Black + ruff + T5 w0 quant.

server/custom_kernels/setup.py

@@ -1,15 +1,14 @@
 from setuptools import setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+
 setup(
-    name='custom_kernels',
+    name="custom_kernels",
     ext_modules=[
         CUDAExtension(
             name="custom_kernels.fused_bloom_attention_cuda",
-                sources=['custom_kernels/fused_bloom_attention_cuda.cu'],
+            sources=["custom_kernels/fused_bloom_attention_cuda.cu"],
             extra_compile_args=["-arch=compute_80", "-std=c++17"],
         )
     ],
-    cmdclass={
+    cmdclass={"build_ext": BuildExtension},
-        'build_ext': BuildExtension
-    }
 )

server/text_generation_server/models/custom_modeling/bloom_modeling.py

@@ -37,7 +37,6 @@ from text_generation_server.utils.layers import (
     TensorParallelEmbedding,
     TensorParallelRowLinear,
     TensorParallelHead,
-    FastLinear
 )
 
 CUSTOM_KERNELS_ENABLED = False

server/text_generation_server/models/custom_modeling/flash_neox_modeling.py

@@ -60,12 +60,18 @@ def load_qkv(config, prefix: str, weights, num_heads, head_size, hidden_size):
     weight = weights.get_sharded(f"{prefix}.weight", dim=0)
     bias = weights.get_sharded(f"{prefix}.bias", dim=0)
 
-    weight = weight.view(
+    weight = (
-        num_heads, 3, head_size, hidden_size,
+        weight.view(
-        ).permute(1, 0, 2, 3).reshape(-1, hidden_size)
+            num_heads,
+            3,
+            head_size,
+            hidden_size,
+        )
+        .permute(1, 0, 2, 3)
+        .reshape(-1, hidden_size)
+    )
     bias = bias.view(num_heads, 3, head_size).permute(1, 0, 2).reshape(-1)
 
-
     linear = get_linear(weight, bias, config.quantize)
     if config.use_parallel_residual:
         return linear
@@ -88,12 +94,18 @@ class FlashNeoxAttention(torch.nn.Module):
 
         rotary_ndims = int(self.head_size * rotary_pct)
         self.rotary_emb = PositionRotaryEmbedding(rotary_ndims, base=rotary_emb_base)
-        self.rotary_emb.inv_freq = nn.Parameter(weights.get_tensor(f"{prefix}.rotary_emb.inv_freq"))
+        self.rotary_emb.inv_freq = nn.Parameter(
+            weights.get_tensor(f"{prefix}.rotary_emb.inv_freq")
+        )
         self.softmax_scale = self.head_size ** (-0.5)
 
         self.query_key_value = load_qkv(
-            config, prefix=f"{prefix}.query_key_value", weights=weights,
+            config,
-            num_heads = self.num_heads, head_size = self.head_size, hidden_size = self.hidden_size
+            prefix=f"{prefix}.query_key_value",
+            weights=weights,
+            num_heads=self.num_heads,
+            head_size=self.head_size,
+            hidden_size=self.hidden_size,
         )
         self.dense = load_row(
             config, prefix=f"{prefix}.dense", weights=weights, bias=True

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -3,7 +3,7 @@ import torch.distributed
 
 from torch import nn
 from transformers.activations import ACT2FN
-from typing import Optional, List
+from typing import Optional
 
 # Flash attention imports
 import flash_attn_cuda
@@ -17,8 +17,9 @@ from text_generation_server.utils.layers import (
 )
 
 
-
+def load_multi_mqa(
-def load_multi_mqa(config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size):
+    config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
+):
     if any("c_attn" in k for k in weights.routing.keys()):
         slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
         shape = slice_.get_shape()
@@ -55,30 +56,35 @@ def load_multi_mqa(config, prefix: str, weights, bias: bool, head_size, num_head
         if config.transpose:
             w = [
                 weights.get_sharded(f"{prefix}.q_attn.weight", dim=1).T,
-                weights.get_tensor(f"{prefix}.kv_attn.weight").T
+                weights.get_tensor(f"{prefix}.kv_attn.weight").T,
             ]
             weight = torch.cat(w, dim=0)
         else:
             w = [
                 weights.get_sharded(f"{prefix}.q_attn.weight", dim=0),
-                weights.get_tensor(f"{prefix}.kv_attn.weight")
+                weights.get_tensor(f"{prefix}.kv_attn.weight"),
             ]
             weight = torch.cat(w, dim=1)
 
