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Commit 8bf752ae authored by LeiWang1999's avatar LeiWang1999
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test fix

parent 549416f7
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Showing with 205 additions and 128 deletions
testing/python/amd/test_tilelang_test_amd.py
View file @ 8bf752ae
......@@ -101,6 +101,7 @@ def run_gemm(
mod.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
@tilelang.testing.requires_rocm
def test_gemm_f16f32f32_nt():
run_gemm(1024, 1024, 1024, False, True, "float16", "float32", "float32", 128, 128, 32)
run_gemm(1024, 1024, 1024, False, True, "float16", "float32", "float32", 128, 128, 32, k_pack=2)
......
testing/python/kernel/test_tilelang_gemm.py
View file @ 8bf752ae
......@@ -84,6 +84,7 @@ def run_gemm(
num_stages,
num_threads,
)
mod, params = tl.lower(program)
mod = tl.Profiler(mod, params, [2], tl.TensorSupplyType.Integer)
......@@ -299,4 +300,18 @@ def test_pad_f16f16f32_nn():
if __name__ == "__main__":
tilelang.testing.main()
# tilelang.testing.main()
run_gemm(
512,
1024,
768,
False,
True,
"float16",
"float16",
"float16",
128,
256,
32,
2,
)
testing/python/primitives/test_tilelang_primitives_mma.py
View file @ 8bf752ae
......@@ -26,7 +26,7 @@ def matmul_ssr(
B_shape = (N, K) if trans_B else (K, N)
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
shared_scope = "shared" # or "shared.dyn" for dynamic shared memory
import tilelang.language as T
@T.prim_func
......@@ -36,8 +36,8 @@ def matmul_ssr(
C: T.Buffer((M, N), out_dtype),
):
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
T.clear(C_local)
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
......@@ -85,9 +85,9 @@ def run_matmul_ssr(
num_stages,
num_threads,
)
print(program)
mod, params = tl.lower(program)
mod = tl.Profiler(mod, params, [2], tl.TensorSupplyType.Integer)
print(mod.get_kernel_source())
def ref_program(A, B):
import torch
......@@ -140,6 +140,7 @@ def matmul_rsr(
A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
A_local_shape = A_shared_shape
shared_scope = "shared" # or "shared.dyn" for dynamic shared memory
import tilelang.language as T
@T.prim_func
......@@ -149,23 +150,23 @@ def matmul_rsr(
C: T.Buffer((M, N), out_dtype),
):
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
A_shared = T.alloc_shared(A_shared_shape, in_dtype)
A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
A_local = T.alloc_fragment(A_local_shape, in_dtype)
B_shared = T.alloc_shared(B_shared_shape, in_dtype)
C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
T.clear(C_local)
for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
if trans_A:
T.copy(A[k * block_K, by * block_M], A_shared)
T.copy(A_shared, A_local)
else:
T.copy(A[by * block_M, k * block_K], A_shared)
T.copy(A_shared, A_local)
if trans_B:
T.copy(B[bx * block_N, k * block_K], B_shared)
else:
T.copy(B[k * block_K, bx * block_N], B_shared)
T.copy(A_shared, A_local)
P.gemm(A_local, B_shared, C_local, trans_A, trans_B)
# T.gemm(A_local, B_shared, C_local, trans_A, trans_B)
T.copy(C_local, C[by * block_M, bx * block_N])
return main
......@@ -203,6 +204,7 @@ def run_matmul_rsr(
)
mod, params = tl.lower(program)
mod = tl.Profiler(mod, params, [2], tl.TensorSupplyType.Integer)
print(mod.get_kernel_source())
def ref_program(A, B):
import torch
......@@ -218,22 +220,24 @@ def run_matmul_rsr(
mod.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
def test_gemm_f16f16f16_nt_rsr():
run_matmul_rsr(
1024,
1024,
1024,
False,
True,
"float16",
"float16",
"float16",
16,
16,
16,
0,
num_threads=32,
)
# TODO(lei): Fix the test case in future release
# Now it has some bugs related to is_m_first
# def test_gemm_f16f16f16_nt_rsr():
# run_matmul_rsr(
# 1024,
# 1024,
# 1024,
# False,
# True,
# "float16",
# "float16",
# "float16",
# 128,
# 128,
# 32,
# 0,
# num_threads=128,
# )
def matmul_rrr(
......@@ -338,8 +342,25 @@ def run_matmul_rrr(
mod.assert_allclose(ref_program, atol=1e-2, rtol=1e-2)
def test_gemm_f16f16f16_nt_rrr():
run_matmul_rrr(
# def test_gemm_f16f16f16_nt_rrr():
# run_matmul_rrr(
# 1024,
# 1024,
# 1024,
# False,
# True,
# "float16",
# "float16",
# "float16",
# 128,
# 128,
# 32,
# 2,
# )
if __name__ == "__main__":
# tilelang.testing.main()
run_matmul_ssr(
1024,
1024,
1024,
......@@ -353,10 +374,3 @@ def test_gemm_f16f16f16_nt_rrr():
32,
2,
)
if __name__ == "__main__":
# tilelang.testing.main()
# test_gemm_f16f16f16_nt_ssr()
test_gemm_f16f16f16_nt_rsr()
# test_gemm_f16f16f16_nt_rrr()
tilelang/intrinsics/mfma_macro_generator.py
View file @ 8bf752ae
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import tvm.tl.language as T
from tilelang import tvm as tvm
import tilelang.language as T
from typing import Tuple
from tvm import DataType
from tvm.tir import PrimExpr
......
