feat: Qwen2 (#1608)

See #1584

---------
Co-authored-by: Cheng Kuan Yong Jason <jasoncky96@gmail.com>

integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2.json

+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 2271,
+        "logprob": null,
+        "text": "Test"
+      },
+      {
+        "id": 1681,
+        "logprob": -8.8515625,
+        "text": " request"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 198,
+        "logprob": -2.9023438,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 2,
+        "logprob": -2.9160156,
+        "special": false,
+        "text": "#"
+      },
+      {
+        "id": 4230,
+        "logprob": -3.1035156,
+        "special": false,
+        "text": " Create"
+      },
+      {
+        "id": 264,
+        "logprob": -1.1025391,
+        "special": false,
+        "text": " a"
+      },
+      {
+        "id": 1681,
+        "logprob": -1.6914062,
+        "special": false,
+        "text": " request"
+      },
+      {
+        "id": 198,
+        "logprob": -1.1953125,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 2035,
+        "logprob": -1.3203125,
+        "special": false,
+        "text": "request"
+      },
+      {
+        "id": 284,
+        "logprob": -0.13537598,
+        "special": false,
+        "text": " ="
+      },
+      {
+        "id": 7388,
+        "logprob": -1.2402344,
+        "special": false,
+        "text": " requests"
+      },
+      {
+        "id": 670,
+        "logprob": -0.2775879,
+        "special": false,
+        "text": ".get"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "\n# Create a request\nrequest = requests.get"
+}

integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_all_params.json

+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 2271,
+        "logprob": null,
+        "text": "Test"
+      },
+      {
+        "id": 1681,
+        "logprob": -8.8515625,
+        "text": " request"
+      }
+    ],
+    "seed": 0,
+    "tokens": [
+      {
+        "id": 311,
+        "logprob": -1.4277344,
+        "special": false,
+        "text": " to"
+      },
+      {
+        "id": 279,
+        "logprob": -0.65478516,
+        "special": false,
+        "text": " the"
+      },
+      {
+        "id": 2473,
+        "logprob": -1.8300781,
+        "special": false,
+        "text": " service"
+      },
+      {
+        "id": 382,
+        "logprob": -0.75,
+        "special": false,
+        "text": ".\n\n"
+      },
+      {
+        "id": 286,
+        "logprob": -0.11621094,
+        "special": false,
+        "text": "       "
+      },
+      {
+        "id": 549,
+        "logprob": 0.0,
+        "special": false,
+        "text": " :"
+      },
+      {
+        "id": 689,
+        "logprob": -0.48608398,
+        "special": false,
+        "text": "return"
+      },
+      {
+        "id": 25,
+        "logprob": 0.0,
+        "special": false,
+        "text": ":"
+      },
+      {
+        "id": 5949,
+        "logprob": -0.5756836,
+        "special": false,
+        "text": " Response"
+      },
+      {
+        "id": 504,
+        "logprob": -0.24499512,
+        "special": false,
+        "text": " from"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "Test request to the service.\n\n        :return: Response from"
+}

integration-tests/models/__snapshots__/test_flash_qwen2/test_flash_qwen2_load.json

