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feat: remove recordStream of normal mode #456

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102 changes: 5 additions & 97 deletions csrc/deep_ep.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -320,16 +320,7 @@ Buffer::get_dispatch_layout(
std::optional<EventHandle> event;
if (async) {
event = EventHandle(comm_stream);
for (auto& t : {topk_idx, num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank}) {
t.record_stream(comm_stream);
if (allocate_on_comm_stream)
t.record_stream(compute_stream);
}
for (auto& to : {num_tokens_per_rdma_rank}) {
to.has_value() ? to->record_stream(comm_stream) : void();
if (allocate_on_comm_stream)
to.has_value() ? to->record_stream(compute_stream) : void();
}
// NOTES: record_stream removed, tensors are now held by Python-layer extra_tensors
} else {
stream_wait(compute_stream, comm_stream);
}
Expand Down Expand Up @@ -606,32 +597,7 @@ Buffer::intranode_dispatch(const torch::Tensor& x,
std::optional<EventHandle> event;
if (async) {
event = EventHandle(comm_stream);
for (auto& t : {x,
is_token_in_rank,
rank_prefix_matrix,
channel_prefix_matrix,
recv_x,
recv_src_idx,
recv_channel_prefix_matrix,
send_head}) {
t.record_stream(comm_stream);
if (allocate_on_comm_stream)
t.record_stream(compute_stream);
}
for (auto& to : {x_scales,
topk_idx,
topk_weights,
num_tokens_per_rank,
num_tokens_per_expert,
cached_channel_prefix_matrix,
cached_rank_prefix_matrix,
recv_topk_idx,
recv_topk_weights,
recv_x_scales}) {
to.has_value() ? to->record_stream(comm_stream) : void();
if (allocate_on_comm_stream)
to.has_value() ? to->record_stream(compute_stream) : void();
}
// NOTES: record_stream removed, tensors are now held by Python-layer extra_tensors
} else {
stream_wait(compute_stream, comm_stream);
}
Expand Down Expand Up @@ -774,16 +740,7 @@ std::tuple<torch::Tensor, std::optional<torch::Tensor>, std::optional<EventHandl
std::optional<EventHandle> event;
if (async) {
event = EventHandle(comm_stream);
for (auto& t : {x, src_idx, send_head, rank_prefix_matrix, channel_prefix_matrix, recv_x}) {
t.record_stream(comm_stream);
if (allocate_on_comm_stream)
t.record_stream(compute_stream);
}
for (auto& to : {topk_weights, recv_topk_weights, bias_0, bias_1}) {
to.has_value() ? to->record_stream(comm_stream) : void();
if (allocate_on_comm_stream)
to.has_value() ? to->record_stream(compute_stream) : void();
}
// NOTES: record_stream removed, tensors are now held by Python-layer extra_tensors
} else {
stream_wait(compute_stream, comm_stream);
}
Expand Down Expand Up @@ -1121,39 +1078,7 @@ Buffer::internode_dispatch(const torch::Tensor& x,
std::optional<EventHandle> event;
if (async) {
event = EventHandle(comm_stream);
for (auto& t : {x,
is_token_in_rank,
recv_x,
rdma_channel_prefix_matrix,
recv_rdma_rank_prefix_sum,
gbl_channel_prefix_matrix,
recv_gbl_rank_prefix_sum}) {
t.record_stream(comm_stream);
if (allocate_on_comm_stream)
t.record_stream(compute_stream);
}
for (auto& to : {x_scales,
topk_idx,
topk_weights,
num_tokens_per_rank,
num_tokens_per_rdma_rank,
num_tokens_per_expert,
cached_rdma_channel_prefix_matrix,
cached_recv_rdma_rank_prefix_sum,
cached_gbl_channel_prefix_matrix,
cached_recv_gbl_rank_prefix_sum,
recv_topk_idx,
recv_topk_weights,
recv_x_scales,
recv_rdma_channel_prefix_matrix,
recv_gbl_channel_prefix_matrix,
send_rdma_head,
send_nvl_head,
recv_src_meta}) {
to.has_value() ? to->record_stream(comm_stream) : void();
if (allocate_on_comm_stream)
to.has_value() ? to->record_stream(compute_stream) : void();
}
// NOTES: record_stream removed, tensors are now held by Python-layer extra_tensors
} else {
stream_wait(compute_stream, comm_stream);
}
Expand Down Expand Up @@ -1338,24 +1263,7 @@ std::tuple<torch::Tensor, std::optional<torch::Tensor>, std::optional<EventHandl
std::optional<EventHandle> event;
if (async) {
event = EventHandle(comm_stream);
for (auto& t : {x,
src_meta,
is_combined_token_in_rank,
rdma_channel_prefix_matrix,
rdma_rank_prefix_sum,
gbl_channel_prefix_matrix,
combined_x,
combined_rdma_head,
combined_nvl_head}) {
t.record_stream(comm_stream);
if (allocate_on_comm_stream)
t.record_stream(compute_stream);
}
for (auto& to : {topk_weights, combined_topk_weights, bias_0, bias_1}) {
to.has_value() ? to->record_stream(comm_stream) : void();
if (allocate_on_comm_stream)
to.has_value() ? to->record_stream(compute_stream) : void();
}
// NOTES: record_stream removed, tensors are now held by Python-layer extra_tensors
} else {
stream_wait(compute_stream, comm_stream);
}
Expand Down
43 changes: 30 additions & 13 deletions deep_ep/buffer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,7 +314,9 @@ def get_dispatch_layout(self, topk_idx: torch.Tensor, num_experts: int,
num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank, event = \
self.runtime.get_dispatch_layout(topk_idx, num_experts, getattr(previous_event, 'event', None),
async_finish, allocate_on_comm_stream)
return num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank, EventOverlap(event)
tensors_to_record = (topk_idx, num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank) if async_finish else None

