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Merged
tqchen merged 1 commit into from
Jan 11, 2024
Merged

[Unity] Update dispatch test cases following the merge from main #16388

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1 change: 1 addition & 0 deletions python/tvm/relax/backend/dispatch_sort_scan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,6 +116,7 @@ def visit_call_(self, call: relax.Call) -> relax.Expr:
tir_call = self.builder_.call_te(
te_func,
call.args[0],
k=call.attrs.k,
axis=call.attrs.axis,
ret_type=call.attrs.ret_type,
is_ascend=not call.attrs.largest,
Expand Down
282 changes: 58 additions & 224 deletions tests/python/relax/test_backend_dispatch_sort_scan.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,84 +38,27 @@ class Before:
def foo(x: R.Tensor((2, 3), "float32", "llvm")):
with R.dataflow():
lv0 = R.cumsum(x, axis=1, dtype="float64", exclusive=False)
gv = R.cumprod(lv0, axis=1, dtype="float64", exclusive=False)
lv1 = R.cumprod(lv0, axis=1, dtype="float64", exclusive=False)
gv = lv1
R.output(gv)
return gv

@I.ir_module
class Expected:
I.module_global_infos({"vdevice": [I.vdevice("llvm", 0)]})
mod = DispatchSortScan()(Before)

@T.prim_func(private=True)
def cumsum(var_A: T.handle, out_buf: T.Buffer((T.int64(2), T.int64(3)), "float64")):
T.func_attr({"tir.noalias": T.bool(True)})
A = T.match_buffer(var_A, (T.int64(2), T.int64(3)), offset_factor=1)
with T.block("cumsum_generic"):
for fused in T.parallel(T.int64(2)):
out_buf[
fused * T.int64(3) // T.int64(3), fused * T.int64(3) % T.int64(3)
] = T.Cast(
"float64",
A[fused * T.int64(3) // T.int64(3), fused * T.int64(3) % T.int64(3)],
)
for _k in range(T.int64(2)):
out_buf[
(fused * T.int64(3) + (_k + T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1))) % T.int64(3),
] = out_buf[
(fused * T.int64(3) + (_k + T.int64(1) - T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1) - T.int64(1))) % T.int64(3),
] + T.Cast(
"float64",
A[
(fused * T.int64(3) + (_k + T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1))) % T.int64(3),
],
)

@T.prim_func(private=True)
def cumprod(var_A: T.handle, out_buf: T.Buffer((T.int64(2), T.int64(3)), "float64")):
T.func_attr({"tir.noalias": T.bool(True)})
A = T.match_buffer(var_A, (T.int64(2), T.int64(3)), "float64", offset_factor=1)
with T.block("cumprod_generic"):
T.reads(A[T.int64(0) : T.int64(2), T.int64(0) : T.int64(3)])
T.writes(out_buf[T.int64(0) : T.int64(2), T.int64(0) : T.int64(3)])
for fused in T.parallel(T.int64(2)):
out_buf[fused * T.int64(3) // T.int64(3), fused * T.int64(3) % T.int64(3)] = A[
fused * T.int64(3) // T.int64(3), fused * T.int64(3) % T.int64(3)
]
for _k in range(T.int64(2)):
out_buf[
(fused * T.int64(3) + (_k + T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1))) % T.int64(3),
] = (
out_buf[
(fused * T.int64(3) + (_k + T.int64(1) - T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1) - T.int64(1))) % T.int64(3),
]
* A[
(fused * T.int64(3) + (_k + T.int64(1))) // T.int64(3),
(fused * T.int64(3) + (_k + T.int64(1))) % T.int64(3),
]
)
vdevices = [I.vdevice("llvm", 0)]
x = relax.Var("x", R.Tensor((2, 3), "float32", vdevices[0]))
bb = relax.BlockBuilder()

@R.function
def foo(
x: R.Tensor((2, 3), dtype="float32", vdevice="llvm")
) -> R.Tensor((2, 3), dtype="float64", vdevice="llvm"):
cls = Expected
with R.dataflow():
lv0 = R.call_tir(cls.cumsum, (x,), out_sinfo=R.Tensor((2, 3), "float64", "llvm"))
gv = R.call_tir(
cls.cumprod,
(lv0,),
out_sinfo=R.Tensor((2, 3), dtype="float64", vdevice="llvm"),
)
R.output(gv)
return gv
with bb.function("foo", (x,), {"global_symbol": "foo"}):
with bb.dataflow():
lv0 = bb.emit_te(topi.cumsum, x, axis=1, dtype="float64", exclusive=False)
out = bb.emit_te(topi.cumprod, lv0, axis=1, dtype="float64", exclusive=False)
out = bb.emit_output(out)
bb.emit_func_output(out)
expected_mod = bb.finalize()
expected_mod.update_global_info("vdevice", vdevices)

mod = DispatchSortScan()(Before)
assert_structural_equal(mod, Expected)
assert_structural_equal(mod, expected_mod)


def test_dispatch_scanop_cuda():
Expand Down Expand Up @@ -172,60 +115,26 @@ class Before:
def foo(x: R.Tensor(("m", 3), "float32", "llvm")):
m = T.int64()
with R.dataflow():
gv = R.sort(x, axis=1, descending=False)
lv = R.sort(x, axis=1, descending=False)
gv = lv
R.output(gv)
return gv