+[
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 2271,
+          "logprob": null,
+          "text": "Test"
+        },
+        {
+          "id": 1681,
+          "logprob": -8.8515625,
+          "text": " request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 198,
+          "logprob": -2.9023438,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2,
+          "logprob": -2.9140625,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 4230,
+          "logprob": -3.1054688,
+          "special": false,
+          "text": " Create"
+        },
+        {
+          "id": 264,
+          "logprob": -1.0966797,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 1681,
+          "logprob": -1.6914062,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 198,
+          "logprob": -1.1923828,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2035,
+          "logprob": -1.3193359,
+          "special": false,
+          "text": "request"
+        },
+        {
+          "id": 284,
+          "logprob": -0.13586426,
+          "special": false,
+          "text": " ="
+        },
+        {
+          "id": 7388,
+          "logprob": -1.2412109,
+          "special": false,
+          "text": " requests"
+        },
+        {
+          "id": 670,
+          "logprob": -0.2775879,
+          "special": false,
+          "text": ".get"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n# Create a request\nrequest = requests.get"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 2271,
+          "logprob": null,
+          "text": "Test"
+        },
+        {
+          "id": 1681,
+          "logprob": -8.8515625,
+          "text": " request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 198,
+          "logprob": -2.9023438,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2,
+          "logprob": -2.9140625,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 4230,
+          "logprob": -3.1054688,
+          "special": false,
+          "text": " Create"
+        },
+        {
+          "id": 264,
+          "logprob": -1.0966797,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 1681,
+          "logprob": -1.6914062,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 198,
+          "logprob": -1.1923828,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2035,
+          "logprob": -1.3193359,
+          "special": false,
+          "text": "request"
+        },
+        {
+          "id": 284,
+          "logprob": -0.13586426,
+          "special": false,
+          "text": " ="
+        },
+        {
+          "id": 7388,
+          "logprob": -1.2412109,
+          "special": false,
+          "text": " requests"
+        },
+        {
+          "id": 670,
+          "logprob": -0.2775879,
+          "special": false,
+          "text": ".get"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n# Create a request\nrequest = requests.get"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 2271,
+          "logprob": null,
+          "text": "Test"
+        },
+        {
+          "id": 1681,
+          "logprob": -8.8515625,
+          "text": " request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 198,
+          "logprob": -2.9023438,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2,
+          "logprob": -2.9140625,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 4230,
+          "logprob": -3.1054688,
+          "special": false,
+          "text": " Create"
+        },
+        {
+          "id": 264,
+          "logprob": -1.0966797,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 1681,
+          "logprob": -1.6914062,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 198,
+          "logprob": -1.1923828,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2035,
+          "logprob": -1.3193359,
+          "special": false,
+          "text": "request"
+        },
+        {
+          "id": 284,
+          "logprob": -0.13586426,
+          "special": false,
+          "text": " ="
+        },
+        {
+          "id": 7388,
+          "logprob": -1.2412109,
+          "special": false,
+          "text": " requests"
+        },
+        {
+          "id": 670,
+          "logprob": -0.2775879,
+          "special": false,
+          "text": ".get"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n# Create a request\nrequest = requests.get"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 2271,
+          "logprob": null,
+          "text": "Test"
+        },
+        {
+          "id": 1681,
+          "logprob": -8.8515625,
+          "text": " request"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 198,
+          "logprob": -2.9023438,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2,
+          "logprob": -2.9140625,
+          "special": false,
+          "text": "#"
+        },
+        {
+          "id": 4230,
+          "logprob": -3.1054688,
+          "special": false,
+          "text": " Create"
+        },
+        {
+          "id": 264,
+          "logprob": -1.0966797,
+          "special": false,
+          "text": " a"
+        },
+        {
+          "id": 1681,
+          "logprob": -1.6914062,
+          "special": false,
+          "text": " request"
+        },
+        {
+          "id": 198,
+          "logprob": -1.1923828,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 2035,
+          "logprob": -1.3193359,
+          "special": false,
+          "text": "request"
+        },
+        {
+          "id": 284,
+          "logprob": -0.13586426,
+          "special": false,
+          "text": " ="
+        },
+        {
+          "id": 7388,
+          "logprob": -1.2412109,
+          "special": false,
+          "text": " requests"
+        },
+        {
+          "id": 670,
+          "logprob": -0.2775879,
+          "special": false,
+          "text": ".get"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\n# Create a request\nrequest = requests.get"
+  }
+]

integration-tests/models/test_flash_qwen2.py

+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_qwen2_handle(launcher):
+    with launcher("Qwen/Qwen1.5-0.5B") as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_qwen2(flash_qwen2_handle):
+    await flash_qwen2_handle.health(300)
+    return flash_qwen2_handle.client
+
+
+@pytest.mark.asyncio
+async def test_flash_qwen2(flash_qwen2, response_snapshot):
+    response = await flash_qwen2.generate(
+        "Test request", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response.generated_text == "\n# Create a request\nrequest = requests.get"
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_qwen2_all_params(flash_qwen2, response_snapshot):
+    response = await flash_qwen2.generate(
+        "Test request",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        stop_sequences=["test"],
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+async def test_flash_qwen2_load(flash_qwen2, generate_load, response_snapshot):
+    responses = await generate_load(flash_qwen2, "Test request", max_new_tokens=10, n=4)
+
+    assert len(responses) == 4
+    assert all(
+        [r.generated_text == responses[0].generated_text for r in responses]
+    ), f"{[r.generated_text  for r in responses]}"
+    assert responses[0].generated_text == "\n# Create a request\nrequest = requests.get"
+
+    assert responses == response_snapshot

server/text_generation_server/models/__init__.py

@@ -54,6 +54,9 @@ try:
     from text_generation_server.models.flash_llama import (
         FlashLlama,
     )
+    from text_generation_server.models.flash_qwen2 import (
+        FlashQwen2,
+    )
     from text_generation_server.models.flash_gemma import (
         FlashGemma,
     )
@@ -81,6 +84,7 @@ if FLASH_ATTENTION:
     __all__.append(FlashMistral)
     __all__.append(FlashMixtral)
     __all__.append(FlashPhi)
+    __all__.append(FlashQwen2)
     __all__.append(FlashStarcoder2)
 
