[GCU] Support llama for GCU

examples/benchmark/wiki_lambada/eval.py

@@ -57,8 +57,8 @@ def get_parser():
         "--device",
         type=str,
         default="gpu",
-        choices=["cpu", "eval_pathgpu", "xpu", "npu"],
-        help="select cpu, gpu, xpu devices.",
+        choices=["cpu", "gpu", "xpu", "npu", "gcu"],
+        help="select cpu, gpu, xpu, gcu devices.",
     )
     parser.add_argument(
         "--dtype",
@@ -67,6 +67,12 @@ def get_parser():
         choices=["bfloat16", "float16", "float32"],
         help="set the dtype of model",
     )
+    parser.add_argument(
+        "--use_flash_attention",
+        type=bool,
+        default=False,
+        help="Whether to use flash attention",
+    )
 
     # load autodist name files, eg: bloom-176b
     parser.add_argument("--load_autodist", action="store_true", help="whether load auto-dist wieght file")
@@ -316,7 +322,7 @@ def do_generation():
         tensor_parallel_output=False,
         tensor_parallel_degree=args.tensor_parallel_degree,
         tensor_parallel_rank=paddle.distributed.get_rank(),
-        use_flash_attention=False,
+        use_flash_attention=args.use_flash_attention,
         dtype=args.dtype,  # todo enable set dtype to avoid additional mem usage
     )
 

paddlenlp/generation/utils.py

@@ -1208,6 +1208,8 @@
                 probs = TopKProcess(probs, top_k, min_tokens_to_keep)
             if top_p is not None and top_p < 1.0:
                 probs = TopPProcess(probs, top_p, min_tokens_to_keep)
+            if paddle.device.is_compiled_with_custom_device("gcu"):
+                probs = paddle.cast(probs, "float32")
 
             # multinomial already support fp16 and bf16 currently, fix issue: https://github.com/PaddlePaddle/Paddle/issues/51852
             next_tokens = paddle.multinomial(probs)

paddlenlp/transformers/llama/fusion_ops.py

@@ -41,7 +41,7 @@
 except ImportError:
     fused_rotary_position_embedding = None
 try:
-    if get_env_device() == "npu":
+    if get_env_device() in ["npu", "gcu"]:
         from paddle.base import core
 
         for lib in os.listdir(os.getenv("CUSTOM_DEVICE_ROOT")):
@@ -53,13 +53,18 @@
 
 
 def fusion_rope(query_states, key_states, value_states, hidden_states, position_ids, past_key_value, rotary_emb):
-    assert past_key_value is None, "fuse rotary not support cache kv for now"
+    if get_env_device() != "gcu":
+        assert past_key_value is None, "fuse rotary not support cache kv for now"
     batch_size, seq_length, num_heads, head_dim = query_states.shape
     _, kv_seq_len, num_key_value_heads, _ = key_states.shape
-    cos, sin = rotary_emb(value_states, seq_len=kv_seq_len)
+    cos, sin, cos_sin = rotary_emb(value_states, seq_len=kv_seq_len)
     if get_env_device() == "npu":
         query_states = core.eager._run_custom_op("fused_rope", query_states, cos, sin)[0]
         key_states = core.eager._run_custom_op("fused_rope", key_states, cos, sin)[0]
+    elif get_env_device() == "gcu":
+        query_states, key_states = core.eager._run_custom_op(
+            "fused_rotary_embedding_gcu", query_states, key_states, cos_sin, position_ids, True
+        )
     else:
         # paddle version > 2.6 or develop support q and k/v with different num_heads
         paddle_version = float(paddle.__version__[:3])
@@ -103,6 +108,8 @@
 def fusion_rms_norm(hidden_states, weight, variance_epsilon):
     if get_env_device() == "npu":
         return core.eager._run_custom_op("rms_norm_npu", hidden_states, weight, variance_epsilon)[0]
+    elif get_env_device() == "gcu":
+        return core.eager._run_custom_op("rms_norm_gcu", hidden_states, weight, variance_epsilon)[0]
     elif get_env_device() == "xpu":
         try:
             import paddle_xpu_nn  # noqa: F821
@@ -158,6 +165,17 @@
                 False,
                 npu_is_casual,
             )[0]
+        elif get_env_device() == "gcu":
+            attn_output = core.eager._run_custom_op(
+                "fused_sdp_flash_attention_gcu",
+                query_states,
+                key_states,
+                value_states,
+                attention_mask,
+                0.0,
+                attention_mask is None,
+                True,
+            )[0]
         else:
             attn_output = F.scaled_dot_product_attention(
                 query_states,

paddlenlp/transformers/llama/modeling.py

@@ -79,7 +79,7 @@
 )
 
 try:
-    if get_env_device() == "npu":
+    if get_env_device() in ["npu", "gcu"]:
 
