diff options
Diffstat (limited to 'ethosu')
| -rw-r--r-- | ethosu/vela/graph_optimiser_util.py | 13 | ||||
| -rw-r--r-- | ethosu/vela/softmax.py | 10 | ||||
| -rw-r--r-- | ethosu/vela/test/test_lut.py | 2 |
3 files changed, 18 insertions, 7 deletions
diff --git a/ethosu/vela/graph_optimiser_util.py b/ethosu/vela/graph_optimiser_util.py index 2822feb8..24a55836 100644 --- a/ethosu/vela/graph_optimiser_util.py +++ b/ethosu/vela/graph_optimiser_util.py @@ -417,7 +417,8 @@ def convert_depthwise_to_conv(op, arch, nng): def convert_to_lut(op, lut_values, lut_name): # Rewrite the operation by Add with scalar 0 + LUT activation - ifm = op.inputs[0] + ifm = op.ifm + ofm = op.ofm if ifm is None: return op assert ifm.dtype.size_in_bytes() == 1 @@ -429,7 +430,7 @@ def convert_to_lut(op, lut_values, lut_name): quantization = QuantizationParameters(0.0, 255.0) quantization.scale_f32 = ifm.quantization.scale_f32 quantization.zero_point = 0 - tens = create_const_tensor(op.inputs[0].name + "_scalar0", [], ifm.dtype, [0], quantization=quantization) + tens = create_const_tensor(ifm.name + "_scalar0", [], ifm.dtype, [0], quantization=quantization) op.add_input_tensor(tens) op.ifm_shapes.append(Shape4D(tens.shape)) # TODO no shape? @@ -437,7 +438,13 @@ def convert_to_lut(op, lut_values, lut_name): # so even if the OFM has a different scale than the IFM, the generated OFM scale instructions # should be the same as the IFM op.forced_output_quantization = ifm.quantization - lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, DataType.int8) + + # the lut tensor datatype needs to match both; the ofm datatype, because these are the values output; and the + # datatype used to generate the lut values (which is probably the ifm datatype), because we want to avoid any + # potential overflow errors in create_lut_tensor() caused by converting Python int (which could represent a uint) + # to NumPy int. this can be guaranteed by checking that the ifm and ofm datatypes are the same + assert ifm.dtype == ofm.dtype + lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, ofm.dtype) op.set_activation_lut(lut_tensor) op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, op) diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py index 575e1e66..5a06c1bd 100644 --- a/ethosu/vela/softmax.py +++ b/ethosu/vela/softmax.py @@ -270,7 +270,7 @@ class SoftMax: ifm2_shape=ifm_max_shape, ) sub_op.set_activation_lut( - create_const_tensor(f"{sub_op.name}_exp_lut", [1, 1, 1, 256], DataType.int32, exp_lut, TensorPurpose.LUT) + create_const_tensor(f"{sub_op.name}_exp_lut", [1, 1, 1, 256], DataType.uint32, exp_lut, TensorPurpose.LUT) ) ifm_exp = add_op_get_ofm(sub_op) # Note: activation.min/max are non-quantized values @@ -505,8 +505,10 @@ class SoftMax: f"{name}_const", [1, 1, 1, 1], DataType.int32, [32767], quantization=no_scale_quant ) add_op = create_add(name, mul2_ofm, const_add, mul2_ofm.quantization.clone(), dtype=DataType.int16) + # lut activation values are int32 type however they are defined as Python ints. If these are converted to + # numpy.int32 it could result in an overflow error. Therefore, they are forced to uint32 to avoid this add_op.set_activation_lut( - create_const_tensor(f"{name}_exp_lut", [1, 1, 1, 512], DataType.int32, self.EXP_LUT, TensorPurpose.LUT) + create_const_tensor(f"{name}_exp_lut", [1, 1, 1, 512], DataType.uint32, self.EXP_LUT, TensorPurpose.LUT) ) ifm_exp = add_op_get_ofm(add_op) @@ -550,11 +552,13 @@ class SoftMax: f"{name}_const", [1, 1, 1, 1], DataType.int32, [32768], quantization=no_scale_quant ) sub11_op = create_sub(name, shifted_sum_minus_one_16, sub11_const, no_scale_quant, dtype=DataType.int16) + # lut activation values are int32 type however they are defined as Python ints. If these are converted to + # numpy.int32 it could result in an overflow error. Therefore, they are forced to uint32 to avoid this sub11_op.set_activation_lut( create_const_tensor( f"{name}_one_over_one_plus_x_lut", [1, 1, 1, 512], - DataType.int32, + DataType.uint32, self.ONE_OVER_ONE_PLUS_X_LUT, TensorPurpose.LUT, ) diff --git a/ethosu/vela/test/test_lut.py b/ethosu/vela/test/test_lut.py index 712be7a2..58e72bbf 100644 --- a/ethosu/vela/test/test_lut.py +++ b/ethosu/vela/test/test_lut.py @@ -35,7 +35,7 @@ from ethosu.vela.test import testutil def set_256_lut(op, key, arch): random.seed(key) values = random.choices(range(256), k=256) - lut_tensor = create_const_tensor(op.name + "_lut", [1, 1, 1, 256], DataType.int8, values, TensorPurpose.LUT) + lut_tensor = create_const_tensor(op.name + "_lut", [1, 1, 1, 256], DataType.uint8, values, TensorPurpose.LUT) scratch_lut_tensor = lut_tensor.clone_into_fast_storage(arch) op.set_activation_lut(scratch_lut_tensor) |