Merge 93e62e73c8 into bd1bdebb47

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

@@ -67,6 +67,10 @@ try:
     from text_generation_server.models.custom_modeling.flash_gemma2_modeling import (
         FlashGemma2ForCausalLM,
     )
+    from text_generation_server.models.custom_modeling.flash_gemma3_modeling import (
+        Gemma3ForConditionalGeneration,
+        FlashGemma3ForCausalLM,
+    )
     from text_generation_server.models.custom_modeling.flash_dbrx_modeling import (
         FlashDbrxForCausalLM,
         DbrxConfig,
@@ -220,6 +224,16 @@ class ModelType(enum.Enum):
         "name": "Gemma2",
         "url": "https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315",
     }
+    GEMMA3 = {
+        "type": "gemma3",
+        "name": "Gemma3",
+        "url": "https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d",
+    }
+    GEMMA3_TEXT = {
+        "type": "gemma3_text",
+        "name": "Gemma3 Text",
+        "url": "https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d",
+    }
     COHERE = {
         "type": "cohere",
         "name": "Cohere",
@@ -630,6 +644,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
@@ -675,6 +690,34 @@ def get_model(
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,
             )
+        elif model_type == GEMMA3:
+            return FlashVlmCausalLM(
+                model_id=model_id,
+                model_class=Gemma3ForConditionalGeneration,
+                revision=revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
+                default_dtype=torch.bfloat16,
+                trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
+                support_chunking=False,
+            )
+        elif model_type == GEMMA3_TEXT:
+            return FlashCausalLM(
+                model_id=model_id,
+                model_class=FlashGemma3ForCausalLM,
+                revision=revision,
+                quantize=quantize,
+                speculator=speculator,
+                dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
+                # Works better for these models
+                default_dtype=torch.bfloat16,
+                trust_remote_code=trust_remote_code,
+                lora_adapter_ids=lora_adapter_ids,
+            )
         elif model_type == COHERE:
             return FlashCausalLM(
                 model_id=model_id,
@@ -864,6 +907,7 @@ def get_model(
                 quantize=quantize,
                 speculator=speculator,
                 dtype=dtype,
+                kv_cache_dtype=kv_cache_dtype,
                 default_dtype=torch.bfloat16,
                 trust_remote_code=trust_remote_code,
                 lora_adapter_ids=lora_adapter_ids,

backends/gaudi/server/text_generation_server/models/custom_modeling/flash_gemma3_modeling.py

