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[Arith] Improve floordiv / floormod rewrite simplifing rules #10591

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Merged
junrushao merged 2 commits into from
Mar 14, 2022
Merged

[Arith] Improve floordiv / floormod rewrite simplifing rules #10591

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a67ae6b
[Arith] Improve floordiv / floormod rewrite simplifing rules
vinx13 Mar 11, 2022
c629f4d
fix test cases
vinx13 Mar 14, 2022
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1 change: 1 addition & 0 deletions src/arith/canonical_simplify.cc
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Expand Up @@ -567,6 +567,7 @@ TVM_STATIC_IR_FUNCTOR(ReprPrinter, vtable)
p->stream << ", ";
p->Print(s);
}
p->stream << ')';
});

// Sub-class RewriteSimplifier::Impl to take benefit of
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12 changes: 12 additions & 0 deletions src/arith/rewrite_simplify.cc
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Expand Up @@ -84,6 +84,9 @@ RewriteSimplifier::Impl::CompareResult RewriteSimplifier::Impl::TryCompare(const
}
}
ConstIntBound dbound = analyzer_->const_int_bound(diff);
if (dbound->min_value == val && dbound->max_value == val) {
return kEQ;
}
if (dbound->min_value > val) {
return kGT;
}
Expand Down Expand Up @@ -819,6 +822,10 @@ PrimExpr RewriteSimplifier::Impl::VisitExpr_(const FloorDivNode* op) {
// Rules involving 3-operands.
TVM_TRY_REWRITE_IF(floordiv(x * c1 + y + z, c2), x * floordiv(c1, c2) + floordiv(y + z, c2),
c2.Eval()->value > 0 && c1.Eval()->value % c2.Eval()->value == 0);
TVM_TRY_REWRITE_IF(floordiv(x * c1 + y + z, c2), floordiv(x, floordiv(c2, c1)),
c1.Eval()->value > 0 && c2.Eval()->value > 0 &&
c2.Eval()->value % c1.Eval()->value == 0 &&
CanProveEqual(floordiv(y.Eval() + z.Eval(), c1.Eval()), 0));

TVM_TRY_REWRITE_IF(floordiv(x * c1 - y + z, c2), x * floordiv(c1, c2) + floordiv(z - y, c2),
c2.Eval()->value > 0 && c1.Eval()->value % c2.Eval()->value == 0);
Expand Down Expand Up @@ -916,6 +923,11 @@ PrimExpr RewriteSimplifier::Impl::VisitExpr_(const FloorModNode* op) {
TVM_TRY_REWRITE_IF(floormod(x * c1 + y, c2), floormod(y, c2),
c2.Eval()->value > 0 && c1.Eval()->value % c2.Eval()->value == 0);

TVM_TRY_REWRITE_IF(floormod(x * c1 + y, c2), floormod(x, floordiv(c2, c1)) * c1 + y,
c1.Eval()->value > 0 && c2.Eval()->value > 0 &&
c2.Eval()->value % c1.Eval()->value == 0 &&
analyzer_->CanProveLess(y.Eval(), c1.Eval()->value));

TVM_TRY_REWRITE_IF(floormod(x + c1, c2), floormod(x, c2),
c2.Eval()->value > 0 && c1.Eval()->value % c2.Eval()->value == 0);

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8 changes: 8 additions & 0 deletions tests/python/unittest/test_arith_rewrite_simplify.py
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Expand Up @@ -504,6 +504,11 @@ def test_floordiv_index_simplify():
ck.verify(fld(y + x * z, z), fld(y, z) + x)
ck.verify(fld(y + z * x, z), fld(y, z) + x)

ck.analyzer.update(y, tvm.arith.ConstIntBound(0, 31), override=True)
ck.analyzer.update(z, tvm.arith.ConstIntBound(0, 3), override=True)
ck.verify(fld(x * 32 + y, 64), fld(x, 2))
ck.verify(fld(x * 128 + y * 4 + z, 512), fld(x, 4))


def test_mod_index_simplify():
ck = RewriteChecker()
Expand Down Expand Up @@ -559,6 +564,9 @@ def test_floormod_index_simplify():
ck.verify(flm(x + (-10), 2), flm(x, 2))
ck.verify(flm(x + y * (-10), 2), flm(x, 2))

ck.analyzer.update(y, tvm.arith.ConstIntBound(0, 31), override=True)
ck.verify(flm(x * 32 + y, 64), flm(x, 2) * 32 + y)


