From dad98696a28b3c2781f13c53b5c463e58f1df891 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Fri, 22 Mar 2024 15:23:37 +0100
Subject: [PATCH 01/13] Added test, docs/, and updated resolve_rounding_mode
function to return new rounding modes.
---
docs/qonnx-custom-ops/quant_op.md | 19 ++++++++++++++++++-
src/qonnx/custom_op/general/quant.py | 16 +++++++++++++++-
tests/custom_op/test_runding_mode.py | 20 ++++++++++++++++++++
3 files changed, 53 insertions(+), 2 deletions(-)
create mode 100644 tests/custom_op/test_runding_mode.py
diff --git a/docs/qonnx-custom-ops/quant_op.md b/docs/qonnx-custom-ops/quant_op.md
index 02d115fb..953fdca7 100644
--- a/docs/qonnx-custom-ops/quant_op.md
+++ b/docs/qonnx-custom-ops/quant_op.md
@@ -21,7 +21,7 @@ This operator is not part of the ONNX standard and is not currently versioned.
<dt><tt>narrow</tt> : int (default is 0)</dt>
<dd>Defines if the value range should be interpreted as narrow, when signed=1. E.g. at 8b regular=[-128, 127] vs narrow=[-127, 127].</dd>
<dt><tt>rounding_mode</tt> : string (default is "ROUND")</dt>
-<dd>Defines how rounding should be applied during quantization. Currently available modes are: "ROUND", "CEIL" and "FLOOR". Here "ROUND" implies a round-to-even operation. Lowercase variants for the rounding mode string are also supported: "round", "ceil", "floor".</dd>
+<dd>Defines how rounding should be applied during quantization. Avaiable options are ROUND, CEIL, FLOOR, UP, DOWN, HALF_UP, HALF_DOWN. The rounding modes are described in the table bellow. The names of rounding modes can be upper case or lower case.</dd>
</dl>
#### Inputs
@@ -46,6 +46,23 @@ This operator is not part of the ONNX standard and is not currently versioned.
</dl>
+#### Rounding modes
+<details>
+<summary>rounding modes</summary>
+| **Number \ ROUNDING_MODE** | ROUND=HALF_EVEN | CEIL | FLOOR | UP | DOWN | HALF_UP | HALF_DOWN |
+|---------------------------- |----------------- |------ |------- |---- |------ |--------- |----------- |
+| 5.5 | 6 | 6 | 5 | 6 | 5 | 6 | 5 |
+| 2.5 | 2 | 3 | 2 | 3 | 2 | 3 | 2 |
+| 1.6 | 2 | 2 | 1 | 2 | 1 | 2 | 2 |
+| 1.1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 |
+| 1.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
+| -1.0 | -1 | -1 | -1 | -1 | -1 | -1 | -1 |
+| -1.1 | -1 | -1 | -2 | -2 | -1 | -1 | -1 |
+| -1.6 | -2 | -1 | -2 | -2 | -1 | -2 | -2 |
+| -2.5 | -2 | -2 | -3 | -3 | -2 | -3 | -2 |
+| -5.5 | -6 | -5 | -6 | -6 | -5 | -6 | -5 |
+</details>
+
#### Examples
<details>
<summary>Quant</summary>
diff --git a/src/qonnx/custom_op/general/quant.py b/src/qonnx/custom_op/general/quant.py
index f552e7a8..15afd048 100644
--- a/src/qonnx/custom_op/general/quant.py
+++ b/src/qonnx/custom_op/general/quant.py
@@ -135,12 +135,26 @@ def resolve_rounding_mode(mode_string):
"""Resolve the rounding mode string of Quant and Trunc ops
to the corresponding numpy functions."""
normalized_mode_string = mode_string.upper()
- if normalized_mode_string == "ROUND":
+ if normalized_mode_string == "ROUND" or normalized_mode_string == "HALF_TO_EVEN":
return np.round
elif normalized_mode_string == "CEIL":
return np.ceil
elif normalized_mode_string == "FLOOR":
return np.floor
+ elif normalized_mode_string == "UP":
+ def round_up(x):
+ return np.sign(x) * np.ceil(np.abs(x))
+ return round_up
+ elif normalized_mode_string == "DOWN":
+ return np.fix
+ elif normalized_mode_string == "HALF_UP":
+ def round_half_up(x):
+ return np.sign(x) * np.floor(np.abs(x) + 0.5)
+ return round_half_up
+ elif normalized_mode_string == "HALF_DOWN":
+ def round_half_down(x):
+ return np.sign(x) * np.ceil(np.abs(x) - 0.5)
+ return round_half_down
else:
raise ValueError(f"Could not resolve rounding mode called: {normalized_mode_string}")
diff --git a/tests/custom_op/test_runding_mode.py b/tests/custom_op/test_runding_mode.py
new file mode 100644
index 00000000..54a81f0e
--- /dev/null
+++ b/tests/custom_op/test_runding_mode.py
@@ -0,0 +1,20 @@
+import pytest
+
+import numpy as np
+
+from qonnx.custom_op.general.quant import resolve_rounding_mode
+
+@pytest.mark.parametrize("rmode,exp", [
+ ("ROUND", np.array([6, 2, 2, 1, 1, -1, -1, -2, -2, -6])),
+ ("CEIL", np.array([6, 3, 2, 2, 1, -1, -1, -1, -2, - 5])),
+ ("FLOOR", np.array([5, 2, 1, 1, 1, -1, -2, -2, -3, -6])),
+ ("UP", np.array([6, 3, 2, 2, 1, -1, -2, -2, -3, -6])),
+ ("DOWN", np.array([5, 2, 1, 1, 1, -1, -1, -1, -2, -5])),
+ ("HALF_UP", np.array([6, 3, 2, 1, 1, -1, -1, -2, -3, -6])),
+ ("HALF_DOWN", np.array([5, 2, 2, 1, 1, -1, -1, -2, -2, -5]))
+ ]
+)
+def test_rounding_modes(rmode, exp):
+ test_array = np.array([5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5])
+ rounding_fn = resolve_rounding_mode(rmode)
+ assert np.array_equal(rounding_fn(test_array), exp)
From 47a88e4fb4d0bc69059297bbea39e69650f95d1f Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Fri, 22 Mar 2024 15:42:21 +0100
Subject: [PATCH 02/13] Fix table visualization.
---
docs/qonnx-custom-ops/quant_op.md | 24 ++++++++++++------------
1 file changed, 12 insertions(+), 12 deletions(-)
diff --git a/docs/qonnx-custom-ops/quant_op.md b/docs/qonnx-custom-ops/quant_op.md
index 953fdca7..b9e11c79 100644
--- a/docs/qonnx-custom-ops/quant_op.md
+++ b/docs/qonnx-custom-ops/quant_op.md
@@ -49,18 +49,18 @@ This operator is not part of the ONNX standard and is not currently versioned.
#### Rounding modes
<details>
<summary>rounding modes</summary>
-| **Number \ ROUNDING_MODE** | ROUND=HALF_EVEN | CEIL | FLOOR | UP | DOWN | HALF_UP | HALF_DOWN |
-|---------------------------- |----------------- |------ |------- |---- |------ |--------- |----------- |
-| 5.5 | 6 | 6 | 5 | 6 | 5 | 6 | 5 |
-| 2.5 | 2 | 3 | 2 | 3 | 2 | 3 | 2 |
-| 1.6 | 2 | 2 | 1 | 2 | 1 | 2 | 2 |
-| 1.1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 |
-| 1.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
-| -1.0 | -1 | -1 | -1 | -1 | -1 | -1 | -1 |
-| -1.1 | -1 | -1 | -2 | -2 | -1 | -1 | -1 |
-| -1.6 | -2 | -1 | -2 | -2 | -1 | -2 | -2 |
-| -2.5 | -2 | -2 | -3 | -3 | -2 | -3 | -2 |
-| -5.5 | -6 | -5 | -6 | -6 | -5 | -6 | -5 |
+| **Number \ ROUNDING_MODE** | ROUND=HALF_EVEN | CEIL | FLOOR | UP | DOWN | HALF_UP | HALF_DOWN |
+|----------------------------|-----------------|------|-------|----|------|---------|-----------|
+| 5.5 | 6 | 6 | 5 | 6 | 5 | 6 | 5 |
+| 2.5 | 2 | 3 | 2 | 3 | 2 | 3 | 2 |
+| 1.6 | 2 | 2 | 1 | 2 | 1 | 2 | 2 |
+| 1.1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 |
+| 1.0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
+| -1.0 | -1 | -1 | -1 | -1 | -1 | -1 | -1 |
+| -1.1 | -1 | -1 | -2 | -2 | -1 | -1 | -1 |
+| -1.6 | -2 | -1 | -2 | -2 | -1 | -2 | -2 |
+| -2.5 | -2 | -2 | -3 | -3 | -2 | -3 | -2 |
+| -5.5 | -6 | -5 | -6 | -6 | -5 | -6 | -5 |
</details>
#### Examples
From e2c15045d9ccf5f4c8162c1555c366c337616fee Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Fri, 22 Mar 2024 15:43:56 +0100
Subject: [PATCH 03/13] Fix table visualization again.
