warmup prefill

remove model where pageattn is not used, set block table to None since it's not used

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

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

@@ -92,7 +92,6 @@ try:
     from text_generation_server.models.custom_modeling.flash_phi_modeling import (
         FlashPhiForCausalLM,
     )
-    from text_generation_server.models.idefics_causal_lm import IdeficsCausalLM
     from text_generation_server.models.mllama_causal_lm import FlashMllamaCausalLMBatch
     from text_generation_server.models.custom_modeling.flash_mllama import (
         FlashMllamaForConditionalGeneration,
@@ -144,7 +143,6 @@ except ImportError as e:
 
 if FLASH_ATTENTION:
     __all__.append(FlashCausalLM)
-    __all__.append(IdeficsCausalLM)
 
 
 class ModelType(enum.Enum):
@@ -301,12 +299,6 @@ class ModelType(enum.Enum):
         "name": "Gptj",
         "url": "https://huggingface.co/EleutherAI/gpt-j-6b",
     }
-    IDEFICS = {
-        "type": "idefics",
-        "name": "Idefics",
-        "url": "https://huggingface.co/HuggingFaceM4/idefics-9b",
-        "multimodal": True,
-    }
     MLLAMA = {
         "type": "mllama",
         "name": "Mllama",
@@ -733,15 +725,6 @@ def get_model(
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
-        elif model_type == IDEFICS:
-            return IdeficsCausalLM(
-                model_id,
-                revision,
-                quantize=quantize,
-                speculator=speculator,
-                dtype=dtype,
-                trust_remote_code=trust_remote_code,
-            )
         elif model_type == QWEN2_VL:
             return FlashVlmCausalLM(
                 model_id=model_id,

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

@@ -69,6 +69,8 @@ from text_generation_server.utils.import_utils import (
 
 import vllm_hpu_extension.environment as environment
 import habana_frameworks.torch as htorch
+import itertools
+from vllm_hpu_extension.ops import batch2block, block2batch
 
 tracer = trace.get_tracer(__name__)
 
@@ -86,6 +88,78 @@ def get_sliding_windows() -> int:
     return SLIDING_WINDOW
 
 
+def prepare_for_decode(
+    dtype, use_contiguous_pa, device, slot, block_tables, batch_size
+):
+    # Prepare values if we need to continue decoding
+    # need for HPUPagedAttentionMetadata preparation
+    def flatten(in_list):
+        return list(itertools.chain(*in_list))
+
+    def gather_list(input, indices, v):
+        return [input[i] if i is not None else v for i in indices]
+
+    def pad_list(input, k, v):
+        input_len = len(input)
+        target_len = (input_len + k - 1) // k * k
+        padding = target_len - input_len
+        return input + [v] * padding
+
+    last_block_usage = slot % BLOCK_SIZE + 1
+    block_groups = [[i] * len(bt) for i, bt in enumerate(block_tables)]
+    block_usage = [
+        [BLOCK_SIZE] * (len(bt) - 1) + [lbu]
+        for bt, lbu in zip(block_tables, last_block_usage)
+        if bt
+    ]
+
+    block_list = flatten(block_tables)
+    block_groups = flatten(block_groups)
+    block_usage = flatten(block_usage)
+
+    assert len(block_list) == len(block_groups)
+    assert len(block_list) == len(block_usage)
+    if use_contiguous_pa:
+        block_bucket_size = max(max(block_list) + 1, len(block_list))
+        #     block_bucket_size = self.bucketing_ctx.get_padded_decode_num_blocks(
+        #         block_bucket_size)
+        indices: List[Any]
+        indices = [None] * block_bucket_size
+        for i, bid in enumerate(block_list):
+            indices[bid] = i
+        block_list = gather_list(block_list, indices, 0)
+        block_groups = gather_list(block_groups, indices, -1)
+        block_usage = gather_list(block_usage, indices, 1)
+    else:
+        block_bucket_size = len(block_list)
+        block_list = pad_list(block_list, block_bucket_size, 0)
+        block_groups = pad_list(block_groups, block_bucket_size, -1)
+        block_usage = pad_list(block_usage, block_bucket_size, 1)
+
+    block_list = torch.tensor(block_list, dtype=torch.int, device=device)
+    block_groups = torch.tensor(block_groups, dtype=torch.int, device=device)
+    block_usage = torch.tensor(block_usage, dtype=dtype, device=device)
+    block_mapping = torch.nn.functional.one_hot(block_groups, num_classes=batch_size)
+    mask = torch.arange(0, BLOCK_SIZE, device=device, dtype=torch.int32).unsqueeze(0)
+    mask = mask >= block_usage.unsqueeze(-1)
+    attn_bias = torch.zeros_like(mask, dtype=dtype).masked_fill_(mask, -math.inf)
+    ones = torch.ones(
+        (block_mapping.size(0),), device=device, dtype=block_mapping.dtype
+    )
+    sums = batch2block(block2batch(ones, block_mapping), block_mapping)
+    block_scales = torch.reciprocal(torch.maximum(ones, sums))
+    return trim_attn_metadata(
+        HPUPagedAttentionMetadata(
+            block_list=block_list,
+            block_groups=block_groups,
+            block_usage=block_usage,
+            block_mapping=block_mapping.to(dtype),
+            attn_bias=attn_bias,
+            block_scales=block_scales,
+        )
+    )
+
+
 @dataclass
 class FlashCausalLMBatch(Batch):
     batch_id: int
@@ -879,83 +953,18 @@ class FlashCausalLMBatch(Batch):
         )
 
