Add Vela codebase
- Added modules ethosu.vela and ethosu.mlw_codec.
- Added README and various configuration files.
Change-Id: I3690f8c8f5966306ecddaeb2793c30ca9c6e2eee
diff --git a/ethosu/vela/high_level_command_stream_generator.py b/ethosu/vela/high_level_command_stream_generator.py
new file mode 100644
index 0000000..364df6f
--- /dev/null
+++ b/ethosu/vela/high_level_command_stream_generator.py
@@ -0,0 +1,315 @@
+# 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:
+# Generate a high-level command stream from a scheduled subgraph with CascadedPasses.
+#
+# Also used during scheduling to work out allowable IFM/OFM overlap, this functionality can be accessed using
+# calc_allowed_ofm_ifm_overlap_for_cascaded_pass().
+
+from .nn_graph import SchedulingStrategy, PassPlacement
+import numpy as np
+from .operation import NpuBlockType
+from .high_level_command_stream import Box, CommandType, Command, NpuStripe, DMA
+
+
+def need_dma(tens):
+ return len(tens.ops) == 1 and tens.ops[0].type == "DMA"
+
+
+def dma_weights_if_necessary(ps, box, weight_tensor):
+ if need_dma(weight_tensor):
+ dma_op = weight_tensor.ops[0]
+ in_tensor = dma_op.inputs[0]
+ yield DMA(in_tensor, weight_tensor, box)
+
+
+def generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx):
+ is_first = idx == 0
+ is_last = idx == len(passes) - 1
+ ps = passes[idx]
+ block_config = block_configs[idx]
+
+ ifm_tensor = ps.ifm_tensor
+ ifm2_tensor = ps.ifm2_tensor
+ ofm_tensor = ps.ofm_tensor
+ weight_tensor = ps.weight_tensor
+ scale_tensor = ps.scale_tensor
+
+ ofm_start = [0] * len(ofm_tensor.shape)
+ ofm_end = list(ofm_tensor.shape)
+
+ 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
+
+ concat_axis = 0
+ concat_offset = 0
+
+ split_offsets = [None, None] # offset for [ifm, ifm2]
+
+ # Fusable activation functions
+ activation_ops = set(("Sigmoid", "Tanh", "Relu", "Relu6", "ReluN1To1"))
+
+ for op in ps.ops:
+ if op.type == "ConcatSliceWrite":
+ concat_axis = op.attrs["concat_axis"]
+ concat_start = op.attrs["concat_start"]
+ concat_end = op.attrs["concat_end"]
+
+ ofm_start[concat_axis] = concat_start
+ ofm_end[concat_axis] = concat_end
+ concat_offset = concat_start
+ ps.primary_op.attrs["fused_memory_function"] = op.type
+ elif op.type in activation_ops:
+ ps.primary_op.attrs["fused_activation_function"] = op.type
+
+ # The ops list has to be reversed here since the Pass Packing is done in reverse
+ ifm_idx = 0
+ for op in reversed(ps.ops):
+ if op.type == "SplitSliceRead":
+ split_offsets[ifm_idx] = op.attrs["split_start"]
+ ps.primary_op.attrs["fused_memory_function"] = op.type
+ ifm_idx += 1
+
+ if strat == SchedulingStrategy.WeightStream:
+ ofm_step = block_config[-1]
+ ofm_stop = ofm_end[-1]
+ if weight_tensor is None or not need_dma(weight_tensor):
+ ofm_step = ofm_stop
+ for start in range(ofm_start[-1], ofm_stop, ofm_step):
+ end = min(start + ofm_step, ofm_stop)
+ ofm_start[-1] = start
+ ofm_end[-1] = end
+ ofm_box = Box(ofm_start, ofm_end)
+ ifm_box = None
+ ifm2_box = None
+
+ if ifm_tensor.shape != []:
+ ifm_box, _, _ = ofm_box.transform_with_strides_and_skirt(
+ strides, skirt, ifm_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0]
+ )
+ else:
+ ifm_box = Box([], [])
+ if ifm2_tensor is not None and ifm2_tensor.shape != []:
+ ifm2_box, _, _ = ofm_box.transform_with_strides_and_skirt(
+ strides, skirt, ifm2_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[1]
+ )
+ else:
+ ifm2_box = Box([], [])
+
+ weight_box = None
+ if weight_tensor is not None:
+ weight_oc_start = start
+ weight_oc_end = end
+ if concat_axis - len(weight_tensor.shape) == -1:
+ weight_oc_start -= concat_offset
+ weight_oc_end -= concat_offset
+
+ weight_box = Box.make_weight_box(
+ weight_tensor.shape,
+ npu_block_type,
+ weight_oc_start,
+ weight_oc_end,
+ weight_tensor.weight_transpose_depthwise,
+ )
+ yield from dma_weights_if_necessary(ps, weight_box, weight_tensor)
+
+ yield NpuStripe(
+ ps,
+ block_config,
+ is_first,
+ is_last,
+ True,
+ True,
+ ifm_tensor,
+ ifm_box,
+ ofm_tensor,
+ ofm_box,
+ weight_tensor,
+ weight_box,
+ scale_tensor,
+ concat_axis,
+ concat_offset,
+ ifm2_tensor=ifm2_tensor,
+ ifm2_box=ifm2_box,
+ )
+
+ elif strat == SchedulingStrategy.IfmStream:
+ y_step = block_config[0]
+ y_start = 0
+ y_dim = 1
+ if len(ofm_tensor.shape) >= 3:
+ y_start = ofm_start[-3]
+ y_dim = ofm_end[-3]
+ if idx > 0:
+ ifm_y_present = 0
+ prev_pass = passes[idx - 1]
+ prev_pass_gen = generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx - 1)
+ else:
+ ifm_y_present = 1