---
docs/qonnx-custom-ops/quant_op.md | 1 +
1 file changed, 1 insertion(+)
diff --git a/docs/qonnx-custom-ops/quant_op.md b/docs/qonnx-custom-ops/quant_op.md
index b9e11c79..68029406 100644
--- a/docs/qonnx-custom-ops/quant_op.md
+++ b/docs/qonnx-custom-ops/quant_op.md
@@ -49,6 +49,7 @@ This operator is not part of the ONNX standard and is not currently versioned.
#### Rounding modes
<details>
<summary>rounding modes</summary>
+
| **Number \ ROUNDING_MODE** | ROUND=HALF_EVEN | CEIL | FLOOR | UP | DOWN | HALF_UP | HALF_DOWN |
|----------------------------|-----------------|------|-------|----|------|---------|-----------|
| 5.5 | 6 | 6 | 5 | 6 | 5 | 6 | 5 |
From baa0df36b48b69a515604a3f5f4c04a00bd0712f Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Fri, 9 Aug 2024 14:06:07 +0200
Subject: [PATCH 04/13] Fixed converter to allow alpha/scale to be a tensor
Fixed rounding_mode specifier in convert_quantized_bits
---
src/qonnx/converters/qkeras/onnx.py | 14 ++++++++++++--
src/qonnx/converters/qkeras/quantizers.py | 14 +++++++++-----
src/qonnx/custom_op/general/quant.py | 2 +-
3 files changed, 22 insertions(+), 8 deletions(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index 1f34d653..3bb1fed9 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -55,7 +55,7 @@ def qlayer_handler(ctx, node, name, args):
quantizers = all_quantizers[keras_name]
if quantizers.get("kernel_quantizer"):
weights = node.inputs[1].get_tensor_value(as_list=True)
- quant_params = get_quant_params(weights, quantizers["kernel_quantizer"])
+ quant_params = get_quant_params(weights, quantizers["kernel_initializer"]['config']['quantizer'])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
for key in quant_params["inputs"].keys():
@@ -63,9 +63,19 @@ def qlayer_handler(ctx, node, name, args):
np_val = np.asarray(quant_params["inputs"][key])
ctx.make_const(name, np_val)
input_nodes.append(name)
- ctx.insert_new_node_on_input(
+ quant_node = ctx.insert_new_node_on_input(
node, "Quant", input_nodes, name=node.name + "_kernel_quantizer", **attr, domain="qonnx"
)
+ scale_node = ctx.make_const(
+ name = node.name + "_kernel_scale",
+ np_val = quant_params['inputs']['scale'].astype(np.float32)
+ )
+ ctx.insert_new_node_on_output(
+ op_type = "Mul",
+ output_name = quant_node.output[0],
+ name = node.name + "_kernel_requantizer",
+ inputs = [quant_node.output[0], scale_node.name]
+ )
if quantizers.get("bias_quantizer") and len(node.input) == 3:
bias = node.inputs[2].get_tensor_value(as_list=True)
diff --git a/src/qonnx/converters/qkeras/quantizers.py b/src/qonnx/converters/qkeras/quantizers.py
index 983cc997..c6a00a00 100644
--- a/src/qonnx/converters/qkeras/quantizers.py
+++ b/src/qonnx/converters/qkeras/quantizers.py
@@ -1,9 +1,9 @@
import qkeras
import six
-
+import numpy as np
def get_quant_params(tensor, qkeras_quantizer):
- if isinstance(qkeras_quantizer, str):
+ if isinstance(qkeras_quantizer, (str, dict)):
qkeras_quantizer = qkeras.get_quantizer(qkeras_quantizer)
return handler_map[qkeras_quantizer.__class__.__name__](tensor, qkeras_quantizer)
@@ -34,11 +34,15 @@ def convert_quantized_bits(tensor, quantizer):
signed = int(config["keep_negative"])
narrow = int(config["symmetric"])
qscale = _get_quantizer_scale(tensor, quantizer)
- assert qscale == 1, "Non-unity alpha is not yet supported"
- scale = 1.0 / 2 ** (int(config["bits"]) - int(config["integer"] + signed))
+ if not isinstance(qscale, np.ndarray):
+ qscale = np.array(qscale)
+ scale = qscale / 2 ** (int(config["bits"]) - int(config["integer"] + signed))
zero_point = 0
bit_width = int(config["bits"])
- rounding_mode = "ROUND"
+ if config['alpha'] == "auto_po2":
+ rounding_mode = "ROUND_UP"
+ else:
+ rounding_mode = "HALF_EVEN"
settings = {
"attributes": {"signed": signed, "narrow": narrow, "rounding_mode": rounding_mode},
diff --git a/src/qonnx/custom_op/general/quant.py b/src/qonnx/custom_op/general/quant.py
index 15afd048..5af3f9f3 100644
--- a/src/qonnx/custom_op/general/quant.py
+++ b/src/qonnx/custom_op/general/quant.py
@@ -135,7 +135,7 @@ def resolve_rounding_mode(mode_string):
"""Resolve the rounding mode string of Quant and Trunc ops
to the corresponding numpy functions."""
normalized_mode_string = mode_string.upper()
- if normalized_mode_string == "ROUND" or normalized_mode_string == "HALF_TO_EVEN":
+ if normalized_mode_string == "ROUND" or normalized_mode_string == "HALF_EVEN":
return np.round
elif normalized_mode_string == "CEIL":
return np.ceil
From de9f73173705cb757daaa80a58044ed8f09a376d Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Fri, 9 Aug 2024 15:08:07 +0200
Subject: [PATCH 05/13] Added a check to see if tensor is representable by the
quantization parameters.
---
src/qonnx/converters/qkeras/onnx.py | 19 ++++++++++++++++++-
1 file changed, 18 insertions(+), 1 deletion(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index 3bb1fed9..cadb10c4 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -4,7 +4,7 @@
from tf2onnx.onnx_opset.nn import BiasAdd, ConvOp
from .quantizers import get_quant_params
-
+from qonnx.custom_op.general.quant import quant
def get_qkeras_onnx_handlers(all_quantizers):
"""Returns the handlers for each kind of layer
@@ -58,6 +58,23 @@ def qlayer_handler(ctx, node, name, args):
quant_params = get_quant_params(weights, quantizers["kernel_initializer"]['config']['quantizer'])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
+ qweights = quant(inp_tensor=np.array(weights),
+ scale=np.array(quant_params['inputs']['scale']),
+ zeropt=np.array(quant_params['inputs']['zero_point']),
+ bitwidth=np.array(quant_params['inputs']['bit_width']),
+ signed=quant_params['attributes']['signed'],
+ narrow=quant_params['attributes']['narrow'],
+ rounding_mode=quant_params['attributes']['rounding_mode']
+ )
+ assert np.array_equal(weights, qweights), f"""Weights of tensor {node.name} are not representable with the given quantization settings.
+ The original weight tensor is: {np.array(weights)} and the quantized tensor is: {qweights};
+ scale: {np.array(quant_params['inputs']['scale'])},
+ zeropt: {np.array(quant_params['inputs']['zero_point'])},
+ bitwidth: {np.array(quant_params['inputs']['bit_width'])},
+ signed: {quant_params['attributes']['signed']},
+ narrow: {quant_params['attributes']['narrow']},
+ rounding_mode: {quant_params['attributes']['rounding_mode']}
+ """
for key in quant_params["inputs"].keys():
name = f"{node.name}_kernel_quantizer_{key}"
np_val = np.asarray(quant_params["inputs"][key])
From 72b994a923e7ea0e012de4f069af4b443ca60d83 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Mon, 12 Aug 2024 11:17:57 +0200
Subject: [PATCH 06/13] Extra Mul node inserted only when neccessary
Commented out assertion on non-representability
---
src/qonnx/converters/qkeras/onnx.py | 59 ++++++++++++++++-------------
1 file changed, 32 insertions(+), 27 deletions(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index cadb10c4..1444865f 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -58,23 +58,23 @@ def qlayer_handler(ctx, node, name, args):
quant_params = get_quant_params(weights, quantizers["kernel_initializer"]['config']['quantizer'])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
- qweights = quant(inp_tensor=np.array(weights),
- scale=np.array(quant_params['inputs']['scale']),
- zeropt=np.array(quant_params['inputs']['zero_point']),
- bitwidth=np.array(quant_params['inputs']['bit_width']),
- signed=quant_params['attributes']['signed'],
- narrow=quant_params['attributes']['narrow'],
- rounding_mode=quant_params['attributes']['rounding_mode']
- )
- assert np.array_equal(weights, qweights), f"""Weights of tensor {node.name} are not representable with the given quantization settings.
