diff options
| -rw-r--r-- | ethosu/vela/graph_optimiser_util.py | 79 | ||||
| -rw-r--r-- | ethosu/vela/tflite_supported_operators.py | 9 |
2 files changed, 33 insertions, 55 deletions
diff --git a/ethosu/vela/graph_optimiser_util.py b/ethosu/vela/graph_optimiser_util.py index b33851a8..e6a79cef 100644 --- a/ethosu/vela/graph_optimiser_util.py +++ b/ethosu/vela/graph_optimiser_util.py @@ -200,35 +200,25 @@ def bypass_memory_only_ops(op): ofm = op.ofm ifm = op.ifm - # Check if ifm/ofm are network ifm/ofm + # Check if ifm is subgraph ifm ifm_is_sg_ifm = ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) - ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in ifm.consumer_list) - ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in ofm.consumer_list) - # Check if ifm/ofm is produced respectively consumed by CPU + # Check if ifm is produced by CPU ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops) - ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list) # This case should be handled prior to this function - assert not ((ifm_is_sg_ifm or ifm_is_sg_ofm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed)) - - if ofm_is_sg_ofm or ofm_is_cpu_consumed: - # Bypassed by replacing ifm with ofm - ofm.ops = [] - for prev_op in ifm.ops: - prev_op.outputs = [ofm] - ofm.ops.append(prev_op) - - # All ifm consumers need to use ofm as input - for ifm_cons in ifm.consumer_list: - for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs): - if cons_ifm == ifm: - ifm_cons.set_input_tensor(ofm, ifm_idx) - else: - # Bypassed by replacing ofm with ifm - for cons in ofm.consumer_list: - for ifm_idx, cons_ifm in enumerate(cons.inputs): - if cons_ifm == ofm: - cons.set_input_tensor(ifm, ifm_idx) + assert not (ifm_is_sg_ifm or ifm_is_cpu_produced) + + # Bypassed by replacing ifm with ofm + ofm.ops = [] + for prev_op in ifm.ops: + prev_op.outputs = [ofm] + ofm.ops.append(prev_op) + + # All ifm consumers need to use ofm as input + for ifm_cons in ifm.consumer_list: + for ifm_idx, cons_ifm in enumerate(ifm_cons.inputs): + if cons_ifm == ifm: + ifm_cons.set_input_tensor(ofm, ifm_idx) def move_splitsliceread_to_consumer(op, cons_op): @@ -261,8 +251,8 @@ def record_optimised(op, arch): DebugDatabase.add_optimised(op, op) -def insert_copy_op_after_tens(tens): - tens_cons_list_copy = tens.consumer_list.copy() +def insert_copy_op_after_ifm(op): + tens = op.ifm # Create a avg_pool nop op with ifm as input copy_tens = tens.clone() @@ -272,12 +262,7 @@ def insert_copy_op_after_tens(tens): copy_op.set_ifm_ofm_shapes() copy_op.run_on_npu = True - # Set copy_ifm consumers - for tens_cons in tens_cons_list_copy: - if tens_cons is not None: - for ifm_idx, cons_inp in enumerate(tens_cons.inputs): - if cons_inp == tens: - tens_cons.set_input_tensor(copy_tens, ifm_idx) + op.set_input_tensor(copy_tens, 0) DebugDatabase.add_optimised(tens.ops[0], copy_op) @@ -286,24 +271,26 @@ def fix_sg_input_output(op, arch, nng): if not op.run_on_npu or op.type not in memory_only_ops: return op - # For the memory only operators we want to remove, tensors are removed. - # But in order to to do this, they cannot be outputs of the sg, - # this need to be fixed prior to the removal. + # For the memory only operators we want to remove, the ifm tensor + # is replaced by the ofm tensor. + # But in order to to do this, the ifm can not be inputs of the sg or + # the ifm can not have more than one consumers. + # This need to be fixed prior to the removal. # Solution is to add a avgpool NOP, to maintain the original tensor. - # This is also valid when reshape ifm/ofm is produced respectively - # consumed by CPU + # This is also valid when reshape ifm is produced by CPU - # Check if operator ifm/ofm are sg ifm/ofm + # Check if operator ifm is subgraph ifm ifm_is_sg_ifm = op.ifm.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) - ifm_is_sg_ofm = any(ifm_cons is None for ifm_cons in op.ifm.consumer_list) - ofm_is_sg_ofm = any(ofm_cons is None for ofm_cons in op.ofm.consumer_list) - # Check if ifm/ofm is produced respectively consumed by CPU + + # Check if ifm is produced by CPU ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops) - ofm_is_cpu_consumed = any(ofm_cons is not None and not ofm_cons.run_on_npu for ofm_cons in op.ofm.consumer_list) - if (ifm_is_sg_ofm or ifm_is_sg_ifm or ifm_is_cpu_produced) and (ofm_is_sg_ofm or ofm_is_cpu_consumed): - # Both ifm and ofm need to persist, but only ifm need a copy, in order to remove the memory only operator. - insert_copy_op_after_tens(op.ifm) + # Check numbers of ifm consumers - if many insert avgpool NOP + ifm_has_multiple_cons = len(op.ifm.consumer_list) > 1 + + if ifm_is_sg_ifm or ifm_is_cpu_produced or ifm_has_multiple_cons: + # Ifm need to persist in order to remove the memory only operator. + insert_copy_op_after_ifm(op) return op diff --git a/ethosu/vela/tflite_supported_operators.py b/ethosu/vela/tflite_supported_operators.py index d42caf58..3d04def3 100644 --- a/ethosu/vela/tflite_supported_operators.py +++ b/ethosu/vela/tflite_supported_operators.py @@ -317,7 +317,6 @@ 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) def is_operator_supported(self, op): ext_type = optype_to_builtintype(op.type) @@ -887,11 +886,3 @@ class TFLiteSupportedOperators: extra = ", ".join(extra) 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, "" |