Generate TFLite files from Tensorflow models

iree-org · Oct 5, 2023 · 1ea715d · 1ea715d
1 parent f2b8505
commit 1ea715d
Show file tree

Hide file tree

Showing 3 changed files with 51 additions and 0 deletions.
diff --git a/common_benchmark_suite/openxla/benchmark/comparative_suite/tf/model_definitions.py b/common_benchmark_suite/openxla/benchmark/comparative_suite/tf/model_definitions.py
@@ -37,6 +37,7 @@
         def_types.ModelArtifactType.STABLEHLO_MLIR,
         def_types.ModelArtifactType.XLA_HLO_DUMP,
         def_types.ModelArtifactType.TF_SAVEDMODEL_V2,
+        def_types.ModelArtifactType.TFLITE_FP32,
     ],
 )
 T5_LARGE_FP32_TF_512XI32_BATCHES = utils.build_batch_models(
@@ -69,6 +70,7 @@
         def_types.ModelArtifactType.STABLEHLO_MLIR,
         def_types.ModelArtifactType.XLA_HLO_DUMP,
         def_types.ModelArtifactType.TF_SAVEDMODEL_V2,
+        def_types.ModelArtifactType.TFLITE_FP32,
     ],
 )
 BERT_LARGE_FP32_TF_384XI32_BATCHES = utils.build_batch_models(
@@ -100,6 +102,8 @@
         def_types.ModelArtifactType.STABLEHLO_MLIR,
         def_types.ModelArtifactType.XLA_HLO_DUMP,
         def_types.ModelArtifactType.TF_SAVEDMODEL_V2,
+        def_types.ModelArtifactType.TFLITE_FP32,
+        def_types.ModelArtifactType.TFLITE_INT8,
     ],
 )
 RESNET50_FP32_TF_224X224X3XF32_BATCHES = utils.build_batch_models(
@@ -130,6 +134,7 @@
         def_types.ModelArtifactType.STABLEHLO_MLIR,
         def_types.ModelArtifactType.XLA_HLO_DUMP,
         def_types.ModelArtifactType.TF_SAVEDMODEL_V2,
+        def_types.ModelArtifactType.TFLITE_FP32,
     ],
 )
 

diff --git a/...enchmark_suite/openxla/benchmark/comparative_suite/tf/scripts/generate_model_artifacts.py b/...enchmark_suite/openxla/benchmark/comparative_suite/tf/scripts/generate_model_artifacts.py
@@ -9,6 +9,7 @@
 import pathlib
 import re
 import multiprocessing
+import numpy as np
 import shutil
 import sys
 import tarfile
@@ -61,6 +62,47 @@ def _generate_mlir(model_dir: pathlib.Path, saved_model_dir: pathlib.Path):
   write_bytecode(str(mlir_path), result)
 
 
+def _generate_tflite(inputs: Tuple[Any, ...], model_dir: pathlib.Path,
+                     saved_model_dir: pathlib.Path):
+  converter = tf.lite.TFLiteConverter.from_saved_model(str(saved_model_dir))
+
+  # Generate fp32 model.
+  try:
+    tflite_model = converter.convert()
+    tflite_model_path = model_dir.joinpath("model_fp32.tflite")
+    with open(tflite_model_path, 'wb') as f:
+      f.write(tflite_model)
+  except Exception as e:
+    print(f"Failed to generate int8 TFLite model. Exception: {e}")
+
+  # Generate int8 model.
+  try:
+
+    def representative_examples():
+      for _ in range(2):
+        random_inputs = []
+        for input in inputs:
+          random_inputs.append(
+              np.random.uniform(low=input.dtype.min,
+                                high=input.dtype.max,
+                                size=input.shape).astype(
+                                    input.dtype.as_numpy_dtype))
+        yield random_inputs
+
+    converter.optimizations = [tf.lite.Optimize.DEFAULT]
+    converter.target_spec.supported_ops = [
+        tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.TFLITE_BUILTINS_INT8
+    ]
+    converter.representative_dataset = representative_examples
+    converter.inference_type = tf.int8
+    tflite_model_int8 = converter.convert()
+    tflite_model_int8_path = model_dir.joinpath("model_int8.tflite")
+    with open(tflite_model_int8_path, 'wb') as f:
+      f.write(tflite_model_int8)
+  except Exception as e:
+    print(f"Failed to generate int8 TFLite model. Exception: {e}")
+
+
 def _generate_artifacts(model: def_types.Model, save_dir: pathlib.Path,
                         auto_upload: bool):
   model_dir = save_dir.joinpath(model.name)
@@ -87,6 +129,7 @@ def _generate_artifacts(model: def_types.Model, save_dir: pathlib.Path,
 
     saved_model_dir = _generate_saved_model(inputs, model_obj, model_dir)
     _generate_mlir(model_dir, saved_model_dir)
+    _generate_tflite(inputs, model_dir, saved_model_dir)
 
     with tarfile.open(model_dir.joinpath("tf-model.tgz"), "w:gz") as tar:
       tar.add(f"{saved_model_dir}/", arcname="")

diff --git a/common_benchmark_suite/openxla/benchmark/def_types.py b/common_benchmark_suite/openxla/benchmark/def_types.py
@@ -15,6 +15,7 @@ class ModelFrameworkType(Enum):
   """Type of framework a model is implemented in."""
   TF_V1 = "tensorflow_v1"
   TF_V2 = "tensorflow_v2"
+  TFLITE = "tflite"
   PYTORCH = "pytorch"
   JAX = "jax"
   GGML = "ggml"
@@ -42,6 +43,8 @@ class ModelArtifactType(Enum):
   """Type of derived model artifact."""
   TF_SAVEDMODEL_V1 = "tf_savedmodel_v1"
   TF_SAVEDMODEL_V2 = "tf_savedmodel_v2"
+  TFLITE_FP32 = "tflite_fp32"
+  TFLITE_INT8 = "tflite_int8"
   XLA_HLO_DUMP = "xla_hlo_dump"
   STABLEHLO_MLIR = "stablehlo_mlir"
   LINALG_MLIR = "linalg_mlir"