Add Qwen3

Signed-off-by: yuanwu <yuan.wu@intel.com>

Dockerfile_gaudi

@@ -122,5 +122,5 @@ ENV OMPI_MCA_btl_vader_single_copy_mechanism NONE
 COPY backends/gaudi/tgi-entrypoint.sh /tgi-entrypoint.sh
 RUN chmod +x /tgi-entrypoint.sh
 
-ENTRYPOINT ["/tgi-entrypoint.sh"]
+#ENTRYPOINT ["/tgi-entrypoint.sh"]
-CMD ["--json-output"]
+#CMD ["--json-output"]

backends/gaudi/server/text_generation_server/cli.py

@@ -57,7 +57,7 @@ def serve(
         ), "MASTER_PORT must be set when sharded is True"
 
     # Remove default handler
-    logger.remove()
+    # logger.remove()
     logger.add(
         sys.stdout,
         format="{message}",
@@ -193,7 +193,7 @@ def download_weights(
     merge_lora: bool = False,
 ):
     # Remove default handler
-    logger.remove()
+    # logger.remove()
     logger.add(
         sys.stdout,
         format="{message}",

backends/gaudi/server/text_generation_server/layers/tensor_parallel.py

@@ -155,6 +155,7 @@ class TensorParallelColumnLinear(SuperLayer):
 
     @classmethod
     def load_multi(cls, config, prefixes: List[str], weights, bias: bool, dim: int):
+        print(f"bias: {bias}")
         if config.quantize == "exl2":
             linears = []
             for prefix in prefixes:

backends/gaudi/server/text_generation_server/models/__init__.py

@@ -109,6 +109,9 @@ try:
     from text_generation_server.models.custom_modeling.flash_qwen2_modeling import (
         Qwen2ForCausalLM,
     )
+    from text_generation_server.models.custom_modeling.flash_qwen3_modeling import (
+        Qwen3ForCausalLM,
+    )
     from text_generation_server.models.custom_modeling.flash_mistral_modeling import (
         FlashMistralForCausalLM,
     )
@@ -293,6 +296,12 @@ class ModelType(enum.Enum):
         "name": "Qwen 2.5 VL",
         "url": "https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e",
     }
+    QWEN3 = {
+        "type": "qwen3",
+        "name": "Qwen 3",
+        "url": "https://huggingface.co/collections/Qwen/qwen3-67dd247413f0e2e4f653967f",
+    }
+
     GALACTICA = {
         "type": "galactica",
         "name": "Galactica",
@@ -785,6 +794,18 @@ def get_model(
                 config_class=Qwen2_5_VLConfig,
                 processor_class=Qwen2_5_VLProcessor,
             )
+        elif model_type == QWEN3:
+            return FlashCausalLM(
+                model_id=model_id,
+                model_class=Qwen3ForCausalLM,
+                revision=revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
+                trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
+            )
         elif model_type == MLLAMA:
             return FlashMllamaCausalLM(
                 model_id=model_id,

backends/gaudi/server/text_generation_server/models/custom_modeling/flash_qwen3_modeling.py

