Merge branch 'vllm-v0.5.0-dtk24.04.1-support-deepseekv2' into 'vllm-v0.5.0-dtk24.04.1'

Support Deepseek-V2 (#4650)

See merge request dcutoolkit/deeplearing/vllm!1

vllm/config.py

@@ -278,6 +278,12 @@ class ModelConfig:
         return self.hf_text_config.hidden_size
 
     def get_head_size(self) -> int:
+        # TODO remove hard code
+        if hasattr(self.hf_text_config, "model_type"
+                   ) and self.hf_text_config.model_type == 'deepseek_v2':
+            # FlashAttention supports only head_size 32, 64, 128, 256,
+            # we need to pad head_size 192 to 256
+            return 256
         if hasattr(self.hf_text_config, "head_dim"):
             return self.hf_text_config.head_dim
         # FIXME(woosuk): This may not be true for all models.

vllm/model_executor/layers/fused_moe/__init__.py

 from vllm.model_executor.layers.fused_moe.fused_moe import (
-    fused_experts, fused_moe, fused_topk, get_config_file_name)
+    fused_experts, fused_moe, fused_topk, get_config_file_name, grouped_topk)
 
 __all__ = [
     "fused_moe",
     "fused_topk",
     "fused_experts",
     "get_config_file_name",
+    "grouped_topk",
 ]

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -367,6 +367,37 @@ def fused_topk(
     return topk_weights, topk_ids
 
 
+# This is used by the Deepseek-V2 model
+def grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: int = 0,
+    topk_group: int = 0,
+):
+    scores = torch.softmax(gating_output, dim=-1)
+    num_token = scores.shape[0]
+    group_scores = scores.view(num_token, num_expert_group,
+                               -1).max(dim=-1).values  # [n, n_group]
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1,
+                           sorted=False)[1]  # [n, top_k_group]
+    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+    score_mask = group_mask.unsqueeze(-1).expand(
+        num_token, num_expert_group,
+        scores.shape[-1] // num_expert_group).reshape(num_token, -1)  # [n, e]
+    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+    topk_weights, topk_ids = torch.topk(tmp_scores,
+                                        k=topk,
+                                        dim=-1,
+                                        sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    return topk_weights, topk_ids
+
+
 def fused_experts(hidden_states: torch.Tensor,
                   w1: torch.Tensor,
                   w2: torch.Tensor,

vllm/model_executor/layers/rotary_embedding.py

@@ -505,6 +505,129 @@ class Phi3SuScaledRotaryEmbedding(nn.Module):
         return query.flatten(-2), key.flatten(-2)
 
