wip

server/text_generation_server/models/__init__.py

@@ -8,6 +8,7 @@ from typing import Optional
 
 from text_generation_server.models.model import Model
 from text_generation_server.models.causal_lm import CausalLM
+from text_generation_server.models.flash_causal_lm import FlashCausalLM
 from text_generation_server.models.bloom import BLOOM, BLOOMSharded
 from text_generation_server.models.seq2seq_lm import Seq2SeqLM
 from text_generation_server.models.galactica import Galactica, GalacticaSharded
@@ -18,17 +19,20 @@ from text_generation_server.models.t5 import T5Sharded
 try:
     from text_generation_server.models.flash_neox import FlashNeoX, FlashNeoXSharded
 
-    FLASH_NEOX = torch.cuda.is_available() and int(os.environ.get("FLASH_NEOX", 0)) == 1
+    FLASH_ATTENTION = (
+        torch.cuda.is_available() and int(os.environ.get("FLASH_ATTENTION", 0)) == 1
+    )
 except ImportError:
-    if int(os.environ.get("FLASH_NEOX", 0)) == 1:
+    if int(os.environ.get("FLASH_ATTENTION", 0)) == 1:
-        logger.exception("Could not import FlashNeoX")
+        logger.exception("Could not import Flash Attention models")
-    FLASH_NEOX = False
+    FLASH_ATTENTION = False
 
 __all__ = [
     "Model",
     "BLOOM",
     "BLOOMSharded",
     "CausalLM",
+    "FlashCausalLM",
     "Galactica",
     "GalacticaSharded",
     "GPTNeoxSharded",
@@ -38,7 +42,7 @@ __all__ = [
     "get_model",
 ]
 
-if FLASH_NEOX:
+if FLASH_ATTENTION:
     __all__.append(FlashNeoX)
     __all__.append(FlashNeoXSharded)
 
@@ -76,10 +80,10 @@ def get_model(
 
     if model_type == "gpt_neox":
         if sharded:
-            neox_cls = FlashNeoXSharded if FLASH_NEOX else GPTNeoxSharded
+            neox_cls = FlashNeoXSharded if FLASH_ATTENTION else GPTNeoxSharded
             return neox_cls(model_id, revision, quantize=quantize)
         else:
-            neox_cls = FlashNeoX if FLASH_NEOX else CausalLM
+            neox_cls = FlashNeoX if FLASH_ATTENTION else CausalLM
             return neox_cls(model_id, revision, quantize=quantize)
 
     if model_type == "t5":

