텍스트 비교

두 텍스트 파일의 차이점을 찾아보세요

실시간 편집

공백 변경 숨기기

변경 없는 행 숨기기

줄바꿈 비활성화

레이아웃

비교 단위

텍스트 스타일

구문 강조

Diffchecker Desktop 가장 안전하게 Diffchecker를 사용하는 방법. 데스크톱 앱을 사용하면 비교 데이터가 외부로 전송되지 않습니다!데스크톱 앱 받기

Comparing TLX between persistent and CLC

생성일 8개월 전비교 결과 만료 없음

35 삭제

행
총
삭제

글자
총
삭제

이 기능을 계속 사용하려면 업그레이드해 주세요 Diffchecker Pro 요금제 보기

158 행

54 추가

행
총
추가

글자
총
추가

이 기능을 계속 사용하려면 업그레이드해 주세요 Diffchecker Pro 요금제 보기

191 행

@triton.jit

def matmul_kernel_tma_ws_blackwell_clc(a_desc, b_desc, c_desc, M, N, K, BLOCK_SIZE_M: tl.constexpr,

def matmul_kernel_tma_ws_blackwell_persistent(a_desc, b_desc, c_desc, M, N, K, BLOCK_SIZE_M: tl.constexpr,

BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, #

GROUP_SIZE_M: tl.constexpr, #

NUM_SMEM_BUFFERS: tl.constexpr, #

NUM_TMEM_BUFFERS: tl.constexpr, #

NUM_SMS: tl.constexpr, #

NUM_CLC_STAGES: tl.constexpr, #

EPILOGUE_SUBTILE: tl.constexpr, #

# allocate NUM_SMEM_BUFFERS buffers

buffers_A = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_K), tl.float16, NUM_SMEM_BUFFERS)

buffers_B = tlx.local_alloc((BLOCK_SIZE_K, BLOCK_SIZE_N), tl.float16, NUM_SMEM_BUFFERS)

# use multiple TMEM buffers to overlap MMA and epilogue

tmem_buffers = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_N), tl.float32, NUM_TMEM_BUFFERS, tlx.storage_kind.tmem)

# allocate barriers

smem_empty_bars = tlx.alloc_barriers(num_barriers=NUM_SMEM_BUFFERS, arrive_count=1)

smem_full_bars = tlx.alloc_barriers(num_barriers=NUM_SMEM_BUFFERS, arrive_count=1)

tmem_full_bars = tlx.alloc_barriers(num_barriers=NUM_TMEM_BUFFERS, arrive_count=1)

tmem_empty_bars = tlx.alloc_barriers(num_barriers=NUM_TMEM_BUFFERS, arrive_count=1)

clc_context = tlx.clc_create_context(NUM_CLC_STAGES, 3)

with tlx.async_tasks():

with tlx.async_task("default"): # epilogue consumer

# common code duplicated for each region to avoid SMEM overhead

start_pid = tl.program_id(axis=0)

num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)

num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

num_pid_in_group = GROUP_SIZE_M * num_pid_n

num_tiles = num_pid_m * num_pid_n

k_tiles = tl.cdiv(K, BLOCK_SIZE_K)

# end of common code

tmem_read_phase = 0

cur_tmem_buf = 0

for tile_id in range(start_pid, num_tiles, NUM_SMS):

tile_id = start_pid

clc_phase_producer = 1

clc_phase_consumer = 0

clc_buf = 0

while tile_id != -1:

clc_buf = clc_buf % NUM_CLC_STAGES

# Debug prints

# if tlx.thread_id(axis=0) == 0:

# tl.device_print("Default WG Processing CtaID", tile_id)

# producer

tlx.clc_producer(clc_context, clc_buf, clc_phase_producer)

# clc_phase_producer ^= 1

clc_phase_producer = clc_phase_producer ^ (clc_buf == (NUM_CLC_STAGES - 1))

pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)

offs_am = pid_m * BLOCK_SIZE_M

offs_bn = pid_n * BLOCK_SIZE_N

tlx.barrier_wait(tmem_full_bars[cur_tmem_buf], tmem_read_phase)

