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[Unity] Use split rather than slice in CombineParallelMatmul #14688

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Merged
vinx13 merged 1 commit into from
Apr 21, 2023
Merged

[Unity] Use split rather than slice in CombineParallelMatmul #14688

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19 changes: 11 additions & 8 deletions src/relax/transform/combine_parallel_matmul.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -176,18 +176,21 @@ runtime::TypedPackedFunc<Map<Var, Expr>(Map<DFPattern, Var>)> GetRewriter(
}
}

PrimExpr begin{0};
Array<PrimExpr> strides{1};
int ind = 0;
Array<IntImm> sections;
for (int i = 0; i < static_cast<int>(indices.size()) - 1; ++i) {
auto width = GetTensorSInfo(rhs[i])->GetShape().value()[rhs_dim - 1].as<IntImmNode>();
ind += width->value;
sections.push_back(IntImm(DataType::Int(64), ind));
}

int lhs_dim = GetTensorSInfo(inp)->ndim;
int slice_axis = std::max<int>(lhs_dim, rhs_dim) - 1;
int split_axis = std::max<int>(lhs_dim, rhs_dim) - 1;
auto chunks = split(matmul_combined, sections, split_axis);

for (size_t i = 0; i < indices.size(); ++i) {
auto width = GetTensorSInfo(rhs[i])->GetShape().value()[rhs_dim - 1];
auto bound_var = matchings[pattern_to_replace[indices[i]]];
auto slice =
strided_slice(matmul_combined, {slice_axis}, {begin}, {begin + width}, strides);
replacements.Set(bound_var, slice);
begin += width;
replacements.Set(bound_var, TupleGetItem(chunks, i));
}
}

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116 changes: 65 additions & 51 deletions tests/python/relax/test_transform_combine_parallel_matmul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,10 +89,11 @@ def expected1(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[1], begin=[0], end=[640], strides=[1])
lv1_1 = R.strided_slice(lv1, axes=[1], begin=[640], end=[1280], strides=[1])
lv2 = R.strided_slice(lv1, axes=[1], begin=[1280], end=[1920], strides=[1])
lv3 = R.concat((lv_1, lv1_1, lv2), axis=1)
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=1)
lv_1 = lv2[0]
lv1_1 = lv2[1]
lv2_1 = lv2[2]
lv3 = R.concat((lv_1, lv1_1, lv2_1), axis=1)
R.output(lv3)
return lv3

Expand All @@ -112,10 +113,11 @@ def expected2(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[2], begin=[0], end=[640], strides=[1])
lv1_1 = R.strided_slice(lv1, axes=[2], begin=[640], end=[1280], strides=[1])
lv2 = R.strided_slice(lv1, axes=[2], begin=[1280], end=[1920], strides=[1])
lv3 = R.concat((lv_1, lv1_1, lv2), axis=1)
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=2)
lv_1 = lv2[0]
lv1_1 = lv2[1]
lv2_1 = lv2[2]
lv3 = R.concat((lv_1, lv1_1, lv2_1), axis=1)
R.output(lv3)
return lv3

Expand All @@ -141,9 +143,10 @@ def expected1(
lv1 = R.matmul(x, lv, out_dtype="float32")
lv2 = R.concat((bias, bias_1, bias_2), axis=0)
lv3 = R.add(lv1, lv2)
lv1_1 = R.strided_slice(lv3, axes=[1], begin=[0], end=[640], strides=[1])
lv3_1 = R.strided_slice(lv3, axes=[1], begin=[640], end=[1280], strides=[1])
lv5 = R.strided_slice(lv3, axes=[1], begin=[1280], end=[1920], strides=[1])
lv4 = R.split(lv3, indices_or_sections=[640, 1280], axis=1)
lv1_1 = lv4[0]
lv3_1 = lv4[1]
lv5 = lv4[2]
lv6 = R.concat((lv1_1, lv3_1, lv5), axis=1)
R.output(lv6)
return lv6
Expand All @@ -165,12 +168,13 @@ def expected2(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[1], begin=[0], end=[640], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=1)
lv_1 = lv2[0]
lv1_1 = R.add(lv_1, bias)
lv2 = R.strided_slice(lv1, axes=[1], begin=[640], end=[1280], strides=[1])
lv3 = R.strided_slice(lv1, axes=[1], begin=[1280], end=[1920], strides=[1])
lv2_1 = lv2[1]
lv3 = lv2[2]
lv4 = R.add(lv3, bias_1)
lv5 = R.concat((lv1_1, lv2, lv4), axis=1)
lv5 = R.concat((lv1_1, lv2_1, lv4), axis=1)
R.output(lv5)
return lv5

