Fix crash

Signed-off-by: yuanwu <yuan.wu@intel.com>

backends/gaudi/server/text_generation_server/layers/linear.py

@@ -25,7 +25,6 @@ class FastLinear(torch.nn.Module):
         return cls(weight, bias)
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
-        print(f"input.shape={input.shape}, self.weight={self.weight.shape}")
         return F.linear(input, self.weight, self.bias)
 
 

backends/gaudi/server/text_generation_server/layers/moe/unquantized.py

@@ -37,7 +37,6 @@ class UnquantizedSparseMoELayer(nn.Module):
         self.weight_block_size = weights.weights_loader.weight_block_size
         self.scoring_func = scoring_func
         self.e_score_correction_bias = e_score_correction_bias
-
         self.gate_up_proj = _load_expert_multi_weights_col(
             prefix=prefix,
             n_experts=n_experts,
@@ -52,7 +51,6 @@ class UnquantizedSparseMoELayer(nn.Module):
             name=down_proj_name,
             weights=weights,
         )
-
         self.hpu_fused_moe = DynamicFusedMOE(n_experts)
         for i in range(n_experts):
             self.hpu_fused_moe.MoeOp.w13_list[i].set_weight(self.gate_up_proj[i])

backends/gaudi/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -31,6 +31,7 @@ from text_generation_server.layers.attention import (
     KVCache,
     get_kv_scales,
 )
+from text_generation_server.utils.log import log_master
 from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
 from text_generation_server.layers.attention import (
     paged_attention,
@@ -46,6 +47,7 @@ from text_generation_server.layers import (
     TensorParallelMultiAdapterLinear,
     TensorParallelAdapterRowLinear,
 )
+from loguru import logger
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
@@ -633,7 +635,14 @@ class FlashLlamaForCausalLM(torch.nn.Module):
         adapter_data: Optional[torch.Tensor] = None,
         cross_attention_states=None,
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        
+        
+        log_master(
+            logger.debug,
+            f"input_ids: {input_ids}, input_ids.shape={input_ids.shape}, input_ids={input_ids[:-20]}"
+        )  
         inputs_embeds = self.embed_tokens(input_ids)
+        print(f"111111111 inputs_embeds: {inputs_embeds}")
         hidden_states = self.model(
             inputs_embeds,
             position_ids,

backends/gaudi/server/text_generation_server/models/flash_causal_lm.py

@@ -1792,7 +1792,7 @@ class FlashCausalLM(Model):
         kwargs = {}
         if htorch.utils.internal.is_lazy():
             kwargs["bypass_hpu_graphs"] = batch.prefilling
-
+        print(f"11111111111111111111input_ids: {input_ids.shape}")
         logits, speculative_logits = self.model.forward(
             input_ids=input_ids,
             position_ids=position_ids,