# flip phase at the end of a round of using TMEM barriers

tmem_read_phase = tmem_read_phase ^ (cur_tmem_buf == NUM_TMEM_BUFFERS - 1)

# load the result from TMEM to registers

acc_tmem = tmem_buffers[cur_tmem_buf]

if EPILOGUE_SUBTILE:

# We load/store the result half by half to reduce SMEM pressure

acc_tmem_subslice1 = tlx.subslice(acc_tmem, 0, BLOCK_SIZE_N // 2)

result = tlx.local_load(acc_tmem_subslice1)

c = result.to(tl.float16)

c_desc.store([offs_am, offs_bn], c)

acc_tmem_subslice2 = tlx.subslice(acc_tmem, BLOCK_SIZE_N // 2, BLOCK_SIZE_N // 2)

result = tlx.local_load(acc_tmem_subslice2)

c = result.to(tl.float16)

c_desc.store([offs_am, offs_bn + BLOCK_SIZE_N // 2], c)

else:

result = tlx.local_load(acc_tmem)

c = result.to(tl.float16)

c_desc.store([offs_am, offs_bn], c)

# done storing this buffer, signal MMA consumer to resume writing to it

tlx.barrier_arrive(tmem_empty_bars[cur_tmem_buf], 1)

cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS

tile_id = tlx.clc_consumer(clc_context, clc_buf, clc_phase_consumer)

# clc_phase_consumer ^= 1

clc_phase_consumer = clc_phase_consumer ^ (clc_buf == (NUM_CLC_STAGES - 1))

clc_buf += 1

# Debug-only: verifying that CLC steals workloads successfully

# if tlx.thread_id(axis=0) == 0:

# tl.device_print("Extracted CtaID", tile_id)

with tlx.async_task(num_warps=1, num_regs=232): # MMA consumer

# common code duplicated for each region to avoid SMEM overhead

start_pid = tl.program_id(axis=0)

num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)

num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

num_pid_in_group = GROUP_SIZE_M * num_pid_n

num_tiles = num_pid_m * num_pid_n

k_tiles = tl.cdiv(K, BLOCK_SIZE_K)

# end of common code

dot_phase = 0 # the current phase of dot op

tmem_write_phase = 1 # sync between epilogue consumer and MMA consumer

cur_tmem_buf = 0

processed_k_iters = 0

for tile_id in range(start_pid, num_tiles, NUM_SMS):

tile_id = start_pid

clc_phase = 0

clc_buf = 0

while tile_id != -1:

clc_buf = clc_buf % NUM_CLC_STAGES

pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)

offs_am = pid_m * BLOCK_SIZE_M

offs_bn = pid_n * BLOCK_SIZE_N

# wait epilogue consumer to be done with the buffer before reusing it

tlx.barrier_wait(tmem_empty_bars[cur_tmem_buf], tmem_write_phase)

# flip phase at the end of a round of using TMEM barriers

tmem_write_phase = tmem_write_phase ^ (cur_tmem_buf == NUM_TMEM_BUFFERS - 1)

# now iterate along K to compute result for the block

for k in range(0, k_tiles):

# processed_k_iters + k means we use the immediate next buffer slot of tile_id x when we start tile_id x+1

buf = (processed_k_iters + k) % NUM_SMEM_BUFFERS

# wait for current phase(round) of load for this buf

tlx.barrier_wait(smem_full_bars[buf], dot_phase)

# buffer is now ready with loaded data, tlx.async_dot will signal `mBarrier` when done

tlx.async_dot(

tlx.async_dot(buffers_A[buf], buffers_B[buf], tmem_buffers[cur_tmem_buf], use_acc=k > 0,

buffers_A[buf],

mBarriers=[smem_empty_bars[buf]], out_dtype=tl.float32)

buffers_B[buf],

tmem_buffers[cur_tmem_buf],

use_acc=k > 0,

mBarriers=[smem_empty_bars[buf]],

out_dtype=tl.float32,

)