@@ -0,0 +1,733 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc. team. All rights reserved.
+#
+# 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 typing import Optional, List, Tuple
+import copy
+
+from text_generation_server.layers import (
+    TensorParallelColumnLinear,
+    TensorParallelEmbedding,
+    TensorParallelRowLinear,
+    get_linear,
+    #
+    SpeculativeHead,
+    TensorParallelMultiAdapterLinear,
+    TensorParallelAdapterRowLinear,
+)
+
+import torch
+
+
+from text_generation_server.models.custom_modeling.vlm import (
+    load_text_model,
+    load_vision_model,
+)
+
+
+from text_generation_server.layers.attention.kv_cache import get_kv_scales
+from text_generation_server.layers.rotary import PositionRotaryEmbedding
+from text_generation_server.layers.layernorm import (
+    FastRMSNorm,
+)
+from text_generation_server.utils.weights import UnquantizedWeight
+from transformers.activations import ACT2FN
+from text_generation_server.layers.attention import (
+    paged_attention,
+    attention,
+    Seqlen,
+    set_block_mapping,
+    HPUPagedAttentionMetadata,
+)
+import habana_frameworks.torch as htorch
+
+ATTENTION_TYPE_GLOBAL = "global"
+ATTENTION_TYPE_LOCAL = "local_sliding"
+
+
+class Gemma3FastRMSNorm(FastRMSNorm):
+    @classmethod
+    def load(cls, prefix: str, weights, eps=1e-6):
+        dtype = weights.dtype
+        weights.dtype = torch.float32
+        weight = weights.get_tensor(f"{prefix}.weight") + 1
+        weights.dtype = dtype
+        new = cls(weight, eps)
+        new.dtype = dtype
+        return new
+
+    # perform the multiplication in full precision and downcast after
+    def forward(self, hidden_states, residual=None):
+        if residual is not None:
+            hidden_states += residual
+        residual = hidden_states
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        hidden_states = hidden_states * self.weight
+        return hidden_states.to(self.dtype), residual
+
+
+def load_attention(config, prefix: str, 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=False,
+        )
+
+
+def _load_gqa(config, prefix: str, weights):
+    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"],
+        dim=0,
+    )
+
+    if isinstance(weight, UnquantizedWeight):
+        weight.weight = weight.weight.to(dtype=weights.dtype).to(device=weights.device)
+
+        head_size = config.head_dim
+        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.weight.shape) == [
+            (num_heads + 2 * num_key_value_heads) * head_size,
+            config.hidden_size,
+        ], f"{list(weight.weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
+
+    return TensorParallelColumnLinear(get_linear(weight, bias=None))
+
+
+class FlashGemma3Attention(torch.nn.Module):
+    def __init__(
+        self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
+    ):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.head_size = config.head_dim
+        self.causal = causal
+        if is_sliding:
+            self.window_size = config.sliding_window
+            # TODO: remove this hack to support local sliding window
+            config = copy.deepcopy(config)
+            config.rope_scaling = dict(rope_type="default")
+            self.rotary_emb = PositionRotaryEmbedding.static(
+                config=config,
+                dim=config.head_dim,
+                base=config.rope_local_base_freq,
+                device=weights.device,
+            )
+        else:
+            self.window_size = -1
+            self.rotary_emb = PositionRotaryEmbedding.static(
+                config=config,
+                dim=config.head_dim,
+                base=config.rope_theta,
+                device=weights.device,
+            )
+
+        self.softmax_scale = (
+            config.query_pre_attn_scalar**-0.5
+            if config.query_pre_attn_scalar is not None
+            else None
+        )
+        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.softcap = None  # config.attn_logit_softcapping
+
+        query_key_value = load_attention(config, prefix, weights)
+        self.query_key_value = TensorParallelMultiAdapterLinear.load(
+            query_key_value,
+            layer_id,
+            ["q_proj", "k_proj", "v_proj"],
+            sizes=[
+                self.head_size * config.num_attention_heads,
+                self.head_size * config.num_key_value_heads,
+                self.head_size * config.num_key_value_heads,
+            ],
+            process_group=weights.process_group,
+        )
+        self.kv_scales = get_kv_scales(weights, f"{prefix}")
+
+        o_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.o_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.o_proj = TensorParallelAdapterRowLinear.load(
+            o_proj,
+            layer_id,
+            "o_proj",
+            process_group=weights.process_group,
+        )
+
+        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.q_norm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.q_norm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.k_norm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.k_norm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.enable_gqa = self.num_heads != self.num_key_value_heads
+
+    def forward(
+        self,
+        hidden_states,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        slots,