@@ -0,0 +1,349 @@
+# 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.
+
+from typing import Optional, Tuple, List
+
+import torch
+from torch import nn
+from text_generation_server.layers.attention import (
+    paged_attention,
+    attention,
+    Seqlen,
+    HPUPagedAttentionMetadata,
+)
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
+from text_generation_server.layers import (
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    TensorParallelColumnLinear,
+    SpeculativeHead,
+)
+
+
+from text_generation_server.layers.layernorm import (
+    FastRMSNorm,
+)
+from .flash_qwen2_modeling import Qwen2MLP
+from text_generation_server.layers.rotary import PositionRotaryEmbedding
+
+
+class Qwen3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config, prefix, weights, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(
+            config, "head_dim", config.hidden_size // config.num_attention_heads
+        )
+        self.num_key_value_groups = (
+            config.num_attention_heads // config.num_key_value_heads
+        )
+        self.num_heads = config.num_attention_heads
+        self.attention_dropout = config.attention_dropout
+        self.softmax_scale = self.head_dim**-0.5
+        self.rotary_emb = PositionRotaryEmbedding.static(
+            config=config,
+            dim=self.head_dim,
+            base=config.rope_theta,
+            device=weights.device,
+        )
+
+        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 = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=False,
+        )
+
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
+
+        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)
+
+        self.max_past = (
+            config.sliding_window if config.sliding_window is not None else -1
+        )
+
+        self.q_norm = FastRMSNorm.load(
+            prefix=f"{prefix}.q_norm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.k_norm = FastRMSNorm.load(
+            prefix=f"{prefix}.k_norm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.sliding_window = config.sliding_window
+        if not (
+            self.config.use_sliding_window
+            and getattr(self.config, "sliding_window", None) is not None
+            and self.layer_idx >= self.config.max_window_layers
+        ):
+            self.sliding_window = None
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        slots,
+        seqlen,
+        hpu_attention_meta,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+        qkv = self.query_key_value(hidden_states)
+        query_states, key_states, value_states = qkv.split(
+            [
+                self.head_dim * self.num_heads,
+                self.head_dim * self.num_key_value_heads,
+                self.head_dim * self.num_key_value_heads,
+            ],
+            dim=1,
+        )
+
+        query_states, _ = self.q_norm(query_states.view(hidden_shape))
+        key_states, _ = self.k_norm(key_states.view(hidden_shape))
+        value_states = value_states.view(hidden_shape)
+        self.rotary_emb(query_states, key_states, cos, sin)
+
+        kv_cache.store(
+            key=key_states,
+            value=value_states,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
+
+        # Prefill
+        if cu_seqlen_prefill is not None:
+            # sdpa
+            attn_output = attention(
+                query=query_states,
+                key=key_states,
+                value=value_states,
+                kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
+                seqlen=seqlen,
+                softmax_scale=self.softmax_scale,
+                window_size_left=self.max_past,
+            )
+        # Decode
+        else:
+            attn_output = paged_attention(
+                query_states,
+                kv_cache,
+                self.kv_head_mapping,
+                self.softmax_scale,
+                seqlen,
+                kv_scales=self.kv_scales,
+                hpu_attention_meta=hpu_attention_meta,
+            )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        return self.o_proj(attn_output)
+
+
+class Qwen3DecoderLayer(nn.Module):
+    def __init__(self, config, prefix, weights, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Qwen3Attention(
+            config=config,
+            prefix=f"{prefix}.self_attn",
+            weights=weights,
+            layer_idx=layer_idx,
+        )
+        self.mlp = Qwen2MLP(config=config, prefix=f"{prefix}.mlp", 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,
+        slots,
+        seqlen,
+        hpu_attention_meta,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states, _ = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states = self.self_attn(
+            hidden_states,
+            cos,
+            sin,
+            cu_seqlen_prefill,
+            kv_cache,
+            slots,
+            seqlen,
+            hpu_attention_meta,
+        )
+
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states, _ = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Qwen3Model(nn.Module):
+    def __init__(self, config, prefix: str, weights):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.layers = nn.ModuleList(
+            [
+                Qwen3DecoderLayer(
+                    config=config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                    weights=weights,
+                    layer_idx=layer_idx,
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = FastRMSNorm.load(
+            prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        position_ids: torch.Tensor,
+        cu_seqlen_prefill: Optional[torch.Tensor],
+        kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
+        slots: torch.Tensor,
+        seqlen: Seqlen,
+        hpu_attention_meta: Optional[HPUPagedAttentionMetadata],
+    ) -> torch.Tensor:
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(
+            position_ids,
+        )
+
+        residual = None
+        for i, decoder_layer in enumerate(self.layers):
+            hidden_states = decoder_layer(
+                hidden_states,
+                residual,
+                cos,
+                sin,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                slots,
+                seqlen,
+                hpu_attention_meta,
+            )
+
+        hidden_states, _ = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        return hidden_states
+
+
+class Qwen3ForCausalLM(nn.Module):
+
+    def __init__(self, prefix: str, config, weights):
+        super().__init__()
+        self.model = Qwen3Model(config=config, prefix="model", weights=weights)
+        self.vocab_size = config.vocab_size
+        if config.tie_word_embeddings:
+            suffix = "model.embed_tokens"
+        else:
+            suffix = "lm_head"
+
+        self.lm_head = SpeculativeHead.load(
+            config,
+            prefix=f"{prefix}.{suffix}" if prefix else suffix,
+            weights=weights,
+        )
+
+        self.embed_tokens = TensorParallelEmbedding(
+            prefix=f"{prefix}.embed_tokens" if prefix else "model.embed_tokens",
+            weights=weights,
+        )
+
+    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]],
+        slots: torch.Tensor,
+        seqlen: Seqlen,
+        hpu_attention_meta: Optional[HPUPagedAttentionMetadata],
+        lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        inputs_embeds = self.embed_tokens(input_ids)
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        hidden_states = self.model(
+            inputs_embeds,
+            position_ids,
+            cu_seqlen_prefill,
+            kv_cache,
+            slots,
+            seqlen,
+            hpu_attention_meta,
+        )
+
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits = self.lm_head(hidden_states)
+
+        return logits

backends/gaudi/server/text_generation_server/models/flash_causal_lm.py

@@ -1391,6 +1391,7 @@ class FlashCausalLM(Model):
             aliases=aliases,
             weights_loader=weights_loader,
         )
+        print(f"weights: {weights}")
 
         prefix = None
         model = model_class(prefix, config, weights)

backends/gaudi/tgi-entrypoint.sh

@@ -10,8 +10,8 @@ fi
 # Check if ATTENTION environment variable is set to paged
 if [[ "$ATTENTION" == "paged" ]]; then
   # Check if Llama-4 is in the command line arguments
-  if [[ "$*" == *"Llama-4"* ]]; then
+  if [[ "$*" == *"Llama-4"* || "$*" == *"Qwen3"* ]]; then
-    echo 'ATTENTION=paged and Llama-4 detected'
+    echo 'ATTENTION=paged and Llama-4 or Qwen3 detected'
     pip install git+https://github.com/huggingface/transformers.git@29338949
   fi
 fi