| # Owner(s): ["module: sparse"] |
| import itertools |
| import random |
| import unittest |
| |
| import torch |
| from torch import nn |
| |
| from torch.sparse.semi_structured import ( |
| _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG, |
| SparseSemiStructuredTensor, |
| to_sparse_semi_structured, |
| ) |
| |
| from torch.testing import make_tensor |
| |
| from torch.testing._internal.common_device_type import ( |
| dtypes, |
| instantiate_device_type_tests, |
| ) |
| |
| from torch.testing._internal.common_dtype import all_types_and_complex |
| import torch._dynamo.test_case |
| |
| from torch.testing._internal.common_utils import ( |
| parametrize, |
| run_tests, |
| subtest, |
| TestCase, |
| TEST_WITH_ROCM, |
| IS_WINDOWS, |
| ) |
| |
| from torch.utils._triton import has_triton |
| |
| CUSPARSELT_NUM_ALG_IDS = 4 |
| |
| SEMI_STRUCTURED_SUPPORTED_DTYPES = _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG.keys() |
| SEMI_STRUCTURED_SUPPORTED_BACKENDS = [] |
| |
| _IS_SM8X = False |
| if torch.cuda.is_available(): |
| _IS_SM8X = torch.cuda.get_device_capability(0)[0] == 8 |
| SEMI_STRUCTURED_SUPPORTED_BACKENDS.append("cutlass") |
| |
| # check if cslt is available for now using this: |
| # TODO when we add cusparselt as a backend, we can update this to be use torch.cusparselt.is_available() |
| try: |
| torch._cslt_compress(torch.ones(128, 256).cuda()) |
| SEMI_STRUCTURED_SUPPORTED_BACKENDS.append("cusparselt") |
| except Exception: |
| pass |
| |
| |
| |
| def rand_sparse_semi_structured_mask( |
| r, c, dtype=torch.float16, device="cuda", choice=None |
| ): |
| """ |
| This function returns a 1:2 sparse matrix of size (r, c). |
| Note that this means this matrix will also be 2:4 and 4:8 sparse as well. |
| """ |
| |
| choices = [[0, 1], [1, 0]] |
| mask_entries = [choice or random.choice(choices) for i in range(r * c // 2)] |
| |
| return ( |
| torch.tensor(mask_entries, dtype=dtype, device=device) |
| .reshape(r, c) |
| .contiguous() |
| ) |
| |
| def rand_sparse_semi_structured(r, c, dtype, device, pattern='2by4', choice=None): |
| if pattern == '2by4': |
| choices = [ |
| [1, 1, 0, 0], |
| [1, 0, 1, 0], |
| [1, 0, 0, 1], |
| [0, 1, 1, 0], |
| [0, 1, 0, 1], |
| [0, 0, 1, 1] |
| ] |
| mask_entries = [choice or random.choice(choices) for i in range(r * c // 4)] |
| elif pattern == '1by2': |
| choices = [ |
| [0, 1], |
| [1, 0] |
| ] |
| mask_entries = [choice or random.choice(choices) for i in range(r * c // 2)] |
| else: |
| assert(false) |
| mask = torch.tensor(mask_entries, dtype=torch.bool).view(r, c).to(device) |
| dense = make_tensor(r, c, dtype=dtype, device=device) |
| dense[dense == 0] = 1 # To prevent zeros except where mask applied. |
| dense = dense.masked_fill(~mask, 0) |
| return dense |
| |
| def rand_sparse_semi_structured_all_patterns(r, c, dtype, device, pattern='2by4'): |
| if pattern == '2by4': |
| choices = [ |
| [[0, 0, 0, 0], [0, 0, 1, 1]], |
| [[0, 0, 0, 1], [0, 0, 1, 1]], |
| [[0, 0, 1, 0], [0, 0, 1, 1]], |
| [[0, 0, 1, 1], [0, 0, 1, 1]], |
| [[0, 1, 0, 0], [0, 1, 0, 1]], |
| [[0, 1, 0, 1], [0, 1, 0, 1]], |
| [[0, 1, 1, 0], [0, 1, 1, 0]], |
| [[0, 1, 1, 1], [0, 1, 1, 0]], |
| [[1, 0, 0, 0], [1, 0, 0, 1]], |
| [[1, 0, 0, 1], [1, 0, 0, 1]], |
| [[1, 0, 1, 0], [1, 0, 1, 0]], |
| [[1, 0, 1, 1], [1, 0, 1, 0]], |
| [[1, 1, 0, 0], [1, 1, 0, 0]], |
| [[1, 1, 0, 1], [1, 1, 0, 0]], |
| [[1, 1, 1, 0], [1, 0, 1, 0]], |
| [[1, 1, 1, 1], [1, 0, 1, 0]], |
| ] |
| mask_rows = [random.randint(0, len(choices) - 1) for i in range(r * c // 4)] |
| else: |
| assert(false) |
| COL_INV, COL_VAL = 0, 1 |
| mask_entries_inv = [choices[i][COL_INV] for i in mask_rows] |
