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1a4268b811
This PR is part of the work to deprecate torch::deploy in OSS. Effectively it does 3 things to get started. 1. Remove test_deploy_interaction as we no longer need to worry about this 2. Remove all torch._running_with_deploy checks and use the False path always (surfaced 1) 3. Remove `USE_DEPLOY` and switch to the default path always Note: MyPy does fail on a bunch of things here as a bunch of older files are touched. It may be better to fix these things on a separate PR Pull Request resolved: https://github.com/pytorch/pytorch/pull/158288 Approved by: https://github.com/albanD
4335 lines
210 KiB
Python
4335 lines
210 KiB
Python
# Owner(s): ["module: sparse"]
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# ruff: noqa: F841
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import torch
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import random
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import io
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import itertools
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import unittest
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import functools
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from contextlib import redirect_stderr
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from torch.testing import make_tensor, FileCheck
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from torch.testing._internal.common_cuda import SM53OrLater, SM80OrLater, TEST_CUSPARSE_GENERIC
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from torch.testing._internal.common_utils import \
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(TEST_WITH_TORCHINDUCTOR, TEST_WITH_ROCM, TEST_CUDA_CUDSS, TEST_SCIPY, TEST_NUMPY, TEST_MKL, IS_WINDOWS, TestCase,
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run_tests, load_tests, coalescedonoff, parametrize, subtest, skipIfTorchDynamo, skipIfRocm,
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skipIfRocmVersionLessThan, IS_FBCODE, IS_REMOTE_GPU, suppress_warnings)
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from torch.testing._internal.common_device_type import \
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(ops, instantiate_device_type_tests, dtypes, OpDTypes, dtypesIfCUDA, onlyCPU, onlyCUDA, skipCUDAIfNoSparseGeneric,
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precisionOverride, skipMeta, skipCUDAIf, skipCPUIfNoMklSparse, skipCUDAIfRocmVersionLessThan,
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largeTensorTest)
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from torch.testing._internal.common_methods_invocations import \
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(op_db, sparse_csr_unary_ufuncs, ReductionOpInfo)
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from torch.testing._internal.common_cuda import _get_torch_cuda_version, TEST_CUDA
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from torch.testing._internal.common_dtype import (
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floating_types, all_types_and_complex_and, floating_and_complex_types, floating_types_and,
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all_types_and_complex, floating_and_complex_types_and)
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from torch.testing._internal.opinfo.definitions.linalg import sample_inputs_linalg_solve
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from torch.testing._internal.opinfo.definitions.sparse import validate_sample_input_sparse
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from test_sparse import CUSPARSE_SPMM_COMPLEX128_SUPPORTED, HIPSPARSE_SPMM_COMPLEX128_SUPPORTED
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import operator
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if TEST_SCIPY:
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import scipy.sparse as sp
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if TEST_NUMPY:
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import numpy as np
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# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
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# sharding on sandcastle. This line silences flake warnings
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load_tests = load_tests
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no_mkl_sparse = IS_WINDOWS or not TEST_MKL
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def _check_cusparse_triangular_solve_available():
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version = _get_torch_cuda_version()
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# cusparseSpSM was added in 11.3.1 but we don't have access to patch version
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min_supported_version = (11, 4)
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return version >= min_supported_version
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def _check_cusparse_spgemm_available():
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# cusparseSpGEMM was added in 11.0
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return not TEST_WITH_ROCM
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def _check_cusparse_sddmm_available():
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if TEST_WITH_ROCM:
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return True
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version = _get_torch_cuda_version()
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# cusparseSDDMM was added in 11.2.1 but we don't have access to patch version
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min_supported_version = (11, 3)
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return version >= min_supported_version
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_sparse_csr_ops = list(filter(lambda op: op.supports_sparse_csr, op_db))
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_sparse_compressed_ops = list(filter(lambda op: (op.supports_sparse_csr or op.supports_sparse_csc
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or op.supports_sparse_bsr or op.supports_sparse_bsc), op_db))
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binary_functions_with_dense_output = ['mm', 'mv', ]
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binary_ops_with_dense_output = list(filter(lambda op: op.name in binary_functions_with_dense_output, op_db))
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UNARY_EWISE_CSR_ALLOW_AUTOGRAD = [
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'abs',
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'conj_physical',
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'deg2rad',
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'neg',
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'positive',
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'frac',
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'nn.functional.relu',
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'log1p',
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'rad2deg'
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]
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# This should be just an import from test_linalg instead of code duplication
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# but https://github.com/pytorch/pytorch/pull/63511#discussion_r733989701
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def _test_addmm_addmv(
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test_case,
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f,
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t,
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m,
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v,
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*,
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alpha=None,
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beta=None,
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transpose_out=False,
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layout=torch.strided,
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mode=None
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):
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"""
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Unified test for checking `f(t, m, v, alpha=alpha, beta=beta)` computation,
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where f is `torch.addmv` or `torch.addmm`.
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`transpose_out` controls whether the out argument is in column-major order.
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`layout` controls whether `m` is converted to specified layout or not.
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Custom behaviour is implemented only for torch.sparse_csr layout.
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"""
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dtype = t.dtype
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numpy_dtype = dtype
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if dtype in {torch.bfloat16}:
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numpy_dtype = torch.float
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if dtype.is_complex:
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alpha = 0.9 + 0.3j if alpha is None else alpha
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beta = 0.5 + 0.6j if beta is None else beta
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else:
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alpha = 1.2 if alpha is None else alpha
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beta = 0.8 if beta is None else beta
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def convert_layout(mat):
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if layout == torch.sparse_csr:
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return mat.to_sparse_csr()
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elif layout == torch.sparse_csc:
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return mat.to_sparse_csc()
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else:
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assert mat.layout == layout
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return mat
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if mode == "all_sparse":
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res1 = f(*map(convert_layout, (t, m, v)), alpha=alpha, beta=beta)
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test_case.assertEqual(res1.layout, layout)
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res1 = res1.to_dense()
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elif mode == "dense_result":
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res1 = f(t, convert_layout(m), convert_layout(v), alpha=alpha, beta=beta)
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else:
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res1 = f(t, convert_layout(m), v, alpha=alpha, beta=beta)
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res2 = torch.full_like(res1, float('nan'))
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if transpose_out:
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res2 = res2.t().clone(memory_format=torch.contiguous_format).t()
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f(t, convert_layout(m), v, alpha=alpha, beta=beta, out=res2)
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res3 = alpha * (m.to(numpy_dtype).cpu().numpy() @ v.to(numpy_dtype).cpu().numpy())
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if beta != 0:
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res3 += (beta * t).to(numpy_dtype).cpu().numpy()
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res3 = torch.from_numpy(res3).to(dtype)
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test_case.assertEqual(res1, res2)
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test_case.assertEqual(res1, res3)
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class TestSparseCSRSampler(TestCase):
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def test_make_crow_indices(self):
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# Here we test the correctness of the crow_indices algorithm
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# and testing it on CPU and with int32 dtype will be
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# sufficient.
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device = torch.device('cpu')
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index_dtype = torch.int32
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for n_rows in range(1, 10):
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for n_cols in range(1, 10):
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for nnz in range(0, n_rows * n_cols + 1):
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crow_indices = self._make_crow_indices(
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n_rows, n_cols, nnz,
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device=device, dtype=index_dtype)
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self.assertEqual(len(crow_indices), n_rows + 1)
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counts = crow_indices[1:] - crow_indices[:-1]
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self.assertEqual(counts.sum(), nnz)
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self.assertGreaterEqual(counts.min(), 0)
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self.assertLessEqual(counts.max(), n_cols)
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def all_sparse_compressed_layouts(test_name='layout'):
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return parametrize(test_name, [
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subtest(torch.sparse_csr, name='SparseCSR'),
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subtest(torch.sparse_csc, name='SparseCSC'),
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subtest(torch.sparse_bsr, name='SparseBSR'),
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subtest(torch.sparse_bsc, name='SparseBSC')])
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def sparse_compressed_nonblock_layouts(test_name='layout'):
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return parametrize(test_name, [
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subtest(torch.sparse_csr, name='SparseCSR'),
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subtest(torch.sparse_csc, name='SparseCSC')])
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sparse_compressed_indices_methods = {
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torch.sparse_csr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
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torch.sparse_csc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
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torch.sparse_bsr: (torch.Tensor.crow_indices, torch.Tensor.col_indices),
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torch.sparse_bsc: (torch.Tensor.ccol_indices, torch.Tensor.row_indices),
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}
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def batched_nonbatched(test_name='batched'):
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return parametrize(test_name, [
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subtest(True, name="Batched"),
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subtest(False, name="NonBatched")
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])
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def hybrid_nonhybrid(test_name='hybrid'):
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return parametrize(test_name, [
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subtest(True, name="Hybrid"),
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subtest(False, name="NonHybrid")
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])
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class TestSparseCompressed(TestCase):
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"""Testing sparse compressed (CSR, CSC, BSR, BSC) tensor generic features.
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"""
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def genTensor(self, size, nnz, *, layout, device=None, dtype=torch.float, index_dtype=torch.int64):
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if device is None:
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device = self.device_type
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return self.genSparseCompressedTensor(size, nnz, device=device, dtype=dtype, index_dtype=index_dtype, layout=layout)
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@all_sparse_compressed_layouts()
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@onlyCPU
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def test_layout(self, layout):
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self.assertIn(str(layout), {'torch.sparse_csr', 'torch.sparse_csc', 'torch.sparse_bsr', 'torch.sparse_bsc'})
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self.assertEqual(type(layout), torch.layout)
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@parametrize('shape_and_device_inference', [subtest(False, name='_'), subtest(True, name='shape_and_device_inference')])
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@parametrize('use_factory_function', [subtest(False, name='_'), subtest(True, name='factory')])
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@parametrize('input_kind', [subtest('tensor', name='from_tensor'), subtest('list', name='from_list')])
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@all_sparse_compressed_layouts()
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@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
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def test_sparse_compressed_constructor(self, layout, device, dtype,
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use_factory_function, shape_and_device_inference, input_kind):
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if input_kind == 'list' and shape_and_device_inference:
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if torch.device(device).type == 'cuda':
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# list inputs to factory/constructor function without
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# specifying device will result a sparse compressed tensor
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# on CPU. So, skip testing against cuda device as unused.
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self.skipTest("nothing to test")
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if dtype not in {torch.float32, torch.complex64, torch.int64, torch.bool}:
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self.skipTest("dtype not supported with list values")
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expected_devices = [torch.device(device)]
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if TEST_CUDA and torch.device(device).type == 'cuda' and torch.cuda.device_count() >= 2 and not shape_and_device_inference:
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expected_devices.append(torch.device('cuda:1'))
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factory_function = {
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torch.sparse_csr: torch.sparse_csr_tensor,
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torch.sparse_csc: torch.sparse_csc_tensor,
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torch.sparse_bsr: torch.sparse_bsr_tensor,
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torch.sparse_bsc: torch.sparse_bsc_tensor,
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}[layout]
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compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
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if input_kind == 'list':
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index_dtypes = [torch.int64]
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else:
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index_dtypes = [torch.int32, torch.int64]
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if dtype.is_floating_point or dtype.is_complex:
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requires_grad_lst = [False, True]
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else:
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requires_grad_lst = [False]
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for index_dtype in index_dtypes:
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for expected_device in expected_devices:
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for (compressed_indices, plain_indices, values), kwargs in self.generate_simple_inputs(
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layout, device=expected_device, dtype=dtype, index_dtype=index_dtype,
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# skip zero-sized tensors for list inputs:
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enable_zero_sized=input_kind != 'list',
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output_tensor=False):
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size = kwargs['size']
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if shape_and_device_inference and 0 in size:
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# skip shape inference for zero-sized tensor
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# inputs because (i) the shape determined from
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# an empty list is ambiguous, and (ii) the
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# size of the plain dimension defined as
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# max(plain_indices) is undefined if
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# plain_indices has no values
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continue
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compressed_indices_expect = compressed_indices
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plain_indices_expect = plain_indices
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values_expect = values
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if input_kind == 'list':
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compressed_indices = compressed_indices.tolist()
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plain_indices = plain_indices.tolist()
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values = values.tolist()
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for requires_grad in requires_grad_lst:
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if use_factory_function:
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if shape_and_device_inference:
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sparse = factory_function(
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compressed_indices, plain_indices, values, requires_grad=requires_grad)
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else:
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sparse = factory_function(
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compressed_indices, plain_indices, values, size,
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dtype=dtype, device=expected_device, requires_grad=requires_grad)
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else:
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if shape_and_device_inference:
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sparse = torch.sparse_compressed_tensor(
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compressed_indices, plain_indices, values,
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layout=layout, requires_grad=requires_grad)
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else:
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sparse = torch.sparse_compressed_tensor(
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compressed_indices, plain_indices, values, size,
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dtype=dtype, layout=layout, device=expected_device, requires_grad=requires_grad)
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self.assertEqual(layout, sparse.layout)
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self.assertEqual(size, sparse.shape)
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self.assertEqual(compressed_indices_expect, compressed_indices_mth(sparse))
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self.assertEqual(plain_indices_expect, plain_indices_mth(sparse))
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self.assertEqual(values_expect, sparse.values())
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self.assertEqual(sparse.device, sparse.values().device)
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self.assertEqual(sparse.device, expected_device)
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self.assertEqual(sparse.values().requires_grad, requires_grad)
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self.assertEqual(sparse.requires_grad, requires_grad)
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self.assertFalse(compressed_indices_mth(sparse).requires_grad)
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self.assertFalse(plain_indices_mth(sparse).requires_grad)
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@skipMeta
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@sparse_compressed_nonblock_layouts()
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@dtypes(*all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half))
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def test_empty(self, layout, device, dtype):
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ns = [5, 2, 0]
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batch_shapes = [(), (2,), (2, 3)]
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compressed_dim = {
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torch.sparse_csr: -2,
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torch.sparse_csc: -1,
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}[layout]
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compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
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for m, n, b in itertools.product(ns, ns, batch_shapes):
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shape = (*b, m, n)
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with torch.sparse.check_sparse_tensor_invariants(enable=False):
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# torch.empty may return invalid sparse compressed tensors
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result = torch.empty(shape, dtype=dtype, device=device, layout=layout)
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self.assertEqual(result.shape, shape)
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self.assertEqual(result.dtype, dtype)
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self.assertEqual(result.device, torch.device(device))
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self.assertEqual(result.layout, layout)
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self.assertEqual(compressed_indices_mth(result).shape, (*b, shape[compressed_dim] + 1,))
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self.assertEqual(plain_indices_mth(result).shape, (*b, 0,))
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self.assertEqual(result.values().shape, (*b, 0,))
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self.assertEqual(result._nnz(), 0)
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self.assertEqual(compressed_indices_mth(result).device, torch.device(device))
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self.assertEqual(plain_indices_mth(result).device, torch.device(device))
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self.assertEqual(result.values().device, torch.device(device))
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self.assertEqual(compressed_indices_mth(result).dtype, torch.int64)
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self.assertEqual(plain_indices_mth(result).dtype, torch.int64)
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self.assertEqual(result.values().dtype, dtype)
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@skipMeta
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@sparse_compressed_nonblock_layouts()
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@dtypes(*all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
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def test_empty_errors(self, layout, device, dtype):
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with self.assertRaisesRegex(RuntimeError,
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"torch.empty: Only batched sparse compressed \\(non-block\\) tensors are supported"
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", but got size"):
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torch.empty((5,), dtype=dtype, device=device, layout=layout)
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@skipMeta
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@all_sparse_compressed_layouts()
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@dtypes(*all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half))
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def test_sparse_compressed_tensor_with_dims(self, layout, device, dtype):
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def get_sparse_compressed_tensor_properties(s):
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if layout in {torch.sparse_csr, torch.sparse_bsr}:
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compressed_indices, plain_indices = s.crow_indices(), s.col_indices()
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else:
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compressed_indices, plain_indices = s.ccol_indices(), s.row_indices()
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values = s.values()
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return dict(shape=s.shape, dtype=s.dtype, device=s.device, nnz=s._nnz(), layout=s.layout,
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compressed_indices_shape=compressed_indices.shape,
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compressed_indices_dtype=compressed_indices.dtype,
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compressed_indices_device=compressed_indices.device,
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plain_indices_shape=plain_indices.shape,
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plain_indices_dtype=plain_indices.dtype,
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plain_indices_device=plain_indices.device,
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values_shape=values.shape,
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values_dtype=values.dtype,
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values_device=values.device)
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for index_dtype in [torch.int32, torch.int64]:
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for t in self.generate_simple_inputs(layout, device=device, dtype=dtype, index_dtype=index_dtype):
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dense_dim = t.dense_dim()
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sparse_dim = t.sparse_dim()
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batch_dim = t.ndim - sparse_dim - dense_dim
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nnz = t.values().shape[batch_dim]
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if layout in {torch.sparse_bsr, torch.sparse_bsc}:
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blocksize = t.values().shape[batch_dim + 1: batch_dim + 1 + sparse_dim]
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else:
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blocksize = ()
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e = torch.ops.aten._sparse_compressed_tensor_with_dims(nnz, dense_dim, t.shape, blocksize, index_dtype,
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dtype=dtype, layout=layout, device=device)
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e_prop, t_prop = get_sparse_compressed_tensor_properties(e), get_sparse_compressed_tensor_properties(t)
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for k, v in e_prop.items():
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self.assertEqual(v, t_prop[k], lambda msg: f'{msg} when comparing {k}, expected {t_prop[k]}, got {v}')
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@skipMeta
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@all_sparse_compressed_layouts()
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@dtypes(*all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
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def test_clone(self, layout, device, dtype):
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for sparse in self.generate_simple_inputs(
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layout, device=device, dtype=dtype, index_dtype=torch.int32):
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cloned_sparse = sparse.clone()
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self.assertEqual(sparse, cloned_sparse)
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@all_sparse_compressed_layouts()
|
|
def test_print(self, layout, device):
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
|
|
printed = []
|
|
for enable_hybrid in [False, True]:
|
|
# using local patterns for test_print stability
|
|
patterns = [
|
|
# 2 x 3 batch of 3 x 2 tensors, trivial blocksize, non-hybrid/hybrid:
|
|
([[[[1, 2, 0],
|
|
[1, 0, 3]],
|
|
[[1, 2, 3],
|
|
[1, 0, 0]],
|
|
[[1, 0, 0],
|
|
[1, 2, 3]]],
|
|
[[[0, 2, 0],
|
|
[1, 2, 3]],
|
|
[[1, 0, 3],
|
|
[1, 2, 0]],
|
|
[[1, 2, 3],
|
|
[0, 2, 0]]]], [(2, 1)], [(), (4,)] if enable_hybrid else [()]),
|
|
# tensor with non-trivial blocksize, non-hybrid/hybrid:
|
|
([[0, 1, 0, 2, 0, 2],
|
|
[0, 1, 0, 0, 2, 0],
|
|
[3, 3, 3, 0, 0, 0],
|
|
[0, 0, 0, 0, 0, 0],
|
|
[0, 5, 0, 6, 6, 6],
|
|
[5, 0, 5, 6, 6, 6],
|
|
[0, 0, 0, 0, 8, 8],
|
|
[7, 7, 7, 0, 8, 8]], [(2, 3)], [(), (4, 2)] if enable_hybrid else [()]),
|
|
]
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for dtype in [torch.float32, torch.float64]:
|
|
for (compressed_indices, plain_indices, values), kwargs in self.generate_simple_inputs(
|
|
layout, device=device, dtype=dtype, index_dtype=index_dtype, enable_hybrid=enable_hybrid,
|
|
enable_non_contiguous_indices=False, enable_non_contiguous_values=False,
|
|
enable_zero_sized=False, output_tensor=False, patterns=patterns):
|
|
size = tuple(kwargs['size'])
|
|
block_ndim = 2 if layout in {torch.sparse_bsr, torch.sparse_bsc} else 0
|
|
base_ndim = 2
|
|
batch_ndim = compressed_indices.dim() - 1
|
|
dense_ndim = values.dim() - batch_ndim - block_ndim - 1
|
|
if enable_hybrid and dense_ndim == 0:
|
|
# non-hybrid cases are covered by the enable_hybrid==False loop
|
|
continue
|
|
batchsize = size[:batch_ndim]
|
|
basesize = size[batch_ndim:batch_ndim + base_ndim]
|
|
densesize = size[batch_ndim + base_ndim:]
|
|
assert len(densesize) == dense_ndim
|
|
printed.append(f"########## {dtype}/{index_dtype}/size={batchsize}+{basesize}+{densesize} ##########")
|
|
x = torch.sparse_compressed_tensor(compressed_indices,
|
|
plain_indices,
|
|
values, size, dtype=dtype, layout=layout, device=device)
|
|
printed.append("# sparse tensor")
|
|
printed.append(str(x))
|
|
printed.append(f"# _{compressed_indices_mth.__name__}")
|
|
printed.append(str(compressed_indices_mth(x)))
|
|
printed.append(f"# _{plain_indices_mth.__name__}")
|
|
printed.append(str(plain_indices_mth(x)))
|
|
printed.append("# _values")
|
|
printed.append(str(x.values()))
|
|
printed.append('')
|
|
printed.append('')
|
|
orig_maxDiff = self.maxDiff
|
|
self.maxDiff = None
|
|
try:
|
|
self.assertExpected('\n'.join(printed))
|
|
self.maxDiff = orig_maxDiff
|
|
except Exception:
|
|
self.maxDiff = orig_maxDiff
|
|
raise
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_copy(self, layout, device, dtype):
|
|
|
|
def run_test(shape, blocksize, nnz, index_type):
|
|
a = self.genSparseCompressedTensor(shape, nnz, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize)
|
|
b = self.genSparseCompressedTensor(shape, nnz, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize)
|
|
|
|
a.copy_(b)
|
|
|
|
self.assertEqual(a, b)
|
|
|
|
ns = [(9, 3), (2, 1), (0, 0)] # (number of dimensions, the corresponding block size)
|
|
batch_shapes = [(), (2,), (2, 3)]
|
|
for ((m, bm), (n, bn), b), index_dtype in zip(itertools.product(ns, ns, batch_shapes), [torch.int32, torch.int64]):
|
|
blocksize = (bm, bn) if layout in {torch.sparse_bsr, torch.sparse_bsc} else ()
|
|
run_test((*b, m, n), blocksize, 0, index_dtype)
|
|
run_test((*b, m, n), blocksize, m * n, index_dtype)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_copy_errors(self, layout, device, dtype):
|
|
blocksize = (2, 3) if layout in {torch.sparse_bsr, torch.sparse_bsc} else ()
|
|
nnz = 6 if layout in {torch.sparse_bsr, torch.sparse_bsc} else 1
|
|
shape1 = (2 * 6, 3 * 6) if layout in {torch.sparse_bsr, torch.sparse_bsc} else (2, 3)
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
a = self.genSparseCompressedTensor(shape1, 0, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize)
|
|
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"copy of sparse compressed tensors having different layouts is not supported."):
|
|
a.copy_(torch.empty(a.shape, dtype=dtype, device=device))
|
|
|
|
b = self.genSparseCompressedTensor(shape1, nnz, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize)
|
|
assert a._nnz() != b._nnz(), (a._nnz(), b._nnz())
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"only sparse compressed tensors with the same number of specified elements are supported."):
|
|
a.copy_(b)
|
|
|
|
shape2 = tuple(reversed(shape1))
|
|
c = self.genSparseCompressedTensor(shape2, nnz, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize)
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"expected shapes of self and src to match along dimension"):
|
|
b.copy_(c)
|
|
|
|
if blocksize:
|
|
blocksize1 = tuple(reversed(blocksize))
|
|
d = self.genSparseCompressedTensor(shape1, nnz, dtype=dtype, layout=layout, device=device,
|
|
index_dtype=index_dtype, blocksize=blocksize1)
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"copy of sparse compressed tensors having different block sizes is not supported"):
|
|
b.copy_(d)
|
|
|
|
def _smallest_divisor(self, n):
|
|
for i in range(2, int(n ** 0.5) + 1):
|
|
if n % i == 0:
|
|
return i
|
|
return n
|
|
|
|
@skipIfTorchDynamo("Not a TorchDynamo suitable test")
|
|
@all_sparse_compressed_layouts()
|
|
@ops(_sparse_compressed_ops)
|
|
@precisionOverride({torch.bfloat16: 1e-2, torch.float16: 1e-2})
|
|
def test_consistency(self, layout, device, dtype, op):
|
|
"""Checks that the op on a strided and on a sparse tensors will
|
|
produce the same results.
|
|
"""
|
|
if not op.supports_sparse_layout(layout):
|
|
self.skipTest(f"{op.name} does not support input with {layout} layout")
|
|
|
|
# FIXME: remove in followup once integer support is landed for segment_reduce
|
|
if (layout == torch.sparse_csr and not dtype.is_floating_point
|
|
and op.name in ('masked.mean', 'masked.amax', 'masked.amin')):
|
|
self.skipTest(f"{op.name} does not support input with {layout} layout and {dtype} dtype")
|
|
|
|
require_mask = isinstance(op, ReductionOpInfo) and 'masked.' in op.name
|
|
|
|
samples = []
|
|
for sample in op.sample_inputs(device, dtype):
|
|
if sample.input.ndim < 2:
|
|
continue
|
|
dense_dim = sample.input.ndim - 2
|
|
blocksize = (tuple(map(self._smallest_divisor, sample.input.shape[:2]))
|
|
if layout in {torch.sparse_bsr, torch.sparse_bsc} else None)
|
|
|
|
def _to_sparse(x):
|
|
if isinstance(x, torch.Tensor):
|
|
if blocksize is None:
|
|
if x.ndim != sample.input.ndim:
|
|
return x
|
|
elif x.ndim != sample.input.ndim + 2 or x.shape[-3] % blocksize[0] or x.shape[-2] % blocksize[1]:
|
|
return x
|
|
return x.clone().to_sparse(layout=layout, blocksize=blocksize, dense_dim=dense_dim)
|
|
return x
|
|
|
|
sparse_sample = sample.transform(_to_sparse)
|
|
# Some strided samples (with inf, nan elements) appear to share
|
|
# storage, so we must clone:
|
|
sample = sample.transform(lambda x: (x.clone() if isinstance(x, torch.Tensor) else x))
|
|
|
|
if validate_sample_input_sparse(op, sparse_sample, check_validate=False) is not sparse_sample:
|
|
# that is, the validation returns the sparse sample
|
|
# wrapped within ErrorInput instance
|
|
continue
|
|
samples.append((sample, sparse_sample))
|
|
|
|
# Fail early to prevent silent success with this test
|
|
if len(samples) == 0:
|
|
raise ValueError("Expected at least one 2 or higher D tensor in samples.")
