from typing import Union import numpy as np import unittest from dataclasses import replace from tinygrad.opt.kernel import Opt, OptOps, KernelOptError, Kernel from tinygrad.codegen.gpudims import get_grouped_dims from tinygrad.uop.ops import UOp, Ops, GroupOp, KernelInfo from tinygrad.device import Device, Buffer, is_dtype_supported from tinygrad.shape.shapetracker import ShapeTracker from tinygrad.shape.view import View from tinygrad.tensor import Tensor, _to_np_dtype from tinygrad.engine.realize import run_schedule, lower_schedule, CompiledRunner, get_program from tinygrad.opt.heuristic import hand_coded_optimizations from tinygrad.helpers import prod, Context, getenv, CI, flatten, dedup, AMX, AMD_LLVM from tinygrad.dtype import DType, dtypes def helper_realized_ast(r:Union[Tensor, list[Tensor]]) -> tuple[UOp, list[Buffer]]: if isinstance(r, Tensor): r = [r] s = Tensor.schedule(*r) run_schedule(s[:-1]) # run all kernels except the last one assert s[-1].ast.op is Ops.SINK, f"helper_realized_ast expects a SINK {s[-1]}" # now all input buffers in s[-1] should be realized # create fresh buffers for the outputs bufs = [Buffer((x).device, x.size, x.dtype).allocate() if i < len(s[-1].ast.src) else x for i,x in enumerate(s[-1].bufs)] return s[-1].ast, bufs def helper_tc_allclose(N:int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0, use_tensor_cores:int=1): a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in) np_a, np_b = a.numpy(), b.numpy() r = a.matmul(b, dtype=dtype_out) if dtype_in == dtypes.bfloat16: r = r.float() realized_ast, bufs = helper_realized_ast(r) k = Kernel(realized_ast) k.apply_tensor_cores(use_tensor_cores, axis=axis, tc_select=tc_select, tc_opt=tc_opt) prg = CompiledRunner(replace(k.to_program(), device=Device.DEFAULT)) if use_tensor_cores == 1: assert len([uop for uop in k.uops if uop.op is Ops.WMMA]) > 0, "wmma not triggered" elif use_tensor_cores == 3: assert len([uop for uop in k.uops if uop.op is Ops.DEFINE_LOCAL]) == 2, "local buffers not triggered" assert len([x for x in k.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included" prg.exec(bufs) if dtype_in == dtypes.half: tc_atol, tc_rtol = 1e-2, 1e-3 elif dtype_in == dtypes.bfloat16: tc_atol, tc_rtol = 1e-2, 1e-2 else: tc_atol, tc_rtol = 5e-3, 1e-4 c = bufs[0].numpy().reshape((M,N)) np.testing.assert_allclose(c, np_a @ np_b, atol=tc_atol, rtol=tc_rtol) def helper_tc_ensure_uops_and_opts_count(N: int, M:int, K:int, dtype_in:DType, dtype_out:DType, axis:int=0, tc_select:int=-1, tc_opt:int=0, ensure_triggered:bool=True): a, b = Tensor.rand(M, K, dtype=dtype_in), Tensor.rand(K, N, dtype=dtype_in) r = a.matmul(b, dtype=dtype_out) sched = r.schedule() realized_ast = sched[-1].ast opts_to_apply = [Opt(OptOps.TC, axis, (tc_select, tc_opt, 1))] realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply))) if ensure_triggered: program = get_program(realized_ast, Device[Device.DEFAULT].renderer) wmmas = len([uop for uop in program.uops if uop.op is Ops.WMMA]) tcs = len([x for x in program.applied_opts if x.op is OptOps.TC]) assert wmmas > 0, "tensor core not triggered" assert tcs == 1, "tensor core opt not included" else: try: program = get_program(realized_ast, Device[Device.DEFAULT].renderer) assert False, "OptOps.TC triggered, expected KernelOptError" except KernelOptError: pass class TestLinearizer(unittest.TestCase): def test_arg_dedup(self): # NOTE: this realize exists because Tensor.numpy calls .contiguous() internally # without contiguous folding, rand.to("CPU") and rand.contiguous().to("CPU") are different UOps. # this test asserts they are the identical Buffer # having different buffers is fine for correctness, because the outputs match. a, b = Tensor.randn(4).realize(), Tensor.randn(4).realize() np_a, np_b = a.numpy(), b.numpy() c = ((a.shrink(((0, 2),)) - a.shrink(((2, 4),))) - (b.shrink(((0, 2),)) - b.shrink(((2, 4),)))) lowered = [x[1] for x in lower_schedule(c.schedule())] for ei in lowered: ei.run() rawbufs = lowered[-1].bufs assert len(rawbufs) == 3 and set(rawbufs[1:]) == {a.uop.base.realized, b.uop.base.realized} np_c = (np_a[:2] - np_a[2:]) - (np_b[:2] - np_b[2:]) np.testing.assert_allclose(np_c, c.numpy(), atol=1e-4, rtol=1e-4) def test_load_removed(self): a = Tensor.rand(1).realize() b = Tensor.rand(1).realize() ta = Tensor.where(Tensor(True), a, b).numpy() tb = Tensor.where(Tensor(False), a, b).numpy() np.testing.assert_equal(a.numpy(), ta) np.testing.assert_equal(b.numpy(), tb) def test_multioutput(self): dtype, st = dtypes.int, ShapeTracker.from_shape((8,)) g0, g1, g2, g3 = [UOp(Ops.DEFINE_GLOBAL, dtype.ptr(), arg=i) for i in range(4)] a = UOp(Ops.LOAD, dtype, src=(g2.view(st),)) b = UOp(Ops.LOAD, dtype, src=(g3.view(st),)) out0 = UOp(Ops.STORE, dtypes.void, src=(g0.view(st), a + b)) out1 = UOp(Ops.STORE, dtypes.void, src=(g1.view(st), a * b)) sink = UOp(Ops.SINK, src=(out0, out1)) a_t = Tensor.full(st.shape, 2).contiguous().realize() b_t = Tensor.full(st.shape, 3).contiguous().realize() lin = helper_linearizer_ast(sink, [a_t, b_t], wanna_output=[a_t.numpy()+b_t.numpy(), a_t.numpy()*b_t.numpy()])[0] stores = [u for u in lin.uops if u.op is Ops.STORE] mutable_bufs = dedup(flatten([[x for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL] for u in stores])) assert len(mutable_bufs) == len(stores) == 2 self.assertSetEqual(set([u.arg for u in mutable_bufs]), set([0,1])) def _test_no_nested_ranges(self, lins, skip=None): for l in lins: range_in_acc = flatten([[x for x in u.src if x.op is Ops.RANGE] for u in l.uops if u.op is Ops.DEFINE_REG]) ranges = [u.op for u in l.uops if (u.op is Ops.RANGE and u in range_in_acc) or (u.op is Ops.ENDRANGE and u.src[0] in range_in_acc)] for i,u in enumerate(ranges): if skip and i in skip: continue assert ranges[i-1] != u, f"multireduce nested the ranges! {ranges[i-1], {u}}" @unittest.expectedFailure def test_const_alu_indexing(self): st = ShapeTracker.from_shape((4,)).to_uop() load = UOp.load(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=1, src=()), st, dtype=dtypes.float) op = load+UOp.const(dtypes.float, 1.0)*UOp.const(dtypes.float, -1) store = UOp.store(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0, src=()), st, op) Tensor.manual_seed(0) x = Tensor.randn(4,).realize() helper_linearizer_ast(store.sink(), [x], wanna_output=[x.numpy()+1*-1], opts=[]) # shapeless CONST in AST is not supported @unittest.expectedFailure def test_const_alu_indexing_one_const_fine(self): st = ShapeTracker.from_shape((4,)).to_uop() load = UOp.load(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=1, src=()), st, dtype=dtypes.float) op = load+UOp.const(dtypes.float, 1.0) store = UOp.store(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0, src=()), st, op) Tensor.manual_seed(0) x = Tensor.randn(4,).realize() helper_linearizer_ast(store.sink(), [x], wanna_output=[x.numpy()+1], opts=[]) @unittest.skipIf(CI and Device.DEFAULT in {"PTX", "AMD", "NV"}, "very slow") def test_indexing_multireduce(self): dataset = Tensor.rand(16384, 256).realize() idxs = Tensor([0,3,5,6]).realize() with Context(FUSE_ARANGE=1): sink = dataset[idxs].contiguous().kernelize().uop.base.src[1].arg.ast real_index = dataset.numpy()[idxs.numpy()].reshape(4, 256, 1, 1) helper_linearizer_ast(sink, [dataset, idxs], wanna_output=[real_index]) def test_two_nested_range(self): a = Tensor.randn(2, ).realize() out = a.reshape(2, 1).expand(2, 3).sum() lin = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)).sum()])[0] ranges = [i for i,u in enumerate(lin.uops) if u.op is Ops.RANGE] assert len(ranges) == 1 # NOTE: it collapses now # RANGE -> LOAD -> RANGE -> ASSIGN #assert any(x.op is Ops.LOAD for x in lin.uops[ranges[0]:ranges[1]]) def test_three_nested_range(self): a = Tensor.randn(2, ).realize() out = a.reshape(2, 1).expand(2, 3).expand(2, 2, 3).sum() lin = helper_linearizer_opt(out, wanna_output=[np.broadcast_to(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)), (2, 2, 3)).sum()])[0] ranges = [i for i,u in enumerate(lin.uops) if u.op is Ops.RANGE] assert len(ranges) == 1 # NOTE: it collapses