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -0,0 +1,651 @@
+import torch
+import torch.distributed
+
+from torch.nn import functional as F
+
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.modeling_utils import PreTrainedModel
+from transformers.models.gpt_neox import GPTNeoXConfig
+
+# Flash attention imports
+import rotary_emb
+import flash_attn_cuda
+import dropout_layer_norm
+
+from flash_attn.layers.rotary import RotaryEmbedding
+
+
+class FastLayerNorm(nn.LayerNorm):
+    def forward(self, hidden_states, residual=None):
+        if hidden_states.shape[-1] > 6144:
+            if residual is not None:
+                hidden_states += residual
+            residual = hidden_states
+
+            return super(FastLayerNorm, self).forward(hidden_states), residual
+        else:
+            (
+                normed_hidden_states,
+                residual,
+                *rest,
+            ) = dropout_layer_norm.dropout_add_ln_fwd(
+                hidden_states,
+                residual,
+                self.weight,
+                self.bias,
+                None,
+                None,
+                None,
+                None,
+                0.0,
+                self.eps,
+                1.0,
+                0,
+                None,
+                False,
+                False,
+            )
+            if residual is None:
+                residual = hidden_states
+
+            return normed_hidden_states, residual
+
+
+class FastLinear(nn.Linear):
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        bias: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(FastLinear, self).__init__(in_features, out_features, bias, device, dtype)
+
+    def transpose_weight(self):
+        self.weight = nn.Parameter(self.weight.T)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.bias is not None:
+            return torch.addmm(self.bias, input, self.weight)
+        return torch.matmul(input, self.weight)
+
+
+class TensorParallelColumnLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        assert out_features % self.tp_world_size == 0
+        out_features = out_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+
+class TensorParallelRowLinear(FastLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        process_group: torch.distributed.ProcessGroup,
+        reduce=True,
+        bias=True,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_world_size = process_group.size()
+        self.reduce = reduce
+        assert in_features % self.tp_world_size == 0
+        in_features = in_features // self.tp_world_size
+
+        super().__init__(
+            in_features=in_features,
+            out_features=out_features,
+            bias=bias,
+            device=device,
+            dtype=dtype,
+        )
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        out = super(TensorParallelRowLinear, self).forward(input)
+        if self.reduce:
+            torch.distributed.all_reduce(out, group=self.process_group)
+
+        return out
+
+
+class TensorParallelEmbedding(nn.Embedding):
+    def __init__(
+        self,
+        num_embeddings,
+        embedding_dim,
+        process_group: torch.distributed.ProcessGroup,
+        padding_idx=None,
+        max_norm=None,
+        norm_type=2.0,
+        scale_grad_by_freq=False,
+        sparse=False,
+        _weight=None,
+        device=None,
+        dtype=None,
+    ):
+        self.process_group = process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+
+        self.original_num_embeddings = num_embeddings
+
+        assert num_embeddings % self.tp_world_size == 0
+        block_size = num_embeddings // self.tp_world_size
+        # inputs in `[min_id, max_id[` are handled by `self` to get embeddings
+        self.min_id = self.tp_rank * block_size
+        self.max_id = (self.tp_rank + 1) * block_size
+
+        # Additional entry that will map to zero
+        # Used for masking
+        self.null_idx = block_size
+
+        super().__init__(
+            block_size,
+            embedding_dim,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+            _weight=_weight,
+            device=device,
+            dtype=dtype,
+        )
+
+    def add_null_idx(self):
+        """Additional 0 entry used for masking"""
+        self.weight = nn.Parameter(F.pad(self.weight, (0, 0, 0, 1)))
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # default all out of bounds values to `self.null_idx` that will then be mapped to 0
+        # translate for [0, self.max_id - self.min_id[
+        input = torch.where(
+            (self.min_id > input) | (input >= self.max_id),
+            self.null_idx,
+            input - self.min_id,
+        )
+        out = super().forward(input)
+        torch.distributed.all_reduce(out, group=self.process_group)
+        return out
+
+
+class PositionRotaryEmbedding(RotaryEmbedding):
+    def _update_cos_sin_cache(self, dtype, device, seqlen):
+        # Reset the tables if the sequence length has changed,
+        # or if we're on a new device (possibly due to tracing for instance)
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+        ):
+            self._seq_len_cached = seqlen
+            t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
+            # Don't do einsum, it converts fp32 to fp16
+            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            freqs = torch.outer(t, self.inv_freq.to(device=t.device))
+            self._cos_cached = torch.cos(freqs).to(dtype)
+            self._sin_cached = torch.sin(freqs).to(dtype)
+
+    def get_cos_sin(self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype):
+        """
+        Return cos and sin for the asked position ids
+        """
+
+        self._update_cos_sin_cache(dtype, position_ids.device, max_s)
+
+        cos = torch.index_select(self._cos_cached, 0, position_ids)
+        sin = torch.index_select(self._sin_cached, 0, position_ids)
+        return cos.unsqueeze(1), sin.unsqueeze(1)
+
+    def forward(self, qkv: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+        rotary_dim = cos.shape[-1]
+        q1 = qkv[:, 0, :, :rotary_dim]
+        q2 = qkv[:, 0, :, rotary_dim : 2 * rotary_dim]
+        k1 = qkv[:, 1, :, :rotary_dim]
+        k2 = qkv[:, 1, :, rotary_dim : 2 * rotary_dim]
+
+        rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
+        rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
+        return qkv
+
+
+class FlashNeoxAttention(torch.nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        hidden_size,
+        rotary_pct,
+        rotary_emb_base,
+        process_group=None,
+        reduce=True,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.head_size = hidden_size // num_heads
+
+        rotary_ndims = int(self.head_size * rotary_pct)
+        self.rotary_emb = PositionRotaryEmbedding(rotary_ndims, base=rotary_emb_base)
+        self.softmax_scale = self.head_size ** (-0.5)
+
+        if process_group is None:
+            self.query_key_value = FastLinear(hidden_size, 3 * hidden_size)
+            self.dense = FastLinear(hidden_size, hidden_size)
+        else:
+            self.num_heads = self.num_heads // process_group.size()
+            self.query_key_value = TensorParallelColumnLinear(
+                hidden_size,
+                3 * hidden_size,
+                process_group=process_group,
+            )
+            self.dense = TensorParallelRowLinear(
+                hidden_size, hidden_size, process_group=process_group, reduce=reduce
+            )
+
+    def shuffle_qkv_dims(self):
+        """Swap dims to avoid an additional permute"""
+        self.query_key_value.weight = torch.nn.Parameter(
+            self.query_key_value.weight.view(
+                self.num_heads, 3, self.head_size, self.hidden_size
+            )
+            .permute(1, 0, 2, 3)
+            .reshape(-1, self.hidden_size)
+        )
+        self.query_key_value.bias = torch.nn.Parameter(
+            self.query_key_value.bias.view(self.num_heads, 3, self.head_size)
+            .permute(1, 0, 2)
+            .reshape(-1)
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        qkv = self.query_key_value(hidden_states)
+        qkv = qkv.view(-1, 3, self.num_heads, self.head_size)
+        qkv_rot = self.rotary_emb(qkv, cos, sin)
+
+        # Prefill
+        if layer_past_present_indices is None:
+            # Copy to layer past
+            layer_past[...] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(qkv[:, 0])
+            # flash attention
+            flash_attn_cuda.fwd(
+                qkv[:, 0],
+                qkv[:, 1],
+                qkv[:, 2],
+                attn_output,
+                cu_seqlens,
+                cu_seqlens,
+                max_s,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                True,
+                False,
+                0,
+                None,
+            )
+        # Decode
+        else:
+            query = qkv_rot[:, 0]
+            # Add present to the layer_past tensor at the correct indices
+            layer_past[layer_past_present_indices] = qkv_rot[:, 1:]
+
+            # output
+            attn_output = torch.empty_like(query)
+            # flash attention
+            flash_attn_cuda.fwd(
+                query,
+                layer_past[:, 0],
+                layer_past[:, 1],
+                attn_output,
+                cu_seqlens_q,
