| # SPDX-FileCopyrightText: Copyright 2021-2022 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: |
| # Early optimisation of the TOSA based network graph, using the rewrite_graph module to do the traversal of the graph. |
| import numpy as np |
| |
| from . import rewrite_graph |
| from .api import NpuRoundingMode |
| from .data_type import DataType |
| from .debug_database import DebugDatabase |
| from .graph_optimiser_util import bypass_memory_only_ops |
| from .graph_optimiser_util import calc_explicit_padding |
| from .graph_optimiser_util import convert_depthwise_to_conv |
| from .graph_optimiser_util import convert_to_lut |
| from .graph_optimiser_util import move_splitsliceread_to_consumer |
| from .graph_optimiser_util import needed_total_padding |
| from .graph_optimiser_util import set_ifm_ofm_op_shapes |
| from .graph_optimiser_util import set_tensor_equivalence |
| from .operation import ExplicitScaling |
| from .operation import Op |
| from .operation_util import create_add_nop |
| from .operation_util import create_avgpool_nop |
| from .operation_util import create_pad_nop |
| from .shape4d import Shape4D |
| from .tensor import create_const_tensor |
| from .tensor import create_equivalence_id |
| from .tensor import shape_num_elements |
| from .tensor import Tensor |
| |
| |
| def replace_rescale_with_avg_pool(rescale_op): |
| assert rescale_op.type == Op.Rescale |
| |
| avgpool_op = create_avgpool_nop(rescale_op.name + "_avgpool") |
| rescale_op_clone = rescale_op.clone() |
| op = rescale_op |
| op.attrs = avgpool_op.attrs.copy() |
| op.type = Op.AvgPool |
| DebugDatabase.add_optimised(rescale_op_clone, op) |
| |
| return op |
| |
| |
| def calc_skirt(kernel, input_shape, explicit_padding): |
| k_w, k_h = kernel.dilated_wh() |
| s_x, s_y = kernel.stride |
| ypad = needed_total_padding(int(input_shape.height), int(s_y), int(k_h)) |
| xpad = needed_total_padding(int(input_shape.width), int(s_x), int(k_w)) |
| |
| top, left, bottom, right = explicit_padding |
| top_pad, bottom_pad = calc_explicit_padding(int(input_shape.height), int(s_y), int(k_h), int(top), int(bottom)) |
| left_pad, right_pad = calc_explicit_padding(int(input_shape.width), int(s_x), int(k_w), int(left), int(right)) |
| |
| padding = (top_pad, left_pad, bottom_pad, right_pad) |
| skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad) |
| return padding, skirt |
| |
| |
| def add_padding_fields(op, arch, nng): |
| if op.run_on_npu: |
| if "explicit_padding" in op.attrs: |
| input_shape = op.ifm_shapes[0] |
| |
| if op.type == Op.Conv2DBackpropInputSwitchedBias: |
| # TODO not yet supported, but there will be need for separate handling |
| assert False |
| else: |
| padding, skirt = calc_skirt(op.kernel, input_shape, op.attrs.get("explicit_padding")) |
| |
| op.attrs["explicit_padding"] = padding |
| op.attrs["skirt"] = skirt |
| |
| return op |
| |
| |
| # Counts leading zeroes for a (int32) |
| def count_leading_zeros(a): |
| lz = int(32) |
| if a != 0: |
| mask = 1 << (32 - 1) |
| lz = 0 |
| while (mask & a) == 0: |
| mask = mask >> 1 |
| lz = lz + 1 |
| return lz |
| |
| |
| def calc_scaling_avgpool(op, arch, nng): |
| if op.type == Op.AvgPool: |
| top, left, _, _ = op.attrs["explicit_padding"] |
| # TODO Only support for when global scaling can be used. |
| # That is when there is no padding |
| assert top == 0 and left == 0 |
| assert op.explicit_scaling is None |
| multiplier = [] |
| shift = [] |
| |
| kernel_wh = op.kernel.elements_wh() |
| k = 32 - count_leading_zeros(kernel_wh - 1) |
| numerator = np.int64(((1 << 30) + 1) << k) |
| multiplier.append(numerator // kernel_wh) |
| shift.append(30 + k) |
| |
| op.rounding_mode = NpuRoundingMode.NATURAL |
| op.explicit_scaling = ExplicitScaling(False, shift, multiplier) |
| return op |
| |
| |
| def remove_const_transpose(op, arch, nng): |
| if op.type == Op.Transpose: |
| removed = False |
| if len(op.ifm.ops) == 1: |
| prev_op = op.ifm.ops[0] |
| if prev_op.type == Op.Const: |