- The original weight tensor is: {np.array(weights)} and the quantized tensor is: {qweights};
- scale: {np.array(quant_params['inputs']['scale'])},
- zeropt: {np.array(quant_params['inputs']['zero_point'])},
- bitwidth: {np.array(quant_params['inputs']['bit_width'])},
- signed: {quant_params['attributes']['signed']},
- narrow: {quant_params['attributes']['narrow']},
- rounding_mode: {quant_params['attributes']['rounding_mode']}
- """
+ #qweights = quant(inp_tensor=np.array(weights),
+ # scale=np.array(quant_params['inputs']['scale']),
+ # zeropt=np.array(quant_params['inputs']['zero_point']),
+ # bitwidth=np.array(quant_params['inputs']['bit_width']),
+ # signed=quant_params['attributes']['signed'],
+ # narrow=quant_params['attributes']['narrow'],
+ # rounding_mode=quant_params['attributes']['rounding_mode']
+ # )
+ #assert np.array_equal(weights, qweights), f"""Weights of tensor {node.name} are not representable with the given quantization settings.
+ # The original weight tensor is: {np.array(weights)} and the quantized tensor is: {qweights};
+ # scale: {np.array(quant_params['inputs']['scale'])},
+ # zeropt: {np.array(quant_params['inputs']['zero_point'])},
+ # bitwidth: {np.array(quant_params['inputs']['bit_width'])},
+ # signed: {quant_params['attributes']['signed']},
+ # narrow: {quant_params['attributes']['narrow']},
+ # rounding_mode: {quant_params['attributes']['rounding_mode']}
+ # """
for key in quant_params["inputs"].keys():
name = f"{node.name}_kernel_quantizer_{key}"
np_val = np.asarray(quant_params["inputs"][key])
@@ -83,16 +83,21 @@ def qlayer_handler(ctx, node, name, args):
quant_node = ctx.insert_new_node_on_input(
node, "Quant", input_nodes, name=node.name + "_kernel_quantizer", **attr, domain="qonnx"
)
- scale_node = ctx.make_const(
- name = node.name + "_kernel_scale",
- np_val = quant_params['inputs']['scale'].astype(np.float32)
- )
- ctx.insert_new_node_on_output(
- op_type = "Mul",
- output_name = quant_node.output[0],
- name = node.name + "_kernel_requantizer",
- inputs = [quant_node.output[0], scale_node.name]
- )
+ if quantizers["kernel_initializer"]['config']['quantizer']['class_name'] == 'quantized_bits':
+ bits = quantizers["kernel_initializer"]['config']['quantizer']['config']['bits']
+ integer = quantizers["kernel_initializer"]['config']['quantizer']['config']['integer']
+ keep_negative = quantizers["kernel_initializer"]['config']['quantizer']['config']['keep_negative']
+ if bits == integer + keep_negative:
+ scale_node = ctx.make_const(
+ name = node.name + "_kernel_scale",
+ np_val = quant_params['inputs']['scale'].astype(np.float32)
+ )
+ ctx.insert_new_node_on_output(
+ op_type = "Mul",
+ output_name = quant_node.output[0],
+ name = node.name + "_kernel_requantizer",
+ inputs = [quant_node.output[0], scale_node.name]
+ )
if quantizers.get("bias_quantizer") and len(node.input) == 3:
bias = node.inputs[2].get_tensor_value(as_list=True)
From 75b40ab81d5b544533373ba8b0654911ab3ec4e4 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Mon, 12 Aug 2024 15:05:29 +0200
Subject: [PATCH 07/13] Added parameterized test for tensor style alpha.
---
tests/keras/test_keras_convert.py | 65 ++++++++++++++++++++++++++++++-
1 file changed, 64 insertions(+), 1 deletion(-)
diff --git a/tests/keras/test_keras_convert.py b/tests/keras/test_keras_convert.py
index 388f39a4..46f445ef 100644
--- a/tests/keras/test_keras_convert.py
+++ b/tests/keras/test_keras_convert.py
@@ -4,6 +4,9 @@
import onnx
import os
import tensorflow as tf
+tf.config.run_functions_eagerly(True)
+tf.keras.utils.set_random_seed(42)
+np.random.seed(42)
from qkeras import QActivation, QConv2D, QDense, binary, quantized_bits, quantized_relu, ternary
from tensorflow.keras.layers import Activation, Conv2D, Dense, Flatten, Input
from tensorflow.keras.models import Model
@@ -323,7 +326,67 @@ def test_qkeras_qdense_4(quantizers, request):
np.testing.assert_allclose(y_qkeras, y_qonnx, rtol=1e-4, atol=1e-4)
os.remove(model_path)
-
+@pytest.mark.parametrize("bits,signed,alpha",[
+ (8, True, [1.000, 1.000, 1.000, 1.000]),
+ (8, False, [1.000, 1.000, 1.000, 1.000]),
+ (4, True, [1.000, 1.000, 1.000, 1.000]),
+ (4, False, [1.000, 1.000, 1.000, 1.000]),
+ (8, True, [0.125, 0.250, 0.500, 1.000]),
+ (8, False, [0.125, 0.250, 0.500, 1.000]),
+ (5, True, [0.250, 0.250, 0.125, 0.125]),
+ (5, False, [0.250, 0.250, 0.125, 0.125]),
+ (4, True, [0.125, 0.250, 0.500, 1.000]),
+ (4, False, [0.125, 0.250, 0.500, 1.000]),
+ (3, True, [0.125, 0.125, 0.250, 0.125]),
+ (3, False, [0.125, 0.125, 0.250, 0.125])
+])
+def test_qkeras_tensor_alpha(bits, signed, alpha, request):
+ random_state = np.random.RandomState(seed=42)
+ max_val = np.array(alpha) * 2**(bits-signed)
+ min_val = -(max_val + 1)
+ w1 = random_state.randint(low=min_val, high=max_val, size=(3, 4))
+ b1 = np.array([0.0, 0.0, 0.0, 0.0])
+ x = x_in = tf.keras.layers.Input(shape=3)
+ x = QActivation(
+ quantized_bits(bits=4, integer=3, keep_negative=True)
+ )(x)
+ x = QDense(
+ 4,
+ kernel_quantizer=quantized_bits(
+ bits=bits, integer=(bits-signed), keep_negative=signed, alpha=alpha
+ ),
+ )(x)
+ x = QActivation(quantized_relu(bits=3, integer=3))(x)
+ model = tf.keras.Model(inputs=[x_in], outputs=[x])
+ model.compile()
+ model.layers[2].set_weights([w1, b1])
+ onnx_model, _ = from_keras(model)
+ model_path = f"model_test_qkeras_tensor_alpha_{request.node.callspec.id}.onnx"
+ onnx.save(onnx_model, model_path)
+ onnx_model = ModelWrapper(onnx_model)
+
+ data = np.array(
+ [
+ [[0.0, 0.0, 0.0]],
+ [[0.0, 1.0, 2.0]],
+ [[2.0, 1.0, 0.0]],
+ [[4.0, 4.0, 4.0]],
+ [[7.0, 7.0, 7.0]],
+ [[6.0, 0.0, 7.0]],
+ [[3.0, 3.0, 3.0]],
+ [[7.0, 0.0, 0.0]],
+ [[0.0, 7.0, 0.0]],
+ [[0.0, 0.0, 7.0]],
+ ]
+ ).astype(np.float32)
+ for x in data:
+ y_qkeras = model.predict(x, verbose=0)
+ idict = {onnx_model.graph.input[0].name: x}
+ odict = oxe.execute_onnx(onnx_model, idict, True)
+ y_qonnx = odict[onnx_model.graph.output[0].name]
+ assert np.array_equal(y_qkeras, y_qonnx)
+ os.remove(model_path)
+
@pytest.mark.parametrize("quantizers", kb_quantizers, ids=kb_quantizers_ids)
def test_qkeras_qconv2d_1(quantizers, request):
kq, bq = quantizers
From 7b5bf4a750ec0a0b66f094f917aa91aa7e677826 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Wed, 14 Aug 2024 08:16:56 +0200
Subject: [PATCH 08/13] Updated QKeras converter to support auto_po2
---
src/qonnx/converters/qkeras/onnx.py | 61 ++++++++++++-----------
src/qonnx/converters/qkeras/qlayers.py | 19 +++++--
src/qonnx/converters/qkeras/quantizers.py | 10 ++--
tests/keras/test_keras_convert.py | 53 +++++++++++++++++++-
4 files changed, 104 insertions(+), 39 deletions(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index 1444865f..1383bec9 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -53,28 +53,13 @@ def qlayer_handler(ctx, node, name, args):
if not keras_name:
return # Not found in quantizers, nothing to do
quantizers = all_quantizers[keras_name]
- if quantizers.get("kernel_quantizer"):
+
+ if quantizers.get("kernel_quantizer_cfg"):
weights = node.inputs[1].get_tensor_value(as_list=True)
- quant_params = get_quant_params(weights, quantizers["kernel_initializer"]['config']['quantizer'])
+ quant_params = get_quant_params(weights, quantizers['kernel_quantizer_cfg'])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
- #qweights = quant(inp_tensor=np.array(weights),
- # scale=np.array(quant_params['inputs']['scale']),
- # zeropt=np.array(quant_params['inputs']['zero_point']),
- # bitwidth=np.array(quant_params['inputs']['bit_width']),
- # signed=quant_params['attributes']['signed'],
- # narrow=quant_params['attributes']['narrow'],
- # rounding_mode=quant_params['attributes']['rounding_mode']
- # )
- #assert np.array_equal(weights, qweights), f"""Weights of tensor {node.name} are not representable with the given quantization settings.