| mask_entries_val = [choices[i][COL_VAL] for i in mask_rows] |
| mask_inv = torch.tensor(mask_entries_inv, dtype=torch.bool).view(r, c).to(device) |
| mask_val = torch.tensor(mask_entries_val, dtype=torch.bool).view(r, c).to(device) |
| dense = make_tensor(r, c, dtype=dtype, device=device) |
| dense[dense == 0] = 1 # To prevent zeros except where mask below applied. |
| dense_inv = dense.masked_fill(~mask_inv, 0) |
| dense_val = dense_inv.masked_fill(~mask_val, 0) |
| return dense_inv, dense_val |
| |
| |
| class SparseSemiStructuredTensorCompileTest(torch._dynamo.test_case.TestCase): |
| |
| def setUp(self): |
| if not _IS_SM8X: |
| self.skipTest('Only runs on SM80') |
| super().setUp() |
| |
| def tearDown(self): |
| super().tearDown() |
| |
| @staticmethod |
| def _test_mlp_contiguous_relu_compile(backend, dense_input_shape): |
| """ |
| Test nn.Linear + .contiguous() + nn.ReLU with SparseSemiStructuredTensor + torch.compile |
| We expect: |
| (1) The sparse tensor subclass should turn nn.Linear into `aten._structured_sparse_linear` + `aten.contiguous()` |
| (2) Inductor should fuse the .contiguous() call into the relu |
| """ |
| |
| class Model(nn.Module): |
| def __init__(self): |
| super().__init__() |
| self.linear = nn.Linear(128, 128) |
| |
| def forward(self, x): |
| x = self.linear(x) |
| x = x.contiguous() |
| return torch.nn.functional.relu(x) |
| |
| SparseSemiStructuredTensor._FORCE_CUTLASS = backend == "cutlass" |
| |
| input = torch.rand(dense_input_shape, device="cuda").half() |
| model = Model().eval().cuda().half() |
| mod_linear = model.linear |
| m, n = mod_linear.weight.shape |
| mask = torch.Tensor([1, 0, 0, 1]).tile((m, n // 4)).bool().cuda() |
| # set masked weight |
| mod_linear.weight = nn.Parameter(mod_linear.weight * mask) |
| |
| dense_result = model(input) |
| mod_linear.weight = nn.Parameter(to_sparse_semi_structured(mod_linear.weight)) |
| sparse_result = model(input) |
| |
| model = torch.compile(model, backend="inductor", fullgraph=True) |
| sparse_compile_result = model(input) |
| |
| # test that sparse_compile_result and dense_result are numerically close |
| assert torch.allclose(dense_result, sparse_compile_result, rtol=1e-3, atol=1e-3) |
| # assert sparse and sparse_compile have the same strides, |
| # as meta registrations may return contiguous tensors when the output is transposed |
| # https://github.com/pytorch/pytorch/pull/114477 |
| assert sparse_result.stride() == sparse_compile_result.stride() |
| |
| @unittest.skipIf(IS_WINDOWS, "torch.compile not supported on windows") |
| @unittest.skipIf("cusparselt" not in SEMI_STRUCTURED_SUPPORTED_BACKENDS, "cusparselt not supported on this machine") |
| def test_mlp_contiguous_relu_compile_cusparselt(self): |
| """ |
| test for cuSPASRELt meta registrations (_cslt_sparse_mm) + torch.compile |
| """ |
| for dense_input_shape in [(1, 128), (64, 128), (128, 128), (64, 128, 128)]: |
| SparseSemiStructuredTensorCompileTest._test_mlp_contiguous_relu_compile("cusparselt", dense_input_shape) |
| |
| @unittest.skipIf(IS_WINDOWS, "torch.compile not supported on windows") |
| def test_mlp_contiguous_relu_compile_cutlass(self): |
| """ |
| test for CUTLASS meta registrations (_sparse_semi_structured_linear) + torch.compile |
| """ |
| for dense_input_shape in [(1, 128), (64, 128), (128, 128), (64, 128, 128)]: |
| SparseSemiStructuredTensorCompileTest._test_mlp_contiguous_relu_compile("cutlass", dense_input_shape) |
| |
| |
| class TestSparseSemiStructured(TestCase): |
| |
| def setUp(self): |
| if not _IS_SM8X: |
| self.skipTest('Only runs on SM80') |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_to_sparse_semi_structured(self, dtype, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| |