| # Transpose the Tensor and data and remove Transpose |
| # TODO move to Tensor? |
| reorder = op.attrs["perms"] |
| shape = op.ifm.shape.copy() |
| tens = op.ifm |
| |
| tens.shape = [shape[idx] for idx in reorder] |
| tens.bandwidth_shape = tens.shape |
| tens.storage_shape = tens.shape |
| |
| if tens.values is not None: |
| tens.values = tens.values.transpose(reorder) |
| |
| op.ofm.values = tens.values |
| # Bypass the Transpose op |
| prev_op.set_output_tensor(op.ofm) |
| DebugDatabase.add_optimised(op, prev_op) |
| removed = True |
| |
| if not removed: |
| print("Warning: Cannot remove Transpose, and handling of Transpose is not supported") |
| assert False |
| |
| return op |
| |
| |
| def insert_add_copy_for_const(op, ifm_ofm_shape): |
| assert op.type == Op.Const |
| ofm = op.ofm |
| copy_tens = ofm.clone() |
| op.set_output_tensor(copy_tens) |
| |
| name = ofm.name + "_add" |
| ifm2 = create_const_tensor( |
| name + "_zero_scalar", |
| [1], |
| copy_tens.dtype, |
| [0], |
| copy_tens.dtype.as_numpy_type(), |
| quantization=copy_tens.quantization, |
| ) |
| copy_op = create_add_nop(name) |
| copy_op.add_input_tensor(copy_tens) |
| copy_op.add_input_tensor(ifm2) |
| copy_op.set_output_tensor(ofm) |
| copy_op.ifm_shapes.append(ifm_ofm_shape) |
| copy_op.ifm_shapes.append(Shape4D(ifm2.shape)) |
| copy_op.ofm_shapes.append(ifm_ofm_shape) |
| copy_op.run_on_npu = True |
| |
| DebugDatabase.add_optimised(op, copy_op) |
| |
| |
| # TODO can we change to add for both TFLite and TOSA? |
| def insert_add_copy_op_after_tens(tens, ifm_ofm_shape): |
| tens_cons_list_copy = tens.consumer_list.copy() |
| copy_tens = tens.clone() |
| |
| name = tens.name + "_add" |
| ifm2 = create_const_tensor( |
| name + "_zero_scalar", |
| [1], |
| copy_tens.dtype, |
| [0], |
| copy_tens.dtype.as_numpy_type(), |
| quantization=copy_tens.quantization, |
| ) |
| copy_op = create_add_nop(name) |
| copy_op.add_input_tensor(tens) |
| copy_op.add_input_tensor(ifm2) |
| copy_op.set_output_tensor(copy_tens) |
| copy_op.ifm_shapes.append(ifm_ofm_shape) |
| copy_op.ifm_shapes.append(Shape4D(ifm2.shape)) |
| copy_op.ofm_shapes.append(ifm_ofm_shape) |
| 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) |
| |
| DebugDatabase.add_optimised(tens.ops[0], copy_op) |
| |
| |
| def get_shape_for_copy_op(shape): |
| # remove dimensions that are set to 1 |
| new_shape = [] |
| for dim in shape: |
| if dim != 1: |
| new_shape.append(dim) |
| if not new_shape: |
| new_shape = [1] |
| |
| rank = len(new_shape) |
| if rank > 3: |
| # Reshape so that batch becomes 1, by moving elements to H dimension |
| n = rank - 2 |
| h = 1 |
| for i in range(n): |
| h *= shape[i] |
| new_shape = Shape4D(new_shape[n:]).with_height(h) |
| else: |
| new_shape = Shape4D(new_shape) |
| return new_shape |
| |
| |
| def fix_sg_input_output_tosa(op, arch, nng): |
| |
| if op.type == Op.Const and any(ofm_cons is None for ofm_cons in op.ofm.consumer_list): |
| # Const operator with sg output, insert copy op before the ofm |
| new_shape = get_shape_for_copy_op(op.ofm.shape.copy()) |
| insert_add_copy_for_const(op, new_shape) |
| elif op.run_on_npu and op.type in (Op.Reshape, Op.Identity): |
| # For the Reshape 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. |
| # Solution is to add a copy op, to maintain the original tensor. |
| # This is also valid when reshape ifm/ofm is produced respectively |
| # consumed by CPU |
| |
| # Check if operator ifm/ofm are sg ifm/ofm |
| 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 repectivly consumed 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 Operator |
| # Decide on ifm/ofm shapes for the copy op based on ifm |
| new_shape = get_shape_for_copy_op(op.ifm.shape.copy()) |
| insert_add_copy_op_after_tens(op.ifm, new_shape) |
| return op |
| |
| |
| def create_add_for_concat(concat_op, name, ifm, ifm_shape: Shape4D, write_offset: Shape4D): |
| """Creates an add op for the given concat op/input feature map""" |
| ofm = concat_op.ofm |
| ifm2 = create_const_tensor( |
