feat: format code (#1070)

server/text_generation_server/models/seq2seq_lm.py

@@ -712,9 +712,11 @@ class Seq2SeqLM(Model):
                     # Decode all tokens
                     output_text, _, _ = self.decode_token(
                         all_decoder_input_ids,
-                        prefix_offset=len(all_decoder_input_ids) - decoder_input_length - 1,
+                        prefix_offset=len(all_decoder_input_ids)
+                        - decoder_input_length
+                        - 1,
                         read_offset=len(all_decoder_input_ids) - decoder_input_length,
-                        skip_special_tokens=True
+                        skip_special_tokens=True,
                     )
 
                     # Get seed

server/text_generation_server/server.py

@@ -16,6 +16,7 @@ from text_generation_server.pb import generate_pb2_grpc, generate_pb2
 from text_generation_server.tracing import UDSOpenTelemetryAioServerInterceptor
 from text_generation_server.models.idefics_causal_lm import IdeficsCausalLMBatch
 
+
 class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
     def __init__(self, model: Model, cache: Cache, server_urls: List[str]):
         self.cache = cache
@@ -26,7 +27,6 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
             # Force inference mode for the lifetime of TextGenerationService
             self._inference_mode_raii_guard = torch._C._InferenceMode(True)
 
-
     async def Info(self, request, context):
         return self.model.info
 
@@ -55,9 +55,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         return generate_pb2.FilterBatchResponse(batch=filtered_batch.to_pb())
 
     async def Warmup(self, request, context):
-        if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
+        if (
+            self.model.batch_type == IdeficsCausalLMBatch
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
-                request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.dtype,
+                self.model.device,
             )
         else:
             batch = self.model.batch_type.from_pb(
@@ -70,9 +76,15 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
         )
 
     async def Prefill(self, request, context):
-        if self.model.batch_type == IdeficsCausalLMBatch: #Hack, i would rather use kwargs in the `from_pb` call
+        if (
+            self.model.batch_type == IdeficsCausalLMBatch
+        ):  # Hack, i would rather use kwargs in the `from_pb` call
             batch = self.model.batch_type.from_pb(
-                request.batch, self.model.tokenizer, self.model.processor, self.model.dtype, self.model.device
+                request.batch,
+                self.model.tokenizer,
+                self.model.processor,
+                self.model.dtype,
+                self.model.device,
             )
         else:
             batch = self.model.batch_type.from_pb(

server/text_generation_server/utils/awq/quantize/qmodule.py

@@ -42,7 +42,9 @@ class WQLinear(nn.Module):
 
     @torch.no_grad()
     def forward(self, x):
-        out_shape = x.shape[:-1] + (self.out_features, )
-        out = awq_inference_engine.gemm_forward_cuda(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8)
+        out_shape = x.shape[:-1] + (self.out_features,)
+        out = awq_inference_engine.gemm_forward_cuda(
+            x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8
+        )
         out = out + self.bias if self.bias is not None else out
         return out.reshape(out_shape)

server/text_generation_server/utils/gptq/quantize.py

@@ -578,7 +578,9 @@ def get_c4_new(nsamples, seed, seqlen, model_id, trust_remote_code):
     return trainloader, valenc
 
 
-def get_loaders(name, nsamples=128, seed=0, seqlen=2048, model_id="", trust_remote_code=False):
+def get_loaders(
+    name, nsamples=128, seed=0, seqlen=2048, model_id="", trust_remote_code=False
+):
     if "wikitext2" in name:
         return get_wikitext2(nsamples, seed, seqlen, model_id, trust_remote_code)
     if "ptb" in name:
@@ -927,7 +929,7 @@ def quantize(
         seed=seed,
         model_id=model_id,
         seqlen=model.seqlen,
-        trust_remote_code=trust_remote_code
+        trust_remote_code=trust_remote_code,
     )
 
     tick = time.time()

server/text_generation_server/utils/layers.py

@@ -38,6 +38,7 @@ if os.getenv("DISABLE_EXLLAMA") == "True":
 elif CAN_EXLLAMA:
     try:
         from text_generation_server.utils.gptq.exllama import Ex4bitLinear
+
         HAS_EXLLAMA = True
     except ImportError:
         pass
@@ -47,6 +48,7 @@ from typing import Optional
 HAS_EETQ = False
 try:
     from EETQ import quant_weights, w8_a16_gemm
+
     HAS_EETQ = True
 except ImportError:
     pass
@@ -74,12 +76,18 @@ def load_layer_norm_no_bias(cls, prefix, weights, eps):
     ln.bias = None
     return ln
 
