mirror of
https://github.com/tile-ai/tilelang.git
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* [Feature] Implement Hoist Loop-Invariant If Pass - Added a new pass to hoist loop-invariant if statements out of loops, improving optimization opportunities. - Introduced classes for collecting and checking conditions, as well as for rewriting statements. - Integrated the new pass into the optimization pipeline and provided a corresponding API for usage. * Add tilelang semantics guide to programming guides (#1740) * Add CallNodeChecker to Loop Unswitching Pass and integrate tilelang cache disabling in benchmark script - Introduced CallNodeChecker class to identify CallNode expressions in loop conditions, enhancing loop-invariant checks. - Updated IsLoopInvariant function to reject conditions containing CallNodes, preventing potential side effects. - Added tilelang.disable_cache() in benchmark_mha_sink_fwd.py to optimize performance during benchmarking. * lint fix * lint fix * Enhance Loop Unswitching Pass with Let Binding Support - Added support for Let-bound variables in the WrittenBufferReadChecker to improve buffer read checks. - Introduced UsesLoopVarThroughLetBindings function to check if conditions depend on loop variables through Let bindings. - Updated IsLoopInvariant function to account for Let bindings when determining loop invariance. - Enhanced HoistableIfFinder to track Let bindings for variables bound to BufferLoad expressions. - Added debug print statements in the OptimizeForTarget function to visualize the module state before and after loop unswitching. * fix * lint fix * Enhance Loop Unswitching with CallNode Exclusion Check - Added CallCheckerExcludingIf class to ensure function calls outside of hoisted if statements are identified, preventing potential synchronization issues during loop unswitching. - Updated loop unswitching logic to incorporate the new call checker, enhancing safety and correctness. - Integrated debug print statements in OptimizeForTarget to visualize module state before and after loop unswitching. - Disabled tilelang cache in the benchmark script for improved performance. * Update TVM subproject to latest commit and remove debug print statements in OptimizeForTarget function * Add support for disabling loop unswitching optimization - Introduced a new configuration option `tl.disable_loop_unswitching` to allow users to disable the loop unswitching optimization. - Updated the Loop Unswitching pass to check this configuration and return the original function if the option is enabled. - Added relevant documentation in the PassConfigKey enumeration for clarity. * update tvm * Refactor: Update TileLang to use tirx instead of tir - Removed references to `tvm.tir` and replaced them with `tvm.tirx` across various files, including examples and backend operations. - Updated target configuration in documentation to reflect the new usage of target config dictionaries instead of CLI-style strings. - Cleaned up `pyproject.toml` and `load_tvm.cmake` by removing obsolete paths. - Enhanced examples for dequantization and GEMM operations to utilize the new `tirx` constructs. - Adjusted various backend operations to ensure compatibility with the new `tirx` namespace. * pass fix * Update submodule 'tvm' to latest commit f24c7b19 * Apply formatting fixes * Update CMakeLists.txt to set HIDE_PRIVATE_SYMBOLS for TileLang and modify target_host initialization in lower.py for compatibility with TVM's target handling. * Refactor CMakeLists.txt and kernel_cache.py to update target names and library dependencies for TVM integration. Adjusted output targets and library names for better compatibility across platforms. * Update repair-wheel-command in pyproject.toml to exclude libz3.so with wildcard for better compatibility with dependency packages. * Fix macOS Z3 dylib lookup * Move macOS Z3 install-name fix to install step * Update TVM submodule for runtime import fix * Update ROCm backend for TVM module factory changes * Fix build-tree Z3 runtime lookup * Fix macOS build-tree Z3 lookup * Support legacy target option strings * Normalize CUDA target in transform test * Qualify Metal stream FFI lookup * Fix PyPI Z3 packaging links * Update TVM for Windows DLL lookup * Register Windows dependency DLL directories * Bump tvm-ffi to match tirx * tvm update * Apply lint fixes * Enhance reduction operations with support for nan propagation and vectorization. Introduce new functions for preferred vectorized size and update existing reduction logic to handle packed operations for bfloat16 and float16 types. Add nan-aware min and max operations in CUDA and ROCm backends, and update related tests to validate functionality. * Refactor ReduceLowerer: Remove unnecessary buffer allocation logic for packed operations in reduce.h * Normalize PyPI Z3 dylib before linking * Drop legacy target option string support * Fix type reference for barrier annotations in CUDA copy operations. Updated `BufferLoadNode` to `tirx::BufferLoadNode` in `GetBarrier` and `LowerCluster` methods to ensure compatibility with recent changes in the TIR API. --------- Co-authored-by: Yu Cheng <yu.cheng@pku.edu.cn> Co-authored-by: Yichen Yan <oraluben@outlook.com>
380 lines
13 KiB
Python
380 lines
13 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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from tilelang import tvm
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import tvm.testing
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from tvm import te
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from tvm import tirx
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from tvm.arith.analyzer import Analyzer
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class IntSetChecker:
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def __init__(self):
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self.analyzer = tvm.arith.Analyzer()
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def verify(self, data, dmap, expected):
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res = self.analyzer.int_set(data, dmap)
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def err_msg():
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return "\ndata={}\ndmap={}\nres={}\nexpected={}".format(data, dmap, res, expected)
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assert self.analyzer.can_prove_equal(res.min_value, expected[0]), err_msg()
