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Add elementwise and broadcast simulator tests #56

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Jul 31, 2024
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Add elementwise and broadcast simulator tests #56

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146 changes: 146 additions & 0 deletions tests/kernel/wave/wave_sim_test.py
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Expand Up @@ -6,6 +6,152 @@
from numpy.testing import assert_allclose


def test_eltwise():
# Input sizes
M = tkl.sym.M
N = tkl.sym.N
# Workgroup tile sizes
BLOCK_M = tkl.sym.BLOCK_M
BLOCK_N = tkl.sym.BLOCK_N
# Address space (for GPU, shared(1) or global(0))
ADDRESS_SPACE = tkl.sym.ADDRESS_SPACE
# Other hyperparameters
LOAD_ELEMS_PER_THREAD = tkl.sym.LOAD_ELEMS_PER_THREAD
STORE_ELEMS_PER_THREAD = tkl.sym.STORE_ELEMS_PER_THREAD

# Expose user-constraints
constraints: list[tkw.Constraint] = [tkw.WorkgroupConstraint(M, BLOCK_M, 0)]
constraints += [tkw.WorkgroupConstraint(N, BLOCK_N, 1)]

constraints += [
tkw.HardwareConstraint(threads_per_wave=64, waves_per_block=(1, 1, 1))
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]

@wave_sim(constraints)
def eltwise(
a: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
b: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
c: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
):
a_reg = tkw.read(a, elements_per_thread=LOAD_ELEMS_PER_THREAD)
b_reg = tkw.read(b, elements_per_thread=LOAD_ELEMS_PER_THREAD)
tkw.write(a_reg + b_reg, c, elements_per_thread=STORE_ELEMS_PER_THREAD)

a = torch.randn(128, 256, dtype=torch.float32)
b = torch.randn(128, 256, dtype=torch.float32)
c = torch.zeros(128, 256, dtype=torch.float32)
eltwise(a, b, c)
assert_allclose(c, a + b)


def test_broadcast_1():
# Input sizes
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Nice!

M = tkl.sym.M
N = tkl.sym.N
# Workgroup tile sizes
BLOCK_M = tkl.sym.BLOCK_M
BLOCK_N = tkl.sym.BLOCK_N
# Address space (for GPU, shared(1) or global(0))
ADDRESS_SPACE = tkl.sym.ADDRESS_SPACE
# Other hyperparameters
LOAD_ELEMS_PER_THREAD = tkl.sym.LOAD_ELEMS_PER_THREAD
STORE_ELEMS_PER_THREAD = tkl.sym.STORE_ELEMS_PER_THREAD

# Expose user-constraints
constraints: list[tkw.Constraint] = [tkw.WorkgroupConstraint(M, BLOCK_M, 0)]
constraints += [tkw.WorkgroupConstraint(N, BLOCK_N, 1)]

constraints += [
tkw.HardwareConstraint(threads_per_wave=64, waves_per_block=(1, 1, 1))
]

@wave_sim(constraints)
def eltwise(
a: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
b: tkl.Memory[N, ADDRESS_SPACE, tkl.f32],
c: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
):
a_reg = tkw.read(a, elements_per_thread=LOAD_ELEMS_PER_THREAD)
b_reg = tkw.read(b, elements_per_thread=LOAD_ELEMS_PER_THREAD)
tkw.write(a_reg + b_reg, c, elements_per_thread=STORE_ELEMS_PER_THREAD)

a = torch.randn(128, 256, dtype=torch.float32)
b = torch.randn(256, dtype=torch.float32)
c = torch.zeros(128, 256, dtype=torch.float32)
eltwise(a, b, c)
assert_allclose(c, a + b)


def test_broadcast_2():
# Input sizes
M = tkl.sym.M
N = tkl.sym.N
# Workgroup tile sizes
BLOCK_M = tkl.sym.BLOCK_M
BLOCK_N = tkl.sym.BLOCK_N
# Address space (for GPU, shared(1) or global(0))
ADDRESS_SPACE = tkl.sym.ADDRESS_SPACE
# Other hyperparameters
LOAD_ELEMS_PER_THREAD = tkl.sym.LOAD_ELEMS_PER_THREAD
STORE_ELEMS_PER_THREAD = tkl.sym.STORE_ELEMS_PER_THREAD

# Expose user-constraints
constraints: list[tkw.Constraint] = [tkw.WorkgroupConstraint(M, BLOCK_M, 0)]
constraints += [tkw.WorkgroupConstraint(N, BLOCK_N, 1)]

constraints += [
tkw.HardwareConstraint(threads_per_wave=64, waves_per_block=(1, 1, 1))
]

@wave_sim(constraints)
def eltwise(
b: tkl.Memory[N, ADDRESS_SPACE, tkl.f32],
c: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
):
b_reg = tkw.read(b, elements_per_thread=LOAD_ELEMS_PER_THREAD)
tkw.write(b_reg, c, elements_per_thread=STORE_ELEMS_PER_THREAD)

b = torch.randn(256, dtype=torch.float32)
c = torch.zeros(128, 256, dtype=torch.float32)
eltwise(b, c)
assert_allclose(c, b + torch.zeros(128, 256, dtype=torch.float32))


def test_broadcast_3():
# Input sizes
M = tkl.sym.M
N = tkl.sym.N
# Workgroup tile sizes
BLOCK_M = tkl.sym.BLOCK_M
BLOCK_N = tkl.sym.BLOCK_N
# Address space (for GPU, shared(1) or global(0))
ADDRESS_SPACE = tkl.sym.ADDRESS_SPACE
# Other hyperparameters
LOAD_ELEMS_PER_THREAD = tkl.sym.LOAD_ELEMS_PER_THREAD
STORE_ELEMS_PER_THREAD = tkl.sym.STORE_ELEMS_PER_THREAD

# Expose user-constraints
constraints: list[tkw.Constraint] = [tkw.WorkgroupConstraint(M, BLOCK_M, 0)]
constraints += [tkw.WorkgroupConstraint(N, BLOCK_N, 1)]

constraints += [
tkw.HardwareConstraint(threads_per_wave=64, waves_per_block=(1, 1, 1))
]

@wave_sim(constraints)
def eltwise(
b: tkl.Memory[N, ADDRESS_SPACE, tkl.f32],
c: tkl.Memory[M, N, ADDRESS_SPACE, tkl.f32],
):
b_reg = tkw.read(b, elements_per_thread=LOAD_ELEMS_PER_THREAD)[0]
tkw.write(b_reg, c, elements_per_thread=STORE_ELEMS_PER_THREAD)

b = torch.randn(256, dtype=torch.float32)
c = torch.zeros(128, 256, dtype=torch.float32)
eltwise(b, c)
assert_allclose(c, b[0] + torch.zeros(128, 256, dtype=torch.float32))


def test_gemm():
# Input sizes
M = tkl.sym.M
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