feat: cohere (#1660)

server/Makefile

@@ -29,5 +29,5 @@ run-dev:
 	SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
 
 export-requirements:
-	poetry export -o requirements_cuda.txt --extras bnb --without-hashes
+	poetry export -o requirements_cuda.txt --without-hashes
 	poetry export -o requirements_rocm.txt --without-hashes

server/pyproject.toml

@@ -17,7 +17,7 @@ grpc-interceptor = "^0.15.0"
 typer = "^0.6.1"
 accelerate = { version = "^0.28.0", optional = true }
 bitsandbytes = { version = "^0.43.0", optional = true }
-safetensors = "^0.4.1"
+safetensors = "^0.4"
 loguru = "^0.6.0"
 opentelemetry-api = "^1.15.0"
 opentelemetry-exporter-otlp = "^1.15.0"
@@ -26,11 +26,11 @@ hf-transfer = "^0.1.2"
 sentencepiece = "^0.1.97"
 tokenizers = "^0.15.0"
 huggingface-hub = "^0.19.3"
-transformers = "^4.38.2"
+transformers = "^4.38"
 einops = "^0.6.1"
 texttable = { version = "^1.6.7", optional = true }
 datasets = { version = "^2.14.0", optional = true }
-peft = { version = "^0.9.0", optional = true }
+peft = { version = "^0.9", optional = true }
 torch = { version = "^2.1.1", optional = true }
 scipy = "^1.11.1"
 pillow = "^10.0.0"

server/requirements_common.txt

-backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
-certifi==2023.11.17 ; python_version >= "3.9" and python_version < "3.13"
-charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
-click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
-colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_platform == "win32" or platform_system == "Windows")
-deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
-einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.61.0 ; python_version >= "3.9" and python_version < "3.13"
-grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.59.3 ; python_version >= "3.9" and python_version < "3.13"
-hf-transfer==0.1.4 ; python_version >= "3.9" and python_version < "3.13"
-huggingface-hub==0.16.4 ; python_version >= "3.9" and python_version < "3.13"
-idna==3.4 ; python_version >= "3.9" and python_version < "3.13"
-loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
-numpy==1.26.2 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-api==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp-proto-grpc==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp-proto-http==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-exporter-otlp==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-instrumentation-grpc==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
-opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
-pillow==10.1.0 ; python_version >= "3.9" and python_version < "3.13"
-protobuf==4.25.1 ; python_version >= "3.9" and python_version < "3.13"
-pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2023.10.3 ; python_version >= "3.9" and python_version < "3.13"
-requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
-scipy==1.11.4 ; python_version >= "3.9" and python_version < "3.13"
-sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==69.0.2 ; python_version >= "3.9" and python_version < "3.13"
-tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.1 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.33.3 ; python_version >= "3.9" and python_version < "3.13"
-typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-typing-extensions==4.8.0 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.1.0 ; python_version >= "3.9" and python_version < "3.13"
-win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
-wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"

server/requirements_cuda.txt

 backoff==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
-bitsandbytes==0.41.3.post2 ; python_version >= "3.9" and python_version < "3.13"
 certifi==2024.2.2 ; python_version >= "3.9" and python_version < "3.13"
 charset-normalizer==3.3.2 ; python_version >= "3.9" and python_version < "3.13"
 click==8.1.7 ; python_version >= "3.9" and python_version < "3.13"
@@ -7,13 +6,13 @@ colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_p
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
+googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-hf-transfer==0.1.5 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -27,21 +26,21 @@ opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_versi
 opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
+packaging==24.0 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
 regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
+safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==69.1.0 ; python_version >= "3.9" and python_version < "3.13"
+setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
 tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.37.1 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.39.0 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.0 ; python_version >= "3.9" and python_version < "3.13"
+typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13"
+urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
 wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"

