Lint & Format fixed

Signed-off-by: Abukhoyer Shaik <[email protected]>

QEfficient/transformers/models/modeling_auto.py

@@ -381,6 +381,39 @@ def compile(
                 aic_num_cores=num_cores,
                 **compiler_options,
             )
+
+        # Construct the qconfig json file path
+        qconfig_file_path = os.path.join(os.path.dirname(self.qpc_path), "qconfig.json")
+        huggingface_config = self.model.config.__dict__
+
+        pytorch_transforms = [cls.__name__ for cls in self._pytorch_transforms]
+        onnx_transforms = [cls.__name__ for cls in self._onnx_transforms]
+
+        onnx_path = str(self.onnx_path)
+        specializations_file_path = str(os.path.join(os.path.dirname(self.qpc_path), "specializations.json"))
+        compile_dir = str(os.path.dirname(self.qpc_path))
+
+        create_and_dump_configs(
+            qconfig_file_path,
+            specializations_file_path,
+            huggingface_config,
+            pytorch_transforms,
+            onnx_transforms,
+            onnx_path,
+            compile_dir,
+            prefill_seq_len,
+            ctx_len,
+            batch_size,
+            full_batch_size,
+            num_devices,
+            num_cores,
+            mxfp6_matmul,
+            mxint8_kv_cache,
+            num_speculative_tokens,
+            enable_qnn,
+            qnn_config,
+        )
+
         return qpc_path
 
     # FIXME: Update this method to match with transformers AutoModelForCausalLM.generate

QEfficient/utils/_utils.py

@@ -12,11 +12,12 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import requests
+import yaml
 from huggingface_hub import login, snapshot_download
 from requests.exceptions import HTTPError
 from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
 
-from QEfficient.utils.constants import QEFF_MODELS_DIR, Constants
+from QEfficient.utils.constants import QEFF_MODELS_DIR, Constants, QnnConstants
 from QEfficient.utils.logging_utils import logger
 
 
@@ -414,15 +415,15 @@ def create_and_dump_configs(
     mxfp6_matmul,
     mxint8_kv_cache,
     num_speculative_tokens,
+    enable_qnn,
+    qnn_config,
 ):
     try:
-        # Parse the XML file
         tree = ET.parse(Constants.SDK_APPS_XML)
         root = tree.getroot()
-        # Try to find the base_version element and get its text
-        version = root.find(".//base_version").text
+        qaic_version = root.find(".//base_version").text
     except (FileNotFoundError, ET.ParseError, AttributeError):
-        version = None
+        qaic_version = None
 
     # Ensure all objects in the configs dictionary are JSON serializable
     def make_serializable(obj):
@@ -433,7 +434,18 @@ def make_serializable(obj):
         elif isinstance(obj, dict):
             return {key: make_serializable(value) for key, value in obj.items()}
         else:
-            return str(obj)  # Convert non-serializable objects to strings
+            return str(obj)
+
+    qnn_config_path = (
+        (qnn_config if qnn_config is not None else "QEfficient/compile/qnn_config.json") if enable_qnn else None
+    )
+    yaml_file_path = os.path.join(os.getenv(QnnConstants.QNN_SDK_PATH_ENV_VAR_NAME), "sdk.yaml")
+    yaml_data = {}
+    try:
+        with open(yaml_file_path, "r") as file:
+            yaml_data = yaml.safe_load(file)
+    except Exception:
+        yaml_data = None
 
     configs = {
         "huggingface_config": make_serializable(huggingface_config),
@@ -444,7 +456,7 @@ def make_serializable(obj):
                 "onnx_path": onnx_path,
             },
             "compilation_config": {
-                "apps_sdk_version": version,
+                "apps_sdk_version": qaic_version,
                 "compile_dir": compile_dir,
                 "specializtions_file_path": specializations_file_path,
                 "prefill_seq_len": prefill_seq_len,
@@ -457,8 +469,15 @@ def make_serializable(obj):
                 "mxint8_kv_cache": mxint8_kv_cache,
                 "num_speculative_tokens": num_speculative_tokens,
             },
+            "qnn_config": {
+                "enable_qnn": enable_qnn,
+                "qnn_config_path": qnn_config_path,
+            },
         },
     }
-    # Dump the configs dictionary to a JSON file
+
+    if yaml_data:
+        configs["qpc_config"]["qnn_config"].update(yaml_data)
+
     with open(config_file_path, "w") as file:
         json.dump(configs, file, indent=4)

tests/qnn_tests/test_causal_lm_models_qnn.py

@@ -5,6 +5,8 @@
 #
 # -----------------------------------------------------------------------------
 
+import os
+
 import numpy as np
 import pytest
 from transformers import AutoModelForCausalLM
@@ -86,9 +88,9 @@ def check_causal_lm_pytorch_vs_kv_vs_ort_vs_ai100(
 
     pytorch_kv_tokens = api_runner.run_kv_model_on_pytorch(qeff_model.model)
 
