| # Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. |
| # |
| # SPDX-License-Identifier: Apache-2.0 |
| # |
| # Licensed under the Apache License, Version 2.0 (the License); you may |
| # not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an AS IS BASIS, WITHOUT |
| # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| |
| |
| # Description: |
| # Packs a subgraph with Neural Network Operations into Passes. Each Pass has one or more Operations. |
| |
| from .nn_graph import Operation, Pass, PassPlacement, TensorPurpose, NpuBlockType, Tensor |
| import collections |
| import enum |
| from .data_type import BaseType, DataType |
| |
| |
| class PassFlags(enum.Flag): |
| Empty = 0 |
| Pre = 1 |
| Main = 2 |
| Post = 4 |
| Mac = 8 |
| Dma = 32 |
| ElementWise = 256 |
| Npu = 512 |
| Cpu = 1024 |
| StartupInit = 2048 |
| MemoryOnly = 4096 |
| PostFusingLimited = 8192 |
| |
| |
| npu_pre_ops = set(("QuantizedResizeBilinear", "SplitSliceRead",)) |
| |
| mac_main_ops = set( |
| ( |
| # convolutions |
| "Conv2DBiasAct", |
| "Conv2D", |
| "QuantizedConv2D", |
| "Conv2DBackpropInputSwitched", |
| # depth-wise convolutions |
| "DepthwiseConv2dBiasAct", |
| "DepthwiseConv2dNative", |
| "QuantizedDepthwiseConv2D", |
| # FC layers |
| "QuantizedMatMul", |
| "MatMul", |
| "FullyConnectedAct", |
| # RNN/LSTM/GRU |
| "BlockLSTM", |
| # pooling |
| "QuantizedMaxPool", |
| "QuantizedAvgPool", |
| "AvgPool", |
| "MaxPool", |
| "AvgPoolAct", |
| "MaxPoolAct", |
| ) |
| ) |
| |
| binary_elem_wise_main_ops = set( |
| ( |
| # binary element-wise |
| "AddAct", |
| "MulAct", |
| "SubAct", |
| "QuantizedAdd", |
| "QuantizedSub", |
| "QuantizedMul", |
| "Mul", |
| "Add", |
| "Sub", |
| "Minimum", |
| "Maximum", |
| ) |
| ) |
| |
| unary_elem_wise_main_ops = set(("LeakyRelu", "Abs")) # Unary element-wise operations |
| |
| elem_wise_main_ops = binary_elem_wise_main_ops | unary_elem_wise_main_ops |
| |
| activation_ops = set(("QuantizedRelu", "QuantizedRelu1", "QuantizedRelu6", "Relu", "Relu6", "ReluN1To1")) |
| npu_post_ops = activation_ops | set( |
| # Bias-add operations: Get rid of these once we have rewrites from Conv2D + BiasAdd + Activation to Conv2DBiasAct. |
| ("Mul", "Add", "QuantizedBiasAdd", "Requantize", "QuantizedBatchNorm", "BiasAdd", "FusedBatchNorm") |
| ) |
| |
| npu_post_fuse_limited_ops = set( |
| # Set of post operators that should not be fused with main/elementwise ops |
| ("ConcatSliceWrite", "Sigmoid", "Tanh") |
| ) |
| |
| elem_wise_ops = elem_wise_main_ops | activation_ops | set(("Sigmoid", "Tanh")) |
| |
| |
| quantization_ops = set(("Dequantize", "QuantizeV2", "Max", "Min")) |
| cpu_ops = ( |
| set(("Softmax", "QuantizedSoftmax", "LRN", "Shape", "QuantizedPad", "Pad", "AddN")) |
| | quantization_ops |
| ) |
| |
| npu_dma_ops = set(("DMA",)) |
| startup_init_ops = set(("Const", "VariableV2", "Placeholder", "SubgraphInput")) |
| memory_only_ops = set(("Squeeze", "Reshape", "QuantizedReshape", "ExpandDims",)) |
| |
| |
| test_sequence = [ |
| ( |
| # ops_set |
| npu_post_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.Pre | PassFlags.Main, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.Post, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| npu_post_fuse_limited_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu | PassFlags.MemoryOnly | PassFlags.Pre | PassFlags.Main, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.PostFusingLimited, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| mac_main_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu |
| | PassFlags.MemoryOnly |
| | PassFlags.ElementWise |
| | PassFlags.Pre |
| | PassFlags.Main |
| | PassFlags.PostFusingLimited, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.Mac | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| elem_wise_main_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu |
| | PassFlags.MemoryOnly |
| | PassFlags.Mac |
| | PassFlags.Pre |
| | PassFlags.Main |
| | PassFlags.PostFusingLimited, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.ElementWise | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| npu_pre_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu | PassFlags.MemoryOnly, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.Mac | PassFlags.Pre | PassFlags.ElementWise, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| npu_dma_ops, |
| # incompatible_pack_flags |
| PassFlags.Cpu | PassFlags.MemoryOnly, |
| # flags_to_set |
| PassFlags.Npu | PassFlags.Dma, |
| # flags_to_clear |
| PassFlags.Empty |
| ), |
| ( |
| # ops_set |
| startup_init_ops, |
| # incompatible_pack_flags |
| PassFlags.Npu | PassFlags.Cpu | PassFlags.MemoryOnly, |
| # flags_to_set |
| PassFlags.StartupInit | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty, |
| ), |
| ( |
| # ops_set |
| memory_only_ops, |
| # incompatible_pack_flags |
| PassFlags.Npu | PassFlags.Cpu, |
| # flags_to_set |
| PassFlags.MemoryOnly | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty |
| ), |
| ( |
| # ops_set |
| cpu_ops, |
| # incompatible_pack_flags |
| PassFlags.Npu | PassFlags.MemoryOnly | PassFlags.Main, |
| # flags_to_set |
| PassFlags.Cpu | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty |
| ), |
| ( # This last one is a fallback for unrecognised operations |
| # ops_set |
| None, |
| # incompatible_pack_flags |
| PassFlags.Npu | PassFlags.MemoryOnly | PassFlags.Main, |
| # flags_to_set |
| PassFlags.Cpu | PassFlags.Main, |
| # flags_to_clear |
| PassFlags.Empty |
| ), |
| ] |
| |
| # Some sanity checking |
| for (operation_set, incompatible_pack_flags, flags_to_set, flags_to_clear) in test_sequence: |
| assert not flags_to_clear & flags_to_set |
| |
| if operation_set is not None: |
| for op in operation_set: |
| assert len(op) > 1 # This is to avoid string literals being decomposed |
| |
| |
| def pack_into_passes(nng, arch, verbose_packing=False): |
| def visit_op(op, ignored): |
| visit_op_refcount[op] += 1 |
| |
| if visit_op_refcount[op] == 1: # First-time visit, go and fix up unused output tensors |
| for tens in op.outputs: |
| if len(tens.consumers()) == 0: |
| visit_op_refcount[op] += 1 |
| |
| assert visit_op_refcount[op] <= len(op.outputs) |
| if visit_op_refcount[op] == len(op.outputs): |
| |
| if op.type in startup_init_ops: |
| startup_list.append(op) |
| else: |
| _, _, _, ofm_tensor = op.get_ifm_ifm2_weights_ofm() |
| if ofm_tensor is None: |
| ofm_tensor = op.outputs[0] |
| build_pass((op,), ofm_tensor) |
| |
| def build_pass(start_ops_to_process, ofm_tensor=None): |
| reverse_ops_list = [] |
| curr_flags = PassFlags.Empty |
| npu_block_type = NpuBlockType.Default |
| |
| reverse_intermediates = [] |
| input_set = set() |
| ifm_tensor = None |
| primary_op = None |
| |
| to_process = collections.deque() |
| for start_op in start_ops_to_process: |
| to_process.append((start_op, None)) |
| |
| while to_process: |
| curr_op, tens = to_process.popleft() |
| |
| if curr_op in reverse_ops_list: |
| continue |
| |