|
|
|
|
# Re-define atol and rtol for operations that result values
|
|
# are random (and hence, non-comparable) be we still want to
|
|
# check the shape, dtype, etc attributes of the results:
|
|
atol = rtol = None
|
|
if op.name == 'randn_like':
|
|
atol = 1e300
|
|
rtol = 1
|
|
|
|
for sample, sparse_sample in samples:
|
|
expected = op(sample.input, *sample.args, **sample.kwargs)
|
|
assert torch.is_tensor(expected)
|
|
output = op(sparse_sample.input, *sparse_sample.args, **sparse_sample.kwargs)
|
|
assert torch.is_tensor(output)
|
|
strided_output = output.to_dense()
|
|
if require_mask and sample.kwargs.get('mask') is not None:
|
|
output_mask = torch.masked._output_mask(op.op, sample.input, *sample.args, **sample.kwargs)
|
|
expected.masked_fill_(~output_mask, 0)
|
|
self.assertEqual(strided_output, expected, atol=atol, rtol=rtol)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@all_sparse_compressed_layouts('layout2')
|
|
@dtypes(*all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
|
|
def test_empty_like(self, layout, layout2, device, dtype):
|
|
for sparse in self.generate_simple_inputs(layout):
|
|
if layout == layout2:
|
|
result = torch.empty_like(sparse, layout=layout2)
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[result.layout]
|
|
torch._validate_sparse_compressed_tensor_args(compressed_indices_mth(result),
|
|
plain_indices_mth(result),
|
|
result.values(),
|
|
result.shape,
|
|
result.layout)
|
|
self.assertEqual(sparse.shape, result.shape)
|
|
else:
|
|
self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"empty_like with different sparse layout is not supported",
|
|
lambda: torch.empty_like(sparse, layout=layout2)
|
|
)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_validate(self, layout, device, dtype):
|
|
def make_zero_batched(t):
|
|
return torch.empty(*((0,) + t.shape), dtype=t.dtype, device=t.device)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for (compressed_indices, plain_indices, values), kwargs in self.generate_simple_inputs(
|
|
layout, device=device, dtype=dtype, index_dtype=index_dtype, output_tensor=False):
|
|
size = kwargs['size']
|
|
torch._validate_sparse_compressed_tensor_args(compressed_indices, plain_indices, values, size, layout)
|
|
|
|
# check empty batch
|
|
torch._validate_sparse_compressed_tensor_args(
|
|
*(make_zero_batched(t) for t in (compressed_indices, plain_indices, values)),
|
|
(0,) + size,
|
|
layout
|
|
)
|
|
|
|
compressed_indices = torch.tensor([0, 0], dtype=index_dtype)
|
|
plain_indices = torch.tensor([], dtype=index_dtype)
|
|
torch._validate_compressed_sparse_indices(layout in {torch.sparse_csr, torch.sparse_bsr},
|
|
compressed_indices, plain_indices, 1, 1, 0)
|
|
|
|
def _generate_invalid_input(self, layout, device):
|
|
from functools import partial
|
|
|
|
def shape(shape, basedim=0):
|
|
blocksize = (1, 1)
|
|
if layout is torch.sparse_csc:
|
|
shape = shape[:basedim] + (shape[basedim + 1], shape[basedim]) + shape[basedim + 2:]
|
|
elif layout is torch.sparse_bsc:
|
|
shape = shape[:basedim] + (shape[basedim + 1] * blocksize[1], shape[basedim] * blocksize[0]) + shape[basedim + 2:]
|
|
elif layout is torch.sparse_bsr:
|
|
shape = shape[:basedim] + (shape[basedim] * blocksize[0], shape[basedim + 1] * blocksize[1]) + shape[basedim + 2:]
|
|
return shape
|
|
|
|
def values(lst, device=device):
|
|
if layout in {torch.sparse_bsr, torch.sparse_bsc}:
|
|
lst = [[[item]] for item in lst]
|
|
return torch.tensor(lst, device=device)
|
|
|
|
tensor = partial(torch.tensor, device=device)
|
|
values = partial(values, device=device)
|
|
|
|
yield ('incontiguous compressed_indices',
|
|
tensor([0, -1, 2, -1, 4, -1])[::2],
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
'expected compressed_indices to be a contiguous tensor per batch')
|
|
|
|
yield ('incontiguous plain_indices',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, -1, 1, -1, 0, -1, 2, -1])[::2],
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
'expected plain_indices to be a contiguous tensor per batch')
|
|
|
|
yield ('0-D compressed_indices',
|
|
tensor(0),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
'compressed_indices must have dimensionality >= 1 but got 0')
|
|
|
|
yield ('compressed/plain_indices mismatch of dimensionalities',
|
|
tensor([[0, 2, 4]]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
'compressed_indices and plain_indices dimensionalities must be equal but got 2 and 1, respectively')
|
|
|
|
if layout in {torch.sparse_csr, torch.sparse_csc}:
|
|
yield ('indices and values mismatch of dimensionalities',
|
|
tensor([[0, 2, 4]]),
|
|
tensor([[0, 1, 0, 2]]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'values must have dimensionality > sum of batch and block dimensionalities \(=1 \+ 0\) but got 1')
|
|
else:
|
|
yield ('indices and values mismatch of dimensionalities',
|
|
tensor([[0, 2, 4]]),
|
|
tensor([[0, 1, 0, 2]]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'values must have dimensionality > sum of batch and block dimensionalities \(=1 \+ 2\) but got 3')
|
|
|
|
yield ('invalid size',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
(2,),
|
|
r'tensor dimensionality must be sum of batch, base, and dense dimensionalities \(=0 \+ 2 \+ 0\) but got 1')
|
|
|
|
yield ('invalid batchsize',
|
|
tensor([[0, 2, 4]]),
|
|
tensor([[0, 1, 0, 2]]),
|
|
values([[1, 2, 3, 4]]),
|
|
shape((2, 2, 3), 1),
|
|
r'all batch dimensions of compressed_indices \(=\[1\]\), plain_indices \(=\[1\]\), '
|
|
r'and values \(=\[1\]\) must be equal to tensor batch dimensions \(=\[2\]\)')
|
|
|
|
if layout is torch.sparse_bsr:
|
|
yield ('invalid blocksize',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
tensor([[[1, 11]], [[2, 22]], [[3, 33]], [[4, 33]]]),
|
|
shape((2, 3)),
|
|
r'tensor shape\[1\] \(=3\) must be divisible with blocksize\[1\] \(=2\) as defined by values shape')
|
|
|
|
if layout is torch.sparse_bsc:
|
|
yield ('invalid blocksize',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
tensor([[[1, 11]], [[2, 22]], [[3, 33]], [[4, 33]]]),
|
|
shape((3, 2)),
|
|
r'tensor shape\[1\] \(=3\) must be divisible with blocksize\[1\] \(=2\) as defined by values shape')
|
|
|
|
yield ('invalid compressed_indices shape',
|
|
tensor([0, 2, 3, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'compressed_indices.shape\[-1\] must be equal to the number of compressed_indices_names \+ 1 \(=3\), but got 4')
|
|
|
|
yield ('invalid compressed_indices shape',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, 1, 0, 1, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'plain_indices.shape\[-1\] must be equal to nnz \(=4\) as defined by values.shape\[0\], but got 5')
|
|
|
|
yield ('compressed/plain_indices mismatch of dtype',
|
|
tensor([0, 2, 4], dtype=torch.int32),
|
|
tensor([0, 1, 0, 2], dtype=torch.int64),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'compressed_indices and plain_indices must have the same dtype, bot got Int and Long, respectively')
|
|
|
|
yield ('invalid compressed/plain_indices dtype',
|
|
tensor([0, 2, 4], dtype=torch.int16),
|
|
tensor([0, 1, 0, 2], dtype=torch.int16),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'compressed_indices and plain_indices dtype must be Int or Long, but got Short')
|
|
|
|
# CUDA kernel asserts are not recoverable, so we skip these for now
|
|
if torch.device(device).type == 'cpu':
|
|
yield ('invalid compressed_indices[0]',
|
|
tensor([1, 2, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'`compressed_indices\[..., 0\] == 0` is not satisfied.')
|
|
|
|
yield ('invalid compressed_indices[0] when nnz == 0',
|
|
tensor([1, 0], dtype=torch.int64),
|
|
tensor([], dtype=torch.int64),
|
|
values([1])[:0],
|
|
shape((1, 1)),
|
|
r'`compressed_indices\[..., 0\] == 0` is not satisfied.')
|
|
|
|
yield ('invalid compressed_indices[-1]',
|
|
tensor([0, 2, 5]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'`compressed_indices\[..., -1\] == nnz` is not satisfied.')
|
|
|
|
yield ('invalid compressed_indices[-1] when nnz == 0',
|
|
tensor([0, 1], dtype=torch.int64),
|
|
tensor([], dtype=torch.int64),
|
|
values([1])[:0],
|
|
shape((1, 1)),
|
|
r'`compressed_indices\[..., -1\] == nnz` is not satisfied.')
|
|
|
|
yield ('invalid compressed_indices.diff(dim=-1)',
|
|
tensor([0, 0, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'0 <= compressed_indices\[..., 1:\] - compressed_indices\[..., :\-1\] <= plain_dim` is not satisfied.')
|
|
|
|
yield ('invalid compressed_indices.diff(dim=-1)',
|
|
tensor([0, 5, 4]),
|
|
tensor([0, 1, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'0 <= compressed_indices\[..., 1:\] - compressed_indices\[..., :\-1\] <= plain_dim` is not satisfied.')
|
|
|
|
yield ('invalid min(plain_indices)',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, -1, 0, 3]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'`0 <= plain_indices < plain_dim` is not satisfied.')
|
|
|
|
yield ('invalid max(plain_indices)',
|
|
tensor([0, 2, 4]),
|
|
tensor([0, 1, 0, 3]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'`0 <= plain_indices < plain_dim` is not satisfied.')
|
|
|
|
yield ('non-coalesced',
|
|
tensor([0, 2, 4]),
|
|
tensor([1, 0, 0, 2]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'`plain_indices\[..., compressed_indices\[..., i - 1\]:compressed_indices\[..., i\]\] '
|
|
'for all i = 1, ..., compressed_dim '
|
|
'are sorted and distinct along the last dimension values` is not satisfied.')
|
|
|
|
if TEST_CUDA and torch.device(device).type == 'cpu':
|
|
yield ('indices and values mismatch of device',
|
|
torch.tensor([0, 2, 4]),
|
|
torch.tensor([0, 1, 0, 1]),
|
|
values([1, 2, 3, 4], device='cuda'),
|
|
shape((2, 3)),
|
|
r'device of compressed_indices \(=cpu\) must match device of values \(=cuda:0\)')
|
|
yield ('compressed_indices and values mismatch of device',
|
|
torch.tensor([0, 2, 4], device='cuda'),
|
|
torch.tensor([0, 1, 0, 1]),
|
|
values([1, 2, 3, 4]),
|
|
shape((2, 3)),
|
|
r'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!')
|
|
yield ('compressed/plain_indices mismatch of device',
|
|
torch.tensor([0, 2, 4], device='cuda'),
|
|
torch.tensor([0, 1, 0, 1]),
|
|
values([1, 2, 3, 4], device='cuda'),
|
|
shape((2, 3)),
|
|
r'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu!')
|
|
|
|
if TEST_CUDA and torch.device(device).type == 'cuda' and torch.cuda.device_count() >= 2:
|
|
yield ('indices and values mismatch of device index',
|
|
torch.tensor([0, 2, 4], device='cuda:0'),
|
|
torch.tensor([0, 1, 0, 1], device='cuda:0'),
|
|
values([1, 2, 3, 4], device='cuda:1'),
|
|
shape((2, 3)),
|
|
r'device of compressed_indices \(=cuda:0\) must match device of values \(=cuda:1\)')
|
|
yield ('compressed_indices and values mismatch of device index',
|
|
torch.tensor([0, 2, 4], device='cuda:0'),
|
|
torch.tensor([0, 1, 0, 1], device='cuda:1'),
|
|
values([1, 2, 3, 4], device='cuda:0'),
|
|
shape((2, 3)),
|
|
r'Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:1!')
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@parametrize('target', [subtest('validate_sparse_compressed_tensor_args'),
|
|
subtest('sparse_compressed_tensor'),
|
|
subtest('sparse_compressed_tensor_no_size')])
|
|
def test_invalid_input(self, layout, device, target):
|
|
for label, compressed_indices, plain_indices, values, size, errmsg in self._generate_invalid_input(layout, device):
|
|
if layout is torch.sparse_bsr:
|
|
errmsg = errmsg.replace('compressed_indices_name', 'row block').replace('plain_indices_name', 'column block')
|
|
elif layout is torch.sparse_bsc:
|
|
errmsg = errmsg.replace('compressed_indices_name', 'column block').replace('plain_indices_name', 'row block')
|
|
elif layout is torch.sparse_csr:
|
|
errmsg = errmsg.replace('compressed_indices_name', 'row').replace('plain_indices_name', 'column')
|
|
elif layout is torch.sparse_csc:
|
|
errmsg = errmsg.replace('compressed_indices_name', 'column').replace('plain_indices_name', 'row')
|
|
if layout in {torch.sparse_csr, torch.sparse_bsr}:
|
|
errmsg = errmsg.replace('compressed_indices', 'crow_indices') \
|
|
.replace('plain_indices', 'col_indices') \
|
|
.replace('plain_dim', 'ncols') \
|
|
.replace('compressed_dim', 'nrows')
|
|
else:
|
|
errmsg = errmsg.replace('compressed_indices', 'ccol_indices') \
|
|
.replace('plain_indices', 'row_indices') \
|
|
.replace('plain_dim', 'nrows') \
|
|
.replace('compressed_dim', 'ncols')
|
|
|
|
if target == 'sparse_compressed_tensor_no_size' and label in {
|
|
'invalid size', 'invalid batchsize', 'invalid compressed_indices shape', 'invalid max(plain_indices)',
|
|
'invalid blocksize'}:
|
|
# Skip invalid size input as a valid size is estimated for other inputs
|
|
continue
|
|
|
|
with self.assertRaisesRegex(RuntimeError, errmsg):
|
|
if target == 'validate_sparse_compressed_tensor_args':
|
|
torch._validate_sparse_compressed_tensor_args(compressed_indices, plain_indices, values, size, layout)
|
|
elif target == 'sparse_compressed_tensor':
|
|
torch.sparse_compressed_tensor(compressed_indices, plain_indices, values, size, layout=layout)
|
|
elif target == 'sparse_compressed_tensor_no_size':
|
|
torch.sparse_compressed_tensor(compressed_indices, plain_indices, values, layout=layout)
|
|
else:
|
|
raise NotImplementedError(target)
|
|
|
|
@skipMeta
|
|
@onlyCPU
|
|
@largeTensorTest("30GB", "cpu")
|
|
def test_invalid_input_csr_large(self):
|
|
rows = 2 ** 31
|
|
with self.assertRaisesRegex(RuntimeError, '32-bit integer overflow in row dimension'):
|
|
torch.sparse_csr_tensor(torch.arange(rows + 1, dtype=torch.int32) // rows,
|
|
torch.tensor([0], dtype=torch.int32),
|
|
torch.tensor([1]), (rows, 1))
|
|
torch.sparse_csr_tensor(torch.arange(rows + 1, dtype=torch.int64) // rows,
|
|
torch.tensor([0], dtype=torch.int64),
|
|
torch.tensor([1]), (rows, 1))
|
|
|
|
cols = 2 ** 31
|
|
with self.assertRaisesRegex(RuntimeError, '32-bit integer overflow in column dimension'):
|
|
torch.sparse_csr_tensor(torch.arange(2, dtype=torch.int32),
|
|
torch.tensor([0], dtype=torch.int32),
|
|
torch.tensor([1]), (1, cols))
|
|
torch.sparse_csr_tensor(torch.arange(2, dtype=torch.int64),
|
|
torch.tensor([0], dtype=torch.int64),
|
|
torch.tensor([1]), (1, cols))
|
|
|
|
nnz = 2 ** 31
|
|
with self.assertRaisesRegex(RuntimeError, '32-bit integer overflow in nnz'):
|
|
# nnz cannot be stored in int32 crow_indices
|
|
# but the `crow_indices[..., -1] == nnz`` check happens after the overflow validation
|
|
# So we can use `nnz - 1` here to avoid `value cannot be converted to type int32 without overflow`
|
|
# during construction of crow_indices
|
|
torch.sparse_csr_tensor(torch.tensor([0, nnz // 2, nnz - 1], dtype=torch.int32),
|
|
torch.arange(nnz // 2, dtype=torch.int32).repeat(2),
|
|
torch.ones(nnz, dtype=torch.int8), (2, nnz // 2))
|
|
torch.sparse_csr_tensor(torch.tensor([0, nnz // 2, nnz], dtype=torch.int64),
|
|
torch.arange(nnz // 2, dtype=torch.int64).repeat(2),
|
|
torch.ones(nnz, dtype=torch.int8), (2, nnz // 2))
|
|
|
|
@skipMeta
|
|
@onlyCPU
|
|
@all_sparse_compressed_layouts()
|
|
def test_dim(self, layout):
|
|
for (compressed_indices, plain_indices, values), kwargs in self.generate_simple_inputs(layout, output_tensor=False):
|
|
size = kwargs['size']
|
|
batch_dim = compressed_indices.dim() - 1
|
|
sparse_dim = 2
|
|
block_dim = 2 if layout in {torch.sparse_bsr, torch.sparse_bsc} else 0
|
|
dense_dim = values.dim() - batch_dim - block_dim - 1
|
|
sparse = torch.sparse_compressed_tensor(compressed_indices, plain_indices, values, size, layout=layout)
|
|
self.assertEqual(sparse.sparse_dim(), sparse_dim)
|
|
self.assertEqual(sparse.dense_dim(), dense_dim)
|
|
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(*all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16))
|
|
def test_to_dtype(self, layout, device, dtype):
|
|
# to_dense does not support hybrid inputs
|
|
for sparse in self.generate_simple_inputs(layout, dtype=dtype, device=device, enable_hybrid=False):
|
|
for to_dtype in all_types_and_complex_and(torch.bool, torch.half, torch.bfloat16):
|
|
sparse_to_dtype = sparse.to(to_dtype)
|
|
dense_to_dtype = sparse.to_dense().to(to_dtype)
|
|
self.assertEqual(sparse_to_dtype.to_dense(), dense_to_dtype)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(torch.double)
|
|
def test_pickle(self, layout, dtype, device):
|
|
import pickle
|
|
|
|
for sparse in self.generate_simple_inputs(layout, device=device, dtype=dtype):
|
|
serialized = pickle.dumps(sparse)
|
|
sparse_loaded = pickle.loads(serialized)
|
|
|
|
self.assertEqual(sparse, sparse_loaded)
|
|
|
|
@all_sparse_compressed_layouts()
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_select_copy(self, device, dtype, index_dtype, layout):
|
|
|
|
def is_view_of(base, other):
|
|
# a shameless copy of TestViewOps.is_view_of
|
|
if (
|
|
not other._is_view() or
|
|
other is base or
|
|
other._base is not base or
|
|
base.device != other.device
|
|
):
|
|
return False
|
|
if base.device.type in ('cpu', 'cuda'):
|
|
if base.untyped_storage().data_ptr() != other.untyped_storage().data_ptr():
|
|
return False
|
|
return True
|
|
|
|
kwargs = dict(device=device, dtype=dtype, index_dtype=index_dtype)
|
|
for sparse, dense in zip(self.generate_simple_inputs(layout, **kwargs),
|
|
self.generate_simple_inputs(torch.strided, **kwargs)):
|
|
if layout in {torch.sparse_csr, torch.sparse_bsr}:
|
|
n_batchdim = sparse.crow_indices().ndim - 1
|
|
elif layout in {torch.sparse_csc, torch.sparse_bsc}:
|
|
n_batchdim = sparse.ccol_indices().ndim - 1
|
|
else:
|
|
assert 0 # unreachable
|
|
self.assertEqual(sparse, dense)
|
|
for dim in range(sparse.ndim):
|
|
if sparse.shape[dim] == 0:
|
|
with self.assertRaisesRegex(IndexError, "index 0 out of range for tensor of size"):
|
|
torch.select_copy(sparse, dim, 0)
|
|
with self.assertRaisesRegex(IndexError, "index 0 out of range for tensor of size"):
|
|
torch.select_copy(dense, dim, 0)
|
|
elif n_batchdim and dim >= n_batchdim and dim < n_batchdim + 2:
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"selecting sparse dimensions is not supported for batched sparse compressed tensors"):
|
|
torch.select_copy(sparse, dim, 0)
|
|
else:
|
|
for index in {0, sparse.shape[dim] // 2, sparse.shape[dim] - 1}:
|
|
dense_select = torch.select_copy(dense, dim, index)
|
|
sparse_select = torch.select_copy(sparse, dim, index)
|
|
self.assertEqual(sparse_select, dense_select)
|
|
self.assertFalse(is_view_of(sparse_select.values(), sparse.values()))
|
|
|
|
|
|
def _npref_block_addmm_addmv(c, a, b, alpha, beta):
|
|
return alpha * (a @ b) + beta * c
|
|
|
|
|
|
class TestSparseCSR(TestCase):
|
|
|
|
def test_csr_stride(self):
|
|
a = self.genSparseCSRTensor((3, 3), 3, dtype=torch.float, device=self.device_type, index_dtype=torch.int64)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Sparse CSR tensors do not have strides"):
|
|
a.stride()
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Sparse CSR tensors do not have strides"):
|
|
a.stride(-1)
|
|
|
|
def test_csr_storage(self):
|
|
a = self.genSparseCSRTensor((3, 3), 3, dtype=torch.float, device=self.device_type, index_dtype=torch.int64)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Cannot access storage of SparseCsrTensorImpl"):
|
|
a.storage()
|
|
|
|
def test_csr_is_contiguous(self):
|
|
a = self.genSparseCSRTensor((3, 3), 3, dtype=torch.float, device=self.device_type, index_dtype=torch.int64)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "Sparse CSR tensors do not have is_contiguous"):
|
|
a.is_contiguous()
|
|
|
|
@onlyCPU
|
|
@largeTensorTest("20GB", "cpu")
|
|
def test_csr_nnz(self):
|
|
# Tests the limits of the number of specified elements in CSR tensors, see gh-102520.
|
|
for nnz in [0, 2**31]:
|
|
rows, cols = 1, max(nnz, 1)
|
|
crow_indices = torch.tensor([0, nnz], dtype=torch.int64)
|
|
col_indices = torch.arange(nnz, dtype=torch.int64)
|
|
values = torch.ones(nnz, dtype=torch.int8)
|
|
a = torch.sparse_csr_tensor(crow_indices, col_indices, values, (rows, cols))
|
|
self.assertEqual(a._nnz(), nnz)
|
|
|
|
def test_csr_double_to_sparse_csr(self):
|
|
a = self.genSparseCSRTensor((3, 3), 3, dtype=torch.float, device=self.device_type, index_dtype=torch.int64)
|
|
a.to_sparse_csr().to_sparse_csr()
|
|
|
|
@all_sparse_compressed_layouts()
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bfloat16, torch.bool))
|
|
def test_select(self, device, dtype, index_dtype, layout):
|
|
compressed_indices_mth = {
|
|
torch.sparse_csr: torch.Tensor.crow_indices,
|
|
torch.sparse_bsr: torch.Tensor.crow_indices,
|
|
torch.sparse_csc: torch.Tensor.ccol_indices,
|
|
torch.sparse_bsc: torch.Tensor.ccol_indices,
|
|
}[layout]
|
|
|
|
plain_indices_mth = {
|
|
torch.sparse_csr: torch.Tensor.col_indices,
|
|
torch.sparse_bsr: torch.Tensor.col_indices,
|
|
torch.sparse_csc: torch.Tensor.row_indices,
|
|
torch.sparse_bsc: torch.Tensor.row_indices,
|
|
}[layout]
|
|
create_tensor_mth = {
|
|
torch.sparse_csr: torch.sparse_csr_tensor,
|
|
torch.sparse_bsr: torch.sparse_bsr_tensor,
|
|
torch.sparse_csc: torch.sparse_csc_tensor,
|
|
torch.sparse_bsc: torch.sparse_bsc_tensor,
|
|
}[layout]
|
|
|
|
shape = (2, 3, 6, 10)
|
|
nnz = 6
|
|
blocksize = (2, 2) if layout in {torch.sparse_bsr, torch.sparse_bsc} else ()
|
|
sparse = self.genSparseCompressedTensor(
|
|
shape, nnz, device=device, layout=layout, dtype=dtype, index_dtype=index_dtype, blocksize=blocksize)
|
|
comp_indices = compressed_indices_mth(sparse)
|
|
plain_indices = plain_indices_mth(sparse)
|
|
values = sparse.values()
|
|
|
|
# select from batch dimensions
|
|
sparse_selected12 = sparse.select(1, 2)
|
|
expected_sparse_selected12 = create_tensor_mth(comp_indices.select(1, 2).contiguous(),
|
|
plain_indices.select(1, 2).contiguous(),
|
|
values.select(1, 2).contiguous(),
|
|
size=(2, 6, 10),
|
|
dtype=dtype,
|
|
device=device)
|
|
self.assertEqual(expected_sparse_selected12, sparse_selected12)
|
|
|
|
# selecting rows/col with batch dims not allowed
|
|
sparse_non_batched = sparse[0, 0]
|
|
# select from sparse dimensions
|
|
for select_args in [(0, 0), (1, 1)]:
|
|
sparse_selected = sparse_non_batched.select(*select_args)
|
|
dense_selected = sparse_non_batched.to_dense().select(*select_args)
|
|
self.assertEqual(dense_selected, sparse_selected)
|
|
|
|
self.assertEqual(sparse[0, 0, 0, 0], sparse.to_dense()[0, 0, 0, 0])
|
|
# assigning to sparse through indexing is disabled
|
|
with self.assertRaisesRegex(TypeError, "Cannot assign to a sparse tensor"):
|
|
sparse[0, 0, 0, 0] = 99.0
|
|
|
|
# select from sparse dimensions without removing batch dims
|
|
msg = "selecting sparse dimensions is not supported for batched sparse compressed tensors."