         for lib in os.listdir(os.getenv("CUSTOM_DEVICE_ROOT")):
             if lib.endswith(".so"):
@@ -410,6 +410,7 @@
         # [1, seqlen, 1, dim]
         self.cos_cached = emb.cos()[None, :, None, :]
         self.sin_cached = emb.sin()[None, :, None, :]
+        self.cos_sin_table = None if get_env_device() != "gcu" else paddle.concat([freqs.cos(), freqs.sin()], axis=-1)
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
@@ -418,6 +419,9 @@
         return (
             cos.cast(x.dtype) if cos.dtype != x.dtype else cos,
             sin.cast(x.dtype) if sin.dtype != x.dtype else sin,
+            self.cos_sin_table.cast(x.dtype)
+            if self.cos_sin_table is not None and self.cos_sin_table.dtype != x.dtype
+            else self.cos_sin_table,
         )
 
 
@@ -439,6 +443,7 @@
         # [1, seqlen, 1, dim]
         self.cos_cached = emb.cos()[None, :, None, :]
         self.sin_cached = emb.sin()[None, :, None, :]
+        self.cos_sin_table = None if get_env_device() != "gcu" else paddle.concat([freqs.cos(), freqs.sin()], axis=-1)
 
 
 class LlamaNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
@@ -471,19 +476,23 @@
         # [1, seqlen, 1, dim]
         scale_cos = emb.cos()[None, :, None, :]
         scale_sin = emb.sin()[None, :, None, :]
-        return scale_cos, scale_sin
+        scale_cos_sin = None if get_env_device() != "gcu" else paddle.concat([freqs.cos(), freqs.sin()], axis=-1)
+        return scale_cos, scale_sin, scale_cos_sin
 
     def forward(self, x, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
         if seq_len > self.max_position_embeddings:
-            scale_cos, scale_sin = self._scale_cos_sin(seq_len=seq_len)
+            scale_cos, scale_sin, scale_cos_sin = self._scale_cos_sin(seq_len=seq_len)
         else:
-            scale_cos, scale_sin = self.cos_cached, self.sin_cached
+            scale_cos, scale_sin, scale_cos_sin = self.cos_cached, self.sin_cached, self.cos_sin_table
         cos = scale_cos[:, :seq_len, :, ...]
         sin = scale_sin[:, :seq_len, :, ...]
         return (
             cos.cast(x.dtype) if cos.dtype != x.dtype else cos,
             sin.cast(x.dtype) if sin.dtype != x.dtype else sin,
+            scale_cos_sin.cast(x.dtype)
+            if scale_cos_sin is not None and scale_cos_sin.dtype != x.dtype
+            else scale_cos_sin,
         )
 
 
@@ -638,7 +647,7 @@
                 )
 
         self.use_fused_rope = config.use_fused_rope
-        if self.use_fused_rope and get_env_device() not in ["npu", "xpu"]:
+        if self.use_fused_rope and get_env_device() not in ["npu", "xpu", "gcu"]:
             if "gpu" not in paddle.device.get_device() or fused_rotary_position_embedding is None:
                 warnings.warn(
                     "Enable fuse rope in the config, but fuse rope is not available. "
@@ -934,7 +943,7 @@
                         sin.cast(value_states.dtype) if sin.dtype != value_states.dtype else sin,
                     )
                 else:
-                    cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+                    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)
 
@@ -1398,7 +1407,7 @@
             y = paddle.to_tensor(paddle.finfo(dtype).min, dtype="float32")
             expanded_attn_mask = expanded_attn_mask.astype("float32")
             expanded_attn_mask = paddle.where(expanded_attn_mask, x, y).astype(dtype)
-        elif get_env_device() == "xpu":
+        elif get_env_device() in ["xpu", "gcu"]:
             x = paddle.to_tensor(0.0, dtype=dtype)
             y = paddle.to_tensor(paddle.finfo(dtype).min, dtype=dtype)
             expanded_attn_mask = expanded_attn_mask.astype(dtype)
@@ -1528,7 +1537,7 @@
             attention_mask, (batch_size, seq_length), cache_length, inputs_embeds.dtype
         )  # [bs, 1, seq_len, seq_len]
         is_casual = False
-        if self.config.use_flash_attention:
+        if self.config.use_flash_attention and get_env_device() != "gcu":
             is_casual = is_casual_mask(attention_mask)
             if get_env_device() != "npu":
                 if is_casual and alibi is None:

paddlenlp/utils/tools.py

@@ -124,6 +124,8 @@
         return "gpu"
     elif "npu" in paddle.device.get_all_custom_device_type():
         return "npu"
+    elif "gcu" in paddle.device.get_all_custom_device_type():
+        return "gcu"
     elif paddle.is_compiled_with_rocm():
         return "rocm"
     elif paddle.is_compiled_with_xpu():