Tianqi Chen
bbfdab79d9
## Summary - Keep the Python test launcher close to plain `pytest -n auto`, move nightly tests under `tests/nightly/python`, remove obsolete launchers and collection bookkeeping, and partition CPU/GPU jobs with explicit `gpu` marker expressions. - Repair exact-pointer regressions at their owning boundaries: packed raw-string ABI values, CUDA/Metal matrix intrinsic pointers, internal TE extern offsets, MetaSchedule scalar annotations, localized auto-tensorization scope matching, and typed DLTensor fixture fields. - Preserve typed workspace calls in TIR and cast pointer-returning external calls in CodeGenC, covered by a plain-TIRx 1024-byte global workspace that is compiled as C++. - Finish phasing out value-bearing Relax `R.Prim` annotations by requiring an explicit dtype, removing obsolete value-based contracts, and expressing the DISCO rank-dependent slices as explicit scalar `call_tir` inputs. - Gate the distributed callback on the optional DISCO runtime, NCCL, and at least two GPUs so capability-limited jobs skip instead of failing. - Remove the non-demonstrating pointer probe, use direct TVMScript comparison for packed strings, and remove the four designated legacy testing modules. The seven repaired CPU categories cover packed raw strings (7 failures), CUDA/Metal matrix access-pointer types (7), internal TE extern offsets (1), a typed DLTensor fixture (1), MetaSchedule scalar annotations (1), CodeGenC workspace return casts (12), and localized auto-tensorization storage-scope matching (19). ## Validation - Base: `ded6ad8dd212869c881efb5590f8a33fc972728e` - Head: `a7277e86dbcfe0638c8c252d36760859c4ab4297` - All 35 locally available original failing node IDs pass across the focused runs. - The full focused TE, TIR builtin-lowering, and CodeGenC files pass: 61 tests. - The complete touched Relax/TVMScript set plus PlanAndUpdateBufferAllocationLocation passes with 784 passed, 20 skipped, and 1 expected failure. - The DISCO callback collects and skips when its runtime or two-GPU environment is unavailable. - Six direct mapping tests, twelve tensor-core sketches, and the dp4a sketch pass unchanged. - The compiler rebuild, branch-wide pre-commit hooks, and full-range whitespace checks pass. - The 13 broad CBLAS/TFLite nodes remain dependency-gated; their owning TE and generated-C regressions compile. No merge is included in this change.
312 lines
14 KiB
Python
312 lines
14 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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# ruff: noqa: F841
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from collections.abc import Callable
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import pytest
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import tvm
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import tvm.testing
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from tvm import relax, tirx
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from tvm.ir import Op, VDevice
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from tvm.script import relax as R
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def test_op_correctness():
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("y", R.Tensor((2, 3), "float32"))
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assert relax.op.add(x, y).op == Op.get("relax.add")
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assert relax.op.divide(x, y).op == Op.get("relax.divide")
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assert relax.op.floor_divide(x, y).op == Op.get("relax.floor_divide")
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assert relax.op.multiply(x, y).op == Op.get("relax.multiply")
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assert relax.op.power(x, y).op == Op.get("relax.power")
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assert relax.op.atan2(x, y).op == Op.get("relax.atan2")
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assert relax.op.subtract(x, y).op == Op.get("relax.subtract")
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assert relax.op.mod(x, y).op == Op.get("relax.mod")
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assert relax.op.floor_mod(x, y).op == Op.get("relax.floor_mod")
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assert relax.op.equal(x, y).op == Op.get("relax.equal")
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assert relax.op.greater(x, y).op == Op.get("relax.greater")
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assert relax.op.greater_equal(x, y).op == Op.get("relax.greater_equal")
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assert relax.op.less(x, y).op == Op.get("relax.less")
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assert relax.op.less_equal(x, y).op == Op.get("relax.less_equal")
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assert relax.op.not_equal(x, y).op == Op.get("relax.not_equal")
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x = relax.Var("x", R.Tensor((2, 3), "int32"))
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y = relax.Var("y", R.Tensor((2, 3), "int32"))
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assert relax.op.bitwise_and(x, y).op == Op.get("relax.bitwise_and")
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assert relax.op.bitwise_or(x, y).op == Op.get("relax.bitwise_or")
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assert relax.op.bitwise_xor(x, y).op == Op.get("relax.bitwise_xor")
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assert relax.op.left_shift(x, y).op == Op.get("relax.left_shift")
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assert relax.op.right_shift(x, y).op == Op.get("relax.right_shift")
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x = relax.Var("x", R.Tensor((2, 3), "bool"))