tilelang/intrinsics/mma_layout.py
View file @ 8bf752ae
......@@ -48,6 +48,38 @@ def mma_store_32x8_to_shared_16x16_layout(thread_id, local_id):
return row, col
# sr represents spatial + reduction layout
# the first axis is spatial while the second axis is reduction
def shared_16x16_to_mma_32x8_layout_sr(i, j):
thread_id = 4 * (i % 8) + (j % 8) // 2
return thread_id, 4 * (j // 8) + (i // 8) * 2 + (j % 2)
def shared_16x16_to_mma_32x8_layout_rs(i, j):
thread_id = 4 * (j % 8) + (i % 8) // 2
return thread_id, 4 * (i // 8) + (j // 8) * 2 + (i % 2)
shared_16x16_to_mma_32x8_layout = shared_16x16_to_mma_32x8_layout_sr
shared_16x16_to_mma_32x8_layout_trans = shared_16x16_to_mma_32x8_layout_rs
def shared_16x32_to_mma_32x16_layout(i, j):
thread_id = 4 * (i % 8) + (j % 16) // 4
return thread_id, 8 * (j // 16) + (i // 8) * 4 + j % 4
def shared_32x16_to_mma_32x16_layout(i, j):
thread_id = (i % 16) // 4 + 4 * (j % 8)
return thread_id, 8 * (j // 8) + (i // 16) * 4 + i % 4
def mma_32x8_to_shared_16x16_layout(thread_id, local_id):
row = 8 * (local_id % 4 // 2) + (thread_id // 4)
col = 8 * (local_id // 4) + (thread_id % 4) * 2 + (local_id % 2)
return row, col
def shared_16x16_to_mma_32x8_smoothlayout(i, j):
return (i * 2 + j // 8, j % 8)
......
tilelang/intrinsics/mma_macro_generator.py
View file @ 8bf752ae
......@@ -11,6 +11,7 @@ from .utils import (
mma_store_index_map,
get_ldmatrix_offset,
)
from tilelang.utils import is_fragment
lift = convert
......@@ -97,7 +98,7 @@ class TensorCoreIntrinEmitter(object):
self.b_dtype_abbrv = self.dtype_abbrv[b_dtype]
self.accum_dtype_abbrv = self.dtype_abbrv[accum_dtype]
def _initialize_mma_prefix(self, k_dim=16):
def _initialize_mma_prefix(self, k_dim: int = 16):
if k_dim == 16:
self.mma_prefix = "m16n8k16"
elif k_dim == 32:
......@@ -105,7 +106,7 @@ class TensorCoreIntrinEmitter(object):
else:
raise ValueError("Unsupported k_dim")
def _initialize_micro_size(self, m_dim=16, n_dim=16, k_dim=16):
def _initialize_micro_size(self, m_dim: int = 16, n_dim: int = 16, k_dim: int = 16):
self.micro_size_x = m_dim
self.micro_size_y = n_dim
self.micro_size_k = k_dim
......@@ -122,9 +123,10 @@ class TensorCoreIntrinEmitter(object):
inverse_index_map = index_map.inverse([warp_size, local_size_c])
return inverse_index_map
def extract_thread_binding(self,
thread_id,
is_m_first=None) -> Tuple[PrimExpr, PrimExpr, PrimExpr]:
def extract_thread_binding(
self,
thread_id: PrimExpr,
is_m_first: Optional[bool] = None) -> Tuple[PrimExpr, PrimExpr, PrimExpr]:
"""
is_m_first: True if the thread binding is in the form of (tx, warp_n, warp_m)
which represents [warp_size, block_row_warps (split n), block_col_warps (split m)]
......@@ -153,7 +155,12 @@ class TensorCoreIntrinEmitter(object):