return num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank, EventOverlap(event, tensors_to_record)

# noinspection PyTypeChecker
def dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
Expand Down Expand Up @@ -386,7 +388,9 @@ def dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
recv_x, recv_x_scales, _, _, _, _, _, _, _, _, event = self.runtime.intranode_dispatch(
x, x_scales, None, None, None, is_token_in_rank, None, num_recv_tokens, rank_prefix_matrix, channel_prefix_matrix,
expert_alignment, num_worst_tokens, config, getattr(previous_event, 'event', None), async_finish, allocate_on_comm_stream)
return (recv_x, recv_x_scales) if x_scales is not None else recv_x, None, None, None, None, EventOverlap(event)

tensors_to_record = (x, x_scales, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, recv_x, recv_x_scales, recv_src_idx) if async_finish else None
return (recv_x, recv_x_scales) if x_scales is not None else recv_x, None, None, None, None, EventOverlap(event, tensors_to_record)
else:
assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None
recv_x, recv_x_scales, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, send_head, event = \
Expand All @@ -395,10 +399,10 @@ def dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
expert_alignment, num_worst_tokens, config,
getattr(previous_event, 'event', None), async_finish, allocate_on_comm_stream)
handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)
return (
recv_x, recv_x_scales
) if x_scales is not None else recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, EventOverlap(
event)
tensors_to_record = (x, x_scales, topk_idx, topk_weights, num_tokens_per_rank, num_tokens_per_expert,
is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix,
recv_x, recv_x_scales, recv_src_idx, recv_topk_idx, recv_topk_weights, send_head) if async_finish else None
return (recv_x, recv_x_scales) if x_scales is not None else recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, EventOverlap(event, tensors_to_record)

# noinspection PyTypeChecker
def combine(self, x: torch.Tensor, handle: Tuple,
Expand Down Expand Up @@ -446,7 +450,8 @@ def combine(self, x: torch.Tensor, handle: Tuple,
channel_prefix_matrix, send_head, config,
getattr(previous_event, 'event',
None), async_finish, allocate_on_comm_stream)
return recv_x, recv_topk_weights, EventOverlap(event)
tensors_to_record = (x, topk_weights, bias_0, bias_1, src_idx, rank_prefix_matrix, channel_prefix_matrix, send_head, recv_x, recv_topk_weights) if async_finish else None
return recv_x, recv_topk_weights, EventOverlap(event, tensors_to_record)