+        seqlen,
+        adapter_data,
+        hpu_attention_meta,
+    ):
+
+        qkv = self.query_key_value(hidden_states, adapter_data)
+        query, kv = qkv.split(
+            [
+                self.head_size * self.num_heads,
+                2 * self.head_size * self.num_key_value_heads,
+            ],
+            dim=1,
+        )
+
+        kv = kv.view(-1, 2, self.num_key_value_heads * self.head_size)
+        key = kv[:, 0]
+        value = kv[:, 1]
+
+        query = query.reshape(-1, self.head_size)
+        key = key.reshape(-1, self.head_size)
+
+        query, _ = self.q_norm(query.contiguous())
+        key, _ = self.k_norm(key.contiguous())
+
+        query = query.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_key_value_heads, self.head_size)
+        value = value.view(-1, self.num_key_value_heads, self.head_size)
+
+        self.rotary_emb(query, key, cos, sin)
+
+        kv_cache.store(
+            key=key,
+            value=value,
+            slots=slots,
+            kv_scales=self.kv_scales,
+        )
+        # Prefill
+        if cu_seqlen_prefill is not None:
+            # sdpa
+            attn_output = attention(
+                query=query,
+                key=key,
+                value=value,
+                kv_cache=kv_cache,
+                kv_scales=self.kv_scales,
+                seqlen=seqlen,
+                softmax_scale=self.softmax_scale,
+                window_size_left=self.window_size,
+                softcap=self.softcap,
+            )
+        # Decode
+        else:
+            attn_output = paged_attention(
+                query,
+                kv_cache,
+                self.kv_head_mapping,
+                self.softmax_scale,
+                seqlen,
+                softcap=self.softcap,
+                kv_scales=self.kv_scales,
+                hpu_attention_meta=hpu_attention_meta,
+            )
+
+        return self.o_proj(
+            attn_output.view(-1, self.num_heads * self.head_size), adapter_data
+        )
+
+
+class Gemma3MLP(nn.Module):
+    def __init__(self, prefix, config, weights, layer_id):
+        super().__init__()
+        act = config.hidden_activation
+        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
+        gate_up_proj = TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
+            weights=weights,
+            dim=0,
+            bias=False,
+        )
+        self.gate_up_proj = TensorParallelMultiAdapterLinear.load(
+            gate_up_proj,
+            layer_id,
+            ["gate_proj", "up_proj"],
+            sizes=[
+                config.intermediate_size,
+                config.intermediate_size,
+            ],
+            process_group=weights.process_group,
+        )
+
+        down_proj = TensorParallelRowLinear.load(
+            config,
+            prefix=f"{prefix}.down_proj",
+            weights=weights,
+            bias=False,
+        )
+        self.down_proj = TensorParallelAdapterRowLinear.load(
+            down_proj,
+            layer_id,
+            "down_proj",
+            process_group=weights.process_group,
+        )
+
+        self.intermediate_size = (
+            config.intermediate_size // weights.process_group.size()
+        )
+
+    def forward(self, hidden_states, adapter_data):
+        gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
+        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], adapter_data
+        )
+
+
+class FlashGemma3Layer(nn.Module):
+    def __init__(
+        self, prefix: str, config, weights, layer_id, causal: bool, is_sliding: bool
+    ):
+        super().__init__()
+        self.self_attn = FlashGemma3Attention(
+            prefix=f"{prefix}.self_attn",
+            config=config,
+            weights=weights,
+            layer_id=layer_id,
+            causal=causal,
+            is_sliding=is_sliding,
+        )
+        self.mlp = Gemma3MLP(
+            prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id
+        )
+
+        self.input_layernorm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
+        )
+        self.post_attention_layernorm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.post_attention_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.pre_feedforward_layernorm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.pre_feedforward_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+        self.post_feedforward_layernorm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.post_feedforward_layernorm",
+            weights=weights,
+            eps=config.rms_norm_eps,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+        cos,
+        sin,
+        cu_seqlen_prefill,
+        kv_cache,
+        slots,
+        seqlen,
+        adapter_data,
+        hpu_attention_meta,
+    ):
+        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,
+            slots,
+            seqlen,
+            adapter_data,
+            hpu_attention_meta,
+        )
+
+        # faster post attention rms norm
+        normed_attn_res_output, _ = self.post_attention_layernorm(attn_output)
+        normed_attn_res_output = normed_attn_res_output + res
+        res = normed_attn_res_output
+
+        pre_normed, _ = self.pre_feedforward_layernorm(normed_attn_res_output)
+        mlp_output = self.mlp(pre_normed, adapter_data)