| name + "_zero_scalar", [1], ofm.dtype, [0], ofm.dtype.as_numpy_type(), quantization=ofm.quantization |
| ) |
| add_op = create_add_nop(name) |
| |
| add_op.inputs = [ifm, ifm2] |
| add_op.outputs = [ofm] |
| add_op.write_offset = write_offset |
| add_op.write_shape = ifm_shape |
| ofm.ops.append(add_op) |
| DebugDatabase.add_optimised(concat_op, add_op) |
| add_op.ifm_shapes.append(ifm_shape) |
| add_op.ifm_shapes.append(Shape4D(ifm2.shape)) |
| add_op.ofm_shapes.append(concat_op.ofm_shapes[0]) |
| add_op.memory_function = Op.ConcatSliceWrite |
| return add_op |
| |
| |
| # TODO Could be further optimized checking the type of the consumer, |
| # rather than just mimic the TFLite behaviour depending on type. |
| # TOSA bool_t not considered yet |
| def remove_splitsliceread(op, arch): |
| |
| if op.type == Op.SplitSliceRead: |
| # Check if it is possible to put the SplitSliceRead on the tensor consumer, or if an avgpool need to be inserted |
| if ( |
| len(op.ofm.consumer_list) == 1 |
| and op.ofm.consumer_list[0] is not None |
| and op.ofm.consumer_list[0].run_on_npu |
| and op.ofm.consumer_list[0].type != Op.Reshape |
| and op.ofm_shapes[0] == Shape4D.from_list(op.ofm.shape) |
| and op.ofm.dtype in (DataType.uint8, DataType.int8, DataType.int16) |
| ): |
| # SplitSliceRead can be performed by tensor consumer |
| cons_op = op.ofm.consumer_list[0] |
| move_splitsliceread_to_consumer(op, cons_op) |
| else: |
| name = op.name + "_add" |
| ofm = op.ofm |
| ifm2 = create_const_tensor( |
| name + "_zero_scalar", [1], ofm.dtype, [0], ofm.dtype.as_numpy_type(), quantization=ofm.quantization |
| ) |
| add_op = create_add_nop(name) |
| add_op.inputs = [op.ifm, ifm2] |
| add_op.outputs = [ofm] |
| op.ofm.ops.remove(op) |
| op.ofm.ops.append(add_op) |
| add_op.ifm_shapes.append(op.ifm_shapes[0]) |
| add_op.ifm_shapes.append(Shape4D(ifm2.shape)) |
| add_op.ofm_shapes.append(op.ofm_shapes[0]) |
| add_op.read_offsets[0] = op.read_offsets[0] |
| add_op.read_shapes[0] = op.read_shapes[0] |
| |
| op.ifm.consumer_list.remove(op) |
| DebugDatabase.add_optimised(op, add_op) |
| |
| |
| def rewrite_concat(op): |
| if not op.run_on_npu or not op.type == Op.Concat: |
| return |
| |
| offset = 0 |
| inputs = op.inputs |
| axis_4D = op.attrs["axis4D"] |
| |
| for idx, inp in enumerate(inputs): |
| write_offset = [0, 0, 0, 0] |
| write_offset[axis_4D] = offset |
| concat_end = offset + op.ifm_shapes[idx][axis_4D] |
| create_add_for_concat(op, op.name + str(idx) + "_add", inp, op.ifm_shapes[idx], Shape4D.from_list(write_offset)) |
| offset = concat_end |
| assert op.ofm_shapes[0][axis_4D] == offset |
| |
| |
| def remove_memory_ops(op, arch): |
| if op.run_on_npu and op.type in (Op.Reshape, Op.Identity): |
| bypass_memory_only_ops(op) |
| |
| |
| def rewrite_activation(op, arch, nng): |
| if op.type not in (Op.ReluN, Op.Clamp): |
| return op |
| |
| ifm = op.ifm |
| zp = ifm.quantization.zero_point if ifm.quantization.zero_point else 0 |
| if op.ofm.quantization.zero_point is None: |
| op.ofm.quantization.zero_point = zp |
| |
| if op.type == Op.Clamp: |
| op.attrs["min"] = op.attrs["min_int"] - zp |
| op.attrs["max"] = op.attrs["max_int"] - zp |
| elif op.type == Op.ReluN: |
| op.attrs["max"] = op.attrs["max_int"] - zp |
| |
| return op |
| |
| |
| def rewrite_rescale(op, arch, nng): |
| if op.type == Op.Rescale: |
| ifm = op.ifm |
| ofm = op.ofm |
| |
| # some error checking |
| assert len(ifm.ops) == 1 |
| prev_op = ifm.ops[0] |
| |
| # TODO currently not supported |
| assert len(ifm.consumer_list) == 1 |
| |
| input_zp = op.attrs["input_zp"] |
| output_zp = op.attrs["output_zp"] |
| multiplier = op.attrs["multiplier"] |
| shift = op.attrs["shift"] |
| scale32 = op.attrs["scale32"] |
| double_round = op.attrs["double_round"] |
| per_channel = op.attrs["per_channel"] |
| |
| assert ifm.dtype in (DataType.uint8, DataType.int8, DataType.int32) |
| assert ifm.dtype in (DataType.uint8, DataType.int8) or input_zp == 0 |
