seamless_communication/ggml/test_unity_cpp.py

import ggml
import ctypes
import torch
import pytest
import numpy as np
import torch
import fairseq2.nn
import fairseq2.nn.transformer
from ctypes import c_void_p
from typing import Any
from pathlib import Path
from typing import Iterator
from ggml import NativeObj
from ggml_convert import convert_model
from seamless_communication.models.unity import load_unity_model

Ctx = ggml.ggml_context_p

UNITY_MODELS = Path(__file__).parent / "examples/unity/models"
PARAMS_256MB = ggml.ggml_init_params(mem_size=256 * 1024 * 1024, mem_buffer=None)


@pytest.fixture(name="ctx")
def _ctx() -> Iterator[Ctx]:
    """Allocate a new context with 256 MB of memory"""
    try:
        ctx = ggml.ggml_init(params=PARAMS_256MB)
        yield ctx
    finally:
        ggml.ggml_free(ctx)


def test_ggml_bindings_work(ctx: Ctx) -> None:
    # Instantiate tensors
    x = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)
    b = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 1)

    # Use ggml operations to build a computational graph
    x2 = ggml.ggml_mul(ctx, x, x)
    f = ggml.ggml_add(ctx, ggml.ggml_mul(ctx, a, x2), b)

    gf = ggml.ggml_build_forward(f)

    # Set the input values
    ggml.ggml_set_f32(x, 2.0)
    ggml.ggml_set_f32(a, 3.0)
    ggml.ggml_set_f32(b, 4.0)

    # Compute the graph
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    # Get the output value
    output = ggml.ggml_get_f32_1d(f, 0)
    assert output == 16.0


def test_ggml_matmul(ctx: Ctx) -> None:
    # Instantiate tensors
    a = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 4, 2)
    x = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 4, 3)

    # Use ggml operations to build a computational graph
    y = ggml.ggml_mul_mat(ctx, a, x)
    assert ggml.shape(y) == (3, 2)
    gf = ggml.ggml_build_forward(y)

    # Set the input values
    ggml.ggml_set_f32(x, 0.0)
    for i in range(4 * 3):
        ggml.ggml_set_f32_1d(x, i, i)

    ggml.ggml_set_f32(a, 0.0)
    ggml.ggml_set_f32_1d(a, 1, 1.0)
    ggml.ggml_set_f32_1d(a, 7, 1.0)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)
    output = [[ggml.ggml_get_f32_1d(y, j * 2 + i) for j in range(3)] for i in range(2)]
    assert output == [[1, 5, 9], [3, 7, 11]]


def test_shape_works(ctx: Ctx) -> None:
    """GGML shape order convention is the reverse from numpy"""
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 10)
    assert ggml.shape(a) == (10,)

    b = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 11, 21)
    assert ggml.shape(b) == (21, 11)

    c = ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F32, 12, 22, 32)
    assert ggml.shape(c) == (32, 22, 12)


def test_nb_works(ctx: Ctx) -> None:
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 10)
    assert ggml.nb(a) == (4, 40, 40, 40)

    b = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F16, 11, 21)
    assert ggml.nb(b) == (2, 22, 462, 462)

    c = ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F32, 12, 22, 32)
    assert ggml.nb(c) == (4, 48, 1056, 33792)


@pytest.mark.xfail(reason="TODO: fix strides")
def test_strides_works(ctx: Ctx) -> None:
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 10)
    assert ggml.strides(a) == np.ones((10,), dtype=np.float32).strides

    b = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 11, 21)
    assert ggml.strides(b) == np.ones((11, 21), dtype=np.float32).strides

    c = ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F32, 12, 22, 32)
    assert ggml.strides(c) == np.ones((12, 22, 32), dtype=np.float32).strides


def test_to_numpy_works_with_f32(ctx: Ctx) -> None:
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 10)
    na = ggml.to_numpy(a)
    for i in range(10):
        ggml.ggml_set_f32_1d(a, i, i)
    assert na[5] == 5
    assert np.allclose(na, np.array(range(10), dtype=np.float32))
    ggml.ggml_set_f32_1d(a, 5, -1.5)
    assert na[5] == -1.5