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assert self.analyzer.can_prove_equal(res.max_value, expected[1]), err_msg()
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def test_basic():
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s = tvm.arith.IntervalSet(2, 3)
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assert s.min_value.value == 2
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assert s.max_value.value == 3
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s = tvm.arith.IntSet.single_point(2)
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assert s.min_value.value == 2
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assert s.max_value.value == 2
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def test_vector():
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base = 10
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stride = 3
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lanes = 2
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s = tvm.arith.IntSet.vector(tvm.tirx.Ramp(base, stride, lanes))
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assert s.min_value.value == base
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assert s.max_value.value == base + stride * (lanes - 1)
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def test_scalable_vector():
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base = 5
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s = tvm.arith.IntSet.vector(tvm.tirx.Ramp(base, 2, tvm.tirx.vscale() * 4))
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assert s.min_value.value == base
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assert s.max_value.same_as(tvm.arith.int_set.pos_inf())
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def test_add_sub():
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ck = IntSetChecker()
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x, y = te.var("x"), te.var("y")
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ck.verify(x + y, {x: tvm.arith.IntervalSet(0, 10)}, (y, 10 + y))
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ck.verify(x + y, {x: tvm.arith.IntervalSet(0, 10), y: tvm.arith.IntervalSet(1, 11)}, (1, 21))
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ck.verify(x - y, {x: tvm.arith.IntervalSet(0, 10), y: tvm.arith.IntervalSet(1, 11)}, (-11, 9))
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def test_mul_div():
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ck = IntSetChecker()
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x, y = te.var("x"), te.var("y")
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tdiv = tvm.tirx.truncdiv
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ck.analyzer.update(y, tvm.arith.ConstIntBound(1, 100), override=True)
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ck.verify(x * y, {x: tvm.arith.IntervalSet(0, 10)}, (0, 10 * y))
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ck.verify(x * 2, {x: tvm.arith.IntervalSet(1, 10)}, (2, 20))
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ck.verify(x * -2, {x: tvm.arith.IntervalSet(1, 10)}, (-20, -2))
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ck.verify(tdiv(x, y), {x: tvm.arith.IntervalSet(0, 10)}, (0, tdiv(10, y)))
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ck.verify(tdiv(x, 2), {x: tvm.arith.IntervalSet(1, 10)}, (0, 5))
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fld = tvm.te.floordiv
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ck.verify(fld(x, y), {x: tvm.arith.IntervalSet(0, 10)}, (0, fld(10, y)))
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ck.verify(fld(x, 2), {x: tvm.arith.IntervalSet(-1, 10)}, (-1, 5))
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def test_mod():
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ck = IntSetChecker()
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x, y = te.var("x"), te.var("y")
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tmod = tvm.tirx.truncmod
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ck.analyzer.update(y, tvm.arith.ConstIntBound(1, 100), override=True)
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ck.verify(tmod(x, y), {x: tvm.arith.IntervalSet(0, 10)}, (0, y - 1))
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ck.verify(tmod(x, 10), {x: tvm.arith.IntervalSet(1, 10)}, (0, 9))
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flm = tvm.te.floormod
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(-10, 10)}, (0, 9))
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(3, 5)}, (3, 5))
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(13, 15)}, (3, 5))
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(3, 15)}, (0, 9))
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(3, 11)}, (0, 9))
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ck.verify(flm(x, 10), {x: tvm.arith.IntervalSet(1, 21)}, (0, 9))
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fld = tvm.te.floordiv
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z = te.var("z")
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ck.analyzer.bind(x, tvm.ir.Range.from_min_extent(0, 3))
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ck.verify(
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flm(y, 8),
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{y: tvm.arith.IntervalSet(z * 8 + x * 4, z * 8 + x * 4 + 3)},
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(
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z * 8 + x * 4 - 8 * fld(z * 8 + x * 4, 8),
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z * 8 + x * 4 + 3 - 8 * fld(z * 8 + x * 4, 8),
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),
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)
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ck1 = IntSetChecker()
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ck1.analyzer.bind(x, tvm.ir.Range.from_min_extent(0, 2))
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ck1.verify(flm(y, 8), {y: tvm.arith.IntervalSet(z * 8 + x * 4, z * 8 + x * 4 + 3)}, (x * 4, x * 4 + 3))
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def test_max_min():
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ck = IntSetChecker()
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x, y = te.var("x"), te.var("y")
