| # SPDX-FileCopyrightText: Copyright 2021-2023 Arm Limited and/or its affiliates <open-source-office@arm.com> |
| # |
| # 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: |
| # Common functions and definitions used during the graph optimization. |
| from typing import Tuple |
| |
| import numpy as np |
| |
| from . import lut |
| from .architecture_features import Accelerator |
| from .data_type import DataType |
| from .debug_database import DebugDatabase |
| from .errors import UnsupportedFeatureError |
| from .errors import VelaError |
| from .operation import Op |
| from .shape4d import Shape4D |
| from .tensor import create_const_tensor |
| from .tensor import QuantizationParameters |
| |
| memory_only_ops = ( |
| Op.Reshape, |
| Op.QuantizedReshape, |
| Op.Squeeze, |
| Op.ExpandDims, |
| Op.Identity, |
| ) |
| |
| |
| def _avoid_nhcwb16_for_concat(tens): |
| # If axis corresponds to C-dimension, NHCWB16 can only be used in the output if all the concat_start's are a |
| # multiple of 16. This as, it is only then the address offset for the ofm, for all operations, will be 16 byte |
| # aligned. For other values of axis the address offsets will be 16 byte aligned, as they are all based on c = 0 |
| # and those addresses are always 16 byte aligned due to the NHCWB16 format. |
| return any(op.write_offset.depth % 16 != 0 for op in tens.ops if op.write_offset is not None) |
| |
| |
| def _avoid_nhcwb16_for_split(tens): |
| # If read offset is not a multiple of 16 in the C-dimension, NHCWB16 need to be avoided in the input |
| |
| # Return True if NHCWB16 needs to be avoided |
| def offset_not_aligned(read_offset): |
| return read_offset is not None and (read_offset.depth % 16) != 0 |
| |
| for cons_op in tens.consumer_list: |
| if cons_op.ifm == tens: |
| if offset_not_aligned(cons_op.read_offsets[0]): |
| return True |
| if cons_op.ifm2 is not None and cons_op.ifm2 == tens: |
| if offset_not_aligned(cons_op.read_offsets[1]): |
| return True |
| return False |
| |
| |
| def _avoid_nhcwb16_for_shapes(tens): |
| # check all producers/consumers to see if any op shape is preventing NHCWB16 |
| for cons_op in tens.consumer_list: |
| if cons_op.ifm == tens: |
| cons_op_shape = cons_op.ifm_shapes[0] |
| elif cons_op.type.is_binary_elementwise_op() and cons_op.ifm2 == tens: |
| cons_op_shape = cons_op.ifm_shapes[1] |
| else: |
| assert False |
| if Shape4D(tens.shape) != cons_op_shape: |
| return True |
| |
| for prod_op in tens.ops: |
| if Shape4D(tens.shape) != prod_op.ofm_shapes[0]: |
| return True |
| |
| return False |
| |
| |
| def _avoid_nhcwb16_for_memory_only(tens): |
| # check all producers/consumers to see if any op is preventing NHCWB16 |
| return any(op.type == Op.Memcpy for op in (tens.consumer_list + tens.ops)) |
| |
| |
| # Check if non linear format can be used |
| def check_format_restrictions(tens, arch): |
| if len(tens.ops) < 1: |
| return |
| if tens.ops[0].type in (Op.Placeholder, Op.SubgraphInput, Op.Const) or any( |
| cons is None for cons in tens.consumer_list |
| ): |
| return |
| |
| # Check if any of the producers/consumers is run on CPU |
| if not all(cons.run_on_npu for cons in tens.consumer_list): |
| return |
| if not all(prod.run_on_npu for prod in tens.ops): |
| return |
| |
| # "Concat" ofm exception: |
| if _avoid_nhcwb16_for_concat(tens): |
| return |
| |
| # "Split" ifm exception: |
| if _avoid_nhcwb16_for_split(tens): |
| return |
| |
| # Shapes checking: check all producers/consumers are NHCWB16 compatible with tens.shape |
| if _avoid_nhcwb16_for_shapes(tens): |
| return |
| |
| # Memory only ifm/ofm exception: DMA ops must use NHCW |
| if _avoid_nhcwb16_for_memory_only(tens): |
| return |
| |
| # Resize bilinear half pixel center implementation requires OFM with linear format to |
| # allow stride modification in H/W dimensions. |
| for op in tens.ops: |
| if op.original_type == Op.ResizeBilinear and op.type == Op.DepthwiseConv2DBias: |
| return |
| |
| for op in tens.consumer_list: |
| if op.type == Op.ReduceSum and ( |
| tens.dtype == DataType.int32 or arch.accelerator_config == Accelerator.Ethos_U65_512 |
| ): |
| # ReduceSum requires NHWC input |
| return |