- # The original weight tensor is: {np.array(weights)} and the quantized tensor is: {qweights};
- # scale: {np.array(quant_params['inputs']['scale'])},
- # zeropt: {np.array(quant_params['inputs']['zero_point'])},
- # bitwidth: {np.array(quant_params['inputs']['bit_width'])},
- # signed: {quant_params['attributes']['signed']},
- # narrow: {quant_params['attributes']['narrow']},
- # rounding_mode: {quant_params['attributes']['rounding_mode']}
- # """
+
for key in quant_params["inputs"].keys():
name = f"{node.name}_kernel_quantizer_{key}"
np_val = np.asarray(quant_params["inputs"][key])
@@ -83,10 +68,10 @@ def qlayer_handler(ctx, node, name, args):
quant_node = ctx.insert_new_node_on_input(
node, "Quant", input_nodes, name=node.name + "_kernel_quantizer", **attr, domain="qonnx"
)
- if quantizers["kernel_initializer"]['config']['quantizer']['class_name'] == 'quantized_bits':
- bits = quantizers["kernel_initializer"]['config']['quantizer']['config']['bits']
- integer = quantizers["kernel_initializer"]['config']['quantizer']['config']['integer']
- keep_negative = quantizers["kernel_initializer"]['config']['quantizer']['config']['keep_negative']
+ if quantizers['kernel_quantizer_cfg']['class_name'] == 'quantized_bits':
+ bits = quantizers['kernel_quantizer_cfg']['config']['bits']
+ integer = quantizers['kernel_quantizer_cfg']['config']['integer']
+ keep_negative = quantizers['kernel_quantizer_cfg']['config']['keep_negative']
if bits == integer + keep_negative:
scale_node = ctx.make_const(
name = node.name + "_kernel_scale",
@@ -99,17 +84,32 @@ def qlayer_handler(ctx, node, name, args):
inputs = [quant_node.output[0], scale_node.name]
)
- if quantizers.get("bias_quantizer") and len(node.input) == 3:
- bias = node.inputs[2].get_tensor_value(as_list=True)
- quant_params = get_quant_params(bias, quantizers["bias_quantizer"])
+ if quantizers.get("bias_quantizer_cfg") and len(node.input) == 3:
+ bias = node.inputs[-1].get_tensor_value(as_list=True)
+ quant_params = get_quant_params(bias, quantizers['bias_quantizer_cfg'])
attr = quant_params["attributes"]
- input_nodes = [node.input[2]]
+ input_nodes = [node.input[-1]]
for key in quant_params["inputs"].keys():
name = f"{node.name}_bias_quantizer_{key}"
np_val = np.asarray(quant_params["inputs"][key])
ctx.make_const(name, np_val)
input_nodes.append(name)
ctx.insert_new_node_on_input(node, "Quant", input_nodes, name=node.name + "_bias_quantizer", **attr, domain="qonnx")
+ if quantizers['bias_quantizer_cfg']['class_name'] == 'quantized_bits':
+ bits = quantizers['bias_quantizer_cfg']['config']['bits']
+ integer = quantizers['bias_quantizer_cfg']['config']['integer']
+ keep_negative = quantizers['bias_quantizer_cfg']['config']['keep_negative']
+ if bits == integer + keep_negative:
+ scale_node = ctx.make_const(
+ name = node.name + "_bias_scale",
+ np_val = quant_params['inputs']['scale'].astype(np.float32)
+ )
+ ctx.insert_new_node_on_output(
+ op_type = "Mul",
+ output_name = quant_node.output[0],
+ name = node.name + "_bias_requantizer",
+ inputs = [quant_node.output[0], scale_node.name]
+ )
if quantizers.get("activation"):
dtypes = [ctx.get_dtype(node.output[0])]
@@ -141,6 +141,9 @@ def qact_handler(ctx, node, name, args):
quantizers = all_quantizers[keras_name]
if quantizers.get("activation"):
dtypes = [ctx.get_dtype(node.output[0])]
+ if "auto" in quantizers['activation']:
+ if not node.graph.get_node_by_output(node.input[0]).is_const():
+ raise AttributeError(f'Automatic quantizers (auto/auto_po2) must have a const input. Invalid topology at node: {name}.')