 MAMBA_AVAILABLE = True
@@ -339,9 +343,7 @@ def get_model(
                 trust_remote_code=trust_remote_code,
             )
         elif sharded:
-            raise NotImplementedError(
-                FLASH_ATT_ERROR_MESSAGE.format("Sharded Golden Gate")
-            )
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma"))
         else:
             return CausalLM(
                 model_id,
@@ -399,6 +401,17 @@ def get_model(
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mistral"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
 
     if model_type == "mixtral":
         sliding_window = config_dict.get("sliding_window", -1)
@@ -413,6 +426,18 @@ def get_model(
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mixtral"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
     if model_type == "starcoder2":
         sliding_window = config_dict.get("sliding_window", -1)
         if (
@@ -425,6 +450,43 @@ def get_model(
                 dtype=dtype,
                 trust_remote_code=trust_remote_code,
             )
+        elif sharded:
+            raise NotImplementedError(
+                FLASH_ATT_ERROR_MESSAGE.format("Sharded Starcoder2")
+            )
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+
+    if model_type == "qwen2":
+        sliding_window = config_dict.get("sliding_window", -1)
+        if (
+            (sliding_window is None or sliding_window == -1) and FLASH_ATTENTION
+        ) or HAS_FLASH_ATTN_V2_CUDA:
+            return FlashQwen2(
+                model_id,
+                revision,
+                quantize=quantize,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
+        elif sharded:
+            raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
+        else:
+            return CausalLM(
+                model_id,
+                revision,
+                quantize=quantize,
+                use_medusa=use_medusa,
+                dtype=dtype,
+                trust_remote_code=trust_remote_code,
+            )
 
     if model_type == "opt":
         return OPTSharded(

server/text_generation_server/models/causal_lm.py

@@ -486,6 +486,9 @@ class CausalLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+        if use_medusa:
+            raise RuntimeError("Medusa decoding is not enabled for AutoModel")
+
         if torch.cuda.is_available():
             device = torch.device("cuda")
             dtype = torch.float16 if dtype is None else dtype

server/text_generation_server/models/custom_modeling/bloom_modeling.py

@@ -870,7 +870,7 @@ class BloomForCausalLM(BloomPreTrainedModel):
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
         **deprecated_arguments,
-    ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
+    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set