now # RANGE -> RANGE -> LOAD -> RANGE -> ASSIGN # NOTE: nothing should toposort between the first two ranges #assert ranges[0]+1 == ranges[1] #assert any(x.op is Ops.LOAD for x in lin.uops[ranges[1]:ranges[2]]) def test_two_nested_range_alt_indexing(self): a = Tensor([2, 2]).realize() out = a.reshape(2, 1).pad(((1, 1), (1, 1)), value=2).sum() lin = helper_linearizer_opt(out, wanna_output=[24])[0] ranges = [i for i,u in enumerate(lin.uops) if u.op is Ops.RANGE] # RANGE -> ALU -> RANGE -> ALU + LOAD -> ASSIGN assert any(x.op in GroupOp.ALU for x in lin.uops[ranges[0]:ranges[1]]) assert not any(x.op is Ops.LOAD for x in lin.uops[ranges[0]:ranges[1]]) assert any(x.op in {*GroupOp.ALU, Ops.LOAD} for x in lin.uops[ranges[1]:]) def test_range_outer_op_before_phi(self): a = Tensor.randn(4, 1).realize() b = Tensor.randn(1, 1).realize() out = (a + b[0]).sum() + b[0] lin = helper_linearizer_opt(out, wanna_output=[(a.numpy()+b.numpy()[0]).sum()+b.numpy()])[0] ranges = [i for i,u in enumerate(lin.uops) if u.op is Ops.RANGE] # LOAD -> RANGE -> LOAD -> ASSIGN assert len([x for x in lin.uops[:ranges[0]] if x.op is Ops.LOAD]) == 1 def test_range_outer_op_before_phi_nested_range(self): a = Tensor.randn(2, ).realize() b = Tensor.randn(1, 1).realize() out = (a.reshape(2, 1).expand(2, 3) + b[0]).sum() + b[0] lin = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3)) + b.numpy()[0]).sum() + b.numpy()])[0] ranges = [i for i,u in enumerate(lin.uops) if u.op is Ops.RANGE] assert len(ranges) == 1 # NOTE: it collapses now #if getenv("PTX"): # LOAD -> RANGE -> CAST -> ALU -> ALU -> LOAD -> ALU -> RANGE -> ALU -> ASSIGN # assert lin.uops[ranges[0]-2].op is Ops.LOAD # assert ranges[1] == ranges[0]+6 # assert [x.op for x in lin.uops[ranges[1]-2:ranges[1]]] == [Ops.LOAD, Ops.ALU] # LOAD -> RANGE -> LOAD -> ALU -> RANGE -> ASSIGN #else: # assert lin.uops[ranges[0]-2].op is Ops.LOAD # assert ranges[1] == ranges[0]+3 # assert [x.op for x in lin.uops[ranges[1]-2:ranges[1]]] == [Ops.LOAD, Ops.ALU] def test_range_outer_op_after_phi(self): a = Tensor.randn(4, 1).realize() out = a.sum() * a.sum() lin = helper_linearizer_opt(out, wanna_output=[a.numpy().sum()*a.numpy().sum()])[0] # RANGE -> LOAD -> ASSIGN -> ALU end = max(i for i,u in enumerate(lin.uops) if u.op is Ops.ENDRANGE) # the INDEX can be first assert lin.uops[end+1].op in GroupOp.ALU or lin.uops[end+2].op in GroupOp.ALU def test_range_outer_op_after_phi_nested_range(self): a = Tensor.randn(2, ).realize() out = a.reshape(2, 1).expand(2, 3).sum() + a.reshape(2, 1).expand(2, 3).sum() lin = helper_linearizer_opt(out, wanna_output=[(np.broadcast_to(a.numpy().reshape(2, 1), (2, 3))).sum()*2])[0] # RANGE -> LOAD -> ASSIGN -> ALU end = max(i for i,u in enumerate(lin.uops) if u.op is Ops.ENDRANGE) # the INDEX can be first assert lin.uops[end+1].op in GroupOp.ALU or lin.uops[end+2].op in GroupOp.ALU def test_load_dedup(self): # for different leaves in the AST, the same loads may occur. a = Tensor.randn(4).realize() # these are of size 3 to avoid float4 coalesce r = a[:-1] + a[1:] k = Kernel(r.schedule()[-1].ast) k.upcast() k.linearize() num_loads = len([uop for uop in k.uops if uop.op is Ops.LOAD]) assert num_loads <= 4, "more load uops than needed" assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?" def test_upcast_cse(self): # when upcasting, within a subtree, there may be common expressions. a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize() r = a.expand([2]) + b.expand([2]) k = Kernel(r.schedule()[-1].ast) k.upcast() k.linearize() num_ops = len([uop for uop in k.uops if uop.op in GroupOp.ALU]) assert num_ops <= 1, "more alu uops than needed" @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_reduce_upcast(self): x, w = Tensor.randn((1,1,3)).realize(), Tensor.randn((1,1,2)).realize() r = Tensor.conv2d(x,w,padding=1).relu() k = Kernel(r.schedule()[-1].ast) k.upcast() k.upcast() k.linearize() accs = [u for u in k.uops if u.op is Ops.DEFINE_REG] stores = [u for u in k.uops if u.op is Ops.STORE] assert len(accs) == 0 # it's removed now assert len(stores) == 1 assert stores[0].src[-1].dtype == dtypes.float.vec(4) # NOTE: can reenable, it does work. it just makes BEAM slow @unittest.expectedFailure @unittest.skipUnless(Device.DEFAULT == "CPU", "test only for CPU") def test_upcast_with_locals_cpu(self): out = Tensor.ones(64,64).contiguous() @ Tensor.ones(64,64).contiguous() k = Kernel(out.schedule()[-1].ast) k.apply_opt(Opt(OptOps.LOCAL, axis=0, arg=4)) prg = k.to_program() self.assertEqual(len(prg.src.split("for")), 5) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason") def test_upcast_with_locals(self): x, y = Tensor.rand(1,128), Tensor.rand(128, 128) r = (x@y).relu() realized_ast = r.schedule()[-1].ast opts_to_apply = [Opt(op=OptOps.GROUP, axis=0, arg=8), Opt(op=OptOps.LOCAL, axis=0, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)] realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply))) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) stores = [u for u in program.uops if u.op is Ops.STORE] # the first store is to lds and can be upcasted assert stores[0].src[-1].dtype == dtypes.float.vec(4) assert any(x.op is Ops.DEFINE_LOCAL for x in stores[0].toposort()) # the second store is to gds with no upcasts assert stores[1].src[-1].dtype == dtypes.float assert any(x.op is Ops.DEFINE_GLOBAL for x in stores[1].toposort()) def test_zero_fold(self): a, b = Tensor.randn(1).realize(), Tensor.randn(1).realize() r = Tensor.stack(a, b) k = Kernel(r.schedule()[-1].ast) k.upcast() k.linearize() num_ops = len([uop for uop in k.uops if uop.op in GroupOp.ALU]) assert num_ops == 0, "more alu uops than needed" def test_sum_acc_dtype(self): for tensor_dtype, acc_dtype in ( (dtypes.bool, dtypes.int), (dtypes.int16, dtypes.int), (dtypes.float16, dtypes.float), (dtypes.bfloat16, dtypes.float)): if is_dtype_supported(tensor_dtype) and is_dtype_supported(acc_dtype): a = Tensor([1, 2, 3], dtype=tensor_dtype).sum() realized_ast = a.schedule()[-1].ast realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple())) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) local = [uop for uop in program.uops if uop.op is Ops.DEFINE_REG] assert local[0].dtype == acc_dtype def test_arg_acc_dtype(self): def helper_arg_acc_dtype(c: Tensor, expected_dtype:DType): realized_ast = c.schedule()[-1].ast realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple())) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) local = [uop for uop in program.uops if uop.op is Ops.DEFINE_REG] assert local[0].dtype == expected_dtype tests = ( (dtypes.float16, None, dtypes.float), (dtypes.bfloat16, None, dtypes.float), (dtypes.float, None, dtypes.float), (dtypes.float16, dtypes.float16, dtypes.float16), (dtypes.bfloat16, dtypes.bfloat16, dtypes.bfloat16), (dtypes.float, dtypes.float16, dtypes.float16), ) for tensor_dtype, acc_dtype, expected_dtype in tests: if is_dtype_supported(tensor_dtype) and is_dtype_supported(acc_dtype) and is_dtype_supported(expected_dtype): a, b = Tensor.rand(8, 8, dtype=tensor_dtype), Tensor.rand(8, 8, dtype=tensor_dtype) helper_arg_acc_dtype(a.sum(dtype=acc_dtype), expected_dtype) helper_arg_acc_dtype(a.matmul(b, dtype=acc_dtype), expected_dtype) helper_arg_acc_dtype(Tensor.einsum("ki,ij->kj", a, b, dtype=acc_dtype), expected_dtype) d, w = Tensor.rand(4, 8, 8, 8, dtype=tensor_dtype), Tensor.rand(8, 8, 2, 2, dtype=tensor_dtype) helper_arg_acc_dtype(d.conv2d(w, dtype=acc_dtype), expected_dtype) # TODO: don't skip bf16 for real device (METAL, AMD) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue # for AMX, tc.dims[2] == 1 so reduceop is None thus tensor_cores are not triggered helper_tc_allclose(tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2], tc.dtype_in, tc.dtype_out, axis=0, tc_opt=0) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_emulation(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue # for AMX, tc.dims[2] == 1 so reduceop is None thus tensor_cores are not triggered