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y = relax.Var("y", R.Tensor((2, 3), "bool"))
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assert relax.op.logical_and(x, y).op == Op.get("relax.logical_and")
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assert relax.op.logical_or(x, y).op == Op.get("relax.logical_or")
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assert relax.op.logical_xor(x, y).op == Op.get("relax.logical_xor")
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def _check_inference(bb: relax.BlockBuilder, call: relax.Call, expected_ty: relax.Type):
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ret = bb.normalize(call)
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tvm.ir.assert_structural_equal(ret.ty, expected_ty)
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binary_arith_ops = [
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(relax.op.add, tirx.Add),
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(relax.op.divide, tirx.Div),
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(relax.op.floor_divide, tirx.FloorDiv),
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(relax.op.multiply, tirx.Mul),
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(relax.op.power, tirx.pow),
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(relax.op.atan2, tirx.atan2),
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(relax.op.subtract, tirx.Sub),
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(relax.op.maximum, tirx.Max),
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(relax.op.minimum, tirx.Min),
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(relax.op.mod, tirx.Mod),
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(relax.op.floor_mod, tirx.FloorMod),
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]
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_arith_infer_ty(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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vdevice0 = VDevice("llvm")
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vdevice1 = VDevice("cuda", 0)
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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x1 = relax.Var("x", R.Tensor((1, 3), "float32"))
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x2 = relax.Var("x", R.Tensor((3, 2, 3), "float32"))
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x3 = relax.Var("x", R.Tensor((3, 1, 3), "float32"))
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x4 = relax.Var("x", R.Tensor("float32", ndim=2))
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x5 = relax.Var("x", R.Tensor())
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x6 = relax.Var("x", R.Tensor("float32", ndim=2, vdevice=vdevice0))
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x7 = relax.Var("x", R.Tensor((2, 3), "float32", vdevice0))
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y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
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y1 = relax.Var("y", R.Tensor((4, 3, 2, 1), "float32"))
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y2 = relax.Var("y", R.Tensor("float32", ndim=2))
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y3 = relax.Var("y", R.Tensor("float32", ndim=-1))
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y4 = relax.Var("y", R.Tensor((2, 3), "float32", vdevice0))
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y5 = relax.Var("y", R.Tensor("float32", ndim=2, vdevice=vdevice0))
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_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, binary_arith_op(x1, y0), relax.TensorType((2, 3), "float32"))
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_check_inference(bb, binary_arith_op(x1, y1), relax.TensorType((4, 3, 2, 3), "float32"))
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_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, binary_arith_op(x3, y2), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, binary_arith_op(x4, y0), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x4, y1), relax.TensorType(dtype="float32", ndim=4))
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_check_inference(bb, binary_arith_op(x4, y2), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x4, y3), relax.TensorType(dtype="float32", ndim=-1))
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_check_inference(bb, binary_arith_op(x5, y0), relax.TensorType(dtype="", ndim=-1))
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_check_inference(
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bb,
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binary_arith_op(x6, y5),
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relax.TensorType(dtype="float32", ndim=2, vdevice=vdevice0),
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)
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_check_inference(
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bb,
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binary_arith_op(x6, y2),
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relax.TensorType(dtype="float32", ndim=2, vdevice=vdevice0),
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)
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_check_inference(bb, binary_arith_op(x7, y4), relax.TensorType((2, 3), "float32", vdevice0))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_infer_ty_binary_arith_prim_value_with_tensor(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("y", R.Prim("float32"))
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_check_inference(bb, binary_arith_op(x, y), relax.TensorType((2, 3), "float32"))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_infer_ty_binary_arith_prim_value_with_prim_value(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Prim("float32"))
