Is tensor representable

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 +46,24 @@ 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>

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
@@ -47,37 +52,85 @@ 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)
     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_quantizer"])
+        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])
             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"
         )
-
-    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["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)
+                )
+                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_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[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])]
@@ -109,6 +162,11 @@ 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 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]]
@@ -148,9 +206,9 @@ 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():

src/qonnx/converters/qkeras/qlayers.py

@@ -104,11 +104,26 @@ def extract_qlayer(layer):
 
     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 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)
+
     # Check if activation is quantized
     if _is_keras_quantizer(keras_config["activation"]):
         keras_config["activation"] = _replace_activation(quantizers["activation"])

src/qonnx/converters/qkeras/quantizers.py

@@ -1,9 +1,11 @@
+import numpy as np
 import qkeras
 import six
+import tensorflow as tf
 
 
 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 +36,12 @@ 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, tf.Tensor)):
+        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"
+    rounding_mode = "HALF_EVEN"
 
     settings = {
         "attributes": {"signed": signed, "narrow": narrow, "rounding_mode": rounding_mode},

src/qonnx/custom_op/general/quant.py

@@ -135,12 +135,32 @@ 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_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}")
 

tests/custom_op/test_runding_mode.py

@@ -0,0 +1,23 @@
+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)