| # 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: |
| # Contains classes that hold commands for the high-level command stream (one command per DMA or NPU stripe). |
| |
| from enum import Enum, IntEnum |
| import numpy as np |
| from .operation import NpuBlockType |
| from .numeric_util import round_up_divide |
| from .range_set import MemoryAccessSet, AccessDirection |
| |
| |
| class Box: |
| def __init__(self, start_coord, end_coord): |
| self.start_coord = list(start_coord) |
| self.end_coord = list(end_coord) |
| assert len(self.start_coord) == len(end_coord) |
| for i in range(len(self.start_coord)): |
| assert self.start_coord[i] <= self.end_coord[i] |
| |
| def transform_with_strides_and_skirt( |
| self, strides, skirt, ifm_shape, npu_block_type, concat_axis=0, concat_offset=0, split_offset=None, k_height=1 |
| ): |
| new_start_coord = list(self.start_coord) |
| new_end_coord = list(self.end_coord) |
| |
| new_start_coord[concat_axis] -= concat_offset |
| new_end_coord[concat_axis] -= concat_offset |
| |
| if split_offset != None: |
| for idx in range(len(split_offset)): |
| new_start_coord[idx] += split_offset[idx] |
| new_end_coord[idx] += split_offset[idx] |
| |
| if split_offset == None and npu_block_type in set((NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct)): |
| # these types of operations do a "dot product" over the entire IFM |
| new_start_coord[-1] = 0 |
| new_end_coord[-1] = ifm_shape[-1] |
| |
| if min(len(new_end_coord), len(ifm_shape)) >= 2: |
| new_end_coord[-2] = min(new_end_coord[-2], ifm_shape[-2]) |
| if min(len(new_end_coord), len(ifm_shape)) >= 3: |
| new_end_coord[-3] = min(new_end_coord[-3], ifm_shape[-3]) |
| |
| pad_top = 0 |
| pad_bottom = 0 |
| if strides is not None and skirt is not None: |
| if len(new_start_coord) >= 2: |
| stride = strides[2] |
| new_start_coord[-2] = max(new_start_coord[-2] * stride - skirt[1], 0) |
| new_end_coord[-2] = min(new_end_coord[-2] * stride + skirt[3], ifm_shape[-2]) |
| |
| if len(new_start_coord) >= 3: |
| stride = strides[1] |
| |
| total_stride = stride * (new_end_coord[-3] - new_start_coord[-3] - 1) |
| new_start_coord[-3] = new_start_coord[-3] * stride - skirt[0] |
| |
| pad_top = max(0, 0 - new_start_coord[-3]) |
| new_start_coord[-3] = max(new_start_coord[-3], 0) |
| |
| while len(ifm_shape) < 3: |
| ifm_shape = [1] + ifm_shape |
| if (new_end_coord[-3] * stride + skirt[2]) > ifm_shape[-3]: |
| # pad_bottom is calculated based the diff between the end position of the weight kernel, |
| # after last stride and the ifm height. |
| k_start = new_start_coord[-3] - pad_top |
| pad_bottom = max(0, k_start + total_stride + k_height - ifm_shape[-3]) |
| |
| new_end_coord[-3] = min(new_end_coord[-3] * stride + skirt[2], ifm_shape[-3]) |
| |
| return Box(new_start_coord, new_end_coord), pad_top, pad_bottom |
| |
| def make_weight_box(weight_shape, npu_block_type, oc_range_start=None, oc_range_end=None, weights_transposed=False): |
| start = [0] * len(weight_shape) |
| end = list(weight_shape) |
| if oc_range_start is not None and oc_range_end is not None: |
| if npu_block_type == NpuBlockType.ConvolutionDepthWise: |
| # input range is output range divided by channel multiplier |
| if weights_transposed: |
| start[-1] = oc_range_start // weight_shape[-2] |
| end[-1] = oc_range_end // weight_shape[-2] |
| else: |
| start[-2] = oc_range_start // weight_shape[-1] |
| end[-2] = oc_range_end // weight_shape[-1] |
| else: |
| start[-1] = oc_range_start |
| end[-1] = oc_range_end |
| for i in range(len(end)): |
| assert 0 <= start[i] < weight_shape[i] |
