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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/11/01 14:31:23 UTC
[GitHub] [tvm] ophirfrish opened a new pull request #9411: Support quantized ABS operator in TFLite frontend
ophirfrish opened a new pull request #9411:
URL: https://github.com/apache/tvm/pull/9411
Added support for quantized ABS operator in TFLite frontend.
cc @areusch @leandron @Mousius @gromero for reviews
Co-authored-by: Matthew Barrett <Ma...@arm.com>
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[GitHub] [tvm] ophirfrish commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
ophirfrish commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740859327
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
This is a miss, I will fix that
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[GitHub] [tvm] ophirfrish commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
ophirfrish commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740859327
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
This is a miss, I will fix that
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[GitHub] [tvm] u99127 commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
u99127 commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740854682
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
why would we have math_ops.rsqrt in an abs test ?
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[GitHub] [tvm] masahi merged pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
masahi merged pull request #9411:
URL: https://github.com/apache/tvm/pull/9411
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[GitHub] [tvm] u99127 commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
u99127 commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740854682
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
why would we have math_ops.rsqrt in an abs test ?
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[GitHub] [tvm] u99127 commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
u99127 commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740854682
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
why would we have math_ops.rsqrt in an abs test ?
--
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[GitHub] [tvm] ophirfrish commented on a change in pull request #9411: Support quantized ABS operator in TFLite frontend
Posted by GitBox <gi...@apache.org>.
ophirfrish commented on a change in pull request #9411:
URL: https://github.com/apache/tvm/pull/9411#discussion_r740859327
##########
File path: tests/python/frontend/tflite/test_forward.py
##########
@@ -3409,6 +3399,71 @@ def test_forward_neg():
_test_neg(np.arange(1, 240, 40, dtype=np.uint8).reshape((2, 1, 3)), quantized=True)
+#######################################################################
+# ABS
+# ----
+
+
+def _test_abs(data, quantized=False):
+ """One iteration of ABS"""
+ if quantized:
+
+ def _create_model():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ op = tf.math.abs(x)
+ return op
+
+ dtype = "int8"
+ model = Model()
+
+ # Save the model
+ export_dir = tempfile.gettempdir() + "/tf_model"
+ tf.saved_model.save(
+ model,
+ export_dir,
+ signatures=model.tf_function.get_concrete_function(
+ tf.TensorSpec(data.shape, tf.float32, name="input"),
+ ),
+ )
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ tmp_data = np.random.rand(*tuple(data.shape))
+ yield [tmp_data.astype(np.float32) * 2 - 1]
+
+ converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_model = converter.convert()
+ return tflite_model
+
+ tflite_model_quant = _create_model()
+ tflite_output = run_tflite_graph(tflite_model_quant, data)
+ in_node = ["serving_default_input_int8"]
+ tvm_output = run_tvm_graph(tflite_model_quant, data, in_node)
+ tvm.testing.assert_allclose(
+ np.squeeze(tvm_output[0]), np.squeeze(tflite_output[0]), rtol=1e-5, atol=1e-2
+ )
+ else:
+ with tf.Graph().as_default():
+ in_data = array_ops.placeholder(shape=data.shape, dtype=data.dtype, name="in_0")
+ out = math_ops.rsqrt(in_data)
Review comment:
This is a miss, I will fix that
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