quant_params = get_quant_params(None, quantizers["activation"])
attr = quant_params["attributes"]
input_nodes = [node.output[0]]
@@ -180,9 +183,9 @@ def bias_handler(ctx, node, name, args):
return # Not found in quantizers, nothing to do
quantizers = all_quantizers[keras_name]
- if quantizers.get("bias_quantizer"):
+ if quantizers.get("bias_quantizer_cfg"):
bias = node.inputs[1].get_tensor_value(as_list=True)
- quant_params = get_quant_params(bias, quantizers["bias_quantizer"])
+ quant_params = get_quant_params(bias, quantizers["bias_quantizer_cfg"])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
for key in quant_params["inputs"].keys():
diff --git a/src/qonnx/converters/qkeras/qlayers.py b/src/qonnx/converters/qkeras/qlayers.py
index fdbca71b..0a4f907d 100644
--- a/src/qonnx/converters/qkeras/qlayers.py
+++ b/src/qonnx/converters/qkeras/qlayers.py
@@ -101,13 +101,26 @@ def _replace_activation(quant_act):
def extract_qlayer(layer):
quantizers = layer.get_quantization_config()
-
+
keras_config = layer.get_config()
- keras_config.pop("kernel_quantizer", None)
- keras_config.pop("bias_quantizer", None)
+ kernel_quant_cfg = keras_config.pop("kernel_quantizer", None)
+ bias_quant_cfg = keras_config.pop("bias_quantizer", None)
keras_config.pop("kernel_range", None)
keras_config.pop("bias_range", None)
+
+ quantizers['kernel_quantizer_cfg'] = kernel_quant_cfg
+ quantizers['bias_quantizer_cfg'] = bias_quant_cfg
+
+ # For some reason downstream can't handle auto_po2, so we just calculate the scale value now
+ if kernel_quant_cfg['config']['alpha'] == "auto_po2":
+ layer.kernel_quantizer_internal(layer.kernel) # sets .scale (see auto_po2)
+ quantizers['kernel_quantizer_cfg']['config']['alpha'] = layer.kernel_quantizer_internal.scale.numpy().flatten().tolist()
+ if bias_quant_cfg['config']['alpha'] == "auto_po2":
+ layer.bias_quantizer_internal(layer.bias)
+ quantizers['bias_quantizer_cfg']['config']['alpha'] = layer.bias_quantizer_internal.scale.numpy().flatten().tolist()
+ quantizers.pop('kernel_quantizer', None)
+ quantizers.pop('bias_quantizer', None)
# Check if activation is quantized
if _is_keras_quantizer(keras_config["activation"]):
diff --git a/src/qonnx/converters/qkeras/quantizers.py b/src/qonnx/converters/qkeras/quantizers.py
index c6a00a00..e38cf710 100644
--- a/src/qonnx/converters/qkeras/quantizers.py
+++ b/src/qonnx/converters/qkeras/quantizers.py
@@ -1,6 +1,8 @@
import qkeras
import six
import numpy as np
+import tensorflow as tf
+
def get_quant_params(tensor, qkeras_quantizer):
if isinstance(qkeras_quantizer, (str, dict)):
@@ -24,7 +26,6 @@ def _get_scale_from_alpha(tensor, quantizer):
def _get_quantizer_scale(tensor, quantizer):
# call the quantizer on the tensor to get its scale
import numpy as np
-
quantizer(np.array(tensor).astype(np.float32))
return quantizer.scale
@@ -34,15 +35,12 @@ def convert_quantized_bits(tensor, quantizer):
signed = int(config["keep_negative"])
narrow = int(config["symmetric"])
qscale = _get_quantizer_scale(tensor, quantizer)
- if not isinstance(qscale, np.ndarray):
+ if not isinstance(qscale, (np.ndarray, tf.Tensor)):
qscale = np.array(qscale)
scale = qscale / 2 ** (int(config["bits"]) - int(config["integer"] + signed))
zero_point = 0
bit_width = int(config["bits"])
- if config['alpha'] == "auto_po2":
- rounding_mode = "ROUND_UP"
- else:
- rounding_mode = "HALF_EVEN"
+ rounding_mode = "HALF_EVEN"
settings = {
"attributes": {"signed": signed, "narrow": narrow, "rounding_mode": rounding_mode},
diff --git a/tests/keras/test_keras_convert.py b/tests/keras/test_keras_convert.py
index 46f445ef..eb3e9b2e 100644
--- a/tests/keras/test_keras_convert.py
+++ b/tests/keras/test_keras_convert.py
@@ -4,7 +4,6 @@
import onnx
import os
import tensorflow as tf
-tf.config.run_functions_eagerly(True)
tf.keras.utils.set_random_seed(42)
np.random.seed(42)
from qkeras import QActivation, QConv2D, QDense, binary, quantized_bits, quantized_relu, ternary
@@ -66,6 +65,58 @@ def test_qkeras_qactivation(quantizer, request):
np.testing.assert_allclose(y_qkeras, y_qonnx, rtol=1e-5, atol=1e-5)
os.remove(model_path)
+@pytest.mark.parametrize("quantizer", [
+ quantized_relu(bits=4, integer=4),
+ quantized_bits(bits=4, integer=4, keep_negative=False, alpha=1),
+ ])
+def test_qkeras_quantizers_rounding_modes(quantizer, request):
+ x = x_in = Input((10,), name="input")
+ x = QActivation(activation=quantizer)(x)
+ model = Model(inputs=[x_in], outputs=[x])
+ model.compile()
+
+ onnx_model, _ = from_keras(model)
+ model_path = f"model_test_qkeras_quantizers_rounding_modes_{request.node.callspec.id}.onnx"
+ onnx.save(onnx_model, model_path)
+ onnx_model = ModelWrapper(onnx_model)
+
+ x_test = np.array([[5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5]]).astype(np.float32)
+ idict = {onnx_model.graph.input[0].name: x_test}
+ odict = oxe.execute_onnx(onnx_model, idict, True)
+ y_qonnx = odict[onnx_model.graph.output[0].name]
+ y_qkeras = model.predict(x_test)
+ assert np.array_equal(y_qkeras, y_qonnx)
+ os.remove(model_path)
+
+@pytest.mark.parametrize("bias", [5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5])
+def test_qkeras_quantizers_autopo2_rounding_modes(bias, request):
+ kq = bq = quantized_bits(4, 4, 1, alpha='auto_po2')
+ # Initialize the kernel & bias to RandonUniform within the range of the quantizers
+ x = x_in = Input((10), name="input")
+ x = QDense(
+ 1,
+ kernel_quantizer=kq,
+ bias_quantizer=bq,
+ kernel_initializer=tf.keras.initializers.Constant(1.0),
+ bias_initializer=tf.keras.initializers.Constant(bias),
+ name="dense_0",
+ )(x)
+ model = Model(inputs=[x_in], outputs=[x])
+ x_test = np.random.uniform(low=-1.0, high=1.0, size=(1, 10)).astype(dtype=np.float32)
+ _ = model.predict(x_test, verbose=0)
+
+ onnx_model, _ = from_keras(model)
+ model_path = f"model_test_qkeras_quantizers_auto_rounding_modes_{request.node.callspec.id}.onnx"
+ onnx.save(onnx_model, model_path)
+ onnx_model = ModelWrapper(onnx_model)
+
+ x_test = np.zeros(shape=(1, 10), dtype=np.float32)
+ idict = {onnx_model.graph.input[0].name: x_test}
+ odict = oxe.execute_onnx(onnx_model, idict, True)
+ y_qonnx = odict[onnx_model.graph.output[0].name]
+ y_qkeras = model.predict(x_test, verbose=0)
+ assert np.array_equal(y_qkeras, y_qonnx)
+ os.remove(model_path)
# pairs of quantizers for kernel and bias
kb_quantizers = [
From 9fd5f6a263fd7b8bb2d11cccdb899f61b4479fb4 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Wed, 14 Aug 2024 08:54:12 +0200
Subject: [PATCH 09/13] Added check for none.
---
src/qonnx/converters/qkeras/qlayers.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/src/qonnx/converters/qkeras/qlayers.py b/src/qonnx/converters/qkeras/qlayers.py
index 0a4f907d..4a62b5d7 100644
--- a/src/qonnx/converters/qkeras/qlayers.py
+++ b/src/qonnx/converters/qkeras/qlayers.py
@@ -113,10 +113,10 @@ def extract_qlayer(layer):
quantizers['bias_quantizer_cfg'] = bias_quant_cfg
# For some reason downstream can't handle auto_po2, so we just calculate the scale value now
- if kernel_quant_cfg['config']['alpha'] == "auto_po2":
+ if kernel_quant_cfg is not None and kernel_quant_cfg['config']['alpha'] == "auto_po2":
layer.kernel_quantizer_internal(layer.kernel) # sets .scale (see auto_po2)
quantizers['kernel_quantizer_cfg']['config']['alpha'] = layer.kernel_quantizer_internal.scale.numpy().flatten().tolist()
- if bias_quant_cfg['config']['alpha'] == "auto_po2":
+ if bias_quant_cfg is not None and bias_quant_cfg['config']['alpha'] == "auto_po2":
layer.bias_quantizer_internal(layer.bias)
quantizers['bias_quantizer_cfg']['config']['alpha'] = layer.bias_quantizer_internal.scale.numpy().flatten().tolist()
quantizers.pop('kernel_quantizer', None)
From d8a66a7db408772f052d3386b74083d2d1c66f51 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Wed, 14 Aug 2024 09:36:25 +0200
Subject: [PATCH 10/13] Fixed pre-commit issues.