| assert 0 < end[i] <= weight_shape[i] |
| |
| return Box(start, end) |
| |
| def get_size_shape(self): |
| return [int(self.end_coord[i] - self.start_coord[i]) for i in range(len(self.end_coord))] |
| |
| def get_size(self): |
| return int(np.prod(self.get_size_shape())) |
| |
| def __str__(self): |
| return "<Box %s - %s>" % (self.start_coord, self.end_coord) |
| |
| __repr__ = __str__ |
| |
| |
| class CommandType(IntEnum): |
| NpuStripe = 0 |
| DMA = 1 |
| Size = 2 |
| |
| |
| class Command: |
| def get_ofm_y_range_for_pass(self, ps_requested): |
| return None |
| |
| def is_npu_pass_command(self): |
| return False |
| |
| def get_memory_accesses(self): |
| return None |
| |
| def get_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops). Should line up with the CommandType enum |
| return np.array((0, 0)) |
| |
| |
| class NpuStripe(Command): |
| def __init__( |
| self, |
| ps, |
| block_config, |
| is_first, |
| is_last, |
| is_first_h_stripe, |
| is_last_h_stripe, |
| ifm_tensor, |
| ifm_box, |
| ofm_tensor, |
| ofm_box, |
| weight_tensor=None, |
| weight_box=None, |
| scale_tensor=None, |
| concat_axis=0, |
| concat_offset=0, |
| ifm2_tensor=None, |
| ifm2_box=None, |
| pad_top=0, |
| pad_bottom=0, |
| ): |
| self.cmdtype = CommandType.NpuStripe |
| self.ps = ps |
| self.block_config = block_config |
| self.is_first = is_first |
| self.is_last = is_last |
| self.is_first_h_stripe = is_first_h_stripe |
| self.is_last_h_stripe = is_last_h_stripe |
| self.ifm_tensor = ifm_tensor |
| self.ifm_box = ifm_box |
| self.ifm2_tensor = ifm2_tensor |
| self.ifm2_box = ifm2_box |
| self.ofm_tensor = ofm_tensor |
| self.ofm_box = ofm_box |
| self.weight_tensor = weight_tensor |
| self.scale_tensor = scale_tensor |
| self.weight_box = weight_box |
| self.concat_axis = concat_axis |
| self.concat_offset = concat_offset |
| self.pad_top = pad_top |
| self.pad_bottom = pad_bottom |
| for i in range(len(self.ofm_box.end_coord)): |
| assert self.ofm_box.end_coord[i] <= self.ofm_tensor.shape[i] |
| |
| def get_memory_accesses(self): |
| res = MemoryAccessSet() |
| if self.ifm_tensor is not None and self.ifm_tensor.shape != []: |
| res.add( |
| self.ifm_tensor.get_address_ranges_for_coordinates(self.ifm_box.start_coord, self.ifm_box.end_coord), |
| AccessDirection.Read, |
| ) |
| if self.ifm2_tensor is not None and self.ifm2_tensor.shape != []: |
| res.add( |
| self.ifm2_tensor.get_address_ranges_for_coordinates(self.ifm2_box.start_coord, self.ifm2_box.end_coord), |
| AccessDirection.Read, |
| ) |
| if self.ofm_tensor is not None: |
| res.add( |
| self.ofm_tensor.get_address_ranges_for_coordinates(self.ofm_box.start_coord, self.ofm_box.end_coord), |
| AccessDirection.Write, |
| ) |
| if self.weight_tensor is not None: |
| res.add( |
| self.weight_tensor.get_address_ranges_for_coordinates( |
| self.weight_box.start_coord, self.weight_box.end_coord |
| ), |
| AccessDirection.Read, |
| ) |
| return res |
| |
| def is_npu_pass_command(self): |
| return True |
| |
| def __str__(self): |
| return "<NPUStripe: ps=%s, ifm_box=%s, ifm2_box=%s, ofm_box=%s, weight_box=%s, block_config=%s>" % ( |
| self.ps.name, |
| self.ifm_box, |
| self.ifm2_box, |
| self.ofm_box, |
| self.weight_box, |
| self.block_config, |
| ) |
| |
| __repr__ = __str__ |
| |
| def get_ofm_y_range_for_pass(self, ps_requested): |
| if ps_requested != self.ps: |
| return None |
| if len(self.ofm_box.start_coord) >= 3: |
| return (self.ofm_box.start_coord[-3], self.ofm_box.end_coord[-3]) |
| return None |
| |
| def get_block_dimensions(self): |
| ofm_box = self.ofm_box |
| block_config = self.block_config |
