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342 lines
15 KiB
342 lines
15 KiB
import unittest, itertools, math
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from typing import Any
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.dtype import DType
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from tinygrad.ops import Ops, UOp
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from tinygrad.helpers import CI
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from tinygrad.codegen.devectorizer import full_graph_rewrite
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import numpy as np
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from tinygrad.device import is_dtype_supported
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def _check_ast_count(desired_count:int, t:Tensor):
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# NOTE: this has side effect because everything can be scheduled only once
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schedule = t.schedule()
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asts = [s for s in schedule if s.ast.op is Ops.SINK]
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assert len(asts) == desired_count, f"{len(asts)} != {desired_count}"
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class TestUnaryOpsConstFolding(unittest.TestCase):
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def test_all_consts_ops(self):
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_check_ast_count(0, Tensor.ones(4).exp())
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_check_ast_count(0, Tensor.ones(4).sqrt())
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_check_ast_count(0, Tensor.ones(4) + Tensor.ones(4))
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_check_ast_count(0, Tensor.ones(4) / Tensor.ones(4))
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def test_cast(self):
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_check_ast_count(0, Tensor.ones(4).cast(dtypes.int16))
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_check_ast_count(0, Tensor.full(4, fill_value=-1).cast(dtypes.uint16))
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@unittest.expectedFailure # no two level fold at lazybuffer
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def test_neg_folding(self):
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_check_ast_count(0, Tensor([1, 2, 3]).mul(-1).neg())
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_check_ast_count(0, Tensor([1, 2, 3]).neg().mul(-1))
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_check_ast_count(0, Tensor([1, 2, 3]).neg().neg())
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def test_neg_realized_no_fold(self):
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x = Tensor.randn(32, 32)
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x = x.clip(0, 1).realize()
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_check_ast_count(1, x.neg())
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class TestBinaryOpsConstFolding(unittest.TestCase):
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def test_add_literal_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) + 0)
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def test_add_tensor_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) + Tensor.zeros(4))
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def test_literal_zero_add(self):
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_check_ast_count(0, 0 + Tensor([1.0, 2, 3, 4]))
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def test_tensor_zero_add(self):
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_check_ast_count(0, Tensor.zeros(4) + Tensor([1.0, 2, 3, 4]))
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def test_sub_literal_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) - 0)
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def test_sub_tensor_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) - Tensor.zeros(4))
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def test_mul_literal_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) * 0)
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def test_mul_tensor_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) * Tensor.zeros(4))
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def test_literal_zero_mul(self):
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_check_ast_count(0, 0 * Tensor([1.0, 2, 3, 4]) * 0)
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def test_tensor_zero_mul(self):
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_check_ast_count(0, Tensor.zeros(4) * Tensor([1.0, 2, 3, 4]))
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def test_mul_literal_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) * 1)
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def test_mul_tensor_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) * Tensor.ones(4))
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def test_literal_one_mul(self):
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_check_ast_count(0, 1 * Tensor([1.0, 2, 3, 4]))
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def test_tensor_one_mul(self):
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_check_ast_count(0, Tensor.ones(4) * Tensor([1.0, 2, 3, 4]))
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def test_bool_tensor_mul_bool(self):
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_check_ast_count(0, Tensor([True, False]) * True)
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_check_ast_count(0, Tensor([True, False]) * False)
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def test_bool_mul_bool_tensor(self):
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_check_ast_count(0, True * Tensor([True, False]))
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_check_ast_count(0, False * Tensor([True, False]))
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def test_div_literal_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) / 1)
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def test_div_tensor_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) / Tensor.ones(4))
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def test_idiv_literal_one(self):
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_check_ast_count(0, Tensor([1, 2, 3, 4]) // 1)
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def test_idiv_tensor_one(self):
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_check_ast_count(0, Tensor([1, 2, 3, 4]) // Tensor.ones(4, dtype=dtypes.int32))
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def test_pow_literal_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** 0)
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def test_pow_tensor_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** Tensor.zeros(4))
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def test_pow_literal_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** 1)
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def test_pow_tensor_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) ** Tensor.ones(4))
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def test_literal_one_pow(self):
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_check_ast_count(0, 1 ** Tensor([1.0, 2, 3, 4]))
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def test_tensor_one_pow(self):
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_check_ast_count(0, Tensor.ones(4) ** Tensor([1.0, 2, 3, 4]))
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class TestBitcastConstFolding(unittest.TestCase):
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def test_scalar_bitcast(self):
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def t(cases: dict[DType, Any]):
