mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-09 15:08:02 -05:00
744af193f0
* remove ExecItem and merge it with ScheduleItem * less diff * fix issues * min diff * don't change bufs in _lower * min diff * update * revert * fixes * diff
807 lines
28 KiB
Python
807 lines
28 KiB
Python
import unittest, math, time
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from tinygrad import Tensor, Device, dtypes, Context
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from tinygrad.uop.ops import UOp, Ops
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from tinygrad.engine.realize import get_runner
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from tinygrad.engine.schedule import ExecItem
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from tinygrad.engine.jit import TinyJit
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from tinygrad.helpers import CI
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import numpy as np
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from extra.thunder.tiny.tk import WARP_THREADS
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from extra.thunder.tiny.tk.kernel import Kernel
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from extra.thunder.tiny.tk.tiles import ST_16X32, RT_16X32, RT_16X16, TileLayout
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@unittest.skipIf(CI or Device.DEFAULT not in ["AMD"], "only amd")
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class TestTK(unittest.TestCase):
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def setUp(self):
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arch = Device["AMD"].arch
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if not arch.startswith("gfx9"):
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self.skipTest(f"arch {arch} not supported")
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@unittest.skipIf(CI, "no wmma in ci")
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def test_simple_matmul(self):
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N = 8192
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BLOCK_SIZE = 64
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with Kernel("simple_matmul", (N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
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warp = ker.warp
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c = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.bfloat16)
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b = ker.gl((1, 1, N, N), dtypes.bfloat16)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
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b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
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c_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.bfloat16, TileLayout.COL)
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c_reg_col = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
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c_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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c_reg_col = warp.zero(c_reg_col)
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for tile in ker.range(N // BLOCK_SIZE):
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a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
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b_smem = warp.load(b_smem, b, (), (0, 0, tile, col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.load(b_reg, b_smem)
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c_reg_col = warp.mma_AB(c_reg_col, a_reg, b_reg)
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c_reg_col = ker.endrange()
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c_smem = warp.store(c_smem, c_reg_col)
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c_reg = warp.load(c_reg, c_smem)
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c = warp.store(c, c_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
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b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
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c = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b, c)
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ei = ExecItem(sink, [t.uop.buffer for t in (c, a, b)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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c = c.float()
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ref = a.matmul(b, dtype=dtypes.float32).float()
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np.testing.assert_allclose(c.numpy(), ref.numpy())
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@unittest.skipIf(CI, "no wmma in ci")
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def test_simple_matmul_transposed(self):
