Vela: bandwidth calculation improvements
- Combine conv and vector_product calculation
- Remove internal bandwidth
- Remove blocks and hw_macs from report
- Use scaled_bws for cycle estimation
Related to: MLBEDSW-3598
Change-Id: I1927a8311ec563f68115e0f2ed077806b86fd717
Signed-off-by: Diqing Zhong <diqing.zhong@arm.com>
diff --git a/ethosu/vela/stats_writer.py b/ethosu/vela/stats_writer.py
index 494b25e..02d95d8 100644
--- a/ethosu/vela/stats_writer.py
+++ b/ethosu/vela/stats_writer.py
@@ -22,7 +22,6 @@
from .nn_graph import PassPlacement
from .npu_performance import BandwidthDirection
-from .npu_performance import MacCount
from .npu_performance import PassCycles
from .numeric_util import round_up_to_int
from .operation import Op
@@ -70,7 +69,7 @@
mem_area.identifier_name() + "_total_bytes",
]
- labels += ["nn_macs", "hardware_macs", "nn_tops", "hardware_tops"]
+ labels += ["nn_macs", "nn_tops"]
labels += ["cycles_" + kind.identifier_name() for kind in PassCycles.all()]
@@ -128,10 +127,8 @@
]
data_items += [
- nng.macs[MacCount.NeuralNetworkMacs],
- nng.macs[MacCount.HardwareMacs],
- nng.macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12,
- nng.macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12,
+ nng.macs,
+ nng.macs * 2 * midpoint_fps / 1e12,
]
data_items += [nng.cycles[kind] for kind in PassCycles.all()]
@@ -164,7 +161,6 @@
bandwidth_names.append(label)
bandwidth_indices.append((mem_area, purpose_candidates, direction_candidates))
- all_macs = MacCount.all()
all_cycles = (
PassCycles.Total,
PassCycles.Npu,
@@ -183,10 +179,9 @@
"block_config_width",
"block_config_input_channels",
"block_config_output_channels",
- "n_blocks_in_pass",
]
+ ["cycles_" + v.identifier_name() for v in all_cycles]
- + [v.identifier_name() for v in all_macs]
+ + ["nn_macs"]
+ bandwidth_names
+ ["sram_used"]
)
@@ -205,9 +200,8 @@
stats += [ps.placement.name]
stats += [cps.strategy.name]
stats += list(ps.block_config)
- stats += [ps.n_blocks]
stats += [round_up_to_int(ps.cycles[v]) for v in all_cycles]
- stats += [round_up_to_int(ps.macs[v]) for v in all_macs]
+ stats += [round_up_to_int(ps.macs)]
for indices in bandwidth_indices:
res = 0
i = indices[0]
@@ -256,17 +250,16 @@
if name:
print("", file=f)
- print("Network summary for", name, file=f)
- print("Accelerator configuration {:>20}".format(arch.accelerator_config.name), file=f)
- print("System configuration {:>20}".format(arch.system_config), file=f)
- print("Memory mode {:>20}".format(arch.memory_mode), file=f)
- print("Accelerator clock {:12d} MHz".format(int(arch.core_clock / 1e6)), file=f)
+ print(f"Network summary for {name}", file=f)
+ print(f"Accelerator configuration {arch.accelerator_config.name:>20}", file=f)
+ print(f"System configuration {arch.system_config:>20}", file=f)
+ print(f"Memory mode {arch.memory_mode:>20}", file=f)
+ print(f"Accelerator clock {int(arch.core_clock / 1e6):12d} MHz", file=f)
for mem_area, label in mem_area_labels:
+ label += " bandwidth"
+ bandwidth = arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000
print(
- "Design peak {:25} {:12.2f} GB/s".format(
- label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000
- ),
- file=f,
+ f"Design peak {label:25} {bandwidth:12.2f} GB/s", file=f,
)
print(file=f)
for mem_area, label in mem_area_labels:
@@ -277,12 +270,12 @@
extra = ""
if (mem_area == MemArea.OnChipFlash or mem_area == MemArea.OffChipFlash) and bits_per_element is not None:
- extra = " ({:.2f} bits per element)".format(bits_per_element[mem_area])
+ extra = f" ({bits_per_element[mem_area]:.2f} bits per element)"
- print("Total {:25} {:12.2f} KiB{}".format(aug_label, memory_used[mem_area] / 1024.0, extra), file=f)