server/requirements_rocm.txt

@@ -6,13 +6,13 @@ colorama==0.4.6 ; python_version >= "3.9" and python_version < "3.13" and (sys_p
 deprecated==1.2.14 ; python_version >= "3.9" and python_version < "3.13"
 einops==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 filelock==3.13.1 ; python_version >= "3.9" and python_version < "3.13"
-fsspec==2023.10.0 ; python_version >= "3.9" and python_version < "3.13"
-googleapis-common-protos==1.62.0 ; python_version >= "3.9" and python_version < "3.13"
+fsspec==2024.2.0 ; python_version >= "3.9" and python_version < "3.13"
+googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-reflection==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio-status==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.60.1 ; python_version >= "3.9" and python_version < "3.13"
-hf-transfer==0.1.5 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-reflection==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio-status==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.62.1 ; python_version >= "3.9" and python_version < "3.13"
+hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.6 ; python_version >= "3.9" and python_version < "3.13"
 loguru==0.6.0 ; python_version >= "3.9" and python_version < "3.13"
@@ -26,21 +26,21 @@ opentelemetry-instrumentation==0.36b0 ; python_version >= "3.9" and python_versi
 opentelemetry-proto==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-sdk==1.15.0 ; python_version >= "3.9" and python_version < "3.13"
 opentelemetry-semantic-conventions==0.36b0 ; python_version >= "3.9" and python_version < "3.13"
-packaging==23.2 ; python_version >= "3.9" and python_version < "3.13"
+packaging==24.0 ; python_version >= "3.9" and python_version < "3.13"
 pillow==10.2.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
 regex==2023.12.25 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
-safetensors==0.3.3 ; python_version >= "3.9" and python_version < "3.13"
+safetensors==0.4.2 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.12.0 ; python_version >= "3.9" and python_version < "3.13"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
-setuptools==69.1.0 ; python_version >= "3.9" and python_version < "3.13"
+setuptools==69.2.0 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.15.2 ; python_version >= "3.9" and python_version < "3.13"
 tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.37.1 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.39.0 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
-typing-extensions==4.9.0 ; python_version >= "3.9" and python_version < "3.13"
-urllib3==2.2.0 ; python_version >= "3.9" and python_version < "3.13"
+typing-extensions==4.10.0 ; python_version >= "3.9" and python_version < "3.13"
+urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"
 win32-setctime==1.1.0 ; python_version >= "3.9" and python_version < "3.13" and sys_platform == "win32"
 wrapt==1.16.0 ; python_version >= "3.9" and python_version < "3.13"

server/text_generation_server/models/__init__.py

@@ -57,6 +57,9 @@ try:
     from text_generation_server.models.flash_qwen2 import (
         FlashQwen2,
     )
+    from text_generation_server.models.flash_cohere import (
+        FlashCohere,
+    )
     from text_generation_server.models.flash_gemma import (
         FlashGemma,
     )
@@ -86,6 +89,8 @@ if FLASH_ATTENTION:
     __all__.append(FlashPhi)
     __all__.append(FlashQwen2)
     __all__.append(FlashStarcoder2)
+    __all__.append(FlashGemma)
+    __all__.append(FlashCohere)
 
 MAMBA_AVAILABLE = True
 try:
@@ -354,6 +359,28 @@ def get_model(
                 trust_remote_code=trust_remote_code,
             )
 
+    if model_type == "cohere":
+        if FLASH_ATTENTION:
+            return FlashCohere(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Cohere"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
     if model_type in ["RefinedWeb", "RefinedWebModel", "falcon"]:
         if sharded:
             if FLASH_ATTENTION:

server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py

+# coding=utf-8
+# Copyright 2024 Cohere team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+import torch.distributed
+
+from torch import nn
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from typing import Optional, List, Tuple
+
+from text_generation_server.utils import paged_attention, flash_attn
+from text_generation_server.utils.layers import (
+    TensorParallelRowLinear,
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    PositionRotaryEmbedding,
+    SpeculativeHead,
+    get_linear,
+    FastRMSNorm,
+)
+
+
+class CohereConfig(PretrainedConfig):
+    def __init__(
+        self,
+        vocab_size=256000,
+        hidden_size=8192,
+        intermediate_size=22528,
+        num_hidden_layers=40,
+        num_attention_heads=64,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=8192,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=5,
+        eos_token_id=255001,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        attention_bias=False,
+        attention_dropout=0.0,
+        logit_scale=1.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.logit_scale = logit_scale
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+def load_attention(config, prefix, weights):
+    if config.num_attention_heads != config.num_key_value_heads:
+        return _load_gqa(config, prefix, weights)
+    else:
+        return TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=config.attention_bias,
+        )
+
+
+def _load_gqa(config, prefix: str, weights):
+    assert config.hidden_size % config.num_attention_heads == 0
+    assert config.num_attention_heads % weights.process_group.size() == 0
+
+    weight = weights.get_multi_weights_col(
+        prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+        quantize=config.quantize,
+        dim=0,
+    )
+
+    if config.quantize not in ["gptq", "awq"]:
+        weight = weight.to(dtype=weights.dtype).to(device=weights.device)
+
+        head_size = config.hidden_size // config.num_attention_heads
+        num_heads = config.num_attention_heads // weights.process_group.size()
+        num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
+        assert list(weight.shape) == [
+            (num_heads + 2 * num_key_value_heads) * head_size,
+            config.hidden_size,
+        ], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
+
+    if config.attention_bias:
+        w = [
+            weights.get_sharded(f"{p}.bias", dim=0)
+            for p in [f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"]
+        ]
+        bias = torch.cat(w, dim=0).to(dtype=weights.dtype).to(device=weights.device)
+    else:
+        bias = None
+
+    return TensorParallelColumnLinear(
+        get_linear(weight, bias=bias, quantize=config.quantize)
+    )
+
+
+class FlashCohereAttention(torch.nn.Module):
+    def __init__(
+        self,
+        prefix: str,
+        config,
+        weights,
+    ):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.num_heads
+
+        self.rotary_emb = PositionRotaryEmbedding.static(
+            config=config,
+            dim=self.head_size,
+            base=config.rope_theta,
+            device=weights.device,
+        )
+
+        self.softmax_scale = self.head_size**-0.5
+
+        if self.num_heads % weights.process_group.size() != 0:
+            raise ValueError(
+                f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
+                f"and `num_shards`: {weights.process_group.size()}"
+            )
+        self.num_heads = self.num_heads // weights.process_group.size()
+        self.num_key_value_heads = (
+            config.num_key_value_heads // weights.process_group.size()
+        )
+
+        self.query_key_value = load_attention(config, prefix, weights)
+
+        self.o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=config.attention_bias,
+        )
+        self.num_groups = self.num_heads // self.num_key_value_heads
+        self.kv_head_mapping = torch.arange(
+            0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
+        ).repeat_interleave(self.num_groups)
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
+        max_s,
+    ):
+        qkv = self.query_key_value(hidden_states)
+        query, kv = qkv.split(
+            [
+                self.head_size * self.num_heads,
+                2 * self.head_size * self.num_key_value_heads,
+            ],
+            dim=1,
+        )
+        query = query.view(-1, self.num_heads, self.head_size)
+        kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
+
+        self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
+
+        paged_attention.reshape_and_cache(
+            kv[:, 0], kv[:, 1], kv_cache[0], kv_cache[1], slots
+        )
+
+        # output tensor
+        attn_output = torch.empty_like(query)
+
+        # Prefill
+        if cu_seqlen_prefill is not None:
+            # flash attention
+            flash_attn.attention(
+                query,
+                torch.select(kv, dim=1, index=0),
+                torch.select(kv, dim=1, index=1),
+                attn_output,
+                cu_seqlen_prefill,
+                max_s,
+                self.softmax_scale,
+            )
+        # Decode
+        else:
+            paged_attention.attention(
+                attn_output,
+                query,
+                kv_cache[0],
+                kv_cache[1],
+                self.kv_head_mapping,
+                self.softmax_scale,
+                block_tables,
+                input_lengths,
+                max_s,
+            )
+
+        return self.o_proj(
+            attn_output.view(-1, self.num_heads * self.head_size), reduce=False
+        )
+
+
+class CohereMLP(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+        act = config.hidden_act
+        self.act = (
+            ACT2FN[act]
+            if "gelu" not in act
+            else lambda x: torch.nn.functional.gelu(
+                x,
+                approximate=(
+                    "tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none"
+                ),
+            )
+        )
+        # Fuse gate and up proj
+        self.gate_up_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
+            weights=weights,
+            dim=0,
+            bias=False,
+        )
+        self.down_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.down_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.intermediate_size = (
+            config.intermediate_size // weights.process_group.size()
+        )
+
+    def forward(self, hidden_states):
+        gate_up_states = self.gate_up_proj(hidden_states)
+        gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
+        return self.down_proj(
+            self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], reduce=False
+        )
+
+
+class FlashCohereLayer(nn.Module):
+    def __init__(self, layer_id, config, weights):
+        super().__init__()
+        prefix = f"model.layers.{layer_id}"
+        self.self_attn = FlashCohereAttention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
+        self.mlp = CohereMLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+
+        self.input_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm",
+            weights=weights,
+            eps=config.layer_norm_eps,
+        )
+        self.process_group = weights.process_group
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
+        max_s,
+    ):
+        normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
+
+        # Self Attention
+        attn_output = self.self_attn(
+            normed_hidden_states,
+            cos,
+            sin,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
+            max_s,
+        )
+
+        mlp_output = self.mlp(normed_hidden_states)
+        output = attn_output + mlp_output
+
+        if self.process_group.size() > 1:
+            torch.distributed.all_reduce(output, group=self.process_group)
+
+        return output, res
+
+
+class FlashCohereModel(torch.nn.Module):
+    def __init__(self, config, weights):
+        super().__init__()
+
+        process_group = weights.process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+        self.embed_tokens = TensorParallelEmbedding(
+            prefix="model.embed_tokens", weights=weights
+        )
+        self.layers = nn.ModuleList(
+            [
+                FlashCohereLayer(
+                    layer_id,
+                    config,
+                    weights,
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = FastRMSNorm.load(
+            prefix="model.norm", weights=weights, eps=config.layer_norm_eps
+        )
+
+        self.gradient_checkpointing = False
+
+        self.head_size = self.layers[0].self_attn.head_size
+        self.num_heads = self.layers[0].self_attn.num_heads
+        self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+
+        # Get rotary cos and sin for this forward
+        # Avoid to index in each layer
+        cos, sin = self.layers[0].self_attn.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_seqlen_prefill,
+                kv_cache[i],
+                block_tables,
+                slots,
+                input_lengths,
+                max_s,
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states
+
+
+class FlashCohereForCausalLM(torch.nn.Module):
+    def __init__(self, config, weights):
+        super().__init__()
+
+        self.model = FlashCohereModel(config, weights)
+        try:
+            self.lm_head = SpeculativeHead.load(
+                config,
+                prefix="lm_head",
+                weights=weights,
+            )
+        except RuntimeError:
+            self.lm_head = SpeculativeHead.load(
+                config,
+                prefix="model.embed_tokens",
+                weights=weights,
+            )
+        self.logit_scale = config.logit_scale
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        block_tables: torch.Tensor,
+        slots: torch.Tensor,
+        input_lengths: torch.Tensor,
+        max_s: int,
+        lm_head_indices: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        hidden_states = self.model(
+            input_ids,
+            position_ids,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
+            max_s,
+        )
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits, speculative_logits = self.lm_head(hidden_states)
+        logits *= self.logit_scale
+        if speculative_logits is not None:
+            speculative_logits *= self.logit_scale
+        return logits, speculative_logits