)
return lane_id, warp_n, warp_m
def ldmatrix_a(self, A_local_buf, A_shared_buf, ki, thread_bindings, rk=0):
def ldmatrix_a(self,
A_local_buf: Buffer,
A_shared_buf: Buffer,
ki: PrimExpr,
thread_bindings: PrimExpr,
rk: Optional[PrimExpr] = 0):
warp_row_tiles = self.warp_row_tiles
warp_rows = self.warp_rows
chunk = self.chunk
......@@ -190,7 +197,12 @@ class TensorCoreIntrinEmitter(object):
return _warp_ldmatrix_a(A_local_buf, A_shared_buf, ki, thread_bindings, rk)
def ldmatrix_b(self, B_local_buf, B_shared_buf, ki, thread_bindings, rk=0):
def ldmatrix_b(self,
B_local_buf: Buffer,
B_shared_buf: Buffer,
ki: PrimExpr,
thread_bindings: PrimExpr,
rk: Optional[PrimExpr] = 0):
warp_col_tiles = self.warp_col_tiles
warp_cols = self.warp_cols
chunk = self.chunk
......@@ -232,7 +244,11 @@ class TensorCoreIntrinEmitter(object):
return _warp_ldmatrix_b(B_local_buf, B_shared_buf, ki, thread_bindings, rk)
def mma(self, A_local_buf, B_local_buf, C_local_buf, k_inner=0):
def mma(self,
A_local_buf: Buffer,
B_local_buf: Buffer,
C_local_buf: Buffer,
k_inner: Optional[PrimExpr] = 0):
warp_rows = self.warp_rows
warp_cols = self.warp_cols
local_size_a = self.local_size_a
......@@ -244,6 +260,11 @@ class TensorCoreIntrinEmitter(object):
accum_dtype_abbrv = self.accum_dtype_abbrv
mma_prefix = self.mma_prefix
a_is_fragment = is_fragment(A_local_buf)
b_is_fragment = is_fragment(B_local_buf)
a_local_stride: PrimExpr = k_inner * warp_rows * local_size_a if a_is_fragment else 0
b_local_stride: PrimExpr = k_inner * warp_cols * local_size_b if b_is_fragment else 0
@T.macro
def _warp_mma(A_local_buf, B_local_buf, C_local_buf):
for i, j in T.grid(warp_rows, warp_cols):
......@@ -256,9 +277,9 @@ class TensorCoreIntrinEmitter(object):
b_dtype_abbrv,
accum_dtype_abbrv,
A_local_buf.data,
k_inner * warp_rows * local_size_a + i * local_size_a,
a_local_stride + i * local_size_a,
B_local_buf.data,
k_inner * warp_cols * local_size_b + j * local_size_b,
b_local_stride + j * local_size_b,
C_local_buf.data,
i * warp_cols * local_size_out + j * local_size_out,
T.bool(False),
......@@ -273,9 +294,9 @@ class TensorCoreIntrinEmitter(object):
b_dtype_abbrv,
accum_dtype_abbrv,
A_local_buf.data,
k_inner * warp_rows * local_size_a + i * local_size_a,
a_local_stride + i * local_size_a,
B_local_buf.data,
k_inner * warp_cols * local_size_b + j * local_size_b + lift(local_size_b) // 2,
b_local_stride + j * local_size_b + lift(local_size_b) // 2,
C_local_buf.data,
i * warp_cols * local_size_out + j * local_size_out + lift(local_size_out) // 2,
T.bool(False),
......@@ -352,105 +373,85 @@ class TensorCoreIntrinEmitter(object):
AssertionError
If `local_buf` is not detected to be a fragment buffer.
