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author | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-09-16 09:39:26 +0200 |
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committer | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2022-09-16 11:01:39 +0200 |
commit | 36424312fcc7c279a929073160ca7191a926e77b (patch) | |
tree | 138eaa1ad66c22110d2e1ff47e204ddbf30ad357 /ethosu/vela | |
parent | 17e53b5d776109e1bd1073c657ff0453ccf3c09e (diff) | |
download | ethos-u-vela-36424312fcc7c279a929073160ca7191a926e77b.tar.gz |
MLBEDSW-6938 Fix PReLU optimisation
Fixed PReLU optimisation to LeakyReLU with negative alpha.
Added optimisation of LeakyReLU to ReLU when alpha is zero.
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Change-Id: I5e66f79b29908fffd95b6115799021138ebb401a
Diffstat (limited to 'ethosu/vela')
-rw-r--r-- | ethosu/vela/tflite_graph_optimiser.py | 30 |
1 files changed, 18 insertions, 12 deletions
diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py index fb98e21b..0630ef41 100644 --- a/ethosu/vela/tflite_graph_optimiser.py +++ b/ethosu/vela/tflite_graph_optimiser.py @@ -1019,11 +1019,12 @@ def convert_lrelu_to_mul_max(op, arch): alpha = np.float32(op.attrs["alpha"]) use_mul_max = 0 < alpha < 1 + is_converted_prelu = "alpha_scaling" in op.attrs if use_mul_max: mul_ifm = ifm new_op = Op.Maximum else: - # Need to use a different approach for alpha > 1 + # Need to use a different approach for alpha < 0 or alpha > 1 no_scale_quant = ifm.quantization.clone() no_scale_quant.scale_f32 = None no_scale_quant.zero_point = 0 @@ -1034,7 +1035,10 @@ def convert_lrelu_to_mul_max(op, arch): 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 + if alpha < 0 and not is_converted_prelu: + # For negative alpha that is not from a converted PReLU we need to use + # int32 Mul below to perform the (negative) alpha scaling + mul_ifm.dtype = DataType.int32 min_op.set_output_tensor(mul_ifm) min_op.set_ifm_ofm_shapes() new_op = Op.RescaleAdd @@ -1050,8 +1054,8 @@ def convert_lrelu_to_mul_max(op, arch): quantization.max = alpha * (quantization.quant_max - quantization.quant_min) quantization.zero_point = 0 alpha_dtype = mul_ifm.dtype - if "alpha_scaling" in op.attrs: - # The LeakyRelu was the result from convert_prelu + if is_converted_prelu: + # The LeakyRelu was the result from convert_prelu and the scaling is provided scalar, alpha_scale, alpha_shift = op.attrs["alpha_scaling"] mul_alpha.type = Op.RescaleMul mul_alpha.rescale = [alpha_scale, alpha_shift] @@ -1062,7 +1066,7 @@ def convert_lrelu_to_mul_max(op, arch): else: quantization.scale_f32 = alpha if alpha_dtype == DataType.int32: - # When the datatype is int32 we need to do the scaling with the multiplication + # When the datatype is int32 (alpha negative) we need to do the scaling with the multiplication scalar, _ = scaling.elementwise_mul_scale(ifm.quantization.scale_f32, alpha, ofm.quantization.scale_f32) else: scalar = 1 @@ -1179,14 +1183,16 @@ def convert_lrelu(op, arch, nng): ifm, ofm = op.get_ifm_ofm() if ifm is None or ofm is None: return op + alpha = op.attrs["alpha"] + if alpha == 0: + # When alpha is 0 the opertion can be converted to a ReLU + op.type = Op.Relu + op.name = op.name.replace("LeakyRelu", op.type.name) + return op 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 - and op.attrs["alpha"] >= 0 - ): + if check_quantized_tens_scaling_equal(ifm, ofm) and ifm.dtype == ofm.dtype == DataType.int16 and 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) @@ -1830,6 +1836,8 @@ def tflite_optimise_graph(nng, arch): convert_conv_to_fc, convert_softmax, convert_prelu, + convert_mul_max_to_abs_or_lrelu, + convert_lrelu, optimise_strided_conv, convert_hardswish_to_lut, rewrite_fully_connected_input, @@ -1840,8 +1848,6 @@ def tflite_optimise_graph(nng, arch): fixup_resize, fixup_bias_tensors, fixup_asymmetric_weights, - convert_mul_max_to_abs_or_lrelu, - convert_lrelu, convert_tanh_sigmoid_to_lut, replace_pad_by_hw_pad, ] |