server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py

@@ -20,16 +20,11 @@
 
 import torch
 import torch.distributed
-import os
-from shutil import copyfile
 
 from torch import nn
 from transformers.activations import ACT2FN
 from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
-from tokenizers import processors
-from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
-from transformers.utils import logging
 
 from text_generation_server.utils import paged_attention, flash_attn
 from text_generation_server.utils.layers import (
@@ -42,162 +37,6 @@ from text_generation_server.utils.layers import (
     FastRMSNorm,
 )
 
-GemmaTokenizer = None
-
-logger = logging.get_logger(__name__)
-VOCAB_FILES_NAMES = {
-    "vocab_file": "tokenizer.model",
-    "tokenizer_file": "tokenizer.json",
-}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
-    },
-    "tokenizer_file": {
-        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
-    },
-}
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
-
-# fmt: off
-DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your \
-answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
- that your responses are socially unbiased and positive in nature.
-
-If a question does not make any sense, or is not factually coherent, explain why instead of answering something not \
-correct. If you don't know the answer to a question, please don't share false information."""
-# fmt: on
-
-
-class GemmaTokenizerFast(PreTrainedTokenizerFast):
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    slow_tokenizer_class = GemmaTokenizer
-    padding_side = "left"
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file=None,
-        tokenizer_file=None,
-        clean_up_tokenization_spaces=False,
-        unk_token="<unk>",
-        bos_token="<bos>",
-        eos_token="<eos>",
-        pad_token="<pad>",
-        add_bos_token=True,
-        add_eos_token=False,
-        use_default_system_prompt=False,
-        **kwargs,
-    ):
-        super().__init__(
-            vocab_file=vocab_file,
-            tokenizer_file=tokenizer_file,
-            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            use_default_system_prompt=use_default_system_prompt,
-            **kwargs,
-        )
-        self._add_bos_token = add_bos_token
-        self._add_eos_token = add_eos_token
-        self.update_post_processor()
-        self.use_default_system_prompt = use_default_system_prompt
-        self.vocab_file = vocab_file
-
-    @property
-    def can_save_slow_tokenizer(self) -> bool:
-        return os.path.isfile(self.vocab_file) if self.vocab_file else False
-
-    def update_post_processor(self):
-        """
-        Updates the underlying post processor with the current `bos_token` and `eos_token`.
-        """
-        bos = self.bos_token
-        bos_token_id = self.bos_token_id
-        if bos is None and self.add_bos_token:
-            raise ValueError("add_bos_token = True but bos_token = None")
-
-        eos = self.eos_token
-        eos_token_id = self.eos_token_id
-        if eos is None and self.add_eos_token:
-            raise ValueError("add_eos_token = True but eos_token = None")
-
-        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
-        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
-
-        special_tokens = []
-        if self.add_bos_token:
-            special_tokens.append((bos, bos_token_id))
-        if self.add_eos_token:
-            special_tokens.append((eos, eos_token_id))
-        self._tokenizer.post_processor = processors.TemplateProcessing(
-            single=single, pair=pair, special_tokens=special_tokens
-        )
-
-    @property
-    def add_eos_token(self):
-        return self._add_eos_token
-
-    @property
-    def add_bos_token(self):
-        return self._add_bos_token
-
-    @add_eos_token.setter
-    def add_eos_token(self, value):
-        self._add_eos_token = value
-        self.update_post_processor()
-
-    @add_bos_token.setter
-    def add_bos_token(self, value):
-        self._add_bos_token = value
-        self.update_post_processor()
-
-    def save_vocabulary(
-        self, save_directory: str, filename_prefix: Optional[str] = None
-    ) -> Tuple[str]:
-        if not self.can_save_slow_tokenizer:
-            raise ValueError(
-                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
-                "tokenizer."
-            )
-
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        out_vocab_file = os.path.join(
-            save_directory,
-            (filename_prefix + "-" if filename_prefix else "")
-            + VOCAB_FILES_NAMES["vocab_file"],
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    @property
-    def default_chat_template(self):
-        raise NotImplementedError
-
-    # TODO ArthurZ let's rely on the template processor instead, refactor all fast tokenizers
-    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
 
