adstraw commented on code in PR #12654: URL: https://github.com/apache/tvm/pull/12654#discussion_r968897620
########## tests/python/contrib/test_hexagon/test_parallel_scalar.py: ########## @@ -0,0 +1,178 @@ +# 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 parallelism for multiple different scalar workloads. """ + +import numpy as np +import tvm + +from tvm.script import tir as T +from numpy.random import default_rng + +TEST_OUTPUT_TEMPLATE = "Test {} with {} operations... \n -Single Thread: {} ms \n -Parallel: {} ms\n -Speedup: {}x\n" + + +def get_add_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations], dtype="float64") + B = T.match_buffer(b, [operations], dtype="float64") + C = T.match_buffer(c, [operations], dtype="float64") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn] = A[vn] + B[vn] + + return operator + + +def get_multiply_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations], dtype="float64") + B = T.match_buffer(b, [operations], dtype="float64") + C = T.match_buffer(c, [operations], dtype="float64") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn] = A[vn] * B[vn] + + return operator + + +def get_sub_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations], dtype="float64") + B = T.match_buffer(b, [operations], dtype="float64") + C = T.match_buffer(c, [operations], dtype="float64") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn] = A[vn] - B[vn] + + return operator + + +def evaluate(hexagon_session, operations, expected, sch): + shape = operations + dtype = "float64" + + target_hexagon = tvm.target.hexagon("v68") + func_tir = tvm.build( + sch.mod["main"], target=tvm.target.Target(target_hexagon, host=target_hexagon) + ) + module = hexagon_session.load_module(func_tir) + + rng = default_rng() + a = rng.random(shape, dtype=dtype) + b = rng.random(shape, dtype=dtype) + c = np.zeros(shape, dtype=dtype) + + a_hexagon = tvm.runtime.ndarray.array(a, device=hexagon_session.device) + b_hexagon = tvm.runtime.ndarray.array(b, device=hexagon_session.device) + c_hexagon = tvm.runtime.ndarray.array(c, device=hexagon_session.device) + + timer = module.time_evaluator("__tvm_main__", hexagon_session.device, number=100, repeat=10) + runtime = timer(a_hexagon, b_hexagon, c_hexagon) + + tvm.testing.assert_allclose(c_hexagon.asnumpy(), expected(a, b)) + + return round(runtime.mean * 1000, 6) + + +class TestMatMulVec: + + operations = tvm.testing.parameter( + 128, + 256, + 512, + 1024, # Single thread runs faster since L2 cache can handle the entire request quickly + 2048, + 4096, # Significant performance degredation once the inputs and outputs cannot all fit in L2 + 8192, + 16384, + ) + + split_factor = tvm.testing.parameter(4) Review Comment: Same comment; can we parameterize the op type to avoid code duplication? ########## tests/python/contrib/test_hexagon/test_parallel_hvx.py: ########## @@ -0,0 +1,255 @@ +# 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 parallelizing HVX workloads and compare them to single thread examples. +""" +import numpy as np +import tvm + +from tvm.script import tir as T +from numpy.random import default_rng + +TEST_OUTPUT_TEMPLATE = "Test {} with {} operations... \n -Single Thread: {} ms \n -Parallel: {} ms\n -Speedup: {}x\n" + + +def get_vrmpy_shape_dtypes(operations): + return ((operations, 128), "uint8", (operations, 128), "uint8", (operations, 32), "int32") + + +def get_vmpy_vadd_shape_dtype(operations): + return ((operations, 128), "uint8", (operations, 128), "uint8", (operations, 128), "int16") + + +def vmpy_expected_producer(shape, a, b): + expected = np.zeros(shape, dtype="int16") + for n in range(shape[0]): + for i in range(0, 128, 2): + expected[n, i // 2] = np.int16(a[n, i]) * np.int16(b[n, i]) + for i in range(1, 128, 2): + expected[n, i // 2 + 64] = np.int16(a[n, i]) * np.int16(b[n, i]) + return expected + + +def vadd_expected_producer(shape, a, b): + expected = np.zeros(shape, dtype="int16") + for n in range(shape[0]): + for i in range(0, 128, 2): + expected[n, i // 2] = np.int16(a[n, i]) + np.int16(b[n, i]) + for i