+
 @classmethod
 def load_conv2d(cls, prefix, weights, in_channels, out_channels, kernel_size, stride):
     weight = weights.get_tensor(f"{prefix}.weight")
     bias = weights.get_tensor(f"{prefix}.bias")
     with init_empty_weights():
-        conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
+        conv2d = cls(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+        )
 
     conv2d.weight = nn.Parameter(weight)
     conv2d.bias = nn.Parameter(bias)
@@ -87,10 +95,17 @@ 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):
+def load_conv2d_no_bias(
+    cls, prefix, weights, in_channels, out_channels, kernel_size, stride
+):
     weight = weights.get_tensor(f"{prefix}.weight")
     with init_empty_weights():
-        conv2d = cls(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride)
+        conv2d = cls(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+        )
 
     conv2d.weight = nn.Parameter(weight)
     conv2d.bias = None
@@ -215,7 +230,10 @@ class Linear4bit(nn.Module):
     def __init__(self, weight, bias, quant_type):
         super().__init__()
         self.weight = Params4bit(
-            weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
+            weight.data,
+            requires_grad=False,
+            compress_statistics=True,
+            quant_type=quant_type,
         )
         self.compute_dtype = None
         self.weight.cuda(weight.device)
@@ -246,7 +264,10 @@ class Linear4bit(nn.Module):
 
 @lru_cache(1)
 def warn_deprecate_bnb():
-    logger.warning("Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce")
+    logger.warning(
+        "Bitsandbytes 8bit is deprecated, using `eetq` is a drop-in replacement, and has much better performnce"
+    )
+
 
 def get_linear(weight, bias, quantize):
     if quantize is None:
@@ -255,7 +276,9 @@ def get_linear(weight, bias, quantize):
         if HAS_EETQ:
             linear = EETQLinear(weight, bias)
         else:
-            raise ImportError("Please install EETQ from https://github.com/NetEase-FuXi/EETQ")
+            raise ImportError(
+                "Please install EETQ from https://github.com/NetEase-FuXi/EETQ"
+            )
     elif quantize == "bitsandbytes":
         warn_deprecate_bnb()
         linear = Linear8bitLt(
@@ -305,7 +328,14 @@ def get_linear(weight, bias, quantize):
             raise NotImplementedError(
                 f"The passed weight is not `awq` compatible, loader needs to be updated."
             )
-        linear = WQLinear(w_bit=bits, group_size=groupsize, qweight=qweight, qzeros=qzeros, scales=scales, bias=bias is not None)
+        linear = WQLinear(
+            w_bit=bits,
+            group_size=groupsize,
+            qweight=qweight,
+            qzeros=qzeros,
+            scales=scales,
+            bias=bias is not None,
+        )
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear
@@ -392,9 +422,7 @@ class TensorParallelColumnLinear(SuperLayer):
     @classmethod
     def load_qkv(cls, config, prefix: str, weights, bias: bool):
         """Specific method when the QKV was joined after the fact"""
-        weight = weights.get_weights_col_packed_qkv(
-            prefix, quantize=config.quantize
-        )
+        weight = weights.get_weights_col_packed_qkv(prefix, quantize=config.quantize)
         if bias:
             raise NotImplementedError("packed_qkv only implemented for baichuan")
         else:
@@ -530,14 +558,16 @@ try:
 
     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 = {"type": os.environ["ROPE_SCALING"], "factor": float(os.environ["ROPE_FACTOR"])}
+            rope_scaling = {
+                "type": os.environ["ROPE_SCALING"],
+                "factor": float(os.environ["ROPE_FACTOR"]),
+            }
             return rope_scaling
         return getattr(config, "rope_scaling", None)
 
@@ -563,9 +593,17 @@ try:
                 if rope_scaling["type"] == "linear":
                     pass
                 elif rope_scaling["type"] == "dynamic":
-                    return DynamicPositionRotaryEmbedding(dim=dim, max_position_embeddings=config.max_position_embeddings, base=base, device=inv_freq.device, scaling_factor=scaling_factor)
+                    return DynamicPositionRotaryEmbedding(
+                        dim=dim,
+                        max_position_embeddings=config.max_position_embeddings,
+                        base=base,
+                        device=inv_freq.device,
+                        scaling_factor=scaling_factor,
+                    )
                 else:
-                    raise NotImplementedError(f"rope scaling type {rope_scaling['type']} is not implemented or invalid")
+                    raise NotImplementedError(
+                        f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
+                    )
             return cls(inv_freq, scaling_factor)
 