# flip phase at the end of a round

dot_phase = dot_phase ^ (buf == NUM_SMEM_BUFFERS - 1)

# wait for last mma to complete

last_buf = (processed_k_iters + k_tiles - 1) % NUM_SMEM_BUFFERS

# in case phase was flipped, we should use the phase value when dot op was issued

last_dot_phase = dot_phase ^ (last_buf == NUM_SMEM_BUFFERS - 1)

tlx.barrier_wait(smem_empty_bars[last_buf], last_dot_phase)

# done filling this buffer, signal epilogue consumer

tlx.barrier_arrive(tmem_full_bars[cur_tmem_buf], 1)

# possibly enter next iteration (next tile) without waiting for epilogue

cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS

processed_k_iters += k_tiles

tile_id = tlx.clc_consumer(clc_context, clc_buf, clc_phase)

# clc_phase ^= 1

clc_phase = clc_phase ^ (clc_buf == (NUM_CLC_STAGES - 1))

clc_buf += 1

with tlx.async_task(num_warps=1, num_regs=232): # producer, TMA load

# common code duplicated for each region to avoid SMEM overhead

start_pid = tl.program_id(axis=0)

num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)

num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)

num_pid_in_group = GROUP_SIZE_M * num_pid_n

num_tiles = num_pid_m * num_pid_n

k_tiles = tl.cdiv(K, BLOCK_SIZE_K)

# end of common code

load_phase = 0 # the current phase of TMA load

# we virtually "flatten" the two layer loop as if we're performing tma loads on

# one big list of data

processed_k_iters = 0

for tile_id in range(start_pid, num_tiles, NUM_SMS):

tile_id = start_pid

clc_phase = 0

clc_buf = 0

while tile_id != -1:

clc_buf = clc_buf % NUM_CLC_STAGES

pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)

offs_am = pid_m * BLOCK_SIZE_M

offs_bn = pid_n * BLOCK_SIZE_N

for k in range(0, k_tiles):

# processed_k_iters + k means we use the immediate next buffer slot of tile_id x when we start tile_id x+1

buf = (processed_k_iters + k) % NUM_SMEM_BUFFERS

# wait for previous phase(round) of dot for this buf

tlx.barrier_wait(smem_empty_bars[buf], load_phase ^ 1)

# buffer is now ready to be used again

offs_k = k * BLOCK_SIZE_K

tlx.barrier_expect_bytes(smem_full_bars[buf],

2 * (BLOCK_SIZE_M + BLOCK_SIZE_N) * BLOCK_SIZE_K) # float16

tlx.async_descriptor_load(a_desc, buffers_A[buf], [offs_am, offs_k], smem_full_bars[buf])

tlx.async_descriptor_load(b_desc, buffers_B[buf], [offs_k, offs_bn], smem_full_bars[buf])

# flip phase at the end of a round

load_phase = load_phase ^ (buf == NUM_SMEM_BUFFERS - 1)

processed_k_iters += k_tiles

tile_id = tlx.clc_consumer(clc_context, clc_buf, clc_phase)

# clc_phase ^= 1

clc_phase = clc_phase ^ (clc_buf == (NUM_CLC_STAGES - 1))

clc_buf += 1

저장된 비교 결과

원본

파일 열기

@triton.jit
def matmul_kernel_tma_ws_blackwell_persistent(a_desc, b_desc, c_desc, M, N, K, BLOCK_SIZE_M: tl.constexpr,
                                              BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,  #
                                              GROUP_SIZE_M: tl.constexpr,  #
                                              NUM_SMEM_BUFFERS: tl.constexpr,  #
                                              NUM_TMEM_BUFFERS: tl.constexpr,  #
                                              NUM_SMS: tl.constexpr,  #
                                              EPILOGUE_SUBTILE: tl.constexpr,  #
                                              ):
    # allocate NUM_SMEM_BUFFERS buffers
    buffers_A = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_K), tl.float16, NUM_SMEM_BUFFERS)
    buffers_B = tlx.local_alloc((BLOCK_SIZE_K, BLOCK_SIZE_N), tl.float16, NUM_SMEM_BUFFERS)
    # use multiple TMEM buffers to overlap MMA and epilogue
    tmem_buffers = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_N), tl.float32, NUM_TMEM_BUFFERS, tlx.storage_kind.tmem)

