diff options
Diffstat (limited to 'ethosu/vela/tflite_supported_operators.py')
| -rw-r--r-- | ethosu/vela/tflite_supported_operators.py | 55 |
1 files changed, 49 insertions, 6 deletions
diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py index 1915d43b..f01a6690 100644 --- a/ethosu/vela/tflite_supported_operators.py +++ b/ethosu/vela/tflite_supported_operators.py @@ -304,6 +304,7 @@ class TFLiteSupportedOperators: # Reshape specific checks: self.specific_constraints[Op.Reshape].append(TFLiteSupportedOperators.constraint_reshape_shape_constant) + self.specific_constraints[Op.Reshape].append(TFLiteSupportedOperators.constraint_reshape_before_mean) # Concat specific checks: for op_type in (Op.Concat, Op.ConcatTFLite): @@ -795,10 +796,9 @@ class TFLiteSupportedOperators: max_prod = cls.mean_kernel_product return h * w <= max_prod, f"Product of height and width is {h * w}" - @classmethod - @docstring_format_args([mean_kernel_product_int8]) - def constraint_mean_height_width_product_int8(cls, op): - """Product of IFM height and width must be no greater than {} when: + @staticmethod + def constraint_mean_height_width_product_int8(op): + """Number of IFM height and width elements might cause accumulator saturation when; The IFM shape has 4 dimensions; and The axis indices specify reduction across 2 dimensions; and The axis indices correspond to the width and height dimensions of the IFM; and @@ -817,8 +817,43 @@ class TFLiteSupportedOperators: return True, "" h = shape[-3] w = shape[-2] - max_prod = cls.mean_kernel_product_int8 - return h * w <= max_prod, f"Product of height and width is {h * w}" + + ifmq, ofmq = op.ifm.quantization, op.ofm.quantization + + # Scale factor + real_scale = ifmq.scale_f32 / ofmq.scale_f32 + + # Min and max value + ifm_min_val = np.iinfo(np.int8).min - ifmq.zero_point + ifm_max_val = np.iinfo(np.int8).max - ifmq.zero_point + + # Accumulator limits + min_acc_limit = np.iinfo(np.int16).min + max_acc_limit = np.iinfo(np.int16).max + + # Theoretical max/min value that accumulator need to store + min_acc_sum = h * w * ifm_min_val * real_scale + ofmq.zero_point + max_acc_sum = h * w * ifm_max_val * real_scale + ofmq.zero_point + + # Max product of heigth and width that will not saturate the accumulator + ifm_min_val = 1 if ifm_min_val == 0 else ifm_min_val + ifm_max_val = 1 if ifm_max_val == 0 else ifm_max_val + if max_acc_sum > abs(min_acc_sum): + max_hw = int((max_acc_limit - ofmq.zero_point) / real_scale / ifm_max_val) + else: + max_hw = int((min_acc_limit - ofmq.zero_point) / real_scale / ifm_min_val) + + extra = [] + + extra.append(f" Possible accumulator range is ({min_acc_sum} - {max_acc_sum})\n") + extra.append(f" Maximum accumulator range is ({min_acc_limit} - {max_acc_limit})\n") + extra.append( + f" Based on the IFM and OFM quantization the IFM height and width must be no greater than {max_hw}" + ) + + extra = "".join(extra) + + return (min_acc_sum >= min_acc_limit and max_acc_sum <= max_acc_limit, f"\n{extra}") @classmethod @docstring_format_args([filter_height_range[1], dilated_height_range[1]]) @@ -867,6 +902,14 @@ class TFLiteSupportedOperators: return valid, f"Op has non-const input(s): {extra}" @staticmethod + def constraint_reshape_before_mean(op): + "Reshape on NPU not supported before MEAN operator" + for next_op in op.outputs[0].consumers(): + if next_op is not None and next_op.type == Op.Mean: + return False, "" + return True, "" + + @staticmethod def constraint_concat_valid_dimensions_non_axis(op): """All Input dimensions must match OFM dimension in all axes except the one defined by the axis attribute""" valid = True |