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y = relax.Var("y", R.Prim("float32"))
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_check_inference(bb, binary_arith_op(x, y), tvm.ir.PrimType("float32"))
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binary_cmp_ops = [
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(relax.op.equal, tirx.EQ),
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(relax.op.greater, tirx.GT),
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(relax.op.greater_equal, tirx.GE),
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(relax.op.less, tirx.LT),
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(relax.op.less_equal, tirx.LE),
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(relax.op.not_equal, tirx.NE),
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]
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@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
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def test_binary_cmp_infer_ty(binary_cmp_op: Callable):
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bb = relax.BlockBuilder()
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vdev0 = VDevice("llvm")
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", R.Tensor((2, 3), "float32"))
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y1 = relax.Var("y", R.Tensor((2, 3), "int32"))
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y2 = relax.Var("y", R.Tensor((2, 3), "float32", vdev0))
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_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y0), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y1), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(x, y2), relax.TensorType((2, 3), "bool", vdev0))
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@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
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def test_infer_ty_binary_cmp_prim_value_to_tensor(binary_cmp_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("y", R.Prim("float32"))
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_check_inference(bb, binary_cmp_op(x, y), relax.TensorType((2, 3), "bool"))
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_check_inference(bb, binary_cmp_op(y, x), relax.TensorType((2, 3), "bool"))
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@pytest.mark.parametrize("binary_cmp_op", [row[0] for row in binary_cmp_ops])
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def test_infer_ty_binary_cmp_prim_value_to_prim_value(binary_cmp_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Prim("float32"))
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y = relax.Var("y", R.Prim("float32"))
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_check_inference(bb, binary_cmp_op(x, y), tvm.ir.PrimType("bool"))
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_check_inference(bb, binary_cmp_op(y, x), tvm.ir.PrimType("bool"))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_infer_ty_shape_symbolic(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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m = tirx.Var("m", "int64")
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n = tirx.Var("n", "int64")
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k = tirx.Var("k", "int64")
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x0 = relax.Var("x", R.Tensor((m, n), "float32"))
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x1 = relax.Var("x", R.Tensor((1, n), "float32"))
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x2 = relax.Var("x", R.Tensor((k, n, m), "float32"))
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x3 = relax.Var("x", R.Tensor((3, 1, n), "float32"))
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x4 = relax.Var("x", R.Tensor("float32", ndim=2))
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y0 = relax.Var("y", R.Tensor((m, n), "float32"))
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y1 = relax.Var("y", R.Tensor((m, n + 2), "float32"))
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y2 = relax.Var("y", R.Tensor((4, k, m, 1), "float32"))
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y3 = relax.Var("y", R.Tensor("float32", ndim=2))
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y4 = relax.Var("y", R.Tensor("float32", ndim=-1))
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_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((m, n), "float32"))
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_check_inference(bb, binary_arith_op(x0, y1), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x1, y0), relax.TensorType((m, n), "float32"))
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_check_inference(bb, binary_arith_op(x1, y2), relax.TensorType((4, k, m, n), "float32"))
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_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType(dtype="float32", ndim=4))
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_check_inference(bb, binary_arith_op(x2, y3), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, binary_arith_op(x3, y3), relax.TensorType(dtype="float32", ndim=3))
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_check_inference(bb, binary_arith_op(x4, y0), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x4, y2), relax.TensorType(dtype="float32", ndim=4))
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_check_inference(bb, binary_arith_op(x4, y3), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x4, y4), relax.TensorType(dtype="float32", ndim=-1))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_infer_ty_shape_var(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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s0 = relax.Var("s0", relax.ShapeType(ndim=2))