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for (from_dt, from_v), (to_dt, to_v) in itertools.product(cases.items(), cases.items()):
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if not math.isnan(from_v):
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r = full_graph_rewrite(UOp.const(from_dt, from_v).bitcast(to_dt).sink()).src[0]
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self.assertEqual(r.op, Ops.CONST, msg:=f"{from_dt} -> {to_dt} ({from_v} -> {to_v})")
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self.assertEqual(r.dtype, to_dt, msg)
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np.testing.assert_equal(r.arg, to_v, msg)
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t({dtypes.int8: 0, dtypes.uint8: 0, dtypes.bool: False})
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t({dtypes.int8: 1, dtypes.uint8: 1, dtypes.bool: True})
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t({dtypes.int8: -1, dtypes.uint8: 2**8-1})
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t({dtypes.int16: -1, dtypes.uint16: 2**16-1, dtypes.float16: float('nan')})
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t({dtypes.int32: -1, dtypes.uint32: 2**32-1, dtypes.float32: float('nan')})
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t({dtypes.int64: -1, dtypes.uint64: 2**64-1, dtypes.float64: float('nan')})
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t({dtypes.int8: -2**7, dtypes.uint8: 2**7})
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t({dtypes.int16: -2**15, dtypes.uint16: 2**15})
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t({dtypes.int32: -2**31, dtypes.uint32: 2**31})
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t({dtypes.int64: -2**63, dtypes.uint64: 2**63})
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t({dtypes.int16: 13496, dtypes.uint16: 13496, dtypes.float16: 0.294921875})
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t({dtypes.int32: 1050081145, dtypes.uint32: 1050081145, dtypes.float32: 0.29485681653022766})
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t({dtypes.int64: 4598983288165178391, dtypes.uint64: 4598983288165178391, dtypes.float64: 0.29485681936461233})
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def test_vec_bitcast(self):
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r = full_graph_rewrite(UOp.const(dtypes.int32.vec(3), (-1, -2**31, 75)).bitcast(dtypes.uint32.vec(3)).sink()).src[0]
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self.assertEqual(r.op, Ops.VECTORIZE)
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self.assertEqual(r.dtype, dtypes.uint32.vec(3))
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self.assertEqual(tuple(x.arg for x in r.src), (2**32-1, 2**31, 75))
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# folds advance indexing into basic indexing
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class TestIndexingConstFolding(unittest.TestCase):
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def test_scalar_index(self):
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t = Tensor.arange(16).float().reshape(1,1,4,4).realize()
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# TODO: fold these
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_check_ast_count(2, t[:,:,Tensor(1),:])
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_check_ast_count(2, t[:,:,Tensor(1)+2,:])
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_check_ast_count(2, t[:,:,Tensor(1),Tensor(0)])
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@unittest.expectedFailure
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def test_const_tensor_index(self):
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# TODO: implement const tensor folded indexing
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t = Tensor.arange(16).float().reshape(1,1,4,4).realize()
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_check_ast_count(0, t[:,:,Tensor.ones(2,1),:])
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_check_ast_count(0, t[:,:,Tensor.ones(1,2)+2,:])
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_check_ast_count(0, t[:,:,Tensor.ones(1,1),Tensor.zeros(2,1,2)])
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class TestMovedConstFolding(unittest.TestCase):
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def test_add_shrunk_zero(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) + Tensor.zeros(6).shrink(((1, 5),)))
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def test_add_padded_zero(self):
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# TODO: it's 1 now, this might be possible to fold
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_check_ast_count(1, Tensor([1.0, 2, 3, 4]) + Tensor.zeros(2).pad(((1, 1),)))
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def test_mul_shrunk_one(self):
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_check_ast_count(0, Tensor([1.0, 2, 3, 4]) * Tensor.ones(6).shrink(((1, 5),)))
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def test_add_padded_one(self):
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_check_ast_count(1, Tensor([1.0, 2, 3, 4]) * Tensor.ones(2).pad(((1, 1),)))
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def test_cast_padded(self):
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# NOTE: this is folded due to CAST_BEFORE_VIEW
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if is_dtype_supported(dtypes.int16):
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_check_ast_count(0, Tensor.ones(4).pad(((1, 1),)).cast(dtypes.int16))
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np.testing.assert_equal(Tensor.ones(4).pad(((1, 1),)).cast(dtypes.int16).numpy(), [0, 1, 1, 1, 1, 0])
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if is_dtype_supported(dtypes.uint16):
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_check_ast_count(0, Tensor.full(4, fill_value=-1).pad(((1, 1),)).cast(dtypes.uint16))
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np.testing.assert_equal(Tensor.full(4, fill_value=-1).pad(((1, 1),)).cast(dtypes.uint16).numpy(), [0, 65535, 65535, 65535, 65535, 0])
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# not folded
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if is_dtype_supported(dtypes.int64):
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_check_ast_count(1, Tensor.ones(4).pad(((1, 1),)).cast(dtypes.int64))
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np.testing.assert_equal(Tensor.ones(4).pad(((1, 1),)).cast(dtypes.int64).numpy(), [0, 1, 1, 1, 1, 0])
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class TestReduceOpsConstFolding(unittest.TestCase):
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def test_const_sum(self):
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_check_ast_count(0, Tensor.ones(4, 5, 6).sum())
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np.testing.assert_equal(Tensor.ones(4, 5, 6).sum().numpy(), 4 * 5 * 6)
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_check_ast_count(0, Tensor.ones(4, 5, 6).sum(axis=0))
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np.testing.assert_equal(Tensor.ones(4, 5, 6).sum(axis=0).numpy(), np.full((5, 6), 4))
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_check_ast_count(0, Tensor(4).sum())
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np.testing.assert_equal(Tensor(4).sum().numpy(), 4)
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def test_padded_const_sum(self):
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_check_ast_count(1, Tensor.ones(4).pad(((1, 1),)).sum())
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np.testing.assert_equal(Tensor.ones(4).pad(((1, 1),)).sum().numpy(), 4)
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# NOTE: cannot just count the non-padded area because some Ops f do not have f(0) = 0.
