diff options
Diffstat (limited to 'ethosu')
-rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 87 |
1 files changed, 79 insertions, 8 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 38e3f603..aaa778ed 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -753,10 +753,74 @@ def convert_prelu(op, arch, nng): if None in (ifm, alpha, ofm): return op + if alpha.values is not None: + # If const alpha check for possible optimisations + alpha_zp = alpha.quantization.zero_point + alpha_scale = alpha.quantization.scale_f32 + # If all alpha values are the same the PReLU can be converted to LeakyRelu + alpha_min = (alpha.values.min().astype(np.int) - alpha_zp) * alpha_scale + alpha_max = (alpha.values.max().astype(np.int) - alpha_zp) * alpha_scale + if alpha_min == alpha_max: + # or even a Relu + if alpha_min == 0: + new_op = Op.Relu + else: + new_op = Op.LeakyRelu + op.attrs["alpha"] = alpha_min + # setup alpha_scaling for bit exact result + ifm_scale = ifm.quantization.scale_f32 + ofm_scale = ofm.quantization.scale_f32 + alpha_scale, alpha_shift = scaling.elementwise_mul_scale(ifm_scale, alpha_scale, ofm_scale) + op.attrs["alpha_scaling"] = (alpha.values.min() - alpha_zp, alpha_scale, alpha_shift) + # Change op type + op.type = new_op + op.name = op.name.replace("Prelu", new_op.name) + del op.inputs[1] # Remove alpha tensor + return op + elif alpha_max < 1: + # If alpha_max is less than 1 convert PReLU to Max(alpha * IFM, identity * IFM) + # Multiply with alpha tensor + mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha") + mul_alpha.add_input_tensor(ifm) + mul_alpha.add_input_tensor(alpha) + fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True) + mul_alpha.set_output_tensor(fm_alpha) + mul_alpha.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, mul_alpha) + if check_quantized_tens_scaling_equal(ifm, ofm): + # No scaling is needed + fm_id = ifm + else: + # Add multiplication with identity + mul_identity = Operation(Op.Mul, op.name + "_mul_identity") + mul_identity.add_input_tensor(ifm) + # Create const tensor containing identity as scalar + quantization = ifm.quantization.clone() + quantization.scale_f32 = np.float32(1) + quantization.zero_point = 0 + one = create_const_tensor("one_const", [], ifm.dtype, [1], quantization=quantization) + mul_identity.add_input_tensor(one) + # Make sure that fm_id is allocated to a different address than fm_alpha + fm_id = ofm.clone(op.name + "_id", set_unique=True) + mul_identity.set_output_tensor(fm_id) + mul_identity.set_ifm_ofm_shapes() + + # Combine scaled and alpha multiplied values + max_op = Operation(Op.Maximum, op.name + "_max") + max_op.add_input_tensor(fm_alpha) + max_op.add_input_tensor(fm_id) + max_op.set_output_tensor(ofm) + max_op.set_ifm_ofm_shapes() + + DebugDatabase.add_optimised(op, max_op) + ifm.consumer_list.remove(op) + return max_op + + # Catch all PReLU conversion for the cases that could not be optimised above no_scale_quant = ifm.quantization.clone() no_scale_quant.scale_f32 = None no_scale_quant.zero_point = 0 - zero = create_const_tensor("zero_const", [1, 1, 1, 1], ifm.dtype, [0], quantization=no_scale_quant) + zero = create_const_tensor("zero_const", [], ifm.dtype, [0], quantization=no_scale_quant) # Select values < 0 min_op = Operation(Op.Minimum, op.name + "_min") @@ -816,7 +880,12 @@ def convert_mul_max_to_abs_or_lrelu(op, arch, nng): mul = muls[0].ops[0] elif len(muls) == 2: # In the case both inputs are Muls, find the one with the same input as the Max - mul = [m for m in muls if len(set(op.inputs + m.ops[0].inputs)) == 1][0].ops[0] + mul_ifms = [m for m in muls if len(set(op.inputs + m.ops[0].inputs)) == 1] + if len(mul_ifms): + mul = mul_ifms[0].ops[0] + else: + # Not using same input + return op else: # No Mul inputs return op @@ -954,17 +1023,19 @@ def convert_lrelu_to_mul_max(op, arch): quantization.min = 0 quantization.max = alpha * (quantization.quant_max - quantization.quant_min) quantization.zero_point = 0 - if np.isinf(1 / alpha): + if "alpha_scaling" in op.attrs: + # The LeakyRelu was the result from convert_prelu + scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"] + mul_alpha.type = Op.RescaleMul + mul_alpha.rescale = [alpha_scale, alpha_shift] + elif np.isinf(1 / alpha): # Handling of alpha near zero quantization.scale_f32 = np.float32(1) scalar = 0 else: quantization.scale_f32 = alpha - scalar = alpha - alpha_tens = create_const_tensor( - op.name + "_alpha_scalar", [], ifm.dtype, [scalar], np.float32, quantization=quantization - ) - alpha_tens.values = np.array([1]) + scalar = 1 + alpha_tens = create_const_tensor(op.name + "_alpha_scalar", [], ifm.dtype, [scalar], quantization=quantization) mul_alpha.add_input_tensor(alpha_tens) fm_alpha = ofm.clone(op.name + "_alpha", set_unique=True) mul_alpha.set_output_tensor(fm_alpha) |