diff options
Diffstat (limited to 'ethosu/vela/tflite_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 54 |
1 files changed, 45 insertions, 9 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index 611046ba..052f824c 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -1017,11 +1017,34 @@ def convert_lrelu_to_mul_max(op, arch): if ifm is None or ofm is None: return op + alpha = np.float32(op.attrs["alpha"]) + use_mul_max = 0 < alpha < 1 + if use_mul_max: + mul_ifm = ifm + new_op = Op.Maximum + else: + # Need to use a different approach for alpha > 1 + no_scale_quant = ifm.quantization.clone() + no_scale_quant.scale_f32 = None + no_scale_quant.zero_point = 0 + zero = create_const_tensor("zero_const", [], ifm.dtype, [0], quantization=no_scale_quant) + + # Select values < 0 + min_op = Operation(Op.Minimum, op.name + "_min") + min_op.add_input_tensor(ifm) + min_op.add_input_tensor(zero) + mul_ifm = ifm.clone(op.name + "_negative", set_unique=True) + mul_ifm.dtype = DataType.int32 + min_op.set_output_tensor(mul_ifm) + min_op.set_ifm_ofm_shapes() + new_op = Op.RescaleAdd + op.rescale = (1, 0) # No scale or shift + DebugDatabase.add_optimised(op, min_op) + # Add multiplication with alpha mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha") - mul_alpha.add_input_tensor(ifm) + mul_alpha.add_input_tensor(mul_ifm) # Create const tensor containing alpha as scalar - alpha = np.float32(op.attrs["alpha"]) quantization = ifm.quantization.clone() quantization.min = 0 quantization.max = alpha * (quantization.quant_max - quantization.quant_min) @@ -1037,15 +1060,24 @@ def convert_lrelu_to_mul_max(op, arch): scalar = 0 else: quantization.scale_f32 = alpha - scalar = 1 - alpha_tens = create_const_tensor(op.name + "_alpha_scalar", [], ifm.dtype, [scalar], quantization=quantization) + scalar, _ = scaling.elementwise_mul_scale(ifm.quantization.scale_f32, alpha, ofm.quantization.scale_f32) + alpha_tens = create_const_tensor( + op.name + "_alpha_scalar", [1, 1, 1, 1], DataType.int32, [scalar], np.int32, 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) mul_alpha.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, mul_alpha) - if check_quantized_tens_scaling_equal(ifm, ofm): + if not use_mul_max: + relu_op = Operation(Op.Relu, op.name + "_relu") + relu_op.add_input_tensor(ifm) + fm_id = ofm.clone(op.name + "_positive_scaled", set_unique=True) + relu_op.set_output_tensor(fm_id) + relu_op.set_ifm_ofm_shapes() + DebugDatabase.add_optimised(op, relu_op) + elif check_quantized_tens_scaling_equal(ifm, ofm): # No identity multiplication is needed fm_id = ifm else: @@ -1069,8 +1101,8 @@ def convert_lrelu_to_mul_max(op, arch): DebugDatabase.add_optimised(op, mul_identity) # Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs - op.type = Op.Maximum - op.name = op.name.replace("LeakyRelu", "Maximum") + op.type = new_op + op.name = op.name.replace("LeakyRelu", new_op.name) op.inputs = [] ifm.consumer_list.remove(op) op.add_input_tensor(fm_alpha) @@ -1145,8 +1177,12 @@ def convert_lrelu(op, arch, nng): if ifm.dtype in (DataType.uint8, DataType.int8) and ifm.dtype == ofm.dtype: # use LUT for int8/uint8 return convert_lrelu_to_lut(op, arch) - if check_quantized_tens_scaling_equal(ifm, ofm) and ifm.dtype == ofm.dtype == DataType.int16: - # use LeakyRelu unmodified for int16 with equal input/output scaling + if ( + check_quantized_tens_scaling_equal(ifm, ofm) + and ifm.dtype == ofm.dtype == DataType.int16 + and op.attrs["alpha"] >= 0 + ): + # use LeakyRelu unmodified for int16 with equal input/output scaling and positive alpha return op return convert_lrelu_to_mul_max(op, arch) |