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[TVM] Revert submodule updates #156

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Feb 24, 2023
Merged

[TVM] Revert submodule updates #156

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9a2a18f
Revert "[Compatible] Fix batch_matmul from Relay (#155)"
comaniac Feb 24, 2023
34fa0a0
Revert "[TVM] Update Submodule (#154)"
comaniac Feb 24, 2023
89a462b
Revert "[TVM] Update Submodule (#153)"
comaniac Feb 24, 2023
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2 changes: 1 addition & 1 deletion 3rdparty/tvm
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Submodule tvm updated from 697c72 to f7aeaf
22 changes: 1 addition & 21 deletions src/op/from_relay/nn.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -15,29 +15,9 @@ namespace raf {
namespace op {
namespace from_relay {

RAF_GENERIC_ATTR_OP_FROM_RELAY("nn.batch_matmul", "raf.op.batch_matmul_nt");
RAF_GENERIC_ATTR_OP_FROM_RELAY("nn.dense", "raf.op.dense");

// TVM's nn.batch_matmul has a transpose_a and transpose_b attribute, but RAF's
// batch_matmul_nt does not. Instead, RAF has 4 variants of batch_matmul for
// different combinations of transpose_a and transpose_b. This function
// converts the batch_matmul with transpose_a and transpose_b attributes to
// the appropriate batch_matmul variant.
RELAY_REGISTER_OP("nn.batch_matmul")
.set_attr<op::FRAFFromRelay>("FRAFFromRelay", [](const Attrs& attrs, const Array<Expr>& args,
const VarValueMap& val_map) {
const auto* relay_attrs = attrs.as<BatchMatmulAttrs>();
auto transpose_a = relay_attrs->transpose_a;
auto transpose_b = relay_attrs->transpose_b;
if (transpose_a && transpose_b) {
return Call(Op::Get("raf.op.batch_matmul_tt"), args);
} else if (transpose_a && !transpose_b) {
return Call(Op::Get("raf.op.batch_matmul_tn"), args);
} else if (!transpose_a && transpose_b) {
return Call(Op::Get("raf.op.batch_matmul_nt"), args);
}
return Call(Op::Get("raf.op.batch_matmul"), args);
});

RAF_OP_FROM_RELAY("nn.conv2d", "raf.op.conv2d",
[&](const Attrs& attrs, const Array<Expr>& args, const VarValueMap& val_map) {
Array<Expr> raf_args = args;
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27 changes: 0 additions & 27 deletions tests/python/pass/test_pass_from_relay.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1068,33 +1068,6 @@ def forward(self, m_x, m_y):
check_from_relay(model, r_func, [m_x, m_y])


@pytest.mark.parametrize("trans", [[False, False], [False, True], [True, False], [True, True]])
def test_batch_matmul(trans):
class TransposeBatchMatmul(raf.Model):
def build(self, trans):
self.op_name = "batch_matmul"
if trans[0] or trans[1]:
self.op_name += f"_{'t' if trans[0] else 'n'}{'t' if trans[1] else 'n'}"

@raf.model.trace
def forward(self, m_x, m_y):
x = raf.relu(m_x)
return getattr(raf, self.op_name)(x, m_y)

model = TransposeBatchMatmul(trans)
m_x, _ = randn((4, 10, 10), dtype="float32")
m_y, _ = randn((4, 10, 10), dtype="float32")

r_x = raf.ir.var("x", shape=(4, 10, 10), dtype="float32")
r_y = raf.ir.var("x", shape=(4, 10, 10), dtype="float32")
r_out = _relay.nn.batch_matmul(
_relay.nn.relu(r_x), r_y, transpose_a=trans[0], transpose_b=trans[1]
)
r_func = _relay.Function(params=[r_x, r_y], body=r_out)

check_from_relay(model, r_func, [m_x, m_y])


@pytest.mark.parametrize("device", get_testable_devices())
@pytest.mark.parametrize("shape", [(), (1,), (1, 2, 3, 4)])
@pytest.mark.parametrize("dtype", ["float64", "float32", "float16"])
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