MLBEDSW-3019: Add profiling debug database
- Added mechanism to track input to output graph transforms for
debugging the resultant command stream.
- Provides base implementation for MLBEDSW-2661
Signed-off-by: Tim Hall <tim.hall@arm.com>
Change-Id: I2dfe8a409fbde7ad0282bfab5acb11ba1c8b82d8
diff --git a/OPTIONS.md b/OPTIONS.md
index 9220151..9aaf67b 100644
--- a/OPTIONS.md
+++ b/OPTIONS.md
@@ -204,6 +204,19 @@
vela network.tflite --recursion-limit 50000
```
+### Enable Debug DB
+
+The neural network debug database allows tracking of optimisations from the
+input network graph to the output command stream. Set this option to enable the
+calculation and writing of an XML file that contains the network debug database
+tables to the output directory.
+**Type: Boolean**
+**Default: Disabled**
+
+```bash
+vela network.tflite --enable-debug-db
+```
+
### Max Block Dependency
Set the maximum value that can be used for the block dependency delay between
diff --git a/ethosu/vela/compiler_driver.py b/ethosu/vela/compiler_driver.py
index 9263305..e089b70 100644
--- a/ethosu/vela/compiler_driver.py
+++ b/ethosu/vela/compiler_driver.py
@@ -31,10 +31,13 @@
from . import scheduler
from . import tensor_allocation
from . import weight_compressor
+from .debug_database import DebugDatabase
from .errors import VelaError
from .nn_graph import PassPlacement
from .nn_graph import TensorAllocator
+from .operation import Op
from .rewrite_graph import verify_graph_health
+from .rewrite_graph import visit_graph_post_order
from .tensor import MemType
from .tensor import Tensor
@@ -127,8 +130,18 @@
return ((lower + upper) / 2, True)
+def _record_operator(op, arch):
+ if op.type != Op.Const:
+ DebugDatabase.add_source(op)
+
+
def compiler_driver(nng, arch, options, scheduler_options):
assert verify_graph_health(nng)
+
+ # Pre-optimisation operator tracking
+ for sg in nng.subgraphs:
+ visit_graph_post_order(sg.output_tensors, arch, [], [_record_operator])
+
nng = graph_optimiser.optimise_graph_a(nng, arch, options.verbose_graph)
assert verify_graph_health(nng)
diff --git a/ethosu/vela/debug_database.py b/ethosu/vela/debug_database.py
new file mode 100644
index 0000000..b5852cd
--- /dev/null
+++ b/ethosu/vela/debug_database.py
@@ -0,0 +1,121 @@
+# 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.
+import csv
+import io
+
+import lxml.etree as xml
+
+from . import numeric_util
+from .operation import Operation
+
+
+class DebugDatabase:
+ NULLREF = -1
+ show_warnings = False
+
+ SOURCE_TABLE = "source"
+ _sourceUID = {}
+ _sourceHeaders = ["id", "operator", "kernel_w", "kernel_h", "ofm_w", "ofm_h", "ofm_d"]
+ _sourceTable = []
+
+ OPTIMISED_TABLE = "optimised"
+ _optimisedUID = {}
+ _optimisedHeaders = ["id", "source_id", "operator", "kernel_w", "kernel_h", "ofm_w", "ofm_h", "ofm_d"]
+ _optimisedTable = []
+
+ QUEUE_TABLE = "queue"
+ _queueHeaders = ["offset", "cmdstream_id", "optimised_id"]
+ _queueTable = []
+
+ STREAM_TABLE = "cmdstream"
+ _streamUID = {}
+ _streamHeaders = ["id", "file_offset"]
+ _streamTable = []
+
+ @classmethod
+ def add_source(cls, op: Operation):
+ assert isinstance(op, Operation)
+ uid = len(cls._sourceUID)
+ cls._sourceUID[op] = uid
+ ofm_shape = numeric_util.full_shape(3, op.outputs[0].shape, 1)
+ cls._sourceTable.append(
+ [uid, op.type, op.kernel.width, op.kernel.height, ofm_shape[-2], ofm_shape[-3], ofm_shape[-1]]
+ )
+
+ @classmethod
+ def add_optimised(cls, parent: Operation, op: Operation):
+ assert isinstance(parent, Operation) and isinstance(op, Operation)
+ if op not in cls._optimisedUID:
+ if parent not in cls._sourceUID:
+ # The the parent wasn't in the source network try to look it
+ # up in the optimised network and use that op's source parent.
