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[Relay] Remove overwriting of matmul shapes when they are static #13615

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Merged
merged 3 commits into from
Dec 15, 2022
Merged

[Relay] Remove overwriting of matmul shapes when they are static #13615

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3a426fd
Remove overwriting of matmul shapes when they are static
Dec 14, 2022
eb51be4
Simplify nesting
Dec 14, 2022
f8a19fb
Add shape check to dense tests.
Dec 14, 2022
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33 changes: 21 additions & 12 deletions src/relay/op/nn/nn.h
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Original file line number Diff line number Diff line change
Expand Up @@ -113,23 +113,32 @@ bool MatmulRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
std::vector<PrimExpr> B_shape(tensor_b->shape.begin(), tensor_b->shape.end());
auto sa = A_shape.size();
auto sb = B_shape.size();
size_t index_swap_A;
size_t index_swap_B;
if (transpose_a && transpose_b) {
auto tmp = A_shape[sa - 2];
A_shape[sa - 2] = B_shape[sb - 1];
B_shape[sb - 1] = tmp;
index_swap_A = sa - 2;
index_swap_B = sb - 1;
} else if (transpose_a) {
auto tmp = A_shape[sa - 2];
A_shape[sa - 2] = B_shape[sb - 2];
B_shape[sb - 2] = tmp;
index_swap_A = sa - 2;
index_swap_B = sb - 2;
} else if (transpose_b) {
auto tmp = A_shape[sa - 1];
A_shape[sa - 1] = B_shape[sb - 1];
B_shape[sb - 1] = tmp;
index_swap_A = sa - 1;
index_swap_B = sb - 1;
} else {
auto tmp = A_shape[sa - 1];
A_shape[sa - 1] = B_shape[sb - 2];
B_shape[sb - 2] = tmp;
index_swap_A = sa - 1;
index_swap_B = sb - 2;
}

// Rewrite dynamic axes to static where constraints allow.
auto tmp = A_shape[index_swap_A];
if (A_shape[index_swap_A].as<tir::AnyNode>()) {
A_shape[index_swap_A] = B_shape[index_swap_B];
}
if (B_shape[index_swap_B].as<tir::AnyNode>()) {
B_shape[index_swap_B] = tmp;
}

// Update input types with new constrained shapes.
reporter->Assign(types[0], TensorType(A_shape, tensor_a->dtype));
reporter->Assign(types[1], TensorType(B_shape, tensor_b_dtype));
}
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3 changes: 3 additions & 0 deletions tests/python/relay/test_op_level1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
import tvm.topi.testing
from tvm.contrib.nvcc import have_fp16
import tvm.testing
from tvm.topi.utils import get_const_tuple

executor_kind = tvm.testing.parameter("graph", "vm")

Expand Down Expand Up @@ -695,6 +696,8 @@ def test_dense(executor_kind):
w = relay.var("w", relay.TensorType((k, n), dtype))
y = relay.nn.dense(x, w)
yy = run_infer_type(y)
# Confirm that input shape has not been rewritten to become dynamic.
assert get_const_tuple(yy.type_args[0].shape) == (4, 2)

n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), 2
x = relay.var("x", relay.TensorType((n, c, h, w), dtype))
Expand Down