in range(1, 128, 2): + expected[n, i // 2 + 64] = np.int16(a[n, i]) + np.int16(b[n, i]) + return expected + + +def vrmpy_expected_producer(shape, a, b): + expected = np.zeros(shape, dtype="int32") + for n in range(shape[0]): + for i in range(32): + for r in range(4): + expected[n, i] = expected[n, i] + np.uint32(a[n, i * 4 + r]) * np.uint32( + b[n, i * 4 + r] + ) + return expected + + +def get_vmpy_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations, 128], dtype="uint8") + B = T.match_buffer(b, [operations, 128], dtype="uint8") + C = T.match_buffer(c, [operations, 128], dtype="int16") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn, T.ramp(0, 1, 128)] = T.call_llvm_intrin( + T.llvm_lookup_intrinsic_id("llvm.hexagon.V6.vmpybusv.128B"), + T.uint32(2), + T.reinterpret(A[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + T.reinterpret(B[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + dtype="int16x128", + ) + + return operator + + +def get_vadd_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations, 128], dtype="uint8") + B = T.match_buffer(b, [operations, 128], dtype="uint8") + C = T.match_buffer(c, [operations, 128], dtype="int16") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn, T.ramp(0, 1, 128)] = T.call_llvm_intrin( + T.llvm_lookup_intrinsic_id("llvm.hexagon.V6.vaddubh.128B"), + T.uint32(2), + T.reinterpret(A[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + T.reinterpret(B[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + dtype="int16x128", + ) + + return operator + + +def get_vrmpy_operator(operations): + @T.prim_func + def operator(a: T.handle, b: T.handle, c: T.handle) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + A = T.match_buffer(a, [operations, 128], dtype="uint8") + B = T.match_buffer(b, [operations, 128], dtype="uint8") + C = T.match_buffer(c, [operations, 32], dtype="int32") + for n in T.grid(operations): + with T.block("C"): + vn = T.axis.remap("S", [n]) + C[vn, T.ramp(0, 1, 32)] = T.call_llvm_intrin( + T.llvm_lookup_intrinsic_id("llvm.hexagon.V6.vrmpyubv.128B"), + T.uint32(2), + T.reinterpret(A[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + T.reinterpret(B[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + dtype="int32x32", + ) + + return operator + + +def evaluate(hexagon_session, shape_dtypes, expected_producer, sch): + a_shape, a_dtype, b_shape, b_dtype, c_shape, c_dtype = shape_dtypes + + target_hexagon = tvm.target.hexagon("v68") + func_tir = tvm.build( + sch.mod["main"], target=tvm.target.Target(target_hexagon, host=target_hexagon) + ) + module = hexagon_session.load_module(func_tir) + + rng = default_rng() + a = rng.integers(0, 16, a_shape, dtype=a_dtype) + b = rng.integers(0, 16, b_shape, dtype=b_dtype) + c = np.zeros(c_shape, dtype=c_dtype) + + a_hexagon = tvm.runtime.ndarray.array(a, device=hexagon_session.device) + b_hexagon = tvm.runtime.ndarray.array(b, device=hexagon_session.device) + c_hexagon = tvm.runtime.ndarray.array(c, device=hexagon_session.device) + + timer = module.time_evaluator("__tvm_main__", hexagon_session.device, number=100, repeat=10) + runtime = timer(a_hexagon, b_hexagon, c_hexagon) + tvm.testing.assert_allclose(c_hexagon.asnumpy(), expected_producer(c_shape, a, b)) + + return round(runtime.mean * 1000, 6) + + +class TestMatMulVec: + + operations = tvm.testing.parameter( + 128, + 256, + 512, + 1024, # Single thread runs faster since L2 cache can handle the entire request quickly + 2048, + 4096, # Significant performance degredation once the inputs and outputs cannot all fit in L2 + 8192, + 16384, + ) + + # Experimentally best split factor but all multiples of 4 perform pretty well. + # This is because there are 4 HVX untis available on the device and pipelining + # works best with parallels of the number of available HVX. + split_factor = tvm.testing.parameter(4) Review Comment: Can you find a way to parameterize the operation type (vrmpy, vmpy, vadd) and create just one test case? There is a lot of duplicated code in the 3 test cases below? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
