def relay_to_tir(mod: tvm.ir.IRModule) -> tvm.ir.IRModule:
"""
This is the hook for python-based lowering of a Relay module which lowers NPU
external functions to TIR.
Parameters
----------
mod : tvm.ir.IRModule
This is the Relay module.
Returns
-------
mod : tvm.ir.IRModule
The Relay module with scheduled NPU external functions.
"""
mod = OutlineCompilerFunctions("ethos-u")(mod)
mod = LegalizeEthosU()(mod)
mod = LUTsOptimizer()(mod)
mod = relay.transform.InferType()(mod)
mod = IdentityOptimizer()(mod)
mod = LayoutOptimizer()(mod)
mod = relay.transform.InferType()(mod)
device_contexts = {
gv: "ethos-u"
for gv, _ in filter(lambda x: util.is_npu_func(x[1]),
mod.functions.items())
}
mod = mod.with_attr("device_contexts", device_contexts)
# Use the cascader if it is enabled for the U55 accelerator, otherwise use copy_constants
# scheduler
if util.is_cascader_enabled():
if util.get_accelerator_config() == "ethos-u65-256":
raise ValueError(
"Cascading is not supported for the U65 accelerator")
workspace_memory_pools = mod.attrs["workspace_memory_pools"]
if not workspace_memory_pools:
raise ValueError(
"Workspace memory pool needs to be provided for the U55 cascader"
)
if len(workspace_memory_pools.pools) != 1:
raise ValueError(
"Exactly one workspace pool needs to be provided for the U55 cascader"
)
memory_pressure = _calculate_memory_pressure(mod)
sram = extract_memory_info(workspace_memory_pools.pools[0],
memory_pressure)
tir_mod = LowerToTIR(
_ethos_u55_cascader(sram, util.is_striping_enabled()))(mod)
else:
tir_mod = LowerToTIR(copy_constants())(mod)
return tir_mod
def relay_to_tir(mod: tvm.ir.IRModule) -> tvm.ir.IRModule:
"""
This is the hook for python-based lowering of a Relay module which lowers NPU
external functions to TIR.
Parameters
----------
mod : tvm.ir.IRModule
This is the Relay module.
Returns
-------
mod : tvm.ir.IRModule
The Relay module with scheduled NPU external functions.
"""
mod = OutlineCompilerFunctions("ethos-u")(mod)
mod = LegalizeEthosU()(mod)
mod = LUTsOptimizer()(mod)
mod = relay.transform.InferType()(mod)
mod = IdentityOptimizer()(mod)
mod = LayoutOptimizer()(mod)
mod = relay.transform.InferType()(mod)
device_contexts = {
gv: "ethos-u"
for gv, _ in filter(lambda x: util.is_npu_func(x[1]),
mod.functions.items())
}
mod = mod.with_attr("device_contexts", device_contexts)
# We are currently using copy_constants scheduler In the long run,
# this should be a single intelligent and a composite scheduler
# that can perform scheduling based on user inputs such as
# scratch memory size.
mod = LowerToTIR(copy_constants)(mod)
return mod
