Lunderberg commented on code in PR #14280:
URL: https://github.com/apache/tvm/pull/14280#discussion_r1133951480
##########
tests/python/unittest/test_tir_transform_lower_warp_memory.py:
##########
@@ -347,5 +348,105 @@ def test_lower_warp_memory_divide_by_factor():
tvm.tir.transform.LowerWarpMemory()(mod)["f_kernel0"]
[email protected]_func
+def func(a: T.handle, b: T.handle) -> None:
+ A = T.match_buffer(a, [32], "float32")
+ B = T.match_buffer(b, [32], "float32")
+ for i in range(32):
+ with T.block("warp_shuffle"):
+ vi = T.axis.spatial(32, i)
+ B[vi] = A[(vi % 4) * 8 + vi // 4] + T.float32(1)
+
+
+def test_warp_shuffle_transform():
+ @tvm.script.ir_module
+ class Before:
+ @T.prim_func
+ def main(A: T.handle("float32", "global"), B: T.handle("float32",
"global")):
+ blockIdx_x = T.env_thread("blockIdx.x")
+ threadIdx_x = T.env_thread("threadIdx.x")
+ T.func_attr(
Review Comment:
It looks like the test case only requires the `"target"` attribute, and only
requires `"kind"` and `"thread_warp_size"` within that. Can we remove the
extra attributes from the unit test?
##########
tests/python/unittest/test_tir_transform_lower_warp_memory.py:
##########
@@ -347,5 +348,105 @@ def test_lower_warp_memory_divide_by_factor():
tvm.tir.transform.LowerWarpMemory()(mod)["f_kernel0"]
[email protected]_func
+def func(a: T.handle, b: T.handle) -> None:
+ A = T.match_buffer(a, [32], "float32")
+ B = T.match_buffer(b, [32], "float32")
+ for i in range(32):
+ with T.block("warp_shuffle"):
+ vi = T.axis.spatial(32, i)
+ B[vi] = A[(vi % 4) * 8 + vi // 4] + T.float32(1)
+
+
+def test_warp_shuffle_transform():
+ @tvm.script.ir_module
+ class Before:
+ @T.prim_func
+ def main(A: T.handle("float32", "global"), B: T.handle("float32",
"global")):
+ blockIdx_x = T.env_thread("blockIdx.x")
+ threadIdx_x = T.env_thread("threadIdx.x")
+ T.func_attr(
+ {
+ "calling_conv": 2,
+ "global_symbol": "main",
+ "target": T.target(
+ {
+ "host": {"keys": ["cpu"], "kind": "llvm", "tag":
""},
+ "keys": ["cuda", "gpu"],
+ "kind": "cuda",
+ "max_num_threads": 1024,
+ "tag": "",
+ "thread_warp_size": 32,
+ }
+ ),
+ "tir.device_thread_axis": [
+ T.iter_var(blockIdx_x, [0, 1], "ThreadIndex",
"blockIdx.x"),
+ T.iter_var(threadIdx_x, [0, 32], "ThreadIndex",
"threadIdx.x"),
+ ],
+ "tir.is_global_func": 1,
+ "tir.noalias": 1,
+ }
+ )
+ T.launch_thread(blockIdx_x, 1)
+ A_warp = T.allocate([32], "float32", "warp")
+ B_warp = T.allocate([32], "float32", "warp")
+ T.launch_thread(threadIdx_x, 32)
+ A_warp_1 = T.Buffer((32,), data=A_warp, scope="warp")
+ A_1 = T.Buffer((32,), data=A)
Review Comment:
Instead of having a separate `A: T.handle` and `A_1: T.Buffer`, the buffer
could be declared as a parameter `A_1: T.Buffer(32)`. It does result in
slightly different TIR, as it follows the style from before `MakePackedAPI` is
applied, but for a unit test would help to emphasize the change being tested.
##########
tests/python/unittest/test_tir_transform_lower_warp_memory.py:
##########
@@ -347,5 +348,105 @@ def test_lower_warp_memory_divide_by_factor():
tvm.tir.transform.LowerWarpMemory()(mod)["f_kernel0"]
[email protected]_func
+def func(a: T.handle, b: T.handle) -> None:
+ A = T.match_buffer(a, [32], "float32")
+ B = T.match_buffer(b, [32], "float32")
+ for i in range(32):
+ with T.block("warp_shuffle"):
+ vi = T.axis.spatial(32, i)
+ B[vi] = A[(vi % 4) * 8 + vi // 4] + T.float32(1)
+
+
+def test_warp_shuffle_transform():
+ @tvm.script.ir_module
+ class Before:
+ @T.prim_func
+ def main(A: T.handle("float32", "global"), B: T.handle("float32",
"global")):
+ blockIdx_x = T.env_thread("blockIdx.x")
+ threadIdx_x = T.env_thread("threadIdx.x")
+ T.func_attr(
+ {
+ "calling_conv": 2,
+ "global_symbol": "main",
+ "target": T.target(
+ {
+ "host": {"keys": ["cpu"], "kind": "llvm", "tag":
""},
+ "keys": ["cuda", "gpu"],
+ "kind": "cuda",
+ "max_num_threads": 1024,
+ "tag": "",
+ "thread_warp_size": 32,
+ }
+ ),
+ "tir.device_thread_axis": [
+ T.iter_var(blockIdx_x, [0, 1], "ThreadIndex",
"blockIdx.x"),
+ T.iter_var(threadIdx_x, [0, 32], "ThreadIndex",
"threadIdx.x"),
+ ],
+ "tir.is_global_func": 1,
+ "tir.noalias": 1,
+ }
+ )
+ T.launch_thread(blockIdx_x, 1)
+ A_warp = T.allocate([32], "float32", "warp")
+ B_warp = T.allocate([32], "float32", "warp")
+ T.launch_thread(threadIdx_x, 32)
+ A_warp_1 = T.Buffer((32,), data=A_warp, scope="warp")
+ A_1 = T.Buffer((32,), data=A)
+ A_warp_1[threadIdx_x] = A_1[threadIdx_x]
+ B_warp_1 = T.Buffer((32,), data=B_warp, scope="warp")
+ T.tvm_storage_sync("warp")
+ B_warp_1[threadIdx_x] = A_warp_1[threadIdx_x % 4 * 8 + threadIdx_x
// 4] + T.float32(1)
Review Comment:
Could we add a comment here, indicating that this line is the one that
should be updated correctly?
##########
tests/python/unittest/test_tir_transform_lower_warp_memory.py:
##########
@@ -347,5 +348,105 @@ def test_lower_warp_memory_divide_by_factor():
tvm.tir.transform.LowerWarpMemory()(mod)["f_kernel0"]
[email protected]_func
+def func(a: T.handle, b: T.handle) -> None:
+ A = T.match_buffer(a, [32], "float32")
+ B = T.match_buffer(b, [32], "float32")
+ for i in range(32):
+ with T.block("warp_shuffle"):
+ vi = T.axis.spatial(32, i)
+ B[vi] = A[(vi % 4) * 8 + vi // 4] + T.float32(1)
+
+
+def test_warp_shuffle_transform():
Review Comment:
The test looks reasonable as-is, though there's also a
`tvm.testing.CompareBeforeAfter` that you could use to further reduce the
boilerplate.
```python
class TestWarpShuffleTransform(tvm.testing.CompareBeforeAfter):
transform = tvm.tir.transform.LowerWarpMemory()
def before(A: T.handle("float32", "global"), B: T.handle("float32",
"global")):
...
def expected(A: T.handle("float32", "global"), B: T.handle("float32",
"global")):
...
```
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