# allocate barriers
    smem_empty_bars = tlx.alloc_barriers(num_barriers=NUM_SMEM_BUFFERS, arrive_count=1)
    smem_full_bars = tlx.alloc_barriers(num_barriers=NUM_SMEM_BUFFERS, arrive_count=1)
    tmem_full_bars = tlx.alloc_barriers(num_barriers=NUM_TMEM_BUFFERS, arrive_count=1)
    tmem_empty_bars = tlx.alloc_barriers(num_barriers=NUM_TMEM_BUFFERS, arrive_count=1)

with tlx.async_tasks():
        with tlx.async_task("default"):  # epilogue consumer
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            num_tiles = num_pid_m * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

tmem_read_phase = 0
            cur_tmem_buf = 0

for tile_id in range(start_pid, num_tiles, NUM_SMS):
                pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
                offs_am = pid_m * BLOCK_SIZE_M
                offs_bn = pid_n * BLOCK_SIZE_N

tlx.barrier_wait(tmem_full_bars[cur_tmem_buf], tmem_read_phase)
                # flip phase at the end of a round of using TMEM barriers
                tmem_read_phase = tmem_read_phase ^ (cur_tmem_buf == NUM_TMEM_BUFFERS - 1)

# load the result from TMEM to registers
                acc_tmem = tmem_buffers[cur_tmem_buf]

if EPILOGUE_SUBTILE:
                    # We load/store the result half by half to reduce SMEM pressure
                    acc_tmem_subslice1 = tlx.subslice(acc_tmem, 0, BLOCK_SIZE_N // 2)
                    result = tlx.local_load(acc_tmem_subslice1)
                    c = result.to(tl.float16)
                    c_desc.store([offs_am, offs_bn], c)

acc_tmem_subslice2 = tlx.subslice(acc_tmem, BLOCK_SIZE_N // 2, BLOCK_SIZE_N // 2)
                    result = tlx.local_load(acc_tmem_subslice2)
                    c = result.to(tl.float16)
                    c_desc.store([offs_am, offs_bn + BLOCK_SIZE_N // 2], c)
                else:
                    result = tlx.local_load(acc_tmem)
                    c = result.to(tl.float16)
                    c_desc.store([offs_am, offs_bn], c)

# done storing this buffer, signal MMA consumer to resume writing to it
                tlx.barrier_arrive(tmem_empty_bars[cur_tmem_buf], 1)

cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS

with tlx.async_task(num_warps=1, num_regs=232):  # MMA consumer
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            num_tiles = num_pid_m * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

dot_phase = 0  # the current phase of dot op
            tmem_write_phase = 1  # sync between epilogue consumer and MMA consumer
            cur_tmem_buf = 0

processed_k_iters = 0
            for tile_id in range(start_pid, num_tiles, NUM_SMS):
                pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
                offs_am = pid_m * BLOCK_SIZE_M
                offs_bn = pid_n * BLOCK_SIZE_N

# wait epilogue consumer to be done with the buffer before reusing it
                tlx.barrier_wait(tmem_empty_bars[cur_tmem_buf], tmem_write_phase)
                # flip phase at the end of a round of using TMEM barriers
                tmem_write_phase = tmem_write_phase ^ (cur_tmem_buf == NUM_TMEM_BUFFERS - 1)

# now iterate along K to compute result for the block
                for k in range(0, k_tiles):
                    # processed_k_iters + k means we use the immediate next buffer slot of tile_id x when we start tile_id x+1
                    buf = (processed_k_iters + k) % NUM_SMEM_BUFFERS
                    # wait for current phase(round) of load for this buf
                    tlx.barrier_wait(smem_full_bars[buf], dot_phase)
                    # buffer is now ready with loaded data, tlx.async_dot will signal `mBarrier` when done
                    tlx.async_dot(
                        buffers_A[buf],
                        buffers_B[buf],
                        tmem_buffers[cur_tmem_buf],
                        use_acc=k > 0,
                        mBarriers=[smem_empty_bars[buf]],
                        out_dtype=tl.float32,
                    )
                    # flip phase at the end of a round
                    dot_phase = dot_phase ^ (buf == NUM_SMEM_BUFFERS - 1)