| |
| out_height = 1 |
| out_width = 1 |
| out_depth = ofm_box.end_coord[-1] - ofm_box.start_coord[-1] |
| if len(ofm_box.end_coord) >= 4: |
| out_width = ofm_box.end_coord[-2] - ofm_box.start_coord[-2] |
| out_height = ofm_box.end_coord[-3] - ofm_box.start_coord[-3] |
| |
| assert out_height >= 0 |
| assert out_width >= 0 |
| assert out_depth >= 0 |
| return ( |
| round_up_divide(out_height, block_config[0]), |
| round_up_divide(out_width, block_config[1]), |
| round_up_divide(out_depth, block_config[3]), |
| ) |
| |
| def get_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops) |
| return np.array((self.get_n_blocks(), 0)) |
| |
| def get_n_blocks(self): |
| h, w, d = self.get_block_dimensions() |
| res = h * w * d |
| assert res >= 0 |
| return res |
| |
| def get_single_block_command(self, block_idx): |
| block_cfg = (self.block_config[0], self.block_config[1], self.block_config[3]) |
| dims = self.get_block_dimensions() |
| strides = dims[1] * dims[2], dims[2], 1 |
| coord = [] |
| idx_left = block_idx |
| for s in strides: |
| c = idx_left // s |
| idx_left -= c * s |
| coord.append(c) |
| |
| assert idx_left == 0 |
| |
| # put in dummy height/widths in case we're dealing with FC layers |
| ofm_start = list(self.ofm_box.start_coord) |
| ofm_end = list(self.ofm_box.end_coord) |
| |
| # cut out a nice block shape |
| for idx in (-1, -2, -3): |
| if len(ofm_start) >= -idx: |
| ofm_start[idx] += block_cfg[idx] * coord[idx] |
| ofm_end[idx] = min(ofm_end[idx], ofm_start[idx] + block_cfg[idx]) |
| |
| ps = self.ps |
| strides = None |
| skirt = None |
| if ps.primary_op is not None: |
| strides = ps.primary_op.attrs.get("strides", None) |
| skirt = ps.primary_op.attrs.get("skirt", None) |
| npu_block_type = ps.npu_block_type |
| |
| ofm_box = Box(ofm_start, ofm_end) |
| ifm_box, _, _ = ofm_box.transform_with_strides_and_skirt( |
| strides, skirt, self.ifm_tensor.shape, npu_block_type, self.concat_axis, self.concat_offset |
| ) |
| |
| weight_box = None |
| if self.weight_tensor is not None: |
| weight_oc_start = ofm_start[-1] |
| weight_oc_end = ofm_end[-1] |
| if self.concat_axis - len(self.weight_tensor.shape) == -1: |
| weight_oc_start -= self.concat_offset |
| weight_oc_end -= self.concat_offset |
| |
| weight_box = Box.make_weight_box( |
| self.weight_tensor.shape, |
| npu_block_type, |
| weight_oc_start, |
| weight_oc_end, |
| self.weight_tensor.weight_transpose_depthwise, |
| ) |
| |
| return NpuStripe( |
| self.ps, |
| self.block_config, |
| self.is_first, |
| self.is_last, |
| self.is_first_h_stripe, |
| self.is_last_h_stripe, |
| self.ifm_tensor, |
| ifm_box, |
| self.ofm_tensor, |
| ofm_box, |
| self.weight_tensor, |
| weight_box, |
| self.scale_tensor, |
| self.concat_axis, |
| self.concat_offset, |
| ) |
| |
| |
| class DMA(Command): |
| def __init__(self, in_tensor, out_tensor, box): |
| self.cmdtype = CommandType.DMA |
| self.in_tensor = in_tensor |
| self.out_tensor = out_tensor |
| self.box = box |
| |
| def __str__(self): |
| return "<DMA: in=%s, out=%s, box=%s>" % (self.in_tensor.name, self.out_tensor.name, self.box) |
| |
| __repr__ = __str__ |
| |
| def get_memory_accesses(self): |
| res = MemoryAccessSet() |
| |
| res.add( |
| self.in_tensor.get_address_ranges_for_coordinates(self.box.start_coord, self.box.end_coord), |
| AccessDirection.Read, |
| ) |
| res.add( |
| self.out_tensor.get_address_ranges_for_coordinates(self.box.start_coord, self.box.end_coord), |
| AccessDirection.Write, |
| ) |
| return res |
| |
| def get_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops) |
| return np.array((0, 1)) |