+                cu_seqlens,
+                1,
+                max_s,
+                0.0,
+                self.softmax_scale,
+                False,
+                False,
+                False,
+                0,
+                None,
+            )
+
+        return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
+
+
+class FlashMLP(nn.Module):
+    def __init__(
+        self, act, hidden_size, intermediate_size, process_group=None, reduce=True
+    ):
+        super().__init__()
+        self.act = (
+            ACT2FN[act]
+            if "gelu" not in act
+            else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
+        )
+
+        if process_group is None:
+            self.dense_h_to_4h = FastLinear(hidden_size, intermediate_size)
+            self.dense_4h_to_h = FastLinear(intermediate_size, hidden_size)
+        else:
+            self.dense_h_to_4h = TensorParallelColumnLinear(
+                hidden_size,
+                intermediate_size,
+                process_group=process_group,
+            )
+            self.dense_4h_to_h = TensorParallelRowLinear(
+                intermediate_size,
+                hidden_size,
+                process_group=process_group,
+                reduce=reduce,
+            )
+        self.process_group = process_group
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_h_to_4h(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.dense_4h_to_h(hidden_states)
+        return hidden_states
+
+
+class FlashNeoXLayer(nn.Module):
+    def __init__(
+        self,
+        num_heads,
+        act,
+        hidden_size,
+        intermediate_size,
+        rotary_pct,
+        rotary_emb_base,
+        layer_norm_eps,
+        use_parallel_residual,
+        process_group=None,
+    ):
+        super().__init__()
+        self.use_parallel_residual = use_parallel_residual
+        self.input_layernorm = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.post_attention_layernorm = FastLayerNorm(hidden_size, eps=layer_norm_eps)
+        self.attention = FlashNeoxAttention(
+            num_heads,
+            hidden_size,
+            rotary_pct,
+            rotary_emb_base,
+            process_group,
+            reduce=not use_parallel_residual,
+        )
+        self.mlp = FlashMLP(
+            act,
+            hidden_size,
+            intermediate_size,
+            process_group,
+            reduce=not use_parallel_residual,
+        )
+        self.process_group = process_group
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlens,
+        max_s,
+        layer_past,
+        layer_past_present_indices,
+        cu_seqlens_q,
+    ):
+        if self.use_parallel_residual:
+            ln1_hidden_states, _ = self.input_layernorm(hidden_states)
+
+            attn_output = self.attention(
+                ln1_hidden_states,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+            ln2_hidden_states, _ = self.post_attention_layernorm(hidden_states)
+
+            mlp_output = self.mlp(ln2_hidden_states)
+            intermediate = mlp_output + attn_output
+
+            # Only reduce once and after the addition instead of once per layer
+            if self.process_group is not None:
+                torch.distributed.all_reduce(intermediate, group=self.process_group)
+
+            return intermediate + hidden_states, None
+        else:
+            hidden_states, residual = self.input_layernorm(hidden_states, residual)
+
+            hidden_states = self.attention(
+                hidden_states,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                layer_past,
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+            hidden_states, residual = self.post_attention_layernorm(
+                hidden_states, residual
+            )
+
+            mlp_output = self.mlp(hidden_states)
+
+            return mlp_output, residual
+
+
+class FlashGPTNeoXPreTrainedModel(PreTrainedModel):
+    config_class = GPTNeoXConfig
+    base_model_prefix = "gpt_neox"
+    supports_gradient_checkpointing = False
+    _no_split_modules = None
+
+
+class FlashGPTNeoXModel(FlashGPTNeoXPreTrainedModel):
+    def __init__(self, config, process_group=None):
+        super().__init__(config)
+        self.config = config
+
+        self.tp_embeddings = False
+        if process_group is not None:
+            self.tp_rank = process_group.rank()
+            self.tp_world_size = process_group.size()
+            if config.vocab_size % self.tp_world_size == 0:
+                self.tp_embeddings = True
+
+        if self.tp_embeddings:
+            self.embed_in = TensorParallelEmbedding(
+                config.vocab_size, config.hidden_size, process_group=process_group
+            )
+        else:
+            self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
+
+        self.layers = nn.ModuleList(
+            [
+                FlashNeoXLayer(
+                    config.num_attention_heads,
+                    config.hidden_act,
+                    config.hidden_size,
+                    config.intermediate_size,
+                    config.rotary_pct,
+                    config.rotary_emb_base,
+                    config.layer_norm_eps,
+                    config.use_parallel_residual,
+                    process_group,
+                )
+                for _ in range(config.num_hidden_layers)
+            ]
+        )
+        self.final_layer_norm = FastLayerNorm(
+            config.hidden_size, eps=config.layer_norm_eps
+        )
+
+        self.gradient_checkpointing = False
+
+        self.head_size = self.layers[0].attention.head_size
+        self.num_heads = self.layers[0].attention.num_heads
+
+    def post_load_weights(self):
+        if isinstance(self.embed_in, TensorParallelEmbedding):
+            self.embed_in.add_null_idx()
+        for layer in self.layers:
+            layer: FlashNeoXLayer
+            layer.attention.shuffle_qkv_dims()
+            layer.attention.query_key_value.transpose_weight()
+            layer.attention.dense.transpose_weight()
+            layer.mlp.dense_h_to_4h.transpose_weight()
+            layer.mlp.dense_4h_to_h.transpose_weight()
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        model = super(FlashGPTNeoXModel, cls).from_pretrained(
+            pretrained_model_name_or_path, *model_args, **kwargs
+        )
+        model.post_load_weights()
+        return model
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values=None,
+    ):
+        hidden_states = self.embed_in(input_ids)
+
+        # Prefill
+        if past_key_values is None:
+            # Create past tensor
+            past_key_values = hidden_states.new_empty(
+                (
+                    len(self.layers),
+                    len(hidden_states),
+                    2,
+                    self.num_heads,
+                    self.head_size,
+                )
+            )
+            layer_past_present_indices = None
+            cu_seqlens_q = None
+        # Decode
+        else:
+            # Create indices from cumulative sequence lengths
+            layer_past_present_indices = cu_seqlens[1:] - 1
+            cu_seqlens_q = torch.arange(
+                cu_seqlens.shape[0], dtype=torch.int32, device=hidden_states.device
+            )
+
+        # Get rotary cos and sin for this forward
+        # Avoid to index in each layer
+        cos, sin = self.layers[0].attention.rotary_emb.get_cos_sin(
+            position_ids, max_s, hidden_states.dtype
+        )
+
+        residual = None
+        for i, layer in enumerate(self.layers):
+            hidden_states, residual = layer(
+                hidden_states,
+                residual,
+                cos,
+                sin,
+                cu_seqlens,
+                max_s,
+                past_key_values[i],
+                layer_past_present_indices,
+                cu_seqlens_q,
+            )
+
+        hidden_states, _ = self.final_layer_norm(hidden_states, residual)
+
+        return hidden_states, past_key_values
+
+
+class FlashGPTNeoXForCausalLM(FlashGPTNeoXPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.tp_parallel:
+            process_group = torch.distributed.distributed_c10d._get_default_group()
+        else:
+            process_group = None
+
+        self.gpt_neox = FlashGPTNeoXModel(config, process_group)
+
+        if self.gpt_neox.tp_embeddings:
+            self.embed_out = FastLinear(
+                config.hidden_size,
+                config.vocab_size // process_group.size(),
+                bias=False,
+            )
+        else:
+            self.embed_out = FastLinear(
+                config.hidden_size, config.vocab_size, bias=False
+            )
+
+    def post_load_weights(self):
+        self.gpt_neox.post_load_weights()
+        self.embed_out.transpose_weight()
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        model = super(FlashGPTNeoXForCausalLM, cls).from_pretrained(
+            pretrained_model_name_or_path, *model_args, **kwargs
+        )
+        model.post_load_weights()
+        return model
+
+    def forward(
+        self,
+        input_ids,
+        position_ids,
+        cu_seqlens,
+        max_s,
+        past_key_values=None,
+    ):
+        hidden_states, present = self.gpt_neox(
+            input_ids, position_ids, cu_seqlens, max_s, past_key_values
+        )
+        return self.embed_out(hidden_states), present