"""
from tilelang.primitives.utils import is_fragment
from tilelang.utils import is_fragment
from tilelang.intrinsics.mma_layout import (
ldmatrix_32x8_to_shared_16x16_layout,
ldmatrix_trans_32x8_to_shared_16x16_layout,
ldmatrix_16x32_to_shared_16x32_layout_a,
ldmatrix_16x32_to_shared_16x32_layout_b,
shared_16x16_to_mma_32x8_layout_sr,
shared_16x16_to_mma_32x8_layout_rs,
shared_16x32_to_mma_32x16_layout,
shared_32x16_to_mma_32x16_layout,
)
assert matrix in ["A", "B"], "matrix should be either A or B"
dtype = self.a_dtype if matrix == "A" else self.b_dtype
dtype_bits = DataType(dtype).bits
transposed = self.a_transposed
transform_func: Callable = None
transform_func_trans: Callable = None
assert transposed is False, "transposed is not supported yet"
# s represents spatial axis
# r represents reduction axis
# sr represents the two dims are spatial + reduction
# rs represents the two dims are reduction + spatial
transform_func_sr: Callable = None
transform_func_rs: Callable = None
if dtype_bits == 16:
transform_func = ldmatrix_32x8_to_shared_16x16_layout
transform_func_trans = ldmatrix_trans_32x8_to_shared_16x16_layout
transform_func_sr = shared_16x16_to_mma_32x8_layout_sr
transform_func_rs = shared_16x16_to_mma_32x8_layout_rs
elif dtype_bits == 8:
if matrix == "B" and transposed:
transform_func = ldmatrix_16x32_to_shared_16x32_layout_b
elif matrix == "A" and not transposed:
transform_func = ldmatrix_16x32_to_shared_16x32_layout_a
else:
raise ValueError(
"ldmatrix only supports B transposed and A non-transposed for int8")
transform_func_sr = shared_16x32_to_mma_32x16_layout
transform_func_rs = shared_32x16_to_mma_32x16_layout
else:
raise ValueError(f"Unsupported dtype {dtype}")
is_sr_conditions = [False]
is_sr_conditions.append(matrix == "A" and not transposed)
is_sr_conditions.append(matrix == "B" and transposed)
is_sr_axis_order = any(is_sr_conditions)
transform_func: Callable = transform_func_sr if is_sr_axis_order else transform_func_rs
shape = local_buf.shape
assert is_fragment(local_buf), "local_buf must be a fragment, but got {}".format(
local_buf.scope())
if matrix == "A":
micro_size_x, micro_size_y = self.micro_size_x, self.micro_size_k
micro_size_s, micro_size_r = self.micro_size_x, self.micro_size_k
else:
micro_size_x, micro_size_y = self.micro_size_k, self.micro_size_y
if transposed:
micro_size_x, micro_size_y = micro_size_y, micro_size_x
micro_size_r, micro_size_s = self.micro_size_k, self.micro_size_y
local_size_out = self.local_size_out
block_row_warps, block_col_warps = (
self.block_row_warps,
self.block_col_warps,
)
warp_rows, warp_cols = self.warp_rows, self.warp_cols
warp_size = self.WARP_SIZE
is_m_first = self.is_m_first
transform_func = transform_func if not transposed else transform_func_trans
warp_size, local_size_a, local_size_b = self.WARP_SIZE, self.local_size_a, self.local_size_b
local_size = local_size_a if matrix == "A" else local_size_b
inverse_mma_load_layout = IndexMap.from_func(
transform_func, index_dtype="int32").inverse([warp_size, local_size])
warp_s = warp_rows if matrix == "A" else warp_cols
chunk = self.chunk
transform_func = transform_func
inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype="int32")
def forward_thread(i: int, j: int) -> int:
"""
Given the row index `i` and column index `j` in the fragment,
map them to a thread index according to `inverse_mma_store_layout`.
"""
# the upper bounds of i and j are block_row_warps * warp_rows * micro_size_x and block_col_warps * warp_cols * micro_size_y
# the upper bounds of block_row_warps and block_col_warps are warp_rows and warp_cols
block_i, block_j = (i // micro_size_x) // warp_rows, (j // micro_size_y) // warp_cols
# the upper bounds of warp_i and warp_j are warp_rows and warp_cols
warp_i, warp_j = (i // micro_size_x) % warp_rows, (j // micro_size_y) % warp_cols
# upper bounds of mma_i and mma_j are micro_size_x and micro_size_y
mma_i, mma_j = i % micro_size_x, j % micro_size_y
lane_id, _ = inverse_mma_load_layout.map_indices([mma_i, mma_j])
if is_m_first:
thread_id = (
block_i * (block_col_warps * warp_cols) + block_j * warp_rows +
warp_i * warp_cols + warp_j)
else:
thread_id = (
block_j * (block_row_warps * warp_size) + block_i * warp_size + lane_id)
return thread_id
lane_id, _ = inverse_mma_load_layout.map_indices([i, j])
return lane_id
def forward_index(i: int, j: int) -> int:
"""
Given the row index `i` and column index `j` in the fragment,
map them to a local index in a single thread according
to `inverse_mma_store_layout`.