+ if parent in cls._optimisedUID:
+ src_uid = cls._optimisedUID[parent][1]
+ else:
+ if DebugDatabase.show_warnings:
+ print("Debug Database: Associated parent '{0}' not in network".format(parent.type))
+ src_uid = DebugDatabase.NULLREF
+ else:
+ src_uid = cls._sourceUID[parent]
+ uid = len(cls._optimisedUID)
+ cls._optimisedUID[op] = (uid, src_uid)
+ ofm_shape = numeric_util.full_shape(3, op.outputs[0].shape, 1)
+ cls._optimisedTable.append(
+ [uid, src_uid, op.type, op.kernel.width, op.kernel.height, ofm_shape[-2], ofm_shape[-3], ofm_shape[-1]]
+ )
+
+ @classmethod
+ def add_stream(cls, key):
+ if key not in cls._streamUID:
+ uid = len(cls._streamUID)
+ cls._streamUID[key] = uid
+ return uid
+
+ @classmethod
+ def set_stream_offset(cls, key, file_offset):
+ assert key in cls._streamUID
+ uid = cls._streamUID[key]
+ cls._streamTable.append([uid, file_offset])
+
+ @classmethod
+ def add_command(cls, stream_id, offset, op: Operation):
+ assert stream_id < len(cls._streamUID)
+ assert op in cls._optimisedUID, "Optimised operator must exist before code generation"
+ optimised_id = cls._optimisedUID[op][0]
+ cls._queueTable.append([offset, stream_id, optimised_id])
+
+ @classmethod
+ def _write_table(cls, root, name, headers, table):
+ # Convert table to CSV
+ out = io.StringIO()
+ writer = csv.writer(out, quoting=csv.QUOTE_NONNUMERIC)
+ writer.writerow(headers)
+ writer.writerows(table)
+
+ # Package table into XML output
+ table = xml.SubElement(root, "table", {"name": name})
+ table.text = xml.CDATA(out.getvalue())
+
+ @classmethod
+ def write(cls, file_path, input_file, output_file):
+ root = xml.Element("debug", {"source": input_file, "optimised": output_file})
+
+ cls._write_table(root, cls.SOURCE_TABLE, cls._sourceHeaders, cls._sourceTable)
+ cls._write_table(root, cls.OPTIMISED_TABLE, cls._optimisedHeaders, cls._optimisedTable)
+ cls._write_table(root, cls.QUEUE_TABLE, cls._queueHeaders, cls._queueTable)
+ cls._write_table(root, cls.STREAM_TABLE, cls._streamHeaders, cls._streamTable)
+
+ xml.ElementTree(root).write(file_path, encoding="utf-8", xml_declaration=True, pretty_print=True)
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py
index e31348b..7304630 100644
--- a/ethosu/vela/graph_optimiser.py
+++ b/ethosu/vela/graph_optimiser.py
@@ -25,6 +25,7 @@
from . import rewrite_graph
from . import scaling
from .data_type import DataType
+from .debug_database import DebugDatabase
from .errors import UnsupportedFeatureError
from .ethos_u55_regs.ethos_u55_regs import resampling_mode
from .numeric_util import clamp_sigmoid
@@ -77,6 +78,7 @@
new_op.attrs["concat_end"] = offset
new_op.run_on_npu = True
tens.ops.append(new_op)
+ DebugDatabase.add_optimised(concat_op, new_op)
assert tens.shape[axis] == offset
# If axis corresponds to C-dimension, NHCWB16 can only be used in the output if all the concat_start's are a
@@ -128,6 +130,7 @@
new_op.attrs["split_end"] = offset_end
new_op.run_on_npu = True
new_op.set_output_tensor(tens)
+ DebugDatabase.add_optimised(split_op, new_op)
return tens
@@ -399,6 +402,7 @@
reshape_op.attrs["new_shape"] = desired_shape
reshape_op.inputs = [inp, new_shape_tens]
reshape_op.set_output_tensor(reshape_out)
+ DebugDatabase.add_optimised(op, reshape_op)
op.inputs[idx] = reshape_out
@@ -492,6 +496,7 @@
reshape_op.attrs["new_shape"] = reshape_input_shape
reshape_op.inputs = [reshape_in, new_shape_tens]
reshape_op.set_output_tensor(out_tens)
+ DebugDatabase.add_optimised(op, reshape_op)
op.outputs[idx] = reshape_in