server/text_generation_server/models/flash_causal_lm.py

@@ -0,0 +1,458 @@
+import torch
+import torch.distributed
+
+from torch.nn import functional as F
+
+from dataclasses import dataclass
+from opentelemetry import trace
+from transformers import AutoTokenizer, PreTrainedTokenizerBase, PreTrainedModel
+from typing import Optional, Tuple, List, Type, Union
+
+from text_generation_server.models import Model
+from text_generation_server.models.types import (
+    Batch,
+    PrefillTokens,
+    Generation,
+    GeneratedText,
+)
+from text_generation_server.pb import generate_pb2
+from text_generation_server.utils import (
+    NextTokenChooser,
+    StoppingCriteria,
+    Sampling,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+@dataclass
+class FlashCausalLMBatch(Batch):
+    batch_id: int
+    requests: List[generate_pb2.Request]
+
+    # Decoder values
+    input_ids: torch.Tensor
+    position_ids: torch.Tensor
+    # cumulative sequence lengths
+    cu_seqlens: torch.Tensor
+    max_seqlen: int
+    past_key_values: Optional[torch.Tensor]
+
+    # All tokens
+    all_input_ids: List[List[int]]
+    all_input_ids_tensor: List[torch.Tensor]
+
+    # Lengths of all generations present in the batch
+    input_lengths: List[int]
+
+    # Generation helpers
+    next_token_choosers: List[NextTokenChooser]
+    stopping_criterias: List[StoppingCriteria]
+
+    def to_pb(self) -> generate_pb2.Batch:
+        return generate_pb2.Batch(
+            id=self.batch_id, requests=self.requests, size=len(self)
+        )
+
+    @classmethod
+    def from_pb(
+        cls,
+        pb: generate_pb2.Batch,
+        tokenizer: PreTrainedTokenizerBase,
+        device: torch.device,
+    ) -> "CausalLMBatch":
+        input_ids = []
+        position_ids = []
+        cu_seqlens = [0]
+        max_seqlen = 0
+
+        input_lengths = []
+        all_input_ids = []
+        all_input_ids_tensor = []
+
+        next_token_choosers = []
+        stopping_criterias = []
+
+        # Cumulative length
+        cumulative_length = 0
+
+        # Parse batch
+        for r in pb.requests:
+            tokenized_input = tokenizer(r.inputs)["input_ids"]
+            input_length = len(tokenized_input)
+            max_seqlen = max(max_seqlen, input_length)
+            input_lengths.append(input_length)
+            all_input_ids.append(tokenized_input)
+
+            tokenized_input = torch.tensor(tokenized_input, device=device)
+            input_ids.append(tokenized_input)
+
+            # Position ids
+            position_ids.append(torch.arange(0, input_length, dtype=torch.int32))
+
+            # Add cumulative lengths of all previous inputs
+            cu_seqlens.append(cumulative_length + input_length)
+
+            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
+            stopping_criteria = StoppingCriteria.from_pb(
+                r.stopping_parameters, tokenizer
+            )
+            stopping_criterias.append(stopping_criteria)
+            all_input_ids_tensor.append(
+                F.pad(tokenized_input, (0, stopping_criteria.max_new_tokens))
+            )
+
+            # Update
+            cumulative_length += input_length
+
+        input_ids = torch.concat(input_ids)
+        position_ids = torch.concat(position_ids)
+        cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32)
+
+        return cls(
+            batch_id=pb.id,
+            requests=pb.requests,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            past_key_values=None,
+            input_lengths=input_lengths,
+            all_input_ids=all_input_ids,
+            all_input_ids_tensor=all_input_ids_tensor,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+        )
+
+    @classmethod
+    @tracer.start_as_current_span("concatenate")
+    def concatenate(cls, batches: List["FlashCausalLMBatch"]) -> "FlashCausalLMBatch":
+        # Batch attributes
+        requests = []
+        input_lengths = []
+        all_input_ids = []
+        all_input_ids_tensor = []
+        next_token_choosers = []
+        stopping_criterias = []
+
+        # Batch tensors
+        input_ids = []
+        position_ids = []
+        cu_seqlens = [torch.tensor([0], dtype=torch.int32)]
+        max_seqlen = 0
+        past_key_values = []
+
+        # Cumulative length
+        cumulative_length = torch.tensor(0)
+
+        for i, batch in enumerate(batches):
+            requests.extend(batch.requests)
+            input_lengths.extend(batch.input_lengths)
+            all_input_ids.extend(batch.all_input_ids)
+            all_input_ids_tensor.extend(batch.all_input_ids_tensor)
+            next_token_choosers.extend(batch.next_token_choosers)
+            stopping_criterias.extend(batch.stopping_criterias)
+
+            # Add cumulative lengths of all previous inputs
+            cu_seqlens.append(batch.cu_seqlens[1:] + cumulative_length)
+
+            input_ids.append(batch.input_ids)
+            position_ids.append(batch.position_ids)
+            past_key_values.append(batch.past_key_values)
+
+            max_seqlen = max(max_seqlen, batch.max_seqlen)
+
+            # Update
+            cumulative_length += batch.cu_seqlens[-1]
+
+        input_ids = torch.concat(input_ids)
+        position_ids = torch.concat(position_ids)
+        # Concat on dim=1 as first dim represents the model layers
+        past_key_values = torch.concat(past_key_values, dim=1)
+        cu_seqlens = torch.concat(cu_seqlens)
+
+        return FlashCausalLMBatch(
+            batch_id=batches[0].batch_id,
+            requests=requests,
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+            past_key_values=past_key_values,
+            input_lengths=input_lengths,
+            all_input_ids=all_input_ids,
+            all_input_ids_tensor=all_input_ids_tensor,
+            next_token_choosers=next_token_choosers,
+            stopping_criterias=stopping_criterias,
+        )
+
+    def __len__(self):
+        return len(self.requests)
+
+
+class FlashCausalLM(Model):
+    def __init__(
+        self,
+        model_cls: Type[PreTrainedModel],
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize=False,
+    ):
+        if torch.cuda.is_available():
+            device = torch.device("cuda")
+            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        else:
+            raise NotImplementedError("FlashCausalLM is only available on GPU")
+
+        if quantize:
+            raise NotImplementedError("FlashCausalLM does not support quantization")
+
+        tokenizer = AutoTokenizer.from_pretrained(
+            model_id, revision=revision, padding_side="left"
+        )
+        self.model = (
+            model_cls.from_pretrained(
+                model_id,
+                revision=revision,
+                torch_dtype=dtype,
+            )
+            .eval()
+            .cuda()
+        )
+
+        super(FlashCausalLM, self).__init__(
+            tokenizer=tokenizer,
+            device=device,
+        )
+
+    @property
+    def batch_type(self) -> Type[FlashCausalLMBatch]:
+        return FlashCausalLMBatch
+
+    def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
+        return self.tokenizer.decode(
+            generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
+        )
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_s: int,
+        past_key_values: Optional = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Model Forward
+        return self.model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            cu_seqlens=cu_seqlens,
+            max_s=max_s,
+            past_key_values=past_key_values,
+        )
+
+    @tracer.start_as_current_span("generate_token")
+    def generate_token(
+        self, batch: FlashCausalLMBatch
+    ) -> Tuple[List[Generation], Optional[FlashCausalLMBatch]]:
+        # Better to send to device here to avoid device issues in concatenate
+        position_ids = batch.position_ids.to(self.device, non_blocking=True)