| A = rand_sparse_semi_structured_mask(128, 256, dtype=dtype) |
| A_sparse = to_sparse_semi_structured(A) |
| |
| assert A.shape == A_sparse.shape |
| assert A.device == A_sparse.device |
| assert A.dtype == A_sparse.dtype |
| |
| assert isinstance(A, torch.Tensor) |
| assert isinstance(A_sparse, SparseSemiStructuredTensor) |
| |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("dense_input_shape", [(128, 1), (128, 64), (128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mm_sparse_first_NN(self, dense_input_shape, dtype, device, backend): |
| """ |
| Ensure torch.mm(A_sparse, B) is correct for float16 and will throw error for int8 |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| |
| A = rand_sparse_semi_structured_mask(256, 128, dtype=dtype) |
| A_sparse = to_sparse_semi_structured(A) |
| |
| B = torch.rand(dense_input_shape, device=A_sparse.device).to(dtype) |
| |
| # Currently we don't support int matmul on GPU, so evaluate on CPU and copy over |
| if dtype is torch.int8: |
| # This should fail |
| if backend == "cutlass": |
| with self.assertRaisesRegex(RuntimeError, "two_four_sgemm_cutlass_dispatch_layouts"): |
| sparse_result = torch.mm(A_sparse, B) |
| else: |
| with self.assertRaisesRegex(RuntimeError, |
| "CUDA error: operation not supported when calling `cusparseLtMatmulDescriptorInit"): |
| sparse_result = torch.mm(A_sparse, B) |
| else: |
| dense_result = torch.mm(A, B) |
| sparse_result = torch.mm(A_sparse, B) |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mm_sparse_first_NT(self, dense_input_shape, dtype, device, backend): |
| """ |
| Ensure torch.mm(A_sparse, B.t()) is correct for float16/bfloat16 |
| and will throw an error for int8 + padding |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| |
| A = rand_sparse_semi_structured_mask(256, 128, dtype=dtype) |
| A_sparse = to_sparse_semi_structured(A) |
| |
| B = torch.rand(dense_input_shape, device=A_sparse.device).to(dtype) |
| |
| # Currently we don't support int matmul on GPU, so evaluate on CPU and copy over |
| if dtype is torch.int8 and dense_input_shape in {(1, 128)}: |
| # padding with int8 throws an error because transposing B yields a contiguous output |
| # and row-row 2:4 sparse @ dense with NN is not supported by cuSPARSELt or CUTLASS. |
| if backend == "cutlass": |
| with self.assertRaisesRegex(RuntimeError, "two_four_sgemm_cutlass_dispatch_layouts"): |
| sparse_result = torch.mm(A_sparse, B.t()) |
| else: |
| with self.assertRaisesRegex(RuntimeError, |
| "CUDA error: operation not supported when calling `cusparseLtMatmulDescriptorInit"): |
| sparse_result = torch.mm(A_sparse, B.t()) |
| elif dtype is torch.int8: |
| # test transpose |
| # NOTE: CUTLASS and cuSPARSELt have slightly different int8 behavior. |
| # CUTLASS will output to an int32 tensor while cuSPARSELt will output to a int8 tensor |
| dense_result = torch.mm(A.cpu(), B.t().cpu()).to(device, dtype=torch.int32 if backend == "cutlass" else torch.int8) |
| sparse_result = torch.mm(A_sparse, B.t()) |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| else: |
| # test transpose |
| dense_result = torch.mm(A, B.t()) |
| sparse_result = torch.mm(A_sparse, B.t()) |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mm_sparse_first_TN(self, dtype, dense_input_shape, device, backend): |
| """ |
| Ensure torch.mm(A_sparse.t(), B) throws error |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = rand_sparse_semi_structured_mask(128, 256, dtype=dtype) |
| A_sparse = to_sparse_semi_structured(A) |
| |
| B = torch.rand(dense_input_shape, device=A_sparse.device).to(dtype) |
