wpan11nv commented on a change in pull request #5498: URL: https://github.com/apache/incubator-tvm/pull/5498#discussion_r419054308
########## File path: tests/python/integration/test_reduce.py ########## @@ -338,6 +338,102 @@ def check_target(device): check_target("cuda") check_target("vulkan") +def test_warp_reduction1(): + m = 32 + n = 128 + A = te.placeholder((m, n), name='A') + k = te.reduce_axis((0, n)) + B = te.compute((m,), lambda i: te.max(A[i][k], axis=k), name='B') + + nthx = 32 + nthy = 4 + block_x = te.thread_axis("blockIdx.x") + thread_x = te.thread_axis((0, nthx), "threadIdx.x") + thread_y = te.thread_axis((0, nthy), "threadIdx.y") + s = te.create_schedule(B.op) + + def check_target(device): + ctx = tvm.context(device, 0) + if not ctx.exist: + print("skip because %s is not enabled.." % device) + return + + # schedule + k = s[B].op.reduce_axis[0] + ko, _ = s[B].split(k, nparts=nthx) + s[B].bind(ko, thread_x) + xo, xi = s[B].split(s[B].op.axis[0], factor=nthy) + s[B].bind(xi, thread_y) + s[B].bind(xo, block_x) + + # validation. + func = tvm.build(s, [A, B], "cuda", name="warp_reduction") + a_np = np.random.uniform(size=(m,n)).astype(A.dtype) + b_np = np.zeros((m,), dtype=A.dtype) + a = tvm.nd.array(a_np, ctx) + b = tvm.nd.array(b_np, ctx) + b_np = np.max(a_np, axis=1) + func(a, b) + tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-3, atol=1e-3) + + check_target("cuda") + +def test_warp_reduction2(): + def fcombine(x, y): + return x[0] + y[0], x[1] * y[1] + + def fidentity(t0, t1): + return tvm.tir.const(0, t0), tvm.tir.const(1, t1) + + add_mul_reducer = te.comm_reducer(fcombine, fidentity, name='add_mul_reducer') + + # compute + m = 16 + n = 256 + A0 = te.placeholder((m, n), name='A0', dtype='float32') + A1 = te.placeholder((m, n), name='Al', dtype='float32') + k = te.reduce_axis((0, n), 'k') + T0, T1 = te.compute((m, ), lambda i: \ + add_mul_reducer((A0[i, k], A1[i, k]), axis=k), name='T') + + nthdx, nthdy = 32, 2 + block_x = te.thread_axis("blockIdx.x") + thread_x = te.thread_axis((0, nthdx), "threadIdx.x") + thread_y = te.thread_axis((0, nthdy), "threadIdx.y") + + def check_target(device): + ctx = tvm.context(device, 0) + if not ctx.exist: Review comment: Just updated the patch.. I do not see HW hooks in codegen. So I move the logic to nvcc compiler. A macro remapping is used. Now no python checks are needed. Also added a mask argument . Not in use though. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org