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_check_ast_count(1, Tensor.ones(4).pad(((1, 1),)).exp().sum())
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np.testing.assert_allclose(Tensor.ones(4).pad(((1, 1),)).exp().sum().numpy(), 4 * math.e + 2)
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def test_bool_zero_max(self):
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_check_ast_count(0, Tensor.full((1, 2), True).shrink(((0, 1), (0, 0))).max((1, 0)))
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np.testing.assert_equal(Tensor.full((1, 2), True).shrink(((0, 1), (0, 0))).max((1, 0)).numpy(), False)
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def test_zero_size_ops(self):
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for reduceop in [lambda x:x.prod(), lambda x:x.sum()]: # lambda x:x.max() NOTE: numpy gives "reduction operation maximum which has no identity"
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_check_ast_count(0, reduceop(Tensor.empty(1, 0)))
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np.testing.assert_equal(reduceop(Tensor.empty(shape:=(1, 0))).numpy(), reduceop(np.empty(shape)))
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def test_zero_size_ops_view(self):
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for reduceop in [lambda x:x.prod(), lambda x:x.sum()]:
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_check_ast_count(0, reduceop(Tensor.empty(1, 0, 4).permute((1, 2, 0)).contiguous()))
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np.testing.assert_equal(reduceop(Tensor.empty(shape:=(1, 0))).numpy(), reduceop(np.empty((shape))))
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def test_zero_size_ops_realized(self):
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for reduceop in [lambda x:x.prod(), lambda x:x.sum()]:
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_check_ast_count(0, reduceop((Tensor.randn(0, 1)+1).realize()))
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np.testing.assert_equal(reduceop((Tensor.randn(shape:=(0, 1))+1).realize()).numpy(), reduceop(np.empty(shape)))
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def test_zero_size_realize_folded(self):
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# non contiguous folded output doesn't realize
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_check_ast_count(0, Tensor.empty(1, 0).sum())
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# contiguous folded const can still schedule
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a = Tensor.empty(1, 0).sum().contiguous()
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_check_ast_count(2, a+2)
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self.assertIs(a.lazydata.base.op, Ops.BUFFER)
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np.testing.assert_equal((Tensor.empty(1, 0).sum().contiguous()+2).numpy(), 2)
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# otherwise we just fuse it
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_check_ast_count(1, (Tensor.empty(1, 0).sum()+2).contiguous())
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np.testing.assert_equal((Tensor.empty(1, 0).sum()+2).numpy(), 2)
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def test_const_prod(self):
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_check_ast_count(0, Tensor.full((2, 3), fill_value=2).prod())
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np.testing.assert_equal(Tensor.full((2, 3), fill_value=2).prod().numpy(), 2**(2*3))
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_check_ast_count(0, Tensor.full((4, 5, 6), fill_value=2).prod(axis=0))
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np.testing.assert_equal(Tensor.full((4, 5, 6), fill_value=2).prod(axis=0).numpy(), np.full((5, 6), 2**4))
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_check_ast_count(0, Tensor(4).prod())
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np.testing.assert_equal(Tensor(4).prod().numpy(), 4)
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def test_const_max(self):
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_check_ast_count(0, Tensor.ones(4, 5, 6).max())
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np.testing.assert_equal(Tensor.ones(4, 5, 6).max().numpy(), 1)
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_check_ast_count(0, Tensor(4).max())
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np.testing.assert_equal(Tensor(4).max().numpy(), 4)
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def test_sum_output_dtype(self):
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# sum output dtype can be different from input
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for dt in dtypes.fields().values():
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if is_dtype_supported(dt):
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t = Tensor.ones(16, dtype=dt).reshape(4, 4)
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assert t.sum().dtype == t.contiguous().sum().dtype
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@unittest.skipIf(CI and Device.DEFAULT in {"GPU", "CUDA", "METAL"}, "no GPU CI")
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class TestMultiConstFolding(unittest.TestCase):
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def test_multi_const_folding_literal(self):
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ds = tuple(f"{Device.DEFAULT}:{i}" for i in range(4))
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t = Tensor.arange(16).float().realize().to(ds)
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# non const folding case creates one ast on each shard
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_check_ast_count(4, t + 1)
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_check_ast_count(4, 1 + t)
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_check_ast_count(4, t * 2)
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_check_ast_count(4, 2 * t)
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# const folded
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_check_ast_count(0, t + 0)
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_check_ast_count(0, 0 + t)