---
src/qonnx/converters/qkeras/onnx.py | 52 ++++++++---------
src/qonnx/converters/qkeras/qlayers.py | 24 ++++----
src/qonnx/converters/qkeras/quantizers.py | 5 +-
src/qonnx/custom_op/general/quant.py | 6 ++
tests/custom_op/test_runding_mode.py | 11 ++--
tests/keras/test_keras_convert.py | 71 +++++++++++++----------
6 files changed, 95 insertions(+), 74 deletions(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index 1383bec9..41b022d7 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -4,7 +4,7 @@
from tf2onnx.onnx_opset.nn import BiasAdd, ConvOp
from .quantizers import get_quant_params
-from qonnx.custom_op.general.quant import quant
+
def get_qkeras_onnx_handlers(all_quantizers):
"""Returns the handlers for each kind of layer
@@ -53,10 +53,10 @@ def qlayer_handler(ctx, node, name, args):
if not keras_name:
return # Not found in quantizers, nothing to do
quantizers = all_quantizers[keras_name]
-
+
if quantizers.get("kernel_quantizer_cfg"):
weights = node.inputs[1].get_tensor_value(as_list=True)
- quant_params = get_quant_params(weights, quantizers['kernel_quantizer_cfg'])
+ quant_params = get_quant_params(weights, quantizers["kernel_quantizer_cfg"])
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
@@ -68,25 +68,24 @@ def qlayer_handler(ctx, node, name, args):
quant_node = ctx.insert_new_node_on_input(
node, "Quant", input_nodes, name=node.name + "_kernel_quantizer", **attr, domain="qonnx"
)
- if quantizers['kernel_quantizer_cfg']['class_name'] == 'quantized_bits':
- bits = quantizers['kernel_quantizer_cfg']['config']['bits']
- integer = quantizers['kernel_quantizer_cfg']['config']['integer']
- keep_negative = quantizers['kernel_quantizer_cfg']['config']['keep_negative']
+ if quantizers["kernel_quantizer_cfg"]["class_name"] == "quantized_bits":
+ bits = quantizers["kernel_quantizer_cfg"]["config"]["bits"]
+ integer = quantizers["kernel_quantizer_cfg"]["config"]["integer"]
+ keep_negative = quantizers["kernel_quantizer_cfg"]["config"]["keep_negative"]
if bits == integer + keep_negative:
scale_node = ctx.make_const(
- name = node.name + "_kernel_scale",
- np_val = quant_params['inputs']['scale'].astype(np.float32)
+ name=node.name + "_kernel_scale", np_val=quant_params["inputs"]["scale"].astype(np.float32)
)
ctx.insert_new_node_on_output(
- op_type = "Mul",
- output_name = quant_node.output[0],
- name = node.name + "_kernel_requantizer",
- inputs = [quant_node.output[0], scale_node.name]
+ op_type="Mul",
+ output_name=quant_node.output[0],
+ name=node.name + "_kernel_requantizer",
+ inputs=[quant_node.output[0], scale_node.name],
)
if quantizers.get("bias_quantizer_cfg") and len(node.input) == 3:
bias = node.inputs[-1].get_tensor_value(as_list=True)
- quant_params = get_quant_params(bias, quantizers['bias_quantizer_cfg'])
+ quant_params = get_quant_params(bias, quantizers["bias_quantizer_cfg"])
attr = quant_params["attributes"]
input_nodes = [node.input[-1]]
for key in quant_params["inputs"].keys():
@@ -95,20 +94,19 @@ def qlayer_handler(ctx, node, name, args):
ctx.make_const(name, np_val)
input_nodes.append(name)
ctx.insert_new_node_on_input(node, "Quant", input_nodes, name=node.name + "_bias_quantizer", **attr, domain="qonnx")
- if quantizers['bias_quantizer_cfg']['class_name'] == 'quantized_bits':
- bits = quantizers['bias_quantizer_cfg']['config']['bits']
- integer = quantizers['bias_quantizer_cfg']['config']['integer']
- keep_negative = quantizers['bias_quantizer_cfg']['config']['keep_negative']
+ if quantizers["bias_quantizer_cfg"]["class_name"] == "quantized_bits":
+ bits = quantizers["bias_quantizer_cfg"]["config"]["bits"]
+ integer = quantizers["bias_quantizer_cfg"]["config"]["integer"]
+ keep_negative = quantizers["bias_quantizer_cfg"]["config"]["keep_negative"]
if bits == integer + keep_negative:
scale_node = ctx.make_const(
- name = node.name + "_bias_scale",
- np_val = quant_params['inputs']['scale'].astype(np.float32)
+ name=node.name + "_bias_scale", np_val=quant_params["inputs"]["scale"].astype(np.float32)
)
ctx.insert_new_node_on_output(
- op_type = "Mul",
- output_name = quant_node.output[0],
- name = node.name + "_bias_requantizer",
- inputs = [quant_node.output[0], scale_node.name]
+ op_type="Mul",
+ output_name=quant_node.output[0],
+ name=node.name + "_bias_requantizer",
+ inputs=[quant_node.output[0], scale_node.name],
)
if quantizers.get("activation"):
@@ -141,9 +139,11 @@ def qact_handler(ctx, node, name, args):
quantizers = all_quantizers[keras_name]
if quantizers.get("activation"):
dtypes = [ctx.get_dtype(node.output[0])]
- if "auto" in quantizers['activation']:
+ if "auto" in quantizers["activation"]:
if not node.graph.get_node_by_output(node.input[0]).is_const():
- raise AttributeError(f'Automatic quantizers (auto/auto_po2) must have a const input. Invalid topology at node: {name}.')
+ raise AttributeError(
+ f"Automatic quantizers (auto/auto_po2) must have a const input. Invalid topology at node: {name}."
+ )
quant_params = get_quant_params(None, quantizers["activation"])
attr = quant_params["attributes"]
input_nodes = [node.output[0]]
diff --git a/src/qonnx/converters/qkeras/qlayers.py b/src/qonnx/converters/qkeras/qlayers.py
index 4a62b5d7..3bfc7fa7 100644
--- a/src/qonnx/converters/qkeras/qlayers.py
+++ b/src/qonnx/converters/qkeras/qlayers.py
@@ -101,26 +101,28 @@ def _replace_activation(quant_act):
def extract_qlayer(layer):
quantizers = layer.get_quantization_config()
-
+
keras_config = layer.get_config()
kernel_quant_cfg = keras_config.pop("kernel_quantizer", None)
bias_quant_cfg = keras_config.pop("bias_quantizer", None)
keras_config.pop("kernel_range", None)
keras_config.pop("bias_range", None)
-
- quantizers['kernel_quantizer_cfg'] = kernel_quant_cfg
- quantizers['bias_quantizer_cfg'] = bias_quant_cfg
+
+ quantizers["kernel_quantizer_cfg"] = kernel_quant_cfg
+ quantizers["bias_quantizer_cfg"] = bias_quant_cfg
# For some reason downstream can't handle auto_po2, so we just calculate the scale value now
- if kernel_quant_cfg is not None and kernel_quant_cfg['config']['alpha'] == "auto_po2":
- layer.kernel_quantizer_internal(layer.kernel) # sets .scale (see auto_po2)
- quantizers['kernel_quantizer_cfg']['config']['alpha'] = layer.kernel_quantizer_internal.scale.numpy().flatten().tolist()
- if bias_quant_cfg is not None and bias_quant_cfg['config']['alpha'] == "auto_po2":
+ if kernel_quant_cfg is not None and kernel_quant_cfg["config"]["alpha"] == "auto_po2":
+ layer.kernel_quantizer_internal(layer.kernel) # sets .scale (see auto_po2)
+ quantizers["kernel_quantizer_cfg"]["config"]["alpha"] = (
+ layer.kernel_quantizer_internal.scale.numpy().flatten().tolist()
+ )
+ if bias_quant_cfg is not None and bias_quant_cfg["config"]["alpha"] == "auto_po2":
layer.bias_quantizer_internal(layer.bias)
- quantizers['bias_quantizer_cfg']['config']['alpha'] = layer.bias_quantizer_internal.scale.numpy().flatten().tolist()
- quantizers.pop('kernel_quantizer', None)
- quantizers.pop('bias_quantizer', None)
+ quantizers["bias_quantizer_cfg"]["config"]["alpha"] = layer.bias_quantizer_internal.scale.numpy().flatten().tolist()