server/text_generation_server/models/custom_modeling/flash_qwen2_modeling.py

+import torch
+import torch.distributed
+
+from torch import nn
+from transformers.activations import ACT2FN
+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,
+)
+
+
+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=True,
+        )
+
+
+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]}"
+
+    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)
+
+    return TensorParallelColumnLinear(
+        get_linear(weight, bias=bias, quantize=config.quantize)
+    )
+
+
+class Qwen2Attention(torch.nn.Module):
+    def __init__(
+        self,
+        prefix: str,
+        config,
+        weights,
+    ):
+        super().__init__()
+        self.max_past = (
+            config.sliding_window if config.sliding_window is not None else -1
+        )
+        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=False,
+        )
+        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,
+        prefill_cache_indices,
+    ):
+        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)
+
+        if prefill_cache_indices is not None:
+            kv_to_cache = kv[prefill_cache_indices]
+        else:
+            kv_to_cache = kv
+
+        paged_attention.reshape_and_cache(
+            kv_to_cache[:, 0], kv_to_cache[:, 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,
+                window_size_left=self.max_past,
+            )
+        # 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))
+
+
+class Qwen2MLP(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])
+
+
+class Qwen2Layer(nn.Module):
+    def __init__(self, layer_id, config, weights):
+        super().__init__()
+        prefix = f"model.layers.{layer_id}"
+        self.self_attn = Qwen2Attention(
+            prefix=f"{prefix}.self_attn", config=config, weights=weights
+        )
+        self.mlp = Qwen2MLP(prefix=f"{prefix}.mlp", config=config, weights=weights)
+        self.input_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = FastRMSNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        block_tables,
+        slots,
+        input_lengths,
+        max_s,
+        prefill_cache_indices,
+    ):
+        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,
+            prefill_cache_indices,
+        )
+
+        # faster post attention rms norm
+        normed_attn_res_output, attn_res = self.post_attention_layernorm(
+            attn_output, res
+        )
+
+        mlp_output = self.mlp(normed_attn_res_output)
+
+        return mlp_output, attn_res
+
+
+class Qwen2Model(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(
+            [
+                Qwen2Layer(
+                    layer_id,
+                    config,
+                    weights,
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = FastRMSNorm.load(
+            prefix="model.norm", weights=weights, eps=config.rms_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,
+        true_max_s: int,
+        prefill_cache_indices: Optional[torch.Tensor],
+    ) -> 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, true_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,
+                prefill_cache_indices,
+            )
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states
+
+
+class Qwen2ForCausalLM(torch.nn.Module):
+    def __init__(self, config, weights):
+        super().__init__()
+
+        self.model = Qwen2Model(config, weights)
+        self.lm_head = SpeculativeHead.load(
+            config,
+            prefix="lm_head",
+            weights=weights,
+        )
+        self.max_past = config.sliding_window
+        self.max_past_tensor = (
+            torch.tensor(config.sliding_window, device=weights.device)
+            if self.max_past is not None
+            else None
+        )
+
+    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,
+        prefill_cache_indices: Optional[torch.Tensor] = None,
+        lm_head_indices: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        true_max_s = max_s
+        if prefill_cache_indices is not None:
+            # Slots also need to be sliced as it has the same size as the whole kv tensor
+            slots = slots[prefill_cache_indices]
+        elif self.max_past is not None:
+            # Clamp in decode mode as paged attention requires clamped values whereas the flash attention
+            # kernel requires the true values
+            input_lengths = torch.clamp(input_lengths, max=self.max_past_tensor)
+
+        hidden_states = self.model(
+            input_ids,
+            position_ids,
+            cu_seqlen_prefill,
+            kv_cache,
+            block_tables,
+            slots,
+            input_lengths,
+            max_s,
+            true_max_s,
+            prefill_cache_indices,
+        )
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits = self.lm_head(hidden_states)
+        return logits

server/text_generation_server/models/custom_modeling/neox_modeling.py

@@ -721,7 +721,7 @@ class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
         )
 
         hidden_states = outputs[0]
-        lm_logits = self.embed_out(hidden_states)
+        lm_logits, speculative_logits = self.embed_out(hidden_states)
 
         lm_loss = None
         if labels is not None:
@@ -739,12 +739,15 @@ class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
             output = (lm_logits,) + outputs[1:]
             return ((lm_loss,) + output) if lm_loss is not None else output
 
-        return CausalLMOutputWithPast(
-            loss=lm_loss,
-            logits=lm_logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
+        return (
+            CausalLMOutputWithPast(
+                loss=lm_loss,
+                logits=lm_logits,
+                past_key_values=outputs.past_key_values,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            ),
+            speculative_logits,
         )
 
     def prepare_inputs_for_generation(

server/text_generation_server/models/custom_modeling/opt_modeling.py

@@ -792,16 +792,19 @@ class OPTForCausalLM(OPTPreTrainedModel):
             return_dict=return_dict,
         )
 
-        logits = self.lm_head(outputs[0]).contiguous()
+        logits, speculative_logits = self.lm_head(outputs)
 
         loss = None
 
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
+        return (
+            CausalLMOutputWithPast(
+                loss=loss,
+                logits=logits,
+                past_key_values=outputs.past_key_values,
+                hidden_states=outputs.hidden_states,
+                attentions=outputs.attentions,
+            ),
+            speculative_logits,
         )
 
     def prepare_inputs_for_generation(

server/text_generation_server/models/flash_mistral.py

@@ -315,7 +315,7 @@ class BaseFlashMistral(FlashCausalLM):
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:
-            raise NotImplementedError("FlashLlama is only available on GPU")
+            raise NotImplementedError("FlashMistral is only available on GPU")
 