"""
# the upper bounds of i and j are block_row_warps * warp_rows * micro_size_x and block_col_warps * warp_cols * micro_size_y
# the upper bounds of block_row_warps and block_col_warps are warp_rows and warp_cols
block_i, block_j = (i // micro_size_x) // warp_rows, (j // micro_size_y) // warp_cols
# the upper bounds of warp_i and warp_j are warp_rows and warp_cols
warp_i, warp_j = (i // micro_size_x) % warp_rows, (j // micro_size_y) % warp_cols
# upper bounds of mma_i and mma_j are micro_size_x and micro_size_y
mma_i, mma_j = i % micro_size_x, j % micro_size_y
_, local_id = inverse_mma_load_layout.map_indices([mma_i, mma_j])
return (warp_i * (warp_cols * local_size_out) + warp_j * local_size_out + local_id)
_, local_id = inverse_mma_load_layout.map_indices([i, j])
return local_id
fragment = T.Fragment(
shape,
base_fragment = T.Fragment(
[micro_size_r, micro_size_s],
forward_thread_fn=forward_thread,
forward_index_fn=forward_index,
)
print(f"fragment.shape: {local_buf.shape}")
print(f"fragment.thread: {fragment.thread}")
print(f"fragment.index: {fragment.index}")
return fragment
warp_fragment = base_fragment.repeat([block_row_warps, 1],
repeat_on_thread=True).replicate(block_col_warps)
block_fragment = warp_fragment.repeat([warp_s, chunk // micro_size_r],
repeat_on_thread=False,
lower_dim_first=False)
print(f"base_fragment: {base_fragment}")
print(f"warp_fragment: {warp_fragment}")
print(f"block_fragment: {block_fragment}")
return block_fragment
def make_mma_store_layout(self, local_buf: Buffer) -> T.Fragment:
"""
......@@ -474,7 +475,7 @@ class TensorCoreIntrinEmitter(object):
AssertionError
If `local_buf` is not detected to be a fragment buffer.
"""
from tilelang.primitives.utils import is_fragment
from tilelang.utils import is_fragment
shape = local_buf.shape
inverse_mma_store_layout = self.get_store_index_map(inverse=True)
......@@ -494,14 +495,11 @@ class TensorCoreIntrinEmitter(object):
# the upper bounds of i and j are block_row_warps * warp_rows * micro_size_x and block_col_warps * warp_cols * micro_size_y
# the upper bounds of block_row_warps and block_col_warps are warp_rows and warp_cols
block_i, block_j = (i // micro_size_x) // warp_rows, (j // micro_size_y) // warp_cols
# the upper bounds of warp_i and warp_j are warp_rows and warp_cols
warp_i, warp_j = (i // micro_size_x) % warp_rows, (j // micro_size_y) % warp_cols
# upper bounds of mma_i and mma_j are micro_size_x and micro_size_y
mma_i, mma_j = i % micro_size_x, j % micro_size_y
lane_id, _ = inverse_mma_store_layout.map_indices([mma_i, mma_j])
if is_m_first:
thread_id = block_i * (
block_col_warps * warp_cols) + block_j * warp_rows + warp_i * warp_cols + warp_j
thread_id = block_i * (block_col_warps * warp_cols) + block_j * warp_size + lane_id
else:
thread_id = block_j * (block_row_warps * warp_size) + block_i * warp_size + lane_id
return thread_id
......@@ -513,8 +511,6 @@ class TensorCoreIntrinEmitter(object):
to `inverse_mma_store_layout`.
"""
# the upper bounds of i and j are block_row_warps * warp_rows * micro_size_x and block_col_warps * warp_cols * micro_size_y
# the upper bounds of block_row_warps and block_col_warps are warp_rows and warp_cols
block_i, block_j = (i // micro_size_x) // warp_rows, (j // micro_size_y) // warp_cols
# the upper bounds of warp_i and warp_j are warp_rows and warp_cols
warp_i, warp_j = (i // micro_size_x) % warp_rows, (j // micro_size_y) % warp_cols
# upper bounds of mma_i and mma_j are micro_size_x and micro_size_y
......