Expand All @@ -192,10 +196,11 @@ def expected1(
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv2 = R.nn.relu(lv1)
lv1_1 = R.strided_slice(lv2, axes=[1], begin=[0], end=[640], strides=[1])
lv3 = R.strided_slice(lv2, axes=[1], begin=[640], end=[1280], strides=[1])
lv5 = R.strided_slice(lv2, axes=[1], begin=[1280], end=[1920], strides=[1])
lv6 = R.concat((lv1_1, lv3, lv5), axis=1)
lv3 = R.split(lv2, indices_or_sections=[640, 1280], axis=1)
lv1_1 = lv3[0]
lv3_1 = lv3[1]
lv5 = lv3[2]
lv6 = R.concat((lv1_1, lv3_1, lv5), axis=1)
R.output(lv6)
return lv6

Expand All @@ -214,11 +219,12 @@ def expected2(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[1], begin=[0], end=[640], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=1)
lv_1 = lv2[0]
lv1_1 = R.nn.gelu(lv_1)
lv2 = R.strided_slice(lv1, axes=[1], begin=[640], end=[1280], strides=[1])
lv3 = R.nn.relu(lv2)
lv4 = R.strided_slice(lv1, axes=[1], begin=[1280], end=[1920], strides=[1])
lv2_1 = lv2[1]
lv3 = R.nn.relu(lv2_1)
lv4 = lv2[2]
lv5 = R.nn.relu(lv4)
lv6 = R.concat((lv1_1, lv3, lv5), axis=1)
R.output(lv6)
Expand All @@ -239,11 +245,13 @@ def expected3(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[1], begin=[0], end=[640], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=1)

lv_1 = lv2[0]
lv1_1 = R.nn.relu(lv_1)
lv2 = R.strided_slice(lv1, axes=[1], begin=[640], end=[1280], strides=[1])
lv3 = R.strided_slice(lv1, axes=[1], begin=[1280], end=[1920], strides=[1])
lv4 = R.concat((lv1_1, lv2, lv3), axis=1)
lv2_1 = lv2[1]
lv3 = lv2[2]
lv4 = R.concat((lv1_1, lv2_1, lv3), axis=1)
R.output(lv4)
return lv4

Expand All @@ -270,10 +278,11 @@ def expected1(
lv2 = R.concat((bias, bias_1, bias_2), axis=0)
lv3 = R.add(lv1, lv2)
lv4 = R.nn.relu(lv3)
lv2_1 = R.strided_slice(lv4, axes=[1], begin=[0], end=[640], strides=[1])
lv5 = R.strided_slice(lv4, axes=[1], begin=[640], end=[1280], strides=[1])
lv8 = R.strided_slice(lv4, axes=[1], begin=[1280], end=[1920], strides=[1])
lv9 = R.concat((lv2_1, lv5, lv8), axis=1)
lv5 = R.split(lv4, indices_or_sections=[640, 1280], axis=1)
lv2_1 = lv5[0]
lv5_1 = lv5[1]
lv8 = lv5[2]
lv9 = R.concat((lv2_1, lv5_1, lv8), axis=1)
R.output(lv9)
return lv9