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
sparse.select(-2, 0)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
sparse.select(-1, 0)
|
|
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_resize(self, device, dtype):
|
|
|
|
def numel(tensor):
|
|
r = 1
|
|
for s in tensor.shape:
|
|
r *= s
|
|
return r
|
|
|
|
batch_shapes = [(), (2,), (2, 3)]
|
|
for index_dtype, b in zip([torch.int32, torch.int64], batch_shapes):
|
|
shape = (*b, 2, 3)
|
|
nnz = 6
|
|
a = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
self.assertEqual(a.numel(), numel(a))
|
|
|
|
new_shape = (*b, 4, 5)
|
|
a.resize_(new_shape)
|
|
|
|
self.assertEqual(a.shape, new_shape)
|
|
# resize to larger shape doesn't add specified elements
|
|
self.assertEqual(a._nnz(), nnz)
|
|
self.assertEqual(a.numel(), numel(a))
|
|
|
|
new_shape = (*b, 1, 5)
|
|
a.resize_(new_shape)
|
|
|
|
self.assertEqual(a.shape, new_shape)
|
|
# resize to smaller shape trims specified elements
|
|
self.assertEqual(a._nnz(), 5)
|
|
self.assertEqual(a.numel(), numel(a))
|
|
|
|
# trim batched dimensions
|
|
a.resize_(new_shape[-2], new_shape[-1])
|
|
self.assertEqual(a.shape, (new_shape[-2], new_shape[-1]))
|
|
self.assertEqual(a._nnz(), 5)
|
|
self.assertEqual(a.numel(), numel(a))
|
|
|
|
@skipMeta
|
|
@dtypes(torch.float, torch.bool)
|
|
@all_sparse_compressed_layouts()
|
|
def test_resize_as_sparse_compressed(self, device, dtype, layout):
|
|
|
|
def _check_resize_b_as_a(b, a):
|
|
br = b.clone()
|
|
br.resize_as_sparse_(a)
|
|
|
|
# shape is inherited from a
|
|
self.assertEqual(a.shape, br.shape)
|
|
# other metadata is not affected
|
|
self.assertEqual(b.layout, br.layout)
|
|
self.assertEqual(b.device, br.device)
|
|
self.assertEqual(b.dtype, br.dtype)
|
|
|
|
def _get_compressed_plain_inds(t):
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[t.layout]
|
|
return compressed_indices_mth(t), plain_indices_mth(t)
|
|
|
|
br_compressed_indices, br_plain_indices = _get_compressed_plain_inds(br)
|
|
br_values = br.values()
|
|
|
|
b_compressed_indices, b_plain_indices = _get_compressed_plain_inds(b)
|
|
a_compressed_indices, a_plain_indices = _get_compressed_plain_inds(a)
|
|
self.assertEqual(a_plain_indices.shape, br_plain_indices.shape)
|
|
self.assertEqual(a_compressed_indices.shape, br_compressed_indices.shape)
|
|
# We don't check the content of br_plain_indices and br_compressed_indices
|
|
# because it is not well-defined (the content depends on the original
|
|
# shape of `b` that `resize_as` ought to discard) nor needed (the
|
|
# subsequent operation likely updates the indices and values of `b` anyway).
|
|
# the device/dtype of indices should always be unaffected
|
|
self.assertEqual(b_plain_indices.dtype, br_plain_indices.dtype)
|
|
self.assertEqual(b_plain_indices.device, br_plain_indices.device)
|
|
self.assertEqual(b_compressed_indices.dtype, br_compressed_indices.dtype)
|
|
self.assertEqual(b_compressed_indices.device, br_compressed_indices.device)
|
|
# values are generated empty, shape is updated
|
|
self.assertEqual(a.values().shape, br_values.shape)
|
|
# the device/dtype of indices should always be unaffected
|
|
b_values = b.values()
|
|
self.assertEqual(b_values.dtype, br_values.dtype)
|
|
self.assertEqual(b_values.device, br_values.device)
|
|
# nnz will be picked up from a via new shape of values
|
|
self.assertEqual(a._nnz(), br._nnz())
|
|
|
|
# post resize the invariants of the layout are respected
|
|
torch._validate_sparse_compressed_tensor_args(br_compressed_indices, br_plain_indices, br_values, br.shape,
|
|
br.layout)
|
|
|
|
block_sparse = layout in (torch.sparse_bsr, torch.sparse_bsc)
|
|
shape = (2, 1, 6, 4)
|
|
nnz = 4
|
|
blocksize = (2, 1) if block_sparse else ()
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
a = self.genSparseCompressedTensor(shape,
|
|
layout=layout,
|
|
device=device,
|
|
index_dtype=index_dtype,
|
|
dtype=dtype,
|
|
nnz=nnz,
|
|
blocksize=blocksize)
|
|
|
|
# same size, resize should not trigger
|
|
b = self.genSparseCompressedTensor(shape,
|
|
layout=layout,
|
|
device=device,
|
|
index_dtype=index_dtype,
|
|
dtype=dtype,
|
|
nnz=nnz,
|
|
blocksize=blocksize)
|
|
|
|
# This test will not always trigger a resize, if the layouts are the same nothing should happen to b.
|
|
# The invariants of the function as checked should still hold
|
|
_check_resize_b_as_a(b, a)
|
|
|
|
# same ndim, but bigger, more nnz, different dtype, different blocksize if blocked
|
|
b = self.genSparseCompressedTensor(tuple(s * 2 for s in shape),
|
|
layout=layout,
|
|
device=device,
|
|
dtype=torch.chalf,
|
|
index_dtype=torch.int64 if index_dtype == torch.int32 else torch.int32,
|
|
nnz=nnz * 2,
|
|
blocksize=tuple(2 * bi for bi in blocksize))
|
|
_check_resize_b_as_a(b, a)
|
|
|
|
# different device, only check on cuda pass as we know we are testing in an environment
|
|
# that has multiple devices
|
|
|
|
# TODO: .cpu() does not seem to work correctly for sparse. Causes a call to `copy_` which
|
|
# complains about incompatible nnz between src and self?
|
|
if torch.device(device).type == 'cuda' and (layout not in (torch.sparse_bsc, torch.sparse_bsr)):
|
|
a_cpu = self.genSparseCompressedTensor(shape,
|
|
layout=layout,
|
|
device='cpu',
|
|
index_dtype=index_dtype,
|
|
dtype=dtype,
|
|
nnz=nnz,
|
|
blocksize=blocksize)
|
|
_check_resize_b_as_a(b, a)
|
|
|
|
# error on a strided
|
|
a_strided = a.to_dense()
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, r'resize_as_sparse_compressed_: src expected sparse compressed tensor layout'):
|
|
b.resize_as_sparse_(a_strided)
|
|
|
|
# error on b strided
|
|
b_strided = b.to_dense()
|
|
with self.assertRaisesRegex(
|
|
RuntimeError, r'resize_as_sparse_compressed_: self expected sparse compressed tensor layout'):
|
|
b_strided.resize_as_sparse_(a)
|
|
|
|
# error if layout does not match, transpose induces layout flip
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"resize_as_sparse_compressed_tensor_: self and src must have the same layout"):
|
|
b.transpose(-2, -1).resize_as_sparse_(a)
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"resize_as_sparse_compressed_tensor_: self and src must have the same layout"):
|
|
b.resize_as_sparse_(a.transpose(-2, -1))
|
|
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_resize_errors(self, device, dtype):
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
shape = (2, 3)
|
|
nnz = 6
|
|
a = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, "torch.resize_: Only batched sparse CSR matrices are supported"):
|
|
new_shape = (4,)
|
|
a.resize_(new_shape)
|
|
|
|
# resizing of columns to smaller size is not implemented
|
|
with self.assertRaisesRegex(
|
|
RuntimeError,
|
|
"torch.resize_: Resizing columns of sparse CSR tensors to a smaller value is not supported.",
|
|
):
|
|
new_shape = (2, 2)
|
|
a.resize_(new_shape)
|
|
|
|
@skipIfTorchDynamo()
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_sparse_csr_from_dense(self, device, dtype):
|
|
dense = torch.tensor([[4, 5, 0], [0, 0, 0], [1, 0, 0]], dtype=dtype, device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 2, 2, 3], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([0, 1, 0], dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([4, 5, 1], dtype=dtype), sparse.values())
|
|
|
|
dense = torch.tensor([[0, 0, 0], [0, 0, 1], [1, 0, 0]], dtype=dtype, device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 0, 1, 2], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([2, 0], dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([1, 1], dtype=dtype), sparse.values())
|
|
|
|
dense = torch.tensor([[2, 2, 2], [2, 2, 2], [2, 2, 2]], dtype=dtype, device=device)
|
|
sparse = dense.to_sparse_csr()
|
|
self.assertEqual(torch.tensor([0, 3, 6, 9], dtype=torch.int64), sparse.crow_indices())
|
|
self.assertEqual(torch.tensor([0, 1, 2] * 3, dtype=torch.int64), sparse.col_indices())
|
|
self.assertEqual(torch.tensor([2] * 9, dtype=dtype), sparse.values())
|
|
|
|
def _test_sparse_compressed_to_dense(self, device, dtype, layout):
|
|
compressed_format_str = str(layout)[-3:]
|
|
|
|
def to_compressed(t):
|
|
return getattr(t, f"to_sparse_{compressed_format_str}")()
|
|
|
|
def compressed_constructor(*input, **kwargs):
|
|
constructor = getattr(torch, f"sparse_{compressed_format_str}_tensor")
|
|
return constructor(*input, **kwargs)
|
|
|
|
def get_dense_shape(shape, batch_ndim):
|
|
if layout is torch.sparse_csc:
|
|
compressed_dims_slice = slice(batch_ndim + 1, batch_ndim - 1, -1)
|
|
else:
|
|
compressed_dims_slice = slice(batch_ndim, batch_ndim + 2)
|
|
return shape[:batch_ndim] + shape[compressed_dims_slice] + shape[batch_ndim + 2:]
|
|
|
|
def transpose(t, batch_ndim):
|
|
if layout is torch.sparse_csc:
|
|
return t.transpose(batch_ndim, batch_ndim + 1)
|
|
return t
|
|
|
|
mn = [5, 2, 0]
|
|
for (m, n) in itertools.product(mn, mn):
|
|
size = (m, n)
|
|
dense = make_tensor(size, dtype=dtype, device=device)
|
|
sparse = to_compressed(dense)
|
|
self.assertEqual(sparse.to_dense(), dense)
|
|
|
|
batch_shape = (2, 3)
|
|
compressed_indices = torch.tensor([0, 3, 5], device=device).repeat(6, 1).reshape(*batch_shape, -1)
|
|
plain_indices = torch.tensor([0, 1, 2, 0, 1], device=device).repeat(6, 1).reshape(*batch_shape, -1)
|
|
values = torch.tensor([1, 2, 1, 3, 4], device=device, dtype=dtype).repeat(6, 1).reshape(*batch_shape, -1)
|
|
sparse = compressed_constructor(compressed_indices, plain_indices, values, dtype=dtype, device=device)
|
|
dense_shape = get_dense_shape(sparse.shape, len(batch_shape))
|
|
dense = torch.tensor([[1, 2, 1], [3, 4, 0]], dtype=dtype, device=device).repeat(6, 1).reshape(dense_shape)
|
|
self.assertEqual(sparse.to_dense(), transpose(dense, len(batch_shape)))
|
|
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_sparse_csr_to_dense(self, device, dtype):
|
|
self._test_sparse_compressed_to_dense(device, dtype, torch.sparse_csr)
|
|
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_sparse_csc_to_dense(self, device, dtype):
|
|
self._test_sparse_compressed_to_dense(device, dtype, torch.sparse_csc)
|
|
|
|
@skipMeta
|
|
@skipCPUIfNoMklSparse
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
def test_coo_to_csr_convert(self, device, dtype, coalesced):
|
|
with self.assertRaisesRegex(RuntimeError, "Input is supposed to be a vector"):
|
|
torch._convert_indices_from_coo_to_csr(
|
|
torch.randint(100, (5, 5), device=device),
|
|
size=100)
|
|
|
|
size = (5, 5)
|
|
sparse_dim = 2
|
|
nnz = 10
|
|
sparse_coo, _, _ = self.genSparseTensor(size, sparse_dim, nnz, coalesced, device, dtype)
|
|
sparse_csr = sparse_coo.to_sparse_csr()
|
|
|
|
self.assertTrue(sparse_csr.is_sparse_csr)
|
|
self.assertEqual(sparse_csr.to_dense(), sparse_coo.to_dense())
|
|
|
|
vec = torch.randn((5, 1), dtype=dtype, device=device)
|
|
coo_product = sparse_coo.matmul(vec)
|
|
csr_product = sparse_csr.matmul(vec)
|
|
|
|
self.assertEqual(coo_product, csr_product)
|
|
|
|
vec = torch.randn((100, 1), dtype=dtype, device=device)
|
|
index = torch.tensor([
|
|
[1, 0, 35, 14, 39, 6, 71, 66, 40, 27],
|
|
[92, 31, 62, 50, 22, 65, 89, 74, 56, 34],
|
|
], dtype=torch.int32)
|
|
values = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=dtype, device=device)
|
|
coo = torch.sparse_coo_tensor(index, values, torch.Size([100, 100]), dtype=dtype, device=device)
|
|
csr = coo.to_sparse_csr()
|
|
|
|
self.assertEqual(coo.matmul(vec), csr.matmul(vec))
|
|
|
|
col_indices = torch.tensor([
|
|
31, 92, 65, 50, 34, 62, 22, 56, 74, 89
|
|
], dtype=torch.int64, device=device)
|
|
self.assertEqual(csr.col_indices(), col_indices)
|
|
|
|
values = torch.tensor([2, 1, 6, 4, 10, 3, 5, 9, 8, 7], dtype=dtype, device=device)
|
|
self.assertEqual(csr.values(), values)
|
|
|
|
@parametrize("blocksize", [2, 4])
|
|
@dtypes((torch.double, torch.int32), (torch.double, torch.int64))
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
@skipMeta
|
|
def test_csr_to_block_csr(self, device, dtypes, blocksize):
|
|
for shape in [(24, 24), (12, 24)]:
|
|
dtype, index_dtype = dtypes
|
|
m, k = shape
|
|
nnz = random.randint(0, m * k)
|
|
t = self.genSparseCSRTensor((m * blocksize, k * blocksize), nnz, dtype=dtype,
|
|
device=device, index_dtype=index_dtype)
|
|
st = sp.csr_matrix((t.values().cpu(), t.col_indices().cpu(), t.crow_indices().cpu()), shape=tuple(t.size()))
|
|
block_t = t.to_sparse_bsr((blocksize, blocksize))
|
|
self.assertEqual(block_t.values().dim(), 3)
|
|
self.assertTrue(block_t.layout == torch.sparse_bsr)
|
|
block_st = st.tobsr(blocksize=(blocksize, blocksize))
|
|
block_st.sort_indices()
|
|
self.assertEqual(block_t.values().cpu(), block_st.data)
|
|
self.assertEqual(block_t.col_indices().cpu(), torch.tensor(block_st.indices).to(index_dtype))
|
|
self.assertEqual(block_t.crow_indices().cpu(), torch.tensor(block_st.indptr).to(index_dtype))
|
|
|
|
@dtypes(torch.double)
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
def test_csr_to_block_csr_errors(self, device, dtype):
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
nnz = 15
|
|
t = self.genSparseCSRTensor((16, 16), nnz, dtype=dtype,
|
|
device=device, index_dtype=index_dtype)
|
|
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
r"tensor sparse size \(.*,.*\) must be divisible by given blocksize \(.*,.*\)"):
|
|
block_t = t.to_sparse_bsr((5, 5))
|
|
|
|
# TODO: Support auto generation of device check for sparse tensors
|
|
# See: https://github.com/pytorch/pytorch/issues/59058
|
|
@onlyCUDA
|
|
@dtypes(torch.double)
|
|
def test_matmul_device_mismatch(self, device, dtype):
|
|
cpu = torch.rand((10, 10))
|
|
cuda = cpu.cuda()
|
|
for s, m1, m2 in itertools.product((cpu, cuda), repeat=3):
|
|
csr = m1.to_sparse()
|
|
if s.device == csr.device == m2.device:
|
|
torch.addmm(s, csr, m2)
|
|
else:
|
|
with self.assertRaisesRegex(RuntimeError, "Expected all tensors to be on the same device"):
|
|
torch.addmm(s, csr, m2)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@skipCUDAIfNoSparseGeneric
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_and_complex_types_and(
|
|
*[torch.half] if SM53OrLater else [],
|
|
*[torch.bfloat16] if SM80OrLater else []))
|
|
def test_csr_matvec(self, device, dtype):
|
|
|
|
if TEST_WITH_ROCM and (dtype == torch.half or dtype == torch.bfloat16):
|
|
self.skipTest("ROCm doesn't work with half dtypes correctly.")
|
|
|
|
side = 100
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
csr = self.genSparseCSRTensor((side, side), 1000, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
vec = torch.randn(side, dtype=dtype, device=device)
|
|
|
|
res = csr.matmul(vec)
|
|
expected = csr.to_dense().matmul(vec)
|
|
|
|
atol, rtol = (2e-3, 1e-3) if dtype == torch.half else (None, None)
|
|
self.assertEqual(res, expected, atol=atol, rtol=rtol)
|
|
|
|
bad_vec = torch.randn(side + 10, dtype=dtype, device=device)
|
|
err_msg = "size mismatch, got"
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
csr.matmul(bad_vec)
|
|
|
|
@onlyCUDA
|
|
# hmm, the test passes ok on CUDA when Rocm is not available:
|
|
@skipCUDAIfRocmVersionLessThan((5, 2))
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_baddbmm(self, device, dtype):
|
|
|
|
# TODO: disable the invariant checks within torch.baddbmm that
|
|
# constructs unconventional csr tensors leading to
|
|
# RuntimeError: tensor dimensionality must be sum of batch,
|
|
# base, and dense dimensionalities (=0 + 2 + 0) but got 3
|
|
# when invariant checking is enabled. When done, undecorate run_test.
|
|
@torch.sparse.check_sparse_tensor_invariants(enable=False)
|
|
def run_test(c, a, a_batched, b, op_b=False, op_out=False, *, dtype=None, device=None):
|
|
alpha = complex(random.random(), random.random()) if dtype.is_complex else random.random()
|
|
beta = complex(random.random(), random.random()) if dtype.is_complex else random.random()
|
|
b = b.mH if (op_b and a.shape == b.shape) else b
|
|
|
|
actual = torch.baddbmm(c, a_batched, b, alpha=alpha, beta=beta)
|
|
|
|
out = torch.empty_like(c.mH if op_out and a.shape == b.shape else c)
|
|
torch.baddbmm(c, a_batched, b, alpha=alpha, beta=beta, out=out)
|
|
|
|
expected = [torch.addmm(c[i], a, b[i], alpha=alpha, beta=beta) for i in range(c.shape[0])]
|
|
expected = torch.stack(expected, 0)
|
|
|
|
self.assertEqual(actual, out)
|
|
self.assertEqual(actual, expected)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for (m, n, k), batch_size, noncontiguous in zip(itertools.product([2, 5], repeat=3), [1, 3], [True, False]):
|
|
nnz = random.randint(0, m * k)
|
|
a = self.genSparseCSRTensor((m, k), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
|
|
# a_batched is a regular CSR tensor but with a batch dimension in the shape
|
|
a_batched = torch.sparse_csr_tensor(
|
|
a.crow_indices(), a.col_indices(), a.values(), (batch_size, m, k), check_invariants=False)
|
|
|
|
b = make_tensor((batch_size, k, n), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
c = make_tensor((batch_size, m, n), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
for op_b, op_out in itertools.product([True, False], repeat=2):
|
|
run_test(c, a, a_batched, b, op_b, op_out, dtype=dtype, device=device)
|
|
|
|
@onlyCUDA
|
|
@skipCUDAIfNoSparseGeneric
|
|
@skipIfRocmVersionLessThan((6, 3))
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_bmm(self, device, dtype):
|
|
def run_test(a, a_batched, b, op_b=False, op_out=False, *, dtype=None, device=None):
|
|
b = b.mH if (op_b and a.shape == b.shape) else b
|
|
|
|
actual = torch.bmm(a_batched, b)
|
|
|
|
out = torch.empty_like(actual.mH if op_out and a.shape == b.shape else actual)
|
|
torch.bmm(a_batched, b, out=out)
|
|
|
|
expected = [torch.mm(a, b[i]) for i in range(b.shape[0])]
|
|
expected = torch.stack(expected, 0)
|
|
|
|
self.assertEqual(actual, out)
|
|
self.assertEqual(actual, expected)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for (m, n, k), batch_size, noncontiguous in zip(itertools.product([2, 5], repeat=3), [1, 3], [True, False]):
|
|
nnz = random.randint(0, m * k)
|
|
a = self.genSparseCSRTensor((m, k), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
|
|
# a_batched is a regular CSR tensor but with a batch
|
|
# dimension in the shape. It is unorthodox in PyTorch
|
|
# to represent a batch sparse tensor in this way,
|
|
# hence checking the tensor invariants is locally
|
|
# turned off.
|
|
a_batched = torch.sparse_csr_tensor(
|
|
a.crow_indices(), a.col_indices(), a.values(), (batch_size, m, k), check_invariants=False)
|
|
|
|
b = make_tensor((batch_size, k, n), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
for op_b, op_out in itertools.product([True, False], repeat=2):
|
|
run_test(a, a_batched, b, op_b, op_out, dtype=dtype, device=device)
|
|
|
|
def run_test_block_addmm_addmv(self,
|
|
addmv_addmm,
|
|
c,
|
|
a,
|
|
b,
|
|
op_b=False,
|
|
op_out=False,
|
|
*,
|
|
dtype=None,
|
|
device=None,
|
|
ref=_npref_block_addmm_addmv):
|
|
alpha = complex(random.random(), random.random()) if dtype.is_complex else random.random()
|
|
beta = complex(random.random(), random.random()) if dtype.is_complex else random.random()
|
|
b = b.mH if (op_b and a.shape == b.shape) else b
|
|
|
|
actual = addmv_addmm(c, a, b, alpha=alpha, beta=beta)
|
|
|
|
out = torch.empty_like(c.mH if op_out and a.shape == b.shape else c)
|
|
addmv_addmm(c, a, b, alpha=alpha, beta=beta, out=out)
|
|
expected = ref(c, a, b, alpha, beta)
|
|
|
|
self.assertEqual(actual, out)
|
|
self.assertEqual(actual, expected, lambda msg: f"{msg}\na={a}\nc={c}\nb={b}\nalpha={alpha} beta={beta}")
|
|
|
|
# TODO: block_size 1 is broken
|
|
@parametrize("block_size", [2, 3])
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@parametrize("noncontiguous", [True, False])
|
|
@skipCPUIfNoMklSparse
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
@skipIfTorchDynamo("raises 'sparse matrix length is ambiguous; use getnnz()'")
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_and_complex_types_and(
|
|
*[torch.half] if SM53OrLater else [],
|
|
*[torch.bfloat16] if SM80OrLater else []))
|
|
@precisionOverride({torch.float32: 1e-3, torch.complex64: 1e-3,
|
|
torch.float64: 1e-5, torch.complex128: 1e-5,
|
|
torch.float16: 1e-3, torch.bfloat16: 1e-3})
|
|
def test_block_addmm(self, device, dtype, index_dtype, block_size, noncontiguous):
|
|
|
|
def make_transposed_addmm_op(f):
|
|
|
|
def tt(t):
|
|
if isinstance(t, torch.Tensor):
|
|
return t.transpose(-2, -1)
|
|
else:
|
|
# assume numpy/scipy spmatrix
|
|
return t.transpose()
|
|
|
|
@functools.wraps(f)
|
|
def wrapper(c, a, b, alpha=None, beta=None, out=None):
|
|
if out is not None:
|
|
# the ref takes no out kwarg
|
|
assert isinstance(out, torch.Tensor)
|
|
# transpose inplace to propagate out to checking context
|
|
out.transpose_(-2, -1)
|
|
return f(tt(c), tt(b), tt(a), alpha=alpha, beta=beta, out=out)
|
|
else:
|
|
return f(tt(c), tt(b), tt(a), alpha=alpha, beta=beta)
|
|
|
|
return wrapper
|
|
|
|
def ref_sp_numpy(c, a, b, alpha=None, beta=None, out=None):
|
|
|
|
def prep_input(t):
|
|
|
|
def to_sp_block_compressed(t):
|
|
|
|
if t.layout is torch.sparse_bsc:
|
|
tt = t.transpose(-1, -2)
|
|
else:
|
|
tt = t
|
|
|
|
t_sp_bsr = sp.bsr_matrix(
|
|
(
|
|
tt.values().cpu().numpy(),
|
|
tt.col_indices().cpu().numpy(),
|
|
tt.crow_indices().cpu().numpy(),
|
|
),
|
|
shape=tt.shape,
|
|
)
|
|
|
|
if t.layout is torch.sparse_bsc:
|
|
return t_sp_bsr.transpose()
|
|
else:
|
|
return t_sp_bsr
|
|
|
|
if t.layout is not torch.strided:
|
|
return to_sp_block_compressed(t)
|
|
else:
|
|
return t.cpu().resolve_conj().numpy()
|
|
|
|
res = _npref_block_addmm_addmv(
|
|
*(prep_input(t) for t in (c, a, b)),
|
|
alpha,
|
|
beta
|
|
)
|
|
|
|
if out is not None:
|
|
out.copy_(res)
|
|
return out
|
|
else:
|
|
return res
|
|
|
|
def ref_half_bfloat16(c, a, b, alpha=None, beta=None, out=None):
|
|
res = alpha * (a.to_dense().to(torch.float32) @ b.to_dense().to(torch.float32)).to(a.dtype) + beta * c
|
|
if out is not None:
|
|
out.copy_(res)
|
|
return out
|
|
else:
|
|
return res
|
|
|
|
if dtype in (torch.half, torch.bfloat16):
|
|
ref = ref_half_bfloat16
|
|
else:
|
|
ref = ref_sp_numpy
|
|
|
|
for (m, n, k) in itertools.product([2, 5], repeat=3):
|
|
nnz = random.randint(0, m * k)
|
|
a = self.genSparseCSRTensor((m, k), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a_data = make_tensor((nnz, block_size, block_size), dtype=dtype, device=device)
|
|
a_data = a_data.mT if noncontiguous else a_data
|
|
a = torch.sparse_bsr_tensor(a.crow_indices(), a.col_indices(),
|
|
a_data, (m * block_size, k * block_size), check_invariants=False)
|
|
b = make_tensor((k * block_size, n * block_size), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
c = make_tensor((m * block_size, n * block_size), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
for op_b, op_out in itertools.product([True, False], repeat=2):
|
|
self.run_test_block_addmm_addmv(torch.addmm, c, a, b, op_b, op_out, dtype=dtype, device=device, ref=ref)
|
|
self.run_test_block_addmm_addmv(make_transposed_addmm_op(torch.addmm),
|
|
c,
|
|
a,
|
|
b,
|
|
op_b,
|
|
op_out,
|
|
dtype=dtype,
|
|
device=device,
|
|
ref=make_transposed_addmm_op(ref))
|
|
|
|
@parametrize("block_size", [2, 3])
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@parametrize("noncontiguous", [True, False])
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_block_addmv(self, device, dtype, index_dtype, block_size, noncontiguous):
|
|
# TODO: Explicitly disable block size 1 support
|
|
# if (TEST_WITH_ROCM or not TEST_CUSPARSE_GENERIC) and block_size == 1:
|
|
# return
|
|
def ref_block_addmv(c, a, b, alpha, beta):
|
|
return _npref_block_addmm_addmv(c, a.to_dense(), b, alpha, beta)
|
|
|
|
for (m, k) in itertools.product([2, 5], repeat=2):
|
|
nnz = random.randint(0, m * k)
|
|
if not noncontiguous:
|
|
a = self.genSparseCSRTensor((m * block_size, k * block_size), nnz,
|
|
dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a = a.to_sparse_bsr((block_size, block_size))
|
|
else:
|
|
a = self.genSparseCSRTensor((m, k), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a_data = make_tensor((nnz, block_size, block_size), dtype=dtype, device=device)
|
|
a_data = a_data.mT if noncontiguous else a_data # Test column-major blocks
|
|
a = torch.sparse_bsr_tensor(a.crow_indices(), a.col_indices(),
|
|
a_data, (m * block_size, k * block_size), check_invariants=False)
|
|
b = make_tensor((k * block_size,), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
c = make_tensor((m * block_size,), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
self.run_test_block_addmm_addmv(torch.addmv, c, a, b, dtype=dtype, device=device, ref=ref_block_addmv)
|
|
|
|
@parametrize("matrix_shape", [(3, 3), (5, 7), (11, 9)], name_fn=lambda x: "shape_{}x{}".format(*x))
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
@onlyCPU
|
|
def test_addmv(self, device, dtype, matrix_shape):
|
|
mat = torch.randn(matrix_shape, dtype=dtype, device=device)
|
|
mat[mat.real < 0] = 0
|
|
sparse_mat = mat.to_sparse_csr()
|
|
mvec = torch.randn((mat.size(1),), dtype=dtype, device=device)
|
|
avec = torch.randn((mat.size(0),), dtype=torch.float64, device=device)
|
|
ref_output = torch.addmv(avec, mat, mvec)
|
|
output = torch.addmv(avec, sparse_mat, mvec)
|
|
self.assertEqual(ref_output, output)
|
|
|
|
@parametrize("block_size", [2, 3])
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@parametrize("noncontiguous", [True, False])
|
|
@skipCPUIfNoMklSparse
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_block_triangular_solve(self, device, dtype, index_dtype, block_size, noncontiguous):
|
|
def run_test(a, b, upper, transpose, unitriangular, op_out):
|
|
if unitriangular and self.device_type == 'cpu':
|
|
# TODO: When unitriangular=True results are not correct on CPU
|
|
return
|
|
|
|
if not upper and self.device_type == 'cpu':
|
|
# TODO: When upper=False some generated inputs might crash on CPU
|
|
return
|
|
|
|
actual = torch.triangular_solve(b, a, upper=upper, unitriangular=unitriangular, transpose=transpose)
|
|
actual_X = actual.solution
|
|
actual_A_clone = actual.cloned_coefficient
|
|
self.assertTrue(actual_A_clone.numel() == 0)
|
|
if a._nnz() == 0:
|
|
self.assertTrue(actual_X.isnan().all())
|
|
return
|
|
|
|
# TODO: replace with torch method when implemented to_dense() on block sparse tensor
|
|
a_bsr = sp.bsr_matrix(
|
|
(
|
|
a.values().cpu().numpy(),
|
|
a.col_indices().cpu().numpy(),
|
|
a.crow_indices().cpu().numpy(),
|
|
),
|
|
shape=a.shape,
|
|
)
|
|
expected_X, _ = torch.triangular_solve(
|
|
b,
|
|
torch.tensor(a_bsr.todense(), device=device),
|
|
transpose=transpose,
|
|
upper=upper,
|
|
unitriangular=unitriangular)
|
|
|
|
if expected_X.isnan().any():
|
|
# TODO: zeros on the diagonal are not handled for CPU path
|
|
# there's no way to query this info from MKL
|
|
if self.device_type == 'cuda' and not TEST_WITH_ROCM:
|
|
self.assertTrue(actual_X.isnan().any() or actual_X.isinf().any())
|
|
return
|
|
|
|
self.assertEqual(actual_X, expected_X)
|
|
|
|
out = torch.empty_like(b.mH if op_out and a.shape == b.shape else b)
|
|
torch.triangular_solve(
|
|
b, a,
|
|
upper=upper, unitriangular=unitriangular, transpose=transpose, out=(out, actual_A_clone)
|
|
)
|
|
self.assertEqual(out, actual_X)
|
|
self.assertEqual(out, expected_X)
|
|
|
|
for (m, k) in itertools.product([2, 3], [1, 3]):
|
|
nnz = random.randint(0, m * m)
|
|
if not noncontiguous:
|
|
a = self.genSparseCSRTensor((m * block_size, m * block_size), nnz,
|
|
dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a = a.to_sparse_bsr((block_size, block_size))
|
|
else:
|
|
a = self.genSparseCSRTensor((m, m), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a_data = make_tensor((nnz, block_size, block_size), dtype=dtype, device=device)
|
|
a_data = a_data.mT if noncontiguous else a_data # Test column-major blocks
|
|
a = torch.sparse_bsr_tensor(a.crow_indices(), a.col_indices(),
|
|
a_data, (m * block_size, m * block_size), check_invariants=False)
|
|
b = make_tensor((m * block_size, k), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
|
|
for (upper, unitriangular, transpose, op_out) in itertools.product([True, False], repeat=4):
|
|
run_test(a, b, upper, unitriangular, transpose, op_out)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@skipIfRocmVersionLessThan((6, 3))
|
|
@dtypes(torch.double)
|
|
def test_mm(self, device, dtype):
|
|
def test_shape(di, dj, dk, nnz0=None, nnz1=None):
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
|
|
def _test_addmm(t, x, y):