         if bias:
             b = [
                 weights.get_sharded(f"{prefix}.q_attn.bias", dim=0),
-                weights.get_tensor(f"{prefix}.kv_attn.bias")
+                weights.get_tensor(f"{prefix}.kv_attn.bias"),
             ]
             bias = torch.cat(b, dim=0)
         else:
             bias = None
 
     weight = weight.to(dtype=weights.dtype).to(device=weights.device)
-    assert list(weight.shape) == [(num_heads + 2) * head_size, hidden_size], f"{weight.shape} != {[(num_heads + 2) * head_size, hidden_size]}"
+    assert list(weight.shape) == [
+        (num_heads + 2) * head_size,
+        hidden_size,
+    ], f"{weight.shape} != {[(num_heads + 2) * head_size, hidden_size]}"
     if bias is not None:
         bias = bias.to(dtype=weights.dtype).to(device=weights.device)
-        assert list(bias.shape) == [(num_heads + 2) * head_size], f"{weight.shape} != {[(num_heads + 2) * head_size]}"
+        assert list(bias.shape) == [
+            (num_heads + 2) * head_size
+        ], f"{weight.shape} != {[(num_heads + 2) * head_size]}"
     return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
 
 
@@ -106,7 +112,9 @@ def load_row(config, prefix: str, weights, bias: bool):
         bias = weights.get_tensor(f"{prefix}.bias")
     else:
         bias = None
-    return TensorParallelRowLinear(get_linear(weight, bias, config.quantize), process_group=weights.process_group)
+    return TensorParallelRowLinear(
+        get_linear(weight, bias, config.quantize), process_group=weights.process_group
+    )
 
 
 class FlashMQAttention(torch.nn.Module):
@@ -131,7 +139,7 @@ class FlashMQAttention(torch.nn.Module):
             bias=True,
             head_size=self.head_size,
             hidden_size=hidden_size,
-            num_heads=self.num_heads
+            num_heads=self.num_heads,
         )
         self.c_proj = load_row(
             config, prefix=f"{prefix}.c_proj", weights=weights, bias=True

server/text_generation_server/models/custom_modeling/t5_modeling.py

@@ -109,9 +109,21 @@ class T5DenseActDense(nn.Module):
         self.wi = TensorParallelColumnLinear.load(
             config, prefix=f"{prefix}.wi", weights=weights, bias=False
         )
+
+        ### XXX: T5 models do not handle well both f16 and quantization.
+        ### Overidding specifically this layer for that reason.
+        ### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
+        ### https://github.com/huggingface/transformers/issues/20287
+        _q = config.quantize
+        _dtype = weights.dtype
+        weights.dtype = torch.float32
+        config.quantize = None
+        self.wo_cast = (torch.float32, _dtype)
         self.wo = TensorParallelRowLinear.load(
             config, prefix=f"{prefix}.wo", weights=weights, bias=False
         )
+        weights.dtype = _dtype
+        config.quantize = _q
 
         self.dropout = nn.Dropout(config.dropout_rate)
         self.act = (
@@ -124,7 +136,10 @@ class T5DenseActDense(nn.Module):
         hidden_states = self.wi(hidden_states)
         hidden_states = self.act(hidden_states)
         hidden_states = self.dropout(hidden_states)
+
+        hidden_states = hidden_states.to(dtype=self.wo_cast[0])
         hidden_states = self.wo(hidden_states)
+        hidden_states = hidden_states.to(dtype=self.wo_cast[1])
         return hidden_states
 
 
@@ -137,9 +152,20 @@ class T5DenseGatedActDense(nn.Module):
         self.wi_1 = TensorParallelColumnLinear.load(
             config, prefix=f"{prefix}.wi_1", weights=weights, bias=False
         )
+        ### XXX: T5 models do not handle well both f16 and quantization.
+        ### Overidding specifically this layer for that reason.
+        ### https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/modeling_t5.py#L316
+        ### https://github.com/huggingface/transformers/issues/20287
+        _q = config.quantize
+        _dtype = weights.dtype
+        weights.dtype = torch.float32
+        config.quantize = None
+        self.wo_cast = (torch.float32, _dtype)
         self.wo = TensorParallelRowLinear.load(
             config, prefix=f"{prefix}.wo", weights=weights, bias=False
         )
+        weights.dtype = _dtype
+        config.quantize = _q
 
         self.dropout = nn.Dropout(config.dropout_rate)
         self.act = (
@@ -154,18 +180,9 @@ class T5DenseGatedActDense(nn.Module):
         hidden_states = hidden_gelu * hidden_linear
         hidden_states = self.dropout(hidden_states)
 