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ck.verify(tvm.te.max(x, x + 1), {x: tvm.arith.IntervalSet(0, 10)}, (1, 11))
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ck.verify(tvm.te.min(x - 1, x + 1), {x: tvm.arith.IntervalSet(0, 10)}, (-1, 9))
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ck.verify(tvm.te.min(x, y), {}, (tvm.te.min(x, y), tvm.te.min(x, y)))
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ck.verify(tvm.te.max(x, y), {}, (tvm.te.max(x, y), tvm.te.max(x, y)))
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def test_select():
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ck = IntSetChecker()
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# x, y = te.var("x"), te.var("y")
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x = te.var("x")
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ck.verify(tvm.tirx.Select(x > 0, x - 1, x + 1), {x: tvm.arith.IntervalSet(0, 10)}, (-1, 11))
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def check_region_bound(expect_region, var_dom, mode, predicate=None):
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"""Helper to check region bound estimation.
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Parameters
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----------
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expect_region: dict
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The keys are of form (begin, end) or PrimExpr as a single point. The values are
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expected estimated region or region dict on different bindings.
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var_dom: dict
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Map var to iteration domain range.
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mode: str
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Specify "lowerbound", "upperbound" or else use strict bound estimation.
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predicate: PrimExpr
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Extra predicate, defaults to True.
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"""
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if predicate is None:
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predicate = tvm.tirx.IntImm("bool", 1)
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region = []
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expect = []
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for k, v in expect_region.items():
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if not isinstance(k, (tuple, list)):
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k = (k, k + 1)
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region.append(tvm.ir.Range.from_min_extent(k[0], Analyzer().simplify(k[1] - k[0])))
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expect.append(v)
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if mode == "lowerbound":
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result = tvm.arith.estimate_region_lower_bound(region=region, var_dom=var_dom, predicate=predicate)
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elif mode == "upperbound":
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result = tvm.arith.estimate_region_upper_bound(region=region, var_dom=var_dom, predicate=predicate)
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else:
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result = tvm.arith.estimate_region_strict_bound(region=region, var_dom=var_dom, predicate=predicate)
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if result is None:
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assert all([_ is None for _ in expect])
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return
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assert len(result) == len(expect)
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for intset, expect_desc in zip(result, expect):
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if isinstance(expect_desc, dict):
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# check range on different free var bindings
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for binding in expect_desc:
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analyzer = Analyzer()
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for k, v in binding:
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analyzer.bind(k, v)
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expect_begin, expect_end = expect_desc[binding]
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result_begin = analyzer.simplify(intset.min_value, 3)
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result_end = analyzer.simplify(intset.max_value + 1, 3)
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assert analyzer.can_prove_equal(result_begin - expect_begin, 0), f"{result_begin} vs {expect_begin}"
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assert analyzer.can_prove_equal(result_end - expect_end, 0), f"{result_end} vs {expect_end}"
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else:
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# check range
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expect_begin, expect_end = expect_desc
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analyzer = Analyzer()
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assert analyzer.can_prove_equal(intset.min_value - expect_begin, 0), f"{intset.min_value} vs {expect_begin}"
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assert analyzer.can_prove_equal(intset.max_value - expect_end + 1, 0), f"{intset.max_value} vs {expect_end - 1}"
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def test_region_bound_not_independent():
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# (i, i+2) and (i+2, i+4) are dependent, this the lowerbound is not available
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i = tvm.tirx.Var("i", "int32")
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var_dom = {
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i: tvm.ir.Range(begin=0, end=64),
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}
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check_region_bound({(i, i + 2): None, (i + 2, i + 4): None}, var_dom, mode="lowerbound")
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check_region_bound({(i, i + 2): (0, 65), (i + 2, i + 4): (2, 67)}, var_dom, mode="upperbound")