-    assert (pytorch_hf_tokens == pytorch_kv_tokens).all(), (
-        "Tokens don't match for HF PyTorch model output and KV PyTorch model output"
-    )
+    assert (
+        pytorch_hf_tokens == pytorch_kv_tokens
+    ).all(), "Tokens don't match for HF PyTorch model output and KV PyTorch model output"
 
     onnx_model_path = qeff_model.export()
     ort_tokens = api_runner.run_kv_model_on_ort(onnx_model_path)
@@ -98,20 +100,21 @@ def check_causal_lm_pytorch_vs_kv_vs_ort_vs_ai100(
     if not get_available_device_id():
         pytest.skip("No available devices to run model on Cloud AI 100")
 
-    _ = qeff_model.compile(
+    qpc_path = qeff_model.compile(
         prefill_seq_len=prompt_len,
         ctx_len=ctx_len,
         num_cores=14,
         mxfp6=False,
         aic_enable_depth_first=False,
         enable_qnn=True,
     )
+    assert os.path.isfile(os.path.join(os.path.dirname(qpc_path), "qconfig.json"))
     exec_info = qeff_model.generate(tokenizer, prompts=Constants.INPUT_STR)
     cloud_ai_100_tokens = exec_info.generated_ids[0]  # Because we always run for single input and single batch size
     gen_len = ort_tokens.shape[-1]
-    assert (ort_tokens == cloud_ai_100_tokens[:, :gen_len]).all(), (
-        "Tokens don't match for ONNXRT output and Cloud AI 100 output."
-    )
+    assert (
+        ort_tokens == cloud_ai_100_tokens[:, :gen_len]
+    ).all(), "Tokens don't match for ONNXRT output and Cloud AI 100 output."
 
     # testing for CB models
     model_hf, _ = load_causal_lm_model(model_config)

tests/transformers/models/test_causal_lm_models.py

@@ -5,6 +5,7 @@
 #
 # -----------------------------------------------------------------------------
 
+import os
 from typing import Optional
 
 import numpy as np
@@ -110,9 +111,9 @@ def check_causal_lm_pytorch_vs_kv_vs_ort_vs_ai100(
 
     pytorch_kv_tokens = api_runner.run_kv_model_on_pytorch(qeff_model.model)
 
-    assert (pytorch_hf_tokens == pytorch_kv_tokens).all(), (
-        "Tokens don't match for HF PyTorch model output and KV PyTorch model output"
-    )
+    assert (
+        pytorch_hf_tokens == pytorch_kv_tokens
+    ).all(), "Tokens don't match for HF PyTorch model output and KV PyTorch model output"
 
     onnx_model_path = qeff_model.export()
     ort_tokens = api_runner.run_kv_model_on_ort(onnx_model_path, is_tlm=is_tlm)
@@ -122,20 +123,21 @@ def check_causal_lm_pytorch_vs_kv_vs_ort_vs_ai100(
     if not get_available_device_id():
         pytest.skip("No available devices to run model on Cloud AI 100")
 
-    _ = qeff_model.compile(
+    qpc_path = qeff_model.compile(
         prefill_seq_len=prompt_len,
         ctx_len=ctx_len,
         num_cores=14,
         mxfp6=False,
         aic_enable_depth_first=False,
         num_speculative_tokens=num_speculative_tokens,
     )
+    assert os.path.isfile(os.path.join(os.path.dirname(qpc_path), "qconfig.json"))
     exec_info = qeff_model.generate(tokenizer, prompts=Constants.INPUT_STR)
     cloud_ai_100_tokens = exec_info.generated_ids[0]  # Because we always run for single input and single batch size
     gen_len = ort_tokens.shape[-1]
-    assert (ort_tokens == cloud_ai_100_tokens[:, :gen_len]).all(), (
-        "Tokens don't match for ONNXRT output and Cloud AI 100 output."
-    )
+    assert (
+        ort_tokens == cloud_ai_100_tokens[:, :gen_len]
+    ).all(), "Tokens don't match for ONNXRT output and Cloud AI 100 output."
 
     # testing for CB models
     model_hf, _ = load_causal_lm_model(model_config)
@@ -204,9 +206,9 @@ def test_causal_lm_export_with_deprecated_api(model_name):
     new_api_ort_tokens = api_runner.run_kv_model_on_ort(new_api_onnx_model_path)
     old_api_ort_tokens = api_runner.run_kv_model_on_ort(old_api_onnx_model_path)
 
-    assert (new_api_ort_tokens == old_api_ort_tokens).all(), (
-        "New API output does not match old API output for ONNX export function"
-    )
+    assert (
+        new_api_ort_tokens == old_api_ort_tokens
+    ).all(), "New API output does not match old API output for ONNX export function"
 
 
 @pytest.mark.on_qaic