| |
| with self.assertRaisesRegex( |
| NotImplementedError, |
| r"arg0: SparseSemiStructuredTensor\(.*transposed=True", |
| ): |
| torch.mm(A_sparse.t(), B) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mm_sparse_second_NT(self, dense_input_shape, dtype, device, backend): |
| """ |
| Ensure torch.mm(A, B_sparse.t()) is correct |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| B = rand_sparse_semi_structured_mask(256, 128, dtype=dtype) |
| B_sparse = to_sparse_semi_structured(B) |
| |
| A = torch.rand(dense_input_shape, device=B_sparse.device).to(dtype) |
| |
| # Currently we don't support int matmul on GPU, so evaluate on CPU and copy over |
| if dtype is torch.int8: |
| dense_result = torch.mm(A.cpu(), B.t().cpu()).to(device, dtype=torch.int32 if backend == "cutlass" else torch.int8) |
| sparse_result = torch.mm(A, B_sparse.t()) |
| else: |
| dense_result = torch.mm(A, B.t()) |
| sparse_result = torch.mm(A, B_sparse.t()) |
| |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mm_sparse_second_NN(self, dense_input_shape, dtype, device, backend): |
| """ |
| Ensure torch.mm(A, B_sparse) throws error |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| B = rand_sparse_semi_structured_mask(256, 128, dtype=dtype) |
| B_sparse = to_sparse_semi_structured(B) |
| |
| A = torch.rand(dense_input_shape, device=B_sparse.device).to(dtype) |
| |
| with self.assertRaisesRegex( |
| NotImplementedError, |
| r"arg1: SparseSemiStructuredTensor\(.*transposed=False", |
| ): |
| sparse_result = torch.mm(A, B_sparse) |
| |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128), (64, 128, 128)]) |
| @parametrize("inference_mode", [subtest(True), subtest(False)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_linear(self, dense_input_shape, inference_mode, device, backend): |
| """ |
| Test nn.Linear has the same numerics |
| """ |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| input = torch.rand((dense_input_shape), device=device).half() |
| model = nn.Linear(128, 256).to(device).half() |
| m, n = model.weight.shape |
| mask = rand_sparse_semi_structured_mask(m, n, device=device, dtype=torch.bool) |
| # set masked weight |
| model.weight = nn.Parameter(model.weight * mask) |
| |
| dense_result = model(input) |
| |
| model.weight = nn.Parameter(to_sparse_semi_structured(model.weight)) |
| |
| if inference_mode: |
| with torch.inference_mode(): |
| sparse_result = model(input) |
| else: |
| sparse_result = model(input) |
| |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @parametrize("dense_input_shape", [(1, 128), (64, 128), (128, 128), (64, 128, 128)]) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_mlp(self, device, dense_input_shape, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = backend == "cutlass" |
| input = torch.rand(dense_input_shape, device=device).half() |
| model = ( |
| nn.Sequential( |
| nn.Linear(128, 256), |
| nn.Linear(256, 128), |
| ) |
| .half() |
| .to(device) |
| ) |
| |
| for i in range(2): |
| m, n = model[i].weight.shape |
| mask = rand_sparse_semi_structured_mask( |
| m, n, device=device, dtype=torch.bool |
| ) |
| # set masked weight |
| model[i].weight = nn.Parameter(model[i].weight * mask) |
| |
| dense_result = model(input) |
| |
| for i in range(2): |
| model[i].weight = nn.Parameter(to_sparse_semi_structured(model[i].weight)) |
| |
| sparse_result = model(input) |
| |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_values(self, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = rand_sparse_semi_structured_mask(128, 128) |
| A_sparse = to_sparse_semi_structured(A) |
| assert A_sparse.values().shape == (128, 64) |
