chore: formatting

server/text_generation_server/models/flash_mixtral.py

@@ -3,17 +3,20 @@ import torch
 from typing import Optional
 
 from text_generation_server.models.flash_mistral import BaseFlashMistral
-from text_generation_server.models.custom_modeling.flash_mixtral_modeling import MixtralConfig, FlashMixtralForCausalLM
+from text_generation_server.models.custom_modeling.flash_mixtral_modeling import (
+    MixtralConfig,
+    FlashMixtralForCausalLM,
+)
 
 
 class FlashMixtral(BaseFlashMistral):
     def __init__(
-            self,
-            model_id: str,
-            revision: Optional[str] = None,
-            quantize: Optional[str] = None,
-            dtype: Optional[torch.dtype] = None,
-            trust_remote_code: bool = False,
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        trust_remote_code: bool = False,
     ):
         super(FlashMixtral, self).__init__(
             config_cls=MixtralConfig,
@@ -22,5 +25,5 @@ class FlashMixtral(BaseFlashMistral):
             revision=revision,
             quantize=quantize,
             dtype=dtype,
-            trust_remote_code=trust_remote_code
+            trust_remote_code=trust_remote_code,
         )

server/text_generation_server/models/idefics_causal_lm.py

@@ -792,7 +792,10 @@ class IdeficsCausalLM(Model):
                         skip_special_tokens=False,
                     )
                     prefill_tokens = Tokens(
-                        prefill_token_ids, prefill_logprobs, prefill_texts, is_special=[]
+                        prefill_token_ids,
+                        prefill_logprobs,
+                        prefill_texts,
+                        is_special=[],
                     )
                 else:
                     prefill_tokens = None
@@ -803,10 +806,10 @@ class IdeficsCausalLM(Model):
                     request.id,
                     prefill_tokens,
                     Tokens(
-                    [next_token_id_squeezed],
-                    [next_token_logprob],
-                    [next_token_text],
-                    [next_token_id_squeezed.item() in self.all_special_ids],
+                        [next_token_id_squeezed],
+                        [next_token_logprob],
+                        [next_token_text],
+                        [next_token_id_squeezed.item() in self.all_special_ids],
                     ),
                     generated_text,
                     top_tokens,

server/text_generation_server/models/model.py

@@ -56,7 +56,7 @@ class Model(ABC):
             dtype=str(self.dtype),
             device_type=self.device.type,
             window_size=self.sliding_window,
-            speculate=self.speculate
+            speculate=self.speculate,
         )
 
     @property

server/text_generation_server/models/seq2seq_lm.py

@@ -736,7 +736,7 @@ class Seq2SeqLM(Model):
                         [self.tokenizer.bos_token_id],
                         [float("nan")],
                         [self.tokenizer.bos_token],
-                        [False]
+                        [False],
                     )
                 else:
                     prefill_tokens = None
@@ -763,10 +763,10 @@ class Seq2SeqLM(Model):
                     request.id,
                     prefill_tokens,
                     Tokens(
-                    [next_token_id_squeezed],
-                    [next_token_logprob],
-                    [next_token_text],
-                    [next_token_id_squeezed.item() in self.all_special_ids],
+                        [next_token_id_squeezed],
+                        [next_token_logprob],
+                        [next_token_text],
+                        [next_token_id_squeezed.item() in self.all_special_ids],
                     ),
                     generated_text,
                     top_tokens,

server/text_generation_server/models/types.py

@@ -66,7 +66,10 @@ class Tokens:
 
     def to_pb(self) -> generate_pb2.Tokens:
         return generate_pb2.Tokens(
-            ids=self.token_ids, logprobs=self.logprobs, texts=self.texts, is_special=self.is_special
+            ids=self.token_ids,
+            logprobs=self.logprobs,
+            texts=self.texts,
+            is_special=self.is_special,
         )
 
     def __len__(self):

server/text_generation_server/server.py

@@ -159,7 +159,13 @@ def serve(
 
         try:
             model = get_model(
-                model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
+                model_id,
+                revision,
+                sharded,
+                quantize,
+                speculate,
+                dtype,
+                trust_remote_code,
             )
         except Exception:
             logger.exception("Error when initializing model")
@@ -207,5 +213,7 @@ def serve(
             await server.stop(0)
 
     asyncio.run(
-        serve_inner(model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code)
+        serve_inner(
+            model_id, revision, sharded, quantize, speculate, dtype, trust_remote_code
+        )
     )

server/text_generation_server/utils/flash_attn.py

@@ -51,7 +51,9 @@ except ImportError as e:
         ) from e
     elif IS_ROCM_SYSTEM:
         for idx in range(torch.cuda.device_count()):
-            if "MI210" not in torch.cuda.get_device_name(idx) and "MI250" not in torch.cuda.get_device_name(idx):
+            if "MI210" not in torch.cuda.get_device_name(
+                idx
+            ) and "MI250" not in torch.cuda.get_device_name(idx):
                 raise ImportError(
                     f"AMD GPU {torch.cuda.get_device_name(idx)} does not support flash-attention"
                 )
@@ -91,8 +93,10 @@ def attention(
         )
     elif HAS_FLASH_ATTN_V2_ROCM:
         if window_size_left != -1:
-            raise ValueError(f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left}).")
-        
+            raise ValueError(
+                f"RoCm version of Flash Attention v2 does not support window attention (window_size_left != -1, got window_size_left={window_size_left})."
+            )
+
         # RoCm flash API does not take the window_size_left and window_size_right arguments.
         return flash_attn_2_cuda.varlen_fwd(
             q,

