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fix some bugs

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root 2025-04-17 00:48:09 +08:00
parent d2cf81423f
commit 921061666c
3 changed files with 7 additions and 7 deletions
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4
ktransformers/local_chat_test.py
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@ -158,12 +158,12 @@ def local_chat(
if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8: if system != "Windows" and (config.architectures[0] == "DeepseekV2ForCausalLM" or config.architectures[0] == "DeepseekV3ForCausalLM") and flashinfer_enabled and get_compute_capability() >= 8:
generated = prefill_and_generate( generated = prefill_and_generate(
model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size, model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_prefill_size,
use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim use_flashinfer_mla = True, num_heads = config.num_attention_heads, head_dim_ckv = config.kv_lora_rank, head_dim_kpe = config.qk_rope_head_dim, q_head_dim = config.qk_rope_head_dim + config.qk_nope_head_dim
) )
else: else:
generated = prefill_and_generate( generated = prefill_and_generate(
model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_prefill_size = chunk_prefill_size, model, tokenizer, input_tensor.cuda(), max_new_tokens, use_cuda_graph, mode = mode, force_think = force_think, chunk_size = chunk_prefill_size,
) )
break break

6
ktransformers/operators/experts.py
View file

@ -680,9 +680,9 @@ class KQwen2MoeSparseMoeBlock(BaseInjectedModule, Qwen2MoeSparseMoeBlock):
y.resize_(*orig_shape) y.resize_(*orig_shape)
return y, router_logits return y, router_logits
hidden_states_expert = hidden_states.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else hidden_states_expert.cpu() hidden_states_expert = hidden_states.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else hidden_states.cpu()
selected_experts_expert = selected_experts.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else selected_experts_expert.cpu() selected_experts_expert = selected_experts.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else selected_experts.cpu()
routing_weights_expert = routing_weights.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else routing_weights_expert.cpu() routing_weights_expert = routing_weights.to(self.experts.device) if isinstance(self.experts, KExpertsBase) else routing_weights.cpu()
shared_expert_output = self.shared_expert(hidden_states) shared_expert_output = self.shared_expert(hidden_states)
shared_expert_output = ( shared_expert_output = (

4
ktransformers/operators/linear.py
View file

@ -138,7 +138,7 @@ class KLinearTorch(KLinearBase):
self.weight = None self.weight = None
self.has_bias = False self.has_bias = False
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor: def forward(self, x: torch.Tensor, bsz_tensor: torch.Tensor=None, **kwargs) -> torch.Tensor:
dtype = x.dtype dtype = x.dtype
out_device = x.device out_device = x.device
# TODO: support CUDA Graph when using cpu, but CPUInfer is recommended. # TODO: support CUDA Graph when using cpu, but CPUInfer is recommended.
@ -201,7 +201,7 @@ class KLinearQ8(KLinearBase):
self.bias = None self.bias = None
self.loaded = False self.loaded = False
def forward(self, x: torch.Tensor) -> torch.Tensor: def forward(self, x: torch.Tensor, bsz_tensor: torch.Tensor=None) -> torch.Tensor:
orig_dtype = x.dtype orig_dtype = x.dtype
out_device = x.device out_device = x.device