@@ -568,6 +573,7 @@
op.attrs["depth_multiplier"], ifm_tensor.shape[3], ofm_tensor.shape[3]
)
)
+ DebugDatabase.add_optimised(op, op)
return op
@@ -616,6 +622,9 @@
reshape_op.set_output_tensor(orig_ofm_tensor)
# Replace this ops OFM to point to the 2D tensor
op.outputs[0] = fc_ofm_tensor
+ # Record optimisation in debug database
+ DebugDatabase.add_optimised(op, reshape_op)
+ DebugDatabase.add_optimised(op, op)
return op
@@ -670,6 +679,10 @@
# Mark the op so that it will be removed as passthrough later on
op.type = Op.Identity
+
+ # Record optimisation in debug database
+ DebugDatabase.add_optimised(op, act_op)
+ DebugDatabase.add_optimised(op, op)
return op
@@ -788,6 +801,10 @@
op.name = op.name.replace("Maximum", new_op.name)
op.outputs[0].name = op.outputs[0].name.replace("Maximum", new_op.name)
op.inputs = [shared_in]
+
+ # Record optimisation in debug database
+ DebugDatabase.add_optimised(op, op)
+
return op
@@ -812,6 +829,7 @@
mul_alpha.add_input_tensor(alpha_tens)
fm_alpha = ofm.clone(op.name + "_alpha")
mul_alpha.set_output_tensor(fm_alpha)
+ DebugDatabase.add_optimised(op, mul_alpha)
if check_quantized_tens_scaling_equal(ifm, ofm):
# No identity multiplication is needed
@@ -832,6 +850,7 @@
mul_identity.add_input_tensor(identity_tens)
fm_id = ofm.clone(op.name + "_id")
mul_identity.set_output_tensor(fm_id)
+ DebugDatabase.add_optimised(op, mul_alpha)
# Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs
op.type = Op.Maximum
@@ -840,6 +859,8 @@
ifm.consumer_list.remove(op)
op.add_input_tensor(fm_alpha)
op.add_input_tensor(fm_id)
+
+ DebugDatabase.add_optimised(op, op)
return op
@@ -1012,6 +1033,7 @@
prev_op.set_activation_lut(op.activation_lut)
# Bypass op
prev_op.set_output_tensor(ofm)
+ DebugDatabase.add_optimised(op, prev_op)
return op
@@ -1052,6 +1074,11 @@
return op
+def _record_optimised(op, arch):
+ if op.type != Op.Const:
+ DebugDatabase.add_optimised(op, op)
+
+
def optimise_graph_a(nng, arch, verbose_graph=False):
if verbose_graph:
nng.print_graph()
@@ -1093,6 +1120,10 @@
nng, sg, arch, [remove_passthrough_tensor], [fuse_activation_function_with_prev, add_padding_fields]
)
+ # Post-optimisation operator debug tracing
+ for sg in nng.subgraphs:
+ rewrite_graph.visit_graph_post_order(sg.output_tensors, arch, [], [_record_optimised])
+
if verbose_graph:
nng.print_graph()
return nng
diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py
index 5673c2d..59376a8 100644
--- a/ethosu/vela/pass_packing.py
+++ b/ethosu/vela/pass_packing.py
@@ -18,6 +18,7 @@
import collections
import enum
+from .debug_database import DebugDatabase
from .nn_graph import Pass
from .nn_graph import PassPlacement
from .operation import create_avgpool_nop
@@ -430,7 +431,6 @@
# Configure a 1x1 AvgPool and attach the op onto it
op = op_list[0]
inp = op.inputs[0]
-
avgpool_op = create_avgpool_nop(op.name + "_avgpool")
avgpool_op.add_input_tensor(inp)
avgpool_out = inp.clone("_avgpooled")
@@ -440,6 +440,7 @@
op.inputs[0] = avgpool_out
op_list.insert(0, avgpool_op)
+ DebugDatabase.add_optimised(op, avgpool_op)
return avgpool_op
return None
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py
index e5e4fb1..e3fedfc 100644
--- a/ethosu/vela/register_command_stream_generator.py
+++ b/ethosu/vela/register_command_stream_generator.py
@@ -32,6 +32,7 @@
from .architecture_features import SHRAMElements
from .data_type import BaseType
from .data_type import DataType
+from .debug_database import DebugDatabase
from .ethos_u55_regs.ethos_u55_regs import acc_format