server/text_generation_server/utils/gptq/exllamav2.py

@@ -11,40 +11,44 @@ logger = getLogger(__name__)
 try:
     from exllamav2_kernels import make_q_matrix, gemm_half_q_half
 except ImportError:
-    logger.error('exllamav2_kernels not installed.')
+    logger.error("exllamav2_kernels not installed.")
     raise
 
 # Dummy tensor to pass instead of g_idx since there is no way to pass "None" to a C++ extension
 none_tensor = torch.empty((1, 1), device="meta")
 
+
 def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda):
     """Matrix multiplication, returns x @ q4"""
     output_shape = x.shape[:-1] + (q4_width,)
     x = x.view(-1, x.shape[-1])
-    output = torch.empty((x.shape[0], q4_width), dtype = torch.half, device = x.device)
+    output = torch.empty((x.shape[0], q4_width), dtype=torch.half, device=x.device)
     gemm_half_q_half(x, q_handle, output, force_cuda)
     return output.view(output_shape)
 
+
 def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
     """
-    Create Q matrix 
+    Create Q matrix
     """
     # EXL2
-    # won't work as the moment because the tensors are not the same. 
+    # won't work as the moment because the tensors are not the same.
     if "q_weight" in w:
         w["q_scale_max"] /= 256
         w["q_perm"] = w["q_perm"].short()
         w["q_invperm"] = w["q_invperm"].short()
-        return make_q_matrix(w["q_weight"],
-                                w["q_perm"],
-                                w["q_invperm"],
-                                w["q_scale"],
-                                w["q_scale_max"],
-                                w["q_groups"],
-                                none_tensor,
-                                none_tensor,
-                                none_tensor,
-                                temp_dq)
+        return make_q_matrix(
+            w["q_weight"],
+            w["q_perm"],
+            w["q_invperm"],
+            w["q_scale"],
+            w["q_scale_max"],
+            w["q_groups"],
+            none_tensor,
+            none_tensor,
+            none_tensor,
+            temp_dq,
+        )
     # GPTQ
     elif "qweight" in w:
         if w["scales"].dtype == torch.float:
@@ -52,31 +56,40 @@ def ext_make_q_matrix(w: dict, temp_dq, key: str = None):
 
         # GPTQ with g_idx (act_order)
         if w.get("g_idx", None) is not None and not (w["g_idx"] == 0).all().item():
-            w["q_perm"] = torch.empty((w["qweight"].shape[0] * 8,), dtype = torch.short, device = w["qweight"].device)
+            w["q_perm"] = torch.empty(
+                (w["qweight"].shape[0] * 8,),
+                dtype=torch.short,
+                device=w["qweight"].device,
+            )
             w["q_invperm"] = torch.empty_like(w["q_perm"])
             # make_q4 segfaults if g_idx is not on cpu in the act-order case. In the non act-order case, None needs to be passed for g_idx.
-            return make_q_matrix(w["qweight"],
-                                 w["q_perm"],
-                                 w["q_invperm"],
-                                 none_tensor,
-                                 none_tensor,
-                                 none_tensor,
-                                 w["qzeros"],
-                                 w["scales"],
-                                 w["g_idx"].cpu(),
-                                 temp_dq)
+            return make_q_matrix(
+                w["qweight"],
+                w["q_perm"],
+                w["q_invperm"],
+                none_tensor,
+                none_tensor,
+                none_tensor,
+                w["qzeros"],
+                w["scales"],
+                w["g_idx"].cpu(),
+                temp_dq,
+            )
         # GPTQ without g_idx
         else:
-            return make_q_matrix(w["qweight"],
-                                none_tensor,
-                                none_tensor,
-                                none_tensor,
-                                none_tensor,
-                                none_tensor,
-                                w["qzeros"],
-                                w["scales"],
-                                none_tensor,
-                                temp_dq)
+            return make_q_matrix(
+                w["qweight"],
+                none_tensor,
+                none_tensor,
+                none_tensor,
+                none_tensor,
+                none_tensor,
+                w["qzeros"],
+                w["scales"],
+                none_tensor,
+                temp_dq,
+            )
+
 