-        # TODO Support this again mayber
+        hidden_states = hidden_states.to(dtype=self.wo_cast[0])
-        # To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
-        # See https://github.com/huggingface/transformers/issues/20287
-        # we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
-        # if (
-        #     isinstance(self.wo.weight, torch.Tensor)
-        #     and hidden_states.dtype != self.wo.weight.dtype
-        #     and self.wo.weight.dtype != torch.int8
-        # ):
-        #     hidden_states = hidden_states.to(self.wo.weight.dtype)
-
         hidden_states = self.wo(hidden_states)
+        hidden_states = hidden_states.to(dtype=self.wo_cast[1])
         return hidden_states
 
 

server/text_generation_server/models/gpt_neox.py

@@ -26,7 +26,7 @@ HAS_BITS_AND_BYTES = True
 try:
     import bitsandbytes as bnb
     from bitsandbytes.nn import Int8Params
-except Exception as e:
+except Exception:
     HAS_BITS_AND_BYTES = False
 
 

server/text_generation_server/models/opt.py

@@ -40,8 +40,9 @@ class OPTSharded(CausalLM):
             trust_remote_code=trust_remote_code,
         )
 
-        )
+        config = AutoConfig.from_pretrained(
-        config = AutoConfig.from_pretrained(model_id, revision=revision,
+            model_id,
+            revision=revision,
             trust_remote_code=trust_remote_code,
         )
         config.quantize = quantize

server/text_generation_server/utils/dist.py

@@ -3,6 +3,7 @@ import torch
 
 from datetime import timedelta
 
+
 class FakeBarrier:
     def wait(self):
         pass
@@ -17,7 +18,9 @@ class FakeGroup:
         return FakeBarrier()
 
     def allgather(self, inputs, local_tensor, **kwargs):
-        assert len(inputs[0]) == len(local_tensor) == 1, f"{len(inputs[0])} != {len(local_tensor)} != 1, and the FakeGroup is supposed to join on simple tensors"
+        assert (
+            len(inputs[0]) == len(local_tensor) == 1
+        ), f"{len(inputs[0])} != {len(local_tensor)} != 1, and the FakeGroup is supposed to join on simple tensors"
         for input_ in inputs:
             input_[0].data = local_tensor[0].data
         return FakeBarrier()

server/text_generation_server/utils/hub.py

@@ -10,8 +10,7 @@ from huggingface_hub import HfApi, hf_hub_download
 from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
 from huggingface_hub.utils import (
     LocalEntryNotFoundError,
-    EntryNotFoundError,
+    EntryNotFoundError,  # Import here to ease try/except in other part of the lib
-    RevisionNotFoundError,  # Import here to ease try/except in other part of the lib
 )
 
 WEIGHTS_CACHE_OVERRIDE = os.getenv("WEIGHTS_CACHE_OVERRIDE", None)

server/text_generation_server/utils/layers.py

@@ -2,14 +2,14 @@ import torch
 
 from torch import nn
 from torch.nn import functional as F
-from typing import Optional, List
+from typing import List
 
 HAS_BITS_AND_BYTES = True
 try:
     import bitsandbytes as bnb
     from bitsandbytes.nn import Int8Params
 
-except ImportError as e:
+except ImportError:
     HAS_BITS_AND_BYTES = False
 
 from accelerate import init_empty_weights
@@ -27,13 +27,15 @@ def load_layer_norm(cls, prefix, weights, eps):
     ln.bias = nn.Parameter(bias)
     return ln
 
+
 torch.nn.LayerNorm.load = load_layer_norm
 
 
 class FastLinear(nn.Module):
     def __init__(
         self,
-        weight, bias,
+        weight,
+        bias,
     ) -> None:
         super().__init__()
         self.weight = nn.Parameter(weight)
@@ -56,10 +58,19 @@ class FastLinear(nn.Module):
 
 
 class Linear8bitLt(nn.Module):
-    def __init__(self, weight, bias, has_fp16_weights=True,
+    def __init__(
-                       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 not memory_efficient_backward, "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
+        assert (
+            not memory_efficient_backward
+        ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
         self.state = bnb.MatmulLtState()
         self.index = index
 
