Flash causal LM case

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

@@ -1702,6 +1702,7 @@ class FlashCausalLM(Model):
             f"{dim}:{seq_len} "
             f"bypass:{bypass} "
             f"free_mem:{free_mem}"
+            ", this may take a while..."
         )
         log_master(logger.info, msg)
 
@@ -1753,6 +1754,10 @@ class FlashCausalLM(Model):
         total_batch_seq = 0.001
         total_mem = 0
         available_mem = prompt_available_memory
+        logger.info(
+            f"Prefill batch size list:{[bsz[0] for bsz in buckets]}\n"
+            f"Prefill sequence length list:{[seq[1] for seq in buckets]}\n"
+        )
         for i, (batch_size, seq_len) in enumerate(buckets):
             if batch_size * seq_len > self.max_batch_prefill_tokens:
                 continue
@@ -1779,6 +1784,8 @@ class FlashCausalLM(Model):
                 total_mem += used_mem
                 total_batch_seq += batch_seq
 
+        logger.info("Prefill warmup successful.\n")
+
         def ordering_function_max_bs(b):
             return (-b[0], b[1])
 
@@ -1790,6 +1797,7 @@ class FlashCausalLM(Model):
         total_batch_seq = 0.001
         total_mem = 0
         available_mem = free_mem - self.mem_reserved
+        logger.info(f"Decode batch size list:{[bsz[0] for bsz in buckets]}\n")
         for i, (batch_size, block_num) in enumerate(buckets):
             if batch_size > block_num:
                 continue
@@ -1814,6 +1822,8 @@ class FlashCausalLM(Model):
                 total_mem += used_mem
                 total_batch_seq += batch_seq
 
+        logger.info("Decode warmup successful.\n")
+
         log_master(
             logger.info,
             f"warmup hpu graph time {int(time.time() - start_time)}s warmup shape count {warmup_shape_count}",