| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| """Test elementwise ops on fpga.""" |
| import os |
| |
| import numpy as np |
| |
| import tvm |
| import tvm.testing |
| from tvm import te |
| |
| os.environ["XCL_EMULATION_MODE"] = "1" |
| os.environ["CL_CONTEXT_EMULATOR_DEVICE_INTELFPGA"] = "1" |
| |
| |
| @tvm.register_func |
| def tvm_callback_vhls_postproc(code): |
| """Hook to inspect the Vivado HLS code before actually run it""" |
| print(code) |
| return code |
| |
| |
| def test_exp(): |
| """Test scheduling and running exp function.""" |
| # graph |
| arr_length = 1024 |
| arr_length_tvm = tvm.runtime.convert(arr_length) |
| placeholder_b = te.placeholder((arr_length_tvm,), name="A") |
| result_b = te.compute(placeholder_b.shape, lambda *i: te.exp(placeholder_b(*i)), name="B") |
| schedule = te.create_schedule(result_b.op) |
| # create iter var and assign them tags. |
| axis1, _ = schedule[result_b].split(result_b.op.axis[0], nparts=1) |
| schedule[result_b].bind(axis1, te.thread_axis("pipeline")) |
| |
| # one line to build the function. |
| def check_device(device, host="llvm"): |
| if not tvm.testing.device_enabled(device): |
| return |
| dev = tvm.device(device, 0) |
| fexp = tvm.build(schedule, [placeholder_b, result_b], device, host, name="myexp") |
| dev = tvm.device(device, 0) |
| # launch the kernel. |
| buff_a = tvm.nd.array(np.random.uniform(size=arr_length).astype(placeholder_b.dtype), dev) |
| buff_b = tvm.nd.array(np.zeros(arr_length, dtype=result_b.dtype), dev) |
| fexp(buff_a, buff_b) |
| tvm.testing.assert_allclose(buff_b.numpy(), np.exp(buff_a.numpy()), rtol=1e-5) |
| |
| check_device("sdaccel") |
| if "AWS_PLATFORM" in os.environ: |
| check_device("sdaccel -device=" + os.environ.get("AWS_PLATFORM")) |
| |
| check_device("aocl_sw_emu") |
| |
| |
| def test_multi_kernel(): |
| """Test scheduling with multiple computes.""" |
| # graph |
| arr_length = 1024 |
| arr_length_tvm = tvm.runtime.convert(arr_length) |
| placeholder_a = te.placeholder((arr_length_tvm,), name="A") |
| placeholder_b = te.placeholder((arr_length_tvm,), name="B") |
| result_c = te.compute( |
| placeholder_a.shape, lambda *i: placeholder_a(*i) + placeholder_b(*i), name="C" |
| ) |
| result_d = te.compute( |
| placeholder_a.shape, lambda *i: placeholder_a(*i) + result_c(*i), name="D" |
| ) |
| schedule = te.create_schedule(result_d.op) |
| # create iter var and assign them tags. |
| axis1, _ = schedule[result_c].split(result_c.op.axis[0], nparts=1) |
| schedule[result_c].bind(axis1, te.thread_axis("pipeline")) |
| axis1, _ = schedule[result_d].split(result_d.op.axis[0], nparts=1) |
| schedule[result_d].bind(axis1, te.thread_axis("pipeline")) |
| |
| # one line to build the function. |
| def check_device(device, host="llvm"): |
| if not tvm.testing.device_enabled(device): |
| return |
| dev = tvm.device(device, 0) |
| fadd = tvm.build( |
| schedule, [placeholder_a, placeholder_b, result_c, result_d], device, host, name="myadd" |
| ) |
| dev = tvm.device(device, 0) |
| # launch the kernel. |
| buff_a = tvm.nd.array(np.random.uniform(size=arr_length).astype(placeholder_a.dtype), dev) |
| buff_b = tvm.nd.array(np.random.uniform(size=arr_length).astype(placeholder_b.dtype), dev) |
| buff_c = tvm.nd.array(np.random.uniform(size=arr_length).astype(result_c.dtype), dev) |
| buff_d = tvm.nd.array(np.random.uniform(size=arr_length).astype(result_d.dtype), dev) |
| fadd(buff_a, buff_b, buff_c, buff_d) |
| tvm.testing.assert_allclose(buff_d.numpy(), buff_a.numpy() * 2 + buff_b.numpy(), rtol=1e-5) |
| |
| check_device("sdaccel") |
| check_device("aocl_sw_emu") |
| |
| |
| if __name__ == "__main__": |
| test_exp() |
| test_multi_kernel() |