| if op.type == Op.Reshape: |
| # Using NHCWB16 format for a no-op reshape is only an option if subsequent |
| # consumers do not also need to perform a reshape or if the OFM is going to |
| # be processed by CPU operations. No-op reshape consumers with empty lists |
| # (those that have no consumers, or null-consumers used as list terminators) |
| # must use normal NHWC output. |
| |
| def incompatible_consumers(oper): |
| if oper and oper.type == Op.Reshape: |
| for consumer in oper.outputs[0].consumer_list: |
| yield from incompatible_consumers(consumer) |
| yield not oper or not oper.run_on_npu |
| |
| if not any(incompatible_consumers(op)): |
| |
| def get_rewrites(oper): |
| if oper and oper.type == Op.Reshape: |
| for consumer in oper.outputs[0].consumer_list: |
| yield from get_rewrites(consumer) |
| yield oper |
| |
| # Detect no-op reshapes by comparing their full input and output tensor shapes. |
| inshape = op.ifm_shapes[0] |
| compatible_shape = [(inshape == oper.ofm_shapes[0]) for oper in get_rewrites(op)] |
| if not (compatible_shape and all(compatible_shape)): |
| return |
| else: |
| return |
| |
| tens.needs_linear_format = False |
| |
| |
| def calc_explicit_padding(input_size, stride, filter_size, pad_before, pad_after) -> Tuple[int, int]: |
| """ |
| Based on explicit padding provided in a PAD operation, returns the corresponding hardware padding |
| that provides equivalent results. |
| """ |
| total_padding = needed_total_padding(input_size, stride, filter_size) |
| |
| # The bottom/right padding might need downward adjustment depending on stride/input size |
| total_minus_before = total_padding - pad_before |
| output_pad_after = pad_after |
| while output_pad_after > 0 and output_pad_after % stride != total_minus_before % stride: |
| output_pad_after -= 1 |
| return pad_before, output_pad_after |
| |
| |
| def needed_total_padding(input_size, stride, filter_size): |
| out_size = (input_size + stride - 1) // stride |
| needed_input = (out_size - 1) * stride + filter_size |
| total_padding = max(0, needed_input - input_size) |
| return total_padding |
| |
| |
| # Set input/output tensor equivalence to the same id for memory operations |
| def set_tensor_equivalence(op, arch, nng): |
| if op.type in memory_only_ops: |
| eid = op.outputs[0].equivalence_id |
| for inp in op.inputs: |
| inp.equivalence_id = eid |
| return op |
| |
| |
| def set_ifm_ofm_op_shapes(op, arch, nng): |
| if op.run_on_npu and op.type.needs_shapes(): |
| if op.ifm_shapes or op.ofm_shapes: |
| # Shapes already set |
| return op |
| op.set_ifm_ofm_shapes() |
| return op |
| |
| |
| def move_splitsliceread_to_consumer(op, cons_op): |
| assert op.type == Op.SplitSliceRead |
| |
| if cons_op.ifm == op.ofm: |
| cons_op.read_offsets[0] = op.read_offsets[0] |
| cons_op.read_shapes[0] = op.read_shapes[0] |
| cons_op.set_input_tensor(op.ifm, cons_op.type.info.indices.ifms[0]) |
| cons_op.ifm_shapes[0] = op.ifm_shapes[0] |
| elif cons_op.type.is_binary_elementwise_op() and cons_op.ifm2 == op.ofm: |
| cons_op.read_offsets[1] = op.read_offsets[0] |
| cons_op.read_shapes[1] = op.read_shapes[0] |
| cons_op.set_input_tensor(op.ifm, cons_op.type.info.indices.ifms[1]) |
| cons_op.ifm_shapes[1] = op.ifm_shapes[0] |
| op.ofm.consumer_list.remove(cons_op) |
| op.ofm.ops = [] |
| op.ifm.consumer_list.remove(op) |
| |
| |
| def check_memory_only_removed(op, arch): |
| if op.run_on_npu and op.type in memory_only_ops: |
| # Memory only operators should have been removed |
| raise VelaError(f"Memory only {op.type} op {op} expected to have been removed, still remains") |
| |
| |
| def record_optimised(op, arch): |
| if op.type not in (Op.Const, Op.Placeholder): |
| DebugDatabase.add_optimised(op, op) |
| |
| |
| def bypass_memory_only_ops(op, arch, nng): |
| if not op.run_on_npu or op.type not in memory_only_ops: |
| return op |
| |
| # Memory only operators can be completely removed if there is a one to one |
| # connection. The reshape OFM can be connected to the previous op. |
| # |
| # Bypassed to |
| # ---> |
| # 1x6x6x10 1x6x6x10 |
| # ADD ADD |
| # | -------> | |
| # 1x6x6x10 | 1x20x3x6 |
| # RESHAPE | MEAN |
| # | ---------| |
| # 1x20x3x10 |
| # MEAN |