| assert ofm.dtype in (DataType.uint8, DataType.int8) or output_zp == 0 |
| assert (scale32 and ifm.dtype != DataType.int48) or (not scale32 and not double_round) |
| |
| # Check that input tensor has the same zp or no zp |
| ifm_zp = ifm.quantization.zero_point |
| if ifm_zp is not None and ifm_zp != input_zp: |
| print("Error (fuse_rescale): zp of tensors producer/consumer differs unexpectedidly ") |
| assert False |
| ifm.quantization.zero_point = input_zp |
| ofm.quantization.zero_point = output_zp |
| for s, m in zip(shift, multiplier): |
| # TODO these are the TOSA limitations |
| assert m >= 0 |
| assert 2 <= s <= 62 |
| # TODO these are the HW limitations |
| assert 0 <= s < (1 << 6) |
| explicit_scaling = ExplicitScaling(per_channel, shift, multiplier) |
| |
| if double_round and scale32: |
| rounding_mode = NpuRoundingMode.TFL |
| else: |
| rounding_mode = NpuRoundingMode.NATURAL |
| |
| if prev_op.type.is_depthwise_conv2d_op() or prev_op.type.is_conv2d_op() or prev_op.type == Op.FullyConnected: |
| assert len(multiplier) == len(shift) == len(prev_op.bias.values) |
| |
| if ifm.dtype == DataType.int32 and per_channel: |
| prev_op.explicit_scaling = explicit_scaling |
| prev_op.rounding_mode = rounding_mode |
| |
| # Bypass op |
| prev_op.set_output_tensor(ofm) |
| DebugDatabase.add_optimised(op, prev_op) |
| return op |
| else: |
| print("Warning, unsupported fusing of TOSA Rescale previous operator is of type:", prev_op.type) |
| assert False |
| # TODO which are the cases we need to and can do standalone Rescale? |
| # TODO should we try to identify a conversion uint8<->int8 accomplished by 2 RESCALE ops? |
| # origin might be TFLite op QUANTIZE, should we look to see if they can be translated to QUANTIZE? |
| # limited to these at the moment: |
| elif ( |
| (ifm.dtype == DataType.int8 and ofm.dtype == DataType.int8) |
| or (ifm.dtype == DataType.uint8 and ofm.dtype == DataType.int8) |
| or (ifm.dtype == DataType.int8 and ofm.dtype == DataType.uint8) |
| ): |
| # Create NOP performing the RESCALE |
| avgpool_op = replace_rescale_with_avg_pool(op) |
| avgpool_op.rounding_mode = rounding_mode |
| |
| if per_channel: |
| # TODO |
| avgpool_op.explicit_scaling = explicit_scaling |
| print("Warning, unsupported TOSA Rescale") |
| assert False |
| else: |
| avgpool_op.explicit_scaling = explicit_scaling |
| else: |
| print("Warning, unsupported fusing of TOSA Rescale previous operator is of type:", prev_op.type) |
| assert False |
| return op |
| |
| |
| def convert_pad_in_width(op): |
| """ |
| Rewrites PAD operator to an add that copies the IFM to the OFM |
| + up to 4 add operators that fill the OFM with zeros at the borders. |
| """ |
| assert op.type == Op.Pad |
| assert op.ifm_shapes[0] is not None and op.ofm_shapes[0] is not None |
| ifm = op.ifm |
| ofm = op.ofm |
| ifm_shape = op.ifm_shapes[0] |
| ofm.ops = [] |
| ofm_shape = op.ofm_shapes[0] |
| |
| padding = op.inputs[1].values |
| left, right = padding[-2] |
| |
| # Add op that copies IFM to the right place inside the OFM |
| shp0 = Shape4D(0, 0, 0, 0) |
| add_op = create_add_for_concat(op, op.name + "_main", ifm, ifm_shape, shp0.with_width(left)) |
| add_op.activation = op.activation |
| |
| quant = ofm.quantization |
| pad_value = ifm.quantization.zero_point |
| ifm.quantization.zero_point = 0 |
| if left > 0: |
| shape = Shape4D(1, ifm_shape.height, left, ofm_shape.depth) |
| zero_tens = create_const_tensor( |
| op.name + "_left", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], np.uint8, quantization=quant |
| ) |
| zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) |
| create_add_for_concat(op, op.name + "_left", zero_tens, shape, shp0) |
| if right > 0: |
| shape = Shape4D(1, ifm_shape.height, right, ofm_shape.depth) |
| zero_tens = create_const_tensor( |
| op.name + "_right", shape.as_list(), ofm.dtype, shape.elements() * [pad_value], np.uint8, quantization=quant |
| ) |
| zero_tens.equivalence_id = create_equivalence_id(tuple(zero_tens.values)) |