         tokenizer = LlamaTokenizerFast.from_pretrained(
             model_id,

server/text_generation_server/models/flash_qwen2.py

+import math
+
+import torch
+import torch.distributed
+
+from opentelemetry import trace
+from transformers.models.qwen2 import Qwen2Tokenizer
+from typing import Optional
+
+from text_generation_server.models.cache_manager import BLOCK_SIZE
+from text_generation_server.models.flash_mistral import (
+    BaseFlashMistral,
+    set_sliding_window,
+)
+from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
+    Qwen2ForCausalLM,
+)
+from transformers.models.qwen2 import Qwen2Config
+from text_generation_server.utils import (
+    initialize_torch_distributed,
+    weight_files,
+    Weights,
+)
+
+tracer = trace.get_tracer(__name__)
+
+
+class FlashQwen2(BaseFlashMistral):
+    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.float16 if dtype is None else dtype
+        else:
+            raise NotImplementedError("FlashQwen2 is only available on GPU")
+
+        tokenizer = Qwen2Tokenizer.from_pretrained(
+            model_id,
+            revision=revision,
+            padding_side="left",
+            truncation_side="left",
+            trust_remote_code=trust_remote_code,
+        )
+
+        config = Qwen2Config.from_pretrained(
+            model_id, revision=revision, trust_remote_code=trust_remote_code
+        )
+        config.quantize = quantize
+        config.use_medusa = use_medusa
+
+        # Set context windows
+        if config.sliding_window is not None:
+            set_sliding_window(
+                config.sliding_window, math.ceil(config.sliding_window / BLOCK_SIZE)
+            )
+
+        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 = Qwen2ForCausalLM(config, weights)
+
+        self.cuda_graphs = {}
+
+        torch.distributed.barrier(group=self.process_group)
+        super(BaseFlashMistral, 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,
+            sliding_window=config.sliding_window,
+        )

server/text_generation_server/models/flash_starcoder2.py

@@ -38,7 +38,7 @@ class FlashStarcoder2(BaseFlashMistral):
             device = torch.device(f"cuda:{rank}")
             dtype = torch.float16 if dtype is None else dtype
         else:
-            raise NotImplementedError("FlashLlama is only available on GPU")
+            raise NotImplementedError("FlashStarcoder2 is only available on GPU")
 
         tokenizer = GPT2TokenizerFast.from_pretrained(
             model_id,

server/text_generation_server/models/galactica.py

@@ -167,6 +167,7 @@ class GalacticaSharded(CausalLM):
         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,
     ):
@@ -194,6 +195,7 @@ class GalacticaSharded(CausalLM):
         )
         config.quantize = quantize
         tokenizer.pad_token_id = config.pad_token_id
+        config.use_medusa = use_medusa
 
         torch.distributed.barrier(group=self.process_group)
         filenames = weight_files(model_id, revision=revision, extension=".safetensors")
@@ -229,10 +231,10 @@ class GalacticaSharded(CausalLM):
     def forward(
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
     ):
-        outputs = self.model.forward(
+        outputs, speculative_logits = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,
             past_key_values=past_key_values,
             use_cache=True,
         )
-        return outputs.logits, outputs.past_key_values
+        return outputs.logits, speculative_logits, outputs.past_key_values

server/text_generation_server/models/gpt_neox.py

@@ -24,6 +24,7 @@ class GPTNeoxSharded(CausalLM):
         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,
     ):
@@ -50,6 +51,7 @@ class GPTNeoxSharded(CausalLM):
             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")
@@ -75,7 +77,7 @@ class GPTNeoxSharded(CausalLM):
     def forward(
         self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
     ):
-        outputs = self.model.forward(
+        outputs, speculative_logits = self.model.forward(
             input_ids=input_ids,
             attention_mask=attention_mask,
             position_ids=position_ids,
@@ -84,4 +86,4 @@ class GPTNeoxSharded(CausalLM):
         )
 
         logits = outputs.logits
-        return logits, outputs.past_key_values
+        return logits, speculative_logits, outputs.past_key_values

server/text_generation_server/models/rw.py

@@ -12,9 +12,13 @@ class RW(CausalLM):
         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,
     ):
+        if use_medusa:
+            raise RuntimeError("Medusa decoding is not enabled for AutoModel")
+
         if torch.cuda.is_available():
             device = torch.device("cuda")
             dtype = torch.float16 if dtype is None else dtype

server/text_generation_server/models/seq2seq_lm.py

@@ -536,6 +536,9 @@ class Seq2SeqLM(Model):
         dtype: Optional[torch.dtype] = None,
         trust_remote_code: bool = False,
     ):
+        if use_medusa:
+            raise RuntimeError("Medusa decoding is not enabled for AutoModel")
+
         if torch.cuda.is_available():
             device = torch.device("cuda")
             dtype = torch.float16 if dtype is None else dtype