+        post_hidden_states, _ = self.post_feedforward_layernorm(mlp_output)
+
+        return post_hidden_states, normed_attn_res_output
+
+
+class FlashGemma3Model(torch.nn.Module):
+    def __init__(self, prefix: str, config, weights, causal: bool):
+        super().__init__()
+
+        process_group = weights.process_group
+        self.tp_rank = process_group.rank()
+        self.tp_world_size = process_group.size()
+
+        self.layers = nn.ModuleList(
+            [
+                FlashGemma3Layer(
+                    prefix=f"{prefix}.layers.{layer_id}",
+                    config=config,
+                    weights=weights,
+                    layer_id=layer_id,
+                    causal=causal,
+                    is_sliding=bool((layer_id + 1) % config.sliding_window_pattern),
+                )
+                for layer_id in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.norm", weights=weights, eps=config.rms_norm_eps
+        )
+
+        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,
+        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,
+        adapter_data: Optional[torch.Tensor],
+        hpu_attention_meta: Optional[HPUPagedAttentionMetadata],
+    ) -> torch.Tensor:
+        if hpu_attention_meta is not None:
+            hpu_attention_meta = set_block_mapping(
+                hpu_attention_meta, inputs_embeds.shape[0]
+            )
+        hidden_states = inputs_embeds
+
+        residual = None
+        lazy_mode = htorch.utils.internal.is_lazy()
+        if lazy_mode:
+            htorch.core.mark_step()
+
+        # Get rotary cos and sin for this forward
+        # Avoid to index in each layer
+
+        residual = None
+        for i, layer in enumerate(self.layers):
+            # Get rotary cos and sin for this forward
+            # Avoid to index in each layer
+            cos, sin = layer.self_attn.rotary_emb.get_cos_sin(position_ids)
+            hidden_states, residual = layer(
+                hidden_states,
+                residual,
+                cos,
+                sin,
+                cu_seqlen_prefill,
+                kv_cache[i],
+                slots,
+                seqlen,
+                adapter_data,
+                hpu_attention_meta,
+            )
+            if lazy_mode:
+                htorch.core.mark_step()
+
+        hidden_states, _ = self.norm(hidden_states, residual)
+
+        return hidden_states
+
+
+class FlashGemma3ForCausalLM(torch.nn.Module):
+    def __init__(self, prefix: str, config, weights, *, causal: bool = True):
+        super().__init__()
+
+        embed_norm = config.hidden_size**0.5
+        if not prefix:
+            prefix = "model"
+        else:
+            prefix = f"{prefix}.model"
+
+        self.embed_tokens = TensorParallelEmbedding(
+            prefix=f"{prefix}.embed_tokens", weights=weights
+        )
+        self.embed_tokens.weight *= embed_norm
+
+        self.model = FlashGemma3Model(
+            prefix=prefix, config=config, weights=weights, causal=causal
+        )
+        self.lm_head = SpeculativeHead.load(
+            prefix=(
+                f"{prefix}.embed_tokens"
+                if config.tie_word_embeddings
+                else f"{prefix}.lm_head"
+            ),
+            config=config,
+            weights=weights,
+        )
+        # self.softcap = config.attn_logit_softcapping
+        # assert isinstance(self.softcap, float)
+        self.softcap = 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]],
+        slots: torch.Tensor,
+        seqlen: Seqlen,
+        hpu_attention_meta: Optional[HPUPagedAttentionMetadata],
+        lm_head_indices: Optional[torch.Tensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        input_embeds = self.embed_tokens(input_ids)
+
+        hidden_states = self.model(
+            input_embeds,
+            position_ids,
+            cu_seqlen_prefill,
+            kv_cache,
+            slots,
+            seqlen,
+            adapter_data,
+            hpu_attention_meta,
+        )
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits, speculative_logits = self.lm_head(hidden_states)
+
+        return logits, speculative_logits
+
+
+class Gemma3MultimodalInputProjection(torch.nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+
+        self.mm_input_projection_weight = weights.get_tensor(
+            "multi_modal_projector.mm_input_projection_weight"
+        )
+
+        self.mm_soft_emb_norm = Gemma3FastRMSNorm.load(
+            prefix=f"{prefix}.mm_soft_emb_norm",
+            weights=weights,
+            eps=config.vision_config.layer_norm_eps,
+        )
+
+        self.patches_per_image = int(
+            config.vision_config.image_size // config.vision_config.patch_size
+        )
+        self.tokens_per_side = int(config.mm_tokens_per_image**0.5)
+        self.kernel_size = self.patches_per_image // self.tokens_per_side
+        self.avg_pool = nn.AvgPool2d(
+            kernel_size=self.kernel_size, stride=self.kernel_size
+        )
+
+    def forward(self, vision_outputs: torch.Tensor):
+        batch_size, _, seq_length = vision_outputs.shape
+
+        reshaped_vision_outputs = vision_outputs.transpose(1, 2)
+        reshaped_vision_outputs = reshaped_vision_outputs.reshape(
+            batch_size, seq_length, self.patches_per_image, self.patches_per_image