helper_tc_allclose(tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2], tc.dtype_in, tc.dtype_out, axis=0, tc_opt=0, use_tensor_cores=3) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_codegen(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue n, m, k = tc.dims[0], tc.dims[1], 2 if AMX else tc.dims[2] a, b = Tensor.rand(m, k, dtype=tc.dtype_in), Tensor.rand(k, n, dtype=tc.dtype_in) r = a.matmul(b, dtype=tc.dtype_out) sched = r.schedule() realized_ast = sched[-1].ast kernel = Kernel(realized_ast) kernel.apply_tensor_cores(1, axis=0, tc_select=-1, tc_opt=2) prg = kernel.to_program() if Device.DEFAULT == "LLVM": assert "0x201000" in prg.src elif Device.DEFAULT == "AMD" and AMD_LLVM: assert "@llvm.amdgcn.wmma" in prg.src elif Device[Device.DEFAULT].renderer.suffix == "PTX": assert "mma.sync.aligned" in prg.src else: assert "__WMMA_" in prg.src @unittest.skipIf((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "broken for AMD") @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_padded(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2) # AMD compiler bug: AMD miscompiles non-zero padded tc kernels with -O3, producing wrong results, nans or hang (see #9606) # Internal bug: zero-stride dimensions combined with a mask may produce wrong index/valid for pad == 1 on AMD @unittest.skipUnless((Device.DEFAULT == "AMD") or (Device.DEFAULT == "PYTHON" and getenv("EMULATE_AMD")), "test for AMD's tc") @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.expectedFailure def test_tensor_cores_padded_amd(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue helper_tc_allclose(tc.dims[0]+(pad:=1), tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_padded_uops(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: pad = 1 # check that TC is triggered for TC_OPT=2 helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=True) # check that TC is not triggered for TC_OPT<2 helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=1, ensure_triggered=False) helper_tc_ensure_uops_and_opts_count(tc.dims[0]+pad, tc.dims[1]+pad, tc.dims[2]+pad, tc.dtype_in, tc.dtype_out, tc_opt=0, ensure_triggered=False) # check excessive padding doesn't trigger padded TC in TC_OPT=2 helper_tc_ensure_uops_and_opts_count(tc.dims[0]//4, tc.dims[1], tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False) helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1]//4, tc.dims[2], tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False) if not AMX: # AMX tc.dims[2] == 1 helper_tc_ensure_uops_and_opts_count(tc.dims[0], tc.dims[1], tc.dims[2]//4, tc.dtype_in, tc.dtype_out, tc_opt=2, ensure_triggered=False) @unittest.skipIf(CI and Device.DEFAULT in {"AMD"}, "AMD CI is really slow here") @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_multi_reduce(self): for tc in Device[Device.DEFAULT].renderer.tensor_cores: if not is_dtype_supported(tc.dtype_in) or not is_dtype_supported(tc.dtype_out): continue # this will be a M=G16, N=G32, M=G16, M=G16, K=R16, K=R16, K=R16 with 9 choices of TC MNK axes golden_result = None for axis in range(9): a = Tensor.rand(16, 16, 29, 29, dtype=tc.dtype_in).realize() b = Tensor.rand(32, 16, 16, 16, dtype=tc.dtype_in).realize() c = a.conv2d(b, padding=1, dtype=tc.dtype_out) realized_ast, real_bufs = helper_realized_ast(c) opts_to_apply = [Opt(OptOps.TC, axis, (-1, 2, 1))] realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply))) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) assert len([uop for uop in program.uops if uop.op is Ops.WMMA]) > 0, "tensor core not triggered" assert len([x for x in program.applied_opts if x.op is OptOps.TC]) == 1, "tensor core opt not included" prg = CompiledRunner(program) # TODO: support this even if numpy doesn't if _to_np_dtype(real_bufs[0].dtype) is None: continue real_bufs[0].copyin(np.zeros((real_bufs[0].size, ), dtype=_to_np_dtype(real_bufs[0].dtype)).data) # Zero to check that all values are filled prg.exec(real_bufs) result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype)) # ensure the results for each choice of axis matches if golden_result is None: golden_result = np.frombuffer(real_bufs[0].as_buffer(), _to_np_dtype(real_bufs[0].dtype)) np.testing.assert_allclose(result, golden_result, atol=0.1, rtol=0.2) # check that get_kernel_actions produces all 9 options from tinygrad.opt.search import get_kernel_actions tc_actions = [k for i, k in get_kernel_actions(Kernel(realized_ast), False).items() if k.applied_opts[0].op == OptOps.TC] available_tc = len([x for x in Device[Device.DEFAULT].renderer.tensor_cores if x.dtype_in == tc.dtype_in and x.dtype_out == tc.dtype_out]) assert len(tc_actions) == 9 * available_tc, f"should contain 9 possible TC actions for every available TC, got {len(tc_actions)}" @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") def test_tensor_cores_unroll_phi(self): tc = Device[Device.DEFAULT].renderer.tensor_cores[0] x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in) r = x.matmul(y, dtype=tc.dtype_out) k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1] for u in k.uops: if u.op is Ops.WMMA: assert u.src[-1].src[0].op != Ops.ASSIGN @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation") def test_tensor_cores_unroll_casted_phi(self): tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0] x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in) r = x.matmul(y, dtype=tc.dtype_out) k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1] for u in k.uops: if u.op is Ops.WMMA: #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2])) assert u.src[-1].src[0].op != Ops.ASSIGN @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"}, "CPU does not support using a different type for accumulation") def test_tensor_cores_unroll_casted_phi_with_children(self): # all ASSIGN children are outside the loop tc = [tc for tc in Device[Device.DEFAULT].renderer.tensor_cores if tc.dtype_in != tc.dtype_out][0] x, y = Tensor.rand(128, 128, dtype=tc.dtype_in), Tensor.rand(128, 128, dtype=tc.dtype_in) r = x.matmul(y, dtype=tc.dtype_out).relu() k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4)]], apply_tc=True, atol=3e-2, rtol=1e-3)[-1] for u in k.uops: if u.op is Ops.WMMA: #assert u.src[-1].dtype == dtypes.float.vec(prod(tc.thread_local_sizes[2])) assert u.src[-1].src[0].op != Ops.ASSIGN @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_simple_unroll_no_between_phi_dependencies(self): x, y = Tensor.rand(128, 128), Tensor.rand(128, 128) r = (x@y).relu() k = helper_linearizer_opt(r, [[Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4)]])[-1] # the uops graph is RANGE -> DEFINE_ACC -> 4x ALU -> 4x ASSIGN -> ENDRANGE for u in k.uops: if u.op is Ops.ASSIGN: assert u.src[1].op in GroupOp.ALU # children of ASSIGN are placed after ENDRANGE if any(x.op is Ops.ASSIGN for x in u.src): end_range = [i for i, x in enumerate(k.uops) if x.op is Ops.ENDRANGE][0] assert end_range < k.uops.index(u) def test_grouped_dims(self): def _assert_grouped_dims(prefix, dims, max_sizes, reverse_dims, expected_sizes, assert_same_length = True): idxs = get_grouped_dims(prefix, dims, max_sizes, reverse_dims) loop_idxs = dedup(flatten([[y for y in x.toposort() if y.op is Ops.SPECIAL] for x in idxs])) loop_idxs = sorted(loop_idxs, key=lambda uop: uop.arg[0]) sizes = [x.arg[1] for x in loop_idxs] assert len(idxs) == len(dims), f"expected idxs to have same length as dims {len(dims)}, got {len(idxs)}" if assert_same_length: assert len(loop_idxs) == min(len(sizes), len(dims)), f"expected idxs to have length {min(len(sizes), len(dims))}, got {len(loop_idxs)}" assert sizes == expected_sizes, f"expected sizes={expected_sizes}, got {sizes=}" # TODO: add these back after uop symbolic # for i in range(len(dims)): # assert idxs[i].max+1 == dims[i], f"idxs[{i}] should have max {dims[i]-1}" # for i in range(len(loop_idxs)): # assert loop_idxs[i].expr.startswith(prefix), f"loop_idxs[{i}] must start with {prefix}" # assert loop_idxs[i].max+1 == sizes[i], f"loop_idxs[{i}] should have max {sizes[i]-1}" # no-op _assert_grouped_dims("gidx", (2,), (16,16,16), False, [2]) _assert_grouped_dims("gidx", (2,3), (16,16,16), False, [2,3]) # check