     def prepare_for_decode(self, dtype, use_contiguous_pa):
-        # Prepare values if we need to continue decoding
-        # need for HPUPagedAttentionMetadata preparation
-        import itertools
-        from vllm_hpu_extension.ops import batch2block, block2batch
-
-        def flatten(in_list):
-            return list(itertools.chain(*in_list))
-
-        def gather_list(input, indices, v):
-            return [input[i] if i is not None else v for i in indices]
-
-        def pad_list(input, k, v):
-            input_len = len(input)
-            target_len = (input_len + k - 1) // k * k
-            padding = target_len - input_len
-            return input + [v] * padding
-
-        device = self.block_tables_tensor.device
-        last_block_usage = self.slots[self.slot_indices] % BLOCK_SIZE + 1
         block_num = self.cache_lengths_tensor // BLOCK_SIZE + 1
         block_tables = []
         for i, bt in enumerate(self.block_tables):
             block_tables.append(bt[0 : block_num[i]])
-        block_groups = [[i] * len(bt) for i, bt in enumerate(block_tables)]
-        block_usage = [
-            [BLOCK_SIZE] * (len(bt) - 1) + [lbu]
-            for bt, lbu in zip(block_tables, last_block_usage)
-            if bt
-        ]
 
-        block_list = flatten(block_tables)
-        block_groups = flatten(block_groups)
-        block_usage = flatten(block_usage)
-        batch = self.input_ids.size(0)
-
-        assert len(block_list) == len(block_groups)
-        assert len(block_list) == len(block_usage)
-        if use_contiguous_pa:
-            block_bucket_size = max(max(block_list) + 1, len(block_list))
-            #     block_bucket_size = self.bucketing_ctx.get_padded_decode_num_blocks(
-            #         block_bucket_size)
-            indices: List[Any]
-            indices = [None] * block_bucket_size
-            for i, bid in enumerate(block_list):
-                indices[bid] = i
-            block_list = gather_list(block_list, indices, 0)
-            block_groups = gather_list(block_groups, indices, -1)
-            block_usage = gather_list(block_usage, indices, 1)
-        else:
-            block_bucket_size = len(block_list)
-            block_list = pad_list(block_list, block_bucket_size, 0)
-            block_groups = pad_list(block_groups, block_bucket_size, -1)
-            block_usage = pad_list(block_usage, block_bucket_size, 1)
-
-        block_list = torch.tensor(block_list, dtype=torch.int, device=device)
-        block_groups = torch.tensor(block_groups, dtype=torch.int, device=device)
-        block_usage = torch.tensor(block_usage, dtype=dtype, device=device)
-        block_mapping = torch.nn.functional.one_hot(block_groups, num_classes=batch)
-        mask = torch.arange(0, BLOCK_SIZE, device=device, dtype=torch.int32).unsqueeze(
-            0
-        )
-        mask = mask >= block_usage.unsqueeze(-1)
-        attn_bias = torch.zeros_like(mask, dtype=dtype).masked_fill_(mask, -math.inf)
-        ones = torch.ones(
-            (block_mapping.size(0),), device=device, dtype=block_mapping.dtype
-        )
-        sums = batch2block(block2batch(ones, block_mapping), block_mapping)
-        block_scales = torch.reciprocal(torch.maximum(ones, sums))
-        self.hpu_attn_meta = trim_attn_metadata(
-            HPUPagedAttentionMetadata(
-                block_list=block_list,
-                block_groups=block_groups,
-                block_usage=block_usage,
-                block_mapping=block_mapping.to(dtype),
-                attn_bias=attn_bias,
-                block_scales=block_scales,
-            )
+        self.hpu_attn_meta = prepare_for_decode(
+            dtype,
+            use_contiguous_pa,
+            self.block_tables_tensor.device,
+            self.slots[self.slot_indices],
+            block_tables,
+            self.input_ids.size(0),
         )
 
     def prepare_for_prefill(self):
@@ -1481,32 +1490,44 @@ class FlashCausalLM(Model):
             self.kv_cache_dtype,
             self.device,
         )
+        for bs in [1, 2, 4, 8]:
+            for seqlen in [32, 64, 128, 256, 512, 1024]:
+                self.warmup_prefill(seqlen, bs)
 