| for operation_set, incompatible_pack_flags, flags_to_set, flags_to_clear in test_sequence: |
| if operation_set is None or curr_op.type in operation_set: |
| if not (curr_flags & incompatible_pack_flags): |
| if flags_to_set & PassFlags.Npu: |
| if not curr_op.run_on_npu: |
| continue |
| |
| reverse_ops_list.append(curr_op) |
| new_block_type = curr_op.attrs.get("npu_block_type", NpuBlockType.Default) |
| if new_block_type != NpuBlockType.Default: |
| assert npu_block_type == NpuBlockType.Default |
| npu_block_type = new_block_type # Only one major block type per pass |
| assert primary_op is None |
| primary_op = curr_op |
| |
| curr_flags &= ~flags_to_clear |
| curr_flags |= flags_to_set |
| |
| if flags_to_set & PassFlags.Npu: |
| if flags_to_set & ( |
| PassFlags.Mac | PassFlags.ElementWise | PassFlags.Post | PassFlags.PostFusingLimited |
| ): |
| assert len(curr_op.inputs) >= 1 |
| if curr_op.type == "BlockLSTM": |
| ifm_tensor = curr_op.inputs[3] |
| else: |
| ifm_tensor = curr_op.inputs[0] |
| assert ifm_tensor.purpose == TensorPurpose.FeatureMap |
| |
| if flags_to_set & PassFlags.Dma: |
| # DMAs are special - Output buffers need to be preserved as intermediates, |
| # if the pass consumes the results |
| if tens is not None: |
| reverse_intermediates.append(tens) |
| |
| if operation_set is None: |
| print("Warning:", curr_op.type, "operation is unknown or unsupported, placing on CPU") |
| |
| for inp in curr_op.inputs: |
| can_pack = True |
| if len(inp.ops) == 1: |
| next_op = inp.ops[0] |
| for outp in next_op.outputs: |
| consumers = outp.consumers() |
| if len(consumers) > 1 or (len(consumers) == 1 and consumers[0] != curr_op): |
| can_pack = False |
| break |
| else: |
| can_pack = False |
| |
| if can_pack: |
| to_process.append((next_op, inp)) |
| else: |
| assert inp is not None |
| input_set.add(inp) |
| |
| break |
| |
| else: |
| # This operation is not compatible with already packed operations, just register the tensor as an input |
| assert tens is not None |
| input_set.add(tens) |
| |
| if curr_flags & PassFlags.Npu and not curr_flags & (PassFlags.ElementWise | PassFlags.Mac): |
| # Make the choice that if we don't have a mac operation, the ambidextrous operations go on the |
| # element wise unit |
| curr_flags |= PassFlags.ElementWise |
| |
| is_element_wise = True |
| for op in reverse_ops_list: |
| if not op.type in elem_wise_ops and not op.type in npu_dma_ops: |
| is_element_wise = False |
| break |
| |
| placement = PassPlacement.Unknown |
| if curr_flags & PassFlags.Npu: |
| assert placement == PassPlacement.Unknown |
| placement = PassPlacement.Npu |
| if curr_flags & PassFlags.Cpu: |
| assert placement == PassPlacement.Unknown |
| placement = PassPlacement.Cpu |
| if curr_flags & PassFlags.MemoryOnly: |
| assert placement == PassPlacement.Unknown |
| placement = PassPlacement.MemoryOnly |
| if curr_flags & PassFlags.StartupInit: |
| assert placement == PassPlacement.Unknown |
| placement = PassPlacement.StartupInit |
| assert placement != PassPlacement.Unknown |
| |
| ops_list = list(reversed(reverse_ops_list)) |
| intermediates = list(reversed(reverse_intermediates)) |
| |
| if primary_op == None: |
| primary_op = create_primary_op(ops_list) |
| if primary_op != None: |
| visit_tensor_refcount[primary_op.inputs[0]] += 1 |
| npu_block_type = primary_op.attrs["npu_block_type"] |
| for input_tens in primary_op.inputs: |
| if input_tens not in input_set: |
| input_set.add(input_tens) |
| |
| ordered_input_list = [] |
