MLBEDSW-6314 Static optimisation for quantise OP

*Quantise op becomes constant if input is known at compile time
*Quantised values calculated if input of op is const and float
*Const inputs to quant op that are int are requantized

Change-Id: Ic94a72a392af709fe6a640d7dacbb5dc2334f16f
Signed-off-by: Ayaan Masood <Ayaan.Masood@arm.com>

1 files changed, 91 insertions, 0 deletions

diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index b8655c97..bfb1ded2 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -23,6 +23,7 @@ from ethosu.vela.data_type import DataType
 from ethosu.vela.graph_optimiser import optimise_graph
 from ethosu.vela.nn_graph import NetworkType
 from ethosu.vela.operation import Op
+from ethosu.vela.operation import Operation
 from ethosu.vela.operation import Padding
 from ethosu.vela.rewrite_graph import verify_graph_health
 from ethosu.vela.tensor import create_const_tensor
@@ -31,6 +32,7 @@ from ethosu.vela.tensor import Tensor
 from ethosu.vela.test import testutil
 from ethosu.vela.tflite_graph_optimiser import calc_explicit_padding
 from ethosu.vela.tflite_graph_optimiser import convert_batched_fc_shape
+from ethosu.vela.tflite_graph_optimiser import optimise_quantize
 from ethosu.vela.tflite_graph_optimiser import replace_pad_by_hw_pad
 from ethosu.vela.tflite_graph_optimiser import rewrite_fully_connected_input
 
@@ -533,3 +535,92 @@ def test_remove_expand_dims():
     assert verify_graph_health(nng)
     nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
     assert verify_graph_health(nng)
+
+
+def test_quant_static_optimisations():
+
+    """
+    Tests if the quant value at vela compile time is calculated correctly
+    """
+
+    quant_ifm = create_const_tensor(
+        "const_quant_ifm", values=np.array(127), value_dtype=np.int8, shape=[], dtype=DataType.int8
+    )
+    quant_ifm.quantization = testutil.default_quant_params()
+    quant_ifm.quantization.scale_f32 = 0.15748031
+    quant_ifm.quantization.quant_min = -128
+    quant_ifm.quantization.quant_max = 127
+
+    quant_ofm = create_const_tensor("const_quant_ofm", values=np.array([]), shape=[], dtype=DataType.int8)
+    quant_ofm.quantization = testutil.default_quant_params()
+    quant_ofm.quantization.scale_f32 = 0.036092404
+    quant_ofm.quantization.zero_point = -128
+    quant_ofm.quantization.quant_min = -128
+    quant_ofm.quantization.quant_max = 127
+
+    # Create quant op
+
+    quant_op = testutil.create_op(Op.Quantize, [quant_ifm], quant_ofm)
+
+    quant_op.run_on_npu = True
+
+    op: Operation = optimise_quantize(quant_op, None, None)
+
+    assert op.ofm.values == 127
+
+    quant_ifm = create_const_tensor(
+        "const_quant_ifm", values=np.array(127), value_dtype=np.int8, shape=[], dtype=DataType.int8
+    )
+    quant_ifm.quantization = testutil.default_quant_params()
+    quant_ifm.quantization.scale_f32 = 0.15748031
+    quant_ifm.quantization.quant_min = -128
+    quant_ifm.quantization.quant_max = 127
+
+    quant_ofm = create_const_tensor("const_quant_ofm", values=np.array([]), shape=[], dtype=DataType.int8)
+    quant_ofm.quantization = testutil.default_quant_params()
+    quant_ofm.quantization.scale_f32 = 0.036092404
+    quant_ofm.quantization.zero_point = -128
+    quant_ofm.quantization.quant_min = -128
+    quant_ofm.quantization.quant_max = 127
+
+    # Create quant op
+
+    quant_op = testutil.create_op(Op.Quantize, [quant_ifm], quant_ofm)
+
+    quant_op.run_on_npu = True
+
+    op: Operation = optimise_quantize(quant_op, None, None)
+
+    assert op.ofm.values == 127
+
+
+def test_optimise_quantize_multiple_values():
+    """
+    Tests if the quant value at vela compile time is calculated correctly
+    when passing multiple values to quantize node
+    """
+
+    quant_ifm = create_const_tensor(
+        "const_quant_ifm", values=np.array([127, 127]), value_dtype=np.int8, shape=[], dtype=DataType.int8
+    )
+    quant_ifm.quantization = testutil.default_quant_params()
+    quant_ifm.quantization.scale_f32 = 0.15748031
+    quant_ifm.quantization.quant_min = -128
+    quant_ifm.quantization.quant_max = 127
+
+    quant_ofm = create_const_tensor("const_quant_ofm", values=np.array([]), shape=[], dtype=DataType.int8)
+    quant_ofm.quantization = testutil.default_quant_params()
+    quant_ofm.quantization.scale_f32 = 0.036092404
+    quant_ofm.quantization.zero_point = -128
+    quant_ofm.quantization.quant_min = -128
+    quant_ofm.quantization.quant_max = 127
+
+    # Create quant op
+
+    quant_op = testutil.create_op(Op.Quantize, [quant_ifm], quant_ofm)
+
+    quant_op.run_on_npu = True
+
+    op: Operation = optimise_quantize(quant_op, None, None)
+
+    assert (op.ofm.values == np.array([127, 127])).all()