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s1 = relax.Var("s1", relax.ShapeType(ndim=2))
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s2 = relax.Var("s2", relax.ShapeType(ndim=4))
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s3 = relax.Var("s3", relax.ShapeType(ndim=1))
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s4 = relax.Var("s4", relax.ShapeType())
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x = relax.Var("x", relax.TensorType(s0, "float32"))
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y0 = relax.Var("y", relax.TensorType(s0, "float32"))
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y1 = relax.Var("y", relax.TensorType(s1, "float32"))
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y2 = relax.Var("y", relax.TensorType(s2, "float32"))
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y3 = relax.Var("y", relax.TensorType(s3, "float32"))
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y4 = relax.Var("y", relax.TensorType(s4, "float32"))
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_check_inference(bb, binary_arith_op(x, y0), relax.TensorType(s0, "float32"))
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_check_inference(bb, binary_arith_op(x, y1), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x, y2), relax.TensorType(dtype="float32", ndim=4))
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_check_inference(bb, binary_arith_op(x, y3), relax.TensorType(dtype="float32", ndim=2))
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_check_inference(bb, binary_arith_op(x, y4), relax.TensorType(dtype="float32"))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_arith_infer_ty_more_input_dtype(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float64"))
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y0 = relax.Var("y", R.Tensor((2, 3), "float64"))
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x1 = relax.Var("x", R.Tensor((2, 3), "int8"))
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y1 = relax.Var("y", R.Tensor((2, 3), "int8"))
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x2 = relax.Var("x", R.Tensor((2, 3), "int64"))
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y2 = relax.Var("y", R.Tensor((2, 3), "int64"))
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_check_inference(bb, binary_arith_op(x0, y0), relax.TensorType((2, 3), "float64"))
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_check_inference(bb, binary_arith_op(x1, y1), relax.TensorType((2, 3), "int8"))
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_check_inference(bb, binary_arith_op(x2, y2), relax.TensorType((2, 3), "int64"))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_infer_ty_shape_unequal_const_int(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", R.Tensor((2, 3), "float32"))
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y0 = relax.Var("y", R.Tensor((2, 4), "float32"))
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with pytest.raises(ValueError):
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bb.normalize(binary_arith_op(x0, y0))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_arith_infer_ty_dtype_mismatch(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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y = relax.Var("y", R.Tensor((2, 3), "int32"))
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with pytest.raises(TypeError):
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bb.normalize(binary_arith_op(x, y))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_arith_infer_ty_vdevice_mismatch(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x = relax.Var("x", R.Tensor((2, 3), "float32", VDevice("llvm")))
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y = relax.Var("y", R.Tensor((2, 3), "int32", VDevice("cuda")))
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with pytest.raises(TypeError):
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bb.normalize(binary_arith_op(x, y))
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_wrong_input_number(binary_arith_op: Callable):
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x = relax.Var("x", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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binary_arith_op(x, x, x)
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with pytest.raises(TypeError):
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binary_arith_op(x)
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with pytest.raises(TypeError):
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binary_arith_op(x, x, x, x)
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@pytest.mark.parametrize("binary_arith_op", [row[0] for row in binary_arith_ops])
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def test_binary_infer_ty_wrong_input_type(binary_arith_op: Callable):
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bb = relax.BlockBuilder()
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x0 = relax.Var("x", relax.ShapeType((2, 3)))
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x1 = relax.Var("x", relax.FuncType([], R.Tensor((2, 3), "float32")))
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y = relax.Var("y", R.Tensor((2, 3), "float32"))
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with pytest.raises(TypeError):
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bb.normalize(binary_arith_op(x0, y))
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with pytest.raises(TypeError):
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bb.normalize(binary_arith_op(x1, y))
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if __name__ == "__main__":
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tvm.testing.main()
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