junrushao commented on code in PR #12895: URL: https://github.com/apache/tvm/pull/12895#discussion_r990227657
########## tests/python/unittest/test_meta_schedule_vnni_integration.py: ########## @@ -0,0 +1,249 @@ +# 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. +# pylint: disable=missing-docstring +import logging +import tempfile +from typing import Optional + +import numpy as np # type: ignore +import pytest +import tvm +from tvm import meta_schedule as ms +from tvm import relay +from tvm._ffi import register_func +from tvm.tir.schedule import BlockRV, Schedule +from tvm.tir.tensor_intrin.x86 import VNNI_DOT_16x4_INTRIN as VNNI_INTRIN + +logging.basicConfig( + format="%(asctime)s.%(msecs)03d %(levelname)s %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logging.getLogger("tvm.meta_schedule").setLevel(logging.DEBUG) + + +def _schedule_dense(m: Optional[int], do_tune: bool): + """Manually schedule a dense block, created from TE compute op via CreatePrimFunc, + using VNNI instruction. + """ + + def schedule_fn(sch, dense_block: Optional[BlockRV] = None) -> bool: + if "dense" not in sch.mod.attrs["task_name"]: + return False + if dense_block is None: + dense_block = sch.get_block("compute") + assert "dense_vnni" in sch.get(dense_block).annotations["schedule_rule"] + + post_blocks = sch.get_consumers(dense_block) + if len(post_blocks) > 0: + # Fuse all intermediate post ops into the last op. + # This is equivalent to the traverse_inline function used in TE schedules. + while True: + next_post_blocks = [] + for post_block in post_blocks: + next_consumers = sch.get_consumers(post_block) + if len(next_consumers) > 0: + sch.compute_inline(post_block) + next_post_blocks += next_consumers + if len(next_post_blocks) == 0: + assert len(post_blocks) == 1 + outer_block = post_blocks[0] + a_y, a_x = sch.get_loops(outer_block)[-2:] + break + post_blocks = next_post_blocks + else: + a_y, a_x, _ = sch.get_loops(dense_block)[-3:] + outer_block = dense_block + if do_tune: + y_factors = sch.sample_perfect_tile(a_y, n=2, max_innermost_factor=128) + a_yo, a_yi = sch.split(a_y, factors=y_factors) + else: + a_yo, a_yi = sch.split(a_y, factors=[None, min(m, 64)]) + a_xo, a_xi = sch.split(a_x, factors=[None, 16]) + sch.reorder(a_yo, a_xo, a_yi, a_xi) + fused = sch.fuse(a_yo, a_xo) + if outer_block != dense_block: + # Handle the case when dense is fused with post ops. + sch.vectorize(a_xi) + sch.compute_at(dense_block, a_yi) + a_xi, a_k = sch.get_loops(dense_block)[-2:] + a_ko, a_ki = sch.split(a_k, factors=[None, 4]) + sch.reorder(a_ko, a_xi, a_ki) + # We need to parallelize before decompose_reduction, otherwise the so-called "Compact dataflow" + # condition is violated. + sch.parallel(fused) + dec = sch.decompose_reduction(dense_block, a_ko) + init_loop = sch.get_loops(dec)[-1] + sch.vectorize(init_loop) + sch.tensorize(a_xi, VNNI_INTRIN) + return True + + return schedule_fn + + +def _relay_dense(m, n, k): + data = relay.var("data", shape=(m, k), dtype="uint8") + weight = relay.var("weight", shape=(n, k), dtype="int8") + bias = relay.var("bias", shape=(n,), dtype="int32") + # dense is tuned by the TIR schedule above, bmm is scheduled by TE (topi/x86/batch_matmul.py) + dense = relay.nn.dense(data, weight, out_dtype="int32") + bias_add = relay.nn.bias_add(dense, bias) + relay.const(1, dtype="int32") + out = relay.nn.batch_matmul( + relay.cast(relay.expand_dims(bias_add, 0), "uint8"), + relay.cast(relay.expand_dims(bias_add, 0), "int8"), + out_dtype="int32", + ) + relay_mod = tvm.IRModule.from_expr(out) + data = np.random.uniform(1, 10, size=(m, k)).astype("uint8") + params = { + "weight": np.random.uniform(1, 10, size=(n, k)).astype("int8"), + "bias": np.random.uniform(1, 10, size=(n,)).astype("int32"), + } + + def f_check(lib, dev): + ref = ( + relay.create_executor( + "vm", + mod=relay_mod, + device=dev, + target="llvm", + ) + .evaluate()(data, params["weight"], params["bias"]) + .numpy() + ) + runtime = tvm.contrib.graph_executor.GraphModule(lib["default"](dev)) + runtime.set_input("data", data) + runtime.run() + out = runtime.get_output(0).numpy() + np.testing.assert_equal(out, ref) + + return relay_mod, params, f_check + + [email protected]("Requires cascadelake") +def test_vnni_schedule_fn_database(): + m, n, k = 1024, 1024, 1024 + target = tvm.target.Target("llvm -mcpu=cascadelake -num-cores 4") + dev = tvm.cpu(0) + relay_mod, params, f_check = _relay_dense(m, n, k) + + with ms.database.ScheduleFnDatabase( + _schedule_dense( + m=m, + do_tune=False, + ) + ), tvm.transform.PassContext( + opt_level=3, + config={"relay.backend.use_meta_schedule": True}, + ): + # pylint: disable=W0105 + """The log should say + Warning: Cannot find workload: tvmgen_default_fused_expand_dims + Warning: Cannot find workload: tvmgen_default_fused_cast + Warning: Cannot find workload: tvmgen_default_fused_cast_1 + Warning: Cannot find workload: tvmgen_default_fused_nn_batch_matmul + + This means batch matmul and others are scheduled by TE, and dense (the one not warned) + is found in the meta schedule tuning database during compilation + """ + # pylint: enable=W0105 + lib = relay.build(relay_mod, target=target, params=params) + f_check(lib, dev) + + [email protected]("Requires cascadelake") +def test_vnni_schedule_fn_tune(): Review Comment: this should work ``` space=ms.space_generator.PostOrderApply( f_block_filter=None, sch_rules="from-target", postprocs=[], mutator_probs="from-target", ), ) ``` -- This is an automated message from the Apache Git Service. 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