| assert (A_sparse.values() == 1).all() |
| |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_indices(self, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = rand_sparse_semi_structured_mask(128, 128) |
| A_sparse = to_sparse_semi_structured(A) |
| assert A_sparse.indices().shape == (128, 8) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_min_sparse_shape(self, dtype, device, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| config = _DTYPE_TO_SEMI_STRUCTURED_SPARSE_CONFIG[dtype] |
| A = rand_sparse_semi_structured_mask(config.sparse_min_rows, config.sparse_min_cols, dtype=dtype, device=device) |
| A_sparse = to_sparse_semi_structured(A) |
| B = torch.rand((config.sparse_min_cols, config.dense_min_cols), device=device).to(dtype) |
| if dtype == torch.int8: |
| dense_res = torch.mm(A.cpu(), B.cpu()).to(device, dtype=torch.int32 if backend == "cutlass" else torch.int8) |
| # int8 sparse matmul not supported for R/R -> R layout, so we transpose one of the arguments to get R/C -> R |
| B_t = B.t().contiguous() |
| sparse_res = torch.mm(A_sparse, B_t.t()) |
| else: |
| dense_res = torch.mm(A, B) |
| sparse_res = torch.mm(A_sparse, B) |
| assert torch.allclose(sparse_res, dense_res, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_unsupported_shape(self, dtype, device, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = rand_sparse_semi_structured_mask(2, 2, dtype=dtype, device=device) |
| with self.assertRaisesRegex(RuntimeError, "Error original_tensor.shape"): |
| A_sparse = to_sparse_semi_structured(A) |
| |
| @dtypes(*all_types_and_complex()) |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_unsupported_dtype(self, dtype, device, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = rand_sparse_semi_structured_mask(128, 128, dtype=dtype, device=device) |
| |
| if dtype not in SEMI_STRUCTURED_SUPPORTED_DTYPES: |
| with self.assertRaisesRegex(RuntimeError, "Error original_tensor.dtype"): |
| A_sparse = to_sparse_semi_structured(A) |
| else: |
| A_sparse = to_sparse_semi_structured(A) |
| |
| @parametrize("backend", SEMI_STRUCTURED_SUPPORTED_BACKENDS) |
| def test_unsupported_dim(self, device, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| A = torch.rand(128, 128, 128, device=device, dtype=torch.float16) |
| |
| with self.assertRaisesRegex(RuntimeError, "Error original_tensor.dim"): |
| A_sparse = to_sparse_semi_structured(A) |
| |
| @unittest.skipIf(TEST_WITH_ROCM, "ROCm doesn't support CUTLASS") |
| @parametrize("backend", ["cutlass"]) |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| def test_linear_cutlass(self, device, dtype, backend): |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| |
| def run_test(batch_shape, m, n, k, device, dtype, dtype_out, add_bias, activation, rtol, atol): |
| pattern = '2by4' if dtype != torch.float32 else '1by2' |
| weight = rand_sparse_semi_structured(m, k, dtype, device, pattern=pattern) |
| input = make_tensor((*batch_shape, n, k), dtype=dtype, device=device) |
| bias = make_tensor((m,), dtype=dtype_out, device=device) if add_bias else None |
| |
| dtype_dense = torch.float32 |
| input_dense = input.to(dtype_dense) |
| weight_dense = weight.to(dtype_dense) |
| bias_dense = bias.to(dtype_dense) if add_bias else None |
| output0 = torch.nn.functional.linear(input_dense, weight_dense, bias=bias_dense) |
| if activation == "relu": |
| relu = torch.nn.ReLU() |
| output0 = relu(output0) |
| elif activation == "silu": |
| silu = torch.nn.SiLU() |
| output0 = silu(output0) |
| |
| compressed = to_sparse_semi_structured(weight) |
| |
| weight_sparse = compressed.values() |
| meta = compressed.indices() |
| |
