comaniac commented on a change in pull request #9261: URL: https://github.com/apache/tvm/pull/9261#discussion_r737956211
########## File path: tests/python/contrib/test_cutlass.py ########## @@ -0,0 +1,121 @@ +# 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. +import math +import pytest +import tvm +from tvm import relay +import numpy as np +from tvm.contrib.cutlass import profile_and_build + + +def get_ref_rt_mod(mod, params): + with tvm.transform.PassContext(opt_level=3): + lib = relay.build(mod, target="cuda", params=params) + dev = tvm.device("cuda", 0) + rt_mod = tvm.contrib.graph_executor.GraphModule(lib["default"](dev)) + return rt_mod, dev + + +def get_output(rt_mod, x): + rt_mod.set_input("data", x) + rt_mod.run() + return rt_mod.get_output(0).asnumpy() + + +def get_dense(M, N, K, out_dtype="float16"): + data = relay.var("data", shape=(M, K), dtype="float16") + weight = relay.var("weight", shape=(N, K), dtype="float16") + return relay.nn.dense(data, weight, out_dtype=out_dtype) + + +def get_dense_bias(M, N, K, out_dtype="float16"): + dense = get_dense(M, N, K, out_dtype=out_dtype) + bias = relay.var("bias", shape=(N,), dtype=out_dtype) + return relay.nn.bias_add(dense, bias) + + +def get_dense_bias_relu(M, N, K, out_dtype="float16"): + return relay.nn.relu(get_dense_bias(M, N, K, out_dtype="float16")) + + +def get_dense_bias_gelu(M, N, K, out_dtype="float16"): + bias_add = get_dense_bias(M, N, K, out_dtype) + mul = bias_add * relay.const((1.0 / math.sqrt(2.0)), dtype=out_dtype) + if out_dtype == "float16": + erf = relay.cast(relay.op.erf(relay.cast(mul, "float32")), "float16") + else: + erf = relay.op.erf(mul) + mul_half = erf * relay.const(0.5, dtype=out_dtype) + add = mul_half + relay.const(0.5, dtype=out_dtype) + return add * bias_add + + +def verify(func, M, N, K, sm=80, atol=1e-5, rtol=1e-5, run_benchmark=False): + if not tvm.get_global_func("relay.ext.cutlass", True): + return + mod = tvm.IRModule.from_expr(func) + typ = relay.transform.InferType()(mod) + out_dtype = typ["main"].body.checked_type.dtype + np_data = np.random.uniform(-1, 1, (M, K)).astype("float16") + np_weight = np.random.uniform(-1, 1, (N, K)).astype("float16") + np_bias = np.random.uniform(-1, 1, (N,)).astype(out_dtype) + + params = {"weight": np_weight, "bias": np_bias} + + rt_mod_ref, dev = get_ref_rt_mod(mod, params) + rt_mod, dev, num_partition = profile_and_build(mod, params, sm, tmp_dir="tmp") Review comment: What I'm worrying about is actually the CI time. I believe the tuning time in this case would definitely be much shorter than AutoTVM/Ansor, so it should be fine in the real use cases. Maybe we could provide some heuristics to control the CI time. For example: 1. A level/mode indicating the number of candidates to be profiled. We could use the lowest level in the CI, and recommend the highest level for users. 2. Simply let users specify the maximum number of candidates. -- 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]