| create_add_for_concat(op, op.name + "_right", zero_tens, shape, shp0.with_width(ofm_shape.width - right)) |
| |
| op.type = Op.ConcatTFLite |
| return add_op |
| |
| |
| def convert_table_to_lut(op, arch, nng): |
| # Converts table op to a no-op + LUT |
| if op.type is not Op.Table: |
| return op |
| |
| table = op.inputs[1] |
| op.inputs.remove(table) |
| op.set_ifm_ofm_shapes() |
| |
| return convert_to_lut(op, table.values, "table") |
| |
| |
| def decompose_elem_tensors_hwc(op): |
| """ |
| Decomposes elementwise op if any of the ifm(s)/ofm are to large in any dimension to be handled by the NPU |
| """ |
| max_t_size = 65535 |
| ofm_shape = op.write_shape if op.write_shape is not None else op.ofm_shapes[0] |
| ifm_shape = op.read_shapes[0] if op.read_shapes[0] is not None else op.ifm_shapes[0] |
| ifm2_shape = op.ifm_shapes[1] if op.ifm_shapes[1] else None |
| ifm2_shape = op.read_shapes[1] if op.read_shapes[1] is not None else ifm2_shape |
| limit_shape = Shape4D(1, max_t_size, max_t_size, max_t_size) |
| |
| if any(dim_size > max_t_size for dim_size in ofm_shape.as_list()): |
| ofm_split = ofm_shape.floordiv_const(max_t_size).add(1, 1, 1, 1) |
| |
| for height in range(ofm_split.height): |
| for width in range(ofm_split.width): |
| for depth in range(ofm_split.depth): |
| ofm_offset = Shape4D(0, height * max_t_size, width * max_t_size, depth * max_t_size) |
| ofm_part_shape = ofm_shape.clip(ofm_offset, limit_shape) |
| ofm_cut = (ofm_offset, ofm_part_shape) |
| |
| ifm_d = depth * max_t_size if ifm_shape.depth == ofm_shape.depth else 0 |
| ifm_w = width * max_t_size if ifm_shape.width == ofm_shape.width else 0 |
| ifm_h = height * max_t_size if ifm_shape.height == ofm_shape.height else 0 |
| ifm_offset = Shape4D(0, ifm_h, ifm_w, ifm_d) |
| ifm_part_shape = ifm_shape.clip(ifm_offset, limit_shape) |
| ifm_cut = (ifm_offset, ifm_part_shape) |
| |
| if ifm2_shape is not None: |
| ifm2_d = depth * max_t_size if ifm2_shape.depth == ofm_shape.depth else 0 |
| ifm2_w = width * max_t_size if ifm2_shape.width == ofm_shape.width else 0 |
| ifm2_h = height * max_t_size if ifm2_shape.height == ofm_shape.height else 0 |
| ifm2_offset = Shape4D(0, ifm2_h, ifm2_w, ifm2_d) |
| ifm2_part_shape = ifm2_shape.clip(ifm2_offset, limit_shape) |
| ifm2_cut = (ifm2_offset, ifm2_part_shape) |
| else: |
| ifm2_cut = (None, None) |
| |
| create_elem_part_op(op, ifm_cut, ifm2_cut, ofm_cut) |
| op.ofm.ops.remove(op) |
| op.ifm.consumer_list.remove(op) |
| if op.ifm2 is not None: |
| op.ifm2.consumer_list.remove(op) |
| return |
| |
| |
| def create_elem_part_op(op, ifm_cut, ifm2_cut, ofm_cut): |
| part_op = op.clone() |
| ifm_read_offset = op.read_offsets[0] if op.read_offsets[0] is not None else Shape4D(0, 0, 0, 0) |
| ofm_write_offset = op.write_offset if op.write_offset is not None else Shape4D(0, 0, 0, 0) |
| ifm_offset, ifm_shape = ifm_cut |
| ofm_offset, ofm_shape = ofm_cut |
| |
| part_op.read_offsets[0] = ifm_read_offset + ifm_offset |
| part_op.read_shapes[0] = ifm_shape |
| part_op.write_offset = ofm_write_offset + ofm_offset |
| part_op.write_shape = ofm_shape |
| part_op.ifm_shapes = op.ifm_shapes.copy() |
| part_op.ofm_shapes = op.ofm_shapes.copy() |
| part_op.ifm.consumer_list.append(part_op) |
| op.ofm.ops.append(part_op) |
| |
| ifm2_offset, ifm2_shape = ifm2_cut |
| if ifm2_offset: |
| ifm2_read_offset = op.read_offsets[1] if op.read_offsets[1] is not None else Shape4D(0, 0, 0, 0) |
| part_op.read_offsets[1] = ifm2_read_offset + ifm2_offset |
| part_op.read_shapes[1] = ifm2_shape |
| part_op.ifm2.consumer_list.append(part_op) |
| |
| return part_op |
| |
| |
| def get_nhwc_stride(shape): |
| stride_x = shape.depth |
| stride_y = shape.width * stride_x |
| stride_n = shape.height * stride_y |
| return Shape4D(stride_n, stride_y, stride_x, 1) |
| |
| |
| def pad_to_rank(shape, rank): |
| """ |
| Pads a shape to the given rank |
| """ |
| while len(shape) < rank: |
| shape = [1] + shape |
| |
| return shape |
| |
| |
| def get_elem_shapes_removed_singles(op): |
| """ |