 class GemmaConfig(PretrainedConfig):
     def __init__(

server/text_generation_server/models/flash_cohere.py

+import torch
+import torch.distributed
+
+from opentelemetry import trace
+from typing import Optional
+from transformers.models.llama import LlamaTokenizerFast
+
+from text_generation_server.models import FlashCausalLM
+from text_generation_server.models.custom_modeling.flash_cohere_modeling import (
+    FlashCohereForCausalLM,
+    CohereConfig,
+)
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class FlashCohere(FlashCausalLM):
+    def __init__(
+        self,
+        model_id: str,
+        revision: Optional[str] = None,
+        quantize: Optional[str] = None,
+        use_medusa: Optional[str] = None,
+        dtype: Optional[torch.dtype] = None,
+        trust_remote_code: bool = False,
+    ):
+        self.process_group, rank, world_size = initialize_torch_distributed()
+        if torch.cuda.is_available():
+            device = torch.device(f"cuda:{rank}")
+            dtype = torch.bfloat16 if dtype is None else dtype
+        else:
+            raise NotImplementedError("FlashCohere is only available on GPU")
+
+        tokenizer = LlamaTokenizerFast.from_pretrained(
+            model_id,
+            revision=revision,
+            padding_side="left",
+            truncation_side="left",
+            trust_remote_code=trust_remote_code,
+            use_fast=True,
+            from_slow=False,
+        )
+
+        config = CohereConfig.from_pretrained(
+            model_id, revision=revision, trust_remote_code=trust_remote_code
+        )
+        config.quantize = quantize
+        config.use_medusa = use_medusa
+
+        torch.distributed.barrier(group=self.process_group)
+
+        filenames = weight_files(model_id, revision=revision, extension=".safetensors")
+        weights = Weights(filenames, device, dtype, process_group=self.process_group)
+        if config.quantize in ["gptq", "awq"]:
+            weights._set_gptq_params(model_id, revision)
+
+        model = FlashCohereForCausalLM(config, weights)
+
+        torch.distributed.barrier(group=self.process_group)
+        super(FlashCohere, self).__init__(
+            model=model,
+            tokenizer=tokenizer,
+            num_layers=len(model.model.layers),
+            num_kv_heads=model.model.num_key_value_heads,
+            head_size=model.model.head_size,
+            dtype=dtype,
+            device=device,
+            rank=rank,
+            world_size=world_size,
+        )

server/text_generation_server/models/flash_gemma.py

@@ -3,10 +3,10 @@ import torch.distributed
 
 from opentelemetry import trace
 from typing import Optional
+from transformers.models.gemma import GemmaTokenizerFast
 
 from text_generation_server.models import FlashCausalLM
 from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
-    GemmaTokenizerFast,
     FlashGemmaForCausalLM,
     GemmaConfig,
 )