    # Note: GGML order of dims is reversed wrt numpy shapes
    b = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 11, 21)
    for i in range(11 * 21):
        ggml.ggml_set_f32_1d(b, i, i)
    nb = ggml.to_numpy(b)
    # assert nb.shape == (21, 11)
    assert nb[0, 5] == 5
    assert nb[3, 5] == 11 * 3 + 5
    assert np.allclose(
        nb, np.array(range(11 * 21), dtype=np.float32).reshape(ggml.shape(b))
    )
    ggml.ggml_set_f32_1d(b, 11 * 3 + 5, -1.5)
    assert nb[3, 5] == -1.5

    sum_rows = ggml.ggml_sum_rows(ctx, b)
    gf = ggml.ggml_build_forward(sum_rows)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)
    np_sum_rows = np.sum(nb, axis=-1, keepdims=True)
    assert np_sum_rows.shape == ggml.shape(sum_rows)
    for i in range(11):
        assert np_sum_rows[i] == ggml.ggml_get_f32_1d(sum_rows, i)

    c = ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F32, 12, 22, 32)
    for i in range(12 * 22 * 32):
        ggml.ggml_set_f32_1d(c, i, i)
    nc = ggml.to_numpy(c)
    assert ggml.shape(c) == (32, 22, 12)
    assert nc[3, 5, 11] == 22 * 12 * 3 + 12 * 5 + 11
    assert np.allclose(
        nc, np.array(range(12 * 22 * 32), dtype=np.float32).reshape(ggml.shape(c))
    )
    ggml.ggml_set_f32_1d(c, 22 * 12 * 3 + 12 * 5 + 11, -1.5)
    assert nc[3, 5, 11] == -1.5


def test_from_numpy_works_with_f32(ctx: Ctx) -> None:
    a = np.random.normal(size=(10,)).astype(dtype=np.float32)
    ga = ggml.from_numpy(ctx, a)
    assert ggml.shape(ga) == (10,)
    assert ggml.nb(ga) == ggml.nb(ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F32, 10))
    assert np.allclose(a, ggml.to_numpy(ga))

    a = np.random.normal(size=(11, 21)).astype(dtype=np.float32)
    ga = ggml.from_numpy(ctx, a)
    assert ggml.shape(ga) == (11, 21)
    assert ggml.nb(ga) == ggml.nb(
        ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, *a.shape[::-1])
    )
    assert np.allclose(a, ggml.to_numpy(ga))

    a = np.random.normal(size=(12, 22, 32)).astype(dtype=np.float32)
    ga = ggml.from_numpy(ctx, a)
    assert ggml.shape(ga) == (12, 22, 32)
    assert ggml.nb(ga) == ggml.nb(
        ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F32, *a.shape[::-1])
    )
    assert np.allclose(a, ggml.to_numpy(ga))


def test_to_numpy_works_with_f16(ctx: Ctx) -> None:
    # We explicitly fill the tensor otherwise they might have non-zero values in them.
    a = ggml.ggml_new_tensor_1d(ctx, ggml.GGML_TYPE_F16, 10)
    na = ggml.to_numpy(a)
    ggml.ggml_set_f32(a, 2.14)
    assert np.allclose(na, np.ones((10,), dtype=np.float16) * 2.14)
    ggml.ggml_set_f32(a, 4.28)
    assert np.allclose(na, np.ones((10,), dtype=np.float16) * 4.28)

    b = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F16, 11, 21)
    nb = ggml.to_numpy(b)
    ggml.ggml_set_f32(b, 4.18)
    assert np.allclose(nb, np.ones((21, 11), dtype=np.float16) * 4.18)
    ggml.ggml_set_f32(b, 5.12)
    assert np.allclose(nb, np.ones((21, 11), dtype=np.float16) * 5.12)