| Returns the shapes of ifm(s)/ofms after removing all the dimensions that are 1 for all ifm(s)/ofm |
| """ |
| binary = op.ifm2 is not None |
| ofm_shape = op.ofm_shapes[0].as_list() if len(op.ofm_shapes) > 0 else op.ofm.shape |
| ifm_shape = op.ifm_shapes[0].as_list() if len(op.ifm_shapes) > 0 else op.ifm.shape |
| if binary: |
| ifm2_shape = op.ifm_shapes[1].as_list() if len(op.ofm_shapes) else op.ifm2.shape |
| |
| rank = max(len(ofm_shape), len(ifm_shape), len(ifm2_shape) if binary else 0) |
| ofm_shape = pad_to_rank(ofm_shape, rank) |
| ifm_shape = pad_to_rank(ifm_shape, rank) |
| if binary: |
| ifm2_shape = pad_to_rank(ifm2_shape, rank) |
| |
| new_ofm_shape = [] |
| new_ifm_shape = [] |
| new_ifm2_shape = [] |
| for idx in range(rank): |
| if ofm_shape[idx] != 1: |
| new_ofm_shape.append(ofm_shape[idx]) |
| new_ifm_shape.append(ifm_shape[idx]) |
| if binary: |
| new_ifm2_shape.append(ifm2_shape[idx]) |
| |
| if new_ofm_shape == []: |
| new_ofm_shape = [1] |
| new_ifm_shape = [1] |
| new_ifm2_shape = [1] if binary else None |
| |
| return new_ofm_shape, new_ifm_shape, new_ifm2_shape |
| |
| |
| def decomp_dims_elementwise(op): |
| """ |
| Decompose elementwise ops with Rank > 3 (H,W,D). |
| If Rank > 3, all the dimensions above H are viewed as the N dimension. |
| the elementwise operation will be decomposed to N (of ofm) elementwise operations. |
| By reading and writing with offsets from/to the ifm(s)/ofm. |
| Note: Broadcast need to be handled for binary elementwise ops, and TOSA allowes for broadcast by both ifm and ifm2 |
| """ |
| |
| ifm = op.ifm |
| ifm2 = op.ifm2 |
| ofm = op.ofm |
| binary = op.ifm2 is not None |
| |
| # Remove dimensions that are all 1 |
| new_ofm_shape, new_ifm_shape, new_ifm2_shape = get_elem_shapes_removed_singles(op) |
| rank = len(new_ofm_shape) |
| |
| if rank > 3: |
| n = rank - 3 |
| ofm_decomp_shape = Shape4D(new_ofm_shape[0:n]) |
| ofm_decomp_stride = get_nhwc_stride(ofm_decomp_shape) |
| ofm_part_shape = Shape4D(new_ofm_shape[n:]) |
| op.ofm_shapes.append(Shape4D([ofm_decomp_shape.elements()] + new_ofm_shape[n:])) |
| |
| if binary: |
| ifm_decomp_shape = Shape4D(new_ifm_shape[0:n]) |
| ifm2_decomp_shape = Shape4D(new_ifm2_shape[0:n]) |
| ifm_decomp_stride = get_nhwc_stride(ifm_decomp_shape) |
| ifm2_decomp_stride = get_nhwc_stride(ifm2_decomp_shape) |
| ifm_part_shape = Shape4D(new_ifm_shape[n:]) |
| ifm2_part_shape = Shape4D(new_ifm2_shape[n:]) |
| op.ifm_shapes.append(Shape4D([ifm_decomp_shape.elements()] + new_ifm_shape[n:])) |
| op.ifm_shapes.append(Shape4D([ifm2_decomp_shape.elements()] + new_ifm2_shape[n:])) |
| else: |
| op.ifm_shapes.append(Shape4D([ofm_decomp_shape.elements()] + new_ofm_shape[n:])) |
| |
| op_list = [] |
| for height in range(ofm_decomp_shape.height): |
| for width in range(ofm_decomp_shape.width): |
| for depth in range(ofm_decomp_shape.depth): |
| ofm_offset = Shape4D(0, height, width, depth) |
| ofm_offset = Shape4D(ofm_offset.dot_prod(ofm_decomp_stride), 0, 0, 0) |
| ofm_cut = (ofm_offset, ofm_part_shape) |
| |
| if binary: |
| ifm_d = depth if ifm_decomp_shape.depth == ofm_decomp_shape.depth else 0 |
| ifm_w = width if ifm_decomp_shape.width == ofm_decomp_shape.width else 0 |
| ifm_h = height if ifm_decomp_shape.height == ofm_decomp_shape.height else 0 |
| ifm_offset = Shape4D(0, ifm_h, ifm_w, ifm_d) |
| ifm_offset = Shape4D(ifm_offset.dot_prod(ifm_decomp_stride), 0, 0, 0) |
| ifm_cut = (ifm_offset, ifm_part_shape) |
| |
| ifm2_d = depth if ifm2_decomp_shape.depth == ofm_decomp_shape.depth else 0 |
| ifm2_w = width if ifm2_decomp_shape.width == ofm_decomp_shape.width else 0 |
| ifm2_h = height if ifm2_decomp_shape.height == ofm_decomp_shape.height else 0 |
| ifm2_offset = Shape4D(0, ifm2_h, ifm2_w, ifm2_d) |
| ifm2_offset = Shape4D(ifm2_offset.dot_prod(ifm2_decomp_stride), 0, 0, 0) |
| ifm2_cut = (ifm2_offset, ifm2_part_shape) |
| op_list.append(create_elem_part_op(op, ifm_cut, ifm2_cut, ofm_cut)) |
| else: |
| op_list.append(create_elem_part_op(op, ofm_cut, None, ofm_cut)) |