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N = 8192
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BLOCK_N, BLOCK_M, BLOCK_K = 64, 64, 128
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with Kernel("simple_matmul_transposed", (N // BLOCK_N, N // BLOCK_M, 1), WARP_THREADS) as ker:
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warp = ker.warp
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c = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.bfloat16)
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b = ker.gl((1, 1, N, N), dtypes.bfloat16)
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a_smem = ker.st((BLOCK_N, BLOCK_K), dtypes.bfloat16, base_shape=ST_16X32)
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b_smem = ker.st((BLOCK_M, BLOCK_K), dtypes.bfloat16, base_shape=ST_16X32)
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a_reg = ker.rt((BLOCK_N, BLOCK_K), dtypes.bfloat16, base_shape=RT_16X32)
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b_reg = ker.rt((BLOCK_M, BLOCK_K), dtypes.bfloat16, base_shape=RT_16X32)
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c_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32, TileLayout.COL, base_shape=RT_16X16)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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c_reg = warp.zero(c_reg)
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for tile in ker.range(N // BLOCK_K):
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a_smem = warp.load(a_smem, a, (), (0, 0, row, tile), axis=2)
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b_smem = warp.load(b_smem, b, (), (0, 0, col, tile), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.load(b_reg, b_smem)
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c_reg = warp.mma_ABt(c_reg, a_reg, b_reg)
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c_reg = ker.endrange()
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c = warp.store(c, c_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
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b = Tensor.rand(1, 1, N, N, dtype="bfloat16").contiguous()
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c = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b, c)
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ei = ExecItem(sink, [t.uop.buffer for t in (c, a, b)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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c = c.float()
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ref = a.matmul(b.transpose(2, 3), dtype=dtypes.float32).float()
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np.testing.assert_allclose(c.numpy(), ref.numpy())
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def test_load_store(self):
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N = 64
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BLOCK_SIZE = 32
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with Kernel("load_store", (N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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a_smem = warp.load(a_smem, a, (), (0, 0, row, col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.copy(b_reg, a_reg)
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b = warp.store(b, b_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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ref = a.float()
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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def test_load_store_local_hop(self):
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N = 64
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BLOCK_SIZE = 32
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with Kernel("load_store_local_hop", (N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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a_smem = warp.load(a_smem, a, (), (0, 0, row, col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.copy(b_reg, a_reg)
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b_smem = warp.store(b_smem, b_reg)
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b_reg = warp.load(b_reg, b_smem)
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b = warp.store(b, b_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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ref = a.float()
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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def test_load_store_multioutput(self):
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N = 64
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BLOCK_SIZE = 32
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with Kernel("load_store_multioutput", (N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, N), dtypes.float32)
