runnable version

server/text_generation_server/models/globals.py

@@ -8,7 +8,7 @@ from text_generation_server.utils.log import log_master
 REQUEST_LOGPROBS = os.getenv("REQUEST_LOGPROBS", "0").lower() in {"1", "true"}
 ATTENTION = os.environ["ATTENTION"]
 # default_prefix_caching = "1" if ATTENTION in {"flashinfer", "flashdecoding"} else "0"
-PREFIX_CACHING = os.environ["PREFIX_CACHING"].lower() in {
+PREFIX_CACHING = os.environ["USE_PREFIX_CACHING"].lower() in {
     "1",
     "true",
 }

server/text_generation_server/models/transformers_flash_causal_lm.py

@@ -5,12 +5,17 @@ from typing import Optional, Tuple, Dict, Any
 import torch
 from opentelemetry import trace
 from loguru import logger
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
 
 from text_generation_server.models.flash_causal_lm import (
     FlashCausalLMBatch,
     FlashCausalLM,
 )
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
 from text_generation_server.utils.import_utils import (
     empty_cache,
     synchronize,
@@ -57,7 +62,7 @@ def _flash_attention_forward_patched(
     query_length: int,
     is_causal: bool,
     softmax_scale: Optional[float] = None,
-    sliding_window: int = -1,
+    sliding_window: Optional[int] = None,
     softcap: Optional[float] = None,
     **kwargs,
 ):
@@ -67,11 +72,11 @@ def _flash_attention_forward_patched(
     kv_scales=KVScales(torch.tensor(1., device=key_states.device), torch.tensor(1., device=key_states.device))
 
     # Correctly reshape the states
-    _, _, num_heads, head_dim = query_states.size()
-    _, _, num_kv_heads, _ = key_states.size()
-    query_states = query_states.view(-1, num_heads, head_dim)
-    key_states = key_states.view(-1, num_kv_heads, head_dim)
-    value_states = value_states.view(-1, num_kv_heads, head_dim)
+    _, num_heads, head_dim = query_states.size()
+    # _, num_kv_heads, _ = key_states.size()
+    # query_states = query_states.view(-1, num_heads, head_dim)
+    # key_states = key_states.view(-1, num_kv_heads, head_dim)
+    # value_states = value_states.view(-1, num_kv_heads, head_dim)
 
     # Take care of updating the cache in-place
     kv_cache.store(
@@ -82,6 +87,7 @@ def _flash_attention_forward_patched(
     )
 
     softmax_scale = 1 / math.sqrt(head_dim) if softmax_scale is None else softmax_scale
+    sliding_window = -1 if sliding_window is None else sliding_window
 
     if kwargs["cu_seqlen_prefill"] is not None:
         attn_output = attention(
@@ -109,7 +115,8 @@ def _flash_attention_forward_patched(
             softcap=softcap,
         )
 
-    attn_output = attn_output.view(attn_output.shape[0], -1)
+    # attn_output = attn_output.view(attn_output.shape[0], -1)
+    attn_output = attn_output.view(-1, num_heads * head_dim)
 
     return attn_output
 
@@ -122,14 +129,21 @@ class TransformersFlashCausalLM(FlashCausalLM):
         quantize: Optional[str] = None,
         speculator: Optional[str] = None,
         dtype: Optional[torch.dtype] = None,
+        default_dtype=torch.float16,
         trust_remote_code: bool = False,
+        tokenizer_class=AutoTokenizer,
+        config_class=AutoConfig,
+        kv_cache_dtype: Optional[torch.dtype] = None,
     ):
+        self.quantize = quantize
+        self.process_group, rank, world_size = initialize_torch_distributed()
+
         if speculator:
             raise RuntimeError("Speculator decoding is not enabled for AutoModel")
 
