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Commit 360db15f authored by Shucai Xiao's avatar Shucai Xiao
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parent f9c38c09
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Showing with 74 additions and 62 deletions
src/include/migraphx/operators.hpp
View file @ 360db15f
......@@ -809,8 +809,8 @@ struct gather
// The dot operation is combination of the onnx GEMM and MatMul operators.
// For GEMM, it support two cases: 1) in the formula alpha * AB + beta * C,
// A and B are 2-D matrics and C is broadcastable to the shape of A*B. For
// the transpose of A and B, we add a tranpose operator beforehand if the
// A and B are 2-D matrics and C is broadcastable to the shape of A*B. For
// the transpose of A and B, we add a tranpose operator beforehand if the
// onnx gemm operator indicates a transpose required. 2) A and B are more
// than 2-D, then the dims except the last 2-D in A and B need to be the
// same, and C should be the same shape as A * B
......@@ -898,36 +898,39 @@ struct dot
// If there are 3 inputs, there are two scenarios:
// 1. A and B are 2-D matrices and C is broadcastable to A * B
// 2. A and B are stack of matrices, then shape for the batch
// should be the same for A and B, and C is the same shape
// as A * B (For now, we add this requirement to simplify the
// should be the same for A and B, and C is the same shape
// as A * B (For now, we add this requirement to simplify the
// implementation. we can remove this requirement later)
if(inputs.size() == 3)
{
auto a_lens = inputs[0].lens();
auto b_lens = inputs[1].lens();
auto a_lens = inputs[0].lens();
auto b_lens = inputs[1].lens();
auto out_lens = a_lens;
auto t = inputs[0].type();
if (inputs[1].lens().size() > 2)
auto t = inputs[0].type();
if(inputs[1].lens().size() > 2)
{
if(!std::equal(a_lens.rbegin() + 2, a_lens.rend(), b_lens.rbegin() + 2))
{
MIGRAPHX_THROW("DOT: dimension mismatch, operand A: {" + to_string_range(a_lens) +
"}, cannot multiply operand B: {" + to_string_range(b_lens) + "}");
MIGRAPHX_THROW("DOT: dimension mismatch, operand A: {" +
to_string_range(a_lens) + "}, cannot multiply operand B: {" +
to_string_range(b_lens) + "}");
}
std::size_t dim_0 = a_lens.size() - 2;
std::size_t dim_1 = a_lens.size() - 1;
if(a_lens[dim_1] != b_lens[dim_0])
MIGRAPHX_THROW("Inner dimensions do not match, operand A: {" + to_string_range(a_lens) +
"}, operand B: {" + to_string_range(b_lens) + "}");
MIGRAPHX_THROW("Inner dimensions do not match, operand A: {" +
to_string_range(a_lens) + "}, operand B: {" +
to_string_range(b_lens) + "}");
out_lens[dim_1] = b_lens[dim_1];
// C should be the same shape as A * B
auto c_lens = inputs[2].lens();
if(!std::equal(c_lens.begin(), c_lens.end(), out_lens.begin()))
{
MIGRAPHX_THROW("DOT: dimension mismatch, operand C: {" + to_string_range(c_lens) +
"}, cannot add to operand A * B: {" + to_string_range(out_lens) + "}");
MIGRAPHX_THROW("DOT: dimension mismatch, operand C: {" +
to_string_range(c_lens) + "}, cannot add to operand A * B: {" +
to_string_range(out_lens) + "}");
}
}
else
......@@ -938,22 +941,23 @@ struct dot
if(a_lens[1] != b_lens[0])
{
MIGRAPHX_THROW("DOT : dimension mismatch, operand A: {" + to_string_range(a_lens) +
"}, cannot multiply operand B: {" + to_string_range(b_lens) + "}");
MIGRAPHX_THROW("DOT : dimension mismatch, operand A: {" +
to_string_range(a_lens) + "}, cannot multiply operand B: {" +
to_string_range(b_lens) + "}");
}
out_lens[1] = b_lens[1];
out_lens[1] = b_lens[1];
// check whether C is broadcastable to A * B
auto c_lens = inputs[2].lens();
if(c_lens.size() > 2 ||
(c_lens.size() == 1 && (c_lens[0] != 1 && c_lens[0] != b_lens[1])) ||
(c_lens.size() == 2 && (c_lens[0] != 1 && c_lens[0] != a_lens[0])) ||
(c_lens.size() == 2 && (c_lens[1] != 1 && c_lens[1] != b_lens[1])))
(c_lens.size() == 1 && (c_lens[0] != 1 && c_lens[0] != b_lens[1])) ||
(c_lens.size() == 2 && (c_lens[0] != 1 && c_lens[0] != a_lens[0])) ||
(c_lens.size() == 2 && (c_lens[1] != 1 && c_lens[1] != b_lens[1])))
{
MIGRAPHX_THROW("DOT: C {" + to_string_range(c_lens) +
"} is not broadcastable to A * B {" + to_string_range(out_lens) +
"}");
"} is not broadcastable to A * B {" + to_string_range(out_lens) +
"}");
}
}
......