         return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
 
-    def tunableop_warmup(self, seqlen: int, max_bt: int):
-        input_ids = torch.zeros(seqlen, dtype=torch.int64, device=self.device)
-        position_ids = torch.zeros(seqlen, dtype=torch.int32, device=self.device)
-        slots = torch.arange(seqlen, dtype=torch.int64, device=self.device)
-
-        # Dummy value, some models (starcoder2) don't accept `None`.
-        input_lengths = torch.ones(seqlen, dtype=torch.int32, device=self.device)
-        cache_lengths_tensor = torch.zeros(
-            seqlen, dtype=torch.int32, device=self.device
-        )
-        cu_seqlen_prefill = torch.tensor(
-            [0, seqlen], device=self.device, dtype=torch.int32
-        )
-
+    def warmup_prefill(self, prompt_len: int, bs: int):
+        input_ids = torch.zeros(
+            prompt_len, dtype=torch.int64, device=self.device
+        ).repeat(bs)
+        position_ids = torch.arange(
+            prompt_len, dtype=torch.int32, device=self.device
+        ).repeat(bs)
+        max_bt = (prompt_len // BLOCK_SIZE + 1) * bs
         block_tables = torch.arange(
             max_bt, dtype=torch.int32, device=self.device
-        ).repeat(seqlen)
-        block_tables = block_tables.reshape((seqlen, max_bt))
+        ).reshape(bs, -1)
+        slot_acc = []
+        for i in range(bs):
+            slots = []
+            for b in block_tables[i]:
+                slots.extend(range(b * BLOCK_SIZE, (b + 1) * BLOCK_SIZE))
+            slot_acc.extend(slots[:prompt_len])
+        slots = torch.tensor(slot_acc, dtype=torch.int64, device=self.device)
+
+        input_lengths = (
+            torch.ones(bs, dtype=torch.int32, device=self.device) * prompt_len
+        )
+        cache_lengths_tensor = torch.zeros(bs, dtype=torch.int32, device=self.device)
+        cu_seqlen_prefill = torch.zeros(bs + 1, device=self.device, dtype=torch.int32)
+        torch.cumsum(input_lengths, -1, out=cu_seqlen_prefill[1:])
 
         seqlen = Seqlen(
             input_lengths=input_lengths,
             cache_lengths=cache_lengths_tensor,
+            cu_seqlen_q=cu_seqlen_prefill,
         )
+        lm_head_indices = input_lengths - 1
 
         # We pass a `cu_seqlen_prefill` in order not to have to deal with paged attention cache allocation/deallocation.
         self.model.forward(
@@ -1514,11 +1535,13 @@ class FlashCausalLM(Model):
             position_ids=position_ids,
             cu_seqlen_prefill=cu_seqlen_prefill,
             kv_cache=self.kv_cache,
-            block_tables=block_tables,
-            seqlen=seqlen,
+            block_tables=None,  # block_table is not used in hpu pageattn. remove it to avoid shape change in hpu graph
             slots=slots,
-            lm_head_indices=None,
+            seqlen=trim_seqlen_metadata(seqlen),
             prefill_cache_indices=None,
+            lm_head_indices=lm_head_indices,
+            adapter_data=None,
+            hpu_attention_meta=None,
         )
 
     def forward(
@@ -1606,7 +1629,7 @@ class FlashCausalLM(Model):
             position_ids=position_ids,
             cu_seqlen_prefill=cu_seqlen_prefill,
             kv_cache=kv_cache,
-            block_tables=block_tables,
+            block_tables=None,
             slots=slots,
             seqlen=trim_seqlen_metadata(seqlen),
             prefill_cache_indices=batch.prefill_cache_indices,
@@ -1637,9 +1660,7 @@ class FlashCausalLM(Model):
             batch.prepare_for_prefill()
         else:
             batch.prepare_for_decode(self.dtype, self.use_contiguous_pa)
-
         prefill_logprobs = batch.prefill_next_token_indices is not None
-
         # Update adapter indices for speculative tokens (if present)
         adapter_meta = batch.adapter_meta
         if batch.speculative_ids is not None:

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

@@ -462,7 +462,7 @@ class FlashVlmCausalLM(FlashCausalLM):
             position_ids=position_ids,
             cu_seqlen_prefill=cu_seqlen_prefill,
             kv_cache=kv_cache,
-            block_tables=block_tables,
+            block_tables=None,  # block_table is not used in hpu pageattn. remove it to avoid shape change in hpu graph
             slots=slots,
             seqlen=trim_seqlen_metadata(seqlen),
             hpu_attention_meta=batch.hpu_attn_meta,

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

@@ -288,7 +288,7 @@ class FlashMllamaCausalLM(FlashVlmCausalLM):
             position_ids=position_ids,
             cu_seqlen_prefill=cu_seqlen_prefill,
             kv_cache=kv_cache,
-            block_tables=block_tables,
+            block_tables=None,  # block_table is not used in hpu pageattn. remove it to avoid shape change in hpu graph
             slots=slots,
             seqlen=trim_seqlen_metadata(seqlen),
             hpu_attention_meta=batch.hpu_attn_meta,

backends/gaudi/server/text_generation_server/server.py

@@ -33,13 +33,11 @@ try:
     from text_generation_server.models.flash_vlm_causal_lm import (
         FlashVlmCausalLMBatch,
     )
-    from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
 
     VLM_BATCH_TYPES = {
         PaliGemmaBatch,
         VlmCausalLMBatch,
         FlashVlmCausalLMBatch,
-        IdeficsCausalLMBatch,
         FlashMllamaCausalLMBatch,
     }
 except (ImportError, NotImplementedError):