 DEVICE = None
 FIXED_BYTES = 0
@@ -106,14 +119,15 @@ class QuantLinear(nn.Module):
         super().__init__()
         if bits != 4:
             raise ValueError(
-                f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization.")
+                f"Exllamav2 kernel supports only bits=4, requested bits={bits}. Something is wrong in the model initialization."
+            )
         self.q_handle = None
         self.q_tensors = None
         self.bits = bits
-        self.maxq = 2 ** self.bits - 1
+        self.maxq = 2**self.bits - 1
         self.infeatures = qweight.shape[0] // self.bits * 32
         self.outfeatures = qweight.shape[1]
-        self.padding = - self.outfeatures % 32
+        self.padding = -self.outfeatures % 32
         self.outfeatures = self.outfeatures + self.padding
 
         self.device = qweight.device
@@ -128,9 +142,12 @@ class QuantLinear(nn.Module):
         outfeatures = self.outfeatures
         assert qweight.shape == (infeatures // 32 * self.bits, outfeatures)
         assert infeatures % self.group_size == 0
-        assert qzeros.shape == (infeatures // self.group_size, outfeatures // 32 * self.bits)
+        assert qzeros.shape == (
+            infeatures // self.group_size,
+            outfeatures // 32 * self.bits,
+        )
         assert scales.shape == (infeatures // self.group_size, outfeatures)
-        assert g_idx.shape == (infeatures, ), f"{g_idx.shape}, {infeatures}"
+        assert g_idx.shape == (infeatures,), f"{g_idx.shape}, {infeatures}"
 
         global FIXED_BYTES, LAYERS
         FIXED_BYTES = max(FIXED_BYTES, self.scratch_space_fixed())
@@ -140,33 +157,31 @@ class QuantLinear(nn.Module):
         assert self.qweight.device.type == "cuda"
         assert self.qweight.device.index is not None
         self.q_tensors = {
-            "qweight":self.qweight,
-            "qzeros":self.qzeros,
-            "scales":self.scales,
-            "g_idx":self.g_idx
+            "qweight": self.qweight,
+            "qzeros": self.qzeros,
+            "scales": self.scales,
+            "g_idx": self.g_idx,
         }
         temp_dq = temp_dq.get_scratch_slice(self.temp_dq_size())
-        self.q_handle = ext_make_q_matrix(
-            self.q_tensors, temp_dq
-        )
-    
-    def forward(self, x, force_cuda = False):
+        self.q_handle = ext_make_q_matrix(self.q_tensors, temp_dq)
+
+    def forward(self, x, force_cuda=False):
         output = ext_gemm_half_q_half(x, self.q_handle, self.outfeatures, force_cuda)
 
         if self.bias is not None:
             output.add_(self.bias)
         return output
-    
+
     def temp_dq_size(self):
         return self.infeatures * self.outfeatures * 2 + 128
-    
+
     def temp_fwd_size(self, max_input_len, max_batch_size):
         return self.outfeatures * max_input_len * max_batch_size * 4 + 128
-    
+
     def scratch_space_fixed(self, max_input_len=4096, max_batch_size=16):
         return self.temp_dq_size() + self.temp_fwd_size(max_input_len, max_batch_size)
-               
-    
+
+
 class ExLlamaV2DeviceTensors:
 
     device_idx: int
@@ -177,13 +192,16 @@ class ExLlamaV2DeviceTensors:
     def __init__(self, device, scratch_bytes):
         self.device = device
         self.scratch_bytes = scratch_bytes
-    
+
     def prepare(self):
-        self.scratch = torch.empty((self.scratch_bytes // 2,), dtype = torch.half, device = self.device)
+        self.scratch = torch.empty(
+            (self.scratch_bytes // 2,), dtype=torch.half, device=self.device
+        )
 
     def get_scratch_slice(self, size_bytes):
 
-        if self.scratch is None: self.prepare()
+        if self.scratch is None:
+            self.prepare()
 
         size_bytes = ((size_bytes + 127) // 128) * 128
         size_half = size_bytes // 2

server/text_generation_server/utils/layers.py

@@ -35,7 +35,9 @@ HAS_EXLLAMA = False
 CAN_EXLLAMA = major >= 8
 V2 = os.getenv("EXLLAMA_VERSION", "2") == "2"
 if V2 and int(os.getenv("WORLD_SIZE", "1")) > 1:
-    logger.warning("Disabling exllama v2 and using v1 instead because there are issues when sharding")
+    logger.warning(
+        "Disabling exllama v2 and using v1 instead because there are issues when sharding"
+    )
     V2 = False
 
 if os.getenv("DISABLE_EXLLAMA") == "True":
@@ -43,17 +45,19 @@ if os.getenv("DISABLE_EXLLAMA") == "True":
 elif CAN_EXLLAMA:
     try:
         if V2:
-            from text_generation_server.utils.gptq.exllamav2 import (QuantLinear as ExllamaQuantLinear,
-                                                                     create_exllama_buffers,
-                                                                     set_device,
-                                                                     )
+            from text_generation_server.utils.gptq.exllamav2 import (
+                QuantLinear as ExllamaQuantLinear,
+                create_exllama_buffers,
+                set_device,
+            )
 