         @classmethod
@@ -583,9 +621,17 @@ try:
                 if rope_scaling["type"] == "linear":
                     pass
                 elif rope_scaling["type"] == "dynamic":
-                    return DynamicPositionRotaryEmbedding(dim=2*inv_freq.shape[0], max_position_embeddings=config.max_position_embeddings, base=10000.0, device=inv_freq.device, scaling_factor=scaling_factor)
+                    return DynamicPositionRotaryEmbedding(
+                        dim=2 * inv_freq.shape[0],
+                        max_position_embeddings=config.max_position_embeddings,
+                        base=10000.0,
+                        device=inv_freq.device,
+                        scaling_factor=scaling_factor,
+                    )
                 else:
-                    raise NotImplementedError(f"rope scaling type {rope_scaling['type']} is not implemented or invalid")
+                    raise NotImplementedError(
+                        f"rope scaling type {rope_scaling['type']} is not implemented or invalid"
+                    )
             return cls(inv_freq, scaling_factor)
 
         def _update_cos_sin_cache(self, dtype, device, seqlen):
@@ -645,8 +691,13 @@ try:
                 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.dim / (self.dim - 2))
-                    self.inv_freq = _create_inv_freq(self.dim, newbase, self.inv_freq.device)
+                    newbase = self.base * (
+                        (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
+                    )
                 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
@@ -656,6 +707,5 @@ try:
                 self._cos_cached = torch.cos(freqs).to(dtype)
                 self._sin_cached = torch.sin(freqs).to(dtype)
 
-
 except ImportError:
     pass

server/text_generation_server/utils/peft.py

@@ -6,6 +6,7 @@ import torch
 from transformers import AutoTokenizer
 from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
 
+
 def download_and_unload_peft(model_id, revision, trust_remote_code):
     torch_dtype = torch.float16
 
@@ -41,6 +42,3 @@ def download_and_unload_peft(model_id, revision, 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/weights.py

@@ -62,7 +62,7 @@ class Weights:
     def get_shape(self, tensor_name: str):
         return self._get_slice(tensor_name).get_shape()
 
-    def get_tensor(self, tensor_name: str, to_device = True):
+    def get_tensor(self, tensor_name: str, to_device=True):
         filename, tensor_name = self.get_filename(tensor_name)
         f = self._get_handle(filename)
         tensor = f.get_tensor(tensor_name)
@@ -110,7 +110,6 @@ class Weights:
         ), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
         return self.get_partial_sharded(tensor_name, dim)
 
-
     def _get_qweight(self, name: str):
         slice_ = self._get_slice(name)
         total_size = slice_.get_shape()[1]
@@ -119,14 +118,16 @@ class Weights:
         world_size = self.process_group.size()
         rank = self.process_group.rank()
 
-        assert single_size % world_size == 0, f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
+        assert (
+            single_size % world_size == 0
+        ), f"Prepacked quantized qkv cannot be sharded across {world_size} shards"
         block_size = single_size // world_size
         start = rank * block_size
         stop = (rank + 1) * block_size
         q = slice_[:, start:stop]
-        k = slice_[:, start+single_size:stop+single_size]
-        v = slice_[:, start+2*single_size:stop+2*single_size]
-        weight = torch.cat([q,k,v], dim=1)
+        k = slice_[:, start + single_size : stop + single_size]
+        v = slice_[:, start + 2 * single_size : stop + 2 * single_size]
+        weight = torch.cat([q, k, v], dim=1)
         weight = weight.to(device=self.device)
         return weight
 
@@ -161,14 +162,16 @@ class Weights:
             world_size = self.process_group.size()
             rank = self.process_group.rank()
 
-            assert single_size % world_size == 0, f"Prepacked qkv cannot be sharded across {world_size} shards"
+            assert (
+                single_size % world_size == 0
+            ), f"Prepacked qkv cannot be sharded across {world_size} shards"
             block_size = single_size // world_size
             start = rank * block_size
             stop = (rank + 1) * block_size
             q = slice_[start:stop]
-            k = slice_[start+single_size:stop+single_size]
-            v = slice_[start+2*single_size:stop+2*single_size]
-            weight = torch.cat([q,k,v], dim=0)
+            k = slice_[start + single_size : stop + single_size]
+            v = slice_[start + 2 * single_size : stop + 2 * single_size]
+            weight = torch.cat([q, k, v], dim=0)
             weight = weight.to(device=self.device)
             weight = weight.to(dtype=self.dtype)
         return weight