from .ethos_u55_regs.ethos_u55_regs import activation
from .ethos_u55_regs.ethos_u55_regs import cmd0
@@ -96,10 +97,13 @@
class CommandStreamEmitter:
+ WORD_SIZE = 4
+
def __init__(self):
self.cmd_stream = []
self.reg_machine = [RegisterMachine(), RegisterMachine()]
self.last_absolute_wait = defaultdict(int)
+ self.offset = 0
def get_reg_machine(self, cmd):
if "DMA" in cmd.name:
@@ -110,7 +114,7 @@
def size_in_bytes(self):
sz = 0
for cmd in self.cmd_stream:
- sz += len(cmd) * 4
+ sz += len(cmd) * CommandStreamEmitter.WORD_SIZE
return sz
def to_list(self):
@@ -154,6 +158,7 @@
# This is not a redundant command, actually write it
self.cmd_stream.append((command,))
+ self.offset += CommandStreamEmitter.WORD_SIZE
def cmd1_with_offset(self, cmd, offset, param=0x0):
offset = int(offset) & 0xFFFFFFFFF
@@ -164,17 +169,20 @@
# This is not a redundant command, actually write it
self.cmd_stream.append((command, offset))
+ self.offset += CommandStreamEmitter.WORD_SIZE * 2
def cmd_wait(self, cmd, channel, outstanding_count):
param = (16 * channel) + outstanding_count
command = ((param & 0xFFFF) << 16) | cmd.value
self.cmd_stream.append((command,))
+ self.offset += CommandStreamEmitter.WORD_SIZE
def cmd_do_operation(self, cmd, param=0):
param = int(param)
command = ((param & 0xFFFF) << 16) | cmd.value
self.cmd_stream.append((command,))
+ self.offset += CommandStreamEmitter.WORD_SIZE
self.get_reg_machine(cmd).switch_bank()
@@ -378,6 +386,9 @@
dep_watermark = Watermark(0, 0)
+ stream_id = DebugDatabase.add_stream(sg)
+ DebugDatabase.set_stream_offset(sg, 0) # Default to zero, can only set during file writing
+
for cmd_index, cmd in enumerate(cmd_stream):
dep_watermark, cmd_waits = get_cmd_wait_dependency(arch, cmd_stream, memory_accesses, cmd_index, dep_watermark)
@@ -1077,6 +1088,7 @@
prev_cmd = cmd
emit_cmd_waits(cmd_waits)
+ DebugDatabase.add_command(stream_id, emit.offset, primary_op)
if npu_block_type == NpuBlockType.ConvolutionMxN:
emit.cmd_do_operation(cmd0.NPU_OP_CONV)
diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py
index 12c2016..efd91a3 100644
--- a/ethosu/vela/softmax.py
+++ b/ethosu/vela/softmax.py
@@ -25,6 +25,7 @@
from . import fp_math
from . import scaling
from .data_type import DataType
+from .debug_database import DebugDatabase
from .operation import Op
from .operation import Operation
from .tensor import create_const_tensor
@@ -220,6 +221,9 @@
def get_graph_8bit(self, ifm, ofm):
exp_lut = self.generate_exp_table(self.op.attrs.get("beta", 1.0), ifm.quantization.scale_f32)
+ ifm = create_reshape_tensor(ifm, ifm.get_full_shape())
+ DebugDatabase.add_optimised(self.op, ifm.ops[0])
+ ofm = create_reshape_tensor(ofm, ofm.get_full_shape(), False)
no_scale_quant = ifm.quantization.clone()
no_scale_quant.scale_f32 = None
no_scale_quant.zero_point = 0
@@ -245,6 +249,7 @@
ifm_max = Tensor([1, maxpool_h, 1, 1], ifm.dtype, maxpool_op.name + "_0")
ifm_max.quantization = no_scale_quant
maxpool_op.set_output_tensor(ifm_max)
+ DebugDatabase.add_optimised(self.op, maxpool_op)
# PASS 1 - Sub+LUT(exp)
sub_op = Operation(Op.Sub, self.op.name + "_sub1")
@@ -261,6 +266,7 @@
ifm_exp.quantization.quant_min = -128
ifm_exp.quantization.quant_max = 127
sub_op.set_output_tensor(ifm_exp)
+ DebugDatabase.add_optimised(self.op, sub_op)
# PASS 2 - SHR
shr2_op = Operation(Op.SHR, self.op.name + "_shr2")
@@ -274,6 +280,7 @@
rescaled_exp = Tensor(ifm.shape, ifm_exp.dtype, shr2_op.name + "_0")
rescaled_exp.quantization = no_scale_quant
shr2_op.set_output_tensor(rescaled_exp)
