Update unitest

llm/predictor.py

@@ -75,6 +75,10 @@ class PredictorArgument:
             "help": "the decoding strategy of generation, which should be one of ['sampling', 'greedy_search', 'beam_search']. Default to sampling"
         },
     )
+    use_flash_attention: bool = field(
+        default=False,
+        metadata={"help": "Whether to use flash attention"},
+    )
 
     mode: str = field(
         default="dynamic", metadata={"help": "the type of predictor, it should be one of [dynamic, static]"}
@@ -241,6 +245,7 @@ class DygraphPredictor(BasePredictor):
         if self.model is None:
             self.model = AutoModelForCausalLM.from_pretrained(
                 config.model_name_or_path,
+                use_flash_attention=config.use_flash_attention,
                 dtype=dtype,
                 tensor_parallel_degree=self.tensor_parallel_degree,
                 tensor_parallel_rank=self.tensor_parallel_rank,
@@ -685,7 +690,9 @@ def create_predictor(
     tensor_parallel_degree: int = 1,
     tensor_parallel_rank: int = 0,
 ):
-    tokenizer = AutoTokenizer.from_pretrained(predictor_args.model_name_or_path)
+    tokenizer = AutoTokenizer.from_pretrained(
+        predictor_args.model_name_or_path,
+    )
     # init chat_template for tokenizer
     init_chat_template(tokenizer, predictor_args.model_name_or_path, predictor_args.chat_template)
 
@@ -727,6 +734,7 @@ def create_predictor(
                 model = AutoModelForCausalLM.from_pretrained(
                     predictor_args.model_name_or_path,
                     dtype=predictor_args.dtype,
+                    use_flash_attention=predictor_args.use_flash_attention,
                     tensor_parallel_degree=tensor_parallel_degree,
                     tensor_parallel_rank=tensor_parallel_rank,
                 )

paddlenlp/data/data_collator.py

@@ -405,7 +405,6 @@ class DataCollatorForSeq2Seq:
             return_tensors=return_tensors,
             return_attention_mask=self.return_attention_mask,
         )
-
         # prepare decoder_input_ids
         if (
             labels is not None

paddlenlp/peft/prefix/utils.py

@@ -17,10 +17,10 @@ import paddle
 
 def bloom_postprocess_past_key_value(past_key_values):
     # (layer_num, bs, head_num/tensor_parallel_degree, prefixlen, head_dim)*2
-    past_key_values = paddle.transpose(past_key_values, perm=[2, 0, 3, 1, 4]).split(2)
+    keys, values = paddle.transpose(past_key_values, perm=[2, 0, 1, 3, 4]).split(2)
     # keys: [layer_num, bs, head_num/tensor_parallel_degree, head_dim, prefixlen]
     # value: [layer_num, bs, head_num/tensor_parallel_degree, prefixlen, head_dim]
-    keys, values = past_key_values[0].transpose([0, 1, 2, 4, 3]), past_key_values[1]
+    # keys, values = past_key_values[0].transpose([0, 1, 2, 4, 3]), past_key_values[1]
     return tuple(zip(keys, values))
 
 

paddlenlp/transformers/bloom/modeling.py

@@ -378,9 +378,23 @@ class BloomAttention(nn.Layer):
         (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
 
         batch_size, q_length, _, _ = query_layer.shape
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            # concatenate along seq_length dimension:
+            #  - key: [batch_size, kv_length, self.num_heads, head_dim]
+            #  - value: [batch_size, kv_length, self.num_heads, head_dim]
+            key_layer = paddle.concat((past_key, key_layer), axis=1)
+            value_layer = paddle.concat((past_value, value_layer), axis=1)
+
+        if use_cache is True:
+            present = (key_layer, value_layer)
+        else:
+            present = None
+
         version = paddle.version.full_version
         version_check = True
-        if version != "0.0.0" and version <= "2.5.2":
+        if self.config.use_flash_attention and version != "0.0.0" and version <= "2.5.2":
             logger.warning(
                 "PaddlePaddle version 2.5.3 or higher is required, please upgrade your PaddlePaddle to 2.5.3 or other higher version."
             )
@@ -397,6 +411,8 @@ class BloomAttention(nn.Layer):
                 key_states,
                 value_states,
                 attn_mask=attention_mask,
+                dropout_p=self.config.attention_dropout,
+                training=self.training,
                 is_causal=False,
             )
             attn_weights = None
@@ -404,39 +420,10 @@ class BloomAttention(nn.Layer):
             attn_output = attn_output.reshape([attn_output.shape[0], attn_output.shape[1], -1])
             output_tensor = self.dense(attn_output)
 