    c = ggml.ggml_new_tensor_3d(ctx, ggml.GGML_TYPE_F16, 12, 22, 32)
    nc = ggml.to_numpy(c)
    ggml.ggml_set_f32(c, 3.16)
    assert np.allclose(nc, np.ones((32, 22, 12), dtype=np.float16) * 3.16)
    ggml.ggml_set_f32(c, 5.08)
    assert np.allclose(nc, np.ones((32, 22, 12), dtype=np.float16) * 5.08)


def test_from_numpy_works_with_f16(ctx: Ctx) -> None:
    a = np.random.normal(size=(10,)).astype(dtype=np.float16)
    ga = ggml.from_numpy(ctx, a)
    assert np.allclose(a, ggml.to_numpy(ga))
    a = np.random.normal(size=(11, 21)).astype(dtype=np.float16)
    ga = ggml.from_numpy(ctx, a)
    assert np.allclose(a, ggml.to_numpy(ga))
    a = np.random.normal(size=(12, 22, 32)).astype(dtype=np.float16)
    ga = ggml.from_numpy(ctx, a)
    assert np.allclose(a, ggml.to_numpy(ga))


def test_to_numpy_works_with_transposed(ctx: Ctx) -> None:
    ga = ggml.ggml_new_tensor_2d(ctx, ggml.GGML_TYPE_F32, 10, 5)
    a = ggml.to_numpy(ga)
    a[...] = np.arange(50).reshape(5, 10).astype(dtype=np.float32)

    gat = ggml.ggml_transpose(ctx, ga)

    gf = ggml.ggml_build_forward(ga)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    at = ggml.to_numpy(gat)

    assert np.allclose(a.T, at)


def test_ning_model_load(ctx: Ctx) -> None:
    pytest.skip("borken")
    model, vocab = ggml.unity_model_load(UNITY_MODELS / "unity-large/ggml-model.bin")
    print(model, vocab)

    example = ggml.from_file(
        ctx, UNITY_MODELS / "unity-large/seqs_before_conformer_block.bin", (1024, 137)
    )

    with ggml.MeasureArena() as arena:
        graph = ggml.unity_audio_encoder_graph(model, example)
        # TODO: why the extra memory ?
        mem_size = ggml.ggml_allocr_alloc_graph(arena, graph) + ggml.GGML_MEM_ALIGN

    with ggml.FixedSizeArena(mem_size) as allocr:
        print(
            f"unity_audio_encoder_graph: compute buffer size: {mem_size/1024/1024} MB"
        )

        eval_res_ptr = ggml.unity_eval(allocr, model, example, 1)
        eval_res = eval_res_ptr.contents
        inpL = ggml.to_numpy(eval_res.nodes[eval_res.n_nodes - 1])
        expected_raw = "-0.1308,0.0346,-0.2656,0.2873,-0.0104,0.0574,0.4033,-0.1125,-0.0460,-0.0496"
        expected = map(float, expected_raw.split(","))
        assert np.allclose(inpL[0, :10], list(expected), atol=1e-4)


@pytest.fixture(scope="module")
def g_model_once() -> Iterator[c_void_p]:
    model_file = Path(__file__).parent / "seamlessM4T_medium.ggml"
    if not model_file.exists():
        convert_model("seamlessM4T_medium", model_file)
    with ggml.load_unity_ggml_file(model_file) as model:
        yield model


@pytest.fixture()
def g_model(ctx: Ctx, g_model_once: c_void_p) -> c_void_p:
    ggml.lib.fairseq2_model_set_inference_ctx(g_model_once, ctx)
    return g_model_once


@pytest.fixture(scope="module")
def pt_model() -> Iterator[Any]:
    model = load_unity_model("seamlessM4T_medium")
    print(model)
    model.eval()
    with torch.inference_mode():
        yield model