+ DebugDatabase.add_optimised(self.op, shr2_op)
# PASS 3 - Reduce sum
reduce_sum_op = Operation(Op.ReduceSum, self.op.name + "_reduce_sum3")
@@ -290,6 +297,7 @@
sum_of_exp = Tensor(reduce_sum_shape, DataType.int32, reduce_sum_op.name + "_0")
sum_of_exp.quantization = no_scale_quant
reduce_sum_op.set_output_tensor(sum_of_exp)
+ DebugDatabase.add_optimised(self.op, reduce_sum_op)
# PASS 4 - CLZ
clz_op = Operation(Op.CLZ, self.op.name + "_clz4")
@@ -297,6 +305,7 @@
headroom_plus_one = Tensor(reduce_sum_shape, DataType.int32, clz_op.name + "_0")
headroom_plus_one.quantization = no_scale_quant
clz_op.set_output_tensor(headroom_plus_one)
+ DebugDatabase.add_optimised(self.op, clz_op)
# PASS 5 - Sub
sub5_op = Operation(Op.Sub, self.op.name + "_sub5")
@@ -314,6 +323,7 @@
right_shift = Tensor(reduce_sum_shape, DataType.int32, sub5_op.name + "_0")
right_shift.quantization = no_scale_quant
sub5_op.set_output_tensor(right_shift)
+ DebugDatabase.add_optimised(self.op, sub5_op)
# PASS 6 - Sub
one = create_const_tensor("one_const", [1, 1, 1, 1], DataType.int32, [1], np.int32, quantization=no_scale_quant)
@@ -323,6 +333,7 @@
headroom = Tensor(reduce_sum_shape, DataType.int32, sub6_op.name + "_0")
headroom.quantization = no_scale_quant
sub6_op.set_output_tensor(headroom)
+ DebugDatabase.add_optimised(self.op, sub6_op)
# PASS 7 - SHL
shl7_op = Operation(Op.SHL, self.op.name + "_shl7")
@@ -331,6 +342,7 @@
shifted_sum = Tensor(reduce_sum_shape, DataType.int32, shl7_op.name + "_0")
shifted_sum.quantization = no_scale_quant
shl7_op.set_output_tensor(shifted_sum)
+ DebugDatabase.add_optimised(self.op, shl7_op)
# PASS 8 - Sub
sub8_op = Operation(Op.Sub, self.op.name + "_sub8")
@@ -343,6 +355,7 @@
shifted_sum_minus_one = Tensor(reduce_sum_shape, DataType.int32, sub8_op.name + "_0")
shifted_sum_minus_one.quantization = no_scale_quant
sub8_op.set_output_tensor(shifted_sum_minus_one)
+ DebugDatabase.add_optimised(self.op, sub8_op)
# PASS 9 - SHL
shl9_op = Operation(Op.SHL, self.op.name + "_shl9")
@@ -351,6 +364,7 @@
shifted_sum_minus_one = Tensor(reduce_sum_shape, DataType.int32, shl9_op.name + "_0")
shifted_sum_minus_one.quantization = no_scale_quant
shl9_op.set_output_tensor(shifted_sum_minus_one)
+ DebugDatabase.add_optimised(self.op, shl9_op)
# PASS 10 - Add
add10_op = Operation(Op.Add, self.op.name + "_add10")
@@ -364,6 +378,7 @@
half_denominator = Tensor(reduce_sum_shape, DataType.int32, add10_op.name + "_0")
half_denominator.quantization = one_scale_quant
add10_op.set_output_tensor(half_denominator)
+ DebugDatabase.add_optimised(self.op, add10_op)
# PASS 11 - Multiply
mul11_op = Operation(Op.Mul, self.op.name + "_mul11")
@@ -382,6 +397,7 @@
rescaled.quantization = one_scale_quant.clone()
rescaled.quantization.scale_f32 = 2.0
mul11_op.set_output_tensor(rescaled)
+ DebugDatabase.add_optimised(self.op, mul11_op)
# PASS 12 - Add
add12_op = Operation(Op.Add, self.op.name + "_add12")
@@ -394,6 +410,7 @@
rescale_w_offset = Tensor(reduce_sum_shape, DataType.int32, add12_op.name + "_0")
rescale_w_offset.quantization = one_scale_quant
add12_op.set_output_tensor(rescale_w_offset)
+ DebugDatabase.add_optimised(self.op, add12_op)
nr_x = rescale_w_offset
F2_one = create_const_tensor(
@@ -411,6 +428,7 @@
half_denominator_times_x.quantization = one_scale_quant.clone()
half_denominator_times_x.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(half_denominator_times_x)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 14, 19, 24 - SUB
sub_op = Operation(Op.Sub, self.op.name + "_sub%d" % (14 + i * 5))