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c = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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a_smem = warp.load(a_smem, a, (), (0, 0, row, col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.copy(b_reg, a_reg)
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b_smem = warp.store(b_smem, b_reg)
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b_reg = warp.load(b_reg, b_smem)
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b = warp.store(b, b_reg, (0, 0, row, col), (), axis=2)
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c = warp.store(c, b_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish(2)
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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c = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b, c)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, c, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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c = c.float()
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ref = a.float()
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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np.testing.assert_allclose(c.numpy(), ref.numpy())
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@unittest.skip("TODO")
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def test_load_store_group(self):
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N = 256
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BLOCK_SIZE = 64
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with Kernel("load_store_group", (N // BLOCK_SIZE, N // BLOCK_SIZE, 1), WARP_THREADS * 2) as ker:
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warp = ker.warp
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group = ker.group(2)
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b = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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col, row = ker.blockIdx_x, ker.blockIdx_y
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a_smem = group.load(a_smem, a, (), (0, 0, row, col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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b_reg = warp.copy(b_reg, a_reg)
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b = warp.store(b, b_reg, (0, 0, row, col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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ref = a.float()
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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def test_add(self):
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N = 64
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BLOCK_SIZE = 32
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with Kernel("add", (1, 1, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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for tile_row in ker.range(N // BLOCK_SIZE):
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for tile_col in ker.range(N // BLOCK_SIZE):
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a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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a_reg += 1
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b = warp.store(b, a_reg, (0, 0, tile_row, tile_col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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ref = a.float() + 1
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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def test_max(self):
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N = 64
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BLOCK_SIZE = 32
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with Kernel("max", (1, 1, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, N), dtypes.float32)
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a = ker.gl((1, 1, N, N), dtypes.float32)
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a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
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a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
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b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
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max_reg = ker.rv(BLOCK_SIZE, dtypes.float32)
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for tile_col in ker.range(N // BLOCK_SIZE):
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max_reg = warp.neg_inf(max_reg.after(tile_col))
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for tile_row in ker.range(N // BLOCK_SIZE):