+        cu_seqlens = batch.cu_seqlens.to(self.device)
+
+        out, present = self.forward(
+            batch.input_ids,
+            position_ids,
+            cu_seqlens,
+            batch.max_seqlen,
+            batch.past_key_values,
+        )
+
+        # List of indices to cache
+        next_batch_keep_indices = []
+
+        # New values for next forward
+        next_batch_input_ids = []
+        next_batch_position_ids = []
+        next_batch_cu_seqlens = [0]
+        next_batch_max_seqlen = 0
+        next_batch_past_key_values = []
+        next_batch_input_lengths = []
+        next_batch_all_input_ids = []
+        next_batch_all_input_ids_tensor = []
+
+        # Cumulative length
+        cumulative_length = 0
+
+        # Results
+        generations: List[Generation] = []
+
+        # Zipped iterator
+        iterator = zip(
+            batch.requests,
+            batch.input_lengths,
+            batch.next_token_choosers,
+            batch.stopping_criterias,
+            batch.all_input_ids,
+            batch.all_input_ids_tensor,
+        )
+
+        # For each member of the batch
+        for i, (
+            request,
+            input_length,
+            next_token_chooser,
+            stopping_criteria,
+            all_input_ids,
+            all_input_ids_tensor,
+        ) in enumerate(iterator):
+            # Indexing metadata
+            start_index = cumulative_length
+            end_index = cumulative_length + input_length
+
+            if batch.past_key_values is None:
+                # Prefill mode
+                # out is of shape [cumulative_sequence_lengths, vocab_size]
+                logits = out[start_index:end_index]
+            else:
+                # Decode mode
+                # out is of shape [batch_size, vocab_size]
+                logits = out[i].unsqueeze(0)
+
+            # Select next token
+            next_token_id, logprobs = next_token_chooser(
+                all_input_ids_tensor[None, :input_length], logits
+            )
+            next_token_id_squeezed = next_token_id.squeeze()
+            next_token_id_item = next_token_id_squeezed.item()
+
+            # Append next token to all tokens
+            all_input_ids.append(next_token_id_item)
+            all_input_ids_tensor[input_length] = next_token_id_item
+            new_input_length = input_length + 1
+
+            # Generated token
+            next_token_logprob = logprobs[-1, next_token_id_item]
+            next_token_text = self.decode_token(
+                next_token_id_item,
+            )
+
+            # Evaluate stopping criteria
+            stop, reason = stopping_criteria(
+                next_token_id_item,
+                next_token_text,
+            )
+
+            if stop:
+                # Decode generated tokens
+                output_text = self.decode(
+                    all_input_ids[-stopping_criteria.current_tokens :]
+                )
+                # Get seed
+                if isinstance(next_token_chooser.choice, Sampling):
+                    seed = next_token_chooser.choice.seed
+                else:
+                    seed = None
+
+                generated_text = GeneratedText(
+                    output_text, stopping_criteria.current_tokens, reason, seed
+                )
+            else:
+                # Keep request in the batch
+                next_batch_keep_indices.append(i)
+                generated_text = None
+
+                # Get sequence present
+                seq_present = present[:, start_index:end_index]
+                # Pad it for next iter attention
+                past = torch.nn.functional.pad(seq_present, (0, 0, 0, 0, 0, 0, 0, 1))
+                next_batch_past_key_values.append(past)
+
+                next_batch_input_ids.append(next_token_id)
+                next_batch_position_ids.append(input_length)
+                # Cumulative sum
+                next_batch_cu_seqlens.append(
+                    next_batch_cu_seqlens[-1] + new_input_length
+                )
+                next_batch_input_lengths.append(new_input_length)
+                next_batch_all_input_ids.append(all_input_ids)
+                next_batch_all_input_ids_tensor.append(all_input_ids_tensor)
+                next_batch_max_seqlen = max(next_batch_max_seqlen, new_input_length)
+
+            # Prefill
+            if stopping_criteria.current_tokens == 1:
+                # Remove generated token to only have prefill and add nan for first prompt token
+                prefill_logprobs = [float("nan")] + logprobs.gather(
+                    1, all_input_ids_tensor[1:input_length].unsqueeze(1)
+                ).squeeze(1)[:-1].tolist()
+                prefill_token_ids = all_input_ids[:-1]
+                prefill_texts = self.tokenizer.batch_decode(
+                    prefill_token_ids,
+                    clean_up_tokenization_spaces=False,
+                    skip_special_tokens=False,
+                )
+                prefill_tokens = PrefillTokens(
+                    prefill_token_ids, prefill_logprobs, prefill_texts
+                )
+            else:
+                prefill_tokens = None
+
+            generation = Generation(
+                request.id,
+                prefill_tokens,
+                next_token_id_item,
+                next_token_logprob,
+                next_token_text,
+                next_token_id_item in self.all_special_ids,
+                generated_text,
+            )
+
+            generations.append(generation)
+            cumulative_length += input_length
+
+        # We finished all generations in the batch; there is no next batch
+        if not next_batch_keep_indices:
+            return generations, None
+
+        # If we finished at least one generation, we need to evict the indices of the generations that finished
+        # from the values of the next batch
+        if len(next_batch_keep_indices) != len(batch):
+            # Apply indices to requests, token_choosers and stopping_criterias that need to be cached
+            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
+            next_batch_next_token_choosers = [
+                batch.next_token_choosers[i] for i in next_batch_keep_indices
+            ]
+            next_batch_stopping_criterias = [
+                batch.stopping_criterias[i] for i in next_batch_keep_indices
+            ]
+        else:
+            next_batch_requests = batch.requests
+            next_batch_next_token_choosers = batch.next_token_choosers
+            next_batch_stopping_criterias = batch.stopping_criterias
+
+        # Create final next batch tensors
+        next_batch_position_ids = torch.tensor(
+            next_batch_position_ids, dtype=torch.int32
+        )
+        next_batch_cu_seqlens = torch.tensor(next_batch_cu_seqlens, dtype=torch.int32)
+        if len(next_batch_keep_indices) > 1:
+            next_batch_input_ids = torch.concat(next_batch_input_ids).squeeze(1)
+            next_batch_past_key_values = torch.concat(next_batch_past_key_values, dim=1)
+        else:
+            next_batch_input_ids = next_batch_input_ids[0].view(1)
+            next_batch_past_key_values = next_batch_past_key_values[0]
+
+        next_batch = FlashCausalLMBatch(
+            batch_id=batch.batch_id,
+            requests=next_batch_requests,
+            input_ids=next_batch_input_ids,
+            position_ids=next_batch_position_ids,
+            cu_seqlens=next_batch_cu_seqlens,
+            max_seqlen=next_batch_max_seqlen,
+            past_key_values=next_batch_past_key_values,
+            input_lengths=next_batch_input_lengths,
+            all_input_ids=next_batch_all_input_ids,
+            all_input_ids_tensor=next_batch_all_input_ids_tensor,
+            next_token_choosers=next_batch_next_token_choosers,
+            stopping_criterias=next_batch_stopping_criterias,
+        )
+        return generations, next_batch