reverse dims _assert_grouped_dims("gidx", (2,3), (16,16,16), True, [3,2]) _assert_grouped_dims("gidx", (2,3,4), (16,16,16), False, [2,3,4]) # test splitting globals: len(dims) == len(max) _assert_grouped_dims("gidx", (64,3,4), (16,16,16), False, [16,12,4]) _assert_grouped_dims("gidx", (64,3,4), (16,4,16), False, [16,3,16]) _assert_grouped_dims("gidx", (64,3,4), (16,16,16), True, [16,3,16]) _assert_grouped_dims("gidx", (128,3,4), (16,4,256), False, [16,3,32]) _assert_grouped_dims("gidx", (4,4,512), (16,4,256), False, [8,4,256]) # prefer group_dim strategy when possible _assert_grouped_dims("gidx", (512,4,2), (8192,2,2), False, [2048,2]) # test splitting globals: len(dims) < len(max) # len(dim) -> len(limited) # 1 -> 2 _assert_grouped_dims("gidx", (128,), (16,16,256), False, [16,8], False) # 1 -> 3 _assert_grouped_dims("gidx", (65536,), (16,16,256), False, [16,16,256], False) # 2 -> 3 _assert_grouped_dims("gidx", (128,128), (16,16,256), False, [16,16,64], False) # test when the only divisor is the square root of dim _assert_grouped_dims("gidx", (121,), (12,12,12), False, [11,11], False) # collapse on onto the left most axis _assert_grouped_dims("gidx", (2,3,4,5), (16,16,16), False, [6,4,5]) _assert_grouped_dims("gidx", (2,3,4,5), (32,16,16), True, [20,3,2]) # _assert_grouped_dims("gidx", (Variable("start_pos",1,2),3,4,5), (32,16,16), True, [20,3,Variable("start_pos",1,2)]) # collapse on left-most available axis (the left most is too small) _assert_grouped_dims("gidx", (2,3,4,5), (4,16,16), False, [2,12,5]) _assert_grouped_dims("gidx", (2,3,4,5), (16,16,16), True, [5,12,2]) # _assert_grouped_dims("gidx", (Variable("start_pos",1,2),3,4,5), (16,16,16), False, [Variable("start_pos",1,2)*3,4,5]) # dim too large and not factorable with self.assertRaises(RuntimeError): get_grouped_dims("gidx", (23,), (16,16,16), False,) with self.assertRaises(RuntimeError): get_grouped_dims("gidx", (128,3,4), (16,2,2), False,) # too large for sizes with self.assertRaises(RuntimeError): get_grouped_dims("gidx", (2,3,4,5,6), (16,16,16)) # # variable too large # with self.assertRaises(AssertionError): # get_grouped_dims("gidx", (Variable("start_pos",0,16),3,4), (16,16,16), False,) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") def test_default_global_reversed(self): # shrink so that the dims do not collapse t = Tensor.ones(5, 6, 7).contiguous().realize().shrink(((0, 4), (0, 5), (0, 6))) k = helper_linearizer_opt(t+1)[0] idxs = dedup([uop for uop in k.uops if uop.op is Ops.SPECIAL]) idxs = sorted(idxs, key=lambda uop: uop.arg[0]) assert idxs[0].arg == ('gidx0', 6), idxs[0].arg assert idxs[1].arg == ('gidx1', 5), idxs[1].arg assert idxs[2].arg == ('gidx2', 4), idxs[2].arg def test_sum_collapse(self): t = Tensor([2]).reshape(1, 1).expand(256, 256).sum() sched = [si for si in t.schedule() if si.ast.op is Ops.SINK] # sum_collapse is a full collapse now assert len(sched) == 1 assert not any(u.op is Ops.REDUCE_AXIS for u in sched[0].ast.toposort()), "found reduce in sum collapse" #lin = Kernel(sched[0].ast) #assert not any(u.op is Ops.RANGE for u in lin.linearize().uops), "found loop in sum collapse" def test_assign_fold(self): a = Tensor.ones(4, 4).contiguous().realize() m = Tensor.ones(4, 4).shrink(((1, 2), None)).pad(((1, 2), None)) a.assign(a+m) a.realize() np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.]) def test_where_fold(self): a = Tensor.ones(4, 4).contiguous().realize() b = a.shrink(((1, 2), None)).pad(((1, 2), None)) a.assign(b.where(2, a)) sched = a.schedule() assert len(sched) == 1 sched_copy = sched[:] run_schedule(sched) np.testing.assert_equal(a.flatten().numpy(), [1.,1.,1.,1.,2.,2.,2.,2.,1.,1.,1.,1.,1.,1.,1.,1.]) realized_ast = sched_copy[-1].ast realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple())) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) assert not any(u.op == Ops.WHERE for u in program.uops), "found where where where should be folded" def test_phi_simplification(self): def helper(t, max_ops=0): k = helper_linearizer_opt(t)[-1] uops = list(k.linearize().uops) # ignore kernel optimized IF statements for now if if_op:=next((u for u in uops if u.op is Ops.IF), None): uops = uops[:uops.index(if_op)] assert len(set([u.op for u in uops if u.op in {Ops.RANGE, Ops.SPECIAL}])) == 1, "has either specials or ranges, not both" assert len([u for u in uops if u.op is Ops.ASSIGN]) == 0, "ASSIGN should have been simplified" # TODO: once uops track min/max this will be fixed #assert len([u for u in uops if u.op is Ops.MAX]) <= max_ops, "no unnecessary MAX ops" helper(Tensor.arange(5.5, (3.5*300), 3.5), max_ops=2) helper(Tensor.arange(-1, -100, -5), max_ops=2) # NOTE: both of these split the reduce (this just wasn't tracked before) #helper(Tensor.arange(-3.2, 6.7, 0.64), max_ops=2) #helper(Tensor.arange(256), max_ops=2) helper(Tensor.arange(255), max_ops=2) @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_grouped_store_phis(self): """ float4 acc0 = float4(0.0,0.0,0.0,0.0); { acc0 = // ... } *((device float4*)(data0+alu2)) = float4(acc0.x,acc0.y,acc0.z,acc0.w); simplifies to: *((device float4*)(data0+alu2)) = acc0; """ x, y = Tensor.randn(64,64), Tensor.randn(64,64) out = x.matmul(y) k = helper_linearizer_opt(out)[-1] # check that the float4 cast collapses store_vals = [u.src[-1] for u in k.uops if u.op is Ops.STORE] for val in store_vals: assert val.dtype == dtypes.float.vec(4) # and val.op is not Ops.VECTORIZE @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_arange_opts(self): a = Tensor.arange(128) helper_linearizer_opt(a, [ [Opt(OptOps.GROUP, 0, 32)], [Opt(OptOps.GROUPTOP, 0, 32)], [Opt(op=OptOps.LOCAL, axis=0, arg=8)], [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0)], [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8)], [Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=8), Opt(op=OptOps.UNROLL, axis=1, arg=4)], # noqa: E501 ]) @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_grouped_store_values(self): x = Tensor.randn((4,3,6,6)).realize() out = x.flip((0,1)).contiguous() k = helper_linearizer_opt(out)[-1] store_val = [u.src[-1] for u in k.uops if u.op is Ops.STORE][0] assert store_val.dtype == dtypes.float.vec(4) and store_val.op is not Ops.VECTORIZE @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_grouped_store_locals_and_globals(self): x, y = Tensor.rand(128, 128), Tensor.rand(128, 128) out = x@y opt = [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 2)] # upcast accs in both reduces k = helper_linearizer_opt(out, opts=[opt])[-1] def get_recursive(uop): return set.union(set(uop.src), [uop], *[get_recursive(v) for v in uop.src]) local_stores = [u for u in k.uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_LOCAL for x in get_recursive(u.src[0]))] global_stores = [u for u in k.uops if u.op is Ops.STORE and any(x.op is Ops.DEFINE_GLOBAL for x in get_recursive(u.src[0]))] barrier = [u for u in k.uops if u.op is Ops.BARRIER][0] # check that the float4 cast collapses for all stores for store in local_stores+global_stores: assert store.src[-1].dtype.count > 1 # and store.src[2].op is not Ops.VECTORIZE # # check the children's vins # TODO: src ALU are not the same, should it? # assert barrier.src == tuple(local_stores) assert len([u for u in k.uops if u.op is Ops.IF and u.src[-1] == barrier]) == 1 @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_grouped_store_local_only(self): x, y = Tensor.rand(1,128), Tensor.rand(128, 128) r = (x@y).relu() k = helper_linearizer_opt(r)[-1] stores = [u for u in k.uops if u.op is Ops.STORE] # the float4 value stores directly in lds and we skip upcast self.assertEqual(stores[0].src[-1].dtype, dtypes.float.vec(4)) #assert stores[0].src[-1].op is not Ops.VECTORIZE # the global store doesn't change assert stores[1].src[-1].dtype == dtypes.float @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_skip_unmatching_upcasts(self): Tensor.manual_seed(0) ast = UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(9600), arg=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(40, 1, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=0, src=()),)), UOp(Ops.LOAD, dtypes.float, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(9600), arg=ShapeTracker(views=(View(shape=(240, 40, 1, 1), strides=(1, 240, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(9600), arg=1, src=()),)),)),)),)) opt = [ Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=16), Opt(op=OptOps.LOCAL, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=3, arg=2) ] k = helper_linearizer_ast(ast, [Tensor.randn(240*40).realize()], opts=[opt])[-1] out = [u for u in k.uops if u.op is Ops.STORE][0] assert out.src[-1].op is Ops.VECTORIZE and out.src[-1].dtype == dtypes.float.vec(4) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4") def test_skip_unmatching_upcasts_with_gep(self): Tensor.manual_seed(0) ast = UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(32, 1, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=0, src=()),)), UOp(Ops.LOAD, dtypes.float, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(256), arg=ShapeTracker(views=(View(shape=(8, 32, 1, 1), strides=(1, 8, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(256), arg=1, src=()),)),)),)),)) opt = [Opt(op=OptOps.LOCAL, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=1, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=0, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=2)] k = helper_linearizer_ast(ast, [Tensor.randn(8*32).realize()], opts=[opt])[-1] out = [u for u in k.uops if u.op is Ops.STORE][0] assert out.src[-1].op is Ops.VECTORIZE and out.src[-1].dtype.count != 1 @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "need backends that support float4") class TestFloat4(unittest.TestCase): @staticmethod def count_float4(uops: list[UOp], n=4): return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.float.vec(n)]), len([uop for uop in uops if uop.op is Ops.STORE and uop.src[-1].dtype == dtypes.float.vec(n)])) @staticmethod def count_half4(uops: list[UOp]): return (len([uop for uop in uops if uop.op is Ops.LOAD and uop.dtype == dtypes.half.vec(4)]), len([uop for uop in uops if uop.op is Ops.STORE and uop.src[-1].dtype == dtypes.half.vec(4)])) def test_float4_basic(self): a = Tensor.empty(2, 8).realize() b = Tensor.empty(2, 8).realize() c = a + b s = c.schedule()[0] realized_ast = s.ast opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)] realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply))) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) assert TestFloat4.count_float4(program.uops) == (2, 1) @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16") def test_float4_multidim(self): a = Tensor.empty(2, 8).realize() b = Tensor.empty(2, 8).realize() c = a + b s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(0, 4) # float4 dimension k.shift_to(0, 2, insert_before=k.shape_len-1) k.upcast() k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (4, 2) @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16") def test_float4_multidim_amx(self): def kernel_for_shape(size, shift): a = Tensor.empty(2, size).realize() b = Tensor.empty(2, size).realize() c = a + b s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(0, 4) k.shift_to(0, shift, insert_before=k.shape_len-1) k.upcast() k.upcast() k.linearize() return k sizes = [12, 8, 16] shifts = [3, 2, 4] excepted_upcast_size = [4, 8, 16] expected_output = [(6,3), (2,1), (2,1)] for i in range(len(sizes)): assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]), excepted_upcast_size[i]) == expected_output[i] def test_float4_unaligned_load(self): a = Tensor.empty(9).realize().shrink(((1, 9),)) b = Tensor.empty(9).realize().shrink(((1, 9),)) c = a + b s = c.schedule()[0] realized_ast = s.ast opts_to_apply = [Opt(op=OptOps.UPCAST, axis=0, arg=4)] realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply))) program = get_program(realized_ast, Device[Device.DEFAULT].renderer) assert TestFloat4.count_float4(program.uops) == (0, 1) @unittest.skipIf(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "CPU with AMX upcasts float up to size 16") def test_float4_multidim_unaligned_load(self): a = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),)) b = Tensor.empty(2, 9).realize().shrink(((0, 2), (1, 9),)) c = a + b s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(len(k.full_unupcasted_shape)-1, 4) # manual trigger float4 dim k.upcast() k.shift_to(len(k.full_unupcasted_shape)-1, 2, insert_before=k.shape_len-1) k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (0, 2) @unittest.skipUnless(Device.DEFAULT in {"CPU", "LLVM"} and AMX, "Only CPU with AMX upcasts float up to size 16") def test_float4_multidim_unaligned_load_amx(self): def kernel_for_shape(size, shift): a = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),)) b = Tensor.empty(2, size).realize().shrink(((0, 2), (1, size),)) c = a + b s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(len(k.full_unupcasted_shape)-1, 4) # manual trigger float4 dim k.upcast() k.shift_to(len(k.full_unupcasted_shape)-1, shift, insert_before=k.shape_len-1) k.upcast() k.linearize() return k sizes = [13, 9, 17] shifts = [3, 2, 4] excepted_upcast_size = [4, 8, 16] expected_output = [(0,3), (0,1), (0,1)] for i in range(len(sizes)): assert TestFloat4.count_float4(kernel_for_shape(sizes[i], shifts[i]).uops, excepted_upcast_size[i]) == expected_output[i] def test_float4_sometimes_unaligned(self): a = Tensor.empty(1, 1, 8).realize() b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5))) c = a.conv2d(b) # only the first and last conv dot products are aligned in a, and b is never aligned, so no # float4 should be emitted (the reduce axis of size 4 is the float4 axis here) s = c.schedule()[0] k = Kernel(s.ast) k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (0, 0) def test_float4_multidim_sometimes_unaligned(self): a = Tensor.empty(1, 1, 7).realize() b = Tensor.empty(1, 1, 5).realize().shrink(((0, 1), (0, 1), (1, 5))) c = a.conv2d(b) # the first conv dot product is aligned in a. If we upcast the output and reduce # dimension, then we could do float4 for only that one set of loads, but we currently # don't. # UPDATE: now we do this fusion s = c.schedule()[0] k = Kernel(s.ast) k.upcast() k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) in {(0,1), (1,1)} def test_float4_noncontiguous(self): a = Tensor.empty(4, 2).realize() b = Tensor.empty(4, 2).realize() c = a + b # we will upcast the top axis of sz 4. they should not be coalesced into float4, # since the top axis is not contiguous. s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(0, 4, top=True) # top axes are float4 axes k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (0, 0) def test_float4_expand(self): a = Tensor.empty(9).realize().shrink(((1, 9),)) b = Tensor.empty(2).realize().reshape((2, 1)).expand((2,4)).reshape((8,)) c = a + b # we will upcast the top axis of sz 4. they should not be coalesced into float4, # since the top axis is not contiguous. s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(0, 4) # float4 axis k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (0, 1) def test_float4_heterogeneous(self): a = Tensor.empty(8).realize() b = Tensor.empty(9).realize().shrink(((1, 9),)) c = a + b # should float4 b but not a s = c.schedule()[0] k = Kernel(s.ast) k.shift_to(0, 4) # float4 axis k.upcast() k.linearize() assert TestFloat4.count_float4(k.uops) == (1, 1) def test_half4_load_unrolled(self): # from llama 7B shard 4 gpus ast = UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(96000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1), strides=(0, 32000, 1, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(96000), arg=0, src=()),)), UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (3,)), src=( UOp(Ops.CAST, dtypes.float, arg=None, src=( UOp(Ops.MUL, dtypes.half, arg=None, src=( UOp(Ops.LOAD, dtypes.half, arg=None, src=( UOp(Ops.VIEW, dtypes.half.ptr(9216), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 4096, 0, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(9216), arg=1, src=()),)),)), UOp(Ops.LOAD, dtypes.half, arg=None, src=( UOp(Ops.VIEW, dtypes.half.ptr(32768000), arg=ShapeTracker(views=(View(shape=(1, 3, 32000, 1024), strides=(0, 0, 1024, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(32768000), arg=2, src=()),)),)),)),)),)),)),)) # TODO: fix this, expected might change but should be positive for expected, opts in [ ((7, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=3), Opt(op=OptOps.UNROLL, axis=0, arg=4)]), ((5, 0), [Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=4)]), ((2, 0), [Opt(op=OptOps.UNROLL, axis=0, arg=4)]), ]: ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts))) program = get_program(ast, Device[Device.DEFAULT].renderer) count = TestFloat4.count_half4(program.uops) assert count == expected, f"{count=}, {expected=}" @unittest.skip("this doesn't happen anymore") def test_float4_acc(self): # from float32 stable diffusion red tinybox ast = UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(33554432), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 262144, 512, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(33554432), arg=0, src=()),)), UOp(Ops.ADD, dtypes.float, arg=None, src=( UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (5, 6, 7)), src=( UOp(Ops.MUL, dtypes.float, arg=None, src=( UOp(Ops.LOAD, dtypes.float, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(67108864), arg=ShapeTracker(views=(View(shape=(1, 1, 1, 256, 4, 514, 4, 514), strides=(0, 0, 0, 262144, 0, 512, 0, 1), offset=-513, mask=((0, 1), (0, 1), (0, 1), (0, 256), (0, 4), (1, 513), (0, 4), (1, 513)), contiguous=False), View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 0, 2056, 1, 4227136, 1058840, 515), offset=0, mask=None, contiguous=False))), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(67108864), arg=1, src=()),)),)), UOp(Ops.LOAD, dtypes.float, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(294912), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 256, 3, 3), strides=(0, 0, 2304, 0, 0, 9, 3, 1), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(294912), arg=2, src=()),)),)),)),)), UOp(Ops.LOAD, dtypes.float, arg=None, src=( UOp(Ops.VIEW, dtypes.float.ptr(128), arg=ShapeTracker(views=(View(shape=(1, 1, 128, 512, 512, 1, 1, 1), strides=(0, 0, 1, 0, 0, 0, 0, 0), offset=0, mask=None, contiguous=False),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(128), arg=3, src=()),)),)),)),)),)) for expected, opts in [ (1, [Opt(op=OptOps.UPCAST, axis=2, arg=4)]), (4, [Opt(op=OptOps.UPCAST, axis=2, arg=4), Opt(op=OptOps.UPCAST, axis=0, arg=4)]), ]: ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts))) program = get_program(ast, Device[Device.DEFAULT].renderer) count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(4)]) assert count == expected, f"{count=}, {expected=}" @unittest.skip("this doesn't happen anymore") def test_float2_acc(self): # from resnet ast = UOp(Ops.SINK, dtypes.void, arg=None, src=( UOp(Ops.STORE, dtypes.void, arg=None, src=( UOp(Ops.VIEW, dtypes.half.ptr(212926464), arg=ShapeTracker(views=(View(shape=(1, 256, 1, 64, 1, 114, 1, 114), strides=(0, 831744, 0, 12996, 0, 114, 0, 1), offset=0, mask=None, contiguous=True),)), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(212926464), arg=0, src=()),)), UOp(Ops.CAST, dtypes.half, arg=None, src=( UOp(Ops.REDUCE_AXIS, dtypes.float, arg=(Ops.ADD, (4, 6)), src=( UOp(Ops.CAST, dtypes.float, arg=None, src=( UOp(Ops.LOAD, dtypes.half, arg=None, src=( UOp(Ops.VIEW, dtypes.half.ptr(462422016), arg=ShapeTracker(views=(View(shape=(256, 64, 3, 56, 2, 3, 56, 2), strides=(1806336, 28224, 3, 504, 0, 1, 9, 0), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 56), (0, 1), (0, 3), (0, 56), (0, 1)), contiguous=False), View(shape=(256, 64, 3, 115, 3, 115), strides=(7225344, 112896, 37632, 336, 112, 1), offset=0, mask=((0, 256), (0, 64), (0, 3), (0, 112), (0, 3), (0, 112)), contiguous=False), View(shape=(256, 64, 456, 456), strides=(7617600, 119025, 345, 1), offset=0, mask=((0, 256), (0, 64), (0, 345), (0, 345)), contiguous=False), View(shape=(1, 256, 1, 64, 4, 114, 4, 114), strides=(0, 13307904, 0, 207936, 51984, 456, 114, 1), offset=0, mask=None, contiguous=True))), src=( # noqa: E501 UOp(Ops.DEFINE_GLOBAL, dtypes.half.ptr(462422016), arg=1, src=()),)),)),)),)),)),)),)) for expected, opts in [ (16, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2), Opt(op=OptOps.LOCAL, axis=2, arg=3), Opt(op=OptOps.UPCAST, axis=3, arg=4)]), # noqa: E501 (4, [Opt(op=OptOps.LOCAL, axis=1, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=2, arg=2)]), ]: ast = ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts))) program = get_program(ast, Device[Device.DEFAULT].renderer) count = len([uop for uop in program.uops if uop.op is Ops.DEFINE_REG and uop.dtype == dtypes.float.vec(2)]) assert count == expected, f"{count=}, {expected=}" class TestHandCodedOpts(unittest.TestCase): def test_masked_upcast(self): layer_1 = Tensor.cat(*[Tensor.empty(5) for _ in range(4)]) layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.empty(6, 20)) s = layer_2.schedule()[-1] k = Kernel(s.ast) k.apply_opts(hand_coded_optimizations(k)) assert len(k.bufs) == 6 # make sure all ops are done in one kernel # masked upcast should upcast masked axis of size 7 # masked upcast should not upcast large (20) last axis # float4/other hcopt shouldn't upcast last axis, since we already have 7 upcast, and the last axis is not very contiguous assert k.upcasted == 1 and k.full_shape[-1] == 7 @unittest.skipIf(Device.DEFAULT in {"METAL", "WEBGPU"}, "METAL/WEBGPU split this kernel since it has 37 buffers") def test_masked_upcast_wino(self): monster = Tensor.stack(*[Tensor.stack(*[Tensor.empty(16) for _ in range(6)]) for _ in range(6)]) s = monster.schedule()[-1] k = Kernel(s.ast) k.apply_opts(hand_coded_optimizations(k)) assert len(k.bufs) == 37 # make sure all ops are done in one kernel # should upcast the two Tensor.stacks assert k.upcasted >= 2 and k.full_shape[k.shape_len-k.upcasted:k.shape_len].count(6) == 2 def test_masked_upcast_wino_full(self): with Context(WINO=1): x,w = Tensor.rand(1,4,8,8, requires_grad=True).realize(), Tensor.rand(4,4,3,3, requires_grad=True).realize() out = Tensor.conv2d(x,w, padding=1) out.mean().backward() upcasts = [] wino_schedule = out.schedule() # collect upcasts of tile transform kernels for i, si in enumerate(wino_schedule): k = Kernel(si.ast) k.apply_opts(hand_coded_optimizations(k)) if k.reduceop is not None: continue # not a tile transform kernel (there is a gemm reduce kernel) if len(k.bufs) < 22: continue # not a tile transform kernel (there's a permute kernel at the end) upcasts.append(tuple(k.full_shape[k.shape_len - k.upcasted:k.shape_len])) assert len(upcasts) == 3 # 3 transformation matrices assert len(wino_schedule) <= 4 # 4 kernels # this test case's inputs are too small, so one of the 4-stacks became a local, which is fine i guess assert upcasts.count((6, 6)) == 2 #and upcasts.count((4, 4)) == 1 backward_schedule = Tensor.schedule(x.grad, w.grad) for si in backward_schedule: k = Kernel(si.ast) k.apply_opts(hand_coded_optimizations(k)) if len(k.bufs) < 20: continue # not a tile transform kernel # heuristic number to make sure that at least some upcasts but not too many upcasts are being done assert 6 <= prod(k.full_shape[k.shape_len - k.upcasted:k.shape_len]) <= 216 assert len(backward_schedule) <= 13 # just the current number, but it could be better def test_masked_upcast_many(self): layer_1 = Tensor.cat(Tensor.rand(3, 4), Tensor.rand(4, 4)) layer_2 = Tensor.cat(layer_1.unsqueeze(0), Tensor.rand(6, 7, 4)) layer_3 = Tensor.cat(layer_2.unsqueeze(0), Tensor.rand(6, 7, 7, 4)) k = helper_linearizer_opt(layer_3)[-1] assert len(k.bufs) == 5 # make sure all ops are done in one kernel # check that we don't do too many upcasts assert prod(k.full_shape[k.shape_len-k.upcasted:k.shape_len]) <= 49 @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") def test_matvec(self): N = 128 a = Tensor.rand(1, N).realize() b = Tensor.rand(N, N).realize() c = a @ b k = helper_linearizer_opt(c)[-1] assert