MLBEDSW-3146: memory transfers cycle estimation
- DMA ops cycle estimation for the first pass
- fix a bug in ifm_blk_depth calculation
- fix a bug in sram bandwidth calculation
- merge dpu and elementwise cycles into npu cycles
- use str.format() in performance print
Change-Id: I78895416f47fc3c652743c5da13fc45630322371
Signed-off-by: Diqing Zhong <diqing.zhong@arm.com>
(cherry picked from commit 5245e97a62c2fe54250f99b06e778f3e0c6dc376)
(cherry picked from commit 16e415677403fc04a90b1a7ec554761d38315640)
diff --git a/ethosu/vela/stats_writer.py b/ethosu/vela/stats_writer.py
index 6fd68f8..3cd769f 100644
--- a/ethosu/vela/stats_writer.py
+++ b/ethosu/vela/stats_writer.py
@@ -154,15 +154,14 @@
for mem_area in mem_areas_to_report():
for purpose, purpose_candidates in purpose_list:
for direction, direction_candidates in direction_list:
- label = "bytes_%s_%s_%s" % (mem_area.identifier_name(), purpose, direction)
+ label = "bytes_{}_{}_{}".format(mem_area.identifier_name(), purpose, direction)
bandwidth_names.append(label)
bandwidth_indices.append((mem_area, purpose_candidates, direction_candidates))
all_macs = MacCount.all()
all_cycles = (
PassCycles.Total,
- PassCycles.Dpu,
- PassCycles.ElementWise,
+ PassCycles.Npu,
PassCycles.Cpu,
PassCycles.SramAccess,
PassCycles.DramAccess,
@@ -253,16 +252,16 @@
if name:
print("", file=f)
print("Network summary for", name, file=f)
- print("Accelerator configuration %20s" % (arch.accelerator_config,), file=f)
- print("System configuration %20s" % (arch.system_config,), file=f)
- print("Accelerator clock %12d MHz" % (arch.npu_clock / 1e6,), file=f)
+ print("Accelerator configuration {:20}".format(arch.accelerator_config), file=f)
+ print("System configuration {:20}".format(arch.system_config), file=f)
+ print("Accelerator clock {:12d} MHz".format(int(arch.npu_clock / 1e6)), file=f)
for mem_area, label in mem_area_labels:
print(
- "Design peak %-25s %12.2f GB/s"
- % (label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000,),
+ "Design peak {:25} {:12.2f} GB/s".format(
+ label + " bandwidth", arch.memory_bandwidths_per_second[mem_area] / 1000.0 / 1000 / 1000
+ ),
file=f,
)
-
print(file=f)
for mem_area, label in mem_area_labels:
if mem_area not in memory_used:
@@ -272,18 +271,19 @@
extra = ""
if (mem_area == MemArea.OnChipFlash or mem_area == MemArea.OffChipFlash) and bits_per_element is not None:
- extra = " (%.2f bits per element)" % (bits_per_element[mem_area],)
+ extra = " ({:.2f} bits per element)".format(bits_per_element[mem_area])
- print("Total %-25s %12.2f KiB%s" % (aug_label, memory_used[mem_area] / 1024.0, extra), file=f)
+ print("Total {:25} {:12.2f} KiB{}".format(aug_label, memory_used[mem_area] / 1024.0, extra), file=f)
print(file=f)
- print("%d passes fused into %d" % (num_passes, num_cascaded_passes), file=f)
+ print("{:d} passes fused into {:d}".format(num_passes, num_cascaded_passes), file=f)
n_cpu_operations = len(cpu_operations)
if n_operations > 0:
print(
- "%d/%d (%4.1f %%) operations falling back to the CPU"
- % (n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100),
+ "{:d}/{:d} ({:4.1%}) operations falling back to the CPU".format(
+ n_cpu_operations, n_operations, n_cpu_operations / n_operations * 100
+ ),
file=f,
)
@@ -294,8 +294,9 @@
return " ".join(str(list(tens.shape)) for tens in lst)
print(
- "CPU operation: %s, inputs %s, outputs %s"
- % (op.type, format_tens_list(op.inputs), format_tens_list(op.outputs)),
+ "CPU operation: {} inputs {}, outputs {}".format(
+ op.type, format_tens_list(op.inputs), format_tens_list(op.outputs)
+ ),
file=f,
)
@@ -308,38 +309,46 @@
fm_bws = bws[TensorPurpose.FeatureMap]
aug_label = label + " bandwidth"
print(
- "Average %-25s %12.2f GB/s" % (aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0,),
+ "Average {:25} {:12.2f} GB/s".format(aug_label, total_bw * midpoint_fps / 1000.0 / 1000.0 / 1000.0),
file=f,
)
print(
- "Input %-25s %12.2f MB/batch"
- % (aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0,),
+ "Input {:25} {:12.2f} MB/batch".format(
+ aug_label, np.sum(fm_bws[BandwidthDirection.Read]) / 1000.0 / 1000.0
+ ),
file=f,
)
- print("Weight %-25s %12.2f MB/batch" % (aug_label, np.sum(weight_bws) / 1000.0 / 1000.0,), file=f)
+ print("Weight {:25} {:12.2f} MB/batch".format(aug_label, np.sum(weight_bws) / 1000.0 / 1000.0), file=f)
print(
- "Output %-25s %12.2f MB/batch"
- % (aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0,),
+ "Output {:25} {:12.2f} MB/batch".format(
+ aug_label, np.sum(fm_bws[BandwidthDirection.Write]) / 1000.0 / 1000.0
+ ),
file=f,
)
- print("Total %-25s %12.2f MB/batch" % (aug_label, total_bw / 1000.0 / 1000.0,), file=f)
+ print("Total {:25} {:12.2f} MB/batch".format(aug_label, total_bw / 1000.0 / 1000.0), file=f)
print(
- "Total %-25s per input %9.2f MB/inference (batch size %d)"
- % (aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size),
+ "Total {:25} per input {:9.2f} MB/inference (batch size {:d})".format(
+ aug_label, total_bw / 1000.0 / 1000.0 / batch_size, batch_size
+ ),
file=f,
)
print(file=f)
- print("Neural network macs %12d MACs/batch" % (macs[MacCount.NeuralNetworkMacs],), file=f)
- print("Hardware macs %12d MACs/batch" % (macs[MacCount.HardwareMacs],), file=f)
print(
- "Network Tops/s %12.2f Tops/s"
- % (macs[MacCount.NeuralNetworkMacs] * 2 * midpoint_fps / 1e12),
+ "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,
)
print(
- "Hardware Tops/s %12.2f Tops/s"
- % (macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12),
+ "Hardware Tops/s {:12.2f} Tops/s".format(
+ macs[MacCount.HardwareMacs] * 2 * midpoint_fps / 1e12
+ ),
file=f,
)
print(file=f)
@@ -347,12 +356,13 @@
for kind in PassCycles.all():
aug_label = kind.display_name() + " cycles"
cyc = cycles[kind]
- print("%-30s %12d cycles/batch" % (aug_label, cyc,), file=f)
+ print("{:30} {:12d} cycles/batch".format(aug_label, int(cyc)), file=f)
print(file=f)
print(
- "Batch Inference time %7.2f ms, %7.2f inferences/s (batch size %d)"
- % (midpoint_inference_time * 1000, midpoint_fps, batch_size),
+ "Batch Inference time {:7.2f} ms, {:7.2f} inferences/s (batch size {:d})".format(
+ midpoint_inference_time * 1000, midpoint_fps, batch_size
+ ),
file=f,
)
print(file=f)