server/text_generation_server/models/flash_neox.py

@@ -1,33 +1,20 @@
 import torch
 import torch.distributed
 
-from torch.nn import functional as F
-
 from accelerate import init_empty_weights
-from dataclasses import dataclass
 from opentelemetry import trace
 from safetensors import safe_open
-from transformers import AutoTokenizer, PreTrainedTokenizerBase, AutoConfig
+from transformers import AutoTokenizer, AutoConfig
-from typing import Optional, Tuple, List, Type, Union
+from typing import Optional, Tuple, List
 
-from text_generation_server.models import Model
+from text_generation_server.models import FlashCausalLM
-from text_generation_server.models.flash_neox_modeling import (
+from text_generation_server.models.custom_modeling.flash_neox_modeling import (
     FlashGPTNeoXForCausalLM,
     TensorParallelEmbedding,
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
 )
-from text_generation_server.models.types import (
-    Batch,
-    PrefillTokens,
-    Generation,
-    GeneratedText,
-)
-from text_generation_server.pb import generate_pb2
 from text_generation_server.utils import (
-    NextTokenChooser,
-    StoppingCriteria,
-    Sampling,
     initialize_torch_distributed,
     weight_files,
 )
@@ -35,437 +22,12 @@ from text_generation_server.utils import (
 tracer = trace.get_tracer(__name__)
 
 
-@dataclass
+class FlashNeoX(FlashCausalLM):
-class FlashNeoXBatch(Batch):
-    batch_id: int
-    requests: List[generate_pb2.Request]
-
-    # Decoder values
-    input_ids: torch.Tensor
-    position_ids: torch.Tensor
-    # cumulative sequence lengths
-    cu_seqlens: torch.Tensor
-    max_seqlen: int
-    past_key_values: Optional[torch.Tensor]
-
-    # All tokens
-    all_input_ids: List[List[int]]
-    all_input_ids_tensor: List[torch.Tensor]
-
-    # Lengths of all generations present in the batch
-    input_lengths: List[int]
-
-    # Generation helpers
-    next_token_choosers: List[NextTokenChooser]
-    stopping_criterias: List[StoppingCriteria]
-
-    def to_pb(self) -> generate_pb2.Batch:
-        return generate_pb2.Batch(
-            id=self.batch_id, requests=self.requests, size=len(self)
-        )
-
-    @classmethod
-    def from_pb(
-        cls,
-        pb: generate_pb2.Batch,
-        tokenizer: PreTrainedTokenizerBase,
-        device: torch.device,
-    ) -> "CausalLMBatch":
-        input_ids = []
-        position_ids = []
-        cu_seqlens = [0]
-        max_seqlen = 0
-
-        input_lengths = []
-        all_input_ids = []
-        all_input_ids_tensor = []
-
-        next_token_choosers = []
-        stopping_criterias = []
-
-        # Cumulative length
-        cumulative_length = 0
-
-        # Parse batch
-        for r in pb.requests:
-            tokenized_input = tokenizer(r.inputs)["input_ids"]
-            input_length = len(tokenized_input)
-            max_seqlen = max(max_seqlen, input_length)
-            input_lengths.append(input_length)
-            all_input_ids.append(tokenized_input)
-
-            tokenized_input = torch.tensor(tokenized_input, device=device)
-            input_ids.append(tokenized_input)
-
-            # Position ids
-            position_ids.append(torch.arange(0, input_length, dtype=torch.int32))
-
-            # Add cumulative lengths of all previous inputs
-            cu_seqlens.append(cumulative_length + input_length)
-
-            next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
-            stopping_criteria = StoppingCriteria.from_pb(
-                r.stopping_parameters, tokenizer
-            )
-            stopping_criterias.append(stopping_criteria)
-            all_input_ids_tensor.append(
-                F.pad(tokenized_input, (0, stopping_criteria.max_new_tokens))
-            )
-
-            # Update
-            cumulative_length += input_length
-
-        input_ids = torch.concat(input_ids)
-        position_ids = torch.concat(position_ids)
-        cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32)
-
-        return cls(
-            batch_id=pb.id,
-            requests=pb.requests,
-            input_ids=input_ids,
-            position_ids=position_ids,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-            past_key_values=None,
-            input_lengths=input_lengths,
-            all_input_ids=all_input_ids,
-            all_input_ids_tensor=all_input_ids_tensor,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-        )
-
-    @classmethod
-    @tracer.start_as_current_span("concatenate")
-    def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
-        # Batch attributes
-        requests = []
-        input_lengths = []
-        all_input_ids = []
-        all_input_ids_tensor = []
-        next_token_choosers = []
-        stopping_criterias = []
-
-        # Batch tensors
-        input_ids = []
-        position_ids = []
-        cu_seqlens = [torch.tensor([0], dtype=torch.int32)]
-        max_seqlen = 0
-        past_key_values = []
-
-        # Cumulative length
-        cumulative_length = torch.tensor(0)
-
-        for i, batch in enumerate(batches):
-            requests.extend(batch.requests)
-            input_lengths.extend(batch.input_lengths)
-            all_input_ids.extend(batch.all_input_ids)
-            all_input_ids_tensor.extend(batch.all_input_ids_tensor)
-            next_token_choosers.extend(batch.next_token_choosers)
-            stopping_criterias.extend(batch.stopping_criterias)
-
-            # Add cumulative lengths of all previous inputs
-            cu_seqlens.append(batch.cu_seqlens[1:] + cumulative_length)
-
-            input_ids.append(batch.input_ids)
-            position_ids.append(batch.position_ids)
-            past_key_values.append(batch.past_key_values)
-
-            max_seqlen = max(max_seqlen, batch.max_seqlen)
-
-            # Update
-            cumulative_length += batch.cu_seqlens[-1]
-
-        input_ids = torch.concat(input_ids)
-        position_ids = torch.concat(position_ids)
-        # Concat on dim=1 as first dim represents the model layers
-        past_key_values = torch.concat(past_key_values, dim=1)
-        cu_seqlens = torch.concat(cu_seqlens)
-
-        return FlashNeoXBatch(
-            batch_id=batches[0].batch_id,
-            requests=requests,
-            input_ids=input_ids,
-            position_ids=position_ids,
-            cu_seqlens=cu_seqlens,
-            max_seqlen=max_seqlen,
-            past_key_values=past_key_values,
-            input_lengths=input_lengths,
-            all_input_ids=all_input_ids,
-            all_input_ids_tensor=all_input_ids_tensor,
-            next_token_choosers=next_token_choosers,
-            stopping_criterias=stopping_criterias,
-        )
-
-    def __len__(self):
-        return len(self.requests)
-
-
-class FlashNeoX(Model):
     def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
-        if torch.cuda.is_available():
-            device = torch.device("cuda")
-            dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
-        else:
-            raise NotImplementedError("FlashNeoX is only available on GPU")
-
-        if quantize:
-            raise NotImplementedError("FlashNeoX does not support quantization")
-
-        tokenizer = AutoTokenizer.from_pretrained(
-            model_id, revision=revision, padding_side="left"
-        )
-        self.model = (
-            FlashGPTNeoXForCausalLM.from_pretrained(
-                model_id,
-                revision=revision,
-                torch_dtype=dtype,
-            )
-            .eval()
-            .cuda()
-        )
-        tokenizer.pad_token_id = (
-            self.model.config.pad_token_id
-            if self.model.config.pad_token_id is not None
-            else self.model.config.eos_token_id
-        )
-
         super(FlashNeoX, self).__init__(
-            tokenizer=tokenizer,
+            FlashGPTNeoXForCausalLM, model_id, revision, quantize
-            device=device,
         )
 
