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Apply magikRUKKOLA's patch from issue #1417 #1422

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Apply magikRUKKOLA's patch from issue #1417 #1422

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14 changes: 9 additions & 5 deletions ktransformers/models/custom_modeling_deepseek_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,18 +42,22 @@ def __init__(

def init_wrapper(self, use_cuda_graph, device, max_batch_size, max_pages):
self.use_cuda_graph = use_cuda_graph
self.workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.int8).to(0)
# Increase buffer sizes to be safe
self.workspace_buffer = torch.empty(256 * 1024 * 1024, dtype=torch.int8).to(0)
self.qo_indptr_buf = torch.empty((max_batch_size+2,), dtype=torch.int32, device=device)
self.paged_kv_indptr_buf = torch.empty((max_batch_size+2,), dtype=torch.int32, device=device)
self.paged_kv_indices_buf = torch.empty((max_pages,), dtype=torch.int32, device=device)
# Make sure this buffer is large enough
self.paged_kv_indices_buf = torch.empty((max_pages * 2,), dtype=torch.int32, device=device)
self.paged_kv_len_buf = torch.empty((max_batch_size+1,), dtype=torch.int32, device=device)
self.bsz_tensor_buf = torch.empty((1, ), dtype=torch.int32, device=device)


self.wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(
self.workspace_buffer, use_cuda_graph=use_cuda_graph,
qo_indptr=self.qo_indptr_buf,kv_indptr=self.paged_kv_indptr_buf,
kv_indices=self.paged_kv_indices_buf,kv_len_arr=self.paged_kv_len_buf,
qo_indptr=self.qo_indptr_buf,
kv_indptr=self.paged_kv_indptr_buf,
kv_indices=self.paged_kv_indices_buf,
kv_len_arr=self.paged_kv_len_buf,
bsz_tensor=self.bsz_tensor_buf,
backend = "fa2",
)
Expand Down Expand Up @@ -145,4 +149,4 @@ def flash_infer_attn_plan(self, batch: ForwardBatchInput, bsz_tensors, num_token
minibatch = batch.minibatch
self.wrapper.plan(minibatch.q_indptr, minibatch.kv_indptr, minibatch.kv_indices,
minibatch.kv_len, num_heads, head_dim_ckv, head_dim_kpe, page_size, causal, sm_scale, q_data_type, kv_data_type, bsz_tensors)


22 changes: 16 additions & 6 deletions ktransformers/server/balance_serve/inference/sampling/sampler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,8 +52,9 @@ def __init__(self, bsz = 1, device = torch.device('cuda'), pretrained_config:Gen
self.is_all_greedy = False

class Sampler(nn.Module):
def __init__(self):
def __init__(self, device=torch.device('cuda')):
super().__init__()
self.device = device

def forward(
self,
Expand All @@ -63,15 +64,20 @@ def forward(
if sampling_config == None:
sampling_config = SamplingOptions()

logits = logits.contiguous()
# Ensure all tensors are on the same device
device = logits.device
logits = logits.contiguous().to(device)
sampling_config.temperatures = sampling_config.temperatures.to(device)

origin_logits = logits.clone()
if sampling_config.is_all_greedy:
# Use torch.argmax if all requests use greedy sampling
probs = logits
batch_next_token_ids = torch.argmax(logits, -1)
else:
# Post process logits
logits.div_(sampling_config.temperatures)
safe_temperatures = sampling_config.temperatures.masked_fill(sampling_config.temperatures == 0, 1.0)
logits.div_(safe_temperatures)
max_top_k_round, batch_size = 32, logits.shape[0]
if sampling_config.need_min_p_sampling:
probs = torch.softmax(logits, dim=-1)
Expand All @@ -82,8 +88,10 @@ def forward(
batch_next_token_ids = min_p_sampling_from_probs(
probs, sampling_config.min_ps
)
torch.cuda.synchronize()
temperature_0_idx = torch.where(sampling_config.temperatures == 0)[0]
batch_next_token_ids[temperature_0_idx] = torch.argmax(origin_logits[temperature_0_idx], -1).to(torch.int32)
if temperature_0_idx.numel() > 0:
batch_next_token_ids[temperature_0_idx] = torch.argmax(origin_logits[temperature_0_idx], -1).to(torch.int32)
else:
# TODO: use different kernel when don't need top_k or top_p
# @TODO get probs
Expand All @@ -94,7 +102,9 @@ def forward(
sampling_config.top_ps,
filter_apply_order="joint",
)
torch.cuda.synchronize()
temperature_0_idx = torch.where(sampling_config.temperatures == 0)[0]
batch_next_token_ids[temperature_0_idx] = torch.argmax(origin_logits[temperature_0_idx], -1).to(torch.int32)
if temperature_0_idx.numel() > 0:
batch_next_token_ids[temperature_0_idx] = torch.argmax(origin_logits[temperature_0_idx], -1).to(torch.int32)

return batch_next_token_ids.to(torch.int32), probs
return batch_next_token_ids.to(torch.int32), probs