k.group_for_reduces == 1 assert k.local_dims == 1 assert k.upcasted == 1 def helper_linearizer_ast(ast:UOp, inputs:list[Tensor], *args, **kwargs): assert isinstance(ast, UOp), "ast must be UOp" inbufs = [x.uop.base.buffer for x in inputs] outbufs = [Buffer(inbufs[-1].device if inbufs else Device.DEFAULT, out.st_arg.size, out.src[1].dtype).allocate() \ for out in ast.src] return _helper_linearizer_opt_ast(ast, outbufs+inbufs, *args, **kwargs) def helper_linearizer_opt(r:Union[Tensor, list[Tensor]], *args, **kwargs): realized_ast, real_bufs = helper_realized_ast(r) return _helper_linearizer_opt_ast(realized_ast, real_bufs, *args, **kwargs) def copyout_outputs(lin:Kernel, outbufs:list[Buffer]) -> list[np.ndarray]: ret = [] for i,x in enumerate(outbufs): shape: tuple[int, ...] = lin.ast.src[i].st_arg.shape ret.append(np.frombuffer(x.as_buffer(), _to_np_dtype(x.dtype)).reshape(shape)) return ret def reset_bufs(bufs:list[Buffer]): for buf in bufs: buf.copyin(np.zeros((buf.size, ), dtype=_to_np_dtype(buf.dtype)).data) # Zero to check that all values are filled def _helper_linearizer_opt_ast(realized_ast:UOp, real_bufs:list[Buffer], opts=[], apply_tc=False, atol=1e-4, rtol=1e-4, color_sizes=[], wanna_output=[]) -> list[Kernel]: lins: list[Kernel] = [] outbufs = [real_bufs[x.src[0].base.arg] for x in realized_ast.src] device = real_bufs[0].device def get_prg(k:Kernel): return CompiledRunner(replace(k.to_program(), device=device)) def check_opt(opts, create_k, expected_color_size): k = create_k() lins.append(k) if apply_tc: assert k.apply_tensor_cores(1, extra_opts=opts), "no tensor core triggered" else: k.apply_opts(opts) if expected_color_size is not None: cs = list(zip(k.colors(), k.full_shape)) assert cs == expected_color_size, f"expected={expected_color_size} got={cs}" prg = get_prg(k) reset_bufs(outbufs) prg.exec(real_bufs) for x,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(x, want, atol=atol, rtol=rtol) # Get baseline if it is not provided, which is not optimized at all. k = Kernel(realized_ast) lins.append(k) prg = get_prg(k) prg.exec(real_bufs) if len(wanna_output) == 0: wanna_output = copyout_outputs(k, outbufs) else: for buf,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol) # Check correctness of handcoded optimiztions. k = Kernel(realized_ast) k.apply_opts(hand_coded_optimizations(k)) lins.append(k) prg = get_prg(k) reset_bufs(outbufs) prg.exec(real_bufs) for buf,want in zip(copyout_outputs(k, outbufs), wanna_output): np.testing.assert_allclose(buf, want, atol=atol, rtol=rtol) for i,x in enumerate(opts): # Check custom transformations if any. check_opt(x, lambda: Kernel(realized_ast), color_sizes[i] if i < len(color_sizes) else None) return lins class TestKernelOpts(unittest.TestCase): @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") def test_local_and_grouped_reduce(self): N = 128 Tensor.manual_seed(1882) a = Tensor.rand(4, 4, N, N) b = Tensor.rand(4, 4, N) r = (b.sqrt() + ((a+1).sum(axis=3).exp())) helper_linearizer_opt(r, [ [Opt(OptOps.LOCAL, 0, 2)], [Opt(OptOps.LOCAL, 0, 8)], [Opt(OptOps.LOCAL, 0, 16)], # Checking how it works with locals [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)], [Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with grouped reduce [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.GROUPTOP, 0, 16)], [Opt(OptOps.LOCAL, 0, 32), Opt(OptOps.GROUPTOP, 0, 2)], # Checking how it works with locals + grouped reduce [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 64)], # Checking how it works with locals + grouped reduce + upcasts [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.UPCAST, 0, 8), Opt(OptOps.UNROLL, 1, 4)], # many local + many group [Opt(OptOps.GROUP, 0, 2)] * 4, [Opt(OptOps.LOCAL, 0, 2)] * 4, [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)] * 4, ]) def test_upcasts(self): N = 16 Tensor.manual_seed(1772) a = Tensor.rand(N, N) b = Tensor.rand(N, N) r = (a+b).sqrt() * ((a+1).exp()) helper_linearizer_opt(r, [ [Opt(OptOps.UPCAST, 0, 2)], [Opt(OptOps.UPCAST, 0, 4)], [Opt(OptOps.UPCAST, 0, 8)], # Checking how it works with upcasts ]) def test_full_upcast(self): Tensor.manual_seed(1772) a = Tensor.rand(4) b = Tensor.rand(4) r = (a+b).sqrt() * ((a+1).exp()) helper_linearizer_opt(r, [ [Opt(OptOps.UPCAST, 0, 4)], # Checking how it works with upcasts ]) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") def test_matmul(self): N = 128 Tensor.manual_seed(1552) a = Tensor.rand(N, N) b = Tensor.rand(N, N) r = a@b helper_linearizer_opt(r, [ [Opt(OptOps.UPCAST, 0, 2)], [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # Checking how it works with upcasts [Opt(OptOps.LOCAL, 0, 2)], [Opt(OptOps.LOCAL, 1, 32)], [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4)], [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 32)], [Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.LOCAL, 1, 8)], # Checking how it works with locals [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)], [Opt(OptOps.GROUPTOP, 0, 32), Opt(OptOps.UNROLL, 0, 4)], # Checking how it works with grouped_reduce [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 32)], [Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 32)], [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 8), Opt(OptOps.GROUPTOP, 0, 4)], # Checking how it works with local+grouped_reduce # Checking all together [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 2)], # Full global upcast + local [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UPCAST, 0, 8)], ]) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") def test_double_reduce(self): N = 128 Tensor.manual_seed(1552) a = Tensor.rand(8, N, 8, N) r = a.sum(axis=(1,3)) helper_linearizer_opt(r, [ # openCL / GPU=1 is 256 max threads [Opt(OptOps.GROUPTOP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 32)], [Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 1, 32)], # Checking how it works with 1 grouped_reduce. [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2)], [Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 64)], # Checking how it works with 2 grouped_reduces. [Opt(OptOps.GROUPTOP, 0, 16), Opt(OptOps.GROUPTOP, 1, 2), Opt(OptOps.UNROLL, 0, 4)], [Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 2, 4)], # Checking how it works with 2 grouped_reduces + upcasts. [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals. [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 2), Opt(OptOps.GROUPTOP, 1, 32), Opt(OptOps.UNROLL, 1, 4)], [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2)], [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 1, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.UNROLL, 1, 4)], # Checking how it works with 2 grouped_reduces + upcasts + locals. [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.LOCAL, 1, 4), Opt(OptOps.GROUPTOP, 0, 4), Opt(OptOps.GROUPTOP, 1, 4), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 0, 2)], # No globals ]) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") def test_invalid_tensor_core_extra_opts(self): N = 128 Tensor.manual_seed(1552) a = Tensor.rand(N, N) b = Tensor.rand(N, N) realized_ast, _ = helper_realized_ast(a@b) invalid_opts = [ [Opt(OptOps.LOCAL, 2, 2)], [Opt(OptOps.UPCAST, 2, 2)], [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.LOCAL, 2, 2)], ] for x in invalid_opts: k = Kernel(realized_ast) with self.assertRaises(AssertionError): assert k.apply_tensor_cores(use_tensor_cores=1, extra_opts=x), "no valid tensor core" # for METAL in runners @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores), "test requires tensor cores with accumulation in half") # testing with half suffices. def test_tensor_core_opts(self): N = 128 Tensor.manual_seed(1552) a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half) r = a.matmul(b, dtype=dtypes.half) atol, rtol = 0.25, 0.01 helper_linearizer_opt(r, [ [], [Opt(OptOps.UPCAST, 0, 4)], [Opt(OptOps.UPCAST, 1, 4)], [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4)], # check upcasts [Opt(OptOps.UNROLL, 0, 2)], # check unroll [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2)], # check combo of unroll and local [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2)], [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4)], [Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4)], # check permutations [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)], [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4)], [Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UNROLL, 0, 4)], ], apply_tc=True, atol=atol, rtol=rtol) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores), "test requires tensor cores with accumulation in half") # testing with half suffices. @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") def test_tensor_core_opts_locals(self): N = 128 Tensor.manual_seed(1552) a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half) r = a.matmul(b, dtype=dtypes.half) atol, rtol = 0.25, 0.01 helper_linearizer_opt(r, [ [Opt(OptOps.UNROLL, 0, 0)], # check full unroll of reduce with locals [Opt(OptOps.LOCAL, 0, 4)], # check local [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.LOCAL, 0, 2)], [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.UPCAST, 1, 4), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 0, 4)], ], apply_tc=True, atol=atol, rtol=rtol) @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared memory") @unittest.skipUnless(any(tc.dtype_in == tc.dtype_out == dtypes.half for tc in Device[Device.DEFAULT].renderer.tensor_cores), "test requires tensor cores with accumulation in half") # testing with half suffices. # NOTE: the METAL test is broken, likely due to a compiler bug. passes on CI with -O0 and with default opt level locally on M3 @unittest.skipIf(Device.DEFAULT == "METAL", "broken for METAL") @unittest.skip("feature was removed") def test_tensor_core_opts_group(self): N = 128 Tensor.manual_seed(1552) a, b = Tensor.rand(N, N, dtype=dtypes.half), Tensor.rand(N, N, dtype=dtypes.half) r = a.matmul(b, dtype=dtypes.half) atol, rtol = 0.25, 0.01 helper_linearizer_opt(r, [ [Opt(OptOps.GROUP, 0, 2)], [Opt(OptOps.GROUPTOP, 0, 4)], [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.GROUP, 0, 2)], [Opt(OptOps.LOCAL, 0, 4), Opt(OptOps.GROUP, 0, 2)], [Opt(OptOps.UNROLL, 0, 4), Opt(OptOps.GROUP, 0, 2)], [Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUP, 0, 2)], [Opt(OptOps.LOCAL, 0, 2), Opt(OptOps.GROUPTOP, 0, 8), Opt(OptOps.UNROLL, 0, 2), Opt(OptOps.UPCAST, 1, 2)], ], apply_tc=True, atol=atol, rtol=rtol) def test_padto_matmul(self): if (CI and Device.DEFAULT in ["AMD", "NV", "CUDA"]): self.skipTest("super slow on CUDA and AMD because of the big grid dims") N = 17 * 17 Tensor.manual_seed(289) a = Tensor.rand(N, N) b = Tensor.rand(N, N) helper_linearizer_opt(a@b, [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 1, 32)], [Opt(OptOps.PADTO, 2, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.PADTO, 2, 32)], # can optimize further post PADTO [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.PADTO, 1, 32), Opt(OptOps.UPCAST, 0, 2), Opt(OptOps.UPCAST, 1, 2),], ]) def test_padto_upcasted_not_ok(self): N = 4 a = Tensor.rand(N, N) b = Tensor.rand(N, N) helper_linearizer_opt(a@b, [ [Opt(OptOps.UPCAST, 0, 0)], [Opt(OptOps.UPCAST, 1, 0)], [Opt(OptOps.UNROLL, 0, 0)], [Opt(OptOps.PADTO, 0, 8)], [Opt(OptOps.PADTO, 1, 8)], [Opt(OptOps.PADTO, 2, 8)], ]) with self.assertRaises(KernelOptError): helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 0, 0), Opt(OptOps.PADTO, 2, 8)]]) with self.assertRaises(KernelOptError): helper_linearizer_opt(a@b, [[Opt(OptOps.UPCAST, 1, 0), Opt(OptOps.PADTO, 2, 8)]]) with self.assertRaises(KernelOptError): helper_linearizer_opt(a@b, [[Opt(OptOps.UNROLL, 0, 0), Opt(OptOps.PADTO, 2, 8)]]) def test_padto_sum_ok(self): N = 18 * 18 # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension a = Tensor.rand(N, N).realize().shrink(((0, 17), (0, 17))) * 100 b = (Tensor.rand(N, N) < 0.5).realize().shrink(((0, 17), (0, 17))) helper_linearizer_opt(a.sum(0), [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) helper_linearizer_opt(a.sum(1), [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) # can pad sum reduce axis if there's no unsafe ops prior to sum for axis in (0, 1): helper_linearizer_opt(a.sum(), [[Opt(OptOps.PADTO, axis, 32)],]) helper_linearizer_opt(a.sum(0), [[Opt(OptOps.PADTO, axis, 32)],]) helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, axis, 32)],]) helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, axis, 32)],]) helper_linearizer_opt(b.sum(dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],]) # TODO: why? if Device.DEFAULT != "WEBGPU": helper_linearizer_opt(b.sum(0, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],]) helper_linearizer_opt(b.sum(1, dtype=dtypes.bool), [[Opt(OptOps.PADTO, axis, 32)],]) # having unsafe ops after sum is fine helper_linearizer_opt(a.sum().exp(), [[Opt(OptOps.PADTO, 0, 32)],]) helper_linearizer_opt(a.sum(0).exp(), [[Opt(OptOps.PADTO, 1, 32)],]) def test_padto_sum_not_ok(self): N = 18 * 18 # NOTE: this setup prevents 17 * 17 contiguous merged into one dimension a = Tensor.rand(N, N).shrink(((0, 17), (0, 17))).exp() # exp is not safe to pad with self.assertRaises(KernelOptError): helper_linearizer_opt(a.exp().sum(), [[Opt(OptOps.PADTO, 0, 32)],]) with self.assertRaises(KernelOptError): helper_linearizer_opt(a.exp().sum(0), [[Opt(OptOps.PADTO, 1, 32)],]) b = a < 1 # lt is not safe to pad with self.assertRaises(KernelOptError): helper_linearizer_opt(b.sum(), [[Opt(OptOps.PADTO, 0, 32)],]) with self.assertRaises(KernelOptError): helper_linearizer_opt(b.sum(0), [[Opt(OptOps.PADTO, 1, 32)],]) def test_padto_max(self): N = 18 * 18 # NOTE: this setup prevents 17 * 17 contiguous merged into one axis a = -Tensor.rand(N, N).shrink(((0, 17), (0, 17))) * 100 helper_linearizer_opt(a.max(0), [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) helper_linearizer_opt(a.max(1), [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) # cannot pad max kernel on reduce with self.assertRaises(KernelOptError): helper_linearizer_opt(a.max(), [[Opt(OptOps.PADTO, 0, 32)],]) with self.assertRaises(KernelOptError): helper_linearizer_opt(a.max(0), [[Opt(OptOps.PADTO, 1, 32)],]) def test_padto_where(self): Tensor.manual_seed(0) N = 17 * 17 a = (Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1).where(1, 0) helper_linearizer_opt(a.max(0), [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) def test_padto_where_multioutput(self): Tensor.manual_seed(0) N = 17 * 17 r = Tensor.randn(N, N).realize().max(axis=0, keepdim=True) > 1 a0 = r.where(1, 0) a1 = r.where(2, 0) helper_linearizer_opt([a0.max(0), a1.max(0)], [ [Opt(OptOps.PADTO, 0, 32)], [Opt(OptOps.PADTO, 0, 32), Opt(OptOps.UPCAST, 0, 8),], ]) @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals") @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared") def test_color_shapes_with_local(self): N = 32 Tensor.manual_seed(1552) a = Tensor.rand(N, N) b = Tensor.rand(N, N) r = a@b opts_shapes = [ ([Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("red",32)]), ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",2),("red",16)]), # check to ensure local_dims are stable for full UNROLL of first_reduce ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]), ([Opt(OptOps.UNROLL, 0, 0),Opt(OptOps.LOCAL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]), # check behavior for full UNROLL on an existing GROUP ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 2)], [("blue",16),("blue",32),("cyan",2),("green",16),("magenta",2)]), ([Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.GROUP, 0, 0),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]), ([Opt(OptOps.GROUP, 0, 0),Opt(OptOps.LOCAL, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",16),("blue",32),("cyan",2),("magenta",32)]), ([Opt(OptOps.GROUP, 0, 2),Opt(OptOps.UNROLL, 0, 0)], [("blue",32),("blue",32),("red",16),("magenta",2)]), ] helper_linearizer_opt(r, [x[0] for x in opts_shapes], color_sizes=[x[1] for x in opts_shapes]) if __name__ == '__main__': unittest.main()