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a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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max_reg = warp.col_reduce(max_reg, a_reg, lambda a, b: a.maximum(b), init_value=-math.inf)
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max_reg = ker.endrange()
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b_reg = warp.map(b_reg, lambda _, idx: max_reg[idx[1], 0])
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for tile_row in ker.range(N // BLOCK_SIZE):
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b = warp.store(b, b_reg, (0, 0, tile_row, tile_col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, N, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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ref = a.float().max(axis=2, keepdim=True).expand(a.shape)
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np.testing.assert_allclose(b.numpy(), ref.numpy())
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def test_max_nonsquare(self):
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N, M = 32, 128
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BLOCK_N, BLOCK_M = 16, 64
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with Kernel("max_nonsquare", (1, 1, 1), WARP_THREADS) as ker:
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warp = ker.warp
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b = ker.gl((1, 1, N, M), dtypes.float32)
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a = ker.gl((1, 1, N, M), dtypes.float32)
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a_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
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a_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32, TileLayout.COL)
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b_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32, TileLayout.COL)
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max_reg = ker.rv(BLOCK_M, dtypes.float32)
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for tile_col in ker.range(M // BLOCK_M):
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max_reg = warp.neg_inf(max_reg.after(tile_col))
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for tile_row in ker.range(N // BLOCK_N):
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a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
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a_reg = warp.load(a_reg, a_smem)
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max_reg = warp.col_reduce(max_reg, a_reg, lambda a, b: a.maximum(b), init_value=-math.inf)
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max_reg = ker.endrange()
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b_reg = warp.map(b_reg, lambda _, idx: max_reg[idx[1], 0])
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for tile_row in ker.range(N // BLOCK_N):
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b = warp.store(b, b_reg, (0, 0, tile_row, tile_col), (), axis=2)
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sink = ker.finish()
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with Context(DEBUG=0):
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a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
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b = Tensor.empty(1, 1, N, M, dtype="float32")
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Tensor.realize(a, b)
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ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
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for _ in range(5): ei.run(wait=True)
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b = b.float()
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|
ref = a.float().max(axis=2, keepdim=True).expand(a.shape)
|
|
|
|
np.testing.assert_allclose(b.numpy(), ref.numpy())
|
|
|
|
def test_sum(self):
|
|
N = 64
|
|
BLOCK_SIZE = 32
|
|
with Kernel("sum", (1, 1, 1), WARP_THREADS) as ker:
|
|
warp = ker.warp
|
|
|
|
b = ker.gl((1, 1, N, N), dtypes.float32)
|
|
a = ker.gl((1, 1, N, N), dtypes.float32)
|
|
|
|
a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
|
|
|
|
a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
|
|
b_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
|
|
|
|
sum_reg = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
|
|
for tile_col in ker.range(N // BLOCK_SIZE):
|
|
sum_reg = warp.zero(sum_reg.after(tile_col))
|
|
|
|
for tile_row in ker.range(N // BLOCK_SIZE):
|
|
a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
|
|
a_reg = warp.load(a_reg, a_smem)
|
|
sum_reg = warp.col_reduce(sum_reg, a_reg, lambda a, b: a + b)
|
|
sum_reg = ker.endrange()
|
|
|
|
b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[1], 0])
|
|
|
|
for tile_row in ker.range(N // BLOCK_SIZE):
|
|
