TileOPs/tests/ops/test_grouped_gemm.py at main · MooreThreads/TileOPs · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128

import pytest
import torch

from tests.test_base import FixtureBase, TestBase
from tileops.ops.grouped_gemm import GroupedGemmOp
from workloads.grouped_gemm import (
    GroupedGemmTest as _GroupedGemmTestWorkload,
)


class GroupedGemmTest(_GroupedGemmTestWorkload, TestBase):
    def ref_program(self, A: torch.Tensor, B: torch.Tensor, batch_sizes: torch.Tensor,
                    batch_offsets: torch.Tensor,
                    batch_padded_offsets: torch.Tensor) -> torch.Tensor:
        if not self.transpose_a:
            # NT / NN: output is (batch_sum, N)
            if self.transpose_b:
                # NT: A @ B^T
                assert A.shape[0] == sum(batch_sizes)
                assert B.shape[0] == len(batch_sizes)
                output = torch.empty((sum(batch_sizes), B.shape[1]), device=A.device, dtype=A.dtype)
                start = 0
                for i, size in enumerate(batch_sizes):
                    size = int(size.item())
                    end = start + size
                    output[start:end] = torch.mm(A[start:end], B[i].transpose(0, 1).contiguous())
                    start = end
            else:
                # NN: A @ B
                assert A.shape[0] == sum(batch_sizes)
                assert B.shape[0] == len(batch_sizes)
                output = torch.empty((sum(batch_sizes), B.shape[2]), device=A.device, dtype=A.dtype)
                start = 0
                for i, size in enumerate(batch_sizes):
                    size = int(size.item())
                    end = start + size
                    output[start:end] = torch.mm(A[start:end], B[i])
                    start = end
        else:
            # TN / TT: output is (batch_count, N, K)
            total_batch = int(batch_sizes.sum().item())
            assert A.shape[0] == total_batch
            N = A.shape[1]
            batch_count = len(batch_sizes)

            if self.transpose_b:
                # TT: A^T @ B^T
                K = B.shape[0]
                assert B.shape[1] == total_batch
                output = torch.zeros((batch_count, N, K), device=A.device, dtype=A.dtype)
                start = 0
                for i, size in enumerate(batch_sizes):
                    size = int(size.item())
                    end = start + size
                    output[i] = torch.mm(A[start:end].transpose(0, 1),
                                         B[:, start:end].transpose(0, 1))
                    start = end
            else:
                # TN: A^T @ B
                K = B.shape[1]
                assert B.shape[0] == total_batch
                output = torch.zeros((batch_count, N, K), device=A.device, dtype=A.dtype)
                start = 0
                for i, size in enumerate(batch_sizes):
                    size = int(size.item())
                    end = start + size
                    output[i] = torch.mm(A[start:end].transpose(0, 1), B[start:end])
                    start = end
        return output


# ---------------------------------------------------------------------------
# Shared helper
# ---------------------------------------------------------------------------


# ---------------------------------------------------------------------------
# Parametrized grouped GEMM test
# ---------------------------------------------------------------------------

class GroupedGemmFixture(FixtureBase):
    PARAMS = [
        ("batch_sum, batch_count, N, K, dtype, transpose_a, transpose_b, tune", [
            pytest.param(
                16384, 4, 4864, 4096, torch.float16, False, True, False,
                marks=pytest.mark.smoke,
            ),
            pytest.param(
                16384, 4, 4864, 4096, torch.float16, False, False, False,
                marks=pytest.mark.full,
            ),
            pytest.param(
                16384, 4, 4864, 4096, torch.float16, True, False, False,
                marks=pytest.mark.full,
            ),
            pytest.param(
                16384, 4, 4864, 4096, torch.float16, True, True, False,
                marks=pytest.mark.full,
            ),
        ]),
    ]


@GroupedGemmFixture
def test_grouped_gemm(batch_sum: int, batch_count: int, N: int, K: int, dtype: torch.dtype,
                      transpose_a: bool, transpose_b: bool, tune: bool) -> None:
    test = GroupedGemmTest(batch_sum, batch_count, N, K, dtype, transpose_a, transpose_b)
    op = GroupedGemmOp(
        batch_sum, batch_count, N, K, dtype, transpose_a=transpose_a, transpose_b=transpose_b,
        tune=tune)
    test.check(op, *test.gen_inputs())


# ---------------------------------------------------------------------------
# Complete variant: forward (NT) + backward dA (NN) + backward dB (TN)
# ---------------------------------------------------------------------------

class GroupedGemmCompleteFixture(FixtureBase):
    PARAMS = [
        ("batch_sum, batch_count, N, K, dtype, tune", [
            pytest.param(16384, 4, 4864, 4096, torch.float16, False, marks=pytest.mark.smoke),
        ]),
    ]


if __name__ == "__main__":
    pytest.main([__file__, "-vvs"])