# wait for last mma to complete
                last_buf = (processed_k_iters + k_tiles - 1) % NUM_SMEM_BUFFERS
                # in case phase was flipped, we should use the phase value when dot op was issued
                last_dot_phase = dot_phase ^ (last_buf == NUM_SMEM_BUFFERS - 1)
                tlx.barrier_wait(smem_empty_bars[last_buf], last_dot_phase)

# done filling this buffer, signal epilogue consumer
                tlx.barrier_arrive(tmem_full_bars[cur_tmem_buf], 1)

# possibly enter next iteration (next tile) without waiting for epilogue
                cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS
                processed_k_iters += k_tiles

with tlx.async_task(num_warps=1, num_regs=232):  # producer, TMA load
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            num_tiles = num_pid_m * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

load_phase = 0  # the current phase of TMA load
            # we virtually "flatten" the two layer loop as if we're performing tma loads on
            # one big list of data
            processed_k_iters = 0

for k in range(0, k_tiles):
                    # processed_k_iters + k means we use the immediate next buffer slot of tile_id x when we start tile_id x+1
                    buf = (processed_k_iters + k) % NUM_SMEM_BUFFERS
                    # wait for previous phase(round) of dot for this buf
                    tlx.barrier_wait(smem_empty_bars[buf], load_phase ^ 1)
                    # buffer is now ready to be used again
                    offs_k = k * BLOCK_SIZE_K
                    tlx.barrier_expect_bytes(smem_full_bars[buf],
                                             2 * (BLOCK_SIZE_M + BLOCK_SIZE_N) * BLOCK_SIZE_K)  # float16
                    tlx.async_descriptor_load(a_desc, buffers_A[buf], [offs_am, offs_k], smem_full_bars[buf])
                    tlx.async_descriptor_load(b_desc, buffers_B[buf], [offs_k, offs_bn], smem_full_bars[buf])
                    # flip phase at the end of a round
                    load_phase = load_phase ^ (buf == NUM_SMEM_BUFFERS - 1)
                processed_k_iters += k_tiles

수정본

파일 열기

def matmul_kernel_tma_ws_blackwell_clc(a_desc, b_desc, c_desc, M, N, K, BLOCK_SIZE_M: tl.constexpr,
                                       BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,  #
                                       GROUP_SIZE_M: tl.constexpr,  #
                                       NUM_SMEM_BUFFERS: tl.constexpr,  #
                                       NUM_TMEM_BUFFERS: tl.constexpr,  #
                                       NUM_SMS: tl.constexpr,  #
                                       NUM_CLC_STAGES: tl.constexpr,  #
                                       EPILOGUE_SUBTILE: tl.constexpr,  #
                                       ):
    # allocate NUM_SMEM_BUFFERS buffers
    buffers_A = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_K), tl.float16, NUM_SMEM_BUFFERS)
    buffers_B = tlx.local_alloc((BLOCK_SIZE_K, BLOCK_SIZE_N), tl.float16, NUM_SMEM_BUFFERS)
    # use multiple TMEM buffers to overlap MMA and epilogue
    tmem_buffers = tlx.local_alloc((BLOCK_SIZE_M, BLOCK_SIZE_N), tl.float32, NUM_TMEM_BUFFERS, tlx.storage_kind.tmem)

clc_context = tlx.clc_create_context(NUM_CLC_STAGES, 3)

with tlx.async_tasks():
        with tlx.async_task("default"):  # epilogue consumer
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

tmem_read_phase = 0
            cur_tmem_buf = 0

tile_id = start_pid

clc_phase_producer = 1
            clc_phase_consumer = 0
            clc_buf = 0
            while tile_id != -1:
                clc_buf = clc_buf % NUM_CLC_STAGES
                # Debug prints
                # if tlx.thread_id(axis=0) == 0:
                # tl.device_print("Default WG Processing CtaID", tile_id)
                # producer
                tlx.clc_producer(clc_context, clc_buf, clc_phase_producer)
                # clc_phase_producer ^= 1
                clc_phase_producer = clc_phase_producer ^ (clc_buf == (NUM_CLC_STAGES - 1))

pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
                offs_am = pid_m * BLOCK_SIZE_M
                offs_bn = pid_n * BLOCK_SIZE_N