|
|
# TODO: addmm doesn't support strided result for sparse inputs.
|
|
# res = beta * t + alpha * (x @ y)
|
|
res = torch.addmm(t, x, y, beta=beta, alpha=alpha)
|
|
expected = torch.addmm(t, x.to_dense(), y.to_dense(), beta=beta, alpha=alpha)
|
|
self.assertEqual(res, expected)
|
|
|
|
res = torch.addmm(t, x, y)
|
|
expected = torch.addmm(t, x.to_dense(), y.to_dense())
|
|
self.assertEqual(res, expected)
|
|
|
|
def _test_mm(x, y):
|
|
res = torch.mm(x, y)
|
|
expected = torch.mm(x.to_dense(), y.to_dense())
|
|
if x.layout is torch.strided or y.layout is torch.strided:
|
|
self.assertEqual(res.layout, torch.strided)
|
|
else:
|
|
self.assertEqual(res.layout, torch.sparse_csr)
|
|
self.assertEqual(res.to_dense(), expected)
|
|
|
|
def _test(t, x, y):
|
|
_test_addmm(t, x, y)
|
|
_test_mm(x, y)
|
|
|
|
if nnz0 is None:
|
|
nnz0 = random.randint(di * dk // 2, di * dk)
|
|
t = torch.randn(di, dj, dtype=dtype, device=device)
|
|
x = self.genSparseCSRTensor((di, dk), nnz0, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
y = torch.randn(dk, dj, dtype=dtype, device=device)
|
|
_test(t, x, y)
|
|
|
|
t = torch.randn(di, dj, dtype=dtype, device=device)
|
|
x = self.genSparseCSCTensor((di, dk), nnz0, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
y = torch.randn(dk, dj, dtype=dtype, device=device)
|
|
_test(t, x, y)
|
|
|
|
if nnz1 is None:
|
|
nnz1 = random.randint(dk * dj // 2, dk * dj)
|
|
t = torch.randn(di, dj, dtype=dtype, device=device)
|
|
x = torch.randn(di, dk, dtype=dtype, device=device)
|
|
y = self.genSparseCSRTensor((dk, dj), nnz1, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
_test(t, x, y)
|
|
|
|
t = torch.randn(di, dj, dtype=dtype, device=device)
|
|
x = torch.randn(di, dk, dtype=dtype, device=device)
|
|
y = self.genSparseCSCTensor((dk, dj), nnz1, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
_test(t, x, y)
|
|
|
|
x_shape, y_shape = x.shape, y.shape
|
|
|
|
gen_csr_csc = [self.genSparseCSRTensor, self.genSparseCSCTensor]
|
|
|
|
# Test mm({CSR, CSC}, {CSR, CSC})
|
|
for gen_x, gen_y in itertools.product(gen_csr_csc, gen_csr_csc):
|
|
x = gen_x(x_shape, nnz0, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
y = gen_y(y_shape, nnz1, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
_test_mm(x, y)
|
|
|
|
def test_empty_inputs(lhs_layout, rhs_layout):
|
|
xd = torch.rand(10, 0, device=device, dtype=dtype)
|
|
yd = xd.transpose(-2, -1)
|
|
zd = torch.rand(0, 0, device=device, dtype=dtype)
|
|
|
|
xls, yls, zls = (t.to_sparse(layout=lhs_layout) for t in (xd, yd, zd))
|
|
xrs, yrs, zrs = (t.to_sparse(layout=rhs_layout) for t in (xd, yd, zd))
|
|
|
|
for ls, rs, ld, rd in [(xls, yrs, xd, yd), (xls, zrs, xd, zd), (zls, yrs, zd, yd), (zls, zrs, zd, zd)]:
|
|
res_sparse = ls @ rs
|
|
res_dense = ld @ rd
|
|
self.assertEqual(res_sparse.to_dense(), res_dense)
|
|
|
|
def test_orthogonal_inputs(lhs_layout, rhs_layout):
|
|
ones = torch.ones(2, 2, device=device, dtype=dtype)
|
|
zeros = torch.zeros(2, 2, device=device, dtype=dtype)
|
|
x = torch.cat((ones, zeros), -1).to_sparse(layout=lhs_layout)
|
|
y = torch.cat((zeros, ones), -2).to_sparse(layout=rhs_layout)
|
|
res = x @ y
|
|
res_expected = torch.zeros(*res.shape, device=device, dtype=dtype, layout=res.layout)
|
|
self.assertEqual(res, res_expected)
|
|
|
|
for lhs_layout, rhs_layout in itertools.product([torch.sparse_csr, torch.sparse_csc], repeat=2):
|
|
test_empty_inputs(lhs_layout, rhs_layout)
|
|
test_orthogonal_inputs(lhs_layout, rhs_layout)
|
|
|
|
for i in [2, 4]:
|
|
for j in [2, 4, 7]:
|
|
for k in [2, 3, 7]:
|
|
test_shape(i, j, k)
|
|
test_shape(4, 4, 4, 0, 0)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_and_complex_types_and(
|
|
*[torch.half] if SM53OrLater and TEST_CUSPARSE_GENERIC else [],
|
|
*[torch.bfloat16] if SM80OrLater and TEST_CUSPARSE_GENERIC else []))
|
|
@precisionOverride({torch.bfloat16: 1e-2, torch.float16: 1e-2})
|
|
def test_sparse_mm(self, device, dtype):
|
|
def test_shape(d1, d2, d3, nnz, transposed, index_dtype):
|
|
if transposed:
|
|
D = torch.randn(d3, d2, dtype=dtype, device=device).t_()
|
|
else:
|
|
D = torch.randn(d2, d3, dtype=dtype, device=device)
|
|
S = self.genSparseCSRTensor((d1, d2), nnz, device=device, dtype=dtype, index_dtype=index_dtype)
|
|
S_dense = S.to_dense()
|
|
self.assertEqual(torch.sparse.mm(S, D), torch.mm(S_dense, D))
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
test_shape(7, 8, 9, 20, False, index_dtype)
|
|
test_shape(7, 8, 9, 20, True, index_dtype)
|
|
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_and_complex_types_and(
|
|
*[torch.half] if SM53OrLater and TEST_CUSPARSE_GENERIC else [],
|
|
*[torch.bfloat16] if SM80OrLater and TEST_CUSPARSE_GENERIC else []))
|
|
@precisionOverride({torch.bfloat16: 3.5e-2, torch.float16: 1e-2})
|
|
def test_sparse_addmm(self, device, dtype):
|
|
def test_shape(m, n, p, nnz, broadcast, index_dtype, alpha_beta=None):
|
|
if alpha_beta is None:
|
|
alpha = random.random()
|
|
beta = random.random()
|
|
else:
|
|
alpha, beta = alpha_beta
|
|
if broadcast:
|
|
D1 = make_tensor((), dtype=dtype, device=device)
|
|
else:
|
|
D1 = make_tensor([n, p], dtype=dtype, device=device)
|
|
D2 = make_tensor([m, p], dtype=dtype, device=device)
|
|
S = self.genSparseCSRTensor([n, m], nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
S_dense = S.to_dense()
|
|
Y = torch.sparse.addmm(D1, S, D2, beta=beta, alpha=alpha)
|
|
Y_dense = torch.addmm(D1, S_dense, D2, beta=beta, alpha=alpha)
|
|
self.assertEqual(Y, Y_dense)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
test_shape(7, 8, 9, 20, False, index_dtype, None)
|
|
test_shape(7, 8, 9, 20, True, index_dtype, None)
|
|
test_shape(7, 8, 9, 20, False, index_dtype, (1, 0))
|
|
test_shape(7, 8, 9, 20, True, index_dtype, (1, 0))
|
|
test_shape(7, 8, 9, 20, False, index_dtype, (1, 1))
|
|
test_shape(7, 8, 9, 20, True, index_dtype, (1, 1))
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@skipIfRocmVersionLessThan((6, 3))
|
|
@dtypes(*floating_and_complex_types())
|
|
@precisionOverride({torch.double: 1e-8, torch.float: 1e-4, torch.bfloat16: 0.6,
|
|
torch.half: 1e-1, torch.cfloat: 1e-4, torch.cdouble: 1e-8})
|
|
@dtypesIfCUDA(*floating_types_and(torch.complex64,
|
|
*[torch.bfloat16] if (SM80OrLater and not TEST_WITH_ROCM) else [],
|
|
*[torch.half] if (SM53OrLater and not TEST_WITH_ROCM) else [],
|
|
*[torch.complex128]
|
|
if CUSPARSE_SPMM_COMPLEX128_SUPPORTED or HIPSPARSE_SPMM_COMPLEX128_SUPPORTED
|
|
else []))
|
|
@sparse_compressed_nonblock_layouts()
|
|
def test_addmm_all_sparse_csr(self, device, dtype, layout):
|
|
M = torch.randn(10, 25, device=device).to(dtype)
|
|
m1 = torch.randn(10, 50, device=device).to(dtype)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, layout=layout, mode="all_sparse")
|
|
|
|
# Test 0-strided
|
|
M = torch.randn(10, 1, device=device).to(dtype).expand(10, 25)
|
|
m1 = torch.randn(10, 1, device=device).to(dtype).expand(10, 50)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, layout=layout, mode="all_sparse")
|
|
|
|
# Test beta=0, M=nan
|
|
M = torch.full((10, 25), float('nan'), device=device).to(dtype)
|
|
m1 = torch.randn(10, 50, device=device).to(dtype)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, beta=0, layout=layout, mode="all_sparse")
|
|
|
|
# Test transpose
|
|
for t1, t2, t3, t4 in itertools.product([True, False], repeat=4):
|
|
def maybe_transpose(cond, m):
|
|
if not cond:
|
|
return m
|
|
return m.t().clone(memory_format=torch.contiguous_format).t()
|
|
|
|
M = maybe_transpose(t1, torch.randn(10, 25, device=device).to(dtype))
|
|
m1 = maybe_transpose(t2, torch.randn(10, 50, device=device).to(dtype))
|
|
m2 = maybe_transpose(t3, torch.randn(50, 25, device=device).to(dtype))
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, transpose_out=t4, layout=layout, mode="all_sparse")
|
|
|
|
@onlyCPU
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(*floating_and_complex_types())
|
|
@sparse_compressed_nonblock_layouts()
|
|
def test_addmm_dense_result(self, device, dtype, layout):
|
|
M = torch.randn(10, 25, device=device).to(dtype)
|
|
m1 = torch.randn(10, 50, device=device).to(dtype)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, layout=layout, mode="dense_result")
|
|
|
|
# Test 0-strided
|
|
M = torch.randn(10, 1, device=device).to(dtype).expand(10, 25)
|
|
m1 = torch.randn(10, 1, device=device).to(dtype).expand(10, 50)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, layout=layout, mode="dense_result")
|
|
|
|
# Test beta=0, M=nan
|
|
M = torch.full((10, 25), float('nan'), device=device).to(dtype)
|
|
m1 = torch.randn(10, 50, device=device).to(dtype)
|
|
m2 = torch.randn(50, 25, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, beta=0, layout=layout, mode="dense_result")
|
|
|
|
# Test transpose
|
|
for t1, t2, t3, t4 in itertools.product([True, False], repeat=4):
|
|
def maybe_transpose(cond, m):
|
|
if not cond:
|
|
return m
|
|
return m.t().clone(memory_format=torch.contiguous_format).t()
|
|
|
|
M = maybe_transpose(t1, torch.randn(10, 25, device=device).to(dtype))
|
|
m1 = maybe_transpose(t2, torch.randn(10, 50, device=device).to(dtype))
|
|
m2 = maybe_transpose(t3, torch.randn(50, 25, device=device).to(dtype))
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, transpose_out=t4, layout=layout, mode="dense_result")
|
|
|
|
@parametrize("k", [0, 1, 8])
|
|
@parametrize("n", [0, 1, 10])
|
|
@parametrize("m", [0, 1, 25])
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(*floating_and_complex_types())
|
|
@dtypesIfCUDA(*floating_types_and(torch.complex64,
|
|
*[torch.bfloat16] if SM80OrLater and not TEST_WITH_ROCM else [],
|
|
*[torch.half] if SM53OrLater and not TEST_WITH_ROCM else [],
|
|
*[torch.complex128]
|
|
if CUSPARSE_SPMM_COMPLEX128_SUPPORTED or HIPSPARSE_SPMM_COMPLEX128_SUPPORTED
|
|
else []))
|
|
@precisionOverride({torch.double: 1e-8, torch.float: 1e-4, torch.bfloat16: 0.6,
|
|
torch.half: 1e-1, torch.cfloat: 1e-4, torch.cdouble: 1e-8})
|
|
def test_addmm_sizes_all_sparse_csr(self, device, dtype, m, n, k):
|
|
M = torch.randn(n, m, device=device).to(dtype)
|
|
m1 = torch.randn(n, k, device=device).to(dtype)
|
|
m2 = torch.randn(k, m, device=device).to(dtype)
|
|
_test_addmm_addmv(self, torch.addmm, M, m1, m2, layout=torch.sparse_csr, mode="all_sparse")
|
|
|
|
M = torch.randn(n, m, device=device).to(dtype).to_sparse_csr()
|
|
m1 = torch.randn(n, k + 1, device=device).to(dtype).to_sparse_csr()
|
|
m2 = torch.randn(k, m, device=device).to(dtype).to_sparse_csr()
|
|
self.assertRaisesRegex(RuntimeError, f"{n}x{k + 1}.*{k}x{m}", lambda: torch.addmm(M, m1, m2))
|
|
self.assertRaisesRegex(RuntimeError, f"{n}x{k + 1}.*{k}x{m}", lambda: torch.mm(m1, m2))
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(torch.float)
|
|
def test_addmm_errors(self, device, dtype):
|
|
# test that the errors are the same for dense and sparse versions
|
|
import re
|
|
|
|
def test1(*, is_sparse):
|
|
# shapes must be compatible for matrix multiplication
|
|
a = make_tensor((2, 3), dtype=dtype, device=device)
|
|
if is_sparse:
|
|
a_sparse = a.to_sparse_csr()
|
|
return torch.addmm(a, a_sparse, a)
|
|
else:
|
|
return torch.addmm(a, a, a)
|
|
|
|
def test2(*, is_sparse):
|
|
# mat2 must be a matrix
|
|
a = make_tensor((2, 3), dtype=dtype, device=device)
|
|
if is_sparse:
|
|
a_sparse = a.to_sparse_csr()
|
|
return torch.addmm(a, a_sparse, a.unsqueeze(0))
|
|
else:
|
|
return torch.addmm(a, a, a.unsqueeze(0))
|
|
|
|
def test3(*, is_sparse):
|
|
# the first input needs to be 1D or 2D
|
|
a = make_tensor((3, 3), dtype=dtype, device=device)
|
|
if is_sparse:
|
|
a_sparse = a.to_sparse_csr()
|
|
return torch.addmm(a.unsqueeze(0), a_sparse, a)
|
|
else:
|
|
return torch.addmm(a.unsqueeze(0), a, a)
|
|
|
|
for test in (test1, test2, test3):
|
|
try:
|
|
test(is_sparse=False)
|
|
except RuntimeError as msg:
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(str(msg))):
|
|
test(is_sparse=True)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(torch.float)
|
|
def test_mm_errors(self, device, dtype):
|
|
# test that the errors are the same for dense and sparse versions
|
|
import re
|
|
|
|
def test1(*, is_sparse):
|
|
# shapes must be compatible for matrix multiplication
|
|
a = make_tensor((2, 3), dtype=dtype, device=device)
|
|
if is_sparse:
|
|
a_sparse = a.to_sparse_csr()
|
|
return torch.mm(a_sparse, a)
|
|
else:
|
|
return torch.mm(a, a)
|
|
|
|
def test2(*, is_sparse):
|
|
# mat2 must be a matrix
|
|
a = make_tensor((2, 3), dtype=dtype, device=device)
|
|
if is_sparse:
|
|
a_sparse = a.to_sparse_csr()
|
|
return torch.mm(a_sparse, a.unsqueeze(0))
|
|
else:
|
|
return torch.mm(a, a.unsqueeze(0))
|
|
|
|
for test in (test1, test2):
|
|
try:
|
|
test(is_sparse=False)
|
|
except RuntimeError as msg:
|
|
with self.assertRaisesRegex(RuntimeError, re.escape(str(msg))):
|
|
test(is_sparse=True)
|
|
|
|
@sparse_compressed_nonblock_layouts()
|
|
@dtypes(torch.float, torch.double)
|
|
def test_add(self, device, layout, dtype):
|
|
def _test_spadd_shape(nnz, shape):
|
|
# sparse.to_dense() uses torch.add internally so if torch.add is wrong,
|
|
# the dense tensor will be wrong but this test would still pass
|
|
# there's a separate test that checks for the correctness of the .to_dense() call
|
|
x = self.genSparseCompressedTensor(shape, nnz,
|
|
dtype=dtype,
|
|
device=device,
|
|
index_dtype=torch.int32,
|
|
layout=layout,
|
|
blocksize=())
|
|
y = torch.randn(*shape, dtype=dtype, device=device)
|
|
r = random.random()
|
|
|
|
res = torch.add(y, x, alpha=r)
|
|
expected = y + r * x.to_dense()
|
|
self.assertEqual(res, expected)
|
|
res_perm = torch.add(x, y, alpha=r)
|
|
self.assertEqual(res_perm, expected)
|
|
|
|
# Non contiguous dense tensor
|
|
s = list(shape)
|
|
s[0] = shape[-1]
|
|
s[-1] = shape[0]
|
|
y = torch.randn(*s, dtype=torch.double, device=device)
|
|
y.transpose_(0, len(s) - 1)
|
|
r = random.random()
|
|
|
|
res = torch.add(y, x, alpha=r)
|
|
expected = y + r * x.to_dense()
|
|
res_perm = torch.add(x, y, alpha=r)
|
|
|
|
self.assertEqual(res, expected)
|
|
self.assertEqual(res_perm, expected)
|
|
|
|
|
|
ns = [2, 5]
|
|
batch_shapes = [(), (2,), (2, 3)]
|
|
for b, m, n in itertools.product(batch_shapes, ns, ns):
|
|
_test_spadd_shape(0, (*b, m, n))
|
|
_test_spadd_shape(m * n // 2, (*b, m, n))
|
|
_test_spadd_shape(m * n, (*b, m, n))
|
|
|
|
@dtypes(torch.float, torch.double)
|
|
def test_mul(self, device, dtype):
|
|
# TODO: This whole test should be migrated to OpInfos
|
|
def _test_spadd_shape(fn, nnz, shape):
|
|
x = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
|
|
y = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
|
|
|
|
# Forward comparison
|
|
res_sparse_sparse = fn(y, x)
|
|
res_dense_sparse = fn(y.to_dense(), x)
|
|
res_sparse_dense = fn(y, x.to_dense())
|
|
expected = fn(y.to_dense(), x.to_dense())
|
|
self.assertEqual(res_sparse_sparse, expected)
|
|
# TODO: While result of mul(dense, csr) is csr, it is not fully compressed.
|
|
# That means it may contain materialized zeros, since the dense argument
|
|
# is converted according to the sparsity pattern of csr. In the future
|
|
# we might require the result to be fully compressed.
|
|
self.assertEqual(res_dense_sparse, expected)
|
|
self.assertEqual(res_sparse_dense, expected)
|
|
|
|
# Grad comparison
|
|
x = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
|
|
y = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
|
|
z = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
|
|
|
|
# csr * csr -> csr with csr, csr gradients
|
|
x_a = x.clone().requires_grad_()
|
|
y_a = y.clone().requires_grad_()
|
|
|
|
fn(y_a, x_a).backward(z)
|
|
|
|
x_dense_a = x.to_dense().requires_grad_()
|
|
y_dense_a = y.to_dense().requires_grad_()
|
|
|
|
fn(y_dense_a, x_dense_a).backward(z.to_dense())
|
|
|
|
self.assertEqual(x_a.grad.layout, torch.sparse_csr)
|
|
self.assertEqual(y_a.grad.layout, torch.sparse_csr)
|
|
|
|
self.assertEqual(x_a.grad.to_dense(), x_dense_a.grad)
|
|
self.assertEqual(y_a.grad.to_dense(), y_dense_a.grad)
|
|
|
|
# TODO: Currently strided Tensors cannot have csr gradients
|
|
# dense * csr -> csr with csr, dense gradients
|
|
x_a = x.clone().requires_grad_()
|
|
y_a = y.to_dense().clone().requires_grad_()
|
|
err_msg = "Function MulBackward0 returned an invalid gradient at index 0 - expected layout Strided but got SparseCsr"
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
fn(y_a, x_a).backward(z)
|
|
|
|
# csr * dense -> csr with dense, csr gradients
|
|
x_a = x.to_dense().clone().requires_grad_()
|
|
y_a = y.clone().requires_grad_()
|
|
err_msg = "Function MulBackward0 returned an invalid gradient at index 1 - expected layout Strided but got SparseCsr"
|
|
with self.assertRaisesRegex(RuntimeError, err_msg):
|
|
fn(y_a, x_a).backward(z)
|
|
|
|
_test_spadd_shape(torch.mul, 100, [100, 100])
|
|
_test_spadd_shape(torch.mul, 0, [100, 100])
|
|
_test_spadd_shape(torch.mul, 100, [100, 1])
|
|
_test_spadd_shape(torch.mul, 100, [1, 100])
|
|
|
|
# TODO: enable hybrid once to_dense supports it
|
|
@parametrize('enable_hybrid', [False])
|
|
@all_sparse_compressed_layouts()
|
|
@dtypes(*all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half))
|
|
def test_mul_scalar(self, layout, device, dtype, enable_hybrid):
|
|
for sparse in self.generate_simple_inputs(
|
|
layout, device=device, dtype=dtype, index_dtype=torch.int32, enable_hybrid=enable_hybrid):
|
|
for scalar_dtype in all_types_and_complex_and(torch.bool, torch.bfloat16, torch.half):
|
|
# ComplexHalf is experimental
|
|
if dtype is torch.half and scalar_dtype.is_complex:
|
|
continue
|
|
|
|
scalar_t = torch.tensor(2, dtype=scalar_dtype)
|
|
for scalar in (scalar_t, scalar_t.item()):
|
|
res_out = sparse.mul(scalar)
|
|
self.assertEqual(res_out, scalar * sparse)
|
|
|
|
res_dense_out = sparse.to_dense().mul(scalar)
