[Gaudi] use pad_token_id to pad input id (#3268)

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

Author: sywangyi

Dockerfile_gaudi

@@ -95,8 +95,6 @@ RUN cd server && \
     make gen-server && \
     pip install --no-deps -r requirements.txt && \
     bash ./dill-0.3.8-patch.sh && \
-    pip install "git+https://github.com/HabanaAI/DeepSpeed.git@${HABANA_VERSION}" && \
-    BUILD_CUDA_EXT=0 pip install git+https://github.com/AutoGPTQ/AutoGPTQ.git@097dd04e --no-build-isolation && \
     pip install . --no-cache-dir
 RUN pip install git+https://github.com/sywangyi/vllm-hpu-extension.git@bmax_fix
 RUN pip install compressed-tensors==0.9.1

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

@@ -975,7 +975,7 @@ def concatenate(
             valid_indices=None,
         )
 
-    def prepare_for_decode(self, dtype, use_contiguous_pa, bucketing_ctx):
+    def prepare_for_decode(self, dtype, use_contiguous_pa, bucketing_ctx, pad_token_id):
         block_num = [length // BLOCK_SIZE + 1 for length in self.cache_lengths]
         block_tables = []
         for i, bt in enumerate(self.block_tables):
@@ -998,7 +998,7 @@ def prepare_for_decode(self, dtype, use_contiguous_pa, bucketing_ctx):
             bucketing_ctx,
         )
         self.input_ids = F.pad(
-            self.input_ids, (0, padded_bs - self.input_ids.shape[0]), value=0
+            self.input_ids, (0, padded_bs - self.input_ids.shape[0]), value=pad_token_id
         )
 
         if self.position_ids.dim() == 2:
@@ -1040,7 +1040,7 @@ def prepare_for_decode(self, dtype, use_contiguous_pa, bucketing_ctx):
         )
 
     def prepare_for_prefill(
-        self, max_padded_input_len, max_padded_bs, max_total_tokens
+        self, max_padded_input_len, max_padded_bs, max_total_tokens, pad_token_id
     ):
         # Prepare values if we need to continue prefilling
         # Speculation must be ignored while we prefill even with chunking
@@ -1064,18 +1064,23 @@ def prepare_for_prefill(
             for input_id in self.input_ids:
                 padded = self.max_input_length - len(input_id) + extra_pad
                 if padded > 0:
-                    input_id = [0] * padded + input_id
+                    input_id = [pad_token_id] * padded + input_id
                 input_ids.append(input_id)
                 input_ids_padded_length.append(padded)
             input_ids = np.concatenate(input_ids, dtype=np.int64)
             self.input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
         elif isinstance(self.input_ids, list):
             input_ids = self.input_ids[0]
             input_ids_padded_length.append(extra_pad)
-            input_ids = [0] * extra_pad + input_ids
+            input_ids = [pad_token_id] * extra_pad + input_ids
             self.input_ids = torch.tensor(input_ids, dtype=torch.int64, device=device)
         else:
-            input_ids = self.input_ids.new_zeros(max_padded_input_len * len(self))
+            input_ids = torch.full(
+                (max_padded_input_len * len(self),),
+                pad_token_id,
+                dtype=torch.int64,
+                device=self.input_ids.device,
+            )
             src_pos = 0
             for i in range(len(self)):
                 end_pos = (i + 1) * max_padded_input_len
@@ -1090,7 +1095,7 @@ def prepare_for_prefill(
             self.input_ids = input_ids
 
         self.input_ids = F.pad(
-            self.input_ids, (0, extra_pad_bs * max_padded_input_len), value=0
+            self.input_ids, (0, extra_pad_bs * max_padded_input_len), value=pad_token_id
         )
 
         self.input_lengths_tensor = torch.tensor(self.input_lengths, dtype=torch.int32)
@@ -1312,8 +1317,9 @@ def prepare_for_prefill(
             self.prefill_next_token_indices = (
                 self.prefill_next_token_indices + input_ids_padded_length_tensor
             )
-        all_input_ids_tensor = torch.zeros(
+        all_input_ids_tensor = torch.full(
             (max_padded_bs, max(max_total_tokens, self.all_input_ids_tensor.shape[-1])),
+            pad_token_id,
             dtype=torch.int64,
             device="hpu",
         )
@@ -1502,6 +1508,19 @@ def __init__(
         )
         self.skip_warmup = os.getenv("VLLM_SKIP_WARMUP", "false").lower() == "true"
         self.max_seq_len_to_capture = 8192
+        if tokenizer.pad_token_id is None:
+            if config.pad_token_id is not None:
+                tokenizer.pad_token_id = config.pad_token_id
+            elif config.eos_token_id is not None:
+                tokenizer.pad_token_id = (
+                    config.eos_token_id[0]
+                    if isinstance(config.eos_token_id, list)
+                    else config.eos_token_id
+                )
+            elif tokenizer.eos_token_id is not None:
+                tokenizer.pad_token_id = tokenizer.eos_token_id
+            else:
+                tokenizer.pad_token_id = 0
         super().__init__(
             model_id=model_id,
             model=model,
@@ -2274,14 +2293,21 @@ def generate_token(
                     ),
                     self.bucketing_ctx.get_padded_prompt_batch_size(len(batch)),
                     self.max_total_tokens,
+                    self.tokenizer.pad_token_id,
                 )
             else:
                 batch.prepare_for_prefill(
-                    batch.max_input_length, len(batch), self.max_total_tokens
+                    batch.max_input_length,
+                    len(batch),
+                    self.max_total_tokens,
+                    self.tokenizer.pad_token_id,
                 )
         else:
             batch.prepare_for_decode(
-                self.dtype, self.use_contiguous_pa, self.bucketing_ctx
+                self.dtype,
+                self.use_contiguous_pa,
+                self.bucketing_ctx,
+                self.tokenizer.pad_token_id,
             )
         if hasattr(self, "set_inputs_embeds") and callable(self.set_inputs_embeds):
             self.set_inputs_embeds(batch)

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

@@ -554,10 +554,10 @@ def from_pb_processor(
         return batch
 
     def prepare_for_prefill(
-        self, max_padded_input_len, max_padded_bs, max_total_tokens
+        self, max_padded_input_len, max_padded_bs, max_total_tokens, pad_token_id
     ):
         super().prepare_for_prefill(
-            max_padded_input_len, max_padded_bs, max_total_tokens
+            max_padded_input_len, max_padded_bs, max_total_tokens, pad_token_id
         )
 
         self.has_image_inputs = False

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

@@ -47,10 +47,10 @@ class FlashMllamaCausalLMBatch(FlashVlmCausalLMBatch):
     cross_attention_states: Optional[torch.Tensor] = None
 
     def prepare_for_prefill(
-        self, max_padded_input_len, max_padded_bs, max_total_tokens
+        self, max_padded_input_len, max_padded_bs, max_total_tokens, pad_token_id
     ):
         super(FlashVlmCausalLMBatch, self).prepare_for_prefill(
-            max_padded_input_len, max_padded_bs, max_total_tokens
+            max_padded_input_len, max_padded_bs, max_total_tokens, pad_token_id
         )
 
     @classmethod