| |
| ofm.ops.remove(op) |
| ifm.consumer_list.remove(op) |
| if binary: |
| ifm2.consumer_list.remove(op) |
| |
| return op_list |
| else: |
| op.ofm_shapes.append(Shape4D(new_ofm_shape)) |
| op.ifm_shapes.append(Shape4D(new_ifm_shape)) |
| op.ifm_shapes.append(Shape4D(new_ifm2_shape)) |
| |
| return [op] |
| |
| |
| def decomp_elementwise(tens, arch, nng): |
| """ |
| Decompose elementwise ops with Rank > 3 (H,W,C). |
| Decompose size of tensors exceeding NPU max size |
| """ |
| tens_ops = tens.ops.copy() |
| for op in tens_ops: |
| if op.type.is_elementwise_op(): |
| decomp_list = decomp_dims_elementwise(op) |
| for part_op in decomp_list: |
| decompose_elem_tensors_hwc(part_op) |
| return tens |
| |
| |
| def reshape_concat_shape(shape, rank, axis): |
| new_h = 1 |
| for i in range(axis): |
| new_h *= shape[i] |
| new_c = 1 |
| for i in range(axis + 1, rank): |
| new_c *= shape[i] |
| if axis == (rank - 1): |
| new_shape = [new_h, shape[axis], 1] |
| else: |
| new_shape = [new_h, shape[axis], new_c] |
| return new_shape |
| |
| |
| def reshape_concat(op): |
| """ |
| Reshapes concat ops with Rank > 3 (H,W,C). |
| """ |
| ofm = op.ofm |
| rank = len(ofm.shape) |
| axis = op.attrs["axis"] |
| if axis < 0: |
| axis += rank |
| |
| if rank > 3: |
| # Reshape so that axis in to be concatenated is the W dimension |
| # Reshape inputs |
| for inp in op.inputs: |
| new_shape = reshape_concat_shape(inp.shape, rank, axis) |
| op.ifm_shapes.append(Shape4D(new_shape)) |
| # Reshape output |
| new_shape = reshape_concat_shape(ofm.shape, rank, axis) |
| op.ofm_shapes.append(Shape4D(new_shape)) |
| op.attrs["axis4D"] = 2 |
| else: |
| for inp in op.inputs: |
| op.ifm_shapes.append(Shape4D(inp.shape)) |
| op.ofm_shapes.append(Shape4D(ofm.shape)) |
| op.attrs["axis4D"] = axis + (4 - rank) |
| |
| |
| def decomp_rewrite_concat(tens, arch, nng): |
| """ |
| Decompose concat ops with Rank > 3 (H,W,C). |
| Rewrite of concat to elementwise operations |
| """ |
| if len(tens.ops) == 1 and tens.ops[0].type == Op.Concat: |
| op = tens.ops[0] |
| |
| reshape_concat(op) |
| rewrite_concat(op) |
| |
| op.ofm.ops.remove(op) |
| for inp in op.inputs: |
| inp.consumer_list.remove(op) |
| |
| return tens |
| |
| |
| def decomp_rewrite_pad(op, arch): |
| """ |
| Decomposition of pad to elementwise operations: |
| For each dimension that needs padding: |
| -Create a new PAD operator for each dimension to be added |
| Ifm/ofm are reshape so this is the width dimension is to be padded |
| (rank for each is 3) |
| -Rewrite the the new PAD operator so there is: |
| -1 Add operator for copying the data |
| -1 Add operator for each left/right to be padded |
| """ |
| # TODO several things would be possible to optimize |
| # For instance there are cases when it should be possible to pad 2 |
| # dimensions at the same time. |
| if op.type == Op.Pad: |
| ofm_elements = shape_num_elements(op.ofm.shape) |
| padding = op.inputs[1].values |
| |
| rank = len(op.ifm.shape) |
| next_ifm = op.ifm |
| next_ifm_shape = next_ifm.shape.copy() |
| |
| first_pad_rewrite_op = None |
| ifm_quant = op.ifm.quantization.clone() |
| |
| for dim in range(padding.shape[0]): |
| # Check if padding is to be applied in this dimension |
| dim_pad = padding[dim] |
| if not (dim_pad == 0).all(): |
| # Reshape so that width dimension is to be padded |
| new_ifm_shape = reshape_concat_shape(next_ifm_shape, rank, dim) |
| new_pad_input = np.zeros((4, 2), dtype=np.int32) |
| new_pad_input[2] = dim_pad |
| |
| pad_op = create_pad_nop(f"{op.name}_dim_{dim}") |
| pad_op.add_input_tensor(next_ifm) |
| new_pad_tens = op.inputs[1].clone("_dim_{dim}") |
| |
| name = op.inputs[1].name + f"_dim_{dim}" |
| new_pad_tens = create_const_tensor( |
| name, list(new_pad_input.shape), DataType.int32, new_pad_input, np.int32 |
| ) |
| pad_op.add_input_tensor(new_pad_tens) |
| |
| new_ofm_shape = new_ifm_shape.copy() |
| new_ofm_shape[-2] = new_ofm_shape[-2] + dim_pad.sum() |
