[Llama.py -> mistral.py] Extract mistral-only relevant code into separate file

vllm/model_executor/mistral.py

@@ -0,0 +1,247 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Mistral adaptation of the LLaMA architecture."""
+
+from collections.abc import Iterable
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.attention import AttentionType
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.models.llama import (
+    LlamaAttention,
+    LlamaDecoderLayer,
+    LlamaForCausalLM,
+    LlamaModel,
+)
+
+from .utils import AutoWeightsLoader
+
+logger = init_logger(__file__)
+
+
+class MistralAttention(LlamaAttention):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        max_position_embeddings: int = 8192,
+        quant_config: QuantizationConfig | None = None,
+        bias: bool = False,
+        bias_o_proj: bool = False,
+        cache_config: CacheConfig | None = None,
+        prefix: str = "",
+        attn_type: str = AttentionType.DECODER,
+    ) -> None:
+        super().__init__(
+            config=config,
+            hidden_size=hidden_size,
+            num_heads=num_heads,
+            num_kv_heads=num_kv_heads,
+            # mistral-nemo has special head dim
+            head_dim=getattr(config, "head_dim", None),
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
+            bias=bias,
+            bias_o_proj=bias_o_proj,
+            cache_config=cache_config,
+            prefix=prefix,
+            attn_type=attn_type,
+        )
+
+        llama_4_scaling_config: dict[str, int | float | str] | None = getattr(
+            config, "llama_4_scaling", None
+        )
+        self.do_llama_4_scaling = llama_4_scaling_config is not None
+        if self.do_llama_4_scaling:
+            assert llama_4_scaling_config is not None
+            self.llama_4_scaling_original_max_position_embeddings = (
+                llama_4_scaling_config["original_max_position_embeddings"]
+            )
+            self.llama_4_scaling_beta = llama_4_scaling_config["beta"]
+
+    def _get_llama_4_attn_scale(self, positions: torch.Tensor) -> torch.Tensor:
+        # Llama4 scaling
+        scaling = 1 + self.llama_4_scaling_beta * torch.log(
+            1
+            + torch.floor(
+                positions / self.llama_4_scaling_original_max_position_embeddings
+            )
+        )
+        # Broadcast over head_dim
+        return scaling.unsqueeze(-1)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        q, k = self.rotary_emb(positions, q, k)
+        if self.do_llama_4_scaling:
+            attn_scale = self._get_llama_4_attn_scale(positions)
+            q = (q * attn_scale).to(q.dtype)
+        attn_output = self.attn(q, k, v)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MistralDecoderLayer(LlamaDecoderLayer):
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        config: LlamaConfig | None = None,
+    ) -> None:
+        super().__init__(
+            vllm_config=vllm_config,
+            prefix=prefix,
+            config=config,
+            attn_layer_type=MistralAttention,
+        )
+
+        self.layer_idx = int(prefix.split(sep=".")[-1])
+        quant_config = self.get_quant_config(vllm_config)
+        config = config or vllm_config.model_config.hf_config
+
+        do_fusion = getattr(
+            quant_config, "enable_quantization_scaling_fusion", False
+        ) and vllm_config.cache_config.cache_dtype.startswith("fp8")
+        if do_fusion:
+            self.input_layernorm.quant_scaling_from = self.self_attn.qkv_proj
+            self.post_attention_layernorm.quant_scaling_from = self.mlp.gate_up_proj
+
+
+@support_torch_compile
+class MistralModel(LlamaModel):
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = MistralDecoderLayer,
+    ):
+        super().__init__(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)
+
+
+class MistralForCausalLM(LlamaForCausalLM):
+    # Mistral: We don't support LoRA on the embedding layers.
+    embedding_modules: dict[str, str] = {}
+
+    # Mistral/Llama models can also be loaded with --load-format mistral
+    # from consolidated.safetensors checkpoints
+    mistral_mapping = {
+        "layers": "model.layers",
+        "attention": "self_attn",
+        "qscale_act": "input_scale",
+        "qscale_weight": "weight_scale",
+        "kv_fake_quantizer.qscale_act": "kv_scale",
+        "q_fake_quantizer.qscale_act": "attn.q_scale",
+        "k_fake_quantizer.qscale_act": "k_scale",
+        "v_fake_quantizer.qscale_act": "v_scale",
+        "wq": "q_proj",
+        "wk": "k_proj",
+        "wv": "v_proj",
+        "wo": "o_proj",
+        "attention_norm": "input_layernorm",
+        "feed_forward": "mlp",
+        "w1": "gate_proj",
+        "w2": "down_proj",
+        "w3": "up_proj",
+        "ffn_norm": "post_attention_layernorm",
+        "tok_embeddings": "model.embed_tokens",
+        "output": "lm_head",
+        "norm": "model.norm",
+    }
+
+    def __init__(
+        self,
+        *,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = MistralDecoderLayer,
+    ):
+        super().__init__(vllm_config=vllm_config, prefix=prefix, layer_type=layer_type)
+
+    def _init_model(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+        layer_type: type[nn.Module] = MistralDecoderLayer,
+    ):
+        return MistralModel(
+            vllm_config=vllm_config, prefix=prefix, layer_type=layer_type
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(
+            self.maybe_remap_mistral(name, loaded_weight)
+            for name, loaded_weight in weights
+        )
+
+    def maybe_remap_mistral(
+        self,
+        name: str,
+        loaded_weight: torch.Tensor,
+    ) -> tuple[str, torch.Tensor]:
+        def permute(w: torch.Tensor, n_heads: int, attn_out: int):
+            attn_in = self.config.head_dim * n_heads
+
+            return (
+                w.view(n_heads, attn_in // n_heads // 2, 2, attn_out)
+                .transpose(1, 2)
+                .reshape(attn_in, attn_out)
+            )
+
+        mapping = self.mistral_mapping
+        modules = name.split(".")
+
+        # rotary embeds should be sliced
+        # If using quantized model in mistral format,
+        # quantization scales (qscale_weight) also need to be sliced
+        if "wk" in modules and modules[-1] == "weight":
+            loaded_weight = permute(
+                loaded_weight, self.config.num_key_value_heads, self.config.hidden_size
+            )
+        elif (
+            "wk" in modules
+            and modules[-1] == "qscale_weight"
+            and loaded_weight.numel() > 1
+        ):
+            loaded_weight = permute(loaded_weight, self.config.num_key_value_heads, 1)
+        elif "wq" in modules and modules[-1] == "weight":
+            loaded_weight = permute(
+                loaded_weight, self.config.num_attention_heads, self.config.hidden_size
+            )
+        elif (
+            "wq" in modules
+            and modules[-1] == "qscale_weight"
+            and loaded_weight.numel() > 1
+        ):
+            loaded_weight = permute(loaded_weight, self.config.num_attention_heads, 1)
+
+        num_modules = len(modules)
+        for i in range(num_modules):
+            item = modules[i]
+            next_item = modules[i + 1] if i < num_modules - 1 else None
+
+            combined_item = f"{item}.{next_item}" if next_item is not None else None
+
+            if combined_item in mapping:
+                name = name.replace(combined_item, mapping[combined_item])
+            elif item in mapping and mapping[item] not in name:
+                name = name.replace(item, mapping[item])
+
+        return name, loaded_weight