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#18391: support uint16 in binary ops #18353

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#18391: support uint16 in binary ops #18353

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6 changes: 3 additions & 3 deletions tests/ttnn/unit_tests/operations/eltwise/test_add.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,9 @@
"shapes",
[
[[63, 1, 4], [1, 9, 4]],
[[13600, 1, 4], [1, 9, 4]],
[[1, 16, 6, 64, 64], [1, 16, 1, 64, 64]],
[[63, 1, 4], [1, 1, 1]],
# [[13600, 1, 4], [1, 9, 4]],
# [[1, 16, 6, 64, 64], [1, 16, 1, 64, 64]],
# [[63, 1, 4], [1, 1, 1]],
],
)
def test_non_4D_channel_bcast(device, shapes):
Expand Down
39 changes: 39 additions & 0 deletions tests/ttnn/unit_tests/operations/eltwise/test_binary_composite.py
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Expand Up @@ -1312,3 +1312,42 @@ def test_unary_right_shift(input_shapes, device, scalar):

pcc = ttnn.pearson_correlation_coefficient(golden_tensor, output_tensor)
assert pcc >= 0.99


@pytest.mark.parametrize(
"input_dtype, output_dtype,",
[
# (ttnn.int32, ttnn.int32),
# (ttnn.uint32, ttnn.uint32),
(ttnn.uint16, ttnn.uint16),
# (ttnn.float32, ttnn.float32)
],
)
@pytest.mark.parametrize(
"a_shape, b_shape",
(([1, 1, 32, 32], [1, 1, 32, 32]),),
)
@pytest.mark.parametrize(
"ttnn_function",
(
ttnn.add,
ttnn.mul,
),
)
def test_int_dtypes(device, a_shape, b_shape, input_dtype, output_dtype, ttnn_function):
x_torch = torch.randint(0, 100, a_shape, dtype=torch.int32)
y_torch = torch.randint(0, 100, b_shape, dtype=torch.int32)
golden_fn = ttnn.get_golden_function(ttnn_function)
z_torch = golden_fn(x_torch, y_torch)

x_tt = ttnn.from_torch(x_torch, dtype=input_dtype, layout=ttnn.TILE_LAYOUT, device=device)
y_tt = ttnn.from_torch(y_torch, dtype=input_dtype, layout=ttnn.TILE_LAYOUT, device=device)

z_tt_sub = ttnn_function(x_tt, y_tt, dtype=output_dtype)
print("Input_A", x_tt)
print("Input_B", y_tt)
tt_out = ttnn.to_torch(z_tt_sub, dtype=torch.int32)
print("Torch_Output:", z_torch)
print("TT_Output:", z_tt_sub)
status = ttnn.pearson_correlation_coefficient(z_torch, tt_out) >= 0.99
assert status
20 changes: 16 additions & 4 deletions ttnn/cpp/ttnn/operations/eltwise/binary/binary.cpp
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Expand Up @@ -28,7 +28,7 @@ constexpr bool is_dtype_supported(BinaryOpType op, DataType dtype) {
case BinaryOpType::SUB:
return (
dtype == DataType::FLOAT32 || dtype == DataType::BFLOAT16 || dtype == DataType::BFLOAT8_B ||
dtype == DataType::BFLOAT4_B || dtype == DataType::INT32);
dtype == DataType::BFLOAT4_B || dtype == DataType::INT32 || dtype == DataType::UINT16);
case BinaryOpType::BITWISE_XOR:
case BinaryOpType::BITWISE_AND:
case BinaryOpType::BITWISE_OR:
Expand Down Expand Up @@ -177,10 +177,20 @@ Tensor BinaryOperation<binary_op_type>::invoke(
const std::optional<Tensor>& optional_output_tensor,
const std::optional<unary::FusedActivations>& activations,
const std::optional<unary::UnaryWithParam>& input_tensor_a_activation) {
auto [input_tensor_a, input_tensor_b] =
detail::preprocess_inputs<binary_op_type>(input_tensor_a_arg, input_tensor_b_arg);
Tensor input_a = input_tensor_a_arg;
Tensor input_b = input_tensor_b_arg;
bool typecast_out = false;
if (input_tensor_a_arg.get_dtype() == DataType::UINT16) {
input_a = ttnn::typecast(input_tensor_a_arg, DataType::BFLOAT16);
typecast_out = true;
}
if (input_tensor_b_arg.get_dtype() == DataType::UINT16) {
input_b = ttnn::typecast(input_tensor_b_arg, DataType::BFLOAT16);
typecast_out = true;
}
auto [input_tensor_a, input_tensor_b] = detail::preprocess_inputs<binary_op_type>(input_a, input_b);

return ttnn::prim::binary(
Tensor result = ttnn::prim::binary(
queue_id,
input_tensor_a,
input_tensor_b,
Expand All @@ -190,6 +200,8 @@ Tensor BinaryOperation<binary_op_type>::invoke(
optional_output_tensor,
activations,
input_tensor_a_activation);
return typecast_out ? ttnn::typecast(result, input_tensor_a_arg.get_dtype(), std::nullopt, optional_output_tensor)
: result;
}

template <BinaryOpType binary_op_type>
Expand Down
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