sub_op.add_input_tensor(F2_one)
@@ -418,6 +436,7 @@
one_minus_half_denominator_times_x = Tensor(reduce_sum_shape, DataType.int32, sub_op.name + "_0")
one_minus_half_denominator_times_x.quantization = one_scale_quant
sub_op.set_output_tensor(one_minus_half_denominator_times_x)
+ DebugDatabase.add_optimised(self.op, sub_op)
# PASS 15, 20, 25 - MUL
mul_op = Operation(Op.Mul, self.op.name + "_mul%d" % (15 + i * 5))
mul_op.add_input_tensor(nr_x)
@@ -426,6 +445,7 @@
to_rescale.quantization = one_scale_quant.clone()
to_rescale.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(to_rescale)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 16, 21, 26 - MUL
shl_op = Operation(Op.Mul, self.op.name + "_mul%d" % (16 + i * 5))
shl_op.add_input_tensor(to_rescale)
@@ -433,6 +453,7 @@
to_add = Tensor(reduce_sum_shape, DataType.int32, shl_op.name + "_0")
to_add.quantization = no_scale_quant
shl_op.set_output_tensor(to_add)
+ DebugDatabase.add_optimised(self.op, shl_op)
# PASS 17, 22, 27 - ADD
add_op = Operation(Op.Add, self.op.name + "_add%d" % (17 + i * 5))
add_op.add_input_tensor(nr_x)
@@ -440,6 +461,7 @@
nr_x = Tensor(reduce_sum_shape, DataType.int32, add_op.name + "_0")
nr_x.quantization = one_scale_quant
add_op.set_output_tensor(nr_x)
+ DebugDatabase.add_optimised(self.op, add_op)
# PASS 28 - Multiply
mul28_op = Operation(Op.Mul, self.op.name + "_mul28")
@@ -450,6 +472,7 @@
scale_factor = Tensor(reduce_sum_shape, DataType.int32, mul28_op.name + "_0")
scale_factor.quantization = one_scale_quant
mul28_op.set_output_tensor(scale_factor)
+ DebugDatabase.add_optimised(self.op, mul28_op)
# PASS 29 - Multiply
mul_op = Operation(Op.Mul, self.op.name + "_mul29")
@@ -459,6 +482,7 @@
scaled_exp.quantization = one_scale_quant.clone()
scaled_exp.quantization.scale_f32 = 2.0
mul_op.set_output_tensor(scaled_exp)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 30 - SHR
shr30_op = Operation(Op.SHR, self.op.name + "_shr30")
@@ -466,6 +490,7 @@
shr30_op.add_input_tensor(scaled_exp)
shr30_op.add_input_tensor(right_shift)
shr30_op.set_output_tensor(ofm)
+ DebugDatabase.add_optimised(self.op, shr30_op)
return shr30_op
@@ -476,6 +501,7 @@
# PASS 0 - Depthwise Maxpool
maxpool_op = self.op.clone("_maxpool0")
maxpool_op.type = Op.MaxPool
+ DebugDatabase.add_optimised(self.op, maxpool_op)
maxpool_h = ifm.shape[1] * ifm.shape[2]
maxpool_w = ifm.shape[3]
maxpool_ifm_shape = [1, maxpool_h, maxpool_w, 1]
@@ -490,6 +516,7 @@
maxpool_ofm = Tensor([1, maxpool_h, 1, 1], ifm.dtype, maxpool_op.name + "_0")
maxpool_ofm.quantization = no_scale_quant
maxpool_op.set_output_tensor(maxpool_ofm)
+ DebugDatabase.add_optimised(self.op, maxpool_op)
# PASS 1 - Sub
sub1_op = Operation(Op.Sub, self.op.name + "_sub1")
@@ -498,6 +525,7 @@
sub1_ofm = Tensor(ifm.shape, DataType.int32, sub1_op.name + "_0")
sub1_ofm.quantization = ifm.quantization.clone()
sub1_op.set_output_tensor(sub1_ofm)
+ DebugDatabase.add_optimised(self.op, sub1_op)
# PASS 2 - Mul
beta = self.op.attrs.get("beta", 1.0)
@@ -516,6 +544,7 @@
mul2_ofm.quantization = ofm.quantization.clone()
mul2_ofm.quantization.scale_f32 = mul2_out_range
mul2_op.set_output_tensor(mul2_ofm)
+ DebugDatabase.add_optimised(self.op, mul2_op)
# PASS 3 - Add+LUT(exp)
add_op = Operation(Op.Add, self.op.name + "_add3")
@@ -533,6 +562,7 @@
exp_ofm = Tensor(mul2_ofm.shape, DataType.int16, add_op.name + "_0")
exp_ofm.quantization = mul2_ofm.quantization.clone()
add_op.set_output_tensor(exp_ofm)
+ DebugDatabase.add_optimised(self.op, add_op)
