wenyi-li Loop + View：编译期常量规则
致命错误：ValueError: Not concrete value
最常见的首次生成错误。所有 pypto.view 的 shape 参数必须是编译期常量（字面量或闭包变量）。
# WRONG — min() 含 loop 变量 offset，是运行时值
for idx in pypto.loop(0, num_iters, 1, ...):
offset = idx * BASIC_BATCH
current = min(BASIC_BATCH, total_size - offset) # runtime!
chunk = pypto.view(x, [current, n], [offset, 0]) # ValueError!
任何含 loop idx 的表达式都是运行时值，不能用于 view shape。
通用正确写法
方法 A：确保整除（推荐）
选择 BASIC_BATCH 使 total_size 可整除，或在 forward 中 assert 整除性：
def create_kernel(total_rows, cols, basic_batch):
assert total_rows % basic_batch == 0
num_iters = total_rows // basic_batch # 闭包常量
@pypto.frontend.jit(...)
def kernel(x: pypto.Tensor((total_rows, cols), ...)) -> ...:
output = pypto.tensor([total_rows, cols], pypto.DT_FP32)
pypto.set_vec_tile_shapes(1, 8192)
for idx in pypto.loop(0, num_iters, 1, name="LOOP", idx_name="idx"):
off = idx * basic_batch
chunk = pypto.view(x, [basic_batch, cols], [off, 0])
result_chunk = chunk * 2.0 # 示例操作
pypto.assemble(result_chunk, [off, 0], output)
return output
return kernel
class ModelNew(torch.nn.Module):
def forward(self, x):
total_rows = x.shape[0] * x.shape[1]
# 调参由 loop_count 空间驱动：先试 16/32，再反推 basic_batch
target_loop_count = 16
assert total_rows % target_loop_count == 0
basic_batch = total_rows // target_loop_count
...
方法 B：主循环 + 尾部
当无法保证整除时：
def create_kernel(total_rows, cols, basic_batch):
full_iterations = total_rows // basic_batch
tail = total_rows % basic_batch
tail_offset = full_iterations * basic_batch
# full_iterations, tail, tail_offset 都是闭包常量
@pypto.frontend.jit(...)
def kernel(x: pypto.Tensor((total_rows, cols), ...)) -> ...:
output = pypto.tensor([total_rows, cols], pypto.DT_FP32)
pypto.set_vec_tile_shapes(1, 8192)
for idx in pypto.loop(0, full_iterations, 1, name="LOOP", idx_name="idx"):
off = idx * basic_batch
chunk = pypto.view(x, [basic_batch, cols], [off, 0])
pypto.assemble(chunk * 2.0, [off, 0], output)
if tail > 0: # 编译期求值（tail 是闭包常量）
tail_chunk = pypto.view(x, [tail, cols], [tail_offset, 0])
pypto.assemble(tail_chunk * 2.0, [tail_offset, 0], output)
return output
return kernel
1D Loop（全局归约/大向量）
LOOP_CHUNKS = 8
def create_frobenius_kernel(flat_size):
chunk_size = flat_size // LOOP_CHUNKS # 闭包常量
@pypto.frontend.jit(...)
def kernel(x: pypto.Tensor((flat_size,), ...)) -> ...:
output = pypto.tensor([flat_size], pypto.DT_FP32)
pypto.set_vec_tile_shapes(16384)
acc = pypto.zeros([1], dtype=pypto.DT_FP32)
for i in pypto.loop(0, LOOP_CHUNKS, 1, name="LOOP_ACC", idx_name="i"):
x_chunk = pypto.view(x, [chunk_size], [i * chunk_size])
part = pypto.sum(x_chunk * x_chunk, dim=0, keepdim=True)
acc[:] = acc + part
norm = pypto.sqrt(acc)
output[:] = x / norm
return output
return kernel
class ModelNew(torch.nn.Module):
def forward(self, x):
x_flat = x.reshape(-1)
assert x_flat.numel() % LOOP_CHUNKS == 0
...
关键原则
- view shape 只能用字面量或闭包变量。loop idx 及含 idx 的表达式是运行时值。
if tail > 0:在编译期求值（tail 是闭包常量），不是运行时分支。- 优先确保整除，避免尾部处理的复杂度。
- Matmul 不要固定 BASIC_BATCH。先在
loop_count空间选中段（1~128常见从16/32起步），再反推BASIC_BATCH；必要时扩到8/64，最后再补端点。