v4.3 update. (#2709)

* v4.3 update.

* Update the cute_dsl_api changelog's doc link

* Update version to 4.3.0

* Update the example link

* Update doc to encourage user to install DSL from requirements.txt

---------

Co-authored-by: Larry Wu <larwu@nvidia.com>

examples/python/CuTeDSL/utils/test_sparse_utils.py

@@ -0,0 +1,104 @@
+import sparse_utils as su
+import cutlass
+import torch
+from cutlass.cute.runtime import from_dlpack
+import numpy as np
+import pytest
+
+
+@pytest.mark.L0
+def test_sparse_cpu():
+    M = 128
+    N = 32
+    K = 32
+    L = 1
+    debug = False
+    # generate sparse tensor
+    a = torch.empty(M, K).random_(-5, 5).to(torch.float16)
+    sparse_utils = su.SparseUtils(M, K, L, cutlass.Float16)
+    if debug:
+        sparse_utils.use_specific_meta_data()
+    a_gen_from_cpu = sparse_utils.generate_sparse_4_2_tensor_with_tensor(a, True)
+    # print(a_gen_from_cpu)
+    # generate compressed tensor and meta data
+    a_compressed_cpu = torch.empty(M, K // 2).to(torch.float16)
+    meta_data_cpu = torch.empty(M, K // 4 // 8).to(torch.uint32)
+    compressor = su.Compressor(M, K, L)
+    compressor.compress(a_gen_from_cpu, a_compressed_cpu, meta_data_cpu, True)
+    # # test with gemm
+    b = torch.empty(N, K).random_(-5, 5).to(torch.float16).cuda()
+    d = torch.empty(M, N).zero_().to(torch.float16).cuda()
+    b_tensor = from_dlpack(b)
+    d_tensor = from_dlpack(d)
+    a_compressed_cpu_tensor = from_dlpack(a_compressed_cpu.cuda())
+    meta_data_cpu_tensor = from_dlpack(meta_data_cpu.cuda())
+    sparse_emulation = su.SparseEmulation(M, N, K, 1)
+    sparse_emulation(a_compressed_cpu_tensor, b_tensor, d_tensor, meta_data_cpu_tensor)
+
+    ref = torch.einsum("mk,nk->mn", a_gen_from_cpu.cpu(), b.cpu())
+    if debug:
+        a_ori = a_gen_from_cpu.cpu().numpy()
+        np.savetxt("a.txt", a_ori, fmt="%f")
+        a_compressed_cpu_ori = a_compressed_cpu.cpu().numpy()
+        np.savetxt("a_compressed_cpu.txt", a_compressed_cpu_ori, fmt="%f")
+        meta_data_cpu_ori = meta_data_cpu.cpu().numpy()
+        np.savetxt("meta_data_cpu.txt", meta_data_cpu_ori, fmt="%f")
+        d_ori = d.cpu().numpy()
+        np.savetxt("d.txt", d_ori, fmt="%f")
+        ref_ori = ref.cpu().numpy()
+        np.savetxt("ref.txt", ref_ori, fmt="%f")
+    torch.testing.assert_close(d.cpu(), ref)
+    print("cpu d == ref")
+
+
+@pytest.mark.L0
+def test_sparse_cuda():
+    M = 128
+    N = 32
+    K = 32
+    L = 1
+    debug = False
+    sparse_utils = su.SparseUtils(M, K, L, cutlass.Float16)
+    if debug:
+        sparse_utils.use_specific_meta_data()
+    # generate sparse tensor
+    a = torch.empty(M, K).random_(-5, 5).to(torch.float16).cuda()
+    a_gen_from_cuda = sparse_utils.generate_4_2_sparse_tensor(False)
+    # print(a_gen_from_cuda)
+    # generate compressed tensor and meta data
+    a_compressed_cuda = torch.empty(M, K // 2).to(torch.float16).cuda()
+    meta_data_cuda = torch.empty(M, K // 4 // 8).to(torch.uint32).cuda()
+    compressor = su.Compressor(M, K, L)
+    compressor.compress(a_gen_from_cuda, a_compressed_cuda, meta_data_cuda, False)
+    # test with gemm
+    b = torch.empty(N, K).random_(-5, 5).to(torch.float16).cuda()
+    d = torch.empty(M, N).zero_().to(torch.float16).cuda()
+    b_tensor = from_dlpack(b)
+    d_tensor = from_dlpack(d)
+    a_compressed_cuda_tensor = from_dlpack(a_compressed_cuda)
+    meta_data_cuda_tensor = from_dlpack(meta_data_cuda)
+    sparse_emulation = su.SparseEmulation(M, N, K, 1)
+    sparse_emulation(
+        a_compressed_cuda_tensor, b_tensor, d_tensor, meta_data_cuda_tensor
+    )
+
+    ref = torch.einsum("mk,nk->mn", a_gen_from_cuda.cpu(), b.cpu())
+    if debug:
+        a_ori = a_gen_from_cuda.cpu().numpy()
+        np.savetxt("a.txt", a_ori, fmt="%f")
+        a_compressed_cuda_ori = a_compressed_cuda.cpu().numpy()
+        np.savetxt("a_compressed_cuda.txt", a_compressed_cuda_ori, fmt="%f")
+        meta_data_cuda_ori = meta_data_cuda.cpu().numpy()
+        np.savetxt("meta_data_cuda.txt", meta_data_cuda_ori, fmt="%f")
+        d_ori = d.cpu().numpy()
+        np.savetxt("d.txt", d_ori, fmt="%f")
+        ref_ori = ref.cpu().numpy()
+        np.savetxt("ref.txt", ref_ori, fmt="%f")
+    torch.testing.assert_close(d.cpu(), ref)
+    print("cuda d == ref")
+
+
+if __name__ == "__main__":
+    cutlass.cuda.initialize_cuda_context()
+    test_sparse_cpu()
+    test_sparse_cuda()