@@ -70,7 +81,11 @@ class Linear8bitLt(nn.Module):
         if threshold > 0.0 and not has_fp16_weights:
             self.state.use_pool = True
 
-        self.weight = Int8Params(weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights)
+        self.weight = Int8Params(
+            weight.data,
+            has_fp16_weights=has_fp16_weights,
+            requires_grad=has_fp16_weights,
+        )
         self.weight.cuda(weight.device)
         self.bias = bias
 
@@ -105,7 +120,8 @@ def get_linear(weight, bias, quantize):
         linear = FastLinear(weight, bias)
     elif quantize == "bitsandbytes":
         linear = Linear8bitLt(
-            weight, bias,
+            weight,
+            bias,
             has_fp16_weights=False,
             threshold=6.0,
         )
@@ -114,7 +130,9 @@ def get_linear(weight, bias, quantize):
     elif quantize == "gptq":
         raise NotImplementedError("Soon")
     else:
-        raise NotImplementedError(f"Quantization `{config.quantize}` is not implemented yet.")
+        raise NotImplementedError(
+            f"Quantization `{config.quantize}` is not implemented yet."
+        )
     return linear
 
 
@@ -126,6 +144,7 @@ class SuperLayer(nn.Module):
     def forward(self, x):
         return self.linear.forward(x)
 
+
 class TensorParallelHead(SuperLayer):
     def __init__(self, linear, process_group):
         super().__init__(linear)
@@ -134,12 +153,17 @@ class TensorParallelHead(SuperLayer):
     @staticmethod
     def load(config, prefix: str, weights):
         weight = weights.get_sharded(f"{prefix}.weight", dim=0)
-        return TensorParallelHead(get_linear(weight, bias=None, quantize=config.quantize), process_group = weights.process_group)
+        return TensorParallelHead(
+            get_linear(weight, bias=None, quantize=config.quantize),
+            process_group=weights.process_group,
+        )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         output = super().forward(input)
         # Logits are sharded, so we need to gather them
-        world_output = [torch.empty_like(output) for _ in range(self.process_group.size())]
+        world_output = [
+            torch.empty_like(output) for _ in range(self.process_group.size())
+        ]
         torch.distributed.all_gather(world_output, output, group=self.process_group)
         world_output = torch.cat(world_output, dim=-1)
         return world_output
@@ -181,13 +205,17 @@ class TensorParallelRowLinear(SuperLayer):
             bias = weights.get_tensor(f"{prefix}.bias")
         else:
             bias = None
-        return TensorParallelRowLinear(get_linear(weight, bias, config.quantize), process_group=weights.process_group)
+        return TensorParallelRowLinear(
+            get_linear(weight, bias, config.quantize),
+            process_group=weights.process_group,
+        )
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         out = super().forward(input)
         torch.distributed.all_reduce(out, group=self.process_group)
         return out
 
+
 class TensorParallelEmbedding(nn.Module):
     def __init__(self, prefix: str, weights, reduce=True):
         super().__init__()
@@ -222,6 +250,7 @@ class TensorParallelEmbedding(nn.Module):
             torch.distributed.all_reduce(out, group=self.process_group)
         return out
 
+
 try:
     import dropout_layer_norm
 

server/text_generation_server/utils/weights.py

@@ -1,7 +1,8 @@
 from pathlib import Path
-from typing import Optional, List
+from typing import List
 from safetensors import safe_open
 
+
 class Weights:
     def __init__(self, filenames: List[Path], device, dtype, process_group):
         routing = {}
@@ -26,8 +27,6 @@ class Weights:
 
         return self._handles[filename]
 
-
-
     def get_filename(self, tensor_name: str) -> str:
         filename = self.routing.get(tensor_name, None)
         if filename is None:
@@ -63,7 +62,9 @@ class Weights:
         start = rank * block_size
         stop = (rank + 1) * block_size
 
-        assert size % world_size == 0, f"The choosen size {size} is not compatible with sharding on {world_size} shards"
+        assert (
+            size % world_size == 0
+        ), f"The choosen size {size} is not compatible with sharding on {world_size} shards"
 
         if dim == 0:
             tensor = slice_[start:stop]
@@ -74,5 +75,3 @@ class Weights:
         tensor = tensor.to(dtype=self.dtype)
         tensor = tensor.to(device=self.device)
         return tensor
-
-