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_check_ast_count(0, t * 0)
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_check_ast_count(0, 0 * t)
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_check_ast_count(0, t * 1)
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_check_ast_count(0, 1 * t)
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np.testing.assert_equal((t + 0).numpy(), np.arange(16))
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np.testing.assert_equal((t * 0).numpy(), [0] * 16)
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np.testing.assert_equal((t * 1).numpy(), np.arange(16))
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_check_ast_count(0, t ** 0)
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_check_ast_count(0, t ** 1)
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_check_ast_count(0, 1 ** t)
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# failing because multi calls .contiguous() on every single sharded uop
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@unittest.expectedFailure
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def test_multi_const_folding_tensor(self):
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ds = tuple(f"{Device.DEFAULT}:{i}" for i in range(4))
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t = Tensor.arange(16).float().realize().to(ds)
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zero = Tensor.zeros(16).realize().to(ds)
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one = Tensor.ones(16).realize().to(ds)
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# const folded
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_check_ast_count(0, t + zero)
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_check_ast_count(0, zero + t)
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_check_ast_count(0, t * zero)
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_check_ast_count(0, zero * t)
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_check_ast_count(0, t * one)
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_check_ast_count(0, one * t)
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np.testing.assert_equal((t + zero).numpy(), np.arange(16))
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np.testing.assert_equal((t * zero).numpy(), [0] * 16)
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np.testing.assert_equal((t * one).numpy(), np.arange(16))
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@unittest.expectedFailure
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def test_multi_todo_pow(self):
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ds = tuple(f"{Device.DEFAULT}:{i}" for i in range(4))
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t = Tensor.arange(16).float().realize().to(ds)
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zero = Tensor.zeros(16).realize().to(ds)
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one = Tensor.ones(16).realize().to(ds)
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# TODO: fix pow folding
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_check_ast_count(0, t ** zero)
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_check_ast_count(0, t ** one)
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_check_ast_count(0, one ** t)
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class TestTautologicalCompare(unittest.TestCase):
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# without const folding, these would have triggered -Wtautological-compare in clang
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def test_lt_false(self):
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# bool < False is always false
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np.testing.assert_equal((Tensor([True, False]) < False).numpy(), [False, False])
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def test_true_lt(self):
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# True < bool is always false
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np.testing.assert_equal((True < Tensor([True, False])).numpy(), [False, False])
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def test_truth_table(self):
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np.testing.assert_equal((Tensor(False) < Tensor(False)).numpy(), False)
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np.testing.assert_equal((Tensor(False) < Tensor(True)).numpy(), True)
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np.testing.assert_equal((Tensor(True) < Tensor(False)).numpy(), False)
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np.testing.assert_equal((Tensor(True) < Tensor(True)).numpy(), False)
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def test_a_eq_a(self):
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# self eq is always true for int or bool
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a = Tensor([1, 2, 3])
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np.testing.assert_equal((a == a).numpy(), [True, True, True])
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# not true for nan
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a = Tensor([math.nan, 1.0, 2.0])
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np.testing.assert_equal((a == a).numpy(), [False, True, True])
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def test_a_ne_a(self):
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# self not eq is always false for int or bool
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a = Tensor([1, 2, 3])
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np.testing.assert_equal((a != a).numpy(), [False, False, False])
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# not true for nan
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a = Tensor([math.nan, 1.0, 2.0])
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np.testing.assert_equal((a != a).numpy(), [True, False, False])
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if __name__ == '__main__':
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unittest.main()
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