コード例 #1
0
ファイル: test_rocblas.py プロジェクト: LANHUIYING/tvm
def test_matmul_add():
    n = 1024
    l = 128
    m = 235
    A = tvm.placeholder((n, l), name='A')
    B = tvm.placeholder((l, m), name='B')
    C = rocblas.matmul(A, B)
    s = tvm.create_schedule(C.op)

    def verify(target="rocm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.rocblas.matmul", True):
            print("skip because extern function is not available")
            return
        ctx = tvm.rocm(0)
        f = tvm.build(s, [A, B, C], target)
        a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
        c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
        f(a, b, c)
        tvm.testing.assert_allclose(
            c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
    verify()
コード例 #2
0
def dense_rocm(data, weight, bias=None):
    """Dense operator for rocm backend.

    Parameters
    ----------
    data : tvm.Tensor
        2-D with shape [batch, in_dim]

    weight : tvm.Tensor
        2-D with shape [out_dim, in_dim]

    bias : tvm.Tensor, optional
        1-D with shape [out_dim]

    Returns
    -------
    output : tvm.Tensor
        2-D with shape [batch, out_dim]
    """
    assert len(data.shape) == 2 and len(weight.shape) == 2, \
        "only support 2-dim dense"
    if bias is not None:
        assert len(bias.shape) == 1
    batch, in_dim = data.shape
    out_dim, _ = weight.shape
    target = tvm.target.current_target()
    if "rocblas" in target.libs:
        matmul = rocblas.matmul(data, weight, False, True)
        if bias is not None:
            matmul = tvm.compute((batch, out_dim), \
                                 lambda i, j: matmul[i, j] + bias[j], \
                                 tag=tag.BROADCAST)
        return matmul
    return dense_default(data, weight, bias)
コード例 #3
0
def test_matmul_add():
    n = 1024
    l = 128
    m = 235
    A = tvm.placeholder((n, l), name='A')
    B = tvm.placeholder((l, m), name='B')
    C = rocblas.matmul(A, B)
    s = tvm.create_schedule(C.op)

    def verify(target="rocm"):
        if not tvm.module.enabled(target):
            print("skip because %s is not enabled..." % target)
            return
        if not tvm.get_global_func("tvm.contrib.rocblas.matmul", True):
            print("skip because extern function is not available")
            return
        ctx = tvm.rocm(0)
        f = tvm.build(s, [A, B, C], target)
        a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
        b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
        c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
        f(a, b, c)
        tvm.testing.assert_allclose(c.asnumpy(),
                                    np.dot(a.asnumpy(), b.asnumpy()),
                                    rtol=1e-5)

    verify()
コード例 #4
0
def dense_rocblas(cfg, data, weight, bias=None, out_dtype=None):
    """Dense operator for rocm backend with cblas.

    Parameters
    ----------
    data : tvm.Tensor
        2-D with shape [batch, in_dim]

    weight : tvm.Tensor
        2-D with shape [out_dim, in_dim]

    bias : tvm.Tensor, optional
        1-D with shape [out_dim]

    out_dtype : str
        The output type. This is used for mixed precision.

    Returns
    -------
    output : tvm.Tensor
        2-D with shape [batch, out_dim]
    """
    assert out_dtype == data.dtype, "Mixed precision not supported."
    matmul = rocblas.matmul(data, weight, False, True)
    batch, in_dim = data.shape
    out_dim, _ = weight.shape
    cfg.add_flop(batch * in_dim * out_dim * 2)
    if bias is not None:
        matmul = tvm.compute((batch, out_dim),
                             lambda i, j: matmul[i, j] + bias[j],
                             tag=tag.BROADCAST)
    return matmul
コード例 #5
0
ファイル: dense.py プロジェクト: LANHUIYING/tvm
def dense_rocm(data, weight, bias=None):
    """Dense operator for rocm backend.

    Parameters
    ----------
    data : tvm.Tensor
        2-D with shape [batch, in_dim]

    weight : tvm.Tensor
        2-D with shape [out_dim, in_dim]

    bias : tvm.Tensor, optional
        1-D with shape [out_dim]

    Returns
    -------
    output : tvm.Tensor
        2-D with shape [batch, out_dim]
    """
    assert len(data.shape) == 2 and len(weight.shape) == 2, \
        "only support 2-dim dense"
    if bias is not None:
        assert len(bias.shape) == 1
    batch, in_dim = data.shape
    out_dim, _ = weight.shape
    target = tvm.target.current_target()
    if "rocblas" in target.libs:
        matmul = rocblas.matmul(data, weight, False, True)
        if bias is not None:
            matmul = tvm.compute((batch, out_dim), \
                                 lambda i, j: matmul[i, j] + bias[j], \
                                 tag=tag.BROADCAST)
        return matmul
    return dense_default(data, weight, bias)
コード例 #6
0
def dense_rocm(cfg, data, weight, bias=None, out_dtype=None):
    """Dense operator for rocm backend.

    Parameters
    ----------
    data : tvm.Tensor
        2-D with shape [batch, in_dim]

    weight : tvm.Tensor
        2-D with shape [out_dim, in_dim]

    bias : tvm.Tensor, optional
        1-D with shape [out_dim]

    out_dtype : str
        The output type. This is used for mixed precision.

    Returns
    -------
    output : tvm.Tensor
        2-D with shape [batch, out_dim]
    """
    assert len(data.shape) == 2 and len(weight.shape) == 2, \
        "only support 2-dim dense"
    if bias is not None:
        assert len(bias.shape) == 1
    if out_dtype is None:
        out_dtype = data.dtype
    batch, in_dim = data.shape
    out_dim, _ = weight.shape
    target = tvm.target.current_target()
    if "rocblas" in target.libs:
        assert out_dtype == data.dtype, "Mixed precision not supported."
        matmul = rocblas.matmul(data, weight, False, True)
        if bias is not None:
            matmul = tvm.compute((batch, out_dim), \
                                 lambda i, j: matmul[i, j] + bias[j], \
                                 tag=tag.BROADCAST)
        return matmul
    return dense_default(data, weight, bias, out_dtype)