 
+def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+class DeepseekScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with YaRN method.
+
+    Credits to Peng et al. github.com/jquesnelle/yarn
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_factor: float,
+        dtype: torch.dtype,
+        *,
+        extrapolation_factor: float = 1,
+        attn_factor: float = 1,
+        beta_fast: int = 32,
+        beta_slow: int = 1,
+        mscale: float = 1,
+        mscale_all_dim: float = 0,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        self.extrapolation_factor = extrapolation_factor
+        self.attn_factor = attn_factor
+        self.beta_fast = beta_fast
+        self.beta_slow = beta_slow
+        # Get n-d magnitude scaling corrected for interpolation.
+        self.mscale = float(
+            yarn_get_mscale(self.scaling_factor, float(mscale)) /
+            yarn_get_mscale(self.scaling_factor, float(mscale_all_dim)) *
+            attn_factor)
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style, dtype)
+
+    def _compute_inv_freq(self, scaling_factor: float) -> torch.Tensor:
+        pos_freqs = self.base**(torch.arange(
+            0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
+                                self.rotary_dim)
+        inv_freq_extrapolation = 1.0 / pos_freqs
+        inv_freq_interpolation = 1.0 / (scaling_factor * pos_freqs)
+
+        low, high = _yarn_find_correction_range(self.beta_fast, self.beta_slow,
+                                                self.rotary_dim, self.base,
+                                                self.max_position_embeddings)
+        # Get n-d rotational scaling corrected for extrapolation
+        inv_freq_mask = (1 - _yarn_linear_ramp_mask(
+            low, high, self.rotary_dim // 2,
+            dtype=torch.float)) * self.extrapolation_factor
+        inv_freq = inv_freq_interpolation * (
+            1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.scaling_factor)
+        t = torch.arange(self.max_position_embeddings * self.scaling_factor,
+                         device="cuda",
+                         dtype=torch.float32)
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = (freqs.cos() * self.mscale)
+        sin = (freqs.sin() * self.mscale)
+        cache = torch.cat((cos, sin), dim=-1)
+        print("Cache shape", cache.shape)
+        return cache
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        offsets: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """PyTorch-native implementation equivalent to forward()."""
+        query_rot = query[..., :self.rotary_dim]
+        key_rot = key[..., :self.rotary_dim]
+        if self.rotary_dim < self.head_size:
+            query_pass = query[..., self.rotary_dim:]
+            key_pass = key[..., self.rotary_dim:]
+
+        self.cos_sin_cache: torch.Tensor = self.cos_sin_cache.to(
+            positions.device)
+        cos_sin = self.cos_sin_cache[torch.add(positions, offsets)
+                                     if offsets is not None else positions]
+        cos, sin = cos_sin.chunk(2, dim=-1)
+        if self.is_neox_style:
+            # NOTE(woosuk): Here we assume that the positions tensor has the
+            # shape [batch_size, seq_len].
+            cos = cos.repeat(1, 1, 2).unsqueeze(-2)
+            sin = sin.repeat(1, 1, 2).unsqueeze(-2)
+        else:
+            cos = cos.repeat_interleave(2, dim=-1).unsqueeze(-2)
+            sin = sin.repeat_interleave(2, dim=-1).unsqueeze(-2)
+
+        rotate_fn = _rotate_neox if self.is_neox_style else _rotate_gptj
+        query_rot = query_rot * cos + rotate_fn(query_rot) * sin
+        key_rot = key_rot * cos + rotate_fn(key_rot) * sin
+
+        if self.rotary_dim < self.head_size:
+            query = torch.cat((query_rot, query_pass), dim=-1)
+            key = torch.cat((key_rot, key_pass), dim=-1)
+        else:
+            query = query_rot
+            key = key_rot
+        return query, key
+
+
+class GemmaRotaryEmbedding(RotaryEmbedding):
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        # https://github.com/huggingface/transformers/blob/v4.41.2/src/transformers/models/gemma/modeling_gemma.py#L107
+        inv_freq = 1.0 / (base**(
+            torch.arange(0, self.rotary_dim, 2, dtype=torch.int64).float() /
+            self.rotary_dim))
+        return inv_freq
+
+
 _ROPE_DICT: Dict[Tuple, RotaryEmbedding] = {}
 
 
@@ -562,7 +685,22 @@ def get_rope(
                                                     base, is_neox_style,
                                                     scaling_factor, dtype,
                                                     **extra_kwargs)
-        elif scaling_type == "su":
+        elif scaling_type == "deepseek_yarn":
+            original_max_position = rope_scaling[
+                "original_max_position_embeddings"]
+            # assert max_position == original_max_position * scaling_factor
+            extra_kwargs = {
+                k: v
+                for k, v in rope_scaling.items()
+                if k in ("extrapolation_factor", "attn_factor", "beta_fast",
+                         "beta_slow", "mscale", "mscale_all_dim")
+            }
+            rotary_emb = DeepseekScalingRotaryEmbedding(
+                head_size, rotary_dim, original_max_position, base,
+                is_neox_style, scaling_factor, dtype, **extra_kwargs)
+        # The correct one should be "longrope" but keep "su" here
+        # for backward compatible
+        elif scaling_type == "su" or scaling_type == "longrope":
             short_factor = rope_scaling["short_factor"]
             long_factor = rope_scaling["long_factor"]
             original_max_position = rope_scaling[

vllm/model_executor/models/__init__.py

@@ -21,6 +21,7 @@ _GENERATION_MODELS = {
     "DbrxForCausalLM": ("dbrx", "DbrxForCausalLM"),
     "DeciLMForCausalLM": ("decilm", "DeciLMForCausalLM"),
     "DeepseekForCausalLM": ("deepseek", "DeepseekForCausalLM"),
+    "DeepseekV2ForCausalLM": ("deepseek_v2", "DeepseekV2ForCausalLM"),
     "FalconForCausalLM": ("falcon", "FalconForCausalLM"),
     "GemmaForCausalLM": ("gemma", "GemmaForCausalLM"),
     "GPT2LMHeadModel": ("gpt2", "GPT2LMHeadModel"),