| output1 = torch._sparse_semi_structured_linear(input, weight_sparse, meta, bias=bias, activation=activation) |
| torch.testing.assert_close(output1.to(dtype_dense), output0, rtol=rtol, atol=atol) |
| |
| if dtype == torch.float32: |
| # Inputs are converted to TF32 internally for sparse GEMM, |
| # so make dense GEMM to do the same for matching results. |
| orig = torch.backends.cuda.matmul.allow_tf32 |
| torch.backends.cuda.matmul.allow_tf32 = True |
| |
| batch_shapes = [[], [3], [3, 1]] |
| dtype_out = {torch.int8: torch.int32, torch.half: torch.half, torch.bfloat16: torch.bfloat16, torch.float32: torch.float32} |
| activations = [None, "relu", "silu"] |
| rtol, atol = 1e-3, 1e-3 |
| if dtype == torch.bfloat16: |
| rtol, atol = 5e-3, 5e-3 |
| elif dtype == torch.float32: |
| rtol, atol = 1e-3, 5e-1 |
| for batch_shape, m, n, k, add_bias, activation in \ |
| itertools.product(batch_shapes, range(3), range(3), range(3), (False, True), activations): |
| if activation == "silu" and dtype == torch.int8: |
| continue # SiLU not supported for integer inputs |
| |
| m = 2 ** m * 32 |
| n = 2 ** n * 32 |
| k = 2 ** k * 128 |
| run_test(batch_shape, m, n, k, device, dtype, dtype_out[dtype], add_bias, activation, rtol, atol) |
| |
| if dtype == torch.float32: |
| torch.backends.cuda.matmul.allow_tf32 = orig |
| |
| |
| @unittest.skipIf(not has_triton(), "Test needs triton and recent GPU arch") |
| @parametrize("backend", ["cutlass"]) |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| def test_conversions(self, device, dtype, backend): |
| if dtype == torch.float32: |
| return |
| |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| |
| def run_test(r, c, device, dtype): |
| pattern = '2by4' if dtype != torch.float32 else '1by2' |
| dense_ref = rand_sparse_semi_structured(r, c, dtype, device, pattern=pattern) |
| |
| compressed = to_sparse_semi_structured(dense_ref) |
| |
| # The torch.ops.aten._to_sparse_semi_structured operator |
| # uses CUTLASS to perform conversion from given dense |
| # matrix to the pair of corresponding sparse and metadata |
| # matrices, with the later used here as a reference to |
| # compare the metadata matrix produced by conversion |
| # performed by SparseSemiStructuredTensor class |
| # constructor against. |
| _, meta_ref = torch.ops.aten._to_sparse_semi_structured(dense_ref) |
| |
| meta = compressed.indices() |
| torch.testing.assert_close(meta, meta_ref, rtol=0, atol=0) |
| |
| dense = compressed.to_dense() |
| torch.testing.assert_close(dense, dense_ref, rtol=0, atol=0) |
| |
| shapes = [[32, 128], [32, 256], [64, 128], [64, 256]] |
| for r, c in shapes: |
| run_test(r, c, device, dtype) |
| |
| @unittest.skipIf(not has_triton(), "Test needs triton and recent GPU arch") |
| @parametrize("backend", ["cutlass"]) |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| def test_conversions_all_patterns(self, device, dtype, backend): |
| if dtype == torch.float32: |
| return |
| |
| SparseSemiStructuredTensor._FORCE_CUTLASS = (backend == "cutlass") |
| r, c = 32, 128 |
| |
| dense_inv, dense_val = rand_sparse_semi_structured_all_patterns(r, c, dtype, device) |
| |
| compressed = to_sparse_semi_structured(dense_inv) |
| dense = compressed.to_dense() |
| |
| torch.testing.assert_close(dense, dense_val, rtol=0, atol=0) |
| |
| class TestCUSPARSELT(TestCase): |
| """ |
| This contains cuSPARSELt specific tests. |
| """ |
| |
| def setUp(self): |
| if not _IS_SM8X: |
| self.skipTest('Only runs on SM80') |
| if "cusparselt" not in SEMI_STRUCTURED_SUPPORTED_BACKENDS: |
| self.skipTest('cuSPARSELt not enabled') |
| else: |
| SparseSemiStructuredTensor._FORCE_CUTLASS = False |
| |
| |