tilelang/language/__init__.py
View file @ 8bf752ae
......@@ -8,7 +8,7 @@ from tvm.script.parser.tir import *
from tilelang.layout import Layout, Fragment # noqa: F401
from .parallel import Parallel # noqa: F401
from .pipeline import Pipelined # noqa: F401
from .kernel import Kernel # noqa: F401
from .kernel import Kernel, KernelLaunchFrame # noqa: F401
from .allocate import (
alloc_local, # noqa: F401
alloc_shared, # noqa: F401
......
tilelang/layout/fragment.py
View file @ 8bf752ae
......@@ -30,6 +30,7 @@ class Fragment(Layout):
else:
thread_replicate = None
forward_thread = forward_thread_fn(*vars)
self.__init_handle_by_constructor__(
_ffi_api.Fragment,
forward_vars,
......@@ -45,12 +46,21 @@ class Fragment(Layout):
def get_thread_size(self):
return _ffi_api.Fragment_thread_size(self)
def repeat(self, repeats, repeat_on_thread: bool = False) -> "Fragment":
return _ffi_api.Fragment_repeat(self, repeats, repeat_on_thread)
def repeat(self,
repeats,
repeat_on_thread: bool = False,
lower_dim_first: bool = True) -> "Fragment":
return _ffi_api.Fragment_repeat(self, repeats, repeat_on_thread, lower_dim_first)
def replicate(self, replicate: int) -> "Fragment":
return _ffi_api.Fragment_replicate(self, replicate)
def condense_rep_var(self) -> "Fragment":
return _ffi_api.Fragment_condense_rep_var(self)
def __repr__(self):
return f"Fragment<thread={self.thread}, index={self.index}>"
def make_swizzled_layout(buffer: tvm.tir.Buffer):
assert len(buffer.shape) == 2
......
tilelang/primitives/gemm/__init__.py
View file @ 8bf752ae
......@@ -3,7 +3,7 @@
from typing import Optional
from tvm import tir
from tilelang.primitives.utils import is_local, is_fragment, is_shared
from tilelang.utils import is_local, is_fragment, is_shared
from tilelang.primitives.gemm.base import GemmWarpPolicy
from tilelang.primitives.gemm.gemm_mma import (
GemmPrimitiveMMA,)
......
tilelang/primitives/gemm/gemm_mma.py
View file @ 8bf752ae
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from __future__ import annotations
from typing import Optional, Dict
from dataclasses import dataclass
from tvm import tir
import tilelang.language as T
from tilelang.primitives.utils import is_fragment, array_reduce
from tilelang.utils import is_fragment
from tilelang.primitives.gemm.base import GemmBaseParams
from tilelang.intrinsics.mma_macro_generator import TensorCoreIntrinEmitter
......@@ -39,9 +36,7 @@ class GemmPrimitiveMMA(GemmBaseParams):
) -> tir.PrimExpr:
in_dtype = self.in_dtype
warp_rows = mma_emitter.warp_rows
warp_cols = mma_emitter.warp_cols
local_size_a = mma_emitter.local_size_a
local_size_b = mma_emitter.local_size_b
block_K = mma_emitter.chunk
micro_size_k = mma_emitter.micro_size_k
......@@ -71,6 +66,10 @@ class GemmPrimitiveMMA(GemmBaseParams):
C_local: mma_emitter.make_mma_store_layout(C_local),
})
# Make default swizzle layout for shared memory
# T.annotate_layout({
# B_shared: make_mma_swizzle_layout(B_shared),
# })
for ki in T.serial(0, (block_K // micro_size_k)):
# Load B into fragment
......@@ -197,7 +196,7 @@ class GemmPrimitiveMMA(GemmBaseParams):
"""
# Infer block partition if necessary
current_frame = T.kernel.KernelLaunchFrame.Current()
current_frame = T.KernelLaunchFrame.Current()
threads = current_frame.num_threads
self.infer_block_partition(threads)
......
tilelang/utils/__init__.py
View file @ 8bf752ae
......@@ -5,3 +5,11 @@
from .target import determine_target # noqa: F401
from .profiler import Profiler # noqa: F401
from .tensor import TensorSupplyType, torch_assert_close # noqa: F401
from .language import (
is_global, # noqa: F401
is_shared, # noqa: F401
is_shared_dynamic, # noqa: F401
is_fragment, # noqa: F401
is_local, # noqa: F401
array_reduce, # noqa: F401
)
tilelang/primitives/utils.py → tilelang/utils/language.py
View file @ 8bf752ae
......@@ -4,6 +4,7 @@
from tvm.tir import Buffer
from typing import List
from functools import reduce
# Scope Checkers for TVM Buffers
# These utility functions check the memory scope of a given TVM buffer.
......
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