+ print(f"Total {aug_label:25} {memory_used[mem_area] / 1024.0:12.2f} KiB{extra}", file=f)
print(file=f)
- print("{:d} passes fused into {:d}".format(num_passes, num_cascaded_passes), file=f)
+ print(f"{num_passes:d} passes fused into {num_cascaded_passes:d}", file=f)
if cpu_operations is None:
cpu_operations = []
@@ -290,9 +283,8 @@
n_cpu_operations = len(cpu_operations)
if n_operations > 0:
print(
- "{:d}/{:d} ({:4.1%}) operations falling back to the CPU".format(
- n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100
- ),
+ f"{n_cpu_operations:d}/{n_operations:d}"
+ f" ({n_cpu_operations / n_operations * 100:4.1%}) operations falling back to the CPU",
file=f,
)
@@ -303,9 +295,8 @@
return " ".join(str(list(tens.shape)) for tens in lst)
print(
- "CPU operation: {} inputs {}, outputs {}".format(
- op.type, format_tens_list(op.inputs), format_tens_list(op.outputs)
- ),
+ f"CPU operation: {op.type}"
+ f" inputs {format_tens_list(op.inputs)}, outputs {format_tens_list(op.outputs)}",
file=f,
)
@@ -318,60 +309,43 @@
fm_bws = bws[TensorPurpose.FeatureMap]
aug_label = label + " bandwidth"
print(
- "Average {:25} {:12.2f} GB/s".format(aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0),
- file=f,
+ f"Average {aug_label:25} {total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0:12.2f} GB/s", file=f,
)
print(
- "Input {:25} {:12.2f} MB/batch".format(
- aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0
- ),
+ f"Input {aug_label:25} {np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0:12.2f} MB/batch",
file=f,
)
- print("Weight {:25} {:12.2f} MB/batch".format(aug_label, np.sum(weight_bws) / 1000.0 / 1000.0), file=f)
+ print(f"Weight {aug_label:25} {np.sum(weight_bws) / 1000.0 / 1000.0:12.2f} MB/batch", file=f)
print(
- "Output {:25} {:12.2f} MB/batch".format(
- aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0
- ),
+ f"Output {aug_label:25} "
+ f"{np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0:12.2f} MB/batch",
file=f,
)
- print("Total {:25} {:12.2f} MB/batch".format(aug_label, total_bw / 1000.0 / 1000.0), file=f)
+ print(f"Total {aug_label:25} {total_bw / 1000.0 / 1000.0:12.2f} MB/batch", file=f)
print(
- "Total {:25} per input {:9.2f} MB/inference (batch size {:d})".format(
- aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size
- ),
+ f"Total {aug_label:25} per input "
+ f"{total_bw / 1000.0 / 1000.0 / batch_size:9.2f} MB/inference (batch size {batch_size:d})",
file=f,
)
print(file=f)
print(
- "Neural network macs {:12d} MACs/batch".format(int(macs[MacCount.NeuralNetworkMacs])),
- file=f,
- )
- print("Hardware macs {:12d} MACs/batch".format(int(macs[MacCount.HardwareMacs])), file=f)
- print(
- "Network Tops/s {:12.2f} Tops/s".format(
- macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12
- ),
- file=f,
+ f"Neural network macs {int(macs):12d} MACs/batch", file=f,
)
print(
- "Hardware Tops/s {:12.2f} Tops/s".format(
- macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12
- ),
- file=f,
+ f"Network Tops/s {macs * 2 * midpoint_fps / 1e12:12.2f} Tops/s", file=f,
)
print(file=f)
for kind in PassCycles.all():
aug_label = kind.display_name() + " cycles"
cyc = cycles[kind]
- print("{:30} {:12d} cycles/batch".format(aug_label, int(cyc)), file=f)
+ print(f"{aug_label:30} {int(cyc):12d} cycles/batch", file=f)
print(file=f)
print(
- "Batch Inference time {:7.2f} ms, {:7.2f} inferences/s (batch size {:d})".format(
- midpoint_inference_time * 1000, midpoint_fps, batch_size
- ),
+ f"Batch Inference time {midpoint_inference_time * 1000:7.2f} ms,"
+ f" {midpoint_fps:7.2f} inferences/s (batch size {batch_size:d})",
file=f,
)
print(file=f)