         device_count = 0
         if torch.cuda.is_available():
-            device = torch.device("cuda")
+            device = torch.device("cuda:0")
             device_count = torch.cuda.device_count()
             dtype = torch.float16 if dtype is None else dtype
         elif hasattr(torch, "xpu") and torch.xpu.is_available():
@@ -157,6 +171,7 @@ class TransformersFlashCausalLM(FlashCausalLM):
             device_map=("auto" if device_count > 1 else None),
             load_in_8bit=quantize == "bitsandbytes",
             trust_remote_code=trust_remote_code,
+            attn_implementation="flash_attention_2"
         )
         if device_count == 1 and quantize != "bitsandbytes":
             model = model.to(device)
@@ -174,10 +189,44 @@ class TransformersFlashCausalLM(FlashCausalLM):
                 tokenizer.add_special_tokens({"pad_token": "[PAD]"})
 
 
-        self.num_layers = len(model.model.layers)
+        self.num_layers = model.config.num_hidden_layers
+        self.num_heads = model.config.num_attention_heads // self.process_group.size()
         self.num_kv_heads = model.config.num_key_value_heads
+        self.num_kv_heads = (
+            self.num_kv_heads // self.process_group.size()
+            if self.num_kv_heads > 1
+            else self.num_kv_heads
+        )
         self.head_size = model.config.hidden_size // model.config.num_attention_heads
 
+        self.cuda_graphs = {}
+        self.kv_cache = []
+        self.kv_cache_dtype = dtype if kv_cache_dtype is None else kv_cache_dtype
+
+        if ATTENTION == "flashinfer":
+            from text_generation_server.layers.attention.flashinfer import (
+                create_prefill_state,
+                create_decode_state,
+                create_prefill_with_paged_kv_state,
+            )
+
+            self.prefill_state = create_prefill_state(device=device)
+            self.prefill_with_paged_kv_state = create_prefill_with_paged_kv_state(
+                device=device
+            )
+
+            self.decode_state = create_decode_state(
+                device=device,
+                num_heads=self.num_heads,
+                num_kv_heads=self.num_kv_heads,
+            )
+
+        self.num_groups = self.num_heads // self.num_kv_heads
+        self.kv_head_mapping = torch.arange(
+            0, self.num_kv_heads, dtype=torch.int32, device=device
+        ).repeat_interleave(self.num_groups)
+
+        torch.distributed.barrier(group=self.process_group)
         # Skip FlashCausalLM init.
         super(FlashCausalLM, self).__init__(
             model_id=model_id,
@@ -186,6 +235,8 @@ class TransformersFlashCausalLM(FlashCausalLM):
             requires_padding=False,
             dtype=dtype,
             device=device,
+            rank=rank,
+            world_size=world_size,
         )
 
     @classmethod
@@ -198,11 +249,18 @@ class TransformersFlashCausalLM(FlashCausalLM):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
-        return cls(model_id, revision, quantize, speculator, dtype, trust_remote_code)
+        return cls(
+            model_id=model_id,
+            revision=revision,
+            quantize=quantize,
+            speculator=speculator,
+            dtype=dtype,
+            trust_remote_code=trust_remote_code,
+        )
 
-    def warmup(self, batch: FlashCausalLMBatch):
+    def warmup(self, batch: FlashCausalLMBatch, max_input_tokens: Optional[int], max_total_tokens: Optional[int],):
         patch_everywhere("_flash_attention_forward", _flash_attention_forward_patched)
-        super().warmup(batch)
+        return super().warmup(batch, max_input_tokens, max_total_tokens)
 
     def forward(
         self, batch: FlashCausalLMBatch, adapter_data: AdapterBatchData
@@ -270,7 +328,8 @@ class TransformersFlashCausalLM(FlashCausalLM):
                     max_s=max_s,
                     prefill_cache_indices=batch.prefill_cache_indices,
                     lm_head_indices=lm_head_indices,
-                )
+                    kv_head_mapping=self.kv_head_mapping,
+                ).logits
                 if batch.prefill_cache_indices is not None:
                     batch.prefill_cache_indices = None
                 return logits, None