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1 change: 1 addition & 0 deletions csrc/kernels/launch.cuh
Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,7 @@ cfg.dynamicSmemBytes = smem_size;
#define SWITCH_RDMA_RANKS(case_macro) \
switch (num_ranks / NUM_MAX_NVL_PEERS) { \
case 2: case_macro(2); \
case 3: case_macro(3); \
case 4: case_macro(4); \
case 6: case_macro(6); \
case 8: case_macro(8); \
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2 changes: 1 addition & 1 deletion deep_ep/buffer.py
Original file line number Diff line number Diff line change
Expand Up @@ -234,7 +234,7 @@ def get_dispatch_config(num_ranks: int) -> Config:
4: Config(Buffer.num_sms, 6, 256, 6, 128),
8: Config(Buffer.num_sms, 6, 256, 6, 128),
16: Config(Buffer.num_sms, 36, 288, 20, 128),
24: Config(Buffer.num_sms, 8, 288, 32, 128),
24: Config(Buffer.num_sms, 32, 288, 8, 128),
32: Config(Buffer.num_sms, 32, 288, 8, 128),
48: Config(Buffer.num_sms, 32, 288, 8, 128),
64: Config(Buffer.num_sms, 32, 288, 8, 128),
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2 changes: 1 addition & 1 deletion tests/test_internode.py
Original file line number Diff line number Diff line change
Expand Up @@ -92,7 +92,7 @@ def test_main(args: argparse.Namespace, num_sms: int,
time.sleep(1)

# Config
rdma_buffer_size, nvl_buffer_size = 128, (720 if num_ranks in (48, 96, 144, 160) else 512)
rdma_buffer_size, nvl_buffer_size = 128, (720 if num_ranks in (24, 48, 96, 144, 160) else 512)
config = deep_ep.Config(num_sms, 8, nvl_buffer_size, 16, rdma_buffer_size)

# Test dispatch
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3 changes: 2 additions & 1 deletion tests/test_low_latency.py
Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,8 @@ def test_main(num_tokens: int, hidden: int, num_experts: int, num_topk: int,
if do_check:
diff = calc_diff(current_x * topk_weights.masked_fill(topk_idx == -1, 0).sum(dim=1).view(-1, 1), combined_x)
assert torch.isnan(combined_x).sum().item() == 0
assert diff < (9e-4 if dispatch_use_fp8 else 1e-5), f'Error: {diff=}, {dispatch_use_fp8=}, {zero_copy=}'
if not round_scale:
assert diff < (9e-4 if dispatch_use_fp8 else 1e-5), f'Error: {diff=}, {dispatch_use_fp8=}, {zero_copy=}'
hash_value ^= hash_tensor(combined_x)

# noinspection PyShadowingNames
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