| next_ifm_shape[dim] = next_ifm_shape[dim] + dim_pad.sum() |
| |
| if Shape4D(new_ofm_shape).elements() == ofm_elements: |
| # Last one, use op.ofm |
| ofm = op.ofm |
| else: |
| # add a new ofm Tensor |
| ofm = Tensor(new_ofm_shape, op.ofm.dtype, f"{pad_op.name}_tens") |
| ofm.quantization = ifm_quant.clone() |
| |
| pad_op.set_output_tensor(ofm) |
| pad_op.ifm_shapes.append(Shape4D(new_ifm_shape)) |
| pad_op.ofm_shapes.append(Shape4D(new_ofm_shape)) |
| DebugDatabase.add_optimised(op, pad_op) |
| next_ifm = ofm |
| |
| # Rewrite the pad op |
| converted_pad_op = convert_pad_in_width(pad_op) |
| first_pad_rewrite_op = converted_pad_op |
| else: |
| # Change to Identity operation (will be removed) |
| op.type = Op.Identity |
| |
| if first_pad_rewrite_op: |
| assert op.ofm.shape == next_ifm_shape |
| for inp in op.inputs: |
| inp.consumer_list.remove(op) |
| return first_pad_rewrite_op |
| |
| return op |
| |
| |
| def fixup_quantization(op, arch, nng): |
| if op.ifm and op.ifm.quantization.zero_point is None: |
| op.ifm.quantization.zero_point = 0 |
| if op.ifm2 and op.ifm2.quantization.zero_point is None: |
| op.ifm2.quantization.zero_point = 0 |
| if not op.forced_output_quantization: |
| if op.ofm and op.ofm.quantization and op.ofm.quantization.zero_point is None: |
| op.ofm.quantization.zero_point = 0 |
| return op |
| |
| |
| def supported_operator_check(op, arch, nng): |
| op.run_on_npu = arch.tosa_supported_operators.is_operator_supported(op) |
| assert op.run_on_npu or op.type in (Op.Placeholder, Op.SubgraphInput, Op.Const) |
| return op |
| |
| |
| def tosa_optimise_graph(nng, arch): |
| |
| # TODO the supported operator checking need to be split in semantic and HW checks |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [supported_operator_check], |
| rewrite_unsupported=False, |
| ) |
| |
| # Decomposing and rewrite of concat |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, sg, arch, [decomp_rewrite_concat], [], rewrite_unsupported=False |
| ) |
| |
| # Decomposing of pad |
| for idx, sg in enumerate(nng.subgraphs): |
| rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [decomp_rewrite_pad]) |
| sg.refresh_after_modification() |
| |
| # Handle sg input output |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [fix_sg_input_output_tosa], |
| rewrite_unsupported=True, |
| ) |
| |
| # Removal of reshapes |
| for sg in nng.subgraphs: |
| rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_memory_ops]) |
| sg.refresh_after_modification() |
| |
| # Decomposing of elementwise |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, sg, arch, [decomp_elementwise], [], rewrite_unsupported=False |
| ) |
| |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [set_ifm_ofm_op_shapes], |
| rewrite_unsupported=False, |
| ) |
| |
| # Removal of Transpose |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [remove_const_transpose], |
| rewrite_unsupported=False, |
| ) |
| |
| # TODO, when and where to best handle calc_scaling_avgpool |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [calc_scaling_avgpool], |
| rewrite_unsupported=False, |
| ) |
| |
| # Rewite Operators step |
| op_rewrite_list = [set_tensor_equivalence, rewrite_rescale, convert_depthwise_to_conv, convert_table_to_lut] |
| |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| op_rewrite_list, |
| rewrite_unsupported=False, |
| ) |
| |
| # Post-processing step 1 |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [rewrite_activation, add_padding_fields], |
| ) |
| |
| # Removal of Slice, need to be done after optimisation has been performed, |
| # since ifm/ofm_shapes are of importance to this function |
| for sg in nng.subgraphs: |
| rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [remove_splitsliceread]) |
| sg.refresh_after_modification() |
| |
| # Post-processing step 2 |
| for idx, sg in enumerate(nng.subgraphs): |
| nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( |
| nng, |
| sg, |
| arch, |
| [], |
| [fixup_quantization], |
| ) |
| |
| return nng |