+ if len(ifm_tensor.shape) >= 3:
+ ifm_y_present = ifm_tensor.shape[-3]
+ prev_pass_gen = []
+ prev_pass = None
+
+ if len(passes) == 1:
+ # no cascading, can just issue one big stripe
+ # but only if we've done allocation and OFM does not overlap IFM
+ if ifm_tensor.address != -1 and ofm_tensor.address != -1:
+ if (
+ ifm_tensor.address + ifm_tensor.storage_size() <= ofm_tensor.address
+ or ofm_tensor.address + ofm_tensor.storage_size() <= ifm_tensor.address
+ ):
+ y_step = y_dim
+
+ weight_box = None
+
+ for start in range(y_start, y_dim, y_step):
+ end = min(start + y_step, y_dim)
+ if len(ofm_tensor.shape) >= 3:
+ ofm_start[-3] = start
+ ofm_end[-3] = end
+ ofm_box = Box(ofm_start, ofm_end)
+
+ k_height = 1
+ if npu_block_type == NpuBlockType.Pooling:
+ if ps.primary_op is not None:
+ k_height = ps.primary_op.attrs["ksize"][1]
+ else:
+ if weight_tensor is not None:
+ k_height = weight_tensor.shape[0]
+
+ ifm_box, pad_top, pad_bottom = ofm_box.transform_with_strides_and_skirt(
+ strides, skirt, ifm_tensor.shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], k_height
+ )
+
+ ifm_y_needed = 1
+ if len(ifm_box.end_coord) >= 3:
+ ifm_y_needed = ifm_box.end_coord[-3]
+ if ifm_y_present < ifm_y_needed:
+ for prev_cmd in prev_pass_gen:
+ yield prev_cmd
+ rng = prev_cmd.get_ofm_y_range_for_pass(prev_pass)
+ if rng is not None:
+ ifm_y_present = max(ifm_y_present, rng[1])
+ if ifm_y_present >= ifm_y_needed:
+ break
+
+ if weight_tensor is not None and weight_box is None:
+ weight_box = Box.make_weight_box(
+ weight_tensor.shape, npu_block_type, weights_transposed=weight_tensor.weight_transpose_depthwise
+ )
+ yield from dma_weights_if_necessary(ps, weight_box, weight_tensor)
+
+ # Check if first/last stripe in pass
+ is_first_h_stripe = start == y_start
+ is_last_h_stripe = (start + y_step) >= y_dim
+
+ stripe = NpuStripe(
+ 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,
+ weight_box,
+ scale_tensor,
+ concat_axis,
+ concat_offset,
+ None,
+ None,
+ pad_top,
+ pad_bottom,
+ )
+ yield stripe
+ else:
+ assert 0, "unknown scheduling strategy"
+
+
+def generate_high_level_command_stream_for_pass_list(strat, passes, block_configs):
+ if strat == SchedulingStrategy.WeightStream:
+ for idx in range(len(passes)):
+ yield from generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx)
+ elif strat == SchedulingStrategy.IfmStream:
+ yield from generate_high_level_command_stream_for_pass(strat, passes, block_configs, len(passes) - 1)
+ else:
+ assert 0, "Unknown streaming strategy"
+
+
+def generate_high_level_command_stream_for_cascaded_pass(cps):
+ yield from generate_high_level_command_stream_for_pass_list(
+ cps.strategy, cps.passes, [ps.block_config for ps in cps.passes]
+ )
+
+
+def generate_high_level_command_stream(nng, sg, arch, verbose_high_level_command_stream):
+ res = []
+ for cps in sg.cascaded_passes:
+ if cps.placement == PassPlacement.Npu:
+ res += list(generate_high_level_command_stream_for_cascaded_pass(cps))
+
+ sg.high_level_command_stream = res
+ if verbose_high_level_command_stream:
+ sg.print_high_level_command_stream()
+
+
+def calc_allowed_ofm_ifm_overlap_for_pass_list(strat, passes, block_configs):
+ highest_ofm_write = 0
+ if not passes[0].ifm_tensor or not passes[-1].ofm_tensor:
+ return 0
+
+ ifm_read = passes[0].ifm_tensor.storage_size
+ min_overlap = 999999999999999999999
+ ofm_size = passes[-1].ofm_tensor.storage_size()
+ if strat == SchedulingStrategy.WeightStream:
+ return 0
+ for cmd in generate_high_level_command_stream_for_pass_list(strat, passes, block_configs):
+ if cmd.is_npu_pass_command():
+ if cmd.is_first:
+ ifm_read = cmd.ifm_tensor.address_offset_for_coordinate(cmd.ifm_box.start_coord, is_top_box=False)
+ if ifm_read is None:
+ return 0
+ if cmd.is_last:
+ write_offset = cmd.ofm_tensor.address_offset_for_coordinate(cmd.ofm_box.end_coord, is_top_box=True)
+ if write_offset is None:
+ return 0
+ highest_ofm_write = max(write_offset, highest_ofm_write)
+
+ if cmd.is_first or cmd.is_last:
+ overlap_required = max(highest_ofm_write - min(ifm_read, ofm_size), 0)
+ can_overwrite = ofm_size - overlap_required
+ min_overlap = min(min_overlap, can_overwrite)
+
+ if cmd.is_first:
+ ifm_read = cmd.ifm_tensor.address_offset_for_coordinate(cmd.ifm_box.end_coord, is_top_box=True)
+
+ min_overlap = max(min_overlap, 0)
+ return min_overlap
+
+
+def calc_allowed_ofm_ifm_overlap_for_cascaded_pass(cps):
+ return calc_allowed_ofm_ifm_overlap_for_pass_list(cps.strategy, cps.passes, [ps.block_config for ps in cps.passes])