             HAS_EXLLAMA = "2"
         else:
-            from text_generation_server.utils.gptq.exllama import (Ex4bitLinear as ExllamaQuantLinear,
-                                                                   create_exllama_buffers,
-                                                                   set_device,
-                                                                   )
+            from text_generation_server.utils.gptq.exllama import (
+                Ex4bitLinear as ExllamaQuantLinear,
+                create_exllama_buffers,
+                set_device,
+            )
 
             HAS_EXLLAMA = "1"
 
@@ -114,7 +118,7 @@ def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, st
 
 @classmethod
 def load_conv2d_no_bias(
-        cls, prefix, weights, in_channels, out_channels, kernel_size, stride
+    cls, prefix, weights, in_channels, out_channels, kernel_size, stride
 ):
     weight = weights.get_tensor(f"{prefix}.weight")
     with init_empty_weights():
@@ -138,9 +142,9 @@ torch.nn.LayerNorm.load_no_bias = load_layer_norm_no_bias
 
 class FastLinear(nn.Module):
     def __init__(
-            self,
-            weight,
-            bias,
+        self,
+        weight,
+        bias,
     ) -> None:
         super().__init__()
         self.weight = nn.Parameter(weight)
@@ -164,9 +168,9 @@ class FastLinear(nn.Module):
 
 class EETQLinear(nn.Module):
     def __init__(
-            self,
-            weight,
-            bias,
+        self,
+        weight,
+        bias,
     ) -> None:
         super().__init__()
         device = weight.device
@@ -185,13 +189,13 @@ class EETQLinear(nn.Module):
 
 class Linear8bitLt(nn.Module):
     def __init__(
-            self,
-            weight,
-            bias,
-            has_fp16_weights=True,
-            memory_efficient_backward=False,
-            threshold=0.0,
-            index=None,
+        self,
+        weight,
+        bias,
+        has_fp16_weights=True,
+        memory_efficient_backward=False,
+        threshold=0.0,
+        index=None,
     ):
         super().__init__()
         assert (
@@ -325,7 +329,9 @@ def get_linear(weight, bias, quantize):
             )
 
         if use_exllama:
-            linear = ExllamaQuantLinear(qweight, qzeros, scales, g_idx, bias, bits, groupsize)
+            linear = ExllamaQuantLinear(
+                qweight, qzeros, scales, g_idx, bias, bits, groupsize
+            )
         else:
             linear = QuantLinear(
                 qweight,
@@ -533,7 +539,6 @@ try:
     else:
         dropout_layer_norm = None
 
-
     class FastLayerNorm(nn.LayerNorm):
         def forward(self, hidden_states, residual=None):
             if hidden_states.shape[-1] > 8192 or IS_ROCM_SYSTEM:
@@ -569,7 +574,6 @@ try:
 
                 return normed_hidden_states, residual
 
-
     class FastRMSNorm(nn.Module):
         def __init__(self, weight: torch.Tensor, eps: float):
             super().__init__()
@@ -601,7 +605,11 @@ try:
                 return self.weight * hidden_states, residual
             elif IS_CUDA_SYSTEM:
                 # faster post attention rms norm
-                normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
+                (
+                    normed_hidden_states,
+                    res,
+                    *rest,
+                ) = dropout_layer_norm.dropout_add_ln_fwd(
                     hidden_states,
                     residual,
                     self.weight,
@@ -638,7 +646,8 @@ try:
                 return out, residual
             else:
                 raise ValueError(
-                    "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction.")
+                    "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
+                )
 
 except ImportError:
     pass
@@ -650,14 +659,12 @@ try:
     elif IS_ROCM_SYSTEM:
         from vllm import pos_encoding_ops
 
-
     def _create_inv_freq(dim, base, device):
         inv_freq = 1.0 / (
-                base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
+            base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim)
         )
         return inv_freq
 
-
     def _get_rope_config(config):
         if os.getenv("ROPE_SCALING", None) is not None:
             rope_scaling = {
@@ -667,7 +674,6 @@ try:
             return rope_scaling
         return getattr(config, "rope_scaling", None)
 
-
     class PositionRotaryEmbedding(nn.Module):
         def __init__(self, inv_freq, scaling_factor):
             super().__init__()
@@ -680,17 +686,23 @@ try:
             self.scaling_factor = scaling_factor
             self.dynamic_args = None
 
-        def forward(self, query: torch.Tensor, key: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+        def forward(
+            self,
+            query: torch.Tensor,
+            key: torch.Tensor,
+            cos: torch.Tensor,
+            sin: torch.Tensor,
+        ):
             # Such controlflows may add some overhead.
             if IS_CUDA_SYSTEM:
                 rotary_dim = cos.shape[-1]
                 q1 = query[..., :rotary_dim]
-                q2 = query[..., rotary_dim: 2 * rotary_dim]
+                q2 = query[..., rotary_dim : 2 * rotary_dim]
 
                 rotary_emb.apply_rotary(q1, q2, cos, sin, q1, q2, False)
 
                 k1 = key[..., :rotary_dim]
-                k2 = key[..., rotary_dim: 2 * rotary_dim]
+                k2 = key[..., rotary_dim : 2 * rotary_dim]
 
                 rotary_emb.apply_rotary(k1, k2, cos, sin, k1, k2, False)
             elif IS_ROCM_SYSTEM:
@@ -700,17 +712,11 @@ try:
                 head_size = query.shape[-1]
 
