cutlass/test/python/emit/pytorch.py

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"""
Tests emitting a CUTLASS kernel to a PyTorch CUDA extension
"""

import random
import tempfile
import unittest

import cutlass

if cutlass.utils.datatypes.torch_available:
    import torch


def _initialize(dtype, M: int, N: int, K: int):
    """
    Utility function to initialize A, B, C, and D matrices corresponding to dimensions M, N, and K

    :param dtype: data type of tensors
    :param M: M dimension of GEMM problem
    :type M: int
    :param N: N dimension of GEMM problem
    :type N: int
    :param K: N dimension of GEMM problem
    :type K: int

    :return: initialized tensors A, B, C, and D
    :rtype: list
    """
    sizes = [(M, K), (K, N), (M, N), (M, N)]
    return [torch.randint(-3, 3, size, device='cuda').to(dtype) for size in sizes]


def _generate_problems(dtype, num):
    """
    Utility function to generate `num` GEMMs of random sizes

    :param dtype: data type of tensors
    :param num: number of GEMMs to generate
    :type num: int

    :return: lists of A, B, C, and D tensors
    :rtype: list
    """
    valid_sizes = [128, 256, 512, 1024]
    As, Bs, Cs, Ds = [], [], [], []
    for _ in range(num):
        M, N, K = [random.choice(valid_sizes) for _ in range(3)]
        A, B, C, D = _initialize(dtype, M, N, K)
        As.append(A)
        Bs.append(B)
        Cs.append(C)
        Ds.append(D)
    return As, Bs, Cs, Ds


@unittest.skipIf(not cutlass.utils.datatypes.torch_available, 'PyTorch must be available to run PyTorch extension tests')
class PyTorchExtensionTest(unittest.TestCase):

    def test_gemm(self):
        random.seed(2023)

        dtype = torch.float16
        plan = cutlass.op.Gemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
        plan.activation = cutlass.epilogue.relu
        op = plan.construct()

        with tempfile.TemporaryDirectory() as tmpdir:
            mod = cutlass.emit.pytorch(op, name='gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)

        A, B, C, _ = _initialize(dtype, 1024, 256, 512)

        D_ref = torch.nn.functional.relu(A @ B)
        D = mod.run(A, B)
        assert torch.allclose(D, D_ref)

        D = mod.run(A, B, C)
        assert torch.allclose(D, D_ref)

        D = mod.run(A, B, C, 1.0)
        assert torch.allclose(D, D_ref)

        D = mod.run(A, B, C, 1.0, 0.0)
        assert torch.allclose(D, D_ref)

        alpha = 2.0
        beta = -1.0
        D_ref = torch.nn.functional.relu((A @ B) * alpha + (beta * C))
        D = mod.run(A, B, C, alpha, beta)
        assert torch.allclose(D, D_ref)

    def test_grouped_gemm(self):
        random.seed(2023)

        dtype = torch.float16
        plan = cutlass.op.GroupedGemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
        op = plan.construct()

        with tempfile.TemporaryDirectory() as tmpdir:
            mod = cutlass.emit.pytorch(op, name='grouped_gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)

        As, Bs, Cs, _ = _generate_problems(dtype, 50)

        def check_all(X, Y):
            for x, y in zip(X, Y):
                assert torch.allclose(x, y)

        Ds_ref = [a @ b for a, b in zip(As, Bs)]
        Ds = mod.run(As, Bs)
        check_all(Ds, Ds_ref)

        Ds = mod.run(As, Bs, Cs)
        check_all(Ds, Ds_ref)

        Ds = mod.run(As, Bs, Cs, 1.0)
        check_all(Ds, Ds_ref)

        Ds = mod.run(As, Bs, Cs, 1.0, 0.0)
        check_all(Ds, Ds_ref)

        alpha = 2.0
        beta = -1.0
        Ds_ref = [(a @ b) * alpha + (beta * c) for a, b, c in zip(As, Bs, Cs)]
        Ds = mod.run(As, Bs, Cs, alpha, beta)
        check_all(Ds, Ds_ref)


if __name__ == '__main__':
    unittest.main()