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# when only a subset of access indices are affine
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i, j, k = tvm.tirx.Var("i", "int32"), tvm.tirx.Var("j", "int32"), tvm.tirx.Var("k", "int32")
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var_dom = {
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i: tvm.ir.Range(begin=0, end=16),
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j: tvm.ir.Range(begin=0, end=16),
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k: tvm.ir.Range(begin=0, end=16),
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}
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check_region_bound(
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{i // 4: None, j * 4 + i % 4: None, tirx.truncdiv(k, 2): None},
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var_dom,
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predicate=j * 4 + i % 4 > 3,
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mode="lowerbound",
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)
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check_region_bound(
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{i // 4: (0, 4), j * 4 + i % 4: (4, 64), tirx.truncdiv(k, 2): (0, 8)},
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var_dom,
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predicate=j * 4 + i % 4 > 3,
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mode="upperbound",
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)
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def test_region_bound_stride_too_wide():
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i = tvm.tirx.Var("i", "int32")
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var_dom = {i: tvm.ir.Range(begin=0, end=64)}
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check_region_bound({(i * 4, i * 4 + 2): None}, var_dom, mode="lowerbound")
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check_region_bound({(i * 4, i * 4 + 2): (0, 254)}, var_dom, mode="upperbound")
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def test_region_bound_small_stride():
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i = tvm.tirx.Var("i", "int32")
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var_dom = {
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i: tvm.ir.Range(begin=0, end=64),
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}
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check_region_bound({(i * 4, i * 4 + 8): (0, 260)}, var_dom, mode="lowerbound")
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def test_region_lower_bound_split_predicate():
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x_o = tvm.tirx.Var("xo", "int32")
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x_i = tvm.tirx.Var("xi", "int32")
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x = x_o * 4 + x_i
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var_dom = {
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x_o: tvm.ir.Range(begin=0, end=16),
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x_i: tvm.ir.Range(begin=0, end=4),
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}
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check_region_bound({(x * 4, x * 4 + 8): (0, 256)}, var_dom, predicate=x < 63, mode="lowerbound")
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check_region_bound(
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{(x * 4, x * 4 + 8): (0, 256), (x * 3, x * 3 + 5): (0, 191)},
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var_dom,
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predicate=x < 63,
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mode="upperbound",
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)
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def test_region_lower_bound_multiple_variables():
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div = tvm.tirx.floordiv
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mod = tvm.tirx.floormod
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x = tvm.tirx.Var("x", "int32")
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wid = tvm.tirx.Var("wid", "int32")
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i = div(x, 16)
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j = div(mod(x, 16), 4) * 8 + mod(x, 4) + div(wid, 32) * 4
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k = wid % 32
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var_dom = {
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x: tvm.ir.Range(begin=0, end=32),
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wid: tvm.ir.Range(begin=0, end=64),
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}
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check_region_bound({i: (0, 2), j: (0, 32), k: (0, 32)}, var_dom, mode="lowerbound")
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def test_region_lower_bound_negative_scale():
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i = tvm.tirx.Var("i", "int32")
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j = tvm.tirx.Var("j", "int32")
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var_dom = {
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i: tvm.ir.Range(begin=0, end=4),
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j: tvm.ir.Range(begin=0, end=4),
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}
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check_region_bound({(1 - i, 5 - i): (-2, 5), (20 - j * 4, 36 - j * 4): (8, 36)}, var_dom, mode="lowerbound")
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def test_region_lower_bound_for_non_perfect_tile():
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h1 = tvm.tirx.Var("h1", "int32")
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h2 = tvm.tirx.Var("h2", "int32")
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h3 = tvm.tirx.Var("h3", "int32")
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# non-uniform tiling, single inner variable
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var_dom = {
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h2: tvm.ir.Range(begin=0, end=10),
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}
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check_region_bound(