-    @property
-    def batch_type(self) -> Type[FlashNeoXBatch]:
-        return FlashNeoXBatch
-
-    def decode(self, generated_ids: Union[torch.Tensor, List[int]]) -> str:
-        return self.tokenizer.decode(
-            generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
-        )
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        position_ids: torch.Tensor,
-        cu_seqlens: torch.Tensor,
-        max_s: int,
-        past_key_values: Optional = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # Model Forward
-        return self.model.forward(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            cu_seqlens=cu_seqlens,
-            max_s=max_s,
-            past_key_values=past_key_values,
-        )
-
-    @tracer.start_as_current_span("generate_token")
-    def generate_token(
-        self, batch: FlashNeoXBatch
-    ) -> Tuple[List[Generation], Optional[FlashNeoXBatch]]:
-        # Better to send to device here to avoid device issues in concatenate
-        position_ids = batch.position_ids.to(self.device, non_blocking=True)
-        cu_seqlens = batch.cu_seqlens.to(self.device)
-
-        out, present = self.forward(
-            batch.input_ids,
-            position_ids,
-            cu_seqlens,
-            batch.max_seqlen,
-            batch.past_key_values,
-        )
-
-        # List of indices to cache
-        next_batch_keep_indices = []
-
-        # New values for next forward
-        next_batch_input_ids = []
-        next_batch_position_ids = []
-        next_batch_cu_seqlens = [0]
-        next_batch_max_seqlen = 0
-        next_batch_past_key_values = []
-        next_batch_input_lengths = []
-        next_batch_all_input_ids = []
-        next_batch_all_input_ids_tensor = []
-
-        # Cumulative length
-        cumulative_length = 0
-
-        # Results
-        generations: List[Generation] = []
-
-        # Zipped iterator
-        iterator = zip(
-            batch.requests,
-            batch.input_lengths,
-            batch.next_token_choosers,
-            batch.stopping_criterias,
-            batch.all_input_ids,
-            batch.all_input_ids_tensor,
-        )
-
-        # For each member of the batch
-        for i, (
-            request,
-            input_length,
-            next_token_chooser,
-            stopping_criteria,
-            all_input_ids,
-            all_input_ids_tensor,
-        ) in enumerate(iterator):
-            # Indexing metadata
-            start_index = cumulative_length
-            end_index = cumulative_length + input_length
-
-            if batch.past_key_values is None:
-                # Prefill mode
-                # out is of shape [cumulative_sequence_lengths, vocab_size]
-                logits = out[start_index:end_index]
-            else:
-                # Decode mode
-                # out is of shape [batch_size, vocab_size]
-                logits = out[i].unsqueeze(0)
-
-            # Select next token
-            next_token_id, logprobs = next_token_chooser(
-                all_input_ids_tensor[None, :input_length], logits
-            )
-            next_token_id_squeezed = next_token_id.squeeze()
-            next_token_id_item = next_token_id_squeezed.item()
-
-            # Append next token to all tokens
-            all_input_ids.append(next_token_id_item)
-            all_input_ids_tensor[input_length] = next_token_id_item
-            new_input_length = input_length + 1
-
-            # Generated token
-            next_token_logprob = logprobs[-1, next_token_id_item]
-            next_token_text = self.decode_token(
-                next_token_id_item,
-            )
-
-            # Evaluate stopping criteria
-            stop, reason = stopping_criteria(
-                next_token_id_item,
-                next_token_text,
-            )
-
-            if stop:
-                # Decode generated tokens
-                output_text = self.decode(
-                    all_input_ids[-stopping_criteria.current_tokens :]
-                )
-                # Get seed
-                if isinstance(next_token_chooser.choice, Sampling):
-                    seed = next_token_chooser.choice.seed
-                else:
-                    seed = None
-
-                generated_text = GeneratedText(
-                    output_text, stopping_criteria.current_tokens, reason, seed
-                )
-            else:
-                # Keep request in the batch
-                next_batch_keep_indices.append(i)
-                generated_text = None
-
-                # Get sequence present
-                seq_present = present[:, start_index:end_index]
-                # Pad it for next iter attention
-                past = torch.nn.functional.pad(seq_present, (0, 0, 0, 0, 0, 0, 0, 1))
-                next_batch_past_key_values.append(past)
-
-                next_batch_input_ids.append(next_token_id)
-                next_batch_position_ids.append(input_length)
-                # Cumulative sum
-                next_batch_cu_seqlens.append(
-                    next_batch_cu_seqlens[-1] + new_input_length
-                )
-                next_batch_input_lengths.append(new_input_length)
-                next_batch_all_input_ids.append(all_input_ids)
-                next_batch_all_input_ids_tensor.append(all_input_ids_tensor)
-                next_batch_max_seqlen = max(next_batch_max_seqlen, new_input_length)
-
-            # Prefill
-            if stopping_criteria.current_tokens == 1:
-                # Remove generated token to only have prefill and add nan for first prompt token
-                prefill_logprobs = [float("nan")] + logprobs.gather(
-                    1, all_input_ids_tensor[1:input_length].unsqueeze(1)
-                ).squeeze(1)[:-1].tolist()
-                prefill_token_ids = all_input_ids[:-1]
-                prefill_texts = self.tokenizer.batch_decode(
-                    prefill_token_ids,
-                    clean_up_tokenization_spaces=False,
-                    skip_special_tokens=False,
-                )
-                prefill_tokens = PrefillTokens(
-                    prefill_token_ids, prefill_logprobs, prefill_texts
-                )
-            else:
-                prefill_tokens = None
-
-            generation = Generation(
-                request.id,
-                prefill_tokens,
-                next_token_id_item,
-                next_token_logprob,
-                next_token_text,
-                next_token_id_item in self.all_special_ids,
-                generated_text,
-            )
-
-            generations.append(generation)
-            cumulative_length += input_length
-
-        # We finished all generations in the batch; there is no next batch
-        if not next_batch_keep_indices:
-            return generations, None
-
-        # If we finished at least one generation, we need to evict the indices of the generations that finished
-        # from the values of the next batch
-        if len(next_batch_keep_indices) != len(batch):
-            # Apply indices to requests, token_choosers and stopping_criterias that need to be cached
-            next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
-            next_batch_next_token_choosers = [
-                batch.next_token_choosers[i] for i in next_batch_keep_indices
-            ]
-            next_batch_stopping_criterias = [
-                batch.stopping_criterias[i] for i in next_batch_keep_indices
-            ]
-        else:
-            next_batch_requests = batch.requests
-            next_batch_next_token_choosers = batch.next_token_choosers
-            next_batch_stopping_criterias = batch.stopping_criterias
-
-        # Create final next batch tensors
-        next_batch_position_ids = torch.tensor(
-            next_batch_position_ids, dtype=torch.int32
-        )
-        next_batch_cu_seqlens = torch.tensor(next_batch_cu_seqlens, dtype=torch.int32)
-        if len(next_batch_keep_indices) > 1:
-            next_batch_input_ids = torch.concat(next_batch_input_ids).squeeze(1)
-            next_batch_past_key_values = torch.concat(next_batch_past_key_values, dim=1)
-        else:
-            next_batch_input_ids = next_batch_input_ids[0].view(1)
-            next_batch_past_key_values = next_batch_past_key_values[0]
-
-        next_batch = FlashNeoXBatch(
-            batch_id=batch.batch_id,
-            requests=next_batch_requests,
-            input_ids=next_batch_input_ids,
-            position_ids=next_batch_position_ids,
-            cu_seqlens=next_batch_cu_seqlens,
-            max_seqlen=next_batch_max_seqlen,
-            past_key_values=next_batch_past_key_values,
-            input_lengths=next_batch_input_lengths,
-            all_input_ids=next_batch_all_input_ids,
-            all_input_ids_tensor=next_batch_all_input_ids_tensor,
-            next_token_choosers=next_batch_next_token_choosers,
-            stopping_criterias=next_batch_stopping_criterias,
-        )
-        return generations, next_batch
-
 