Expand All @@ -297,12 +306,13 @@ def expected2(
lv1 = R.matmul(x, lv, out_dtype="float32")
lv2 = R.concat((bias, bias_1, bias_2), axis=0)
lv3 = R.add(lv1, lv2)
lv1_1 = R.strided_slice(lv3, axes=[1], begin=[0], end=[640], strides=[1])
lv4 = R.split(lv3, indices_or_sections=[640, 1280], axis=1)
lv1_1 = lv4[0]
lv2_1 = R.nn.relu(lv1_1)
lv4 = R.strided_slice(lv3, axes=[1], begin=[640], end=[1280], strides=[1])
lv6 = R.strided_slice(lv3, axes=[1], begin=[1280], end=[1920], strides=[1])
lv4_1 = lv4[1]
lv6 = lv4[2]
lv7 = R.nn.relu(lv6)
lv8 = R.concat((lv2_1, lv4, lv7), axis=1)
lv8 = R.concat((lv2_1, lv4_1, lv7), axis=1)
R.output(lv8)
return lv8

Expand All @@ -323,14 +333,15 @@ def expected3(
with R.dataflow():
lv = R.concat((y, y_1, y_2), axis=1)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv_1 = R.strided_slice(lv1, axes=[1], begin=[0], end=[640], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=1)
lv_1 = lv2[0]
lv1_1 = R.add(lv_1, bias)
lv2 = R.nn.relu(lv1_1)
lv3 = R.strided_slice(lv1, axes=[1], begin=[640], end=[1280], strides=[1])
lv4 = R.strided_slice(lv1, axes=[1], begin=[1280], end=[1920], strides=[1])
lv2_1 = R.nn.relu(lv1_1)
lv3 = lv2[1]
lv4 = lv2[2]
lv5 = R.add(lv4, bias_1)
lv6 = R.nn.relu(lv5)
lv7 = R.concat((lv2, lv3, lv6), axis=1)
lv7 = R.concat((lv2_1, lv3, lv6), axis=1)
R.output(lv7)
return lv7

Expand Down Expand Up @@ -370,11 +381,12 @@ def expected1(
with R.dataflow():
lv = R.concat((w0, w2), axis=2)
lv1 = R.matmul(x, lv, out_dtype="float32")
lv0 = R.strided_slice(lv1, axes=[2], begin=[0], end=[640], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640], axis=2)
lv0 = lv2[0]
lv1_1 = R.matmul(x, w1, out_dtype="void")
lv2 = R.strided_slice(lv1, axes=[2], begin=[640], end=[1280], strides=[1])
lv2_1 = lv2[1]
lv3 = R.matmul(x, w3, out_dtype="void")
out = lv0, lv1_1, lv2, lv3
out = lv0, lv1_1, lv2_1, lv3
R.output(out)
return out

Expand Down Expand Up @@ -449,16 +461,18 @@ def expected1(
with R.dataflow():
lv = R.concat((w0, w1, w2), axis=1)
lv1 = R.matmul(x1, lv, out_dtype="float32")
lv0 = R.strided_slice(lv1, axes=[2], begin=[0], end=[640], strides=[1])
lv1_1 = R.strided_slice(lv1, axes=[2], begin=[640], end=[1280], strides=[1])
lv2 = R.split(lv1, indices_or_sections=[640, 1280], axis=2)
lv0 = lv2[0]
lv1_1 = lv2[1]
lv_1 = R.concat((w3, w4), axis=1)
lv1_2 = R.matmul(x2, lv_1, out_dtype="float32")
lv2 = R.concat((b0, b1), axis=0)
lv3 = R.add(lv1_2, lv2)
lv5 = R.strided_slice(lv3, axes=[2], begin=[0], end=[640], strides=[1])
lv2_1 = R.strided_slice(lv1, axes=[2], begin=[1280], end=[1920], strides=[1])
lv6 = R.strided_slice(lv3, axes=[2], begin=[640], end=[1280], strides=[1])
out = lv0, lv1_1, lv2_1, lv5, lv6
lv2_1 = R.concat((b0, b1), axis=0)
lv3 = R.add(lv1_2, lv2_1)
lv4 = R.split(lv3, indices_or_sections=[640], axis=2)
lv5 = lv4[0]
lv2_2 = lv2[2]
lv6 = lv4[1]
out = lv0, lv1_1, lv2_2, lv5, lv6
R.output(out)
return out

Expand Down