fix high dim

server/text_generation_server/models/transformers_flash_causal_lm.py

@@ -44,7 +44,7 @@ def tgi_flash_attention_forward(
     **kwargs,
 ):
 
-    kv_cache = kwargs["kv_cache"][kwargs["layer_idx"]]
+    kv_cache = kwargs["kv_cache"][module.layer_idx]
     # This means no scale
     kv_scales=KVScales(torch.tensor(1., device=key_states.device), torch.tensor(1., device=key_states.device))
 
@@ -97,7 +97,6 @@ def tgi_flash_attention_forward(
             softcap=softcap,
         )
 
-    # attn_output = attn_output.view(attn_output.shape[0], -1)
     attn_output = attn_output.view(-1, num_heads * head_dim)
 
     return attn_output, None
@@ -244,6 +243,42 @@ class TransformersFlashCausalLM(FlashCausalLM):
             trust_remote_code=trust_remote_code,
         )
 
+
+    def _model_forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        seqlen,
+        max_s,
+        prefill_cache_indices,
+        lm_head_indices,
+    ):
+        hidden_states = self.model.model.forward(
+            input_ids=input_ids[None, ...],  # expand dim to easily fit transformers
+            position_ids=position_ids[None, ...],  # expand dim to easily fit transformers
+            past_key_values=None,  # we use self.kv_cache instead of transformers cache object
+            use_cache=False,  # we use self.kv_cache instead of transformers cache object
+            return_dict=True,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            block_tables=block_tables,
+            slots=slots,
+            seqlen=seqlen,
+            max_s=max_s,
+            prefill_cache_indices=prefill_cache_indices,
+            kv_head_mapping=self.kv_head_mapping,
+        )[0].squeeze(dim=0)
+        # And compute logits from the lm_head, slicing correctly the indices
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits = self.model.lm_head.forward(hidden_states)
+        return logits
+
+
     def forward(
         self, batch: FlashCausalLMBatch, adapter_data: AdapterBatchData
     ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
@@ -297,13 +332,9 @@ class TransformersFlashCausalLM(FlashCausalLM):
                     max_q=batch.max_input_length,
                     max_k=batch.max_current_length,
                 )
-                # Use only the Model, not ModelForCausalLM
-                hidden_states = self.model.model.forward(
-                    input_ids=input_ids[None, ...],  # expand dim to easily fit transformers
-                    position_ids=position_ids[None, ...],
-                    past_key_values=None,
-                    use_cache=False,  # we use self.kv_cache instead of transformers cache object
-                    return_dict=True,
+                logits = self._model_forward(
+                    input_ids=input_ids,
+                    position_ids=position_ids,
                     cu_seqlen_prefill=cu_seqlen_prefill,
                     kv_cache=kv_cache,
                     block_tables=block_tables,
@@ -311,10 +342,8 @@ class TransformersFlashCausalLM(FlashCausalLM):
                     seqlen=seqlen,
                     max_s=max_s,
                     prefill_cache_indices=batch.prefill_cache_indices,
-                    kv_head_mapping=self.kv_head_mapping,
-                )[0].squeeze(dim=0)
-                # And compute logits from the lm_head, slicing correctly the indices
-                logits = self.model.lm_head.forward(hidden_states[lm_head_indices])
+                    lm_head_indices=lm_head_indices,
+                )
                 if batch.prefill_cache_indices is not None:
                     batch.prefill_cache_indices = None
                 return logits, None
@@ -363,3 +392,180 @@ class TransformersFlashCausalLM(FlashCausalLM):
         # Slice output to the correct shape
         logits = cuda_graph["logits"][:bs]
         return logits, None
+
+
+    def cuda_graph_warmup(self, bs: int, max_s: int, max_bt: int):
+        max_bs = max(self.cuda_graphs.keys()) if self.cuda_graphs else None
+        input_lengths = [max_s] * bs
+        cache_lengths = [0] * bs
+        if max_bs is None:
+            input_ids = torch.zeros(bs, dtype=torch.int64, device=self.device)
+            position_ids = torch.zeros(bs, dtype=torch.int32, device=self.device)
+            slots = torch.arange(bs, dtype=torch.int64, device=self.device)
+            input_lengths_tensor = (
+                torch.ones(bs, dtype=torch.int32, device=self.device) * max_s
+            )
+            cache_lengths_tensor = torch.zeros(
+                bs, dtype=torch.int32, device=self.device
+            )
+            block_tables = torch.arange(
+                max_bt, dtype=torch.int32, device=self.device
+            ).repeat(bs)
+            block_tables = block_tables.reshape((bs, max_bt))
+            if ATTENTION == "flashinfer":
+                block_tables = block_tables_to_ragged(
+                    block_tables=block_tables,
+                    input_lengths=input_lengths,
+                    cache_lengths=cache_lengths,
+                    input_lengths_tensor=input_lengths_tensor,
+                    cache_lengths_tensor=cache_lengths_tensor,