src/targets/gpu/gemm.cpp
View file @ 360db15f
......@@ -369,43 +369,47 @@ argument miopen_gemm::compute(context& ctx,
bool is_3inputs = (args.size() == 4);
if(is_3inputs)
{
fill_result(output_shape, args[3], args[2]);
fill_result(output_shape, args[3], args[2]);
output_shape.visit_type([&](auto as) {
auto n_dim = output_shape.lens().size();
auto dim_1 = n_dim - 1;
auto dim_0 = n_dim - 2;
auto alpha_r = to_rocblas_type(as(op.alpha));
auto beta_r = to_rocblas_type(as(op.beta));
bool transa = args[0].get_shape().transposed();
bool transb = args[1].get_shape().transposed();
rocblas_int lda = args[0].get_shape().strides()[transa ? dim_1 : dim_0];
rocblas_int ldb = args[1].get_shape().strides()[transb ? dim_1 : dim_0];
rocblas_int ldc = args[3].get_shape().strides()[dim_0];
auto out_lens = output_shape.lens();
rocblas_int m = out_lens[dim_0];
rocblas_int n = out_lens[dim_1];
rocblas_int k = args[0].get_shape().lens()[dim_1];
auto num_matrices = std::accumulate(out_lens.rbegin() + 2, out_lens.rend(), std::size_t{1}, std::multiplies<std::size_t>());
auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data())); };
generic_rocblas_batched_gemm(as,
ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
&alpha_r,
to_pointer(args[1]),
ldb,
k * n,
to_pointer(args[0]),
lda,
m * k,
&beta_r,
to_pointer(args[3]),
ldc,
m * n,
num_matrices);
auto n_dim = output_shape.lens().size();
auto dim_1 = n_dim - 1;
auto dim_0 = n_dim - 2;
auto alpha_r = to_rocblas_type(as(op.alpha));
auto beta_r = to_rocblas_type(as(op.beta));
bool transa = args[0].get_shape().transposed();
bool transb = args[1].get_shape().transposed();
rocblas_int lda = args[0].get_shape().strides()[transa ? dim_1 : dim_0];
rocblas_int ldb = args[1].get_shape().strides()[transb ? dim_1 : dim_0];
rocblas_int ldc = args[3].get_shape().strides()[dim_0];
auto out_lens = output_shape.lens();
rocblas_int m = out_lens[dim_0];
rocblas_int n = out_lens[dim_1];
rocblas_int k = args[0].get_shape().lens()[dim_1];
auto num_matrices = std::accumulate(out_lens.rbegin() + 2,
out_lens.rend(),
std::size_t{1},
std::multiplies<std::size_t>());
auto to_pointer = [&](auto&& arg) { return to_rocblas_type(as.from(arg.data())); };
generic_rocblas_batched_gemm(
as,
ctx.get_stream().get_rocblas(),
transb ? rocblas_operation_transpose : rocblas_operation_none,
transa ? rocblas_operation_transpose : rocblas_operation_none,
n,
m,
k,
&alpha_r,
to_pointer(args[1]),
ldb,
k * n,
to_pointer(args[0]),
lda,
m * k,
&beta_r,
to_pointer(args[3]),
ldc,
m * n,
num_matrices);
});
......
test/op_shape_test.cpp
View file @ 360db15f
......@@ -423,15 +423,19 @@ TEST_CASE(dot)
{
migraphx::shape s_m1{migraphx::shape::float_type, {1, 1, 4, 5}};
migraphx::shape s_m2{migraphx::shape::float_type, {1, 2, 5, 7}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {1, 2, 4, 7}},
migraphx::op::dot{}, s_m1, s_m2);
expect_shape(migraphx::shape{migraphx::shape::float_type, {1, 2, 4, 7}},
migraphx::op::dot{},
s_m1,
s_m2);
}
{
migraphx::shape s_m1{migraphx::shape::float_type, {1, 2, 4, 5}};
migraphx::shape s_m2{migraphx::shape::float_type, {2, 1, 5, 7}};
expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 2, 4, 7}},
migraphx::op::dot{}, s_m1, s_m2);
expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 2, 4, 7}},
migraphx::op::dot{},
s_m1,
s_m2);
}
{
......
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