     self.assertGreaterEqual(msg["inference_speed"], 220)  # Lanai
 
-    def test_qwen2_5(self):
+    def test_static_qwen2_5(self):
         if not self.required_envs():
             self.skipTest("missing required envs")
 
@@ -4338,11 +4338,14 @@ def test_qwen2_5(self):
             "--decoder_model",
             "qwen2_5",
             "--model_mode",
-            "hybrid",
-            "--prefill_ar_len",
-            "32",
+            "kv",
             "--max_seq_len",
-            "128",
+            "1024",
+            "--eval_perplexity",
+            "--tasks",
+            "wikitext",
+            "--limit",
+            "1",
         ]
         if self.compile_only:
             cmds.extend(["--compile_only"])
@@ -4355,8 +4358,6 @@ def test_qwen2_5(self):
         if self.pre_gen_pte:
             cmds.extend(["--pre_gen_pte", self.pre_gen_pte])
 
-        # Accuracy is bad for now. Just check user's prompt is returned.
-        golden_start_with = "My favourite condiment is "
         p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
         with Listener((self.ip, self.port)) as listener:
             conn = listener.accept()
@@ -4365,12 +4366,13 @@ def test_qwen2_5(self):
             if "Error" in msg:
                 self.fail(msg["Error"])
             else:
-                model_out = msg["result"][0]
-                self.assertTrue(
-                    model_out.startswith(golden_start_with),
-                    f"Expected Output: {golden_start_with}. Actual Output: {model_out}",
-                )
-                self.assertGreaterEqual(msg["inference_speed"], 95)  # Lanai
+                inference_speed_ref = {"SM8650": 110, "SM8750": 130}
+                self.assertLessEqual(msg["wiki_ppl"], 25)
+                self.assertLessEqual(msg["pte_size"], 800000000)  # 800mb
+                if self.model in inference_speed_ref:
+                    self.assertGreaterEqual(
+                        msg["inference_speed"], inference_speed_ref[self.model]
+                    )
 
 
 class TestExampleOssScript(TestQNN):

examples/qualcomm/oss_scripts/llama/README.md

@@ -114,11 +114,14 @@ We have two distinct mechanisms for updating the key-value (KV) cache, which can
 </table>
 
 ### Additional Configs when running the script
+
+#### 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
 ```
 
+#### 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}
@@ -149,3 +152,28 @@ python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL
 
 You can enable MaskedSoftmax feature by providing the flag `--enable_masked_softmax`. It is designed to optimize the LLMs accuracy and performance executed on HTP backend. MaskedSoftmax is used to replace the Softmax(Add(In, Mask)) structure in attention block in LLMs during backend optimization. For more details, please refer to QNN documents.
 Note that it is only supported starting from QNN 2.35.
+
+#### Perplexity Evaluation
+This script supports perplexity evaluation and is capable of assessing perplexity scores across 3 phases: prepare_pt2e(CPU FP), convert_pt2e(CPU QDQ), QNN on device.
+
+To evaluate the perplexity across all 3 phases, users should provide the `--eval_perplexity` flag and specify the evaluation task. Please notice when this flag is provided, the `--prompt ${PROMPT}` will be ignored.
+
+For example, using the Qwen model and 1 wikitext sample as the evaluation task, users can assess all 3 phases perplexity score in a single run by including the appropriate configuration:
+```bash
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --prompt "What is 1+1?" --temperature 0 --model_mode kv --max_seq_len 1024 --ptq 16a8w --decoder_model qwen2_5 --eval_perplexity --tasks wikitext --limit 1
+```
+
+For the example script above, 1 wikitext sample is used to evaluate all 3 phases. However, there are cases where a user may want to use one sample for quantization calibration and multiple samples for perplexity evaluation. In this case, the process should be split into two runs. In the 1st run, the model is compiled using one sample. In the 2nd run, the user can provide a different configuration for QNN device execution.
+Example:
+```bash
+# 1st run to compile with --limit 1
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --prompt "What is 1+1?" --temperature 0 --model_mode kv --max_seq_len 1024 --ptq 16a8w --decoder_model qwen2_5 --eval_perplexity --tasks wikitext --limit 1 --compile_only
+```
+```bash
+# 2nd run to perform QNN device execution with --limit 3
+python examples/qualcomm/oss_scripts/llama/llama.py -b build-android -s ${SERIAL_NUM} -m ${SOC_MODEL} --prompt "What is 1+1?" --temperature 0 --model_mode kv --max_seq_len 1024 --ptq 16a8w --decoder_model qwen2_5 --eval_perplexity --tasks wikitext --limit 3 --pre_gen_pte ${PATH_TO_ARTIFACT_IN_1ST_RUN} --quant_attrs_path ${PATH_TO_ARTIFACT_IN_1ST_RUN}/kv_llama_qnn_quant_attrs.json
+```
+
+#### Tasks quantization calibration
+If `--tasks ${TASK}` is not provided, the program will use `--prompt ${PROMPT}` as the dataset for quantization calibration.
+Regardless of whether `--eval_perplexity` is provided, as long as `--tasks ${TASK}` is specified, the specified tasks will be used for model quantization calibration instead of the prompt.

examples/qualcomm/oss_scripts/llama/decoder_constants.py

@@ -0,0 +1,20 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# All rights reserved
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+HUGGING_FACE_REPO_IDS = {"qwen2_5": "Qwen/Qwen2.5-0.5B"}
+
+EVAL_MODE = {
+    "kv": 0,
+    "hybrid": 1,
+    "lookahead": 2,
+}
+
+DECODER_MODEL_VERSION = {
+    "stories260k": "llama2",
+    "stories110m": "llama2",
+    "llama3_2": "llama3",
+    "qwen2_5": "qwen2_5",
+}