-            query_layer = query_layer.transpose([0, 2, 1, 3])
-            key_layer = key_layer.transpose([0, 2, 3, 1])
-            value_layer = value_layer.transpose([0, 2, 1, 3])
-            if layer_past is not None:
-                past_key, past_value = layer_past
-                # concatenate along seq_length dimension:
-                #  - key: [batch_size, self.num_heads, head_dim, kv_length]
-                #  - value: [batch_size, self.num_heads, kv_length, head_dim]
-                key_layer = paddle.concat((past_key, key_layer), axis=3)
-                value_layer = paddle.concat((past_value, value_layer), axis=2)
-
-            if use_cache:
-                present = (key_layer, value_layer)
-            else:
-                present = None
         else:
-
             query_layer = query_layer.transpose([0, 2, 1, 3])
             key_layer = key_layer.transpose([0, 2, 3, 1])
             value_layer = value_layer.transpose([0, 2, 1, 3])
-            if layer_past is not None:
-                past_key, past_value = layer_past
-                # concatenate along seq_length dimension:
-                #  - key: [batch_size, self.num_heads, head_dim, kv_length]
-                #  - value: [batch_size, self.num_heads, kv_length, head_dim]
-                key_layer = paddle.concat((past_key, key_layer), axis=3)
-                value_layer = paddle.concat((past_value, value_layer), axis=2)
-
-            if use_cache is True:
-                present = (key_layer, value_layer)
-            else:
-                present = None
-
             _, _, _, kv_length = key_layer.shape
 
             query_layer = query_layer.reshape([batch_size * self.num_heads, q_length, self.head_dim])
@@ -449,7 +436,6 @@ class BloomAttention(nn.Layer):
             attention_scores = baddbmm(
                 alibi, batch1=query_layer, batch2=key_layer, beta=self.beta, alpha=self.inv_norm_factor
             )
-
             # change view to [batch_size, num_heads, q_length, kv_length]
             # attention_scores = matmul_result.reshape([batch_size, self.num_heads, q_length, kv_length])
 
@@ -949,14 +935,13 @@ class BloomModel(BloomPreTrainedModel):
         seq_length_with_past = seq_length
         past_key_values_length = 0
         if past_key_values[0] is not None:
-            past_key_values_length = past_key_values[0][0].shape[3]
+            past_key_values_length = past_key_values[0][0].shape[1]
             seq_length_with_past = seq_length_with_past + past_key_values_length
 
         if attention_mask is None:
             attention_mask = paddle.ones([batch_size, seq_length_with_past], dtype="bool")
         elif attention_mask.dtype != paddle.bool:
             attention_mask = paddle.cast(attention_mask, "bool")
-
         if len(attention_mask.shape) > 2:
             _attention_mask = paddle.ones([batch_size, seq_length_with_past], dtype="bool")
             alibi = build_alibi_tensor(_attention_mask, self.config.n_head, dtype=hidden_states.dtype)