 class FlashNeoXSharded(FlashNeoX):
     def __init__(
@@ -508,7 +70,7 @@ class FlashNeoXSharded(FlashNeoX):
         model.post_load_weights()
         self.model = model.eval().to(dtype)
         torch.distributed.barrier(group=self.process_group)
-        super(FlashNeoX, self).__init__(
+        super(FlashCausalLM, self).__init__(
             tokenizer=tokenizer,
             device=device,
         )

server/text_generation_server/models/santacoder.py

@@ -6,12 +6,6 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 
 from text_generation_server.models import CausalLM
 
-FIM_PREFIX = "<fim-prefix>"
-FIM_MIDDLE = "<fim-middle>"
-FIM_SUFFIX = "<fim-suffix>"
-FIM_PAD = "<fim-pad>"
-EOD = "<|endoftext|>"
-
 
 class SantaCoder(CausalLM):
     def __init__(self, model_id: str, revision: Optional[str] = None, quantize=False):
@@ -28,18 +22,6 @@ class SantaCoder(CausalLM):
         tokenizer = AutoTokenizer.from_pretrained(
             model_id, revision=revision, padding_side="left"
         )
-        tokenizer.add_special_tokens(
-            {
-                "additional_special_tokens": [
-                    EOD,
-                    FIM_PREFIX,
-                    FIM_MIDDLE,
-                    FIM_SUFFIX,
-                    FIM_PAD,
-                ],
-                "pad_token": EOD,
-            }
-        )
 
         self.model = (
             AutoModelForCausalLM.from_pretrained(