|
|
# BUG: dispatcher ignores mul.Scalar(Tensor, Scalar)
|
|
# This issues is circumvented in the mul(Tensor, Tensor) kernel.
|
|
self.assertEqual(res_out, res_dense_out)
|
|
|
|
if dtype == torch.result_type(sparse, scalar):
|
|
res_in_dense = sparse.to_dense().mul_(scalar)
|
|
res_in = sparse.clone().mul_(scalar)
|
|
self.assertEqual(res_in, res_in_dense)
|
|
self.assertEqual(res_out, res_in)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_sparse_add(self, device, dtype):
|
|
def run_test(m, n, index_dtype):
|
|
|
|
alpha = random.random()
|
|
nnz1 = random.randint(0, m * n)
|
|
nnz2 = random.randint(0, m * n)
|
|
nnz3 = random.randint(0, m * n)
|
|
|
|
if TEST_WITH_ROCM:
|
|
# ROCm fails when nnz = 0
|
|
nnz1, nnz2, nnz3 = max(1, nnz1), max(1, nnz2), max(1, nnz3)
|
|
|
|
S1 = self.genSparseCSRTensor([m, n], nnz1, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
S2 = self.genSparseCSRTensor([m, n], nnz2, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
S3 = self.genSparseCSRTensor([m, n], nnz3, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
sparse_args = [S1, S2, S3]
|
|
dense_args = [t.to_dense() for t in sparse_args]
|
|
arg_idx = list(range(len(sparse_args)))
|
|
out_idx = arg_idx + [None]
|
|
|
|
for idx1, idx2, idx3 in itertools.product(arg_idx, arg_idx, out_idx):
|
|
s1 = sparse_args[idx1]
|
|
s2 = sparse_args[idx2]
|
|
s3 = None if idx3 is None else sparse_args[idx3]
|
|
d1 = dense_args[idx1]
|
|
d2 = dense_args[idx2]
|
|
d3 = None if idx3 is None else dense_args[idx3]
|
|
|
|
expected = torch.add(d1, d2, alpha=alpha, out=d3)
|
|
actual = torch.add(s1, s2, alpha=alpha, out=s3)
|
|
self.assertEqual(actual.crow_indices().dtype, index_dtype)
|
|
self.assertEqual(actual.col_indices().dtype, index_dtype)
|
|
self.assertEqual(actual, expected)
|
|
self.assertEqual(s3, d3)
|
|
if s3 is not None:
|
|
self.assertEqual(s3.crow_indices().dtype, index_dtype)
|
|
self.assertEqual(s3.col_indices().dtype, index_dtype)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for m, n in itertools.product([3, 5], [3, 5]):
|
|
run_test(m, n, index_dtype)
|
|
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_sparse_add_errors(self, device, dtype):
|
|
def run_test(index_type):
|
|
a = self.genSparseCSRTensor((2, 2), 3, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
b = self.genSparseCSRTensor((2, 1), 2, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
with self.assertRaisesRegex(RuntimeError, "Expected input tensors to have the same shape"):
|
|
torch.add(a, b)
|
|
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
run_test(index_dtype)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@skipCUDAIf(
|
|
not _check_cusparse_triangular_solve_available(),
|
|
"cuSparse Generic API SpSV is not available"
|
|
)
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
@precisionOverride({torch.float32: 1e-3, torch.complex64: 1e-3,
|
|
torch.float64: 1e-8, torch.complex128: 1e-8})
|
|
def test_sparse_triangular_solve(self, device, dtype):
|
|
|
|
def run_test(n, k, upper, unitriangular, transpose, zero):
|
|
if not unitriangular:
|
|
triangle_function = torch.triu if upper else torch.tril
|
|
else:
|
|
# Make sure diagonal elements are not materialized.
|
|
# This is to exercise `unitriangular=True` not relying on
|
|
# explicit presence of these indices.
|
|
if upper:
|
|
def remove_diagonal(t):
|
|
return t.triu(-1)
|
|
|
|
else:
|
|
def remove_diagonal(t):
|
|
return t.tril(-1)
|
|
|
|
triangle_function = remove_diagonal
|
|
|
|
make_A = torch.zeros if zero else make_tensor
|
|
A = make_A((n, n), dtype=dtype, device=device)
|
|
A = triangle_function(A)
|
|
A_sparse = A.to_sparse_csr()
|
|
B = make_tensor((n, k), dtype=dtype, device=device)
|
|
|
|
expected = torch.triangular_solve(B, A, upper=upper, unitriangular=unitriangular, transpose=transpose)
|
|
expected_X = expected.solution
|
|
|
|
actual = torch.triangular_solve(B, A_sparse, upper=upper, unitriangular=unitriangular, transpose=transpose)
|
|
actual_X = actual.solution
|
|
actual_A_clone = actual.cloned_coefficient
|
|
self.assertTrue(actual_A_clone.numel() == 0)
|
|
if A_sparse._nnz() == 0:
|
|
self.assertTrue(actual_X.isnan().all())
|
|
return
|
|
self.assertEqual(actual_X, expected_X)
|
|
|
|
# test out with C contiguous strides
|
|
out = torch.empty_strided((n, k), (k, 1), dtype=dtype, device=device)
|
|
torch.triangular_solve(
|
|
B, A_sparse,
|
|
upper=upper, unitriangular=unitriangular, transpose=transpose, out=(out, actual_A_clone)
|
|
)
|
|
self.assertEqual(out, expected_X)
|
|
|
|
# test out with F contiguous strides
|
|
out = torch.empty_strided((n, k), (1, n), dtype=dtype, device=device)
|
|
torch.triangular_solve(
|
|
B, A_sparse,
|
|
upper=upper, unitriangular=unitriangular, transpose=transpose, out=(out, actual_A_clone)
|
|
)
|
|
self.assertEqual(out, expected_X)
|
|
self.assertEqual(out.stride(), (1, n))
|
|
|
|
# test out with discontiguous strides
|
|
out = torch.empty_strided((2 * n, k), (1, 2 * n), dtype=dtype, device=device)[::2]
|
|
if n > 0 and k > 0:
|
|
self.assertFalse(out.is_contiguous())
|
|
self.assertFalse(out.t().is_contiguous())
|
|
before_stride = out.stride()
|
|
torch.triangular_solve(
|
|
B, A_sparse,
|
|
upper=upper, unitriangular=unitriangular, transpose=transpose, out=(out, actual_A_clone)
|
|
)
|
|
self.assertEqual(out, expected_X)
|
|
self.assertEqual(out.stride(), before_stride)
|
|
|
|
ks = [0, 1, 3]
|
|
ns = [5, 3, 0]
|
|
for (k, n), (upper, unitriangular, transpose, zero) in itertools.product(itertools.product(ks, ns),
|
|
itertools.product([True, False], repeat=4)):
|
|
run_test(n, k, upper, unitriangular, transpose, zero)
|
|
|
|
@skipCUDAIf(
|
|
not _check_cusparse_sddmm_available(),
|
|
"cuSparse Generic API SDDMM is not available"
|
|
)
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
@precisionOverride({torch.float32: 1e-3, torch.complex64: 1e-3,
|
|
torch.float64: 1e-8, torch.complex128: 1e-8})
|
|
def test_sampled_addmm(self, device, dtype):
|
|
def run_test(c, a, b, op_a, op_b, *, alpha=None, beta=None):
|
|
if dtype.is_complex:
|
|
alpha = random.random() + 0.3j if alpha is None else alpha
|
|
beta = random.random() + 0.6j if beta is None else beta
|
|
else:
|
|
alpha = random.random() if alpha is None else alpha
|
|
beta = random.random() if beta is None else beta
|
|
|
|
if op_a and a.shape == b.shape:
|
|
a = a.mH
|
|
if op_b and a.shape == b.shape:
|
|
b = b.mH
|
|
|
|
actual = torch.sparse.sampled_addmm(c, a, b, alpha=alpha, beta=beta)
|
|
|
|
out = torch.sparse_csr_tensor(
|
|
*map(torch.clone, (actual.crow_indices(), actual.col_indices())),
|
|
torch.empty_like(actual.values()),
|
|
size=actual.shape
|
|
)
|
|
torch.sparse.sampled_addmm(c, a, b, alpha=alpha, beta=beta, out=out)
|
|
|
|
spy_c = torch.sparse_csr_tensor(c.crow_indices(), c.col_indices(), torch.ones_like(c.values()), size=c.shape)
|
|
expected = alpha * (a @ b) * spy_c.to_dense() + beta * c.to_dense()
|
|
self.assertEqual(actual.to_dense(), out.to_dense())
|
|
self.assertEqual(actual.to_dense(), expected)
|
|
|
|
mnk = list(itertools.product([2, 5], repeat=3))
|
|
|
|
# Add a test case for size 0 a and b tensors
|
|
mnk = mnk + [(5, 5, 0)]
|
|
|
|
batch_shapes = [(), (2,), (2, 3)]
|
|
tf = [True, False]
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for (m, n, k), b, noncontiguous, bcast_c in itertools.product(mnk, batch_shapes, tf, tf):
|
|
if bcast_c and len(b) == 0:
|
|
continue
|
|
nnz = random.randint(0, m * n)
|
|
c_batch = () if bcast_c else b
|
|
c = self.genSparseCSRTensor((*c_batch, m, n), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
a = make_tensor((*b, m, k), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
b = make_tensor((*b, k, n), dtype=dtype, device=device, noncontiguous=noncontiguous)
|
|
for op_a, op_b in itertools.product([True, False], repeat=2):
|
|
run_test(c, a, b, op_a, op_b)
|
|
|
|
@skipCUDAIf(
|
|
not _check_cusparse_sddmm_available(),
|
|
"cuSparse Generic API SDDMM is not available"
|
|
)
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_sampled_addmm_autograd(self, device, dtype):
|
|
from torch.testing._internal.common_methods_invocations import sample_inputs_sparse_sampled_addmm
|
|
|
|
samples = list(sample_inputs_sparse_sampled_addmm(None, device, dtype, requires_grad=True))
|
|
|
|
for sample, dense_covector in zip(samples, [True, False]):
|
|
c = sample.input
|
|
a = sample.args[0]
|
|
b = sample.args[1]
|
|
|
|
# Compute sparse result
|
|
output = torch.sparse.sampled_addmm(c, a, b, **sample.kwargs)
|
|
covector = torch.randn_like(output).to_dense() if dense_covector else torch.randn_like(output)
|
|
output.backward(covector)
|
|
|
|
# Compute dense result and compare with sparse result
|
|
c1, a1, b1 = (x.detach().to_dense().requires_grad_(True) for x in [c, a, b])
|
|
dense_output = sample.kwargs['alpha'] * (a1 @ b1) * torch.ones_like(c).to_dense() + sample.kwargs['beta'] * c1
|
|
self.assertEqual(output, dense_output)
|
|
dense_covector = covector.to_dense()
|
|
dense_output.backward(dense_covector)
|
|
self.assertEqual(c.grad, c1.grad)
|
|
self.assertEqual(a.grad, a1.grad)
|
|
self.assertEqual(b.grad, b1.grad)
|
|
|
|
@onlyCUDA
|
|
# It works on ROCm and CUDA issue is currently active
|
|
@skipCUDAIf(not TEST_WITH_ROCM, "Causes CUDA memory exception, see https://github.com/pytorch/pytorch/issues/72177")
|
|
@skipCUDAIf(
|
|
not _check_cusparse_sddmm_available(),
|
|
"cuSparse Generic API SDDMM is not available"
|
|
)
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
@precisionOverride({torch.float32: 1e-3, torch.complex64: 1e-3,
|
|
torch.float64: 1e-8, torch.complex128: 1e-8})
|
|
def test_sampled_addmm_zero_sized(self, device, dtype):
|
|
def run_test(c, a, b):
|
|
actual = torch.sparse.sampled_addmm(c, a, b)
|
|
self.assertEqual(actual.shape, c.shape)
|
|
|
|
for m, n, k in itertools.product([0, 5], repeat=3):
|
|
c = torch.empty(m, n, dtype=dtype, device=device, layout=torch.sparse_csr)
|
|
a = make_tensor((m, k), dtype=dtype, device=device)
|
|
b = make_tensor((k, n), dtype=dtype, device=device)
|
|
run_test(c, a, b)
|
|
|
|
@onlyCUDA
|
|
@skipCUDAIf(
|
|
not _check_cusparse_sddmm_available(),
|
|
"cuSparse Generic API SDDMM is not available"
|
|
)
|
|
@dtypes(torch.float32, torch.float64, torch.complex64, torch.complex128)
|
|
def test_sampled_addmm_errors(self, device, dtype):
|
|
# test that the errors are the same for dense and sparse sampled versions
|
|
# import re
|
|
|
|
# shapes must be compatible for matrix multiplication
|
|
a = make_tensor((2, 3), dtype=dtype, device=device)
|
|
a_sparse = a.to_sparse_csr()
|
|
with self.assertRaisesRegex(RuntimeError, r"cannot be multiplied"):
|
|
torch.sparse.sampled_addmm(a_sparse, a, a)
|
|
|
|
# mat1 must be a matrix
|
|
with self.assertRaisesRegex(RuntimeError, r"Expected mat1 to be a matrix"):
|
|
torch.sparse.sampled_addmm(a_sparse, a[..., 0, :], a)
|
|
|
|
# mat2 must be a matrix
|
|
with self.assertRaisesRegex(RuntimeError, r"Expected mat2 to be a matrix"):
|
|
torch.sparse.sampled_addmm(a_sparse, a, a[..., 0, :])
|
|
|
|
a = make_tensor((2, 2), dtype=dtype, device=device)
|
|
b = make_tensor((3, 3), dtype=dtype, device=device)
|
|
b_sparse = b.to_sparse_csr()
|
|
with self.assertRaisesRegex(RuntimeError, r"self.shape\[-2\] must match mat1.shape\[-2\]"):
|
|
torch.sparse.sampled_addmm(b_sparse, a, a)
|
|
|
|
b = make_tensor((2, 3), dtype=dtype, device=device)
|
|
b_sparse = b.to_sparse_csr()
|
|
with self.assertRaisesRegex(RuntimeError, r"self.shape\[-1\] must match mat2.shape\[-1\]"):
|
|
torch.sparse.sampled_addmm(b_sparse, a, a)
|
|
|
|
a = make_tensor((2, 2), dtype=dtype, device=device)
|
|
a_sparse = a.to_sparse_csr()
|
|
with self.assertRaisesRegex(RuntimeError, r"Expected mat1 to have strided layout"):
|
|
torch.sparse.sampled_addmm(a_sparse, a_sparse, a_sparse)
|
|
|
|
with self.assertRaisesRegex(RuntimeError, r"Expected mat2 to have strided layout"):
|
|
torch.sparse.sampled_addmm(a_sparse, a, a_sparse)
|
|
|
|
@onlyCPU
|
|
@dtypes(torch.float32, torch.float64, torch.bfloat16, torch.float16)
|
|
@precisionOverride({torch.bfloat16: 0.01})
|
|
def test_sparse_mm_reduce_sum(self, device, dtype):
|
|
def run_test(m, n, k, nnz, train):
|
|
sparse = self.genSparseCSRTensor((m, k), nnz, dtype=dtype, device=device, index_dtype=torch.int64)
|
|
dense = sparse.to_dense()
|
|
|
|
mat = torch.randn(k, n, dtype=dtype)
|
|
ref_mat = mat.clone()
|
|
|
|
if train:
|
|
sparse.requires_grad_()
|
|
mat.requires_grad_()
|
|
dense.requires_grad_()
|
|
ref_mat.requires_grad_()
|
|
|
|
ref_out = torch.mm(dense, ref_mat)
|
|
out = torch.sparse.mm(sparse, mat, 'sum')
|
|
|
|
self.assertEqual(out, ref_out)
|
|
|
|
if train:
|
|
ref_out.sum().backward()
|
|
out.sum().backward()
|
|
|
|
grad_input = sparse.grad
|
|
ref_grad_input = dense.grad
|
|
grad_mat = mat.grad
|
|
ref_grad_mat = ref_mat.grad
|
|
|
|
self.assertEqual(grad_input.to_dense(), ref_grad_input)
|
|
self.assertEqual(grad_mat, ref_grad_mat)
|
|
|
|
run_test(4, 5, 4, 10, False)
|
|
run_test(4, 4, 4, 16, True)
|
|
|
|
@skipIfTorchDynamo()
|
|
@onlyCPU
|
|
@dtypes(torch.float32, torch.float64, torch.bfloat16, torch.float16)
|
|
@precisionOverride({torch.bfloat16: 0.02, torch.float16: 0.01})
|
|
def test_sparse_mm_reduce(self, device, dtype):
|
|
def run_test(m, n, k, nnz, reduce_type, index_dtype, train):
|
|
csr = self.genSparseCSRTensor((m, n), nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
mat = torch.randn(n, k, dtype=dtype)
|
|
ref_mat = mat.clone()
|
|
ref_values = csr.values().clone()
|
|
|
|
out_int32 = index_dtype == torch.int32
|
|
coo_indices = torch._convert_indices_from_csr_to_coo(
|
|
csr.crow_indices(),
|
|
csr.col_indices(),
|
|
out_int32=out_int32)
|
|
row, col = coo_indices[0], coo_indices[1]
|
|
|
|
def ref(row, col, val, mat):
|
|
out = torch.zeros([m, k], dtype=dtype)
|
|
weight = mat.index_select(0, col)
|
|
src = weight.mul(val.view(-1, 1))
|
|
index = row.view(-1, 1).expand_as(weight)
|
|
index = index.to(dtype=torch.int64)
|
|
# scatter_reduce expect index to be int64
|
|
out.scatter_reduce_(0, index, src, reduce=reduce_type, include_self=False)
|
|
return out
|
|
|
|
if train:
|
|
csr.requires_grad_()
|
|
mat.requires_grad_()
|
|
ref_values.requires_grad_()
|
|
ref_mat.requires_grad_()
|
|
|
|
ref_out = ref(row, col, ref_values, ref_mat)
|
|
out = torch.sparse.mm(csr, mat, reduce_type)
|
|
self.assertEqual(out, ref_out)
|
|
|
|
if train and dtype not in (torch.bfloat16, torch.float16):
|
|
ref_out.sum().backward()
|
|
out.sum().backward()
|
|
|
|
grad_values = csr.grad.values()
|
|
grad_weight = mat.grad
|
|
ref_grad_values = ref_values.grad
|
|
ref_grad_weight = ref_mat.grad
|
|
self.assertEqual(grad_values, ref_grad_values)
|
|
self.assertEqual(grad_weight, ref_grad_weight)
|
|
|
|
for train in [False, True]:
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
for reduce_type in ["sum", "mean", "amax", "amin"]:
|
|
# by setting nnz < M, create empty rows
|
|
run_test(3, 4, 11, 1, reduce_type, index_dtype, train)
|
|
run_test(3, 4, 11, 6, reduce_type, index_dtype, train)
|
|
run_test(3, 4, 11, 12, reduce_type, index_dtype, train)
|
|
# we are doing blocking with 4x vector length in the kernel,
|
|
# so need to test when K > 4x vector length
|
|
run_test(4, 7, 33, 13, reduce_type, index_dtype, train)
|
|
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_coo_csr_conversion(self, device, dtype):
|
|
for m, n in itertools.product([5, 2, 0], [5, 2, 0]):
|
|
size = (m, n)
|
|
dense = make_tensor(size, dtype=dtype, device=device)
|
|
coo_sparse = dense.to_sparse()
|
|
csr_sparse = coo_sparse.to_sparse_csr()
|
|
|
|
self.assertEqual(csr_sparse.to_dense(), dense)
|
|
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_csr_coo_conversion(self, device, dtype):
|
|
for m, n in itertools.product([5, 2, 0], [5, 2, 0]):
|
|
size = (m, n)
|
|
dense = make_tensor(size, dtype=dtype, device=device)
|
|
csr_sparse = dense.to_sparse_csr()
|
|
coo_sparse = csr_sparse.to_sparse()
|
|
|
|
self.assertEqual(coo_sparse.to_dense(), dense)
|
|
|
|
# Currently, there is no rule in PyTorch for filling zeros in the outputs
|
|
# from operations on Sparse CSR tensors. Hence only those operators are supported
|
|
# which have 0->0 correspondence, example: sin(0) = 0, tan(0) = 0 but
|
|
# cos(0) = 1 (and hence it's not supported).
|
|
# Note: here, we do this test only for unary operators
|
|
@ops(sparse_csr_unary_ufuncs)
|
|
def test_zero_to_zero_correspondence_unary(self, device, dtype, op):
|
|
zero = torch.zeros((1, 2), dtype=dtype, device=device)
|
|
tensor_explicit_zeros = torch.sparse_csr_tensor([0, 1], [1], [0], dtype=dtype, device=device)
|
|
|
|
output_zero = op(zero)
|
|
expected_zero = zero.to(output_zero.dtype)
|
|
|
|
output_explicit_zeros = op(tensor_explicit_zeros).to_dense()
|
|
expected_explicit_zeros = tensor_explicit_zeros.to_dense().to(output_explicit_zeros.dtype)
|
|
|
|
for (output, expected) in [
|
|
(output_zero, expected_zero),
|
|
(output_explicit_zeros, expected_explicit_zeros)
|
|
]:
|
|
self.assertEqual(output, expected, f"This operator ({op.name}) should not be supported for "
|
|
"Sparse CSR as it breaks 0->0 correspondence.")
|
|
|
|
for inp in [zero.to_sparse_csr(), tensor_explicit_zeros]:
|
|
self.assertEqual(op(inp).values().numel(), inp.values().numel(),
|
|
f"{op.name} fails to preserve sparsity pattern.")
|
|
|
|
@ops(sparse_csr_unary_ufuncs)
|
|
def test_sparse_csr_unary_out(self, device, dtype, op):
|
|
samples = op.sample_inputs(device, dtype)
|
|
|
|
if not op.supports_out:
|
|
self.skipTest("Skipped! Out not supported")
|
|
|
|
for sample in samples:
|
|
assert torch.is_tensor(sample.input)
|
|
# Sparse CSR only supports 2D tensors as inputs
|
|
# Fail early to prevent silent success with this test
|
|
if sample.input.ndim != 2:
|
|
raise ValueError("Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
|
|
|
|
sample.input = sample.input.to_sparse_csr()
|
|
expect = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
out = self.genSparseCSRTensor(sample.input.size(), sample.input._nnz(),
|
|
device=sample.input.device, dtype=expect.dtype,
|
|
index_dtype=sample.input.crow_indices().dtype)
|
|
op(sample.input, *sample.args, **sample.kwargs, out=out)
|
|
|
|
self.assertEqual(out, expect)
|
|
|
|
@ops(sparse_csr_unary_ufuncs)
|
|
def test_sparse_csr_unary_inplace(self, device, dtype, op):
|
|
samples = op.sample_inputs(device, dtype)
|
|
|
|
if op.inplace_variant is None:
|
|
self.skipTest("Skipped! Inplace variant not supported!")
|
|
|
|
for sample in samples:
|
|
assert torch.is_tensor(sample.input)
|
|
# Sparse CSR only supports 2D tensors as inputs
|
|
# Fail early to prevent silent success with this test
|
|
if sample.input.ndim != 2:
|
|
raise ValueError("Expected 2D tensor but got tensor with dimension: {sample.input.ndim}.")
|
|
|
|
sample.input = sample.input.to_sparse_csr()
|
|
expect = op(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
if not torch.can_cast(expect.dtype, dtype):
|
|
with self.assertRaisesRegex(RuntimeError, "result type"):
|
|
op.inplace_variant(sample.input, *sample.args, **sample.kwargs)
|
|
continue
|
|
|
|
if sample.input.is_complex() and op.name == "abs":
|
|
with self.assertRaisesRegex(RuntimeError, "not supported"):
|
|
op.inplace_variant(sample.input, *sample.args, **sample.kwargs)
|
|
continue
|
|
|
|
actual = op.inplace_variant(sample.input, *sample.args, **sample.kwargs)
|
|
|
|
self.assertIs(actual, sample.input)
|
|
self.assertEqual(actual, expect)
|
|
|
|
@skipIfTorchDynamo("Not a TorchDynamo suitable test")
|
|
@ops(sparse_csr_unary_ufuncs, dtypes=OpDTypes.supported, allowed_dtypes=[torch.double, torch.cdouble])
|
|
def test_autograd_sparse_csr_unary(self, device, dtype, op):
|
|
if op.name not in UNARY_EWISE_CSR_ALLOW_AUTOGRAD:
|
|
self.skipTest(f"Skipped! Unary op {op.name} not supported with CSR input and autograd")
|
|
|
|
samples = list(op.sample_inputs(device, dtype))
|
|
|
|
# Fail early to prevent silent success with this test
|
|
ndims_equals_2d = (s.input.ndim == 2 for s in samples)
|
|
if not any(ndims_equals_2d):
|
|
raise ValueError("Expected at least one 2D tensor in samples.")
|
|
|
|
for sample in samples:
|
|
# We must skip samples of low dimensionality, we can't convert them to sparsed compressed layouts
|
|
if sample.input.ndim < 2:
|
|
continue
|
|
sparse_input = sample.input.to_sparse_csr().requires_grad_(True)
|
|
|
|
def fn(input):
|
|
output = op.gradcheck_wrapper(op.get_op(), input, *sample.args, **sample.kwargs)
|
|
if sample.output_process_fn_grad is not None:
|
|
return sample.output_process_fn_grad(output)
|
|
return output
|
|
|
|
# Compute sparse result
|
|
output = fn(sparse_input)
|
|
covector = torch.randn_like(output)
|
|
output.backward(covector)
|
|
self.assertTrue(torch.is_tensor(sparse_input.grad))
|
|
self.assertTrue(sparse_input.grad.is_sparse_csr)
|
|
|
|
# Compute dense result and compare with sparse result
|
|
dense_input = sparse_input.detach().to_dense().requires_grad_(True)
|
|
dense_output = fn(dense_input)
|
|
dense_covector = covector.to_dense()
|
|
dense_output.backward(dense_covector)
|
|
self.assertEqual(sparse_input.grad, dense_input.grad)
|
|
|
|
@skipCUDAIf(
|
|
not _check_cusparse_sddmm_available(),
|
|
"cuSparse Generic API SDDMM is not available"
|
|
)
|
|
@dtypes(torch.float64)
|
|
def test_autograd_dense_output_addmm(self, device, dtype):
|
|
from torch.testing._internal.common_methods_invocations import sample_inputs_addmm
|
|
|
|
samples = list(sample_inputs_addmm(None, device, dtype, requires_grad=True))
|
|
|
|
# Fail early to prevent silent success with this test
|
|
ndims_equals_2d = (s.args[0].ndim == 2 for s in samples)
|
|
if not any(ndims_equals_2d):
|
|
raise ValueError("Expected at least one 2D tensor in samples to convert to sparse.")