# PASS 4 - Reduce sum
reduce_sum_op = Operation(Op.ReduceSum, self.op.name + "_reduce_sum4")
@@ -549,6 +579,7 @@
sum_of_exp = Tensor(reduce_sum_shape, DataType.int32, reduce_sum_op.name + "_0")
sum_of_exp.quantization = no_scale_quant
reduce_sum_op.set_output_tensor(sum_of_exp)
+ DebugDatabase.add_optimised(self.op, reduce_sum_op)
# PASS 5 - CLZ
clz_op = Operation(Op.CLZ, self.op.name + "_clz5")
@@ -556,6 +587,7 @@
headroom_plus_one = Tensor(reduce_sum_shape, DataType.int32, clz_op.name + "_0")
headroom_plus_one.quantization = no_scale_quant
clz_op.set_output_tensor(headroom_plus_one)
+ DebugDatabase.add_optimised(self.op, clz_op)
# PASS 6 - Sub
sub6_op = Operation(Op.Sub, self.op.name + "_sub6")
@@ -568,6 +600,7 @@
reciprocal_right_shift = Tensor(reduce_sum_shape, DataType.int32, sub6_op.name + "_0")
reciprocal_right_shift.quantization = no_scale_quant
sub6_op.set_output_tensor(reciprocal_right_shift)
+ DebugDatabase.add_optimised(self.op, sub6_op)
# PASS 7 - SHL
shl7_op = Operation(Op.SHL, self.op.name + "_shl7")
@@ -580,6 +613,7 @@
constant_one = Tensor(reduce_sum_shape, DataType.int32, shl7_op.name + "_0")
constant_one.quantization = no_scale_quant
shl7_op.set_output_tensor(constant_one)
+ DebugDatabase.add_optimised(self.op, shl7_op)
# PASS 8 - Sub
sub8_op = Operation(Op.Sub, self.op.name + "_sub8")
@@ -588,6 +622,7 @@
sum_of_exps_minus_one = Tensor(reduce_sum_shape, DataType.int32, sub8_op.name + "_0")
sum_of_exps_minus_one.quantization = no_scale_quant
sub8_op.set_output_tensor(sum_of_exps_minus_one)
+ DebugDatabase.add_optimised(self.op, sub8_op)
# PASS 9 - SHL
shl9_op = Operation(Op.SHL, self.op.name + "_shl9")
@@ -596,6 +631,7 @@
shifted_sum_minus_one = Tensor(reduce_sum_shape, DataType.int32, shl9_op.name + "_0")
shifted_sum_minus_one.quantization = no_scale_quant
shl9_op.set_output_tensor(shifted_sum_minus_one)
+ DebugDatabase.add_optimised(self.op, shl9_op)
# PASS 10 - SHR
shr10_op = Operation(Op.SHR, self.op.name + "_shr10")
@@ -608,6 +644,7 @@
shifted_sum_minus_one_16 = Tensor(reduce_sum_shape, DataType.int32, shr10_op.name + "_0")
shifted_sum_minus_one_16.quantization = shifted_sum_minus_one.quantization.clone()
shr10_op.set_output_tensor(shifted_sum_minus_one_16)
+ DebugDatabase.add_optimised(self.op, shr10_op)
# PASS 11 - Sub+LUT(one over one plus x)
sub11_op = Operation(Op.Sub, self.op.name + "_sub11")
@@ -630,6 +667,7 @@
reciprocal_scale = Tensor(reduce_sum_shape, DataType.int16, sub11_op.name + "_0")
reciprocal_scale.quantization = no_scale_quant
sub11_op.set_output_tensor(reciprocal_scale)
+ DebugDatabase.add_optimised(self.op, sub11_op)
# PASS 12 - Multiply
mul_op = Operation(Op.Mul, self.op.name + "_mul12")
@@ -638,11 +676,13 @@
mul_ofm = Tensor(exp_ofm.shape, DataType.int32, mul_op.name + "_0")
mul_ofm.quantization = no_scale_quant
mul_op.set_output_tensor(mul_ofm)
+ DebugDatabase.add_optimised(self.op, mul_op)
# PASS 13 - SHR
shr13_op = Operation(Op.SHR, self.op.name + "_shr13")
shr13_op.add_input_tensor(mul_ofm)
shr13_op.add_input_tensor(reciprocal_right_shift)
shr13_op.set_output_tensor(ofm)
+ DebugDatabase.add_optimised(self.op, shr13_op)
return shr13_op
diff --git a/ethosu/vela/vela.py b/ethosu/vela/vela.py
index 4b43751..5df20d2 100644
--- a/ethosu/vela/vela.py
+++ b/ethosu/vela/vela.py
@@ -31,6 +31,7 @@
from . import stats_writer
from . import tflite_writer
from ._version import __version__
+from .debug_database import DebugDatabase
from .errors import InputFileError
from .nn_graph import PassPlacement
