optimize gpt2 by using linear instead of conv1D

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

optimum/exporters/ipex/cache_utils.py

@@ -44,7 +44,7 @@ def __init__(
         self.batch_size = batch_size
         # Used in `generate` to keep tally of how many tokens the cache has seen
         self._seen_tokens = torch.zeros([batch_size], dtype=torch.int32, device=device)
-        self.block_size = 16
+        self.block_size = 64
         self.num_blocks = (max_cache_len // self.block_size + (max_cache_len % self.block_size != 0)) * batch_size
         self.block_tables = -1 * torch.ones([self.num_blocks], dtype=torch.int32, device=device).reshape(
             batch_size, -1

optimum/exporters/ipex/modeling_utils.py

@@ -614,22 +614,6 @@ def forward(
         if past_len == 0:
             # prefill, remove padding
             seq_len_tensor = torch.cat((input_lens.new_tensor([0]), input_lens.cumsum(-1).int()))
-            # varlen_attention(
-            #     query.contiguous() if query.device.type == "xpu" else query,
-            #     key.contiguous() if key.device.type == "xpu" else key,
-            #     value.contiguous() if value.device.type == "xpu" else value,
-            #     attn_output,
-            #     seq_len_tensor,
-            #     seq_len_tensor,
-            #     input_lens.max(),
-            #     input_lens.max(),
-            #     0.0,
-            #     1.0 / math.sqrt(self.head_dim),
-            #     False,
-            #     True,
-            #     False,
-            #     None,
-            # )
             PagedAttention.flash_attn_varlen_func(
                 attn_output,
                 query,
@@ -734,9 +718,16 @@ class _IPEXGPT2Attention(_IPEXAttention):
     def __init__(self, module, config) -> None:
         self.num_key_value_heads = config.num_key_value_heads
         super().__init__(module, config)
+        _setattr_from_module(self, module)
+        self.c_attn_linear = nn.Linear(self.c_attn.weight.shape[0], self.c_attn.weight.shape[1])
+        self.c_attn_linear.weight = nn.Parameter(self.c_attn.weight.t())
+        self.c_attn_linear.bias = self.c_attn.bias
+        self.c_proj_linear = nn.Linear(self.c_proj.weight.shape[0], self.c_proj.weight.shape[1])
+        self.c_proj_linear.weight = nn.Parameter(self.c_proj.weight.t())
+        self.c_proj_linear.bias = self.c_proj.bias
 
     def qkv_gemm(self, hidden_states):
-        query, key, value = self.c_attn(hidden_states).split(self.split_size, dim=-1)
+        query, key, value = self.c_attn_linear(hidden_states).split(self.split_size, dim=-1)
         query = query.view(-1, self.num_heads, self.head_dim)
         key = key.view(-1, self.num_heads, self.head_dim)
         value = value.view(-1, self.num_heads, self.head_dim)
@@ -748,7 +739,6 @@ def rope(self, query, key, *args, **kwargs):
     def postprocess_attention_output(self, attn_output):
         attn_output = attn_output.reshape(-1, attn_output.shape[-2] * attn_output.shape[-1])
         attn_output = self.c_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
         return attn_output