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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/09/12 20:27:37 UTC
[GitHub] [tvm] adstraw commented on a diff in pull request #12654: [Hexagon] Create test examples to show parallelization
adstraw commented on code in PR #12654:
URL: https://github.com/apache/tvm/pull/12654#discussion_r968897620
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tests/python/contrib/test_hexagon/test_parallel_scalar.py:
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@@ -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?
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