diff options
Diffstat (limited to 'ethosu/vela/supported_operators.py')
| -rw-r--r-- | ethosu/vela/supported_operators.py | 28 |
1 files changed, 18 insertions, 10 deletions
diff --git a/ethosu/vela/supported_operators.py b/ethosu/vela/supported_operators.py index 2e35d771..9d315189 100644 --- a/ethosu/vela/supported_operators.py +++ b/ethosu/vela/supported_operators.py @@ -135,6 +135,7 @@ class SupportedOperators: self.generic_constraints.append(SupportedOperators.constraint_tens_quant_scale) self.generic_constraints.append(SupportedOperators.constraint_tens_quant_per_axis) self.generic_constraints.append(SupportedOperators.constraint_faf) + self.generic_constraints.append(SupportedOperators.constraint_quant_scale_inf) # Setup specific constraints. Note: the order matters self.specific_constraints = defaultdict(list) @@ -180,11 +181,6 @@ class SupportedOperators: self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_type) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_height_range) self.specific_constraints[op_type].append(SupportedOperators.constraint_filter_product_range) - # TODO: Check ReduceSum restrictions - - # Relu specific checks: - for op_type in SupportedOperators.relu_ops: - self.specific_constraints[op_type].append(SupportedOperators.constraint_quant_scale_inf) # Resizing specific checks: for op_type in SupportedOperators.resizing_ops: @@ -552,11 +548,23 @@ class SupportedOperators: @staticmethod def constraint_quant_scale_inf(op): - "The IFM quantization scale divided by the OFM quantization scale must not be infinite" - ifm_scale = op.ifm.quantization.scale_f32 - ofm_scale = op.ofm.quantization.scale_f32 - valid = not np.isinf(ifm_scale / ofm_scale) - return valid, f"Op has infinite quantization scale. ifm_scale={ifm_scale} ofm_scale={ofm_scale}" + "Input and Output tensors must have quantization scales that fit within float32 precision" + if op.ofm is not None and op.ofm.is_quantized(): + ofm_scale = op.ofm.quantization.scale_f32 + if ofm_scale < np.finfo(np.float32).tiny: + return ( + False, + f"The quantization scale of the output tensor is {ofm_scale}, " + + f"minimum supported is: {np.finfo(np.float32).tiny}", + ) + if op.ifm is not None and op.ifm.is_quantized(): + ifm_scale = op.ifm.quantization.scale_f32 + if np.isinf(ifm_scale / ofm_scale): + return ( + False, + f"IFM scale divided by OFM scale is infinite, ifm_scale={ifm_scale} ofm_scale={ofm_scale}", + ) + return True, "Op's quantization is ok" @staticmethod def constraint_depth_multiplier(op): |