junrushao commented on code in PR #13005: URL: https://github.com/apache/tvm/pull/13005#discussion_r1020503370
########## tests/python/contrib/test_hexagon/test_async_dma_pipeline.py: ########## @@ -0,0 +1,353 @@ +# 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 different strategies for loading data into vtcm before running HVX workloads. """ + +import numpy as np +import tvm +import pytest + +from tvm.script import tir as T +from numpy.random import default_rng + +from tvm.tir.function import TensorIntrin + +VRMPY_SIZE_B = 128 +VRMPY_SIZE_INT32 = 32 + + +def conv_approximation(size_a, size_w): + a_shape = (size_a, VRMPY_SIZE_B) + w_shape = (size_w, VRMPY_SIZE_B) + out_shape = (size_a, VRMPY_SIZE_INT32) + + @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, a_shape, dtype="uint8") + W = T.match_buffer(b, w_shape, dtype="uint8") + C = T.match_buffer(c, out_shape, dtype="int32") + for n, i in T.grid(size_a, size_w): + with T.block("C"): + vn, vi = T.axis.remap("SR", [n, i]) + T.reads(A[vn, 0:VRMPY_SIZE_B], W[vi, 0:VRMPY_SIZE_B], C[vn, 0:VRMPY_SIZE_INT32]) + T.writes(C[vn, 0:VRMPY_SIZE_INT32]) + with T.init(): + for x in T.serial(VRMPY_SIZE_INT32): + C[vn, x] = 0 + C[vn, T.ramp(0, 1, 32)] = T.call_llvm_intrin( + T.llvm_lookup_intrinsic_id("llvm.hexagon.V6.vrmpyubv.acc.128B"), + T.uint32(3), + C[vn, T.ramp(0, 1, 32)], + T.reinterpret(A[vn, T.ramp(0, 1, 128)], dtype="int32x32"), + T.reinterpret(W[vi, T.ramp(0, 1, 128)], dtype="int32x32"), + dtype="int32x32", + ) + # Currently async DMA lowering does not add any wait to the end of schedules so + # for timing purposes we are manually adding a wait to ensure that all copies + # are complete when the schedule exits. + T.evaluate( + T.tvm_call_packed( + "device_api.hexagon.dma_wait", + 0, # QueueId + 0, # Wait for 0 in flight + dtype="int32", + ) + ) + + return tvm.tir.Schedule(operator) + + +def evaluate(hexagon_session, sch, a, b, size_a, expected_output, use_async_copy=0): + target_hexagon = tvm.target.hexagon("v68", link_params=True) + with tvm.transform.PassContext(config={"tir.use_async_copy": use_async_copy}): + func_tir = tvm.build( + sch.mod["main"], target=tvm.target.Target(target_hexagon, host=target_hexagon) + ) + module = hexagon_session.load_module(func_tir) + + 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( + np.zeros((size_a, VRMPY_SIZE_INT32), dtype="int32"), device=hexagon_session.device + ) + + if tvm.testing.utils.IS_IN_CI: + # Run with reduced number and repeat for CI + timer = module.time_evaluator("__tvm_main__", hexagon_session.device, number=1, repeat=1) + else: + timer = module.time_evaluator("__tvm_main__", hexagon_session.device, number=10, repeat=10) + + time = timer(a_hexagon, b_hexagon, c_hexagon) + tvm.testing.assert_allclose(c_hexagon.asnumpy(), expected_output) + return round(time.mean * 1000, 4) + + [email protected] +def input_a(size_a): + return default_rng().integers(0, 8, (size_a, VRMPY_SIZE_B), dtype="uint8") + + [email protected] +def input_w(size_w): + return default_rng().integers(0, 8, (size_w, VRMPY_SIZE_B), dtype="uint8") + + [email protected] +def expected_output(size_a, size_w, input_a, input_w): + if tvm.testing.utils.IS_IN_CI and (size_a > 1024 or size_w > 1): + pytest.skip("Skipping test since it takes too long in CI.") + expected_output = np.zeros((size_a, VRMPY_SIZE_INT32), dtype="int32") + for n in range(size_a): + for x in range(size_w): + for i in range(VRMPY_SIZE_INT32): + for r in range(4): + expected_output[n, i] += np.uint32(input_a[n, i * 4 + r]) * np.uint32( + input_w[x, i * 4 + r] + ) + return expected_output + + +def get_single_dma_schedule(size_a, size_w): + a_shape = (size_a, VRMPY_SIZE_B) + w_shape = (size_w, VRMPY_SIZE_B) + out_shape = (size_a, VRMPY_SIZE_INT32) + + a_bytes = size_a * VRMPY_SIZE_B + w_bytes = size_w * VRMPY_SIZE_B + + @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, a_shape, dtype="uint8", mem_scope="global") + W = T.match_buffer(b, w_shape, dtype="uint8", mem_scope="global") + C = T.match_buffer(c, out_shape, dtype="int32", mem_scope="global") + A_global_vtcm = T.alloc_buffer(a_shape, dtype="uint8", mem_scope="global.vtcm") + W_global_vtcm = T.alloc_buffer(w_shape, dtype="uint8", mem_scope="global.vtcm") + C_global_vtcm = T.alloc_buffer(out_shape, dtype="int32", mem_scope="global.vtcm") Review Comment: @nverke just wanted to report an issue that i happened to detect using the new TVMScript parser `T.alloc_buffer` doesn't accept the parameter `mem_scope`, but instead it uses `scope`. Current generation of TVMScript parser doesn't report any error of such misuses (which is weird) but instead assumes `scope="global"`. To debug what's exactly happening, you may print out the method using `operator.show`, which is: ```python @T.prim_func def func(a_buffer: T.Buffer[(1024, 128), "uint8"], w_buffer: T.Buffer[(1, 128), "uint8"], c_buffer: T.Buffer[(1024, 32), "int32"]): # function attr dict T.func_attr({"global_symbol": "main", "tir.noalias": True}) # body # with T.block("root") a_global_vtcm = T.alloc_buffer([1024, 128], dtype="uint8") ## <==== note it's "global" rather than "global.vtcm" w_global_vtcm = T.alloc_buffer([1, 128], dtype="uint8") c_global_vtcm = T.alloc_buffer([1024, 32], dtype="int32") T.evaluate(T.tvm_call_packed("device_api.hexagon.mem_copy_DLTensor", T.tvm_stack_make_array(a_global_vtcm.data, T.tvm_stack_make_shape(1024, 128, dtype="handle"), 0, 2, "uint8", 0, dtype="handle"), T.tvm_stack_make_array(a_buffer.data, T.tvm_stack_make_shape(1024, 128, dtype="handle"), 0, 2, "uint8", 0, dtype="handle"), T.Cast("int32", 131072), dtype="int32")) T.evaluate(T.tvm_call_packed("device_api.hexagon.mem_copy_DLTensor", T.tvm_stack_make_array(w_global_vtcm.data, T.tvm_stack_make_shape(1, 128, dtype="handle"), 0, 2, "uint8", 0, dtype="handle"), T.tvm_stack_make_array(w_buffer.data, T.tvm_stack_make_shape(1, 128, dtype="handle"), 0, 2, "uint8", 0, dtype="handle"), T.Cast("int32", 128), dtype="int32")) for n, index_0 in T.grid(1024, 1): with T.block("c_buffer"): vn_index, vi_index = T.axis.remap("SR", [n, index_0]) T.reads(a_global_vtcm[vn_index, 0 : 128], w_global_vtcm[vi_index, 0 : 128], c_global_vtcm[vn_index, 0 : 32]) T.writes(c_global_vtcm[vn_index, 0 : 32]) with T.init(): for x in T.serial(32): c_global_vtcm[vn_index, x] = 0 c_global_vtcm[vn_index, 0:32] = c_global_vtcm[vn_index, 0:32] + T.call_llvm_intrin(3885, T.uint32(2), T.reinterpret(a_global_vtcm[vn_index, 0:128], dtype="int32x32"), T.reinterpret(w_global_vtcm[vi_index, 0:128], dtype="int32x32"), dtype="int32x32") T.evaluate(T.tvm_call_packed("device_api.hexagon.mem_copy_DLTensor", T.tvm_stack_make_array(c_buffer.data, T.tvm_stack_make_shape(1024, 128, dtype="handle"), 0, 2, "int32", 0, dtype="handle"), T.tvm_stack_make_array(c_global_vtcm.data, T.tvm_stack_make_shape(1024, 128, dtype="handle"), 0, 2, "int32", 0, dtype="handle"), T.Cast("int32", 131072), dtype="int32")) ``` -- This is an automated message from the Apache Git Service. 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