def test_min_index_simplify():
ck = RewriteChecker()
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8 changes: 4 additions & 4 deletions tests/python/unittest/test_meta_schedule_feature_extractor_per_store_feature.py
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Expand Up @@ -315,7 +315,7 @@ def _create_schedule():
25.0,
16.000022888183594,
15.000043869018555,
10.001408576965332,
10.001408194392809,
0.0,
],
rtol=1e-5,
Expand Down Expand Up @@ -951,8 +951,8 @@ def _create_schedule():
0.0,
0.0,
0.0,
22.00000034396526,
22.00000034396526,
21.584962959341485,
21.584962959341485,
21.000000687930438,
0.0,
0.0,
Expand Down Expand Up @@ -1032,7 +1032,7 @@ def _create_schedule():
0.0,
0.0,
3.169925001442312,
10.001408194392809,
9.61654884377899,
8.005624549193879,
14.000088052430122,
1.584962500721156,
Expand Down
8 changes: 4 additions & 4 deletions tests/python/unittest/test_tir_transform_renormalize_split_pattern.py
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Expand Up @@ -89,12 +89,12 @@ class After_simplified:
def main(inputs: T.Buffer[(8192,), "float32"], weight: T.Buffer[(2097152,), "float32"], conv2d_transpose_nhwc: T.Buffer[(16384,), "float32"]) -> None:
# function attr dict
T.func_attr({"global_symbol": "main", "tir.noalias": True})
T.preflattened_buffer(inputs, [1, 4, 4, 512], dtype="float32", data=inputs.data)
T.preflattened_buffer(weight, [4, 4, 512, 256], dtype="float32", data=weight.data)
T.preflattened_buffer(conv2d_transpose_nhwc, [1, 8, 8, 256], dtype="float32", data=conv2d_transpose_nhwc.data)
# var definition
threadIdx_x = T.env_thread("threadIdx.x")
blockIdx_x = T.env_thread("blockIdx.x")
T.preflattened_buffer(inputs, [1, 4, 4, 512], dtype="float32", data=inputs.data)
T.preflattened_buffer(weight, [4, 4, 512, 256], dtype="float32", data=weight.data)
T.preflattened_buffer(conv2d_transpose_nhwc, [1, 8, 8, 256], dtype="float32", data=conv2d_transpose_nhwc.data)
# body
T.launch_thread(blockIdx_x, 64)
conv2d_transpose_nhwc_local = T.allocate([8], "float32", "local")
Expand All @@ -107,7 +107,7 @@ def main(inputs: T.Buffer[(8192,), "float32"], weight: T.Buffer[(2097152,), "flo
for ax0_ax1_ax2_ax3_fused_0 in T.serial(24):
PadInput_shared[ax0_ax1_ax2_ax3_fused_0 * 32 + threadIdx_x] = T.if_then_else(4 <= ax0_ax1_ax2_ax3_fused_0 and ax0_ax1_ax2_ax3_fused_0 < 20 and 1 <= blockIdx_x // 32 * 2 + ax0_ax1_ax2_ax3_fused_0 % 4 and blockIdx_x // 32 * 2 + ax0_ax1_ax2_ax3_fused_0 % 4 < 5, inputs[blockIdx_x // 32 * 1024 + ax0_ax1_ax2_ax3_fused_0 * 512 + i6_0 * 32 + threadIdx_x - 2560], T.float32(0), dtype="float32")
for ax0_ax1_ax2_ax3_fused_0 in T.serial(32):
weight_shared[T.ramp(ax0_ax1_ax2_ax3_fused_0 * 128 + threadIdx_x * 4, 1, 4)] = weight[T.ramp(ax0_ax1_ax2_ax3_fused_0 // 2 * 131072 + i6_0 * 8192 + (ax0_ax1_ax2_ax3_fused_0 * 16 + threadIdx_x // 2) % 32 * 256 + blockIdx_x % 32 * 8 + threadIdx_x % 2 * 4, 1, 4)]
weight_shared[T.ramp(ax0_ax1_ax2_ax3_fused_0 * 128 + threadIdx_x * 4, 1, 4)] = weight[T.ramp(ax0_ax1_ax2_ax3_fused_0 // 2 * 131072 + i6_0 * 8192 + ax0_ax1_ax2_ax3_fused_0 % 2 * 4096 + threadIdx_x // 2 * 256 + blockIdx_x % 32 * 8 + threadIdx_x % 2 * 4, 1, 4)]
for i6_1, i2_3, i4_2, i5_2, i6_2, i1_4, i2_4 in T.grid(4, 2, 4, 4, 8, 2, 2):
conv2d_transpose_nhwc_local[i1_4 * 4 + i2_3 * 2 + i2_4] = conv2d_transpose_nhwc_local[i1_4 * 4 + i2_3 * 2 + i2_4] + T.if_then_else((i1_4 + i4_2) % 2 == 0 and (i2_4 + i5_2) % 2 == 0, PadInput_shared[threadIdx_x // 8 * 128 + (i1_4 + i4_2) // 2 * 128 + (i2_4 + i5_2) // 2 * 32 + i2_3 * 32 + i6_1 * 8 + i6_2], T.float32(0), dtype="float32") * weight_shared[i6_1 * 64 + i6_2 * 8 + threadIdx_x % 8 + 3840 - i5_2 * 256 - i4_2 * 1024]
for ax1, ax2 in T.grid(2, 4):
Expand Down