|
|
|
|
for sample in samples:
|
|
a = sample.args[0].relu().to_sparse_csr()
|
|
if sample.args[0].shape == sample.args[1].shape:
|
|
import warnings
|
|
warnings.warn("Broken for square matrices, see https://github.com/pytorch/pytorch/issues/116565")
|
|
continue
|
|
|
|
# This path tests the autograd path wrt dense inputs
|
|
for addmm in [torch.addmm, torch.sparse.addmm]:
|
|
|
|
def fn(c, b):
|
|
output = addmm(c, a, b, **sample.kwargs)
|
|
if sample.output_process_fn_grad is not None:
|
|
return sample.output_process_fn_grad(output)
|
|
return output
|
|
|
|
self.assertTrue(torch.autograd.gradcheck(fn, [sample.input, sample.args[1]], fast_mode=True))
|
|
|
|
# noncontiguous
|
|
c = make_tensor(sample.input.shape, device=device, dtype=dtype, noncontiguous=True, requires_grad=True)
|
|
b = make_tensor(sample.args[1].shape, device=device, dtype=dtype, noncontiguous=True, requires_grad=True)
|
|
self.assertTrue(torch.autograd.gradcheck(fn, [c, b], fast_mode=True))
|
|
|
|
# Now test the autograd path wrt sparse inputs
|
|
for reverse in [True, False]:
|
|
c, b = sample.input, sample.args[1]
|
|
if reverse and a.shape != b.shape:
|
|
continue
|
|
|
|
def fn(a):
|
|
inputs = (c, b, a) if reverse else (c, a, b)
|
|
output = addmm(*inputs, **sample.kwargs)
|
|
if sample.output_process_fn_grad is not None:
|
|
return sample.output_process_fn_grad(output)
|
|
return output
|
|
|
|
# gradcheck doesn't work for sparse CSR yet, compare against dense path
|
|
# Compute sparse result
|
|
a = a.detach().requires_grad_(True)
|
|
output = fn(a)
|
|
covector = torch.randn_like(output)
|
|
output.backward(covector)
|
|
self.assertTrue(torch.is_tensor(a.grad))
|
|
if addmm == torch.sparse.addmm:
|
|
self.assertTrue(a.grad.is_sparse_csr)
|
|
else:
|
|
self.assertTrue(a.grad.layout == torch.strided)
|
|
|
|
# Compute dense result and compare with sparse result
|
|
dense_a = a.detach().to_dense().requires_grad_(True)
|
|
dense_output = fn(dense_a)
|
|
self.assertEqual(output, dense_output)
|
|
dense_covector = covector.to_dense()
|
|
dense_output.backward(dense_covector)
|
|
|
|
if addmm == torch.sparse.addmm:
|
|
self.assertEqual(a.grad, dense_a.grad.sparse_mask(a))
|
|
else:
|
|
self.assertEqual(a.grad, dense_a.grad)
|
|
|
|
@skipCPUIfNoMklSparse
|
|
@dtypes(torch.float64)
|
|
def test_autograd_dense_output_addmv(self, device, dtype):
|
|
from torch.testing._internal.common_methods_invocations import sample_inputs_addmv
|
|
|
|
samples = list(sample_inputs_addmv(None, device, dtype, requires_grad=True))
|
|
|
|
# Fail early to prevent silent success with this test
|
|
ndims_equals_2d = (s.args[0].ndim == 2 for s in samples)
|
|
if not any(ndims_equals_2d):
|
|
raise ValueError("Expected at least one 2D tensor in samples to convert to sparse.")
|
|
|
|
for sample in samples:
|
|
# TODO: Remove detach once we have autograd support for CSR input
|
|
a = sample.args[0].to_sparse_csr().detach()
|
|
|
|
def fn(c, b):
|
|
output = torch.addmv(c, a, b, **sample.kwargs)
|
|
if sample.output_process_fn_grad is not None:
|
|
return sample.output_process_fn_grad(output)
|
|
return output
|
|
|
|
self.assertTrue(torch.autograd.gradcheck(fn, [sample.input, sample.args[1]], fast_mode=True))
|
|
|
|
# noncontiguous
|
|
c = make_tensor(sample.input.shape, device=device, dtype=dtype, noncontiguous=True, requires_grad=True)
|
|
b = make_tensor(sample.args[1].shape, device=device, dtype=dtype, noncontiguous=True, requires_grad=True)
|
|
self.assertTrue(torch.autograd.gradcheck(fn, [c, b], fast_mode=True))
|
|
|
|
@skipIfTorchDynamo("Not a TorchDynamo suitable test")
|
|
@ops(binary_ops_with_dense_output, dtypes=OpDTypes.supported, allowed_dtypes=[torch.double, ])
|
|
def test_autograd_dense_output(self, device, dtype, op):
|
|
if op.name == "mv" and no_mkl_sparse and self.device_type == 'cpu':
|
|
self.skipTest("MKL Sparse is not available")
|
|
|
|
samples = list(op.sample_inputs(device, dtype, requires_grad=True))
|
|
|
|
# Fail early to prevent silent success with this test
|
|
ndims_equals_2d = (s.input.ndim == 2 for s in samples)
|
|
if not any(ndims_equals_2d):
|
|
raise ValueError("Expected at least one 2D tensor in samples.")
|
|
|
|
# Here we assume that the signature is op(sparse_input, dense_input) -> dense_output
|
|
for sample in samples:
|
|
# TODO: Remove detach once we have autograd support for CSR input
|
|
sparse_input = sample.input.to_sparse_csr().detach()
|
|
|
|
def fn(*args):
|
|
output = op.gradcheck_wrapper(op.get_op(), sparse_input, *args, **sample.kwargs)
|
|
if sample.output_process_fn_grad is not None:
|
|
return sample.output_process_fn_grad(output)
|
|
return output
|
|
|
|
self.assertTrue(torch.autograd.gradcheck(fn, sample.args, fast_mode=True))
|
|
|
|
# noncontiguous
|
|
args = [make_tensor(a.shape, device=device, dtype=dtype, noncontiguous=True, requires_grad=True) for a in sample.args]
|
|
self.assertTrue(torch.autograd.gradcheck(fn, args, fast_mode=True))
|
|
|
|
@dtypes(*all_types_and_complex())
|
|
def test_direct_coo_csr_conversion(self, device, dtype):
|
|
for m, n in itertools.product([5, 2, 0], [5, 2, 0]):
|
|
size = (m, n)
|
|
dense = make_tensor(size, dtype=dtype, device=device)
|
|
coo_sparse = dense.to_sparse_coo()
|
|
|
|
self.assertEqual(coo_sparse.to_sparse_csr().to_sparse_coo(), coo_sparse)
|
|
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_sum(self, device, dtype):
|
|
def run_test(shape, nnz, index_type):
|
|
a = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
self.assertEqual(a.sum(), a.values().sum())
|
|
if dtype in floating_types():
|
|
a.requires_grad_(True)
|
|
a.sum().backward()
|
|
self.assertEqual(a.grad, torch.ones(shape, dtype=dtype, device=device))
|
|
for shape, index_dtype in itertools.product(
|
|
[(10, 5), (10, 10)],
|
|
[torch.int32, torch.int64]):
|
|
run_test(shape, 0, index_dtype)
|
|
run_test(shape, max(shape), index_dtype)
|
|
run_test(shape, shape[0] * shape[1], index_dtype)
|
|
|
|
@skipIfTorchDynamo()
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
@all_sparse_compressed_layouts()
|
|
def test_transpose(self, device, dtype, layout):
|
|
|
|
def _check_transpose_view(subject, transpose):
|
|
self.assertTrue(transpose.values()._is_view())
|
|
self.assertTrue(transpose._is_view())
|
|
self.assertTrue(transpose._base is subject)
|
|
|
|
def _check_layout_invariants(transpose):
|
|
self.assertEqual(transpose.device, torch.device(device))
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[transpose.layout]
|
|
compressed_indices, plain_indices = compressed_indices_mth(transpose), plain_indices_mth(transpose)
|
|
torch._validate_sparse_compressed_tensor_args(compressed_indices, plain_indices, transpose.values(),
|
|
transpose.shape, transpose.layout)
|
|
|
|
def check_good_transpose(subject, subject_dense, dim0, dim1, expected_layout):
|
|
transpose = subject.transpose(dim0, dim1)
|
|
# correct layout
|
|
self.assertEqual(transpose.layout, expected_layout)
|
|
# transpose must be return a view
|
|
_check_transpose_view(subject, transpose)
|
|
# result uses unsafe construction, so we check invariants
|
|
_check_layout_invariants(transpose)
|
|
self.assertEqual(transpose.to_dense(), subject_dense.transpose(dim0, dim1))
|
|
|
|
round_trip = transpose.transpose(dim0, dim1)
|
|
self.assertEqual(round_trip.layout, subject.layout)
|
|
# transpose must be return a view
|
|
_check_transpose_view(subject, round_trip)
|
|
# result uses unsafe construction, so we check invariants
|
|
_check_layout_invariants(round_trip)
|
|
self.assertEqual(round_trip.to_dense(), subject_dense)
|
|
|
|
def check_same_dim_transpose(subject, subject_dense, dim):
|
|
transpose = subject.transpose(dim, dim)
|
|
# correct layout
|
|
self.assertEqual(transpose.layout, subject.layout)
|
|
# transpose must be return a view
|
|
_check_transpose_view(subject, transpose)
|
|
# result uses unsafe construction, so we check invariants
|
|
_check_layout_invariants(transpose)
|
|
self.assertEqual(transpose.to_dense(), subject_dense)
|
|
|
|
def check_dim_type_mismatch_throws(subject, name0, dim0, name1, dim1):
|
|
mismatch_name = f"{dim0}\\({name0}\\) and {dim1}\\({name1}\\)"
|
|
err = r"transpose\(\): can only transpose dimensions of the same type \(Batch, Sparse, Dense\), got " + mismatch_name
|
|
|
|
with self.assertRaisesRegex(RuntimeError, err):
|
|
subject.transpose(dim0, dim1)
|
|
|
|
def run_test(shape, nnz, index_type, n_dense, blocksize=()):
|
|
subject = self.genSparseCompressedTensor(shape,
|
|
nnz,
|
|
layout=layout,
|
|
device=device,
|
|
index_dtype=index_type,
|
|
blocksize=blocksize,
|
|
dense_dims=n_dense,
|
|
dtype=dtype)
|
|
|
|
|
|
sparse0 = len(shape) - n_dense - 1
|
|
sparse1 = sparse0 - 1
|
|
|
|
dense0 = sparse0 + 1 if n_dense > 0 else None
|
|
dense1 = dense0 + 1 if n_dense > 1 else None
|
|
|
|
n_batch = len(shape) - n_dense - 2
|
|
batch0 = sparse1 - 1 if n_batch > 0 else None
|
|
batch1 = 0 if n_batch > 1 else None
|
|
|
|
sparse_dims = (sparse0, sparse1)
|
|
dense_dims = (dense0, dense1)
|
|
batch_dims = (batch0, batch1)
|
|
|
|
named0 = [(name, d[0]) for name, d in zip(["Batch", "Sparse", "Dense"], (batch_dims, sparse_dims, dense_dims))]
|
|
named1 = [(name, d[1]) for name, d in zip(["Batch", "Sparse", "Dense"], (batch_dims, sparse_dims, dense_dims))]
|
|
|
|
flipped_layout = {
|
|
torch.sparse_csr: torch.sparse_csc,
|
|
torch.sparse_csc: torch.sparse_csr,
|
|
torch.sparse_bsr: torch.sparse_bsc,
|
|
torch.sparse_bsc: torch.sparse_bsr
|
|
}[layout]
|
|
if n_dense > 0:
|
|
# expect all transpose to throw
|
|
for (name0, dim0), (name1, dim1) in itertools.product(named0, named1):
|
|
msg = r"transpose\(\): hybrid sparse compressed tensors with dense dimensions are not supported"
|
|
if (dim0 is not None) and (dim1 is not None):
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
subject.transpose(dim0, dim1)
|
|
else:
|
|
subject_dense = subject.to_dense()
|
|
for (name0, dim0), (name1, dim1) in itertools.product(named0, named1):
|
|
if dim0 is not None:
|
|
check_same_dim_transpose(subject, subject_dense, dim0)
|
|
|
|
if dim1 is not None:
|
|
if name0 == name1:
|
|
expected_layout = flipped_layout if name0 == "Sparse" else layout
|
|
check_good_transpose(subject, subject_dense, dim0, dim1, expected_layout)
|
|
else:
|
|
check_dim_type_mismatch_throws(subject, name0, dim0, name1, dim1)
|
|
|
|
# batch/sparse, sparse/dense only and full hybrid cases
|
|
shape_ndense = list(itertools.product([(2, 4, 6, 2), (10, 6, 4, 2), (2, 4, 4, 2, 6)], [0, 1, 2]))
|
|
# sparse only cases
|
|
shape_ndense += [[(4, 8), 0], [(2, 2), 0], [(8, 4), 0]]
|
|
for (shape, n_dense), index_dtype in itertools.product(shape_ndense, [torch.int32, torch.int64]):
|
|
n_batch = len(shape) - n_dense - 2
|
|
sparse_shape = shape[n_batch: n_batch + 2]
|
|
if layout in (torch.sparse_bsr, torch.sparse_bsc):
|
|
# for blocked all combinations of 2,1 should be valid blocksizes
|
|
run_test(shape, 0, index_dtype, n_dense, blocksize=(2, 2))
|
|
run_test(shape, max(sparse_shape), index_dtype, n_dense, blocksize=(2, 2))
|
|
run_test(shape, sparse_shape[0] * sparse_shape[1], index_dtype, n_dense, blocksize=(2, 2))
|
|
# repeat the realistic sparseity case with varried block sizes
|
|
run_test(shape, max(sparse_shape), index_dtype, n_dense, blocksize=(2, 1))
|
|
run_test(shape, max(sparse_shape), index_dtype, n_dense, blocksize=(1, 2))
|
|
run_test(shape, max(sparse_shape), index_dtype, n_dense, blocksize=(1, 1))
|
|
else:
|
|
run_test(shape, 0, index_dtype, n_dense)
|
|
run_test(shape, max(sparse_shape), index_dtype, n_dense)
|
|
run_test(shape, sparse_shape[0] * sparse_shape[1], index_dtype, n_dense)
|
|
|
|
# TODO: This is a stopgap for a rigorous extension of our autograd tests
|
|
# to test the functionality of detach
|
|
@skipMeta
|
|
@dtypes(*all_types_and_complex_and(torch.half, torch.bool, torch.bfloat16))
|
|
def test_exercise_detach(self, device, dtype):
|
|
shape = (3, 3)
|
|
nnz = 4
|
|
for index_dtype in [torch.int32, torch.int64]:
|
|
inp = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=index_dtype)
|
|
detached_inp = inp.detach()
|
|
self.assertEqual(inp, detached_inp)
|
|
|
|
def _construct_sp_matrix(self, tensor, layout, blocksize=(2, 2)):
|
|
if tensor.layout in [torch.sparse_coo, torch.sparse_csr, torch.sparse_csc, torch.strided]:
|
|
tensor = tensor.to_dense()
|
|
else:
|
|
raise NotImplementedError(repr(tensor))
|
|
if layout is torch.sparse_csr:
|
|
return sp.csr_matrix(tensor.cpu().numpy())
|
|
if layout is torch.sparse_csc:
|
|
return sp.csc_matrix(tensor.cpu().numpy())
|
|
if layout is torch.sparse_bsr:
|
|
return sp.bsr_matrix(tensor.cpu().numpy(), blocksize=blocksize).sorted_indices()
|
|
if layout is torch.sparse_bsc:
|
|
# SciPy doesn't have native BSC support - but our tests don't need the full
|
|
# functionality so fake it by using a transposed BSR matrix.
|
|
class FakeBscMatrix:
|
|
def __init__(self, matrix):
|
|
self._matrix = matrix
|
|
self.shape = tuple(reversed(matrix.shape))
|
|
self.indptr = matrix.indptr
|
|
self.indices = matrix.indices
|
|
self.data = [x.transpose() for x in matrix.data]
|
|
|
|
@staticmethod
|
|
def from_matrix(matrix, blocksize):
|
|
blocksize = tuple(reversed(blocksize))
|
|
matrix = matrix.transpose()
|
|
return FakeBscMatrix(sp.bsr_matrix(matrix, blocksize=blocksize))
|
|
|
|
def sorted_indices(self):
|
|
sub = self._matrix.sorted_indices()
|
|
return FakeBscMatrix(sub)
|
|
|
|
return FakeBscMatrix.from_matrix(tensor.cpu().numpy(), blocksize=blocksize).sorted_indices()
|
|
raise NotImplementedError(repr(tensor))
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts('to_layout')
|
|
@all_sparse_compressed_layouts('from_layout')
|
|
def test_compressed_layout_conversions_coverage(self, device, from_layout, to_layout):
|
|
"""This test performs a smoke test for covered conversion and verifies
|
|
that an exception is thrown for unsupported conversions.
|
|
|
|
TODO: This test covers a subset of
|
|
TestSparseAny.test_to_sparse tests and can be
|
|
eliminated. Keeping the test until the new
|
|
`Tensor.to_sparse(*, layout, blocksize)` has landed.
|
|
"""
|
|
|
|
allowed_pairwise_layouts_sets = {
|
|
frozenset({torch.sparse_csc}),
|
|
frozenset({torch.sparse_csr}),
|
|
frozenset({torch.sparse_csc, torch.sparse_csr}),
|
|
frozenset({torch.sparse_csc, torch.sparse_bsc}),
|
|
frozenset({torch.sparse_csc, torch.sparse_bsr}),
|
|
frozenset({torch.sparse_csr, torch.sparse_bsc}),
|
|
frozenset({torch.sparse_csr, torch.sparse_bsr}),
|
|
frozenset({torch.sparse_bsc}),
|
|
frozenset({torch.sparse_bsr}),
|
|
frozenset({torch.sparse_bsc, torch.sparse_bsr}),
|
|
}
|
|
block_layouts = (torch.sparse_bsr, torch.sparse_bsc)
|
|
|
|
def _to_from_layout(layout_a, layout_b, a):
|
|
expect_error = True
|
|
if {layout_a, layout_b} in allowed_pairwise_layouts_sets:
|
|
expect_error = False
|
|
|
|
# BSR -> CSR is not yet supported
|
|
if (layout_a, layout_b) == (torch.sparse_bsr, torch.sparse_csr):
|
|
expect_error = True
|
|
# BSR -> CSC is not yet supported
|
|
if (layout_a, layout_b) == (torch.sparse_bsr, torch.sparse_csc):
|
|
expect_error = True
|
|
# BSC -> CSR is not yet supported
|
|
if (layout_a, layout_b) == (torch.sparse_bsc, torch.sparse_csr):
|
|
expect_error = True
|
|
# BSC -> CSC is not yet supported
|
|
if (layout_a, layout_b) == (torch.sparse_bsc, torch.sparse_csc):
|
|
expect_error = True
|
|
# CSR -> BSR only works for non-batched inputs
|
|
if (layout_a, layout_b) == (torch.sparse_csr, torch.sparse_bsr):
|
|
if a.dim() > 2:
|
|
expect_error = True
|
|
# CSR -> BSC only works for non-batched inputs
|
|
if (layout_a, layout_b) == (torch.sparse_csr, torch.sparse_bsc):
|
|
if a.dim() > 2:
|
|
expect_error = True
|
|
# CSC -> BSR only works for non-batched inputs
|
|
if (layout_a, layout_b) == (torch.sparse_csc, torch.sparse_bsr):
|
|
if a.dim() > 2:
|
|
expect_error = True
|
|
# CSC -> BSC only works for non-batched inputs
|
|
if (layout_a, layout_b) == (torch.sparse_csc, torch.sparse_bsc):
|
|
if a.dim() > 2:
|
|
expect_error = True
|
|
|
|
blocksize_a = (1, 1) if layout_a in {torch.sparse_bsr, torch.sparse_bsc} else None
|
|
blocksize_b = (1, 1) if layout_b in {torch.sparse_bsr, torch.sparse_bsc} else None
|
|
b = a.to_sparse(layout=layout_a, blocksize=blocksize_a)
|
|
if expect_error:
|
|
with self.assertRaises(RuntimeError):
|
|
b.to_sparse(layout=layout_b, blocksize=blocksize_b)
|
|
else:
|
|
c = b.to_sparse(layout=layout_b, blocksize=blocksize_b)
|
|
self.assertEqual(a.to_dense(), c.to_dense())
|
|
|
|
# change of blocksize upon conversion is not yet supported.
|
|
if b.layout in block_layouts:
|
|
for block_layout in block_layouts:
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"conversion from.*to.*with blocksize changed from.*to.*is not supported"):
|
|
b.to_sparse(layout=block_layout, blocksize=(3, 3))
|
|
|
|
batch_dims = [(), (2,), (2, 2), (2, 2, 2)]
|
|
sparse_dims = (6, 12)
|
|
for batch_dim in batch_dims:
|
|
a = make_tensor(batch_dim + sparse_dims, dtype=torch.float, device=device)
|
|
_to_from_layout(from_layout, to_layout, a)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@batched_nonbatched()
|
|
@hybrid_nonhybrid()
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
def test_dense_to_from_sparse_compressed(self, device, hybrid, batched, layout):
|
|
"""This test tests conversion from dense to/from CSR and CSC
|
|
by comparing to SciPy's implementation.
|
|
|
|
Here we test only those conversion combinations that SciPy
|
|
supports to ensure that PyTorch conversions are in the same
|
|
page with SciPy. Independent from SciPy, all conversion
|
|
combinations are tested in TestSparseAny.test_to_sparse.
|
|
"""
|
|
|
|
blocked_layouts = (torch.sparse_bsr, torch.sparse_bsc)
|
|
|
|
# helpers
|
|
|
|
def _check_against_scipy_matrix(pt_matrix, dense, blocksize, **kwargs):
|
|
# scipy has no bsc layout, so we check against the bsr layout of the transposed dense
|
|
if layout == torch.sparse_bsc:
|
|
sp_matrix = self._construct_sp_matrix(dense.t(), layout=torch.sparse_bsr, blocksize=blocksize[::-1])
|
|
else:
|
|
sp_matrix = self._construct_sp_matrix(dense, layout=layout, blocksize=blocksize)
|
|
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
|
|
|
|
self.assertEqual(layout, pt_matrix.layout)
|
|
if layout == torch.sparse_bsc:
|
|
self.assertEqual(sp_matrix.shape[::-1], pt_matrix.shape)
|
|
else:
|
|
self.assertEqual(sp_matrix.shape, pt_matrix.shape)
|
|
|
|
self.assertEqual(torch.tensor(sp_matrix.indptr, dtype=torch.int64), compressed_indices_mth(pt_matrix))
|
|
self.assertEqual(torch.tensor(sp_matrix.indices, dtype=torch.int64), plain_indices_mth(pt_matrix))
|
|
if layout == torch.sparse_bsc:
|
|
# we must transpose the blocks before comparing
|
|
self.assertEqual(torch.tensor(sp_matrix.data), pt_matrix.values().transpose(-2, -1))
|
|
else:
|
|
self.assertEqual(torch.tensor(sp_matrix.data), pt_matrix.values())
|
|
|
|
def _check_hybrid_matrix(pt_matrix, dense, blocksize, **kwargs):