@pytest.mark.xfail(reason="TODO")
def test_hparams_code_is_up_to_date() -> None:
    model_file = Path(__file__).parent / "seamlessM4T_medium.ggml"

    hparams_header_file = model_file.with_suffix(".hparams.h")
    hparams_struct = hparams_header_file.read_text().strip()
    actual_code = (UNITY_MODELS.parent / "unity_model_loader.h").read_text()
    assert hparams_struct in actual_code


def test_numpy_mul_mat(ctx: Ctx) -> None:
    slen, d_in, d_out = (5, 4, 2)
    # torch.nn and fairseq2.nn assumes (seq_len, dim) to represent inputs,
    x = np.zeros((slen, d_in), dtype=np.float32)  # (seq_len, dim_in)
    x[0, :] = [1, 1 / 3, 0, 0]

    weight = np.eye(d_out, d_in, dtype=np.float32)
    weight[1, 1] = 1
    # assert weight.shape == (d_out, d_in) # (dim_out, dim_in)
    y_exp = x @ weight.T  # (seq_len, dim_out)

    gx = ggml.from_numpy(ctx, x)  # (dim_in, seq_len)
    gw = ggml.from_numpy(ctx, weight)  # (dim_in, dim_out)
    # gb = ggml.from_numpy(ctx, linear.bias.numpy())  # (dim_out)
    # GGML linear impl
    assert ggml.ggml_can_mul_mat(gw, gx)
    # gy = ggml.ggml_add(ctx, ggml.ggml_mul_mat(ctx, gw, gx), gb)  # (dim_out, seq_len)
    gy = ggml.ggml_mul_mat(ctx, gw, gx)  # (dim_out, seq_len)

    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gf.nodes[gf.n_nodes - 1])
    assert np.allclose(y_exp, y)


@torch.no_grad()
def test_torch_spda_vs_ggml_flash_attn(ctx: Ctx) -> None:
    slen, d_in, num_heads = (5, 4, 2)
    torch.random.manual_seed(0)
    q = torch.zeros((num_heads, slen, d_in))
    torch.nn.init.uniform_(q, -1, 1)
    k = torch.zeros((num_heads, slen, d_in))
    torch.nn.init.uniform_(k, -1, 1)
    v = torch.zeros((num_heads, slen, d_in))
    torch.nn.init.uniform_(v, -1, 1)

    # Note: we are using x for both keys and queries, so every position
    # attends mostly to itself, hence y_exp looks a bit like arange(slen)
    y_exp = torch.nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True)
    y_exp = y_exp.numpy()
    gq = ggml.from_numpy(ctx, q.numpy())
    gk = ggml.from_numpy(ctx, k.numpy())
    # ggml flash attention expect a different order of axis for v:
    # (H, slen, H_dim) -> (H, H_dim, slen)
    gv = ggml.from_numpy(ctx, v.transpose(1, 2).contiguous().numpy())
    assert ggml.shape(gv) == (num_heads, d_in, slen)
    gy = ggml.ggml_flash_attn(ctx, gq, gk, gv, True)
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gy)
    assert np.allclose(y_exp, y)


def test_ggml_softmax_vs_torch(ctx: Ctx) -> None:
    x = torch.empty((5, 8, 4))
    torch.nn.init.uniform_(x, -1, 1)
    y_exp = torch.softmax(x, dim=-1).numpy()

    gx = ggml.from_numpy(ctx, x.numpy())
    gy = ggml.ggml_soft_max(ctx, gx)
    y = ggml.to_numpy(gy)

    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    assert np.allclose(y_exp, y, rtol=1e-3)


def test_forward_ffn(ctx: Ctx, g_model: c_void_p, pt_model: Any) -> None:
    x = torch.empty((21, 1024))  # (seq_len, model_dim)
    torch.nn.init.uniform_(x, -1 / 32, 1 / 32)

