diff options
Diffstat (limited to 'ethosu/vela')
| -rw-r--r-- | ethosu/vela/register_command_stream_generator.py | 5 | ||||
| -rw-r--r-- | ethosu/vela/scaling.py | 9 | ||||
| -rw-r--r-- | ethosu/vela/weight_compressor.py | 11 |
3 files changed, 20 insertions, 5 deletions
diff --git a/ethosu/vela/register_command_stream_generator.py b/ethosu/vela/register_command_stream_generator.py index faae2cf3..5fa71aa7 100644 --- a/ethosu/vela/register_command_stream_generator.py +++ b/ethosu/vela/register_command_stream_generator.py @@ -27,7 +27,7 @@ from .ethos_u55_regs.ethos_u55_regs import * from .tensor import MemArea, TensorBlockTraversal from .operation import NpuBlockType from .numeric_util import quantise_float32, round_up, round_away_zero, round_up_to_int, clamp_sigmoid, clamp_tanh -from .data_type import BaseType +from .data_type import BaseType, DataType import numpy as np from .shared_buffer_allocation import SharedBufferAllocation from .architecture_features import SharedBufferArea, SHRAMElements, ArchitectureFeatures @@ -615,6 +615,9 @@ def generate_register_command_stream(nng, sg, arch, verbose=False): else: # Convolution assert cmd.weight_tensor.block_traversal != TensorBlockTraversal.Default + # Reduced precision quantization and natural rounding used for int16 + if cmd.ifm_tensor.dtype == DataType.int16: + rounding_mode = rounding.NATURAL emit.cmd0_with_param(cmd0.NPU_SET_KERNEL_HEIGHT_M1, cmd.weight_tensor.shape[0] - 1) emit.cmd0_with_param(cmd0.NPU_SET_KERNEL_WIDTH_M1, cmd.weight_tensor.shape[1] - 1) if cmd.weight_tensor.block_traversal == TensorBlockTraversal.PartKernelFirst: diff --git a/ethosu/vela/scaling.py b/ethosu/vela/scaling.py index b255f938..785cddc6 100644 --- a/ethosu/vela/scaling.py +++ b/ethosu/vela/scaling.py @@ -42,6 +42,15 @@ def quantise_scale(scale): return significand_q31, shift +# Reduced precision quantization for int16 +def reduced_quantise_scale(scale): + multiplier, shift = quantise_scale(scale) + reduced_multiplier = int((multiplier + (1 << 15)) >> 16) + reduced_shift = shift - 16 + + return reduced_multiplier, reduced_shift + + # Calculate global OFM scale for Average Pooling def quantise_pooling_scale(nr_kernel_elements, rescale_bits=0): _, k = math.frexp(nr_kernel_elements - 1) diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py index 0b4ac696..92197248 100644 --- a/ethosu/vela/weight_compressor.py +++ b/ethosu/vela/weight_compressor.py @@ -25,7 +25,7 @@ import math import numpy as np from collections import namedtuple from .numeric_util import round_up -from .scaling import quantise_scale +from .scaling import quantise_scale, reduced_quantise_scale from .tensor import TensorPurpose, TensorSubPurpose, TensorFormat, TensorBlockTraversal from .operation import NpuBlockType from .architecture_features import Block @@ -287,7 +287,7 @@ def calc_scales_and_pack_biases(tens, arch, oc_quantum, rescale_for_faf=False): if not rescale_for_faf: if ifm_dtype == DataType.uint8: scales = [np.double(ifm_scale * weight_scale) / np.double(ofm_scale) for weight_scale in weight_scales] - elif ifm_dtype == DataType.int8: + elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16: scales = [ (np.double(ifm_scale) * np.double(weight_scale)) / np.double(ofm_scale) for weight_scale in weight_scales @@ -297,13 +297,16 @@ def calc_scales_and_pack_biases(tens, arch, oc_quantum, rescale_for_faf=False): else: if ifm_dtype == DataType.uint8: scales = [np.double(ifm_scale * weight_scale * 0x3000) for weight_scale in weight_scales] - elif ifm_dtype == DataType.int8: + elif ifm_dtype == DataType.int8 or ifm_dtype == DataType.int16: scales = [(np.double(ifm_scale * 0x3000) * np.double(weight_scale)) for weight_scale in weight_scales] else: assert False, str(ifm_dtype) + " not implemented" # quantise all of the weight scales into (scale_factor, shift) - quantised_scales = [quantise_scale(scale) for scale in scales] + if ifm_dtype == DataType.int16: + quantised_scales = [reduced_quantise_scale(scale) for scale in scales] + else: + quantised_scales = [quantise_scale(scale) for scale in scales] for _, shift in quantised_scales: assert shift >= 16 |