| @parametrize("dense_input_shape", [(128, 128)]) |
| def test_cslt_sparse_mm_int8_in_fp16_out(self, dense_input_shape, device): |
| A = rand_sparse_semi_structured_mask(128, 128, dtype=torch.int8) |
| A_compressed = torch._cslt_compress(A) |
| |
| B = torch.rand(dense_input_shape, device=device).to(torch.int8) |
| |
| dense_result = torch.mm(A.cpu().to(torch.int64), B.t().cpu().to(torch.int64)).to(device, dtype=torch.float16) |
| sparse_result = torch._cslt_sparse_mm(A_compressed, B.t(), out_dtype=torch.float16) |
| assert torch.allclose(dense_result, sparse_result, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(torch.float16, torch.bfloat16) |
| def test_cslt_sparse_mm_alpha(self, dtype, device): |
| A = torch.Tensor([0, 0, 1, 1]).tile((128, 64)).to(dtype).cuda() |
| B = torch.ones((256, 128), device=device).to(dtype) |
| alpha = torch.Tensor([2**(-i) for i in range(128)]).cuda() |
| |
| A_compressed = torch._cslt_compress(A) |
| sparse_result = torch._cslt_sparse_mm(A_compressed, B, alpha=alpha) |
| |
| alpha_scaled = torch.stack([alpha] * 128).t() |
| dense_result = alpha_scaled * torch.mm(A.to(torch.float32), B.to(torch.float32)) |
| dense_result = dense_result.to(dtype) |
| |
| assert torch.allclose(sparse_result, dense_result, rtol=1e-3, atol=1e-3) |
| |
| def test_cslt_sparse_mm_alpha_int8_in_f16_out(self, device): |
| A = torch.Tensor([0, 0, 10, 10]).tile((128, 64)).to(torch.int8).cuda() |
| B = torch.ones((128, 256), device=device).to(torch.int8).t() |
| alpha = torch.Tensor([2**(-i) for i in range(128)]).cuda() |
| |
| A_compressed = torch._cslt_compress(A) |
| sparse_result = torch._cslt_sparse_mm(A_compressed, B, alpha=alpha, out_dtype=torch.float16).cpu() |
| |
| alpha_scaled = torch.stack([alpha] * 128).t() |
| dense_result = alpha_scaled.cpu() * torch.mm(A.to(torch.int32).cpu(), B.to(torch.int32).cpu()) |
| dense_result = dense_result.to(torch.float16) |
| |
| assert torch.allclose(sparse_result, dense_result, rtol=1e-3, atol=1e-3) |
| |
| @parametrize("alg_id", range(CUSPARSELT_NUM_ALG_IDS)) |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| def test_cslt_sparse_mm_alg_id(self, device, dtype, alg_id): |
| # alg_id=3 not supported for float32 dtype |
| if dtype == torch.float32 and alg_id == 3: |
| return |
| A = rand_sparse_semi_structured_mask(128, 128, dtype=dtype) |
| A_compressed = torch._cslt_compress(A) |
| B = torch.ones((128, 128), device=device).to(dtype) |
| |
| A_compressed = torch._cslt_compress(A) |
| sparse_result = torch._cslt_sparse_mm(A_compressed, B.t(), alg_id=alg_id) |
| |
| dense_result = torch.mm(A.to(torch.float32), B.to(torch.float32)) |
| dense_result = dense_result.to(dtype) |
| |
| assert torch.allclose(sparse_result, dense_result, rtol=1e-3, atol=1e-3) |
| |
| @dtypes(*SEMI_STRUCTURED_SUPPORTED_DTYPES) |
| def test_cslt_sparse_mm_search(self, device, dtype): |
| A = rand_sparse_semi_structured_mask(128, 128, dtype=dtype) |
| A_compressed = torch._cslt_compress(A) |
| B = torch.ones((128, 128), device=device).to(dtype) |
| |
| A_compressed = torch._cslt_compress(A) |
| alg_id = torch._cslt_sparse_mm_search(A_compressed, B.t()) |
| # for cuSPARSELt v0.4.0 there is a bug where although there are 5 alg_ids, we run into an error |
| # when setting using the last one (4) |
| # in cuSPARSELt v0.5.0 there are only 4 alg_ids total, so we should remove the +1 here when we update. |
| assert alg_id in range(CUSPARSELT_NUM_ALG_IDS + 1) |
| |
| |
| instantiate_device_type_tests(TestSparseSemiStructured, globals(), only_for="cuda") |
| instantiate_device_type_tests(TestCUSPARSELT, globals(), only_for="cuda") |
| |
| if __name__ == "__main__": |
| run_tests() |
