diff options
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/graph_optimiser.py | 30 |
1 files changed, 21 insertions, 9 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index f6209ed2..4696446c 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -35,6 +35,7 @@ from .operation import NpuBlockType from .operation import Op from .operation import Operation from .softmax import SoftMax +from .tensor import check_quantized_tens_scaling_equal from .tensor import create_const_tensor from .tensor import create_reshape_tensor from .tensor import QuantizationParameters @@ -341,7 +342,7 @@ def convert_batched_fc_to_conv(op, arch, nng): # There is a preceding Reshape # Compare input of prev_op and input of op, to see if prev_op can be removed ifm_prev_op = prev_op.inputs[0] - if ifm_prev_op.shape == ifm.shape and ifm_prev_op.quantization.is_scaling_equal(ifm.quantization): + if ifm_prev_op.shape == ifm.shape and check_quantized_tens_scaling_equal(ifm_prev_op, ifm.quantization): # prev_op can be removed op.set_input_tensor(ifm_prev_op, 0) else: @@ -369,7 +370,7 @@ def convert_batched_fc_to_conv(op, arch, nng): # There is a subsequent Reshape # Compare desired shape and output of consumer op, to see if consumer op can be removed ofm_cons_op = ofm.consumer_list[0].outputs[0] - if desired_shape == ofm_cons_op.shape and ofm.quantization.is_scaling_equal(ofm_cons_op.quantization): + if desired_shape == ofm_cons_op.shape and check_quantized_tens_scaling_equal(ofm, ofm_cons_op): op.outputs[0] = ofm_cons_op op.outputs[0].ops = [op] else: @@ -613,7 +614,7 @@ def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng): ofm = op.outputs[0] # Relu with differing IFM and OFM scaling cannot be fused with another primary op # and requires its own to be inserted - if not ifm.is_scaling_equal(ofm): + if not check_quantized_tens_scaling_equal(ifm, ofm): # Override this op with its own primary op (avgpool) relu_fused_op = create_avgpool_nop(op.name + "_avgpool") # And fuse the original activation function to it @@ -727,9 +728,12 @@ def convert_mul_max_to_abs_or_lrelu(op, arch, nng): if mul.activation: return op ifm, ofm = op.get_ifm_ofm() + if ifm is None or ofm is None: + return op + if ifm.dtype not in (DataType.uint8, DataType.int8) or ifm.dtype != ofm.dtype: return op - if not ifm.is_scaling_equal(ofm) or not ifm.is_scaling_equal(mul_ofm): + if not check_quantized_tens_scaling_equal(ifm, ofm) or not check_quantized_tens_scaling_equal(ifm, mul_ofm): # rewrite to LeakyRelu currently only makes sense if the quantization is identical return op @@ -780,6 +784,8 @@ def convert_lrelu_to_mul_max(op, arch): # Converts LeakyRelu to Max(alpha * IFM, identity * IFM) # (the opposite of convert_mul_max_to_abs_or_lrelu) ifm, ofm = op.get_ifm_ofm() + if ifm is None or ofm is None: + return op # Add multiplication with alpha mul_alpha = Operation(Op.Mul, op.name + "_mul_alpha") @@ -796,7 +802,7 @@ def convert_lrelu_to_mul_max(op, arch): fm_alpha = ofm.clone(op.name + "_alpha") mul_alpha.set_output_tensor(fm_alpha) - if ifm.is_scaling_equal(ofm): + if check_quantized_tens_scaling_equal(ifm, ofm): # No identity multiplication is needed fm_id = ifm else: @@ -829,6 +835,8 @@ def convert_lrelu_to_mul_max(op, arch): def convert_to_lut(op, lut_values, lut_name): # Rewrite the operation by Add with scalar 0 + LUT activation ifm = op.inputs[0] + if ifm is None: + return op assert ifm.dtype.size_in_bytes() == 1 op.type = Op.Add op.name = op.name + "_lut_" + lut_name @@ -908,10 +916,12 @@ def convert_lrelu(op, arch, nng): if op.type != Op.LeakyRelu: return op ifm, ofm = op.get_ifm_ofm() + if ifm is None or ofm is None: + 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 ifm.is_scaling_equal(ofm) and ifm.dtype == ofm.dtype and ifm.dtype == DataType.int16: + 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 return op return convert_lrelu_to_mul_max(op, arch) @@ -953,9 +963,9 @@ def remove_unwanted_reshapes(op, arch, nng): cons_op_ofm = cons_op.outputs[0] if len(prev_op_ifm.shape) == len(cons_op_ofm.shape): # Check if quantization is the same in the input and output for the reshape ops - if prev_op_ifm.quantization.is_scaling_equal( - prev_op_ofm.quantization - ) and cons_op_ifm.quantization.is_scaling_equal(cons_op_ofm.quantization): + if check_quantized_tens_scaling_equal(prev_op_ifm, prev_op_ofm) and check_quantized_tens_scaling_equal( + cons_op_ifm, cons_op_ofm + ): op.set_input_tensor(prev_op_ifm, 0) op.set_output_tensor(cons_op_ofm) return op @@ -966,6 +976,8 @@ def fuse_activation_function_with_prev(op, arch, nng): if not op.attrs.get("is_nop", False) or op.activation is None: return op ifm, ofm = op.get_ifm_ofm() + if ifm is None or ofm is None: + return op # finds the input(s) to the operation prev_op = ifm.ops[0] # Note: the below checks on prev_op require that a first optimize pass on the full graph has been performed |