|
|
# Calculate COO indices for sparse matrix.
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
|
|
compressed_indices = compressed_indices_mth(pt_matrix)
|
|
plain_indices = plain_indices_mth(pt_matrix)
|
|
coo_indices = torch._convert_indices_from_csr_to_coo(compressed_indices, plain_indices)
|
|
row_indices, col_indices = {
|
|
torch.sparse_csr: (coo_indices[0, ], coo_indices[1, ]),
|
|
torch.sparse_csc: (coo_indices[1, ], coo_indices[0, ]),
|
|
torch.sparse_bsr: (coo_indices[0, ], coo_indices[1, ]),
|
|
torch.sparse_bsc: (coo_indices[1, ], coo_indices[0, ]),
|
|
}[pt_matrix.layout]
|
|
|
|
# If sparse matrix layout blocked, rearrange dense matrix
|
|
# so that the shape past first two dimensions match the
|
|
# shape of sparse matrix values.
|
|
dense_to_check = dense
|
|
if blocksize:
|
|
dense_shape = dense.shape
|
|
dense_to_check_shape = (dense.shape[0] // blocksize[0],
|
|
blocksize[0],
|
|
dense.shape[1] // blocksize[1],
|
|
blocksize[1]) + dense.shape[2:]
|
|
dense_to_check = dense_to_check.reshape(dense_to_check_shape).transpose(1, 2)
|
|
|
|
# Verify that non-zero values of the sparse matrix are
|
|
# equal to corresponding values of the dense matrix.
|
|
self.assertEqual(pt_matrix.values(), dense_to_check[row_indices, col_indices])
|
|
|
|
# Verify that the remaining elements of the dense matrix
|
|
# are 0, i.e. that dense are sparse matrix are fully
|
|
# equal.
|
|
mask = torch.ones_like(dense_to_check, dtype=torch.bool)
|
|
mask[row_indices, col_indices] = False
|
|
self.assertTrue(torch.all(torch.masked_select(dense_to_check, mask) == 0))
|
|
|
|
def _check_batched(pt_tensor, dense, check_batch=None, batch_shape=(), blocksize=(), **kwargs):
|
|
self.assertEqual(layout, pt_tensor.layout)
|
|
self.assertEqual(pt_tensor.shape, dense.shape)
|
|
compressed_indices_mth, plain_indices_mth = sparse_compressed_indices_methods[layout]
|
|
for batch_index in np.ndindex(batch_shape):
|
|
pt_matrix = pt_tensor[batch_index]
|
|
dense_matrix = dense[batch_index]
|
|
dense_dim = pt_matrix.dim() - 2
|
|
dense_matrix_pt = dense_matrix.to_sparse(layout=layout,
|
|
blocksize=blocksize or None,
|
|
dense_dim=dense_dim)
|
|
# sanity check, selecting batch of to_<layout> and dense[batch].to_<layout> should give the same result
|
|
self.assertEqual(pt_matrix, dense_matrix_pt)
|
|
check_batch(pt_matrix, dense_matrix, blocksize, **kwargs)
|
|
|
|
def _generate_subject(sparse_shape, batch_shape, hybrid_shape):
|
|
shape = batch_shape + sparse_shape + hybrid_shape
|
|
n_batch_dim = len(batch_shape)
|
|
n_hybrid_dim = len(hybrid_shape)
|
|
# generate a dense tensor
|
|
dense = make_tensor(shape, dtype=torch.float, device=device)
|
|
|
|
# introduce some sparsty, mask is sparse shape, element applies to entire dense sub-tensor (hybrid) and is
|
|
# applied to each batch
|
|
mask = make_tensor(sparse_shape, dtype=torch.bool, device=device)
|
|
# manually expand to match hybrid shape
|
|
if hybrid:
|
|
mask = mask.view(sparse_shape + tuple(1 for _ in range(n_hybrid_dim)))
|
|
mask = mask.expand(sparse_shape + hybrid_shape)
|
|
|
|
# mask will broadcast over the batch dims if present
|
|
|
|
return dense * mask
|
|
|
|
# note: order is important here, the hybrid-ness decides the inner content check which is used to build the
|
|
# batched checker (if needed)
|
|
check_content = _check_against_scipy_matrix
|
|
if hybrid:
|
|
check_content = _check_hybrid_matrix
|
|
if batched:
|
|
check_content = functools.partial(_check_batched, check_batch=check_content)
|
|
|
|
sparse_sizes = [(6, 10), (0, 10), (6, 0), (0, 0)]
|
|
blocksizes = [(2, 2), (1, 1), (1, 2)] if layout in blocked_layouts else [()]
|
|
batch_sizes = [(3,), (1, 3), (2, 1, 3)] if batched else [()]
|
|
hybrid_sizes = [(4, ), (2, 2)] if hybrid else [()]
|
|
|
|
# general cases, always run
|
|
for sparse_shape, blocksize, batch_shape, hybrid_shape in itertools.product(
|
|
sparse_sizes, blocksizes, batch_sizes, hybrid_sizes):
|
|
dense = _generate_subject(sparse_shape, batch_shape, hybrid_shape)
|
|
sparse = dense.to_sparse(layout=layout, blocksize=blocksize or None, dense_dim=len(hybrid_shape))
|
|
check_content(sparse, dense, blocksize=blocksize, batch_shape=batch_shape, hybrid_shape=hybrid_shape)
|
|
dense_back = sparse.to_dense()
|
|
self.assertEqual(dense, dense_back)
|
|
|
|
# special cases for batched tensors
|
|
if batched:
|
|
# batched sparse tensors need only have the same number of non-zeros in each batch not necessarily the
|
|
# same sparsity pattern in each batch
|
|
sparse_shape = sparse_sizes[0]
|
|
hybrid_shape = hybrid_sizes[0]
|
|
batch_shape = batch_sizes[0]
|
|
shape = batch_shape + sparse_shape + hybrid_shape
|
|
dense = make_tensor(shape, dtype=torch.float, device=device)
|
|
blocksize = blocksizes[0]
|
|
# number of elements/blocks in each batch (total not nnz)
|
|
batch_mask_shape = sparse_shape
|
|
if layout in blocked_layouts:
|
|
# if we are blocked the mask is generated for the block valued elements
|
|
batch_mask_shape = sparse_shape[0] // blocksize[0], sparse_shape[1] // blocksize[1]
|
|
|
|
# random bool vector w/ length equal to max possible nnz for the sparse_shape
|
|
mask_source = make_tensor(batch_mask_shape, dtype=torch.bool, device=device).flatten()
|
|
n_batch = functools.reduce(operator.mul, batch_shape, 1)
|
|
|
|
# stack random permutations of the source for each batch
|
|
mask = torch.stack([mask_source[torch.randperm(mask_source.numel())]
|
|
for _ in range(n_batch)], dim=0).reshape(batch_shape + batch_mask_shape)
|
|
if layout in blocked_layouts:
|
|
# for blocked we need to do a bit of extra work to expand the mask from blocked-space to element-space
|
|
mask_shape = mask.shape
|
|
mask = mask.view(mask_shape + (1, 1))
|
|
mask = mask.expand(mask_shape + blocksize)
|
|
mask = mask.transpose(-3, -2)
|
|
mask = mask.flatten(-4, -3).flatten(-2, -1)
|
|
mask_shape = mask.shape
|
|
mask = mask.view(mask_shape + (1,) * len(hybrid_shape))
|
|
mask = mask.expand(mask_shape + hybrid_shape)
|
|
dense = dense * mask
|
|
sparse = dense.to_sparse(layout=layout, blocksize=blocksize or None, dense_dim=len(hybrid_shape))
|
|
check_content(sparse, dense, blocksize=blocksize, batch_shape=batch_shape, hybrid_shape=hybrid_shape)
|
|
|
|
dense_back = sparse.to_dense()
|
|
self.assertEqual(dense, dense_back)
|
|
|
|
# if batches have different nnz we expect the conversion to throw
|
|
mask_0 = mask[0]
|
|
mask_1 = mask[0].clone().fill_(True)
|
|
mask_2 = mask[0].clone().fill_(False)
|
|
mask_true = mask_source.clone().fill_(True)
|
|
mask_false = mask_source.clone().fill_(False)
|
|
mask = torch.stack([(mask_0, mask_1, mask_2)[i % 3] for i in range(n_batch)], dim=0).reshape(batch_shape + mask_0.shape)
|
|
dense = make_tensor(shape, dtype=torch.float, device=device)
|
|
dense = dense * mask
|
|
msg = "Expect the same number of specified elements per batch."
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
dense.to_sparse(layout=layout, blocksize=blocksize or None)
|
|
|
|
# Should throw if there is a zero in the batch size
|
|
dense = make_tensor((0,) + shape, dtype=torch.float, device=device)
|
|
layout_code = str(layout).split("_")[-1]
|
|
msg = f"to_sparse_{layout_code}: Expected product of batch dimensions to be non-zero."
|
|
with self.assertRaisesRegex(RuntimeError, msg):
|
|
dense.to_sparse(layout=layout, blocksize=blocksize or None)
|
|
|
|
@skipMeta
|
|
@all_sparse_compressed_layouts()
|
|
@coalescedonoff
|
|
@dtypes(torch.double)
|
|
@unittest.skipIf(not TEST_SCIPY, "SciPy not found")
|
|
def test_sparse_to_sparse_compressed(self, device, dtype, coalesced, layout):
|
|
"""
|
|
This test tests conversion from COO to CSR and CSC and CSC to CSR and CSC
|
|
by comparing to SciPy's implementation.
|
|
|
|
Here we test only those conversion combinations that SciPy
|
|
supports to ensure that PyTorch conversions are in the same
|
|
page with SciPy. Independent from SciPy, all conversion
|
|
combinations are tested in TestSparseAny.test_to_sparse.
|
|
"""
|
|
|
|
blocksize_kw = {}
|
|
if layout in (torch.sparse_bsc, torch.sparse_bsr):
|
|
blocksize_kw['blocksize'] = (2, 2)
|
|
# block modes don't support 0 width/height
|
|
shapes = [(6, 10)]
|
|
elif layout in (torch.sparse_csc, torch.sparse_csr):
|
|
shapes = [(0, 10), (6, 0), (6, 10), (0, 0)]
|
|
else:
|
|
raise NotImplementedError("unhandled layout")
|
|
|
|
if layout in (torch.sparse_bsc, torch.sparse_csc):
|
|
compressed_indices_mth = torch.Tensor.ccol_indices
|
|
plain_indices_mth = torch.Tensor.row_indices
|
|
elif layout in (torch.sparse_bsr, torch.sparse_csr):
|
|
compressed_indices_mth = torch.Tensor.crow_indices
|
|
plain_indices_mth = torch.Tensor.col_indices
|
|
else:
|
|
raise NotImplementedError("unhandled layout")
|
|
|
|
for shape in shapes:
|
|
sparse_dim = 2
|
|
nnz = shape[0] * shape[1] // 2
|
|
sparse, _, _ = self.genSparseTensor(shape, sparse_dim, nnz, coalesced, device, dtype)
|
|
sp_matrix = self._construct_sp_matrix(sparse, layout)
|
|
pt_matrix = sparse.to_sparse(layout=layout, **blocksize_kw)
|
|
|
|
self.assertEqual(layout, pt_matrix.layout)
|
|
self.assertEqual(sp_matrix.shape, pt_matrix.shape)
|
|
self.assertEqual(torch.tensor(sp_matrix.indptr, dtype=torch.int64), compressed_indices_mth(pt_matrix))
|
|
self.assertEqual(torch.tensor(sp_matrix.indices, dtype=torch.int64), plain_indices_mth(pt_matrix))
|
|
self.assertEqual(torch.tensor(sp_matrix.data), pt_matrix.values())
|
|
|
|
sparse_csc = sparse.to_sparse_csc()
|
|
sp_matrix = self._construct_sp_matrix(sparse_csc, layout)
|
|
pt_matrix = sparse_csc.to_sparse(layout=layout, **blocksize_kw)
|
|
|
|
self.assertEqual(layout, pt_matrix.layout)
|
|
self.assertEqual(sp_matrix.shape, pt_matrix.shape)
|
|
self.assertEqual(torch.tensor(sp_matrix.indptr, dtype=torch.int64), compressed_indices_mth(pt_matrix))
|
|
self.assertEqual(torch.tensor(sp_matrix.indices, dtype=torch.int64), plain_indices_mth(pt_matrix))
|
|
self.assertEqual(torch.tensor(sp_matrix.data), pt_matrix.values())
|
|
|
|
@unittest.skipIf(not TEST_CUDA_CUDSS, "The test requires cudss")
|
|
@dtypes(*floating_types())
|
|
def test_linalg_solve_sparse_csr_cusolver(self, device, dtype):
|
|
# https://github.com/krshrimali/pytorch/blob/f5ee21dd87a7c5e67ba03bfd77ea22246cabdf0b/test/test_sparse_csr.py
|
|
|
|
try:
|
|
spd = torch.rand(4, 3)
|
|
A = spd.T @ spd
|
|
b = torch.rand(3).cuda()
|
|
A = A.to_sparse_csr().cuda()
|
|
x = torch.sparse.spsolve(A, b)
|
|
except RuntimeError as e:
|
|
if "Calling linear solver with sparse tensors requires compiling " in str(e):
|
|
self.skipTest("PyTorch was not built with cuDSS support")
|
|
|
|
samples = sample_inputs_linalg_solve(None, device, dtype)
|
|
|
|
for sample in samples:
|
|
if sample.input.ndim != 2:
|
|
continue
|
|
|
|
out = torch.zeros(sample.args[0].size(), dtype=dtype, device=device)
|
|
if sample.args[0].ndim != 1 and sample.args[0].size(-1) != 1:
|
|
with self.assertRaisesRegex(RuntimeError, "b must be a 1D tensor"):
|
|
out = torch.linalg.solve(sample.input.to_sparse_csr(), *sample.args, **sample.kwargs)
|
|
break
|
|
if not sample.args[0].numel():
|
|
with self.assertRaisesRegex(RuntimeError,
|
|
"Expected non-empty other tensor, but found empty tensor"):
|
|
torch.linalg.solve(sample.input.to_sparse_csr(), *sample.args, **sample.kwargs, out=out)
|
|
break
|
|
|
|
expect = torch.linalg.solve(sample.input, *sample.args, **sample.kwargs)
|
|
sample.input = sample.input.to_sparse_csr()
|
|
if sample.args[0].ndim != 1 and sample.args[0].size(-1) == 1:
|
|
expect = expect.squeeze(-1)
|
|
sample.args = (sample.args[0].squeeze(-1), )
|
|
out = torch.linalg.solve(sample.input, *sample.args, **sample.kwargs)
|
|
self.assertEqual(expect, out)
|
|
|
|
|
|
def skipIfNoTriton(cls):
|
|
from torch.utils._triton import has_triton
|
|
|
|
# no-op if triton is present
|
|
if has_triton():
|
|
return cls
|
|
else:
|
|
|
|
@functools.wraps(cls, updated=())
|
|
class skipped_cls(cls):
|
|
def setUp(self):
|
|
self.skipTest("Triton is not available.")
|
|
|
|
return skipped_cls
|
|
|
|
@skipIfNoTriton
|
|
class TestSparseCompressedTritonKernels(TestCase):
|
|
|
|
def _to_block_triangular_inplace(self, d, row_block, col_block):
|
|
"""
|
|
This function modifies `d` to become (upper/lower) block-triangular in-place.
|
|
It is assumed that `d.shape[-2]` is divisible by `row_block` and
|
|
`d.shape[-1]` is divisible by `col_block`.
|
|
"""
|
|
|
|
from torch.sparse._triton_ops import tile_to_blocksize
|
|
|
|
m, n = d.shape[-2:]
|
|
d_tiled = tile_to_blocksize(d, (row_block, col_block))
|
|
d_tiled = d_tiled.moveaxis(-4, -1).moveaxis(-4, -1)
|
|
if m // row_block > n // col_block:
|
|
d_tiled.tril_()
|
|
else:
|
|
d_tiled.triu_()
|
|
|
|
return d
|
|
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_bsr_softmax(self, device, dtype):
|
|
from functools import partial
|
|
from torch.sparse._triton_ops import bsr_softmax
|
|
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=1.0, high=3.0)
|
|
|
|
# NOTE: batch dims with zero sizes are not supported in `to_sparse_bsr`.
|
|
batches = [(), (2,), (2, 2)]
|
|
size = [6, 12, 0]
|
|
block_size = [2, 3]
|
|
|
|
# General correctness
|
|
for row_block, col_block, b, m, n in itertools.product(block_size, block_size, batches, size, size):
|
|
input = tensor(b + (m, n))
|
|
input.diagonal(dim1=-2, dim2=-1).fill_(m * n)
|
|
input = self._to_block_triangular_inplace(input, row_block, col_block)
|
|
|
|
bsr = input.to_sparse_bsr((row_block, col_block))
|
|
coo = input.to_sparse().to(torch.float)
|
|
|
|
res_tri = bsr_softmax(bsr)
|
|
res_coo = torch.sparse.softmax(coo, -1)
|
|
self.assertEqual(res_tri, res_coo.to(input.dtype))
|
|
|
|
# Test long rows which exceed Triton's max numel limit set to 2 ** 17
|
|
input = tensor(b + (1, 150000))
|
|
bsr = input.to_sparse_bsr(1)
|
|
self.assertEqual(input.softmax(-1), bsr_softmax(bsr))
|
|
|
|
@parametrize("block_size", [16, 32, 64])
|
|
@parametrize("index_dtype", [torch.int32, torch.int64])
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf((not TEST_WITH_TORCHINDUCTOR) or (IS_FBCODE and IS_REMOTE_GPU),
|
|
"Skipped for internal with remote GPUs")
|
|
def test_triton_bsr_dense_bmm(self, device, dtype, index_dtype, block_size):
|
|
from functools import partial
|
|
from torch.sparse._triton_ops import bsr_dense_mm
|
|
|
|
def kernel_impl(*args, **kwargs):
|
|
return bsr_dense_mm(*args, skip_checks=True, **kwargs)
|
|
|
|
kernel = torch._TritonLibrary.registerOp(
|
|
"_triton_bsr_dense_mm_out",
|
|
"_triton_bsr_dense_mm_out(Tensor bsr, Tensor dense, *, Tensor(a!) out) -> Tensor(a!)",
|
|
kernel_impl,
|
|
"SparseCsrCUDA"
|
|
)
|
|
|
|
# kernel != kernel_impl means dispatch was already registered.
|
|
# This is exactly what we need!
|
|
self.assertTrue(kernel is not kernel_impl)
|
|
|
|
# Note that each value in a non-zero block is in range block_size * [low^2, high^2).
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=0.5, high=1.5)
|
|
|
|
# NOTE: batch dims with zero sizes are not supported in `to_sparse_bsr`.
|
|
batches = [(), (2,), (2, 2)]
|
|
size = [128, 256, 0]
|
|
|
|
# Whether to make inputs orthogonal so that the product is zero
|
|
make_orthogonal = [True, False]
|
|
|
|
for bd, bs, m, n, k, is_ortho in itertools.product(batches, batches, size, size, size, make_orthogonal):
|
|
bsr = tensor(bs + (m, k))
|
|
# NOTE: do not get confused, it will be transposed
|
|
dense = tensor(bd + (n, k))
|
|
|
|
if is_ortho:
|
|
bsr = torch.cat((bsr, torch.zeros_like(bsr)), dim=-1)
|
|
dense = torch.cat((torch.zeros_like(dense), dense), dim=-1)
|
|
|
|
bsr = bsr.to_sparse_bsr(block_size)
|
|
|
|
if bsr.dim() == 2 and dtype != torch.float:
|
|
# Test against linear to check dispatch
|
|
# which takes place for torch.half and torch.bfloat16.
|
|
res_dense = torch.nn.functional.linear(dense, bsr.to_dense())
|
|
res_tri_out = torch.empty_like(res_dense)
|
|
res_tri = torch.nn.functional.linear(dense, bsr, out=res_tri_out)
|
|
|
|
# Check dispatch worked with non-trivial outputs
|
|
if m > 0 and n > 0 and k > 0:
|
|
self.assertTrue(kernel.kernel_invoked)
|
|
kernel.kernel_invoked = False
|
|
else:
|
|
# Otherwise check correctness against bmm
|
|
# since nn.linear does not support bsr.dim() > 2.
|
|
res_dense = bsr.to_dense() @ dense.transpose(-2, -1)
|
|
res_tri_out = torch.empty_like(res_dense)
|
|
res_tri = kernel(bsr, dense.transpose(-2, -1), out=res_tri_out)
|
|
|
|
self.assertTrue(res_tri is res_tri_out)
|
|
self.assertEqual(res_tri, res_dense)
|
|
|
|
res_dense = bsr.to_dense() @ dense.transpose(-2, -1)
|
|
# check whether bsr_dense_mm handles different grid sizes
|
|
# None means max possible grid size which is CUDA-dependent.
|
|
grid_size = (None, 2, 4)
|
|
grid_gen = itertools.product(grid_size, repeat=3)
|
|
for grid in grid_gen:
|
|
res_tri = torch.sparse._triton_ops.bsr_dense_mm(
|
|
bsr,
|
|
dense.transpose(-2, -1),
|
|
max_grid=grid,
|
|
)
|
|
self.assertEqual(res_tri, res_dense)
|
|
|
|
@onlyCUDA
|
|
@dtypes(torch.half)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU,
|
|
"Skipped for internal with remote GPUs")
|
|
def test_triton_bsr_dense_bmm_error_messages(self, device, dtype):
|
|
from torch.sparse._triton_ops import bsr_dense_mm
|
|
|
|
rhs = torch.rand(32, 32, dtype=dtype, device=device)
|
|
lhs = rhs.to_sparse_bsr(16)
|
|
with self.assertRaisesRegex(ValueError, "only BSR sparse format is supported"):
|
|
bsr_dense_mm(lhs.to_sparse_bsc(16), rhs)
|
|
with self.assertRaisesRegex(ValueError, "on the same GPU device"):
|
|
bsr_dense_mm(lhs, rhs.cpu())
|
|
if torch.cuda.device_count() > 1:
|
|
with self.assertRaisesRegex(ValueError, "on the same GPU device"):
|
|
bsr_dense_mm(lhs.to("cuda:0"), rhs.to("cuda:1"))
|
|
with self.assertRaisesRegex(ValueError, "all inputs are expected to be of the same dtype"):
|
|
bsr_dense_mm(lhs, rhs.to(torch.float))
|
|
with self.assertRaisesRegex(ValueError, r"and one of \(half, bfloat16, float32\)"):
|
|
bsr_dense_mm(lhs.to(torch.double), rhs.to(torch.double))
|
|
with self.assertRaisesRegex(ValueError, "all inputs involved in the matrix product are expected to be at least 2D"):
|
|
bsr_dense_mm(lhs, torch.rand(1, dtype=dtype, device=device))
|
|
with self.assertRaisesRegex(ValueError,
|
|
"sizes involved in the matrix product are not compatible for matrix multiplication"):
|
|
bsr_dense_mm(lhs, torch.rand(1, 1, dtype=dtype, device=device))
|
|
with self.assertRaisesRegex(ValueError,
|
|
r"dense.size\(-1\) == 15 should be divisible by 16"):
|
|
bsr_dense_mm(lhs, torch.rand(32, 15, dtype=dtype, device=device))
|
|
# Blocksizes check
|
|
for blocksize in (15, 30):
|
|
n = blocksize * 2
|
|
rhs = torch.rand(n, n, dtype=dtype, device=device)
|
|
lhs = rhs.to_sparse_bsr(blocksize)
|
|
with self.assertRaisesRegex(ValueError, "should be at least 16 and a power of 2"):
|
|
bsr_dense_mm(lhs, rhs)
|
|
# out check
|
|
rhs = torch.rand(2, 32, 32, dtype=dtype, device=device)
|
|
lhs = rhs.to_sparse_bsr(16)
|
|
with self.assertRaisesRegex(ValueError, r"`out` argument has wrong shape"):
|
|
out = torch.rand(2, 30, 30, dtype=dtype, device=device)
|
|
bsr_dense_mm(lhs, rhs, out=out)
|
|
with self.assertRaisesRegex(ValueError, r"only row-major/col-major `out`"):
|
|
out = torch.rand(32, 32, 2, dtype=dtype, device=device).transpose(0, -1)
|
|
bsr_dense_mm(lhs, rhs, out=out)
|
|
|
|
@parametrize("block_size", [16, 32, 64])
|
|
@onlyCUDA
|
|
@skipIfRocm
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
@precisionOverride({torch.float16: 1e-3})
|
|
def test_triton_scaled_dot_product_attention(self, device, dtype, block_size):
|
|
from functools import partial
|
|
from torch.sparse._triton_ops import _scaled_dot_product_attention
|
|
|
|
# Note that each value in a non-zero block is in range block_size * [low^2, high^2).
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=0.3, high=1.2)
|
|
|
|
def broadcast_input(*ts):
|
|
batch_dims = torch.broadcast_shapes(*(t.shape[:-2] for t in ts))
|
|
yield from (torch.broadcast_to(t, batch_dims + t.shape[-2:]) for t in ts)
|
|
|
|
# NOTE: batch dims with zero sizes are not supported in `to_sparse_bsr`.
|
|
batches = [(), (2,), (2, 2)]
|
|
size = [128, 256, 0]
|
|
|
|
for bam, bq, bk, bv, m, n, k in itertools.product(batches, batches, batches, batches, size, size, size):
|
|
query = tensor(bq + (m, k))
|
|
key = tensor(bk + (n, k))
|
|
value = tensor(bv + (n, k))
|
|
|
|
# We make attn_mask block lower/upper triangular so that BSR and Strided
|
|
# function variants are directly comparable.
|
|
attn_mask = torch.ones(bam + (m, n), device=device, dtype=torch.bool)
|
|
attn_mask = self._to_block_triangular_inplace(attn_mask, block_size, block_size)
|
|
attn_mask_bsr = attn_mask.to_sparse_bsr(block_size)
|
|
|
|
# NOTE: only boolean mask is directly compatible with the Strided version
|
|
# without any pre-/post-processing. Hence we test against a boolean mask.
|
|
for scale in (None, 1. / 16):
|
|
if scale is None and query.size(-1) == 0:
|
|
scale = 1
|
|
# We cast to double here as this dispatches to the MATH backend which
|
|
# introduces additional rounding steps over the fused implementations
|
|
expected = torch.nn.functional.scaled_dot_product_attention(
|
|
*broadcast_input(query.double(), key.double(), value.double(), attn_mask), scale=scale
|
|
).to(dtype)
|
|
|
|
for mask_dtype in (torch.bool, dtype):
|
|
res = _scaled_dot_product_attention(query, key, value, attn_mask_bsr.to(mask_dtype), scale=scale)
|
|
self.assertEqual(res, expected)
|
|
|
|
|
|
@parametrize("block_size", [16, 32, 64])
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_sampled_addmm(self, device, dtype, block_size):