+        )
+        reshaped_vision_outputs = reshaped_vision_outputs.contiguous()
+
+        pooled_vision_outputs = self.avg_pool(reshaped_vision_outputs)
+        pooled_vision_outputs = pooled_vision_outputs.flatten(2)
+        pooled_vision_outputs = pooled_vision_outputs.transpose(1, 2)
+
+        normed_vision_outputs, _ = self.mm_soft_emb_norm(pooled_vision_outputs)
+
+        projected_vision_outputs = torch.matmul(
+            normed_vision_outputs, self.mm_input_projection_weight
+        )
+        return projected_vision_outputs.type_as(vision_outputs)
+
+
+class Gemma3ForConditionalGeneration(nn.Module):
+    def __init__(self, prefix, config, weights):
+        super().__init__()
+
+        self.config = config
+
+        if config.vision_config is not None:
+
+            config.vision_config.quantize = config.quantize
+
+            self.post_vision_model_layernorm = nn.LayerNorm.load(
+                prefix="vision_tower.vision_model.post_layernorm",
+                weights=weights,
+                eps=config.vision_config.layer_norm_eps,
+            )
+
+            self.multimodal_projector = Gemma3MultimodalInputProjection(
+                prefix="multi_modal_projector",
+                config=config,
+                weights=weights,
+            )
+
+            text_config = config.text_config
+            text_config.speculator = config.speculator
+            text_config.quantize = config.quantize
+
+            self.vision_model = load_vision_model(
+                prefix="vision_tower" if not prefix else f"{prefix}.vision_tower",
+                config=config.vision_config,
+                weights=weights,
+            )
+
+            self.text_model = load_text_model(
+                prefix="language_model" if not prefix else f"{prefix}.language_model",
+                config=config.text_config,
+                weights=weights,
+            )
+        else:
+            config.text_config.quantize = config.quantize
+            config.text_config.speculator = config.speculator
+            self.text_model = load_text_model(
+                prefix=prefix,
+                config=config.text_config,
+                weights=weights,
+            )
+
+        self.pad_token_id = (
+            config.pad_token_id if config.pad_token_id is not None else -1
+        )
+        self.dtype = weights.dtype
+
+    def get_vision_embeds(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_attention_mask: Optional[torch.FloatTensor] = None,
+        image_sizes: Optional[torch.Tensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+    ):
+        pixel_values = pixel_values.to(dtype=self.dtype)
+        image_outputs = self.vision_model(pixel_values)
+        vision_outputs = self.post_vision_model_layernorm(
+            image_outputs.last_hidden_state
+        )
+        image_features = self.multimodal_projector(vision_outputs)
+        image_features = image_features.view(-1, image_features.shape[-1])
+        return image_features
+
+    def get_inputs_embeds(
+        self,
+        input_ids: torch.Tensor,
+        vision_embeds: torch.Tensor = None,
+    ):
+        inputs_embeds = self.text_model.embed_tokens(input_ids)
+
+        if vision_embeds is not None:
+            # Replace the image token embeddings with the vision features
+            image_token_mask = (input_ids == self.config.image_token_index).to(
+                input_ids.device
+            )
+            inputs_embeds[image_token_mask] = vision_embeds.view(
+                -1, vision_embeds.shape[-1]
+            )
+        return inputs_embeds
+
+    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],
+        lm_head_indices: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        adapter_data: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        if cu_seqlen_prefill is not None:
+            position_ids += 1
+
+        if attention_mask is not None:
+            min_dtype = torch.finfo(inputs_embeds.dtype).min
+            # prefill may be larger than sliding window
+            effective_seq_len = max(
+                position_ids.shape[0], self.config.text_config.sliding_window
+            )
+            sliding_window_mask = torch.tril(
+                torch.ones_like(attention_mask, dtype=torch.bool),
+                diagonal=-self.config.text_config.sliding_window,
+            )
+            attention_mask_local = torch.where(
+                sliding_window_mask, min_dtype, attention_mask
+            )
+            offset = max(0, position_ids.shape[0] - effective_seq_len)
+            attention_mask_local = attention_mask_local[
+                :, :, :, offset : offset + effective_seq_len
+            ]
+        else:
+            attention_mask_local = None
+
+        hidden_states = self.text_model.model(
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            cu_seqlen_prefill=cu_seqlen_prefill,
+            kv_cache=kv_cache,
+            slots=slots,
+            seqlen=seqlen,
+            hpu_attention_meta=hpu_attention_meta,
+            adapter_data=adapter_data,
+        )
+
+        if lm_head_indices is not None:
+            hidden_states = hidden_states[lm_head_indices]
+        logits, speculative_logits = self.text_model.lm_head(hidden_states)
+
+        return logits, speculative_logits