# load the result from TMEM to registers
                acc_tmem = tmem_buffers[cur_tmem_buf]

# done storing this buffer, signal MMA consumer to resume writing to it
                tlx.barrier_arrive(tmem_empty_bars[cur_tmem_buf], 1)

cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS

tile_id = tlx.clc_consumer(clc_context, clc_buf, clc_phase_consumer)
                # clc_phase_consumer ^= 1
                clc_phase_consumer = clc_phase_consumer ^ (clc_buf == (NUM_CLC_STAGES - 1))
                clc_buf += 1

# Debug-only: verifying that CLC steals workloads successfully
                # if tlx.thread_id(axis=0) == 0:
                # tl.device_print("Extracted CtaID", tile_id)

with tlx.async_task(num_warps=1, num_regs=232):  # MMA consumer
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

dot_phase = 0  # the current phase of dot op
            tmem_write_phase = 1  # sync between epilogue consumer and MMA consumer
            cur_tmem_buf = 0

processed_k_iters = 0
            tile_id = start_pid
            clc_phase = 0
            clc_buf = 0
            while tile_id != -1:
                clc_buf = clc_buf % NUM_CLC_STAGES
                pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
                offs_am = pid_m * BLOCK_SIZE_M
                offs_bn = pid_n * BLOCK_SIZE_N

# now iterate along K to compute result for the block
                for k in range(0, k_tiles):
                    # processed_k_iters + k means we use the immediate next buffer slot of tile_id x when we start tile_id x+1
                    buf = (processed_k_iters + k) % NUM_SMEM_BUFFERS
                    # wait for current phase(round) of load for this buf
                    tlx.barrier_wait(smem_full_bars[buf], dot_phase)
                    # buffer is now ready with loaded data, tlx.async_dot will signal `mBarrier` when done
                    tlx.async_dot(buffers_A[buf], buffers_B[buf], tmem_buffers[cur_tmem_buf], use_acc=k > 0,
                                  mBarriers=[smem_empty_bars[buf]], out_dtype=tl.float32)
                    # flip phase at the end of a round
                    dot_phase = dot_phase ^ (buf == NUM_SMEM_BUFFERS - 1)

# done filling this buffer, signal epilogue consumer
                tlx.barrier_arrive(tmem_full_bars[cur_tmem_buf], 1)

# possibly enter next iteration (next tile) without waiting for epilogue
                cur_tmem_buf = (cur_tmem_buf + 1) % NUM_TMEM_BUFFERS
                processed_k_iters += k_tiles
                tile_id = tlx.clc_consumer(clc_context, clc_buf, clc_phase)
                # clc_phase ^= 1
                clc_phase = clc_phase ^ (clc_buf == (NUM_CLC_STAGES - 1))
                clc_buf += 1

with tlx.async_task(num_warps=1, num_regs=232):  # producer, TMA load
            # common code duplicated for each region to avoid SMEM overhead
            start_pid = tl.program_id(axis=0)
            num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
            num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
            num_pid_in_group = GROUP_SIZE_M * num_pid_n
            k_tiles = tl.cdiv(K, BLOCK_SIZE_K)
            # end of common code

tile_id = start_pid
            clc_phase = 0
            clc_buf = 0
            while tile_id != -1:
                clc_buf = clc_buf % NUM_CLC_STAGES
                pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M)
                offs_am = pid_m * BLOCK_SIZE_M
                offs_bn = pid_n * BLOCK_SIZE_N