Relay BNNS Integration

Relay BNNS Integration#

Author: Egor Churaev

Introduction#

Apple BNNS library is a collection of functions that can be used to construct neural networks for inference (and train). It’s supported in macOS, iOS, tvOS, and watchOS. BNNS provides primitives executed on all CPU supported on those platforms and optimized for high performance and low-energy consumption. This integration will offload as many operators as possible from Relay to BNNS.

BNNS runtime is a part of platform API and available on all modern Apple operating systems. Application using BNNS will not depends on any additional external dependencies.

BNNS functions uses Apple private hardware capabilities which are not exposed yet by Apple. Example of such capabilities can be AMX Apple cpu extension.

This guide will demonstrate how to build TVM with BNNS codegen and runtime enabled. It will also provide example code to compile and run models using BNNS runtime. Finally, we document the supported operators.

Building TVM with BNNS support#

To turn on TVM BNNS codegen and TVM BNNS runtime you need to turn on the only USE_BNNS flag

  • USE_BNNS=ON/OFF - This flag will enable compiling a network with offloading subgraphs to BNNS primitives and will link tvm library to the BNNS runtime module.

Enabling of this flag will cause to search the default Accelerate Frameworks on current target SDK. The minimal versions of required SDK is macOS 11.0, iOS 14.0, tvOS 14.0 and watchOS 7.0.

Example setting in config.cmake file:

set(USE_BNNS ON)

BNNS partitioning of Relay graph#

Operations to be offloaded on BNNS execution must be annotated before passing of module for compilation. All ops annotated by partition_for_bnns will be offloaded for BNNS execution. The rest of the ops will go through the LLVM compilation and code generation.

Important note: BNNS support primitives only with constant weights. To satisfy this requirements we have to map constants to related tensor abstraction in relay representation. To freeze tensors and operate with them as constants you may need to call ONNX importer with special flag "freeze_params=True" or performer binding manually. In general cases all relay importers don't do that by default. For your convenience "partition_for_bnns" can do this for you if params dictionary is passed as the argument.

from tvm.relay.op.contrib.bnns import partition_for_bnns
model = partition_for_bnns(model, params=params)

Input data layout for operations to be offloaded to BNNS execution#

BNNS kernels support only planar format of input data. The partitioner will require to have NCHW input layout for conv2d input.

To use BNNS integration for models with interleave input layout, they should be converted before passing of module to partition_for_bnns. The layout conversion will happen only for explicitly enumerated types of ops. It might happen that depending on topology there might be regular data reorder around conv2d to interleave and planar layout. This will be reflected in performance penalties and affect execution time. It is recommended to analyze the whole topology and extend below list to convert all intermediate tensors to NCHW data layout.

Example of input layouts change:

# For models with NHWC input layout
with tvm.transform.PassContext(opt_level=3):
    mod = relay.transform.InferType()(mod)
    mod = relay.transform.ConvertLayout({"nn.conv2d": ["NCHW", "default"],
                                        "nn.bias_add": ["NCHW", "default"],
                                        "nn.relu": ["NCHW"]})(mod)

Example: Build and Deploy Mobilenet v2 1.0 with BNNS#

Create a Relay graph from a MXNet Mobilenet v2 1.0 model.

import tvm
from tvm import relay
import mxnet
from mxnet.gluon.model_zoo.vision import get_model

dtype = "float32"
input_shape = (1, 3, 224, 224)
block = get_model('mobilenetv2_1.0', pretrained=True)
module, params = relay.frontend.from_mxnet(block, shape={'data': input_shape}, dtype=dtype)

Markup the parts of graphs to be offloaded to BNNS primitives. All ops which are supported by the BNNS integration will be handled by BNNS invocations, the rest of the ops will go through the regular TVM llvm compilation and code generation.

After that you need to compile new module with target corresponding to required Apple platform

from tvm.relay.op.contrib.bnns import partition_for_bnns

# target for macOS Big Sur 11.1:
target = "llvm -mtriple=x86_64-apple-darwin20.2.0"

model = partition_for_bnns(model, params=params)  # to markup operations to be offloaded to BNNS
with tvm.transform.PassContext(opt_level=3):
    lib = relay.build(model, target=target, params=params)

Export the module.

lib.export_library('compiled.dylib')

Load module and run inference on the target machine with TVM built with USE_BNNS enabled

import tvm
import numpy as np
from tvm.contrib import graph_executor

dev = tvm.cpu(0)
loaded_lib = tvm.runtime.load_module('compiled.dylib')
gen_module = tvm.contrib.graph_executor.GraphModule(loaded_lib['default'](dev))

dtype = "float32"
input_shape = (1, 3, 224, 224)
input_data = np.random.uniform(0, 1, input_shape).astype(dtype)
gen_module.run(data=input_data)

Operator support#

Relay Node

Remarks

nn.conv2d

nn.batch_norm

Supported by BNNS integration only in nn.conv2d-batch_norm pattern

nn.dense

nn.batch_matmul

nn.bias_add

Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense fusion

add

Supported by BNNS integration only as a bias part of nn.conv2d or nn.dense fusion

nn.relu

Supported by BNNS integration only as a part of nn.conv2d or nn.dense fusion

nn.gelu

Supported by BNNS integration only as a part of nn.conv2d or nn.dense fusion