@I.ir_module
class Expected:
I.module_global_infos({"vdevice": [I.vdevice("llvm", 0)]})

@T.prim_func(private=True)
def sort(var_A: T.handle, var_sort_cpu: T.handle):
T.func_attr({"tir.noalias": T.bool(True)})
m = T.int64()
data_buf = T.match_buffer(var_A, (m, T.int64(3)), align=8)
out_buf = T.match_buffer(var_sort_cpu, (m, T.int64(3)), align=8)
with T.block("sort_cpu"):
T.reads()
T.writes()
T.call_packed(
"tvm.contrib.sort.sort",
T.tvm_stack_make_array(
data_buf.data,
T.tvm_stack_make_shape(m, T.int64(3)),
0,
2,
T.float32(0),
T.int64(0),
),
T.tvm_stack_make_array(
out_buf.data,
T.tvm_stack_make_shape(m, T.int64(3)),
0,
2,
T.float32(0),
T.int64(0),
),
1,
T.bool(True),
)
vdevices = [I.vdevice("llvm", 0)]
m = tir.Var("m", "int64")
x = relax.Var("x", R.Tensor((m, 3), "float32", vdevices[0]))
bb = relax.BlockBuilder()

@R.function
def foo(
x: R.Tensor(("m", 3), dtype="float32", vdevice="llvm")
) -> R.Tensor(("m", 3), dtype="float32", vdevice="llvm"):
m = T.int64()
cls = Expected
with R.dataflow():
gv = R.call_tir(
cls.sort, (x,), out_sinfo=R.Tensor((m, 3), dtype="float32", vdevice="llvm")
)
R.output(gv)
return gv
with bb.function("foo", (x,), {"global_symbol": "foo"}):
with bb.dataflow():
out = bb.emit_te(topi.sort, x, axis=1, is_ascend=True)
out = bb.emit_output(out)
bb.emit_func_output(out)
expected_mod = bb.finalize()
expected_mod.update_global_info("vdevice", vdevices)

mod = DispatchSortScan()(Before)
assert_structural_equal(mod, Expected)
assert_structural_equal(mod, expected_mod)


def test_dispatch_sort_cuda():
Expand Down Expand Up @@ -295,55 +204,26 @@ class Before:
def foo(x: R.Tensor(("m", 3), "float32", "llvm")):
m = T.int64()
with R.dataflow():
gv = R.argsort(x, axis=1, descending=False)
lv = R.argsort(x, axis=1, descending=False, dtype="int32")
gv = lv
R.output(gv)
return gv

@I.ir_module
class Expected:
I.module_global_infos({"vdevice": [I.vdevice("llvm", 0)]})

@T.prim_func(private=True)
def argsort(var_A: T.handle, var_argsort_cpu: T.handle):
T.func_attr({"tir.noalias": T.bool(True)})
m = T.int64()
data_buf = T.match_buffer(var_A, (m, T.int64(3)), align=8)
out_buf = T.match_buffer(var_argsort_cpu, (m, T.int64(3)), "int32", align=8)
with T.block("argsort_cpu"):
T.reads(data_buf[T.int64(0) : m, T.int64(0) : T.int64(3)])
T.writes(out_buf[T.int64(0) : m, T.int64(0) : T.int64(3)])
T.call_packed(
"tvm.contrib.sort.argsort",
T.tvm_stack_make_array(
data_buf.data,
T.tvm_stack_make_shape(m, T.int64(3)),
0,
2,
T.float32(0),
T.int64(0),
),
T.tvm_stack_make_array(
out_buf.data, T.tvm_stack_make_shape(m, T.int64(3)), 0, 2, 0, T.int64(0)
),
1,
T.bool(True),
)
vdevices = [I.vdevice("llvm", 0)]
m = tir.Var("m", "int64")
x = relax.Var("x", R.Tensor((m, 3), "float32", vdevices[0]))
bb = relax.BlockBuilder()