                 # Inplace operation, updating query and key.
-                pos_encoding_ops.rotary_embedding(
-                    query,
-                    key,
-                    head_size,
-                    cos,
-                    sin,
-                    True
-                )
+                pos_encoding_ops.rotary_embedding(query, key, head_size, cos, sin, True)
             else:
                 raise ValueError(
-                    "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction.")
+                    "Your system seem to be not supported. Please check your install or open an issue at https://github.com/huggingface/text-generation-inference/issues with a clear reproduction."
+                )
 
         @classmethod
         def static(cls, config, dim, base, device):
@@ -732,15 +738,16 @@ try:
                 elif rope_scaling["type"] == "yarn":
                     return YarnPositionRotaryEmbedding(
                         dim=2 * inv_freq.shape[0],
-                        max_position_embeddings=rope_scaling["original_max_position_embeddings"],
+                        max_position_embeddings=rope_scaling[
+                            "original_max_position_embeddings"
+                        ],
                         base=10000.0,
                         device=inv_freq.device,
                         scaling_factor=scaling_factor,
                         extrapolation_factor=1,
                         attn_factor=1,
                         beta_fast=32,
-                        beta_slow=1
-
+                        beta_slow=1,
                     )
                 else:
                     raise NotImplementedError(
@@ -773,15 +780,16 @@ try:
                 elif rope_scaling["type"] == "yarn":
                     return YarnPositionRotaryEmbedding(
                         dim=2 * inv_freq.shape[0],
-                        max_position_embeddings=rope_scaling["original_max_position_embeddings"],
+                        max_position_embeddings=rope_scaling[
+                            "original_max_position_embeddings"
+                        ],
                         base=10000.0,
                         device=inv_freq.device,
                         scaling_factor=scaling_factor,
                         extrapolation_factor=1,
                         attn_factor=1,
                         beta_fast=32,
-                        beta_slow=1
-
+                        beta_slow=1,
                     )
                 else:
                     raise NotImplementedError(
@@ -793,9 +801,9 @@ try:
             # 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
+                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)
@@ -809,7 +817,7 @@ try:
                 self._sin_cached = torch.sin(freqs).to(dtype)
 
         def get_cos_sin(
-                self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype
+            self, position_ids: torch.Tensor, max_s: int, dtype: torch.dtype
         ):
             """
             Return cos and sin for the asked position ids
@@ -827,7 +835,6 @@ try:
             # Note: this unsqueeze is not necessary on RoCm + VLLM ROPE implementation, but we leave it as is to avoid yet an other controlflow.
             return cos.unsqueeze(1), sin.unsqueeze(1)
 
-
     class DynamicPositionRotaryEmbedding(PositionRotaryEmbedding):
         def __init__(self, dim, max_position_embeddings, base, device, scaling_factor):
             inv_freq = _create_inv_freq(dim, base, device)
@@ -840,14 +847,14 @@ try:
             # 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
+                seqlen > self._seq_len_cached
+                or self._cos_cached.device != device
+                or self._cos_cached.dtype != dtype
             ):
                 if seqlen > self.max_position_embeddings:
                     newbase = self.base * (
-                            (self.scaling_factor * seqlen / self.max_position_embeddings)
-                            - (self.scaling_factor - 1)
+                        (self.scaling_factor * seqlen / self.max_position_embeddings)
+                        - (self.scaling_factor - 1)
                     ) ** (self.dim / (self.dim - 2))
                     self.inv_freq = _create_inv_freq(
                         self.dim, newbase, self.inv_freq.device
@@ -861,24 +868,28 @@ try:
                 self._cos_cached = torch.cos(freqs).to(dtype)
                 self._sin_cached = torch.sin(freqs).to(dtype)
 
-
     # Inverse dim formula to find dim based on number of rotations
     import math
 
-
-    def find_correction_dim(num_rotations, dim, base=10000, max_position_embeddings=2048):
-        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
-
+    def find_correction_dim(
+        num_rotations, dim, base=10000, max_position_embeddings=2048
+    ):
+        return (
+            dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))
+        ) / (2 * math.log(base))
 
     # Find dim range bounds based on rotations
-    def find_correction_range(low_rot, high_rot, dim, base=10000, max_position_embeddings=2048):
-        low = math.floor(find_correction_dim(
-            low_rot, dim, base, max_position_embeddings))
-        high = math.ceil(find_correction_dim(
-            high_rot, dim, base, max_position_embeddings))
+    def find_correction_range(
+        low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
+    ):
+        low = math.floor(
+            find_correction_dim(low_rot, dim, base, max_position_embeddings)
+        )
+        high = math.ceil(
+            find_correction_dim(high_rot, dim, base, max_position_embeddings)
+        )
         return max(low, 0), min(high, dim - 1)  # Clamp values just in case
 