    # Test FFN without LayerNorm
    y_exp = pt_model.text_encoder.layers[0].ffn(x).numpy()
    gx = ggml.from_numpy(ctx, x)
    gy = ggml.forward(
        "StandardFeedForwardNetwork", g_model, "text_encoder.layers.0.ffn", gx
    )
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gf.nodes[gf.n_nodes - 1])
    assert np.allclose(y_exp, y, atol=1e-6)


def test_forward_layer_norm(ctx: Ctx, g_model: c_void_p, pt_model: Any) -> None:
    x = torch.empty((21, 1024))
    torch.nn.init.uniform_(x, -1, 1)

    y_exp = pt_model.text_encoder.layers[0].ffn_layer_norm(x).numpy()
    gx = ggml.from_numpy(ctx, x)
    gy = ggml.forward("LayerNorm", g_model, "text_encoder.layers.0.ffn_layer_norm", gx)
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gf.nodes[gf.n_nodes - 1])
    assert np.allclose(y_exp, y, rtol=1e-3, atol=1e-4)


def _name(tensor: ggml.ggml_tensor_p) -> bytes:
    try:
        return tensor.contents.name  # type: ignore[no-any-return]
    except ValueError:
        return b"???"


def test_forward_self_attn(ctx: Ctx, g_model: c_void_p, pt_model: Any) -> None:
    x = torch.empty((1, 21, 1024))
    torch.random.manual_seed(0)
    torch.nn.init.uniform_(x, -1, 1)

    self_attn = pt_model.text_encoder.layers[0].self_attn

    # Note: we use different lengths for queries and keys,
    # this tests the implementation in decoding context too.
    # Note2: ggml_flash_attn requires that we have more keys than queries
    gxq = ggml.from_numpy(ctx, x[0, :11, :])
    gx = ggml.from_numpy(ctx, x[0])
    ggml.ggml_set_name(gx, b"x")
    gy = ggml.forward(
        "MultiheadAttention",
        g_model,
        "text_encoder.layers.0.self_attn",
        gxq,
        gx,
        gx,
        None,  # TODO: tests with causal attention masks
    )
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    # q_exp = self_attn._project_q(x[:, :11, :], None, None).squeeze(0).numpy()

    y = ggml.to_numpy(gy)
    nodes = {}

    for i in range(gf.n_nodes):
        name = _name(gf.nodes[i])
        children = [_name(gf.nodes[i].contents.src[j]) for j in range(2)]
        print(name, f"op({gf.nodes[i].contents.op})", children)
        nodes[name] = ggml.to_numpy(gf.nodes[i])

    attn_weights_hook = fairseq2.nn.transformer.StoreAttentionWeights([])
    self_attn.register_attn_weight_hook(attn_weights_hook)

    y_exp = self_attn(x[:, :11, :], None, x, x).numpy()
    y_exp = y_exp.squeeze(0)  # remove batch dimension

    # q = nodes[b"q"]
    # assert q.shape == q_exp.shape
    # assert np.allclose(q_exp, q, atol=1e-5)

    attn_exp, attn_weights_exp = map(
        lambda t: t.squeeze(0).numpy(), attn_weights_hook._storage[0]
    )

    # with flash_attn we don't have attn_weights
    flash_attn = b"attn_weights" not in nodes

    if not flash_attn:
        attn_weights = nodes[b"attn_weights"]
        assert attn_weights_exp.shape == attn_weights.shape
        # GGML is very agressively reducing small softmax weights to 0.
        # Not sure to what this is due.
        assert np.allclose(attn_weights_exp, attn_weights, atol=1e-3)
        attn_exp = attn_exp.transpose(0, 2, 1)

    attn = nodes[b"attn"]
    assert attn_exp.shape == attn.shape
    # Because of rounding errors in softmax, it's even worse here.
    # flash attention have a better numerical precision though.
    assert np.allclose(attn_exp, attn, atol=1e-4 if flash_attn else 1e-2)

    assert y.shape == y_exp.shape
    assert np.allclose(y_exp, y, atol=1e-4 if flash_attn else 1e-2)


