some cleaning

huggingface · Jun 27, 2024 · 02ac451 · 02ac451
1 parent 3760102
commit 02ac451
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Showing 2 changed files with 4 additions and 44 deletions.
diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py
@@ -111,7 +111,6 @@ def __init__(
         prefix: str,
         config,
         weights,
-        layer_idx,
     ):
         super().__init__()
         self.num_heads = config.num_attention_heads
@@ -144,7 +143,6 @@ def __init__(
 
         self.query_key_value = load_attention(config, prefix, weights, index)
         self.index = index
-        self.layer_idx = layer_idx
 
         o_proj = TensorParallelRowLinear.load(
             config,
@@ -165,8 +163,6 @@ def __init__(
             0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
         ).repeat_interleave(self.num_groups)
 
-        self.step = 0
-
     def forward(
         self,
         hidden_states,
@@ -198,18 +194,6 @@ def forward(
         # output tensor
         attn_output = torch.empty_like(query)
 
-        if self.layer_idx < 4:
-            torch.save(query, f"query_states_step{self.step}_layer{self.layer_idx}.pt")
-            if cu_seqlen_prefill is not None:
-                torch.save(
-                    torch.select(kv, dim=1, index=0),
-                    f"key_states_step{self.step}_layer{self.layer_idx}.pt",
-                )
-                torch.save(
-                    torch.select(kv, dim=1, index=1),
-                    f"value_states_step{self.step}_layer{self.layer_idx}.pt",
-                )
-
         # Prefill
         if cu_seqlen_prefill is not None:
             # flash attention
@@ -236,14 +220,9 @@ def forward(
                 max_s,
             )
 
-        attn_output = attn_output.view(-1, self.num_heads * self.head_size)
-        if self.layer_idx < 4:
-            torch.save(
-                attn_output, f"attn_output_step{self.step}_layer{self.layer_idx}.pt"
-            )
-
-        self.step += 1
-        return self.o_proj(attn_output, adapter_data)
+        return self.o_proj(
+            attn_output.view(-1, self.num_heads * self.head_size), adapter_data
+        )
 
 
 class LlamaMLP(nn.Module):
@@ -342,14 +321,13 @@ def forward(self, hidden_states, adapter_data):
 
 
 class FlashLlamaLayer(nn.Module):
-    def __init__(self, index, prefix, config, weights, layer_idx):
+    def __init__(self, index, prefix, config, weights):
         super().__init__()
         self.self_attn = FlashLlamaAttention(
             index=index,
             prefix=f"{prefix}.self_attn",
             config=config,
             weights=weights,
-            layer_idx=layer_idx,
         )
         self.mlp = LlamaMLP(
             prefix=f"{prefix}.mlp", config=config, weights=weights, index=index
@@ -422,7 +400,6 @@ def __init__(self, prefix, config, weights):
                     ),
                     config=config,
                     weights=weights,
-                    layer_idx=layer_id,
                 )
                 for layer_id in range(config.num_hidden_layers)
             ]

diff --git a/server/text_generation_server/models/flash_causal_lm.py b/server/text_generation_server/models/flash_causal_lm.py
@@ -1149,23 +1149,6 @@ def forward(
             cuda_graph = None
 
         if cu_seqlen_prefill is not None or cuda_graph is None:
-            logger.info(f"input_ids {input_ids} {input_ids.shape}")
-            logger.info(f"position_ids {position_ids} {position_ids.shape}")
-            logger.info(
-                f"cu_seqlen_prefill {cu_seqlen_prefill} {cu_seqlen_prefill.shape if cu_seqlen_prefill is not None else 'NONE'}"
-            )
-            logger.info(
-                f"kv_cache {type(kv_cache)}, len={len(kv_cache)}, {len(kv_cache[0])}, shape={kv_cache[0][0].shape}"
-            )
-            logger.info(
-                f"block_tables {type(block_tables)} {block_tables.shape} {block_tables}"
-            )
-            logger.info(f"slots {type(slots)} {slots.shape} {slots}")
-            logger.info(f"input_lengths {input_lengths}")
-            logger.info(f"max_s {max_s}")
-            logger.info(f"prefill_cache_indices {batch.prefill_cache_indices}")
-            logger.info(f"lm_head_indices {lm_head_indices}")
-            logger.info(f"adapter_data {adapter_data}")
             logits, speculative_logits = self.model.forward(
                 input_ids=input_ids,
                 position_ids=position_ids,