-
     def linear_ramp_mask(min, max, dim):
         if min == max:
             max += 0.001  # Prevent singularity
@@ -887,16 +898,25 @@ try:
         ramp_func = torch.clamp(linear_func, 0, 1)
         return ramp_func
 
-
     def get_mscale(scale=1):
         if scale <= 1:
             return 1.0
         return 0.1 * math.log(scale) + 1.0
 
-
     class YarnPositionRotaryEmbedding(PositionRotaryEmbedding):
-        def __init__(self, dim, max_position_embeddings, base, device, scaling_factor, *, extrapolation_factor,
-                     attn_factor, beta_fast, beta_slow):
+        def __init__(
+            self,
+            dim,
+            max_position_embeddings,
+            base,
+            device,
+            scaling_factor,
+            *,
+            extrapolation_factor,
+            attn_factor,
+            beta_fast,
+            beta_slow,
+        ):
             inv_freq = _create_inv_freq(dim, base, device)
             super().__init__(inv_freq, scaling_factor)
             self.dim = dim
@@ -906,16 +926,17 @@ try:
             self.attn_factor = attn_factor
             self.beta_fast = beta_fast
             self.beta_slow = beta_slow
-            self.mscale = float(get_mscale(
-                self.scaling_factor) * self.attn_factor)  # Get n-d magnitude scaling corrected for interpolation
+            self.mscale = float(
+                get_mscale(self.scaling_factor) * self.attn_factor
+            )  # Get n-d magnitude scaling corrected for interpolation
 
         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
+                seqlen > self._seq_len_cached
+                or self._cos_cached.device != device
+                or self._cos_cached.dtype != dtype
             ):
                 if seqlen > self.max_position_embeddings:
                     inv_freq_extrapolation = _create_inv_freq(
@@ -923,15 +944,26 @@ try:
                     )
                     freqs = 1.0 / inv_freq_extrapolation
                     inv_freq_interpolation = 1.0 / (self.scaling_factor * freqs)
-                    low, high = find_correction_range(self.beta_fast, self.beta_slow, self.dim, self.base,
-                                                      self.max_position_embeddings)
-                    inv_freq_mask = (1 - linear_ramp_mask(low, high, self.dim // 2).float().to(
-                        device)) * self.extrapolation_factor  # Get n-d rotational scaling corrected for extrapolation
-                    inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
+                    low, high = find_correction_range(
+                        self.beta_fast,
+                        self.beta_slow,
+                        self.dim,
+                        self.base,
+                        self.max_position_embeddings,
+                    )
+                    inv_freq_mask = (
+                        1
+                        - linear_ramp_mask(low, high, self.dim // 2).float().to(device)
+                    ) * self.extrapolation_factor  # Get n-d rotational scaling corrected for extrapolation
+                    inv_freq = (
+                        inv_freq_interpolation * (1 - inv_freq_mask)
+                        + inv_freq_extrapolation * inv_freq_mask
+                    )
 
                     self.inv_freq = inv_freq
-                    self.mscale = float(get_mscale(
-                        self.scaling_factor) * self.attn_factor)  # Get n-d magnitude scaling corrected for interpolation
+                    self.mscale = float(
+                        get_mscale(self.scaling_factor) * self.attn_factor
+                    )  # Get n-d magnitude scaling corrected for interpolation
 
                 self._seq_len_cached = seqlen
                 t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)

server/text_generation_server/utils/medusa.py

@@ -2,6 +2,7 @@ import torch
 from dataclasses import dataclass
 from text_generation_server.utils.layers import TensorParallelHead, FastLinear
 
+
 @dataclass
 class Output:
     logits: torch.FloatTensor = None
@@ -11,7 +12,9 @@ class Output:
 class ResBlock(torch.nn.Module):
     def __init__(self, config, prefix, weights):
         super().__init__()
-        self.linear = FastLinear.load(config, prefix=f"{prefix}.linear", weights=weights, bias=True)
+        self.linear = FastLinear.load(
+            config, prefix=f"{prefix}.linear", weights=weights, bias=True
+        )
         self.act = torch.nn.SiLU()
 
     def forward(self, x):
@@ -19,15 +22,13 @@ class ResBlock(torch.nn.Module):
 
 
 class MedusaModel(torch.nn.Module):
-    def __init__(
-        self,
-        config,
-        weights,
-        lm_head
-    ):
+    def __init__(self, config, weights, lm_head):
         super().__init__()
         self.heads = torch.nn.ModuleList(
-            [MedusaHead(config, prefix=f"{i}", weights=weights) for i in range(config["medusa_num_heads"])]
+            [
+                MedusaHead(config, prefix=f"{i}", weights=weights)
+                for i in range(config["medusa_num_heads"])
+            ]
         )
         self.lm_head = lm_head
 