b = warp.store(b, b_reg, (0, 0, tile_row, tile_col), (), axis=2)
|
|
|
|
sink = ker.finish()
|
|
|
|
with Context(DEBUG=0):
|
|
a = Tensor.rand(1, 1, N, N, dtype="float32").contiguous()
|
|
b = Tensor.empty(1, 1, N, N, dtype="float32")
|
|
Tensor.realize(a, b)
|
|
|
|
ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
|
|
for _ in range(5): ei.run(wait=True)
|
|
b = b.float()
|
|
|
|
ref = a.float().sum(axis=2, keepdim=True).expand(a.shape)
|
|
|
|
np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
|
|
|
|
def test_sum_nonsquare(self):
|
|
N, M = 32, 128
|
|
BLOCK_N, BLOCK_M = 16, 64
|
|
with Kernel("sum_nonsquare", (1, 1, 1), WARP_THREADS) as ker:
|
|
warp = ker.warp
|
|
|
|
b = ker.gl((1, 1, N, M), dtypes.float32)
|
|
a = ker.gl((1, 1, N, M), dtypes.float32)
|
|
|
|
a_smem = ker.st((BLOCK_N, BLOCK_M), dtypes.float32)
|
|
|
|
a_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32, TileLayout.COL)
|
|
b_reg = ker.rt((BLOCK_N, BLOCK_M), dtypes.float32, TileLayout.COL)
|
|
|
|
sum_reg = ker.rv(BLOCK_M, dtypes.float32)
|
|
|
|
for tile_col in ker.range(M // BLOCK_M):
|
|
sum_reg = warp.zero(sum_reg.after(tile_col))
|
|
|
|
for tile_row in ker.range(N // BLOCK_N):
|
|
a_smem = warp.load(a_smem, a, (), (0, 0, tile_row, tile_col), axis=2)
|
|
a_reg = warp.load(a_reg, a_smem)
|
|
sum_reg = warp.col_reduce(sum_reg, a_reg, lambda a, b: a + b)
|
|
sum_reg = ker.endrange()
|
|
|
|
b_reg = warp.map(b_reg, lambda _, idx: sum_reg[idx[1], 0])
|
|
|
|
for tile_row in ker.range(N // BLOCK_N):
|
|
b = warp.store(b, b_reg, (0, 0, tile_row, tile_col), (), axis=2)
|
|
|
|
sink = ker.finish()
|
|
|
|
with Context(DEBUG=0):
|
|
a = Tensor.rand(1, 1, N, M, dtype="float32").contiguous()
|
|
b = Tensor.empty(1, 1, N, M, dtype="float32")
|
|
Tensor.realize(a, b)
|
|
|
|
ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
|
|
for _ in range(5): ei.run(wait=True)
|
|
b = b.float()
|
|
|
|
ref = a.float().sum(axis=2, keepdim=True).expand(a.shape)
|
|
|
|
np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
|
|
|
|
def test_softmax(self):
|
|
N = 64
|
|
BLOCK_SIZE = 32
|
|
with Kernel("softmax", (1, 1, 1), WARP_THREADS) as ker:
|
|
warp = ker.warp
|
|
|
|
b = ker.gl((1, 1, BLOCK_SIZE, N), dtypes.float32)
|
|
a = ker.gl((1, 1, BLOCK_SIZE, N), dtypes.float32)
|
|
|
|
a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
|
|
|
|
a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
|
|
|
|
max_vec_last = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
max_vec = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
norm_vec = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
|
|
max_vec = warp.neg_inf(max_vec)
|
|
norm_vec = warp.zero(norm_vec)
|
|
|
|
for tile_col in ker.range(N // BLOCK_SIZE):
|
|
a_smem_ = warp.load(a_smem, a, (), (0, 0, 0, tile_col), axis=2)
|
|
a_reg_ = warp.load(a_reg, a_smem_)
|
|
|
|
a_reg_ *= 1.0 / math.log(2)
|
|
|
|
max_vec_last = warp.copy(max_vec_last.after(tile_col), max_vec)
|
|
max_vec = warp.row_reduce(max_vec.after(max_vec_last), a_reg_, lambda a, b: a.maximum(b), init_value=-math.inf)
|
|
a_reg_ = (a_reg_ - max_vec).exp2()
|
|
max_vec_last = (max_vec_last - max_vec).exp2()
|
|
norm_vec *= max_vec_last
|
|
norm_vec = warp.row_reduce(norm_vec, a_reg_, lambda a, b: a + b)
|
|
norm_vec = ker.endrange()
|
|
max_vec = max_vec.after(norm_vec)
|
|
|
|
for tile_col in ker.range(N // BLOCK_SIZE):
|
|
a_smem_ = warp.load(a_smem, a, (), (0, 0, 0, tile_col), axis=2)
|
|
a_reg_ = warp.load(a_reg, a_smem_)
|
|
|
|
a_reg_ *= 1.0 / math.log(2)
|
|
a_reg_ = (a_reg_ - max_vec).exp2()
|
|
a_reg_ /= norm_vec
|
|
|
|
b = warp.store(b, a_reg_, (0, 0, 0, tile_col), (), axis=2)
|
|
|
|
sink = ker.finish()
|
|
|
|
with Context(DEBUG=0):
|
|
a = Tensor.rand(1, 1, BLOCK_SIZE, N, dtype="float32")
|
|
b = Tensor.empty(1, 1, BLOCK_SIZE, N, dtype="float32")
|
|
Tensor.realize(a, b)
|
|
|
|
ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
|
|
for _ in range(5): ei.run(wait=True)
|
|
b = b.float()
|
|
|
|
ref = a.float().softmax(axis=3)
|
|
|
|
np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
|
|
|
|
def test_softmax_col(self):
|
|
N = 64
|
|
BLOCK_SIZE = 32
|
|
with Kernel("softmax_col", (1, 1, 1), WARP_THREADS) as ker:
|
|
warp = ker.warp
|
|
|
|
b = ker.gl((1, 1, N, BLOCK_SIZE), dtypes.float32)
|
|
a = ker.gl((1, 1, N, BLOCK_SIZE), dtypes.float32)
|
|
|
|
a_smem = ker.st((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32)
|
|
|
|
a_reg = ker.rt((BLOCK_SIZE, BLOCK_SIZE), dtypes.float32, TileLayout.COL)
|
|
|
|
max_vec_last = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
max_vec = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
norm_vec = ker.rv(BLOCK_SIZE, dtypes.float32)
|
|
|
|
max_vec = warp.neg_inf(max_vec)
|
|
norm_vec = warp.zero(norm_vec)
|
|
|
|
for tile_row in ker.range(N // BLOCK_SIZE):
|
|
