def clip_strategy_vta(attrs, inputs, out_type, target):
strategy = OpStrategy()
strategy.add_implementation(compute_clip_vta,
_strategy.wrap_topi_schedule(
topi.generic.schedule_injective),
name="clip.vta")
return strategy
def conv2d_strategy_ethosu(attrs, inputs, out_type, target):
strategy = OpStrategy()
strategy.add_implementation(
create_ethosu_conv2d_compute,
_strategy.wrap_topi_schedule(schedule_injective),
name="ethosu_conv2d",
)
return strategy
def multiply_strategy_vta(attrs, inputs, out_type, target):
strategy = OpStrategy()
strategy.add_implementation(
_strategy.wrap_topi_compute(multiply_packed),
_strategy.wrap_topi_schedule(schedule_multiply_packed),
name="multiply.vta",
)
return strategy
def dense_strategy_vta(attrs, inputs, out_type, target):
"""dense vta strategy"""
if inputs[0].shape == 4: # this implies the layout is packed
strategy = OpStrategy()
strategy.add_implementation(
_strategy.wrap_compute_dense(dense_packed),
_strategy.wrap_topi_schedule(schedule_dense_packed),
name="dense_packed.vta")
return strategy
# If it's not packed, run on ARM CPU
arm_tgt = tvm.target.arm_cpu(target.model)
return _strategy.x86.dense_strategy_cpu(attrs, inputs, out_type, arm_tgt)
def conv2d_transpose_strategy_vta(attrs, inputs, out_type, target):
"""conv2d_transpose vta strategy"""
dilation = topi.util.get_const_tuple(attrs.dilation)
layout = attrs.data_layout
assert dilation == (1, 1), "support for dilation limited to (1, 1)"
if is_packed_layout(layout):
strategy = OpStrategy()
strategy.add_implementation(
_strategy.wrap_compute_conv2d_transpose(conv2d_transpose_packed),
_strategy.wrap_topi_schedule(schedule_conv2d_transpose_packed),
name="conv2d_transpose_packed.vta")
return strategy
# If it's not packed, run on ARM CPU
arm_tgt = tvm.target.arm_cpu(target.model)
return _strategy.arm_cpu.conv2d_transpose_strategy_arm_cpu(attrs, inputs, out_type, arm_tgt)
def conv2d_strategy_vta(attrs, inputs, out_type, target):
"""conv2d vta strategy"""
strategy = OpStrategy()
kernel = inputs[1]
dilation = topi.utils.get_const_tuple(attrs.dilation)
groups = attrs.groups
layout = attrs.data_layout
assert dilation == (1, 1), "support for dilation limited to (1, 1)"
if is_packed_layout(layout):
if groups == 1:
env = get_env()
assert env.LOG_INP_WIDTH == 3, "only support 8bit inp for now"
assert env.LOG_WGT_WIDTH == 3, "only support 8bit wgt for now"
assert kernel.dtype == "int8"
strategy.add_implementation(
_strategy.wrap_compute_conv2d(conv2d_packed, True),
_strategy.wrap_topi_schedule(schedule_conv2d_packed),
name="conv2d_packed.vta",
)
else: # group_conv2d
strategy.add_implementation(
_strategy.wrap_compute_conv2d(group_conv2d_packed,
has_groups=True),
_strategy.wrap_topi_schedule(schedule_group_conv2d_packed),
name="group_conv2d_packed.vta",
)
return strategy
# If it's not packed, run on ARM CPU
arm_tgt = tvm.target.arm_cpu(target.model)
return _strategy.arm_cpu.conv2d_strategy_arm_cpu(attrs, inputs, out_type,
arm_tgt)