@R.function
def foo(
x: R.Tensor(("m", 3), dtype="float32", vdevice="llvm")
) -> R.Tensor(("m", 3), dtype="int32", vdevice="llvm"):
m = T.int64()
cls = Expected
with R.dataflow():
gv = R.call_tir(
cls.argsort, (x,), out_sinfo=R.Tensor((m, 3), dtype="int32", vdevice="llvm")
)
R.output(gv)
return gv
with bb.function("foo", (x,), {"global_symbol": "foo"}):
with bb.dataflow():
out = bb.emit_te(topi.argsort, x, axis=1, is_ascend=True, dtype="int32")
out = bb.emit_output(out)
bb.emit_func_output(out)
expected_mod = bb.finalize()
expected_mod.update_global_info("vdevice", vdevices)

mod = DispatchSortScan()(Before)
assert_structural_equal(mod, Expected)
assert_structural_equal(mod, expected_mod)


def test_dispatch_argsort_cuda():
Expand Down Expand Up @@ -410,71 +290,26 @@ class Before:
def foo(x: R.Tensor(("m", 3), "float32", "llvm")):
m = T.int64()
with R.dataflow():
gv = R.topk(x, k=2, axis=1, largest=True)
lv = R.topk(x, k=2, axis=1, largest=True)
gv = lv
R.output(gv)
return gv

@I.ir_module
class Expected:
I.module_global_infos({"vdevice": [I.vdevice("llvm", 0)]})

@T.prim_func(private=True)
def topk(var_A: T.handle, var_topk_cpu_v0: T.handle, var_topk_cpu_v1: T.handle):
T.func_attr({"tir.noalias": T.bool(True)})
m = T.int64()
data_buf = T.match_buffer(var_A, (m, T.int64(3)), align=8)
value_buf = T.match_buffer(var_topk_cpu_v0, (m, T.int64(1)), align=8)
indices_buf = T.match_buffer(var_topk_cpu_v1, (m, T.int64(1)), "int32", align=8)
with T.block("topk_cpu"):
T.reads(data_buf[T.int64(0) : m, T.int64(0) : T.int64(3)])
T.writes(
value_buf[T.int64(0) : m, T.int64(0)], indices_buf[T.int64(0) : m, T.int64(0)]
)
T.call_packed(
"tvm.contrib.sort.topk",
T.tvm_stack_make_array(
data_buf.data,
T.tvm_stack_make_shape(m, T.int64(3)),
0,
2,
T.float32(0),
T.int64(0),
),
T.tvm_stack_make_array(
value_buf.data, T.tvm_stack_make_shape(m, 1), 0, 2, T.float32(0), T.int64(0)
),
T.tvm_stack_make_array(
indices_buf.data, T.tvm_stack_make_shape(m, 1), 0, 2, 0, T.int64(0)
),
1,
1,
"both",
T.bool(False),
)
vdevices = [I.vdevice("llvm", 0)]
m = tir.Var("m", "int64")
x = relax.Var("x", R.Tensor((m, 3), "float32", vdevices[0]))
bb = relax.BlockBuilder()

@R.function
def foo(
x: R.Tensor(("m", 3), dtype="float32", vdevice="llvm")
) -> R.Tuple(
R.Tensor(("m", 1), dtype="float32", vdevice="llvm"),
R.Tensor(("m", 1), dtype="int32", vdevice="llvm"),
):
m = T.int64()
cls = Expected
with R.dataflow():
gv = R.call_tir(
cls.topk,
(x,),
out_sinfo=[
R.Tensor((m, 1), dtype="float32", vdevice="llvm"),
R.Tensor((m, 1), dtype="int32", vdevice="llvm"),
],
)
R.output(gv)
return gv
with bb.function("foo", (x,), {"global_symbol": "foo"}):
with bb.dataflow():
out = bb.emit_te(topi.topk, x, k=2, axis=1, is_ascend=False, dtype="int32")
out = bb.emit_output(out)
bb.emit_func_output(out)
expected_mod = bb.finalize()
expected_mod.update_global_info("vdevice", vdevices)

mod = DispatchSortScan()(Before)
assert_structural_equal(mod, Expected)
assert_structural_equal(mod, expected_mod)


def test_dispatch_topk_cuda():
Expand All @@ -494,12 +329,11 @@ def foo(x: R.Tensor((2, 3), "float32", "cuda")):

vdevices = [I.vdevice("cuda", 0)]
x = relax.Var("x", R.Tensor((2, 3), "float32", vdevices[0]))
y = relax.Var("y", R.Tensor((2, 3), "float32"))
bb = relax.BlockBuilder()
with target:
with bb.function("foo", (x,), {"global_symbol": "foo"}):
with bb.dataflow():
out = bb.emit_te(topi.cuda.topk, x, axis=1, is_ascend=False, dtype="int32")
out = bb.emit_te(topi.cuda.topk, x, k=2, axis=1, is_ascend=False, dtype="int32")
out = bb.emit_output(out)
bb.emit_func_output(out)
expected_mod = bb.finalize()
Expand Down