def test_StandardTransformerEncoderLayer_forward(
    ctx: Ctx, g_model: c_void_p, pt_model: Any
) -> None:
    x = torch.empty((1, 21, 1024))
    padding_mask = torch.ones((1, 21))
    torch.random.manual_seed(0)
    torch.nn.init.uniform_(x, -1, 1)

    layer = pt_model.text_encoder.layers[0]

    gx = ggml.from_numpy(ctx, x[0])
    ggml.ggml_set_name(gx, b"x")
    gpad = ggml.from_numpy(ctx, padding_mask[0])
    ggml.ggml_set_name(gpad, b"padding_mask")
    gy = ggml.forward(
        "StandardTransformerEncoderLayer",
        g_model,
        "text_encoder.layers.0",
        gx,
        None,  # TODO support padding mask
    )
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gy)

    y_exp, _ = layer(x, padding_mask)
    y_exp = y_exp.squeeze(0).numpy()  # remove batch dimension

    assert y.shape == y_exp.shape
    assert np.allclose(y_exp, y, atol=1e-4)


def test_StandardTransformerEncoder_forward(
    ctx: Ctx, g_model: c_void_p, pt_model: Any
) -> None:
    x = torch.empty((1, 21, 1024))
    padding_mask = torch.ones((1, 21))
    torch.random.manual_seed(0)
    torch.nn.init.uniform_(x, -1, 1)

    gx = ggml.from_numpy(ctx, x[0])
    ggml.ggml_set_name(gx, b"x")
    gpad = ggml.from_numpy(ctx, padding_mask[0])
    ggml.ggml_set_name(gpad, b"padding_mask")
    gy = ggml.forward(
        "StandardTransformerEncoder",
        g_model,
        "text_encoder",
        gx,
        None,  # TODO support padding mask
    )
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gy)

    y_exp, _ = pt_model.text_encoder(x, padding_mask)
    y_exp = y_exp.squeeze(0).numpy()  # remove batch dimension

    assert y.shape == y_exp.shape
    assert np.allclose(y_exp, y, atol=1e-4)


def test_causal_attention_mask(ctx: Ctx):
    x = torch.zeros((5, 10))
    generator = fairseq2.nn.transformer.CausalAttentionMaskGenerator()
    mask_exp = generator(x)

    gx = ggml.from_numpy(ctx, x)
    gmask = ggml.causal_attention_mask(ctx, gx)
    mask = ggml.to_numpy(gmask)

    assert mask_exp.shape == (10, 10)
    assert mask.shape == (10, 10)
    assert np.allclose(mask, mask_exp)



def test_StandardTransformerDecoder_forward(
    ctx: Ctx, g_model: c_void_p, pt_model: Any
) -> None:
    x = torch.empty((1, 13, 1024))
    encoder_out = torch.empty((1, 21, 1024))
    padding_mask = torch.ones((1, 13))
    torch.random.manual_seed(0)
    torch.nn.init.uniform_(x, -1, 1)
    torch.nn.init.uniform_(encoder_out, -1, 1)
    gx = ggml.from_numpy(ctx, x[0])
    ggml.ggml_set_name(gx, b"x")
    gpad = ggml.from_numpy(ctx, padding_mask[0])
    ggml.ggml_set_name(gpad, b"padding_mask")
    genc = ggml.from_numpy(ctx, encoder_out[0])
    gy = ggml.forward(
        "StandardTransformerDecoder",
        g_model,
        "text_decoder",
        gx,
        None,  # TODO support padding mask,
        genc,
        None,
    )
    gf = ggml.ggml_build_forward(gy)
    ggml.ggml_graph_compute_with_ctx(ctx, ctypes.pointer(gf), 1)

    y = ggml.to_numpy(gy)

    y_exp, _ = pt_model.text_decoder(x, padding_mask, encoder_out, None)
    y_exp = y_exp.squeeze(0).numpy()  # remove batch dimension

    assert y.shape == y_exp.shape
    assert np.allclose(y_exp, y, atol=1e-4)