| input_refcounts = collections.defaultdict(int) |
| for op in ops_list: |
| for inp in op.inputs: |
| if inp in input_set: |
| if input_refcounts[inp] == 0: |
| ordered_input_list.append(inp) |
| input_refcounts[inp] += 1 |
| |
| name = ops_list[0].name |
| non_dma_ops = [op for op in ops_list if op.type != "DMA"] |
| if non_dma_ops: |
| name = non_dma_ops[0].name |
| ps = Pass(name, placement, is_element_wise, npu_block_type) |
| ps.ops = ops_list |
| ps.primary_op = primary_op |
| ps.inputs = ordered_input_list |
| ps.intermediates = intermediates |
| ps.outputs = list(ops_list[-1].outputs) |
| ps.ifm_tensor = ifm_tensor |
| |
| # ElementWise operation, 2 IFMs |
| if ps.primary_op and ps.primary_op.type in binary_elem_wise_main_ops: |
| ps.ifm_tensor = ps.inputs[0] |
| |
| if len(ps.inputs) == 1: |
| # Only 1 input, IFM and IFM2 are the same tensor |
| ps.ifm2_tensor = ps.inputs[0] |
| else: |
| ps.ifm2_tensor = ps.inputs[1] |
| else: |
| ps.ifm_tensor = ifm_tensor |
| ps.ifm2_tensor = None |
| |
| ps.ofm_tensor = ofm_tensor |
| assert ps.placement != PassPlacement.Npu or ps.ofm_tensor is not None |
| ps.weight_tensor = ps.get_primary_op_ifm_weights()[1] |
| ps.scale_tensor = ps.get_primary_op_ifm_weights_biases_ofm()[2] |
| |
| for op in ps.ops: |
| op.scheduled_pass = ps |
| |
| reverse_pass_list.append(ps) |
| |
| for inp, refcount in input_refcounts.items(): |
| for _ in range(refcount): |
| visit_tensor(inp) |
| |
| return ps |
| |
| def visit_tensor(tens): |
| visit_tensor_refcount[tens] += 1 |
| assert visit_tensor_refcount[tens] <= len(tens.consumers()) |
| if visit_tensor_refcount[tens] == len(tens.consumers()): |
| for op in reversed(tens.ops): |
| visit_op(op, tens) |
| |
| def create_primary_op(ops_list): |
| if any(op.type in (npu_pre_ops | npu_post_ops | npu_post_fuse_limited_ops) for op in ops_list): |
| # Configure a 1x1 AvgPool and attach the op onto it |
| op = ops_list[0] |
| inp = op.inputs[0] |
| avgpool_name = op.name + "_avgpool" |
| avgpool_op = Operation("AvgPool", avgpool_name) |
| avgpool_op.inputs = [inp] |
| avgpool_op.inputs[0].consumer_list.append(avgpool_op) |
| avgpool_op.attrs["padding"] = b"VALID" |
| avgpool_op.attrs["npu_block_type"] = NpuBlockType.Pooling |
| avgpool_op.attrs["stride_w"] = 1 |
| avgpool_op.attrs["stride_h"] = 1 |
| avgpool_op.attrs["filter_width"] = 1 |
| avgpool_op.attrs["filter_height"] = 1 |
| avgpool_op.attrs["strides"] = [1, 1, 1, 1] |
| avgpool_op.attrs["ksize"] = [1, 1, 1, 1] |
| avgpool_op.attrs["skirt"] = [0, 0, 0, 0] |
| avgpool_op.attrs["explicit_padding"] = [0, 0, 0, 0] |
| avgpool_out = inp.clone("_avgpooled") |
| avgpool_out.consumer_list.append(op) |
| avgpool_out.ops = [avgpool_op] |
| avgpool_op.outputs = [avgpool_out] |
| |
| op.inputs[0] = avgpool_out |
| ops_list.insert(0, avgpool_op) |
| |
| return avgpool_op |
| |
| return None |
| |
| for sg in nng.subgraphs: |
| reverse_pass_list = [] |
| visit_op_refcount = collections.defaultdict(int) |
| visit_tensor_refcount = collections.defaultdict(int) |
| |
| startup_list = [] |
| |
| for tens in sg.output_tensors: |
| visit_tensor(tens) |
| |
| if startup_list: |
| startup_ps = build_pass(startup_list) |
| startup_ps.outputs = [op.outputs[0] for op in startup_list] # Need to fixup the outputs |
| startup_ps.name = "startup_weight_initialisation" |
| |
| sg.passes = list(reversed(reverse_pass_list)) |
| sg.build_pass_links() |
| |
| if verbose_packing: |
| nng.print_passes() |
| |
| return nng |