Merge query/key/value projection layers

src/petals/models/llama/block.py

@@ -3,13 +3,118 @@ LLaMA intermediate layer
 Based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
 See commit history for authorship.
 """
+import math
 from typing import Optional, Tuple
 
 import torch
-from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
+import torch.nn as nn
+from transformers.models.llama.modeling_llama import (
+    LlamaAttention,
+    LlamaConfig,
+    LlamaDecoderLayer,
+    LlamaMLP,
+    LlamaModel,
+    LlamaRMSNorm,
+    apply_rotary_pos_emb,
+    repeat_kv,
+)
 
 
-class WrappedLlamaBlock(LlamaDecoderLayer):
+class OptimizedLlamaAttention(LlamaAttention):
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.qkv_proj = nn.Linear(
+            self.hidden_size, (self.num_heads + 2 * self.num_key_value_heads) * self.head_dim, bias=False
+        )
+        self.qkv_sizes = [
+            self.num_heads * self.head_dim,
+            self.num_key_value_heads * self.head_dim,
+            self.num_key_value_heads * self.head_dim,
+        ]
+        self.attn_norm_constant = math.sqrt(self.head_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+        assert (
+            self.config.pretraining_tp == 1
+        ), "OptimizedLlamaAttention assumes that config.pretraining_tp is equal to 1"
+        assert not output_attentions, "output_attentions=True is not supported"
+
+        query_states, key_states, value_states = torch.split(self.qkv_proj(hidden_states), self.qkv_sizes, dim=2)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / self.attn_norm_constant
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+class OptimizedLlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: LlamaConfig):
+        nn.Module.__init__(self)
+        self.hidden_size = config.hidden_size
+        self.self_attn = OptimizedLlamaAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+
+class WrappedLlamaBlock(OptimizedLlamaDecoderLayer):
     def forward(
         self,
         hidden_states: torch.Tensor,

src/petals/utils/convert_block.py

@@ -50,6 +50,19 @@ def convert_block(
     if freeze:
         block.requires_grad_(False)
 
+    if hasattr(block, "self_attn") and hasattr(block.self_attn, "qkv_proj"):
+        offset = 0
+        for data in [
+            block.self_attn.q_proj.weight.data,
+            block.self_attn.k_proj.weight.data,
+            block.self_attn.v_proj.weight.data,
+        ]:
+            block.self_attn.qkv_proj.weight.data[offset : offset + data.size(0)].copy_(data)
+            offset += data.size(0)
+        del block.self_attn.q_proj
+        del block.self_attn.k_proj
+        del block.self_attn.v_proj
+
     block = make_tensor_parallel(block, config, tensor_parallel_devices, output_device=output_device)
 
     if quant_type != QuantType.NONE: