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Merged
junrushao merged 1 commit into from
Aug 4, 2023
Merged

[Unity][DLight] Use less shared memory for gemv #15482

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16 changes: 10 additions & 6 deletions python/tvm/dlight/gpu/gemv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -219,27 +219,31 @@ def get_extent(loop_rv: tir.schedule.LoopRV):
else:
len_ty = min(len_s, 4)

# Use `split_k` to prevent too large shared memory usage
split_k: int = 4

_, tx = sch.split(r, [None, len_tx], preserve_unit_iters=True)
# Schedule the RF block
rf = sch.rfactor(tx, 0)
batch, bx, r, tx, _ = sch.get_loops(rf)
sch.reorder(bx, tx, r)
ro, ri = sch.split(r, [split_k, None], preserve_unit_iters=True)
bx, ty = sch.split(bx, [None, len_ty], preserve_unit_iters=True)

sch.bind(batch, "blockIdx.y")
sch.bind(bx, "blockIdx.x")
sch.bind(ty, "threadIdx.y")
sch.bind(tx, "threadIdx.x")
unit = sch.add_unit_loop(r)
sch.annotate(unit, "pragma_auto_unroll_max_step", unroll_number)
sch.annotate(unit, "pragma_unroll_explicit", 1)
sch.annotate(ro, "pragma_auto_unroll_max_step", unroll_number)
sch.annotate(ro, "pragma_unroll_explicit", 1)

if target.kind.name == "cuda":
# Cache read the vector
def cache_shared(index: int):
block: tir.Block = sch.get(rf)
type_bytes: int = get_bytes(block.reads[index].buffer.dtype)
cache = sch.cache_read(rf, index, "shared")
sch.compute_at(cache, unit, preserve_unit_loops=True)
sch.compute_at(cache, ro, preserve_unit_loops=True)
fused = sch.fuse(*sch.get_loops(cache)[5:])
loop: tir.For = sch.get(fused)
vec_length = vec_bytes // type_bytes
Expand All @@ -256,7 +260,7 @@ def cache_local(index: int):
type_bytes: int = get_bytes(block.reads[index].buffer.dtype)
vec_length = vec_bytes // type_bytes
cache = sch.cache_read(rf, index, "local")
sch.compute_at(cache, r, preserve_unit_loops=True)
sch.compute_at(cache, ri, preserve_unit_loops=True)
fused = sch.fuse(*sch.get_loops(cache)[6:])
loop: tir.For = sch.get(fused)
if isinstance(loop.extent, tir.IntImm) and loop.extent.value % vec_length == 0:
Expand All @@ -273,7 +277,7 @@ def cache_local(index: int):
# TODO: cache scale buffer in Decode-GEMV to shared memory

sch.set_scope(rf, 0, "local")
sch.decompose_reduction(rf, r)
sch.decompose_reduction(rf, ro)
# Schedule the write back block
sch.reverse_compute_at(block, ty, preserve_unit_loops=True)
_, _, _, tx, *s = sch.get_loops(block)
Expand Down
80 changes: 41 additions & 39 deletions tests/python/dlight/test_gpu_gemv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,40 +97,40 @@ def expected(lv1637: T.Buffer((1, 32, 1, 128), "float16"), p_lv1638: T.handle, p
for ax1_fused_0 in T.thread_binding(n, thread="blockIdx.x"):
for ax1_fused_1 in T.thread_binding(1, thread="threadIdx.y"):
for ax2_fused_1 in T.thread_binding(32, thread="threadIdx.x"):
for u in T.serial(1, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
with T.block("NT_matmul_rf_init"):
vax2_fused_1, v0 = T.axis.remap("SS", [ax2_fused_1, ax0_fused])
v1 = T.axis.spatial(n, ax1_fused_0 + ax1_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1])
var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1] = T.float16(0)
for ax2_fused_0_0 in T.serial(4, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
for ax0_ax1_ax2_ax3_fused_0 in range(1):
for ax0_ax1_ax2_ax3_fused_1 in T.thread_binding(1, thread="threadIdx.y"):
for ax0_ax1_ax2_ax3_fused_2 in T.thread_binding(32, thread="threadIdx.x"):
for ax0_ax1_ax2_ax3_fused_3 in T.vectorized(4):
for ax0_ax1_ax2_ax3_fused_3 in T.vectorized(1):
with T.block("lv1637_shared"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(32, ax0_fused)
v2 = T.axis.spatial(1, 0)
v3 = T.axis.spatial(128, ax0_ax1_ax2_ax3_fused_0 * 128 + ax0_ax1_ax2_ax3_fused_1 * 128 + ax0_ax1_ax2_ax3_fused_2 * 4 + ax0_ax1_ax2_ax3_fused_3)
v3 = T.axis.spatial(128, ax2_fused_0_0 * 32 + ax0_ax1_ax2_ax3_fused_0 * 32 + ax0_ax1_ax2_ax3_fused_1 * 32 + ax0_ax1_ax2_ax3_fused_2 + ax0_ax1_ax2_ax3_fused_3)
T.reads(lv1637[v0, v1, v2, v3])
T.writes(lv1637_shared[v0, v1, v2, v3])
lv1637_shared[v0, v1, v2, v3] = lv1637[v0, v1, v2, v3]
with T.block("NT_matmul_rf_init"):
vax2_fused_1, v0 = T.axis.remap("SS", [ax2_fused_1, ax0_fused])
v1 = T.axis.spatial(n, ax1_fused_0 + ax1_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1])
var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1] = T.float16(0)
for ax2_fused_0 in range(4):
for ax2_fused_0_1 in range(1):
for ax0_ax1_ax2_ax3_fused in T.vectorized(1):
with T.block("lv1637_shared_local"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(32, ax0_fused)
v2 = T.axis.spatial(1, 0)
v3 = T.axis.spatial(128, ax2_fused_0 * 32 + ax2_fused_1)
v3 = T.axis.spatial(128, ax2_fused_0_0 * 32 + ax2_fused_1)
T.reads(lv1637_shared[v0, v1, v2, v3])
T.writes(lv1637_shared_local[v0, v1, v2, v3])
lv1637_shared_local[v0, v1, v2, v3] = lv1637_shared[v0, v1, v2, v3]
for u_1 in range(1):
for u in range(1):
with T.block("NT_matmul_rf_update"):
vax2_fused_1, v0 = T.axis.remap("SS", [ax2_fused_1, ax0_fused])
v1 = T.axis.spatial(n, ax1_fused_0 + ax1_fused_1)
vax2_fused_0 = T.axis.reduce(4, ax2_fused_0)
vax2_fused_0 = T.axis.reduce(4, ax2_fused_0_0 + ax2_fused_0_1)
T.reads(var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1], lv1637_shared_local[0, v0, 0, vax2_fused_0 * 32 + vax2_fused_1], lv1638[0, v0, v1, vax2_fused_0 * 32 + vax2_fused_1])
T.writes(var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1])
var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1] = var_NT_matmul_intermediate_rf_local[vax2_fused_1, 0, v0, 0, v1] + lv1637_shared_local[0, v0, 0, vax2_fused_0 * 32 + vax2_fused_1] * lv1638[0, v0, v1, vax2_fused_0 * 32 + vax2_fused_1]
Expand Down Expand Up @@ -186,39 +186,40 @@ def expected(lv571: T.Buffer((22016, 512), "uint32"), lv572: T.Buffer((22016, 12
for ax0_fused_0 in T.thread_binding(2752, thread="blockIdx.x"):
for ax0_fused_1 in T.thread_binding(8, thread="threadIdx.y"):
for ax1_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"):
for u in T.serial(1, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
for ax0_ax1_ax2_fused_0 in range(2):
with T.block("NT_matmul_rf_init"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(22016, ax0_fused_0 * 8 + ax0_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = T.float16(0)
for ax1_0_fused_0_0 in T.serial(4, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
for ax0_ax1_ax2_fused_0 in range(1):
for ax0_ax1_ax2_fused_1 in T.thread_binding(8, thread="threadIdx.y"):
for ax0_ax1_ax2_fused_2 in T.thread_binding(32, thread="threadIdx.x"):
for ax0_ax1_ax2_fused_3 in T.vectorized(8):
for ax0_ax1_ax2_fused_3 in T.vectorized(4):
with T.block("lv1654_shared"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(1, 0)
v2 = T.axis.spatial(4096, ax0_ax1_ax2_fused_0 * 2048 + ax0_ax1_ax2_fused_1 * 256 + ax0_ax1_ax2_fused_2 * 8 + ax0_ax1_ax2_fused_3)
v2 = T.axis.spatial(4096, ax1_0_fused_0_0 * 1024 + ax0_ax1_ax2_fused_0 * 1024 + ax0_ax1_ax2_fused_1 * 128 + ax0_ax1_ax2_fused_2 * 4 + ax0_ax1_ax2_fused_3)
T.reads(lv1654[v0, v1, v2])
T.writes(lv1654_shared[v0, v1, v2])
lv1654_shared[v0, v1, v2] = lv1654[v0, v1, v2]
with T.block("NT_matmul_rf_init"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(22016, ax0_fused_0 * 8 + ax0_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = T.float16(0)
for ax1_0_fused_0 in range(16):
for ax1_0_fused_0_1 in range(4):
for ax0_ax1_ax2_fused_0 in range(1):
for ax0_ax1_ax2_fused_1 in T.vectorized(8):
with T.block("lv1654_shared_local"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(1, 0)
v2 = T.axis.spatial(4096, ax1_0_fused_0 * 256 + ax1_0_fused_1 * 8 + ax0_ax1_ax2_fused_0 * 8 + ax0_ax1_ax2_fused_1)
v2 = T.axis.spatial(4096, ax1_0_fused_0_0 * 1024 + ax1_0_fused_0_1 * 256 + ax1_0_fused_1 * 8 + ax0_ax1_ax2_fused_0 * 8 + ax0_ax1_ax2_fused_1)
T.reads(lv1654_shared[v0, v1, v2])
T.writes(lv1654_shared_local[v0, v1, v2])
lv1654_shared_local[v0, v1, v2] = lv1654_shared[v0, v1, v2]
for ax1_1 in range(8):
with T.block("NT_matmul_rf_update"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(22016, ax0_fused_0 * 8 + ax0_fused_1)
vax1_0_fused_0, vax1_1 = T.axis.remap("RR", [ax1_0_fused_0, ax1_1])
vax1_0_fused_0 = T.axis.reduce(16, ax1_0_fused_0_0 * 4 + ax1_0_fused_0_1)
vax1_1 = T.axis.reduce(8, ax1_1)
T.reads(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0], lv1654_shared_local[0, 0, vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1], lv571[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 8], lv572[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 32])
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] + lv1654_shared_local[0, 0, vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1] * ((T.Cast("float16", T.bitwise_and(T.shift_right(lv571[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 8], T.Cast("uint32", (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) % 8) * T.uint32(4)), T.uint32(15))) - T.float16(7)) * lv572[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 32])
Expand Down Expand Up @@ -278,39 +279,40 @@ def expected(lv771: T.Buffer((32000, 512), "uint32"), lv772: T.Buffer((32000, 12
for ax0_fused_0 in T.thread_binding(4000, thread="blockIdx.x"):
for ax0_fused_1 in T.thread_binding(8, thread="threadIdx.y"):
for ax1_0_fused_1 in T.thread_binding(32, thread="threadIdx.x"):
for u in T.serial(1, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
for ax0_ax1_ax2_fused_0 in range(2):
with T.block("NT_matmul_rf_init"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(32000, ax0_fused_0 * 8 + ax0_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = T.float16(0)
for ax1_0_fused_0_0 in T.serial(4, annotations={"pragma_auto_unroll_max_step": 256, "pragma_unroll_explicit": 1}):
for ax0_ax1_ax2_fused_0 in range(1):
for ax0_ax1_ax2_fused_1 in T.thread_binding(8, thread="threadIdx.y"):
for ax0_ax1_ax2_fused_2 in T.thread_binding(32, thread="threadIdx.x"):
for ax0_ax1_ax2_fused_3 in T.vectorized(8):
for ax0_ax1_ax2_fused_3 in T.vectorized(4):
with T.block("lv3216_shared"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(1, 0)
v2 = T.axis.spatial(4096, ax0_ax1_ax2_fused_0 * 2048 + ax0_ax1_ax2_fused_1 * 256 + ax0_ax1_ax2_fused_2 * 8 + ax0_ax1_ax2_fused_3)
v2 = T.axis.spatial(4096, ax1_0_fused_0_0 * 1024 + ax0_ax1_ax2_fused_0 * 1024 + ax0_ax1_ax2_fused_1 * 128 + ax0_ax1_ax2_fused_2 * 4 + ax0_ax1_ax2_fused_3)
T.reads(lv3216[v0, v1, v2])
T.writes(lv3216_shared[v0, v1, v2])
lv3216_shared[v0, v1, v2] = lv3216[v0, v1, v2]
with T.block("NT_matmul_rf_init"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(32000, ax0_fused_0 * 8 + ax0_fused_1)
T.reads()
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = T.float16(0)
for ax1_0_fused_0 in range(16):
for ax1_0_fused_0_1 in range(4):
for ax0_ax1_ax2_fused_0 in range(1):
for ax0_ax1_ax2_fused_1 in T.vectorized(8):
with T.block("lv3216_shared_local"):
v0 = T.axis.spatial(1, 0)
v1 = T.axis.spatial(1, 0)
v2 = T.axis.spatial(4096, ax1_0_fused_0 * 256 + ax1_0_fused_1 * 8 + ax0_ax1_ax2_fused_0 * 8 + ax0_ax1_ax2_fused_1)
v2 = T.axis.spatial(4096, ax1_0_fused_0_0 * 1024 + ax1_0_fused_0_1 * 256 + ax1_0_fused_1 * 8 + ax0_ax1_ax2_fused_0 * 8 + ax0_ax1_ax2_fused_1)
T.reads(lv3216_shared[v0, v1, v2])
T.writes(lv3216_shared_local[v0, v1, v2])
lv3216_shared_local[v0, v1, v2] = lv3216_shared[v0, v1, v2]
for ax1_1 in range(8):
with T.block("NT_matmul_rf_update"):
vax1_0_fused_1 = T.axis.spatial(32, ax1_0_fused_1)
v0 = T.axis.spatial(32000, ax0_fused_0 * 8 + ax0_fused_1)
vax1_0_fused_0, vax1_1 = T.axis.remap("RR", [ax1_0_fused_0, ax1_1])
vax1_0_fused_0 = T.axis.reduce(16, ax1_0_fused_0_0 * 4 + ax1_0_fused_0_1)
vax1_1 = T.axis.reduce(8, ax1_1)
T.reads(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0], lv3216_shared_local[0, 0, vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1], lv771[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 8], lv772[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 32])
T.writes(var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0])
var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] = var_NT_matmul_intermediate_rf_local[vax1_0_fused_1, 0, 0, v0] + lv3216_shared_local[0, 0, vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1] * ((T.Cast("float16", T.bitwise_and(T.shift_right(lv771[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 8], T.Cast("uint32", (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) % 8) * T.uint32(4)), T.uint32(15))) - T.float16(7)) * lv772[v0, (vax1_0_fused_0 * 256 + vax1_0_fused_1 * 8 + vax1_1) // 32])
Expand Down