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{
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h3 * 8 + h2: {
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(): (
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tvm.tirx.max(h3 * 8, 1),
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tvm.tirx.min(0, h3 * 8 - 214) + 224,
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),
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((h3, 0),): (1, 10), # h3 == 0: region is [1, 10)
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((h3, 10),): (h3 * 8, h3 * 8 + 10), # 0 < h3 <= 26: region is [h3 * 8, h3 * 8 + 10)
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((h3, 27),): (h3 * 8, 224), # h3 > 26: region is [h3 * 8, 224)
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}
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},
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var_dom,
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predicate=tvm.tirx.all(h3 * 8 + h2 >= 1, h3 * 8 + h2 < 224),
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mode="lowerbound",
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)
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# non-uniform tiling, two inner variables
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var_dom = {
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h1: tvm.ir.Range(begin=0, end=5),
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h2: tvm.ir.Range(begin=0, end=2),
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}
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check_region_bound(
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{
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h3 * 8 + h2 * 5 + h1: {
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(): (
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tvm.tirx.max(h3 * 8, 1),
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tvm.tirx.min(0, h3 * 8 - 214) + 224,
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),
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((h3, 0),): (1, 10),
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((h3, 10),): (h3 * 8, h3 * 8 + 10),
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((h3, 27),): (h3 * 8, 224),
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}
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},
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var_dom,
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predicate=tvm.tirx.all(h3 * 8 + h2 * 5 + h1 >= 1, h3 * 8 + h2 * 5 + h1 < 224),
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mode="lowerbound",
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)
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# lowerbound should fail on incompatible predicates
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check_region_bound(
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{h3 * 8 + h2 * 5 + h1: None},
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var_dom,
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predicate=tvm.tirx.all(h3 * 8 + h2 * 5 + h1 >= 1, h3 * 8 + h1 * 2 + h2 < 224),
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mode="lowerbound",
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)
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check_region_bound(
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{h3 * 8 + h2 * 5 + h1: (h3 * 8, h3 * 8 + 10)},
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var_dom,
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predicate=tvm.tirx.all(h3 * 8 + h2 * 5 + h1 >= 1, h3 * 8 + h1 * 2 + h2 < 224),
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mode="upperbound",
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)
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def test_region_lower_bound_unfusable():
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var_dom = {
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tvm.tirx.Var("i", "int32"): tvm.ir.Range(8),
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tvm.tirx.Var("j", "int32"): tvm.ir.Range(4),
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}
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i, j = var_dom
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check_region_bound({(i + j) // 2: (0, 6)}, var_dom, mode="lowerbound")
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def test_union_lower_bound():
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neg_inf = tvm.arith.int_set.neg_inf()
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pos_inf = tvm.arith.int_set.pos_inf()
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set_0 = tvm.arith.IntervalSet(min_value=neg_inf, max_value=0)
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set_1 = tvm.arith.IntervalSet(min_value=1, max_value=pos_inf)
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result = tvm.arith.int_set.union_lower_bound([set_0, set_1])
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assert result.min_value.same_as(neg_inf)
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assert result.max_value.same_as(pos_inf)
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set_2 = tvm.arith.IntervalSet(min_value=pos_inf, max_value=neg_inf)
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result = tvm.arith.int_set.union_lower_bound([set_0, set_1, set_2])
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assert result.min_value.same_as(neg_inf)
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assert result.max_value.same_as(pos_inf)
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|
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def test_modular_set():
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ck = IntSetChecker()
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x = tvm.te.var("x", dtype="int32")
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y = tvm.te.var("y", dtype="int32")
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expr = (x * 2048 + y * 16) % 7168
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ck.verify(expr, {x: tvm.arith.IntervalSet(0, 128), y: tvm.arith.IntervalSet(0, 3584)}, (0, 7152))
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if __name__ == "__main__":
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tvm.testing.main()
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