|
|
from functools import partial
|
|
from torch.sparse._triton_ops import sampled_addmm, broadcast_batch_dims_bsr
|
|
|
|
# Note that each value in a non-zero block is in range block_size * [low^2, high^2).
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=0.3, high=1.2)
|
|
|
|
# NOTE: batch dims with zero sizes are not supported in `to_sparse_bsr`.
|
|
batches = [(), (2,), (2, 2)]
|
|
size = [128, 256, 0]
|
|
|
|
delta_k = (-3,)
|
|
for bi, bm1, bm2, m, n, k, dk in itertools.product(batches, batches, batches, size, size, size, delta_k):
|
|
# Test not powers of 2 ks as well.
|
|
k = max(0, k + dk)
|
|
# Non-trivial sparsity pattern.
|
|
# Plus with tril inputs the result is also tril,
|
|
# so we can compare BSR and CSR implementations.
|
|
input = tensor(bi + (m, n)).tril_()
|
|
bsr = input.to_sparse_bsr(block_size)
|
|
mat1 = tensor(bm1 + (m, k)).tril_()
|
|
mat2 = tensor(bm2 + (k, n)).tril_()
|
|
|
|
batch_dim = torch.broadcast_shapes(input.shape[:-2], mat1.shape[:-2], mat2.shape[:-2])
|
|
|
|
csr = input.broadcast_to(batch_dim + input.shape[-2:]).to_sparse_csr().to(torch.float)
|
|
mat1csr = mat1.broadcast_to(batch_dim + mat1.shape[-2:]).to(torch.float)
|
|
mat2csr = mat2.broadcast_to(batch_dim + mat2.shape[-2:]).to(torch.float)
|
|
|
|
input_broadcasted_clone = broadcast_batch_dims_bsr(
|
|
"test_triton_sampled_addmm",
|
|
bsr, mat1, mat2
|
|
).clone()
|
|
input_broadcasted_clone = torch.sparse_compressed_tensor(
|
|
input_broadcasted_clone.crow_indices(),
|
|
input_broadcasted_clone.col_indices(),
|
|
# For testing `out=` let's make values to have "weird" strides
|
|
# so that if the kernel modifies values to it's needs, the result
|
|
# is being copied into out.values.
|
|
input_broadcasted_clone.values().transpose(-3, -2).contiguous().transpose(-3, -2),
|
|
layout=input_broadcasted_clone.layout,
|
|
size=input_broadcasted_clone.shape
|
|
)
|
|
|
|
scalars = (0.0, 2.0)
|
|
for alpha, beta, out in itertools.product(scalars, scalars, (None, input_broadcasted_clone)):
|
|
res_tri = sampled_addmm(bsr, mat1, mat2, alpha=alpha, beta=beta, out=out)
|
|
if out is not None:
|
|
self.assertTrue(res_tri is out)
|
|
|
|
batch_broadcasted_shape = torch.broadcast_shapes(*(t.shape[:-2] for t in (input, mat1, mat2)))
|
|
self.assertTrue(res_tri.shape == batch_broadcasted_shape + (m, n))
|
|
|
|
res_csr = torch.sparse.sampled_addmm(csr, mat1csr, mat2csr, alpha=alpha, beta=beta).to(input.dtype)
|
|
self.assertEqual(res_tri.to_dense(), res_csr.to_dense())
|
|
|
|
# Check different grid sizes to make sure that input slicing works
|
|
# if this input is larger than the grid.
|
|
grid_size = (3, None)
|
|
grid_gen = itertools.product(grid_size, repeat=2)
|
|
for grid in grid_gen:
|
|
res_tri_grid = sampled_addmm(bsr, mat1, mat2, alpha=alpha, beta=beta, max_grid=grid)
|
|
self.assertEqual(res_tri, res_tri_grid)
|
|
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_scatter_mm(self, device, dtype):
|
|
from torch.sparse._triton_ops import scatter_mm
|
|
from functools import partial
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=0.5, high=1.5)
|
|
sizes = [8, 16]
|
|
for m, k, n in itertools.product(sizes, sizes, sizes):
|
|
blocks = torch.stack([tensor(m, k), tensor(m, k)])
|
|
others = torch.stack([tensor(k, n), tensor(k, n)])
|
|
|
|
expected = torch.stack([blocks[0] @ others[0] + blocks[1] @ others[0],
|
|
blocks[0] @ others[1],
|
|
blocks[1] @ others[1]])
|
|
|
|
indices_data = (
|
|
'scatter_mm',
|
|
torch.tensor([0, 2, 3, 4], dtype=torch.int32, device=device),
|
|
torch.tensor([[0, 0], [1, 0], [0, 1], [1, 1]], dtype=torch.int32, device=device))
|
|
|
|
result = scatter_mm(blocks, others, indices_data=indices_data)
|
|
|
|
self.assertEqual(result, expected)
|
|
|
|
indices_data = (
|
|
'bsr_strided_mm',
|
|
torch.tensor([0, 2, 4, 5, 6], dtype=torch.int32, device=device),
|
|
torch.tensor([0, n, 2 * n * m, 2 * n * m + n], dtype=torch.int32, device=device),
|
|
torch.tensor([1, 0, 1, 0, 1, 1], dtype=torch.int32, device=device),
|
|
torch.tensor([0, 2 * k * n, n, 2 * k * n + n, 2 * k * n, 2 * k * n + n],
|
|
dtype=torch.int32, device=device),
|
|
dict(SPLIT_N=2, is_compressed=False, TILE_M=m, TILE_N=n, GROUP_SIZE=1)
|
|
)
|
|
|
|
for bsize in [(), (2,), (3, 4)]:
|
|
other = tensor(*bsize, 2 * k, 2 * n)
|
|
expected = torch.cat([
|
|
torch.cat([blocks[1], blocks[0]], dim=1),
|
|
torch.cat([torch.zeros_like(blocks[0]), blocks[1]], dim=1)], dim=0) @ other
|
|
result = scatter_mm(blocks, other, indices_data=indices_data)
|
|
self.assertEqual(result, expected)
|
|
|
|
@parametrize("blocksize", [2, '2x3', 16, '16x32', 32, 64])
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_bsr_scatter_mm(self, device, dtype, blocksize):
|
|
import triton
|
|
from torch.sparse._triton_ops import bsr_scatter_mm, bsr_scatter_mm_indices_data
|
|
from functools import partial
|
|
if isinstance(blocksize, str):
|
|
blocksize = tuple(map(int, blocksize.split('x')))
|
|
else:
|
|
blocksize = (blocksize,) * 2
|
|
# Note that each value in a non-zero block is in range blocksize * [low^2, high^2).
|
|
tensor = partial(make_tensor, device=device, dtype=dtype, low=0.5, high=1.5)
|
|
|
|
# NOTE: batch dims with zero sizes are not supported in `to_sparse_bsr`.
|
|
batches = [(), (2,), (2, 2)]
|
|
sizes = [blocksize[0], 2 * blocksize[0], 4 * blocksize[0]]
|
|
sizes_K = [blocksize[1], 2 * blocksize[1]]
|
|
|
|
for bd, bs, M, K, N, has_zero_row_block in itertools.product(batches, batches[:1], sizes, sizes_K, sizes, (False, True)):
|
|
bsr_dense = tensor(bs + (M, K))
|
|
if has_zero_row_block:
|
|
if M > blocksize[0]:
|
|
bsr_dense[:blocksize[0]].zero_()
|
|
else:
|
|
continue
|
|
bsr = bsr_dense.to_sparse_bsr(blocksize)
|
|
dense = tensor(bd + (K, N))
|
|
expected = bsr.to_dense() @ dense
|
|
|
|
for indices_format in ('bsr_strided_mm', 'bsr_strided_mm_compressed', 'scatter_mm'):
|
|
if indices_format in {'bsr_strided_mm', 'bsr_strided_mm_compressed'}:
|
|
SPLIT_N_list = [N]
|
|
while SPLIT_N_list[-1] > 1:
|
|
SPLIT_N_list.append(max(1, SPLIT_N_list[-1] // 2))
|
|
else:
|
|
SPLIT_N_list = [1]
|
|
for SPLIT_N in SPLIT_N_list:
|
|
indices_data = bsr_scatter_mm_indices_data(
|
|
bsr, dense, indices_format=indices_format, SPLIT_N=SPLIT_N)
|
|
try:
|
|
result = bsr_scatter_mm(bsr, dense, indices_data=indices_data)
|
|
except triton.compiler.OutOfResources:
|
|
# ensure that there was at least one successful test:
|
|
assert SPLIT_N < SPLIT_N_list[0]
|
|
break
|
|
|
|
self.assertEqual(result, expected)
|
|
torch.sparse._triton_ops._bsr_scatter_mm_indices_data.cache_clear()
|
|
|
|
def test_TensorAsKey(self, device):
|
|
from torch.sparse._triton_ops import TensorAsKey
|
|
assertEqualOptions = dict(exact_dtype=True, exact_device=True, exact_layout=True)
|
|
|
|
t = torch.tensor([1, 2, 3, 4], dtype=torch.int64, device=device)
|
|
key = TensorAsKey(t)
|
|
self.assertTrue(key == TensorAsKey(t))
|
|
self.assertTrue(key.obj is t)
|
|
|
|
t2 = t[:]
|
|
key2 = TensorAsKey(t2)
|
|
self.assertTrue(key == key2)
|
|
self.assertEqual(key2.obj, t, **assertEqualOptions)
|
|
# deleting object leads to dead key
|
|
del t2
|
|
self.assertTrue(key2.obj is None)
|
|
self.assertTrue(key.obj is t)
|
|
|
|
# key with different storage offset and shape:
|
|
self.assertFalse(key == TensorAsKey(t[1:]))
|
|
|
|
# key with different strides:
|
|
self.assertFalse(key == TensorAsKey(t[::2]))
|
|
|
|
# when object dies, make sure that key represents a dead
|
|
# object as well:
|
|
del t
|
|
self.assertTrue(key.obj is None)
|
|
|
|
# Storing a tensor as a dict key:
|
|
d = {}
|
|
t3 = torch.tensor([1, 2, 3, 4], dtype=torch.int32, device=device)
|
|
key3 = TensorAsKey(t3)
|
|
d[key3] = 123
|
|
self.assertTrue(d.get(key3) == 123)
|
|
t3_ = t3[:]
|
|
self.assertTrue(d.get(TensorAsKey(t3_)) == 123)
|
|
self.assertTrue(d.get(TensorAsKey(t3.clone())) is None)
|
|
|
|
d[TensorAsKey(t3_)] = 567
|
|
self.assertTrue(d.get(key3) == 567)
|
|
|
|
# t3 and t3_ reference the same data, so, the key becomes dead
|
|
# (that is, its .obj property returns None) until all
|
|
# references are deleted:
|
|
del t3
|
|
self.assertTrue(key3.obj is not None)
|
|
self.assertTrue(d.get(key3) == 567)
|
|
del t3_
|
|
self.assertTrue(key3.obj is None)
|
|
self.assertTrue(d.get(key3) == 567)
|
|
|
|
# Storing a tensor as a dict key and value:
|
|
d = {}
|
|
t4 = torch.tensor([1, 2, 3, 4], dtype=torch.int32, device=device)
|
|
key4 = TensorAsKey(t4)
|
|
d[key4] = (t4, 123)
|
|
self.assertEqual(d.get(key4), (t4, 123), **assertEqualOptions)
|
|
# when object is deleted, the key represents an alive object
|
|
# because the object is referenced by the dict item value:
|
|
del t4
|
|
self.assertTrue(key4.obj is not None)
|
|
# This also means that the life time of the tensor is same as
|
|
# the life time of the corresponding dict item:
|
|
del d[key4]
|
|
self.assertTrue(key4.obj is None)
|
|
|
|
# Storing a tensor as a dict key and value wrapped with TensorAsKey:
|
|
d = {}
|
|
t5 = torch.tensor([1, 2, 3, 4], dtype=torch.int32, device=device)
|
|
key5 = TensorAsKey(t5)
|
|
d[key5] = (key5, 567)
|
|
self.assertEqual(d.get(key5), (key5, 567), **assertEqualOptions)
|
|
self.assertTrue(key5.obj is not None)
|
|
# when object is deleted, it will be dead as the wrapped value
|
|
# hold the tensor instance as a weakref:
|
|
del t5
|
|
self.assertTrue(key5.obj is None)
|
|
# but key is still valid:
|
|
self.assertEqual(d.get(key5), (key5, 567), **assertEqualOptions)
|
|
|
|
@suppress_warnings
|
|
@parametrize("op", ['bsr_dense_addmm', 'bsr_dense_mm', 'bsr_dense_linear', '_int_bsr_dense_addmm'])
|
|
@parametrize("blocksize", [16, '16x32', 32])
|
|
@parametrize("out_dtype", ['unspecified', 'int32'])
|
|
@onlyCUDA
|
|
@dtypes(torch.half, torch.bfloat16, torch.float, torch.int8)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float, torch.int8)
|
|
@precisionOverride({torch.float16: 6e-1})
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_kernel(self, op, device, dtype, blocksize, out_dtype):
|
|
from torch.sparse._triton_ops import bsr_dense_addmm, bsr_dense_mm, _int_bsr_dense_addmm
|
|
from torch.sparse._triton_ops_meta import (create_blocked_tensor, get_meta,
|
|
optimize_bsr_dense_addmm, dump)
|
|
if out_dtype == "unspecified":
|
|
out_dtype = None
|
|
elif op == "bsr_dense_addmm":
|
|
out_dtype = getattr(torch, out_dtype)
|
|
if out_dtype.is_floating_point != dtype.is_floating_point:
|
|
self.skipTest("incompatible out dtype")
|
|
else:
|
|
self.skipTest("out dtype not implemented")
|
|
|
|
def bsr_dense_linear(input, weights, bias=None):
|
|
return torch.nn.functional.linear(input, weights, bias=bias).transpose(-1, -2)
|
|
|
|
operation = dict(bsr_dense_addmm=bsr_dense_addmm, bsr_dense_mm=bsr_dense_mm, bsr_dense_linear=bsr_dense_linear,
|
|
_int_bsr_dense_addmm=_int_bsr_dense_addmm)[op]
|
|
|
|
def reference(input, mat1, mat2, beta=1, alpha=1, left_alpha=None, right_alpha=None, op=op):
|
|
assert mat1.layout is torch.strided
|
|
assert mat2.layout is torch.strided
|
|
if dtype is torch.int8:
|
|
if op == '_int_bsr_dense_addmm':
|
|
mat12 = torch._int_mm(mat1, mat2)
|
|
else:
|
|
# workaround RuntimeError: "addmm_cuda" not implemented for 'Char'
|
|
if out_dtype is not None:
|
|
mat12 = torch._int_mm(mat1, mat2).to(out_dtype)
|
|
else:
|
|
mat12 = torch._int_mm(mat1, mat2).to(torch.int8)
|
|
else:
|
|
mat12 = mat1 @ mat2
|
|
if alpha != 1:
|
|
mat12 *= alpha
|
|
if left_alpha is not None:
|
|
mat12 = left_alpha.reshape(*left_alpha.shape[:-1], -1, 1) * mat12
|
|
if right_alpha is not None:
|
|
mat12 = mat12 * right_alpha.reshape(*right_alpha.shape[:-1], 1, -1)
|
|
return beta * input + mat12
|
|
|
|
if op == '_int_bsr_dense_addmm':
|
|
# _int_bsr_dense_addmm is same as bsr_dense_addmm except
|
|
# with int8 inputs, _int_bsr_dense_addmm returns int32
|
|
# result. This is covered by operation and reference
|
|
# definitions above and all other definitions below are
|
|
# identical between _int_bsr_dense_addmm and
|
|
# bsr_dense_addmm.
|
|
if dtype.is_floating_point or dtype.is_complex:
|
|
self.skipTest(f"Redundant test: {op} on {dtype} tensors")
|
|
op = 'bsr_dense_addmm'
|
|
|
|
def nc_copy(t, axes=(-1,)):
|
|
"""Return a copy of input.
|
|
|
|
The returned copy will be a non-contiguous tensor.
|
|
"""
|
|
if t.layout is torch.strided:
|
|
shape = list(t.shape)
|
|
for a in axes:
|
|
shape[a] *= 2
|
|
r = torch.empty(shape, dtype=t.dtype, device=t.device)
|
|
s = r[tuple(slice(None, None, 2 if t.shape[i] != r.shape[i] else None) for i in range(t.ndim))]
|
|
s.copy_(t)
|
|
return s
|
|
elif t.layout is torch.sparse_bsr:
|
|
compressed_indices = t.crow_indices()
|
|
plain_indices = t.col_indices()
|
|
return torch.sparse_compressed_tensor(compressed_indices, plain_indices, nc_copy(t.values()),
|
|
t.shape, layout=t.layout)
|
|
else:
|
|
raise NotImplementedError(t.layout)
|
|
|
|
if isinstance(blocksize, str):
|
|
BM, BK = tuple(map(int, blocksize.split('x')))
|
|
else:
|
|
BM, BK = (blocksize,) * 2
|
|
|
|
if op in {"bsr_dense_linear"} and BM != BK:
|
|
# todo: eliminate this skip
|
|
self.skipTest(f"{op} does not support non-square blocks")
|
|
|
|
if op in {"bsr_dense_linear"} and dtype is torch.int8:
|
|
# todo: eliminate this skip
|
|
self.skipTest(f"{op} does not support int8")
|
|
|
|
if dtype is torch.int8 and min(BM, BK) < 32:
|
|
self.skipTest("triton kernel does not support support int8 blocks smaller than 32")
|
|
|
|
beta_lst = dict(bsr_dense_addmm=[0, 1, 2], bsr_dense_mm=[0], bsr_dense_linear=[1])[op]
|
|
alpha_lst = dict(bsr_dense_addmm=[0, 1, 2], bsr_dense_mm=[1], bsr_dense_linear=[1])[op]
|
|
sparsity_lst = [0, 0.5, 1]
|
|
blocks_per_row_lst = [1, 2]
|
|
blocks_per_col_lst = [1, 2]
|
|
result_cols_lst = [16, 32, 64]
|
|
has_left_alpha_lst = dict(bsr_dense_addmm=[False, True], bsr_dense_mm=[False], bsr_dense_linear=[False])[op]
|
|
has_right_alpha_lst = dict(bsr_dense_addmm=[False, True], bsr_dense_mm=[False], bsr_dense_linear=[False])[op]
|
|
high = 1.5 + int(dtype is torch.int8)
|
|
for beta, alpha, sparsity, blocks_per_row, blocks_per_col, N, has_left_alpha, has_right_alpha in itertools.product(
|
|
beta_lst, alpha_lst, sparsity_lst, blocks_per_row_lst, blocks_per_col_lst, result_cols_lst,
|
|
has_left_alpha_lst, has_right_alpha_lst):
|
|
M = BM * blocks_per_row
|
|
K = BK * blocks_per_col
|
|
mat1 = create_blocked_tensor(0, M, K, (BM, BK), sparsity, dtype, device=device)
|
|
bsr = mat1.to_sparse_bsr((BM, BK))
|
|
mat2 = make_tensor(K, N, dtype=dtype, device=device, low=0.5, high=high)
|
|
input = make_tensor(M, N, dtype=dtype, device=device, low=0.5, high=high)
|
|
|
|
left_alpha = make_tensor(M, dtype=dtype, device=device, low=0.5, high=high) if has_left_alpha else None
|
|
right_alpha = make_tensor(N, dtype=dtype, device=device, low=0.5, high=high) if has_right_alpha else None
|
|
|
|
if 0 and op == "bsr_dense_addmm":
|
|
# Find optimal kernel parameters, the speed-up is
|
|
# about 10x for running this test.
|
|
#
|
|
# Enable this if-block when the test method is
|
|
# updated, run the test, and finally, disable the
|
|
# if-block.
|
|
key = (M, K, N, BM, BK, beta == 0, beta == 1, alpha == 1)
|
|
meta = get_meta(op, key, version=(0, dtype, 0.5))
|
|
if meta is None:
|
|
optimize_bsr_dense_addmm(M, K, N, BM, BK, beta=beta, alpha=alpha, dtype=dtype, sparsity=0.5)
|
|
assert meta is not None
|
|
dump() # this will update torch/sparse/_triton_ops_meta.py
|
|
|
|
expected = reference(input, mat1, mat2, beta=beta, alpha=alpha, left_alpha=left_alpha, right_alpha=right_alpha)
|
|
if out_dtype is not None:
|
|
expected = expected.to(out_dtype)
|
|
out = expected.new_empty(input.shape, dtype=out_dtype)
|
|
else:
|
|
out = None
|
|
kwargs = dict(bsr_dense_addmm=dict(beta=beta, alpha=alpha, out=out,
|
|
left_alpha=left_alpha, right_alpha=right_alpha), bsr_dense_mm={},
|
|
bsr_dense_linear=dict(bias=input.transpose(-1, -2)))[op]
|
|
|
|
args = dict(bsr_dense_addmm=(input, bsr, mat2), bsr_dense_mm=(bsr, mat2),
|
|
bsr_dense_linear=(mat2.transpose(-1, -2), bsr))[op]
|
|
result = operation(*args, **kwargs)
|
|
self.assertEqual(result, expected)
|
|
|
|
# Test non-contiguous input tensors:
|
|
nc_mat2 = nc_copy(mat2)
|
|
nc_input = nc_copy(input)
|
|
nc_bsr = nc_copy(bsr)
|
|
|
|
args = dict(bsr_dense_addmm=(input, bsr, nc_mat2), bsr_dense_mm=(bsr, nc_mat2),
|
|
bsr_dense_linear=(nc_mat2.transpose(-1, -2), bsr))[op]
|
|
result = operation(*args, **kwargs)
|
|
self.assertEqual(result, expected)
|
|
|
|
# todo: add bsr_dense_linear to the set below (currently,
|
|
# nn.linear has unnecessarily restrictive arguments
|
|
# checks).
|
|
if op in {'bsr_dense_addmm', 'bsr_dense_mm'}:
|
|
args = dict(bsr_dense_addmm=(input, nc_bsr, mat2), bsr_dense_mm=(nc_bsr, mat2),
|
|
bsr_dense_linear=(mat2.transpose(-1, -2), nc_bsr))[op]
|
|
result = operation(*args, **kwargs)
|
|
self.assertEqual(result, expected)
|
|
|
|
if op in {'bsr_dense_addmm', 'bsr_dense_linear'}:
|
|
args = dict(bsr_dense_addmm=(nc_input, bsr, nc_mat2),
|
|
bsr_dense_linear=(nc_mat2.transpose(-1, -2), bsr))[op]
|
|
kwargs = dict(bsr_dense_addmm=dict(beta=beta, alpha=alpha, left_alpha=left_alpha, right_alpha=right_alpha, out=out),
|
|
bsr_dense_linear=dict(bias=nc_input.transpose(-1, -2)))[op]
|
|
result = operation(*args, **kwargs)
|
|
self.assertEqual(result, expected)
|
|
|
|
@parametrize("op", ['bsr_dense_addmm', '_int_bsr_dense_addmm'])
|
|
@onlyCUDA
|
|
@parametrize("out_dtype", ['unspecified', 'int32'])
|
|
@dtypes(torch.half, torch.bfloat16, torch.float, torch.int8)
|
|
@dtypesIfCUDA(torch.half, *[torch.bfloat16] if SM80OrLater else [], torch.float, torch.int8)
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_tune(self, op, device, dtype, out_dtype):
|
|
from torch.sparse._triton_ops import bsr_dense_addmm, _int_bsr_dense_addmm
|
|
from torch.sparse._triton_ops_meta import (create_blocked_tensor, tune_bsr_dense_addmm, tune__int_bsr_dense_addmm, get_meta)
|
|
|
|
if out_dtype == "unspecified":
|
|
out_dtype = None
|
|
elif op == "bsr_dense_addmm":
|
|
out_dtype = getattr(torch, out_dtype)
|
|
if out_dtype.is_floating_point != dtype.is_floating_point:
|
|
self.skipTest("incompatible out dtype")
|
|
else:
|
|
self.skipTest("out dtype not implemented")
|
|
|
|
operation = dict(bsr_dense_addmm=bsr_dense_addmm, _int_bsr_dense_addmm=_int_bsr_dense_addmm)[op]
|
|
tuner = dict(bsr_dense_addmm=tune_bsr_dense_addmm,
|
|
_int_bsr_dense_addmm=tune__int_bsr_dense_addmm)[op]
|
|
|
|
if op == '_int_bsr_dense_addmm':
|
|
M, K, N = 32, 32, 32
|
|
blocksize = (32, 32)
|
|
else:
|
|
M, K, N = 16, 16, 32
|
|
blocksize = (16, 16)
|
|
sparsity = 1.0
|
|
bsr = create_blocked_tensor(0, M, K, blocksize, sparsity, dtype, device).to_sparse_bsr(blocksize)
|
|
sparsity = 1 - bsr._nnz() * blocksize[0] * blocksize[1] / (M * K)
|
|
input = make_tensor(K, N, dtype=dtype, device=device)
|
|
dense = make_tensor(K, N, dtype=dtype, device=device)
|
|
version_dtype = dtype
|
|
if out_dtype is None:
|
|
out = None
|
|
else:
|
|
out = input.new_empty(input.shape, dtype=out_dtype)
|
|
if dtype is not out_dtype:
|
|
version_dtype = (dtype, out_dtype)
|
|
|
|
if op in {'bsr_dense_addmm', '_int_bsr_dense_addmm'}:
|
|
args = (input, bsr, dense)
|
|
|
|
def get_current_meta():
|
|
version = (0, version_dtype, sparsity)
|
|
meta_key = (M, K, N, *blocksize, False, True, True)
|
|
return get_meta(op, meta_key, version=version, exact=True)
|
|
else:
|
|
raise NotImplementedError(op)
|
|
|
|
self.assertEqual(get_current_meta(), None)
|
|
|
|
meta = tuner(*args, **dict(store=True, verbose=False, out=out))
|
|
self.assertEqual(get_current_meta(), meta)
|
|
|
|
expected = operation(*args, **dict(out=None if out_dtype is None else out.clone()))
|
|
result = operation(*args, **dict(meta=meta, out=out))
|
|
self.assertEqual(result, expected)
|
|
|
|
@onlyCUDA
|
|
@unittest.skipIf(IS_FBCODE and IS_REMOTE_GPU, "Test requires Triton")
|
|
def test_triton_bsr_dense_addmm_meta(self, device):
|
|
from torch.sparse._triton_ops import bsr_dense_addmm_meta
|
|
from torch.sparse._triton_ops_meta import update as update_bsr_dense_addmm_meta
|
|
|
|
dtype = torch.float32
|
|
Ms = Ks = 16
|
|
beta = 0.0
|
|
alpha = 1.0
|
|
|
|
def get_meta(M, K, N, sparsity=None):
|
|
return bsr_dense_addmm_meta(M, K, N, Ms, Ks, beta, alpha, dtype=dtype, sparsity=sparsity,
|
|
_version="test_triton_bsr_dense_addmm_meta")
|
|
|
|
def update_meta(M, K, N, value, sparsity=0.5):
|
|
key = (M, K, N, Ms, Ks, beta == 0, beta == 1, alpha == 1)
|
|
update_bsr_dense_addmm_meta("bsr_dense_addmm", torch.cuda.get_device_name(),
|
|
("test_triton_bsr_dense_addmm_meta", dtype, sparsity),
|
|
key, value)
|
|
|
|
def get_meta_with_checks(M, K, N, warn_count=0, sparsity=None):
|
|
f = io.StringIO()
|
|
with redirect_stderr(f):
|
|
result = get_meta(M, K, N, sparsity=sparsity)
|
|
msg = f.getvalue()
|
|
FileCheck().check_count(
|
|
str=f"UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M={M} K={K} N={N}",
|
|
count=warn_count, exactly=True
|
|
).run(msg)
|
|
return result
|
|
|
|
# Test warn_once when requesting non-existing tuned parameters multiple times
|
|
f = io.StringIO()
|
|
with redirect_stderr(f):
|
|
for i in range(5):
|
|
get_meta(16, 16, 16)
|
|
for i in range(5):
|
|
get_meta(16, 16, 32)
|
|
|
|
msg = f.getvalue()
|
|
FileCheck().check_count(
|
|
str="UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=16 K=16 N=16", count=1, exactly=True
|
|
).run(msg)
|
|
FileCheck().check_count(
|
|
str="UserWarning: bsr_dense_addmm uses non-optimal triton kernel parameters for M=16 K=16 N=32", count=1, exactly=True
|
|
).run(msg)
|
|
|
|
# Test warn_once when tuned parameters are missing
|
|
default_meta = dict(GROUP_SIZE_ROW=4, SPLIT_N=2, num_stages=1, num_warps=4)
|
|
self.assertEqual(get_meta_with_checks(32, 32, 32, warn_count=1), default_meta)
|
|
|
|
# Test (no)warn_once when tuned parameters are available
|
|
update_meta(32, 32, 48, (2, 8, 5, 6))
|
|
expected_meta = dict(GROUP_SIZE_ROW=2, SPLIT_N=8, num_stages=5, num_warps=6)
|
|
self.assertEqual(get_meta_with_checks(32, 32, 48, warn_count=0), expected_meta)
|
|
|
|
# Test non-existing tuned parameters with non-default sparsity
|
|
# while for default sparsity 0.5 the parameters are available
|
|
self.assertEqual(get_meta_with_checks(32, 32, 48, warn_count=0, sparsity=0.6), expected_meta)
|
|
|
|
# Test non-existing tuned parameters while there exists
|
|
# parameters with consistent N // SPLIT_N ratio:
|
|
self.assertEqual(get_meta_with_checks(32, 32, 72, warn_count=0),
|
|
dict(GROUP_SIZE_ROW=2, SPLIT_N=12, num_stages=5, num_warps=6))
|
|
# ... or not:
|
|
self.assertEqual(get_meta_with_checks(32, 32, 64, warn_count=1),
|
|
dict(GROUP_SIZE_ROW=4, SPLIT_N=4, num_stages=1, num_warps=4))
|
|
|
|
|
|
# e.g., TestSparseCSRCPU and TestSparseCSRCUDA
|
|
instantiate_device_type_tests(TestSparseCSR, globals())
|
|
instantiate_device_type_tests(TestSparseCompressed, globals())
|
|
instantiate_device_type_tests(TestSparseCompressedTritonKernels, globals())
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|