@@ -40,9 +41,16 @@ class MedusaModel(torch.nn.Module):
 class MedusaHead(torch.nn.Module):
     def __init__(self, config, prefix, weights):
         super().__init__()
-        self.blocks = torch.nn.ModuleList([ResBlock(config, prefix=f"{prefix}.{i}", weights=weights) for i in range(config["medusa_num_layers"])])
+        self.blocks = torch.nn.ModuleList(
+            [
+                ResBlock(config, prefix=f"{prefix}.{i}", weights=weights)
+                for i in range(config["medusa_num_layers"])
+            ]
+        )
         n = len(self.blocks)
-        self.out = FastLinear.load(config, prefix=f"{prefix}.{n}", weights=weights, bias=False)
+        self.out = FastLinear.load(
+            config, prefix=f"{prefix}.{n}", weights=weights, bias=False
+        )
 
     def forward(self, x):
         for block in self.blocks:

server/text_generation_server/utils/paged_attention.py

@@ -7,23 +7,26 @@ from vllm import attention_ops
 _PARTITION_SIZE = 512
 
 
-def reshape_and_cache(key: torch.Tensor, value: torch.Tensor, key_cache: torch.Tensor, value_cache: torch.Tensor,
-                      slots: torch.Tensor):
-    cache_ops.reshape_and_cache(
-        key, value, key_cache, value_cache, slots
-    )
+def reshape_and_cache(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    slots: torch.Tensor,
+):
+    cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots)
 
 
 def attention(
-        out: torch.Tensor,
-        query: torch.Tensor,
-        key_cache: torch.Tensor,
-        value_cache: torch.Tensor,
-        kv_head_mapping: torch.Tensor,
-        softmax_scale: float,
-        block_tables: torch.Tensor,
-        input_lengths: torch.Tensor,
-        max_s: int,
+    out: torch.Tensor,
+    query: torch.Tensor,
+    key_cache: torch.Tensor,
+    value_cache: torch.Tensor,
+    kv_head_mapping: torch.Tensor,
+    softmax_scale: float,
+    block_tables: torch.Tensor,
+    input_lengths: torch.Tensor,
+    max_s: int,
 ):
     # Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
     # Copyright 2023 The vLLM team. All rights
@@ -45,9 +48,7 @@ def attention(
     # value_cache => [num_blocks, num_heads, head_size, block_size]
     block_size = value_cache.shape[3]
     num_seqs, num_heads, head_size = query.shape
-    max_num_partitions = (
-            (max_s + _PARTITION_SIZE - 1) //
-            _PARTITION_SIZE)
+    max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
     # NOTE(woosuk): We use a simple heuristic to decide whether to use
     # PagedAttention V1 or V2. If the number of partitions is 1, we use
     # V1 to avoid the overhead of reduction. Also, if the number of

server/text_generation_server/utils/peft.py

@@ -38,7 +38,9 @@ def download_and_unload_peft(model_id, revision, trust_remote_code):
     os.makedirs(model_id, exist_ok=True)
     cache_dir = model_id
     logger.info(f"Saving the newly created merged model to {cache_dir}")
-    tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=trust_remote_code)
+    tokenizer = AutoTokenizer.from_pretrained(
+        base_model_id, trust_remote_code=trust_remote_code
+    )
     model.save_pretrained(cache_dir, safe_serialization=True)
     model.config.save_pretrained(cache_dir)
     tokenizer.save_pretrained(cache_dir)

server/text_generation_server/utils/speculate.py

-
 SPECULATE = None
 
+
 def get_speculate() -> int:
     global SPECULATE
     return SPECULATE
 
+
 def set_speculate(speculate: int):
     global SPECULATE
     SPECULATE = speculate
-
-

server/text_generation_server/utils/tokens.py

@@ -16,6 +16,7 @@ from text_generation_server.utils.logits_process import (
 from text_generation_server.utils.watermark import WatermarkLogitsProcessor
 from transformers import PreTrainedTokenizerBase, RepetitionPenaltyLogitsProcessor
 
+
 class NextTokenChooser:
     def __init__(
         self,
@@ -145,21 +146,31 @@ class StoppingCriteria:
             pb.ignore_eos_token,
         )
 
-def create_n_gram_speculation(input_ids: torch.Tensor, next_ids: torch.Tensor, accepted_ids: torch.Tensor, speculate: int, verbose: bool):
+
+def create_n_gram_speculation(
+    input_ids: torch.Tensor,
+    next_ids: torch.Tensor,
+    accepted_ids: torch.Tensor,
+    speculate: int,
+    verbose: bool,
+):
     # Very trivial approach, find first match in the string.
     # This is much less refined than actual n-gram but seems to work
     # relatively OK in grounded mode and is by far much faster with
     # much less worst case complexity as everything happens on device.
     B = accepted_ids.shape[0]
     device = input_ids.device
-    seeds = next_ids[accepted_ids.cumsum(dim=-1) -1 ]
+    seeds = next_ids[accepted_ids.cumsum(dim=-1) - 1]
     indices = (input_ids == seeds.unsqueeze(-1)).max(dim=1).indices + 1
-    all_indices = indices.unsqueeze(-1).expand(B, speculate) + torch.arange(speculate, device=device)
+    all_indices = indices.unsqueeze(-1).expand(B, speculate) + torch.arange(
+        speculate, device=device
+    )
     all_indices = torch.clamp(all_indices, max=input_ids.shape[1] - 1)
 
     speculative_ids = input_ids.gather(dim=-1, index=all_indices)
     return speculative_ids
 