a_smem_ = warp.load(a_smem, a, (), (0, 0, tile_row, 0), axis=2)
|
|
a_reg_ = warp.load(a_reg, a_smem_)
|
|
|
|
a_reg_ *= 1.0 / math.log(2)
|
|
|
|
max_vec_last = warp.copy(max_vec_last.after(tile_row), max_vec)
|
|
max_vec = warp.col_reduce(max_vec.after(max_vec_last), a_reg_, lambda a, b: a.maximum(b), init_value=-math.inf)
|
|
a_reg_ = (a_reg_ - max_vec).exp2()
|
|
max_vec_last = (max_vec_last - max_vec).exp2()
|
|
norm_vec *= max_vec_last
|
|
norm_vec = warp.col_reduce(norm_vec, a_reg_, lambda a, b: a + b)
|
|
norm_vec = ker.endrange()
|
|
max_vec = max_vec.after(norm_vec)
|
|
|
|
for tile_row in ker.range(N // BLOCK_SIZE):
|
|
a_smem_ = warp.load(a_smem, a, (), (0, 0, tile_row, 0), axis=2)
|
|
a_reg_ = warp.load(a_reg.after(norm_vec), a_smem_)
|
|
|
|
a_reg_ *= 1.0 / math.log(2)
|
|
a_reg_ = (a_reg_ - max_vec).exp2()
|
|
a_reg_ /= norm_vec
|
|
|
|
b = warp.store(b, a_reg_, (0, 0, tile_row, 0), (), axis=2)
|
|
|
|
sink = ker.finish()
|
|
|
|
with Context(DEBUG=0):
|
|
a = Tensor.rand(1, 1, N, BLOCK_SIZE, dtype="float32")
|
|
b = Tensor.empty(1, 1, N, BLOCK_SIZE, dtype="float32")
|
|
Tensor.realize(a, b)
|
|
|
|
ei = ExecItem(sink, [t.uop.buffer for t in (b, a)], prg=get_runner(Device.DEFAULT, sink))
|
|
for _ in range(5): ei.run(wait=True)
|
|
b = b.float()
|
|
|
|
ref = a.float().softmax(axis=2)
|
|
|
|
np.testing.assert_allclose(b.numpy(), ref.numpy(), atol=1e-5, rtol=1e-5)
|
|
|
|
def test_fa(self):
|
|
NUM_WORKERS = 1
|
|
B, N, H, H_KV, D = 2, 8192, 32, 8, 128
|
|
Q_BLOCK_SIZE = 16
|
|
KV_BLOCK_SIZE = 16
|
|
GROUP_SIZE = H // H_KV
|
|
with Kernel("fa", (H, N // (Q_BLOCK_SIZE*NUM_WORKERS), B), NUM_WORKERS * WARP_THREADS) as ker:
|
|
warp = ker.warp
|
|
|
|
# kernel
|
|
o = ker.gl((B, N, H, D), dtypes.bfloat16)
|
|
q = ker.gl((B, N, H, D), dtypes.bfloat16)
|
|
k = ker.gl((B, N, H_KV, D), dtypes.bfloat16)
|
|
v = ker.gl((B, N, H_KV, D), dtypes.bfloat16)
|
|
|
|
head = ker.blockIdx_x
|
|
head_kv = head // GROUP_SIZE
|
|
batch = ker.blockIdx_z
|
|
q_seq = ker.blockIdx_y * NUM_WORKERS + ker.warpid
|
|
|
|
k_smem = ker.st((KV_BLOCK_SIZE, D), dtypes.bfloat16)
|
|
v_smem = ker.st((KV_BLOCK_SIZE, D), dtypes.bfloat16)
|
|
|
|
q_reg_fl = ker.rt((Q_BLOCK_SIZE, D), dtypes.float32)
|
|
q_reg = ker.rt((Q_BLOCK_SIZE, D), dtypes.bfloat16)
|
|
q_reg_transposed = ker.rt((D, Q_BLOCK_SIZE), dtypes.bfloat16, TileLayout.COL)
|
|
k_reg = ker.rt((KV_BLOCK_SIZE, D), dtypes.bfloat16)
|
|
k_reg_transposed = ker.rt((D, KV_BLOCK_SIZE), dtypes.bfloat16, TileLayout.COL)
|
|
v_reg = ker.rt((KV_BLOCK_SIZE, D), dtypes.bfloat16, TileLayout.COL)
|
|
o_reg = ker.rt((D, Q_BLOCK_SIZE), dtypes.float32, TileLayout.COL)
|
|
o_reg_transposed = ker.rt((Q_BLOCK_SIZE, D), dtypes.float32)
|
|
att_block = ker.rt((KV_BLOCK_SIZE, Q_BLOCK_SIZE), dtypes.float32, TileLayout.COL)
|
|
att_block_mma = ker.rt((KV_BLOCK_SIZE, Q_BLOCK_SIZE), dtypes.bfloat16, TileLayout.COL)
|
|
max_vec_last = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
|
|
max_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
|
|
norm_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
|
|
scale_vec = ker.rv(KV_BLOCK_SIZE, dtypes.float32)
|
|
|
|
max_vec = warp.neg_inf(max_vec)
|
|
norm_vec = warp.zero(norm_vec)
|
|
o_reg = warp.zero(o_reg)
|
|
scale_vec = warp.ones(scale_vec)
|
|
|
|
# load q tile
|
|
q_reg_fl = warp.load(q_reg_fl, q, (), (batch, q_seq, head, 0), axis=1)
|
|
q_reg_fl *= (1.0 / math.sqrt(D)) * (1.0 / math.log(2))
|
|
q_reg = warp.copy(q_reg, q_reg_fl)
|
|
q_reg_transposed = warp.transpose(q_reg_transposed, q_reg)
|
|
|
|
for kv_idx in ker.range(N // KV_BLOCK_SIZE):
|
|
k_smem = warp.load(k_smem, k, (), (batch, kv_idx, head_kv, 0), axis=1)
|
|
v_smem = warp.load(v_smem, v, (), (batch, kv_idx, head_kv, 0), axis=1)
|
|
|
|
k_reg = warp.load(k_reg, k_smem)
|
|
v_reg = warp.load(v_reg, v_smem)
|
|
|
|
# mma qk^t
|
|
att_block = warp.zero(att_block.after(kv_idx))
|
|
k_reg_transposed = warp.transpose(k_reg_transposed, k_reg)
|
|
att_block = warp.mma_AtB(att_block, k_reg_transposed, q_reg_transposed)
|
|
|
|
# mask for causal
|
|
q_base = q_seq * Q_BLOCK_SIZE + (warp.laneid % 16)
|
|
kv_base = kv_idx * KV_BLOCK_SIZE + (warp.laneid // 16) * 4
|
|
att_block = warp.map(att_block,
|
|
lambda x, idx: ((kv_base + idx[0]*16 + idx[2]) > (q_base + idx[1]*16)).alu(Ops.WHERE, UOp.ufix(x._uop, -math.inf), x))
|
|
|
|
# softmax
|
|
max_vec_last = warp.copy(max_vec_last.after(kv_idx), max_vec)
|
|
max_vec = warp.row_reduce(max_vec.after(max_vec_last), att_block, lambda a, b: a.maximum(b), init_value=-math.inf)
|
|
|
|
scale_vec = warp.map(scale_vec.after(max_vec_last, max_vec), lambda _, idx: max_vec_last[*idx] - max_vec[*idx])
|
|
scale_vec = scale_vec.exp2()
|
|
|
|
o_reg *= scale_vec
|
|
norm_vec *= scale_vec
|
|
|
|
att_block -= max_vec
|
|
att_block = att_block.exp2()
|
|
|
|
norm_vec = warp.row_reduce(norm_vec.after(scale_vec), att_block, lambda a, b: a + b)
|
|
|
|
# mma av
|
|
att_block_mma = warp.copy(att_block_mma.after(kv_idx, norm_vec), att_block)
|
|
o_reg = warp.mma_AtB(o_reg, v_reg, att_block_mma)
|
|
o_reg = ker.endrange()
|
|
norm_vec = norm_vec.after(o_reg)
|
|
|
|
o_reg /= norm_vec
|
|
|
|
o_reg_transposed = warp.transpose(o_reg_transposed, o_reg)
|
|
o = warp.store(o, o_reg_transposed, (batch, q_seq, head, 0), (), axis=1)
|
|
|
|
sink = ker.finish()
|
|
|
|
with Context(DEBUG=0):
|
|
q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16).contiguous()
|
|
k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
|
|
v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
|
|
out = Tensor.empty(B, N, H, D, dtype=dtypes.bfloat16)
|
|
Tensor.realize(q, k, v, out)
|
|
|
|
ei = ExecItem(sink, [t.uop.buffer for t in (out, q, k, v)], prg=get_runner(Device.DEFAULT, sink))
|
|
for _ in range(5):
|
|
et = ei.run(wait=True)
|
|
attn_flops = 2 * B * H * N * N * D + \
|
|
4 * B * H * N * N + \
|
|
2 * B * H * N * N * D
|
|
print(f"{attn_flops/(et*1e9):2f} GFLOPS")
|
|
out = out.float()
|
|
|
|
q_permuted = q.permute(0, 2, 1, 3)
|
|
k_permuted = k.permute(0, 2, 1, 3)
|
|
v_permuted = v.permute(0, 2, 1, 3)
|
|
ref = q_permuted.scaled_dot_product_attention(k_permuted, v_permuted, is_causal=True, enable_gqa=True).float()
|
|
ref = ref.permute(0, 2, 1, 3)
|
|
|
|
np.testing.assert_allclose(out.numpy(), ref.numpy(), atol=2e-2, rtol=2e-2)
|
|
|
|
def test_fast_fa(self):
|
|
from extra.thunder.tiny.fa import flash_attention
|
|
|
|
B, N, H, H_KV, D = 2, 8192, 32, 8, 128
|
|
|
|
with Context(DEBUG=0):
|
|
q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16).contiguous()
|
|
k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
|
|
v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16).contiguous()
|
|
Tensor.realize(q, k, v)
|
|
|
|
q, k, v = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
|
|
|
|
fa_jitted = TinyJit(flash_attention)
|
|
|
|
for _ in range(10):
|
|
st = time.perf_counter()
|
|
out = fa_jitted(q, k, v, is_causal=True)
|
|
et = time.perf_counter() - st
|
|
attn_flops = 2 * B * H * N * N * D + \
|
|
4 * B * H * N * N + \
|
|
2 * B * H * N * N * D
|
|
print(f"{attn_flops/(et*1e9):2f} GFLOPS")
|
|
out = out.float().transpose(1, 2)
|
|
|
|
ref = q.scaled_dot_product_attention(k, v, is_causal=True, enable_gqa=True).float().transpose(1, 2)
|
|
|
|
np.testing.assert_allclose(out.numpy(), ref.numpy(), atol=2e-2, rtol=2e-2)
|
|
|
|
def test_fast_fa_bwd(self):
|
|
from extra.thunder.tiny.fa import flash_attention
|
|
|
|
Tensor.manual_seed(42)
|
|
|
|
B, N, H, H_KV, D = 1, 32, 2, 1, 32
|
|
|
|
with Context(DEBUG=0):
|
|
q = Tensor.randn(B, N, H, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
|
|
k = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
|
|
v = Tensor.randn(B, N, H_KV, D, dtype=dtypes.bfloat16, requires_grad=True).contiguous()
|
|
Tensor.realize(q, k, v)
|
|
|
|
do = Tensor.ones(B, N, H, D, dtype=dtypes.float32).contiguous()
|
|
Tensor.realize(do)
|
|
|
|
q_, k_, v_ = q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)
|
|
out = flash_attention(q_, k_, v_)
|
|
out = out.float().transpose(1, 2)
|
|
out.backward(do)
|
|
Tensor.realize(q.grad, k.grad, v.grad)
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with Context(DEBUG=0):
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q_ref = q.detach().clone().requires_grad_(True)
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k_ref = k.detach().clone().requires_grad_(True)
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v_ref = v.detach().clone().requires_grad_(True)
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Tensor.realize(q_ref, k_ref, v_ref)
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q_ref_, k_ref_, v_ref_ = q_ref.transpose(1, 2), k_ref.transpose(1, 2), v_ref.transpose(1, 2)
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ref = q_ref_.scaled_dot_product_attention(k_ref_, v_ref_)
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ref = ref.float().transpose(1, 2)
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ref.backward(do)
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Tensor.realize(q_ref.grad, k_ref.grad, v_ref.grad)
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np.testing.assert_allclose(q.grad.numpy(), q_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
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np.testing.assert_allclose(v.grad.numpy(), v_ref.grad.numpy(), atol=2e-2, rtol=2e-2)
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np.testing.assert_allclose(k.grad.numpy(), k_ref.grad.numpy(), atol=5e-2, rtol=2e-2)
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if __name__ == "__main__":
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unittest.main()
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