# noinspection PyTypeChecker
def internode_dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
Expand Down Expand Up @@ -479,7 +484,11 @@ def internode_dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Te
x, x_scales, topk_idx, topk_weights, None, None, is_token_in_rank, None, num_recv_tokens, num_rdma_recv_tokens,
rdma_channel_prefix_matrix, recv_rdma_rank_prefix_sum, gbl_channel_prefix_matrix, recv_gbl_rank_prefix_sum,
expert_alignment, config, getattr(previous_event, 'event', None), async_finish, allocate_on_comm_stream)
return (recv_x, recv_x_scales) if x_scales is not None else recv_x, None, None, None, None, EventOverlap(event)

tensors_to_record =(x, x_scales, is_token_in_rank, recv_x, recv_x_scales,
rdma_channel_prefix_matrix, recv_rdma_rank_prefix_sum, gbl_channel_prefix_matrix, recv_gbl_rank_prefix_sum,
recv_rdma_channel_prefix_matrix, recv_src_meta, send_rdma_head, send_nvl_head) if async_finish else None
return (recv_x, recv_x_scales) if x_scales is not None else recv_x, None, None, None, None, EventOverlap(event, tensors_to_record)
else:
assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None
recv_x, recv_x_scales, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, \
Expand All @@ -494,10 +503,15 @@ def internode_dispatch(self, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Te
handle = (is_token_in_rank, rdma_channel_prefix_matrix, gbl_channel_prefix_matrix, recv_rdma_channel_prefix_matrix,
recv_rdma_rank_prefix_sum, recv_gbl_channel_prefix_matrix, recv_gbl_rank_prefix_sum, recv_src_meta, send_rdma_head,
send_nvl_head)
return (
recv_x, recv_x_scales
) if x_scales is not None else recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, EventOverlap(
event)
tensors_to_record = (x, x_scales, topk_idx, topk_weights, num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert,
is_token_in_rank, recv_x, recv_x_scales, recv_topk_idx, recv_topk_weights,
rdma_channel_prefix_matrix, gbl_channel_prefix_matrix,
recv_rdma_channel_prefix_matrix, recv_rdma_rank_prefix_sum,
recv_gbl_channel_prefix_matrix, recv_gbl_rank_prefix_sum,
recv_src_meta, send_rdma_head, send_nvl_head) if async_finish else None

return (recv_x, recv_x_scales) if x_scales is not None else recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, EventOverlap(event, tensors_to_record)


# noinspection PyTypeChecker
def internode_combine(self, x: torch.Tensor, handle: Union[tuple, list],
Expand Down Expand Up @@ -527,7 +541,10 @@ def internode_combine(self, x: torch.Tensor, handle: Union[tuple, list],
send_rdma_head, send_nvl_head, config,
getattr(previous_event, 'event',
None), async_finish, allocate_on_comm_stream)
return combined_x, combined_topk_weights, EventOverlap(event)
tensors_to_record = (x, topk_weights, bias_0, bias_1, src_meta, is_combined_token_in_rank,
rdma_channel_prefix_matrix, rdma_rank_prefix_sum, gbl_channel_prefix_matrix,
send_rdma_head, send_nvl_head, combined_x, combined_topk_weights) if async_finish else None
return combined_x, combined_topk_weights, EventOverlap(event, tensors_to_record)

def clean_low_latency_buffer(self, num_max_dispatch_tokens_per_rank: int, hidden: int, num_experts: int) -> None:
"""
Expand Down
6 changes: 5 additions & 1 deletion deep_ep/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,9 +33,12 @@ def __init__(self, event: Optional[EventHandle] = None, extra_tensors: Optional[
def current_stream_wait(self) -> None:
"""
The current stream `torch.cuda.current_stream()` waits for the event to be finished.
After synchronization completes, tensor references are released to allow memory reuse.
"""
assert self.event is not None
self.event.current_stream_wait()
# Release tensor references after synchronization is complete
self.extra_tensors = None

def __enter__(self) -> Any:
"""
Expand All @@ -56,9 +59,10 @@ def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
Utility for overlapping and Python `with` syntax.

Please follow the example in the `__enter__` function.
After synchronization completes, tensor references are released to allow memory reuse.
"""
if self.event is not None:
self.event.current_stream_wait()
self.current_stream_wait()


def check_nvlink_connections(group: dist.ProcessGroup):
Expand Down