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

@@ -23,6 +23,12 @@ def load_text_model(prefix, config, weights, name=None):
         )
 
         return FlashGemma2ForCausalLM(prefix, config, weights)
+    elif config.model_type == "gemma3" or config.model_type == "gemma3_text":
+        from text_generation_server.models.custom_modeling.flash_gemma3_modeling import (
+            FlashGemma3ForCausalLM,
+        )
+
+        return FlashGemma3ForCausalLM(prefix, config, weights)
     elif config.model_type == "paligemma":
         from text_generation_server.models.custom_modeling.flash_gemma_modeling import (
             FlashGemmaForCausalLM,
@@ -42,13 +48,20 @@ def load_vision_model(prefix, config, weights):
         return CLIPVisionTransformer(
             prefix=f"{prefix}.vision_model", config=config, weights=weights
         )
-    if config.model_type == "siglip_vision_model":
+    if (
+        config.model_type == "siglip_vision_model"
+        or config.model_type == "gemma3_vision"
+    ):
         from text_generation_server.models.custom_modeling.siglip import (
             SiglipVisionTransformer,
         )
 
+        # TODO: ensure that using the prefix doesn't break any existing models
+        # that rely on the old prefix (update the old models if necessary)
         return SiglipVisionTransformer(
-            prefix="vision_tower.vision_model", config=config, weights=weights
+            prefix=f"{prefix}.vision_model",
+            config=config,
+            weights=weights,
         )
     else:
         raise RuntimeError(f"Unsupported model type {config.model_type}")

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

@@ -80,19 +80,6 @@ from vllm_hpu_extension.profiler import HabanaMemoryProfiler, format_bytes
 
 tracer = trace.get_tracer(__name__)
 
-# Will be set in init
-SLIDING_WINDOW: Optional[int] = None
-
-
-def set_sliding_window(sliding_window: int):
-    global SLIDING_WINDOW
-    SLIDING_WINDOW = sliding_window
-
-
-def get_sliding_windows() -> int:
-    global SLIDING_WINDOW
-    return SLIDING_WINDOW
-
 
 def prepare_for_decode(
     dtype, use_contiguous_pa, device, slots, block_tables, batch_size, bucketing_ctx
@@ -1112,7 +1099,6 @@ class FlashCausalLMBatch(Batch):
             self.cache_lengths_tensor, (0, extra_pad_bs), value=0
         )
 
-        sliding_window = get_sliding_windows()
         position_ids = []
         slot_indices = []
         prefill_cache_indices = []
@@ -1178,9 +1164,7 @@ class FlashCausalLMBatch(Batch):
 
             # Create tensor to slice into the kv tensor in prefill
             # hpu need request_prefill_cache_indices to skip padding in kv cache
-            sliding_window = get_sliding_windows()
+            sliding_window = input_length
-            if sliding_window is None:
-                sliding_window = input_length
             cumulative_length += input_ids_padded_length[i]
             if sliding_window is not None:
                 request_prefill_cache_indices = torch.arange(
@@ -1457,9 +1441,7 @@ class FlashCausalLM(Model):
         if text_config is not None:
             config = text_config
 
-        if getattr(config, "sliding_window", None) is not None:
+        if getattr(config, "sliding_window", None) is None:
-            set_sliding_window(config.sliding_window)
-        else:
             config.sliding_window = None
 
         self.num_layers = config.num_hidden_layers

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

@@ -1001,17 +1001,8 @@ class FlashVlmCausalLM(FlashCausalLM):
 
         attention_mask = None
         attention_mask_forward = None
-        if self.model.config.model_type == "gemma3" and cu_seqlen_prefill is not None:
-            attention_mask = self.model.get_attention_mask(
-                input_ids, cu_seqlen_prefill, self.dtype, bool_mask=True
-            )
-            min_dtype = torch.finfo(self.dtype).min
-            attention_mask_forward = torch.where(attention_mask, 0, min_dtype).to(
-                input_ids.device
-            )
-            attention_mask = attention_mask.reshape(-1)
         if self.model.config.model_type == "llama4":
-            attention_mask = (input_ids != 0).long()
+            attention_mask = (input_ids != self.tokenizer.pad_token_id).long()
             attention_mask_forward = attention_mask.view(input_lengths.shape[0], -1)
 
         if cu_seqlen_prefill is None and self.max_past() is not None:

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

@@ -79,7 +79,7 @@ class Model(ABC):
             requires_padding=self.requires_padding,
             dtype=str(self.dtype),
             device_type=self.device.type,
-            window_size=self.sliding_window,
+            window_size=None,
             speculate=self.speculate,
             block_size=BLOCK_SIZE,
         )