juda commented on code in PR #11911: URL: https://github.com/apache/tvm/pull/11911#discussion_r918473322
########## apps/pt_tvmdsoop/tests/test_as_torch.py: ########## @@ -0,0 +1,148 @@ +#!/usr/bin/env python + +# 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 script for tvm torch module""" +import numpy as np + +import torch +import torch.nn + +import tvm +import tvm.testing +from tvm.contrib.torch import as_torch +from tvm.script import tir as T + + +@as_torch +def matmul(M: int, N: int, K: int, dtype: str): + @T.prim_func + def main(a: T.handle, b: T.handle, c: T.handle) -> None: + A = T.match_buffer(a, [M, K], dtype=dtype) + B = T.match_buffer(b, [N, K], dtype=dtype) + C = T.match_buffer(c, [M, N], dtype=dtype) + for i, j, k in T.grid(M, N, K): + with T.block(): + vi, vj, vk = T.axis.remap("SSR", [i, j, k]) + with T.init(): + C[vi, vj] = T.float32(0) + C[vi, vj] = C[vi, vj] + A[vi, vk] * B[vj, vk] + + return main + + +@as_torch [email protected]_module +class MyModule: + @T.prim_func + def main(a: T.handle, b: T.handle): + # We exchange data between function by handles, which are similar to pointer. + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + # Create buffer from handles. + A = T.match_buffer(a, (8,), dtype="float32") + B = T.match_buffer(b, (8,), dtype="float32") + for i in range(8): + # A block is an abstraction for computation. + with T.block("B"): + # Define a spatial block iterator and bind it to value i. + vi = T.axis.spatial(8, i) + B[vi] = A[vi] + 1.0 + + +@as_torch [email protected]_module +class ModuleGPU: + @T.prim_func + def main(A: T.Buffer[8, "float32"], B: T.Buffer[8, "float32"]) -> None: + T.func_attr({"global_symbol": "main", "tir.noalias": True}) + for i_0 in T.thread_binding(2, thread="blockIdx.x"): + for i_2 in T.thread_binding(2, thread="threadIdx.x"): + for i_1 in T.serial(2): + with T.block("B"): + vi = T.axis.spatial(8, i_0 * 4 + i_1 * 2 + i_2) + T.reads(A[vi]) + T.writes(B[vi]) + B[vi] = A[vi] + T.float32(1) + + +class MinuesOnes(torch.nn.Module): + def __init__(self): + super(MinuesOnes, self).__init__() + self.engine = MyModule + + def forward(self, *input): + self.engine.forward(*input) + return input[-1] - 1 + + +def test_tvmscript_torch_matmul(): + s1 = np.ones((128, 128)).astype("float32") + s2 = np.ones((128, 128)).astype("float32") Review Comment: fixed -- 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: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