+ quantizers.pop("kernel_quantizer", None)
+ quantizers.pop("bias_quantizer", None)
# Check if activation is quantized
if _is_keras_quantizer(keras_config["activation"]):
diff --git a/src/qonnx/converters/qkeras/quantizers.py b/src/qonnx/converters/qkeras/quantizers.py
index e38cf710..3d232390 100644
--- a/src/qonnx/converters/qkeras/quantizers.py
+++ b/src/qonnx/converters/qkeras/quantizers.py
@@ -1,6 +1,6 @@
+import numpy as np
import qkeras
import six
-import numpy as np
import tensorflow as tf
@@ -26,6 +26,7 @@ def _get_scale_from_alpha(tensor, quantizer):
def _get_quantizer_scale(tensor, quantizer):
# call the quantizer on the tensor to get its scale
import numpy as np
+
quantizer(np.array(tensor).astype(np.float32))
return quantizer.scale
@@ -36,7 +37,7 @@ def convert_quantized_bits(tensor, quantizer):
narrow = int(config["symmetric"])
qscale = _get_quantizer_scale(tensor, quantizer)
if not isinstance(qscale, (np.ndarray, tf.Tensor)):
- qscale = np.array(qscale)
+ qscale = np.array(qscale)
scale = qscale / 2 ** (int(config["bits"]) - int(config["integer"] + signed))
zero_point = 0
bit_width = int(config["bits"])
diff --git a/src/qonnx/custom_op/general/quant.py b/src/qonnx/custom_op/general/quant.py
index 5af3f9f3..b0b50b9a 100644
--- a/src/qonnx/custom_op/general/quant.py
+++ b/src/qonnx/custom_op/general/quant.py
@@ -142,18 +142,24 @@ def resolve_rounding_mode(mode_string):
elif normalized_mode_string == "FLOOR":
return np.floor
elif normalized_mode_string == "UP":
+
def round_up(x):
return np.sign(x) * np.ceil(np.abs(x))
+
return round_up
elif normalized_mode_string == "DOWN":
return np.fix
elif normalized_mode_string == "HALF_UP":
+
def round_half_up(x):
return np.sign(x) * np.floor(np.abs(x) + 0.5)
+
return round_half_up
elif normalized_mode_string == "HALF_DOWN":
+
def round_half_down(x):
return np.sign(x) * np.ceil(np.abs(x) - 0.5)
+
return round_half_down
else:
raise ValueError(f"Could not resolve rounding mode called: {normalized_mode_string}")
diff --git a/tests/custom_op/test_runding_mode.py b/tests/custom_op/test_runding_mode.py
index 54a81f0e..eb48d644 100644
--- a/tests/custom_op/test_runding_mode.py
+++ b/tests/custom_op/test_runding_mode.py
@@ -4,15 +4,18 @@
from qonnx.custom_op.general.quant import resolve_rounding_mode
-@pytest.mark.parametrize("rmode,exp", [
+
+@pytest.mark.parametrize(
+ "rmode,exp",
+ [
("ROUND", np.array([6, 2, 2, 1, 1, -1, -1, -2, -2, -6])),
- ("CEIL", np.array([6, 3, 2, 2, 1, -1, -1, -1, -2, - 5])),
+ ("CEIL", np.array([6, 3, 2, 2, 1, -1, -1, -1, -2, -5])),
("FLOOR", np.array([5, 2, 1, 1, 1, -1, -2, -2, -3, -6])),
("UP", np.array([6, 3, 2, 2, 1, -1, -2, -2, -3, -6])),
("DOWN", np.array([5, 2, 1, 1, 1, -1, -1, -1, -2, -5])),
("HALF_UP", np.array([6, 3, 2, 1, 1, -1, -1, -2, -3, -6])),
- ("HALF_DOWN", np.array([5, 2, 2, 1, 1, -1, -1, -2, -2, -5]))
- ]
+ ("HALF_DOWN", np.array([5, 2, 2, 1, 1, -1, -1, -2, -2, -5])),
+ ],
)
def test_rounding_modes(rmode, exp):
test_array = np.array([5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5])
diff --git a/tests/keras/test_keras_convert.py b/tests/keras/test_keras_convert.py
index eb3e9b2e..964a2e3c 100644
--- a/tests/keras/test_keras_convert.py
+++ b/tests/keras/test_keras_convert.py
@@ -4,8 +4,6 @@
import onnx
import os
import tensorflow as tf
-tf.keras.utils.set_random_seed(42)
-np.random.seed(42)
from qkeras import QActivation, QConv2D, QDense, binary, quantized_bits, quantized_relu, ternary
from tensorflow.keras.layers import Activation, Conv2D, Dense, Flatten, Input
from tensorflow.keras.models import Model
@@ -15,6 +13,10 @@
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.infer_shapes import InferShapes
+# For reproducibility
+tf.keras.utils.set_random_seed(42)
+np.random.seed(42)
+
act_quantizers = [
quantized_bits(8, 4, 0, alpha=1),
quantized_bits(8, 4, 1, alpha=1),
@@ -65,16 +67,20 @@ def test_qkeras_qactivation(quantizer, request):
np.testing.assert_allclose(y_qkeras, y_qonnx, rtol=1e-5, atol=1e-5)
os.remove(model_path)
-@pytest.mark.parametrize("quantizer", [
- quantized_relu(bits=4, integer=4),
- quantized_bits(bits=4, integer=4, keep_negative=False, alpha=1),
- ])
-def test_qkeras_quantizers_rounding_modes(quantizer, request):
+
+@pytest.mark.parametrize(
+ "quantizer",
+ [
+ quantized_relu(bits=4, integer=4),
+ quantized_bits(bits=4, integer=4, keep_negative=False, alpha=1),
+ ],
+)
+def test_qkeras_quantizers_rounding_modes(quantizer, request):
x = x_in = Input((10,), name="input")
x = QActivation(activation=quantizer)(x)
model = Model(inputs=[x_in], outputs=[x])
model.compile()
-
+
onnx_model, _ = from_keras(model)
model_path = f"model_test_qkeras_quantizers_rounding_modes_{request.node.callspec.id}.onnx"
onnx.save(onnx_model, model_path)
@@ -88,9 +94,10 @@ def test_qkeras_quantizers_rounding_modes(quantizer, request):
assert np.array_equal(y_qkeras, y_qonnx)
os.remove(model_path)
+
@pytest.mark.parametrize("bias", [5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5])
def test_qkeras_quantizers_autopo2_rounding_modes(bias, request):
- kq = bq = quantized_bits(4, 4, 1, alpha='auto_po2')
+ kq = bq = quantized_bits(4, 4, 1, alpha="auto_po2")
# Initialize the kernel & bias to RandonUniform within the range of the quantizers
x = x_in = Input((10), name="input")
x = QDense(
@@ -118,6 +125,7 @@ def test_qkeras_quantizers_autopo2_rounding_modes(bias, request):
assert np.array_equal(y_qkeras, y_qonnx)
os.remove(model_path)
+
# pairs of quantizers for kernel and bias
kb_quantizers = [
(quantized_bits(8, 4, 0, alpha=1), quantized_bits(8, 4, 0, alpha=1)),
@@ -377,35 +385,35 @@ def test_qkeras_qdense_4(quantizers, request):
np.testing.assert_allclose(y_qkeras, y_qonnx, rtol=1e-4, atol=1e-4)
os.remove(model_path)
-@pytest.mark.parametrize("bits,signed,alpha",[
- (8, True, [1.000, 1.000, 1.000, 1.000]),
- (8, False, [1.000, 1.000, 1.000, 1.000]),
- (4, True, [1.000, 1.000, 1.000, 1.000]),
- (4, False, [1.000, 1.000, 1.000, 1.000]),
- (8, True, [0.125, 0.250, 0.500, 1.000]),
- (8, False, [0.125, 0.250, 0.500, 1.000]),
- (5, True, [0.250, 0.250, 0.125, 0.125]),
- (5, False, [0.250, 0.250, 0.125, 0.125]),
- (4, True, [0.125, 0.250, 0.500, 1.000]),
- (4, False, [0.125, 0.250, 0.500, 1.000]),
- (3, True, [0.125, 0.125, 0.250, 0.125]),
- (3, False, [0.125, 0.125, 0.250, 0.125])
-])
+
+@pytest.mark.parametrize(
+ "bits,signed,alpha",
+ [
+ (8, True, [1.000, 1.000, 1.000, 1.000]),
+ (8, False, [1.000, 1.000, 1.000, 1.000]),
+ (4, True, [1.000, 1.000, 1.000, 1.000]),
+ (4, False, [1.000, 1.000, 1.000, 1.000]),
+ (8, True, [0.125, 0.250, 0.500, 1.000]),
+ (8, False, [0.125, 0.250, 0.500, 1.000]),
+ (5, True, [0.250, 0.250, 0.125, 0.125]),
+ (5, False, [0.250, 0.250, 0.125, 0.125]),
+ (4, True, [0.125, 0.250, 0.500, 1.000]),
+ (4, False, [0.125, 0.250, 0.500, 1.000]),
+ (3, True, [0.125, 0.125, 0.250, 0.125]),
+ (3, False, [0.125, 0.125, 0.250, 0.125]),
+ ],
+)
def test_qkeras_tensor_alpha(bits, signed, alpha, request):
random_state = np.random.RandomState(seed=42)
- max_val = np.array(alpha) * 2**(bits-signed)
