HLS Backend Example

TVM supports Xilinx FPGA board with SDAccel. Here is a tutorial for how to deploy TVM to AWS F1 FPGA instance.

Note: This feature is still experimental. We cannot use SDAccel to deploy an end to end neural networks for now.

We use two python scripts for this tutorial.

  • build.py - a script to synthesize FPGA bitstream.

import tvm
from tvm import te

tgt_host="llvm"
tgt="sdaccel"

n = te.var("n")
A = te.placeholder((n,), name='A')
B = te.placeholder((n,), name='B')
C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")

s = te.create_schedule(C.op)
px, x = s[C].split(C.op.axis[0], nparts=1)

s[C].bind(px, tvm.thread_axis("pipeline"))

fadd = tvm.build(s, [A, B, C], tgt, target_host=tgt_host, name="myadd")

fadd.save("myadd.o")
fadd.imported_modules[0].save("myadd.xclbin")

tvm.contrib.cc.create_shared("myadd.so", ["myadd.o"])
  • run.py - a script to use FPGA as an accelerator.

import tvm
import numpy as np
import os

tgt="sdaccel"

fadd = tvm.runtime.load("myadd.so")
if os.environ.get("XCL_EMULATION_MODE"):
    fadd_dev = tvm.runtime.load("myadd.xclbin")
else:
    fadd_dev = tvm.runtime.load("myadd.awsxclbin")
fadd.import_module(fadd_dev)

ctx = tvm.context(tgt, 0)

n = 1024
a = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx)
b = tvm.nd.array(np.random.uniform(size=n).astype("float32"), ctx)
c = tvm.nd.array(np.zeros(n, dtype="float32"), ctx)

fadd(a, b, c)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())

Setup

  • Launch an instance using the FPGA Developer AMI. We don’t need an F1 instance for emulation and synthesis, so it is recommended to use a lower cost instance for them.

  • Setup AWS FPGA development kit.

git clone https://github.com/aws/aws-fpga.git
cd aws-fpga
source sdaccel_setup.sh
source ${XILINX_SDX}/settings64.sh
  • Setup TVM with OpenCL enabled.

Emulation

  • Create emconfig.json for emulation.

emconfigutil --platform ${AWS_PLATFORM} --nd 1
  • Copy emconfig.json to the python binary directory. It is because the current Xilinx toolkit assumes that both host binary and the emconfig.json file are in the same path.

cp emconfig.json $(dirname $(which python))
  • Run software emulation

export XCL_EMULATION_MODE=1
export XCL_TARGET=sw_emu

python build.py
python run.py
  • Run hardware emulation

export XCL_EMULATION_MODE=1
export XCL_TARGET=hw_emu

python build.py
python run.py

Synthesis

  • Run synthesis with the following script.

unset XCL_EMULATION_MODE
export XCL_TARGET=hw

python build.py
  • Create AWS FPGA image and upload it to AWS S3.

${SDACCEL_DIR}/tools/create_sdaccel_afi.sh -xclbin=myadd.xclbin -o=myadd \
    -s3_bucket=<bucket-name> -s3_dcp_key=<dcp-folder-name> -s3_logs_key=<logs-folder-name>

This also generates an awsxclbin file, which is necessary to use the AWS FPGA image on F1 instances.

Run

  • Launch Amazon EC2 F1 instance.

  • Copy myadd.so, myadd.awsxclbin, and run.py to the F1 instance.

  • Setup AWS FPGA development kit.

git clone https://github.com/aws/aws-fpga.git
cd aws-fpga
source sdaccel_setup.sh
  • Setup TVM with OpenCL enabled.

  • Become root and setup environment variables.

sudo sh
source ${INSTALL_ROOT}/setup.sh
  • Run

python run.py