| # |
| # In the above the ADD OFM = RESHAPE IFM is removed and replaced by |
| # the RESHAPE OFM. |
| # |
| # Then there are two cases when bypassing is not possible. One is when |
| # the IFM is produced by the CPU. This tensor must be preserved. It |
| # cannot be removed from the graph. The other case is when the IFM has |
| # multiple consumers, then it is not possible to just bypass the op and |
| # there is a need for a DMA (nop). |
| # |
| # Converts to |
| # ---> |
| # 1x6x6x10 1x6x6x10 |
| # -----ADD----- -----ADD----- |
| # | | | | |
| # 1x6x6x10 1x6x6x10 1x6x6x10 1x6x6x10 |
| # RESHAPE MEAN DMA OP MEAN |
| # | | |
| # 1x20x3x6 1x20x3x6 |
| # MEAN MEAN |
| # |
| # If the DMA IFM and DMA OFM ends up in the same memory area |
| # the DMA op will be removed when the cmd stream is generated. |
| |
| ifm_has_multiple_cons = len(op.ifm.consumer_list) > 1 |
| ifm_is_cpu_produced = any(ifm_prod is not None and not ifm_prod.run_on_npu for ifm_prod in op.ifm.ops) |
| |
| if ifm_has_multiple_cons or ifm_is_cpu_produced: |
| # Convert to a memcpy op |
| op.type = Op.Memcpy |
| DebugDatabase.add_optimised(op, op) |
| else: |
| # Bypass op |
| ofm = op.ofm |
| ifm = op.ifm |
| ofm.ops = [] |
| for prev_op in ifm.ops: |
| prev_op.outputs = [ofm] |
| ofm.ops.append(prev_op) |
| |
| return op |
| |
| |
| def convert_depthwise_to_conv(op, arch, nng): |
| # Depthwise is equivalent to a single conv2d if the ifm depth is 1 and |
| # the ofm depth equals the depth multipler. |
| # If those conditions are true, then we can perform a simple |
| # switch of the operator type (and weight order) |
| |
| if op.type == Op.DepthwiseConv2DBias and (op.attrs["depth_multiplier"] != 1): |
| ifm_shape = op.ifm_shapes[0] |
| weight_tensor = op.inputs[1] |
| ofm_shape = op.ofm_shapes[0] |
| if (ifm_shape.depth == 1) and (ofm_shape.depth == op.attrs["depth_multiplier"]): |
| # Change op type to Conv2d |
| op.type = Op.Conv2DBias |
| del op.attrs["channel_multiplier"] |
| del op.attrs["depth_multiplier"] |
| |
| weight_tensor.values = np.transpose(weight_tensor.values, (0, 1, 3, 2)) |
| weight_tensor.set_all_shapes(list(weight_tensor.values.shape)) |
| DebugDatabase.add_optimised(op, op) |
| else: |
| raise UnsupportedFeatureError( |
| f"Unsupported 'DEPTHWISE_CONV_2D' with depth_multiplier = {op.attrs['depth_multiplier']},", |
| f" ifm channels = {ifm_shape.depth}, ofm channels = {ofm_shape.depth}", |
| ) |
| return op |
| |
| |
| def convert_to_lut(op, lut_values, lut_name): |
| # Rewrite the operation by Add with scalar 0 + LUT activation |
| ifm = op.ifm |
| ofm = op.ofm |
| if ifm is None: |
| return op |
| assert ifm.dtype.size_in_bytes() == 1 |
| op.type = Op.Add |
| op.name = op.name + "_lut_" + lut_name |
| # Mark as no-op to enable potential fusing optimizations |
| op.attrs["is_nop"] = True |
| # Create an input tensor containing scalar zero |
| quantization = QuantizationParameters(0.0, 255.0) |
| quantization.scale_f32 = ifm.quantization.scale_f32 |
| quantization.zero_point = 0 |
| tens = create_const_tensor(ifm.name + "_scalar0", [], ifm.dtype, [0], quantization=quantization) |
| op.add_input_tensor(tens) |
| op.ifm_shapes.append(Shape4D(tens.shape)) # TODO no shape? |
| |
| # The LUT must be applied without any preceding rescaling (the LUT itself performs the rescale), |
| # so even if the OFM has a different scale than the IFM, the generated OFM scale instructions |
| # should be the same as the IFM |
| op.forced_output_quantization = ifm.quantization |
| |
| # the lut tensor datatype needs to match both; the ofm datatype, because these are the values output; and the |
| # datatype used to generate the lut values (which is probably the ifm datatype), because we want to avoid any |
| # potential overflow errors in create_lut_tensor() caused by converting Python int (which could represent a uint) |
| # to NumPy int. this can be guaranteed by checking that the ifm and ofm datatypes are the same |
| assert ifm.dtype == ofm.dtype |
| lut_tensor = lut.create_lut_tensor(op.name + "_values", lut_values, ofm.dtype) |
| op.set_activation_lut(lut_tensor) |
| op.set_ifm_ofm_shapes() |
| DebugDatabase.add_optimised(op, op) |
| return op |