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[fix] fix test case; moe error in second iter

pull/6083/head
duanjunwen 2024-10-14 07:38:02 +00:00
parent a11b4b50a7
commit abd455189d
3 changed files with 38 additions and 13 deletions
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8
colossalai/shardformer/layer/_operation.py
View File

@ -129,7 +129,7 @@ class LinearWithAsyncCommunication(torch.autograd.Function):
"""
@staticmethod
def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce, fp8_communication=False, use_zbv=True):
def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce, fp8_communication=False, use_zbv=False):
ctx.save_for_backward(input_, weight, bias)
ctx.use_bias = bias is not None
ctx.process_group = process_group
@ -1094,9 +1094,11 @@ def matmul_with_async_comm(input_, weight, bias, process_group, async_grad_allre
)
def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce, fp8_communication=False):
def linear_with_async_comm(
input_, weight, bias, process_group, async_grad_allreduce, fp8_communication=False, use_zbv=False
):
return LinearWithAsyncCommunication.apply(
input_, weight, bias, process_group, async_grad_allreduce, fp8_communication
input_, weight, bias, process_group, async_grad_allreduce, fp8_communication, use_zbv
)

32
colossalai/shardformer/layer/linear.py
View File

@ -85,6 +85,7 @@ class Linear1D_Col(ParallelModule):
weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
fp8_communication: bool = False,
use_zbv: bool = False,
**kwargs,
):
super().__init__(weight=weight, bias_=bias_, **kwargs)
@ -100,6 +101,7 @@ class Linear1D_Col(ParallelModule):
self.device = device
self.process_group = process_group
self.fp8_communication = fp8_communication
self.use_zbv = use_zbv
if skip_bias_add and not bias:
raise ValueError("cannot skip bias addition if bias is None")
@ -206,7 +208,13 @@ class Linear1D_Col(ParallelModule):
input_parallel, self.process_group, self.seq_parallel_dim, fp8_communication=self.fp8_communication
)
output_parallel = linear_with_async_comm(
input_parallel, self.weight, bias, self.process_group, False, fp8_communication=self.fp8_communication
input_parallel,
self.weight,
bias,
self.process_group,
False,
fp8_communication=self.fp8_communication,
use_zbv=self.use_zbv,
)
elif self.seq_parallel_mode == "ring":
output_parallel = linear_gather_forward_reducescatter_backward(
@ -214,7 +222,13 @@ class Linear1D_Col(ParallelModule):
)
else:
output_parallel = linear_with_async_comm(
input_parallel, self.weight, bias, self.process_group, True, fp8_communication=self.fp8_communication
input_parallel,
self.weight,
bias,
self.process_group,
True,
fp8_communication=self.fp8_communication,
use_zbv=self.use_zbv,
)
if self.gather_output:
@ -272,6 +286,7 @@ class Linear1D_Row(ParallelModule):
bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
stream_chunk_num: int = 1,
fp8_communication: bool = False,
use_zbv: bool = False,
):
super().__init__()
@ -287,6 +302,7 @@ class Linear1D_Row(ParallelModule):
self.seq_parallel_dim = seq_parallel_dim
self.num_partitions = dist.get_world_size(self.process_group)
self.fp8_communication = fp8_communication
self.use_zbv = use_zbv
if skip_bias_add and not bias:
raise ValueError("cannot skip bias addition if bias is None")
@ -428,10 +444,14 @@ class Linear1D_Row(ParallelModule):
output = torch.cat(output_parallel_list, dim=-1)
else:
if self.seq_parallel_mode is None:
output_parallel = linear_with_async_comm(input_, self.weight, None, self.process_group, False)
output_parallel = linear_with_async_comm(
input_, self.weight, None, self.process_group, False, use_zbv=self.use_zbv
)
output = reduce_forward(output_parallel, self.process_group, fp8_communication=self.fp8_communication)
elif self.seq_parallel_mode == "split_gather":
output_parallel = linear_with_async_comm(input_, self.weight, None, self.process_group, False)
output_parallel = linear_with_async_comm(
input_, self.weight, None, self.process_group, False, use_zbv=self.use_zbv
)
output = reducescatter_forward_gather_backward(
output_parallel, self.process_group, self.seq_parallel_dim, fp8_communication=self.fp8_communication
)
@ -444,7 +464,9 @@ class Linear1D_Row(ParallelModule):
ring=True,
)
else:
output_parallel = linear_with_async_comm(input_, self.weight, None, self.process_group, False)
output_parallel = linear_with_async_comm(
input_, self.weight, None, self.process_group, False, use_zbv=self.use_zbv
)
output = reduce_forward(output_parallel, self.process_group)
if not self.skip_bias_add:

11
tests/test_pipeline/test_schedule/test_zerobubble_pp.py
View File

@ -752,8 +752,9 @@ def run_with_hybridplugin(test_config):
@parameterize(
"config",
[
(0, 1, 4, 1, 1),
(1, 2, 2, 1, 1),
# TODO:ERR in second iter
# (0, 1, 4, 1, 1),
# (1, 2, 2, 1, 1),
(1, 2, 1, 2, 1),
(1, 2, 1, 1, 2),
],
@ -905,9 +906,9 @@ def run_with_booster_moehybridplugin(config: Tuple[int, ...]):
torch_optimizer.zero_grad()
assert_loose_close(parallel_output, torch_output_sum, dtype=dtype)
print(f"rank {dist.get_rank()} config {test_config} test passed")
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()
clear_layout_converter()
Randomizer.reset_index()
torch.cuda.empty_cache()
def run_dist(rank, world_size, port):