| # SPDX-FileCopyrightText: Copyright 2020-2023 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: |
| # Contains classes that hold commands for the high-level command stream (one command per DMA or NPU stripe). |
| from typing import List |
| from typing import Optional |
| |
| import numpy as np |
| |
| from .architecture_features import Block |
| from .numeric_util import round_up_divide |
| from .operation import NpuBlockType |
| from .shape4d import Shape4D |
| |
| |
| 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] |
| |
| @staticmethod |
| def wrap(a, b): |
| """Wrap broadcasted tensor boxes in order to |
| prevent out of bounds during box creation""" |
| tmp = [0, 0, 0, 0] |
| for i, val in enumerate(a): |
| if int(val) != 0: |
| tmp[i] = a[i] |
| if a[i] >= b[i] and b[i] != 0: |
| tmp[i] = a[i] % b[i] |
| return Shape4D(tmp) |
| |
| def transform_with_strides_and_skirt( |
| self, |
| strides: List[int], |
| skirt: List[int], |
| ifm_shape: Shape4D, |
| npu_block_type: NpuBlockType, |
| concat_offsets: List[int], |
| k_dilated_height: int, |
| split_offset: Optional[Shape4D] = None, |
| split_shape: Optional[Shape4D] = None, |
| upscaling_factor: int = 1, |
| op_type=None, |
| ): |
| new_start_coord = list(self.start_coord) |
| new_end_coord = list(self.end_coord) |
| |
| new_start_coord = np.subtract(new_start_coord, concat_offsets) |
| new_end_coord = np.subtract(new_end_coord, concat_offsets) |
| |
| if split_offset is not None: |
| for idx in range(len(split_offset)): |
| new_start_coord[idx] += split_offset[idx] |
| new_end_coord[idx] += split_offset[idx] |
| |
| if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum): |
| # these types of operations do a "dot product" or sum over the entire IFM |
| if split_offset is None: |
| new_start_coord[-1] = 0 |
| new_end_coord[-1] = ifm_shape.depth |
| else: |
| new_start_coord[-1] = split_offset[-1] |
| new_end_coord[-1] = new_start_coord[-1] + split_shape[-1] |
| |
| if len(new_end_coord) >= 1: |
| new_end_coord[-1] = min(new_end_coord[-1], ifm_shape.depth) |
| if len(new_end_coord) >= 2: |
| new_end_coord[-2] = min(new_end_coord[-2], ifm_shape.width * upscaling_factor) |
| if len(new_end_coord) >= 3: |
| original_end_coord = list(new_end_coord) |
| new_end_coord[-3] = min(new_end_coord[-3], ifm_shape.height * upscaling_factor) |
| |
| 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] |
| # if the current op was combined with a split slice read then the valid ifm range is given by the output |
| # of the split op (which is defined by the read offset and the read shape) |
| if split_offset is None: |
| 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.width) |
| else: |
| new_start_coord[-2] = max(new_start_coord[-2] * stride - skirt[1], split_offset[-2]) |
| new_end_coord[-2] = min(new_end_coord[-2] * stride + skirt[3], split_offset[-2] + split_shape[-2]) |
| |
| if len(new_start_coord) >= 3: |
| stride = strides[1] |
| skirt_top_remainder = skirt[0] % upscaling_factor |
| |
| total_stride = stride * (new_end_coord[-3] - new_start_coord[-3] - 1) |
| new_start_coord[-3] = new_start_coord[-3] * stride - skirt[0] + skirt_top_remainder |
| |
| pad_top = max(0, 0 - new_start_coord[-3]) + skirt_top_remainder |
| new_start_coord[-3] = max(new_start_coord[-3], 0) |
| |
| if (new_end_coord[-3] * stride + skirt[2]) > (ifm_shape.height * upscaling_factor): |
| # pad_bottom is calculated based the diff between the end position of the weight kernel, |
| # after last stride and the ifm height. |
| if upscaling_factor != 1 and original_end_coord[-3] > ifm_shape.height * upscaling_factor: |
| # Special case for Transpose Convolution with VALID padding. |
| pad_bottom = original_end_coord[-3] - (ifm_shape.height * upscaling_factor) |
| else: |
| k_start = new_start_coord[-3] - pad_top |
| pad_bottom = max( |
| 0, k_start + total_stride + k_dilated_height - (ifm_shape.height * upscaling_factor) |
| ) |
| |
| # Adjust for upscaling |
| new_start_coord[-3] = max(new_start_coord[-3] // upscaling_factor, 0) |
| new_end_coord[-3] = new_end_coord[-3] * stride + skirt[2] + (skirt[2] % upscaling_factor) |
| new_end_coord[-3] = max(min(new_end_coord[-3] // upscaling_factor, ifm_shape.height), 1) |
| |
| # Wrap the IFMs of broadcasted binary elementwise ops |
| # at the limits of the non-broadcasted volumes |
| # Non-broadcasted ops aren't affected by the wrapping |
| if op_type is not None and op_type.is_binary_elementwise_op(): |
| tmp = list(ifm_shape) |
| one = Shape4D(1, 1, 1, 1) |
| new_start_coord = Box.wrap(new_start_coord, tmp) |
| new_end_coord = Box.wrap(Shape4D(list(new_end_coord)) - one, tmp) + one |
| |
| 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 is_subbox_of(self, other): |
| if self.start_coord and self.end_coord: |
| assert len(self.start_coord) == len(other.start_coord) |
| assert len(self.end_coord) == len(other.end_coord) |
| return all(a >= b for (a, b) in zip(self.start_coord, other.start_coord)) and all( |
| a <= b for (a, b) in zip(self.end_coord, other.end_coord) |
| ) |
| |
| 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 get_block(self) -> Block: |
| return Block.from_shape(self.get_size_shape()) |
| |
| def __str__(self): |
| return "<Box %s - %s>" % (self.start_coord, self.end_coord) |
| |
| __repr__ = __str__ |
| |
| |
| class Command: |
| def is_npu_pass_command(self): |
| return False |
| |
| def get_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops). |
| return np.array((0, 0)) |
| |
| |
| class NpuStripe(Command): |
| def __init__( |
| self, |
| ps, |
| block_config, |
| 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, |
| ifm2_tensor=None, |
| ifm2_box=None, |
| pad_top=0, |
| pad_bottom=0, |
| reversed_operands=False, |
| ): |
| self.ps = ps |
| self.block_config = block_config |
| 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.pad_top = pad_top |
| self.pad_bottom = pad_bottom |
| self.reversed_operands = reversed_operands |
| for i in range(len(self.ofm_box.end_coord)): |
| assert self.ofm_box.end_coord[i] <= ps.ofm_shapes[0][i] |
| |
| 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_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 |
| |
| |
| class DMA(Command): |
| def __init__(self, ps, in_tensor, out_tensor, box): |
| self.ps = ps |
| 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_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops) |
| return np.array((0, 1)) |
| |
| |
| class NOP(Command): |
| def __init__(self, ps, in_tensor, out_tensor): |
| self.ps = ps |
| self.in_tensor = in_tensor |
| self.out_tensor = out_tensor |
| |
| def __str__(self): |
| return f"<NOP: in={self.in_tensor.name}, out={self.out_tensor.name}>" |
| |
| __repr__ = __str__ |
| |
| def get_operation_count(self): |
| # returns numpy array of (DPU blocks, dma_ops) |
| return np.array((0, 0)) |