from .nn_graph import TensorAllocator
@@ -39,14 +40,18 @@
from .tensor import Tensor
-def process(fname, arch, model_reader_options, compiler_options, scheduler_options):
+def process(input_name, enable_debug_db, arch, model_reader_options, compiler_options, scheduler_options):
if compiler_options.timing:
start = time.time()
- nng = model_reader.read_model(fname, model_reader_options)
+ os.makedirs(compiler_options.output_dir, exist_ok=True)
+ output_basename = os.path.join(compiler_options.output_dir, os.path.splitext(os.path.basename(input_name))[0])
+ DebugDatabase.show_warnings = enable_debug_db
+
+ nng = model_reader.read_model(input_name, model_reader_options)
if not nng:
- raise InputFileError(fname, "input file could not be read")
+ raise InputFileError(input_name, "input file could not be read")
if compiler_options.verbose_operators:
nng.print_operators()
@@ -58,16 +63,21 @@
compiler_driver.compiler_driver(nng, arch, compiler_options, scheduler_options)
- passes_csv_file = "%s/%s_pass-breakdown_%s.csv" % (compiler_options.output_dir, nng.name, arch.system_config)
+ passes_csv_file = "{0}_pass-breakdown_{1}.csv".format(output_basename, arch.system_config)
stats_writer.write_pass_metrics_csv(nng, passes_csv_file)
- summary_csv_file = "%s/%s_summary_%s.csv" % (compiler_options.output_dir, nng.name, arch.system_config)
+ summary_csv_file = "{0}_summary_{1}.csv".format(output_basename, arch.system_config)
stats_writer.write_summary_metrics_csv(nng, summary_csv_file, arch)
stats_writer.print_performance_metrics(nng, show_cpu_operations=compiler_options.show_cpu_operations, arch=arch)
- if fname.endswith(".tflite"):
- tflite_writer.write_tflite(nng, "%s/%s_vela.tflite" % (compiler_options.output_dir, nng.name))
+ output_filename = output_basename + "_vela.tflite"
+ if input_name.endswith(".tflite"):
+ tflite_writer.write_tflite(nng, output_filename)
+
+ if enable_debug_db:
+ debug_filename = output_basename + "_debug.xml"
+ DebugDatabase.write(debug_filename, input_name, output_filename)
if compiler_options.timing:
stop = time.time()
@@ -123,6 +133,13 @@
parser.add_argument(
"--output-dir", type=str, default="output", help="Output directory to write files to (default: %(default)s)"
)
+ parser.add_argument(
+ "--enable-debug-db",
+ action="store_true",
+ default=None,
+ help="Enables the calculation and writing of a network debug database to output directory",
+ )
+
parser.add_argument("--config", type=str, help="Location of vela configuration file")
parser.add_argument("--verbose-graph", action="store_true", help="Verbose graph rewriter")
@@ -319,9 +336,7 @@
model_reader_options = model_reader.ModelReaderOptions()
- os.makedirs(args.output_dir, exist_ok=True)
-
- nng = process(args.network, arch, model_reader_options, compiler_options, scheduler_options)
+ nng = process(args.network, args.enable_debug_db, arch, model_reader_options, compiler_options, scheduler_options)
if args.show_subgraph_io_summary:
print_subgraph_io_summary(nng)
diff --git a/setup.py b/setup.py
index 07ab2d1..cc30636 100644
--- a/setup.py
+++ b/setup.py
@@ -56,7 +56,7 @@
keywords=["ethos-u", "vela compiler", "tflite", "npu"],
packages=find_namespace_packages(include=["ethosu.*"]),
python_requires="~=3.6", # We support only 3.6+
- install_requires=["flatbuffers==1.11.0", "numpy>=1.16.6"],
+ install_requires=["flatbuffers==1.11.0", "numpy>=1.16.6", "lxml>=4.6.1"],
entry_points={"console_scripts": ["vela = ethosu.vela.vela:main"]},
ext_modules=[mlw_module],
setup_requires=["setuptools_scm"],