+                    max_current_length=max_s,
+                )
+        else:
+            if bs > max_bs:
+                raise RuntimeError(
+                    "Cuda graphs should be generated in decreasing order size to reduce VRAM usage"
+                )
+            input_ids = self.cuda_graphs[max_bs]["input_ids"][:bs]
+            position_ids = self.cuda_graphs[max_bs]["position_ids"][:bs]
+            if ATTENTION == "flashinfer":
+                block_tables = self.cuda_graphs[max_bs]["block_tables"][: bs * max_bt]
+            else:
+                block_tables = self.cuda_graphs[max_bs]["block_tables"][:bs]
+            slots = self.cuda_graphs[max_bs]["slots"][:bs]
+            input_lengths_tensor = self.cuda_graphs[max_bs]["input_lengths"][:bs]
+            cache_lengths_tensor = self.cuda_graphs[max_bs]["cache_lengths"][:bs]
+
+        if ATTENTION == "flashinfer":
+            from text_generation_server.layers.attention.flashinfer import (
+                create_decode_state_cuda_graphs,
+            )
+
+            block_tables_ptr = torch.zeros(
+                bs + 1, dtype=torch.int32, device=self.device
+            )
+            last_page_len = torch.ones(bs, dtype=torch.int32, device=self.device)
+            state = create_decode_state_cuda_graphs(
+                device=input_ids.device,
+                block_tables=block_tables,
+                block_tables_ptr=block_tables_ptr,
+                last_page_len=last_page_len,
+                num_heads=self.num_heads,
+                num_kv_heads=self.num_kv_heads,
+            )
+        else:
+            state = None
+
+        if (
+            hasattr(self.model, "config")
+            and hasattr(self.model.config, "model_type")
+            and self.model.config.model_type == "qwen2_vl"
+        ):
+            if position_ids.dim() == 1:
+                position_ids = self.model.get_position_ids(input_ids)
+
+        graph = torch.cuda.CUDAGraph()
+        self.cuda_graphs[bs] = {
+            "input_ids": input_ids,
+            "position_ids": position_ids,
+            "kv_cache": self.kv_cache,
+            "block_tables": block_tables,
+            "slots": slots,
+            "input_lengths": input_lengths_tensor,
+            "cache_lengths": cache_lengths_tensor,
+            "state": state,
+            "graph": graph,
+        }
+
+        torch.cuda.synchronize()
+        # Run once outside to warmup
+        with self._forward_context(
+            block_tables=block_tables,
+            cu_seqlen_prefill=None,
+            input_lengths_tensor=input_lengths_tensor,
+            state=state,
+            cache_lengths_tensor=cache_lengths_tensor,
+        ):
+            seqlen = Seqlen(
+                input_lengths=input_lengths_tensor,
+                cache_lengths=cache_lengths_tensor,
+                cu_seqlen_q=None,
+                max_q=1,
+                max_k=max_s,
+            )
+            self._model_forward(
+                input_ids=input_ids,
+                position_ids=position_ids,
+                cu_seqlen_prefill=None,
+                kv_cache=self.kv_cache,
+                block_tables=block_tables,
+                slots=slots,
+                seqlen=seqlen,
+                max_s=max_s,
+                prefill_cache_indices=None,
+                lm_head_indices=None,
+            )
+            del seqlen
+
+            torch.cuda.synchronize()
+
+            with torch.cuda.graph(graph, pool=MEM_POOL):
+                seqlen = Seqlen(
+                    input_lengths=input_lengths_tensor,
+                    cache_lengths=cache_lengths_tensor,
+                    cu_seqlen_q=None,
+                    max_q=1,
+                    max_k=max_s,
+                )
+                logits = self._model_forward(
+                    input_ids=input_ids,
+                    position_ids=position_ids,
+                    cu_seqlen_prefill=None,
+                    kv_cache=self.kv_cache,
+                    block_tables=block_tables,
+                    slots=slots,
+                    seqlen=seqlen,
+                    max_s=max_s,
+                    prefill_cache_indices=None,
+                    lm_head_indices=None,
+                )
+                self.cuda_graphs[bs]["logits"] = logits
+                self.cuda_graphs[bs]["speculative_logits"] = None
+        torch.cuda.synchronize()
+
+
+    def tunableop_warmup(self, seqlen: 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
+        )
+        max_s = seqlen
+        seqlen = Seqlen(
+            input_lengths=input_lengths,
+            cache_lengths=cache_lengths_tensor,
+            cu_seqlen_q=cu_seqlen_prefill,
+            max_q=1,
+            max_k=seqlen,
+        )
+
+        # We pass a `cu_seqlen_prefill` in order not to have to deal with paged attention cache allocation/deallocation.
+        self._model_forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=self.kv_cache,
+            block_tables=None,
+            seqlen=seqlen,
+            slots=slots,
+            max_s=max_s,
+            lm_head_indices=None,
+            prefill_cache_indices=None,
+        )