+
 class HeterogeneousNextTokenChooser:
     def __init__(
         self,
@@ -228,7 +239,15 @@ class HeterogeneousNextTokenChooser:
         self.dtype = dtype
         self.device = device
 
-    def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor, speculate: int, speculated_ids: Optional[torch.Tensor] = None, speculative_scores: Optional[torch.Tensor] = None, verbose=False):
+    def __call__(
+        self,
+        input_ids: torch.Tensor,
+        scores: torch.Tensor,
+        speculate: int,
+        speculated_ids: Optional[torch.Tensor] = None,
+        speculative_scores: Optional[torch.Tensor] = None,
+        verbose=False,
+    ):
         if speculated_ids is not None:
             B = scores.shape[0] // (speculated_ids.shape[1] + 1)
             S = speculated_ids.shape[1] + 1
@@ -249,12 +268,11 @@ class HeterogeneousNextTokenChooser:
             for warper in self.warpers:
                 _scores = warper(input_ids, _scores)
 
-
             _next_ids = self.choice(_scores)
             scores[:, j] = _scores
             next_ids[:, j] = _next_ids
-        next_ids = next_ids.view(B*S)
-        scores = scores.view( B* S, -1)
+        next_ids = next_ids.view(B * S)
+        scores = scores.view(B * S, -1)
 
         if speculated_ids is not None:
             accepted_ids = []
@@ -262,7 +280,7 @@ class HeterogeneousNextTokenChooser:
             S = speculated_ids.shape[1] + 1
             indices = []
             for i in range(B):
-                _next_ids = next_ids[i*S: (i + 1)*S]
+                _next_ids = next_ids[i * S : (i + 1) * S]
                 _speculated_ids = speculated_ids[i]
                 validate_speculative = _next_ids[:-1] == _speculated_ids
                 index = i * S
@@ -278,7 +296,9 @@ class HeterogeneousNextTokenChooser:
                         break
                 accepted_ids.append(accepted)
 
-            accepted_ids = torch.tensor(accepted_ids, device=input_ids.device, dtype=input_ids.dtype)
+            accepted_ids = torch.tensor(
+                accepted_ids, device=input_ids.device, dtype=input_ids.dtype
+            )
             next_ids = next_ids[indices]
             scores = scores[indices]
             indices = torch.arange(B, device=input_ids.device) * S
@@ -296,7 +316,9 @@ class HeterogeneousNextTokenChooser:
                 speculative_ids = Greedy()(speculative_scores)
             else:
                 # n-gram
-                speculative_ids = create_n_gram_speculation(input_ids, next_ids, accepted_ids, speculate, verbose)
+                speculative_ids = create_n_gram_speculation(
+                    input_ids, next_ids, accepted_ids, speculate, verbose
+                )
         else:
             speculative_ids = None
 

server/text_generation_server/utils/weights.py

@@ -16,7 +16,7 @@ class Weights:
         dtype,
         process_group,
         aliases: Optional[Dict[str, List[str]]] = None,
-        prefix: Optional[str] = None
+        prefix: Optional[str] = None,
     ):
         routing = {}
         for filename in filenames:
@@ -213,7 +213,8 @@ class Weights:
 
             bits, groupsize = self._get_gptq_params()
             from text_generation_server.utils.layers import HAS_EXLLAMA
-            use_exllama = bits==4  and HAS_EXLLAMA and quantize == "gptq"
+
+            use_exllama = bits == 4 and HAS_EXLLAMA and quantize == "gptq"
             weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
         else:
             w = [self.get_sharded(f"{p}.weight", dim=0) for p in prefixes]
@@ -283,7 +284,7 @@ class Weights:
             if use_exllama:
                 qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
                 scales = self.get_sharded(f"{prefix}.scales", dim=0)
-                g_idx = self.get_sharded(f"{prefix}.g_idx", dim= 0)
+                g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
                 g_idx = g_idx - g_idx[0]
             else:
                 # The triton kernel reorders the scales/zero points instead of the weight/activation.

update_doc.py

@@ -21,14 +21,14 @@ def main():
     block = []
     for line in lines:
         if line.startswith("  -") or line.startswith("      -"):
-            rendered_block = '\n'.join(block)
+            rendered_block = "\n".join(block)
             if header:
                 final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
             else:
                 final_doc += f"```shell\n{rendered_block}\n```\n"
             block = []
             tokens = line.split("<")
-            if len(tokens)>1:
+            if len(tokens) > 1:
                 header = tokens[-1][:-1]
             else:
                 header = line.split("--")[-1]
@@ -36,7 +36,7 @@ def main():
 
         block.append(line)
 
-    rendered_block = '\n'.join(block)
+    rendered_block = "\n".join(block)
     final_doc += f"## {header}\n```shell\n{rendered_block}\n```\n"
     block = []