+ max_val = np.array(alpha) * 2 ** (bits - signed)
min_val = -(max_val + 1)
w1 = random_state.randint(low=min_val, high=max_val, size=(3, 4))
b1 = np.array([0.0, 0.0, 0.0, 0.0])
x = x_in = tf.keras.layers.Input(shape=3)
- x = QActivation(
- quantized_bits(bits=4, integer=3, keep_negative=True)
- )(x)
+ x = QActivation(quantized_bits(bits=4, integer=3, keep_negative=True))(x)
x = QDense(
4,
- kernel_quantizer=quantized_bits(
- bits=bits, integer=(bits-signed), keep_negative=signed, alpha=alpha
- ),
+ kernel_quantizer=quantized_bits(bits=bits, integer=(bits - signed), keep_negative=signed, alpha=alpha),
)(x)
x = QActivation(quantized_relu(bits=3, integer=3))(x)
model = tf.keras.Model(inputs=[x_in], outputs=[x])
@@ -437,7 +445,8 @@ def test_qkeras_tensor_alpha(bits, signed, alpha, request):
y_qonnx = odict[onnx_model.graph.output[0].name]
assert np.array_equal(y_qkeras, y_qonnx)
os.remove(model_path)
-
+
+
@pytest.mark.parametrize("quantizers", kb_quantizers, ids=kb_quantizers_ids)
def test_qkeras_qconv2d_1(quantizers, request):
kq, bq = quantizers
From cd453206cf3ed1c90026924b24ae7fc2af6e785e Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Wed, 14 Aug 2024 10:12:38 +0200
Subject: [PATCH 11/13] Added check if tensor is repsentable with quant
setting.s
---
src/qonnx/converters/qkeras/onnx.py | 25 ++++++++++++++++++++++++-
1 file changed, 24 insertions(+), 1 deletion(-)
diff --git a/src/qonnx/converters/qkeras/onnx.py b/src/qonnx/converters/qkeras/onnx.py
index 41b022d7..bbccde8d 100644
--- a/src/qonnx/converters/qkeras/onnx.py
+++ b/src/qonnx/converters/qkeras/onnx.py
@@ -1,10 +1,15 @@
+import logging
import numpy as np
from tf2onnx.late_rewriters import channel_order_rewriters
from tf2onnx.onnx_opset.math import DirectOp, MatMul
from tf2onnx.onnx_opset.nn import BiasAdd, ConvOp
+from qonnx.custom_op.general.quant import quant
+
from .quantizers import get_quant_params
+logger = logging.getLogger(__name__)
+
def get_qkeras_onnx_handlers(all_quantizers):
"""Returns the handlers for each kind of layer
@@ -47,6 +52,23 @@ def _extract_node_name(onnx_node, keras_quantizers):
return None
+def check_tensor_is_representable(tensor, quant_params, node):
+ "Gives a Warning iftensor is not representable with the providede quantization settings"
+ qtensor = quant(
+ inp_tensor=np.array(tensor),
+ scale=np.array(quant_params["inputs"]["scale"]),
+ zeropt=np.array(quant_params["inputs"]["zero_point"]),
+ bitwidth=np.array(quant_params["inputs"]["bit_width"]),
+ signed=quant_params["attributes"]["signed"],
+ narrow=quant_params["attributes"]["narrow"],
+ rounding_mode=quant_params["attributes"]["rounding_mode"],
+ )
+ if not np.array_equal(tensor, qtensor):
+ logger.warn(
+ f"Tensor of node: {node.name} is not representable with the provided quantization settings: {quant_params}"
+ )
+
+
def qlayer_handler(ctx, node, name, args):
all_quantizers = args[0]
keras_name = _extract_node_name(node, all_quantizers)
@@ -57,9 +79,9 @@ def qlayer_handler(ctx, node, name, args):
if quantizers.get("kernel_quantizer_cfg"):
weights = node.inputs[1].get_tensor_value(as_list=True)
quant_params = get_quant_params(weights, quantizers["kernel_quantizer_cfg"])
+ check_tensor_is_representable(weights, quant_params, node)
attr = quant_params["attributes"]
input_nodes = [node.input[1]]
-
for key in quant_params["inputs"].keys():
name = f"{node.name}_kernel_quantizer_{key}"
np_val = np.asarray(quant_params["inputs"][key])
@@ -86,6 +108,7 @@ def qlayer_handler(ctx, node, name, args):
if quantizers.get("bias_quantizer_cfg") and len(node.input) == 3:
bias = node.inputs[-1].get_tensor_value(as_list=True)
quant_params = get_quant_params(bias, quantizers["bias_quantizer_cfg"])
+ check_tensor_is_representable(bias, quant_params, node)
attr = quant_params["attributes"]
input_nodes = [node.input[-1]]
for key in quant_params["inputs"].keys():
From 7e98eb3287de5c36e463084fc5724a449751c064 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Mon, 26 Aug 2024 09:16:02 +0200
Subject: [PATCH 12/13] reformated with pre-commit hooks.
---
src/qonnx/custom_op/general/quant.py | 6 ++++++
tests/custom_op/test_runding_mode.py | 11 +++++++----
2 files changed, 13 insertions(+), 4 deletions(-)
diff --git a/src/qonnx/custom_op/general/quant.py b/src/qonnx/custom_op/general/quant.py
index 15afd048..5cdc1294 100644
--- a/src/qonnx/custom_op/general/quant.py
+++ b/src/qonnx/custom_op/general/quant.py
@@ -142,18 +142,24 @@ def resolve_rounding_mode(mode_string):
elif normalized_mode_string == "FLOOR":
return np.floor
elif normalized_mode_string == "UP":
+
def round_up(x):
return np.sign(x) * np.ceil(np.abs(x))
+
return round_up
elif normalized_mode_string == "DOWN":
return np.fix
elif normalized_mode_string == "HALF_UP":
+
def round_half_up(x):
return np.sign(x) * np.floor(np.abs(x) + 0.5)
+
return round_half_up
elif normalized_mode_string == "HALF_DOWN":
+
def round_half_down(x):
return np.sign(x) * np.ceil(np.abs(x) - 0.5)
+
return round_half_down
else:
raise ValueError(f"Could not resolve rounding mode called: {normalized_mode_string}")
diff --git a/tests/custom_op/test_runding_mode.py b/tests/custom_op/test_runding_mode.py
index 54a81f0e..eb48d644 100644
--- a/tests/custom_op/test_runding_mode.py
+++ b/tests/custom_op/test_runding_mode.py
@@ -4,15 +4,18 @@
from qonnx.custom_op.general.quant import resolve_rounding_mode
-@pytest.mark.parametrize("rmode,exp", [
+
+@pytest.mark.parametrize(
+ "rmode,exp",
+ [
("ROUND", np.array([6, 2, 2, 1, 1, -1, -1, -2, -2, -6])),
- ("CEIL", np.array([6, 3, 2, 2, 1, -1, -1, -1, -2, - 5])),
+ ("CEIL", np.array([6, 3, 2, 2, 1, -1, -1, -1, -2, -5])),
("FLOOR", np.array([5, 2, 1, 1, 1, -1, -2, -2, -3, -6])),
("UP", np.array([6, 3, 2, 2, 1, -1, -2, -2, -3, -6])),
("DOWN", np.array([5, 2, 1, 1, 1, -1, -1, -1, -2, -5])),
("HALF_UP", np.array([6, 3, 2, 1, 1, -1, -1, -2, -3, -6])),
- ("HALF_DOWN", np.array([5, 2, 2, 1, 1, -1, -1, -2, -2, -5]))
- ]
+ ("HALF_DOWN", np.array([5, 2, 2, 1, 1, -1, -1, -2, -2, -5])),
+ ],
)
def test_rounding_modes(rmode, exp):
test_array = np.array([5.5, 2.5, 1.6, 1.1, 1.0, -1.0, -1.1, -1.6, -2.5, -5.5])
From fafc4d7f5496fed4129bc820a2815c99cd0105b0 Mon Sep 17 00:00:00 2001
From: jvreca <jure.vreca@ijs.si>
Date: Mon, 26 Aug 2024 09:18:49 +0200
Subject: [PATCH 13/13] Removed the _TO_ to make it consitant with others
---
src/qonnx/custom_op/general/quant.py | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/src/qonnx/custom_op/general/quant.py b/src/qonnx/custom_op/general/quant.py
index 5cdc1294..b0b50b9a 100644
--- a/src/qonnx/custom_op/general/quant.py
+++ b/src/qonnx/custom_op/general/quant.py
@@ -135,7 +135,7 @@ def resolve_rounding_mode(mode_string):
"""Resolve the rounding mode string of Quant and Trunc ops
to the corresponding numpy functions."""
normalized_mode_string = mode_string.upper()
- if normalized_mode_string == "ROUND" or normalized_mode_string == "HALF_TO_EVEN":
+ if normalized_mode_string == "ROUND" or normalized_mode_string == "HALF_EVEN":
return np.round
elif normalized_mode_string == "CEIL":
return np.ceil