paddlenlp/transformers/chatglm/modeling.py

@@ -245,9 +245,19 @@ class ChatGLMAttention(nn.Layer):
         q_layer, k_layer, v_layer = paddle.split(mixed_layer, 3, axis=-1)
         # [s, b, n, h/n]
         q_layer, k_layer = self._core_attention(q_layer, k_layer, position_ids, rotary_embeds)
+
+        if cache is not None:
+            cache_k, cache_v = cache[0], cache[1]
+            # [s + c, b, n, h/n]
+            k_layer = paddle.concat([cache_k, k_layer], axis=0)
+            v_layer = paddle.concat([cache_v, v_layer], axis=0)
+
+        cache_kv = None
+        if use_cache:
+            cache_kv = (k_layer, v_layer)
         version = paddle.version.full_version
         version_check = True
-        if version != "0.0.0" and version <= "2.5.2":
+        if self.config.use_flash_attention and version != "0.0.0" and version <= "2.5.2":
             logger.warning(
                 "PaddlePaddle version 2.5.3 or higher is required, please upgrade your PaddlePaddle to 2.5.3 or other higher version."
             )
@@ -255,15 +265,6 @@ class ChatGLMAttention(nn.Layer):
         if self.config.use_flash_attention and version_check:
             # Paddle Flash Attention input [ bz, seqlen, nhead, head_dim]
             # Torch Flash Attention input [ bz, nhead, seqlen, head_dim]
-            if cache is not None:
-                cache_k, cache_v = cache[0], cache[1]
-                # [s + c, b, n, h/n]
-                k_layer = paddle.concat([cache_k, k_layer], axis=0)
-                v_layer = paddle.concat([cache_v, v_layer], axis=0)
-            cache_kv = None
-            if use_cache:
-                cache_kv = (k_layer, v_layer)
-
             # [s, b, n, h/n] = > [batch_size, seq_len, num_heads, head_dim]
             q_layer = paddle.transpose(q_layer, [1, 0, 2, 3])
             k_layer = paddle.transpose(k_layer, [1, 0, 2, 3])
@@ -286,18 +287,9 @@ class ChatGLMAttention(nn.Layer):
 
             output, attention_probs = attn_output, attn_weights
         else:
-            if cache is not None:
-                cache_k, cache_v = cache[0], cache[1]
-                # [s + c, b, n, h/n]
-                k_layer = paddle.concat([cache_k, k_layer], axis=0)
-                v_layer = paddle.concat([cache_v, v_layer], axis=0)
 
             seq_length, batch_size, num_heads, hidden_size = k_layer.shape
 
-            cache_kv = None
-            if use_cache:
-                cache_kv = (k_layer, v_layer)
-
             attention_scale_coeff = float(layer_id) + 1.0
             if self.attention_scale:
                 # [s, b, n, h/n]

tests/fixtures/llm/finetune.yaml

@@ -17,6 +17,7 @@ finetune:
     fp16_opt_level: "O2"
     do_train: true
     do_eval: true
+    use_flash_attention: true
     disable_tqdm: true
     load_best_model_at_end: true
     eval_with_do_generation: false

tests/fixtures/llm/predictor.yaml

@@ -3,6 +3,7 @@ inference-predict:
     mode: dynamic 
     max_length: 40
     batch_size: 2
+    use_flash_attention: false
     decode_strategy: greedy_search
     dtype: float16
     data_file: tests/fixtures/llm/data/train.json

tests/fixtures/llm/prefix_tuning.yaml

@@ -17,6 +17,7 @@ prefix_tuning:
     do_train: true
     do_eval: true
     disable_tqdm: true
+    use_flash_attention: false
     load_best_model_at_end: true
     eval_with_do_generation: false
     metric_for_best_model: "accuracy"

tests/llm/test_predictor.py

@@ -77,6 +77,30 @@ class PredictorTest(LLMTest, unittest.TestCase):
         else:
             self.assertGreaterEqual(count / len(result_0), 0.4)
 
+    def test_flash_attention(self):
+        self.run_predictor({"inference_model": False, "use_flash_attention": False})
+        result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))
+
+        self.run_predictor({"inference_model": False, "use_flash_attention": True})
+        result_1 = self._read_result(os.path.join(self.output_dir, "predict.json"))
+
+        # compare the generation result of dygraph & flash attention model
+        assert len(result_0) == len(result_1)
+
+        count, full_match = 0, 0
+        for inference_item, no_inference_item in zip(result_0, result_1):
+            if self.model_name_or_path == "__internal_testing__/tiny-random-llama":
+                min_length = 5
+            else:
+                min_length = min(len(inference_item), len(no_inference_item))
+            count += int(inference_item[: min_length // 2] == no_inference_item[: min_length // 2])
+            full_match += int(inference_item[:min_length] == no_inference_item[:min_length])
+
+        if self.model_name_or_path == "__internal_testing__/tiny-random-llama":
+            self.assertGreaterEqual(count / len(result_0), 0.2)
+        else:
+            self.assertEqual(full_match / len(result_0), 1.0)
+
     def test_wint8(self):
         self.run_predictor({"inference_model": True, "quant_type": "weight_only_int8"})
         result_0 = self._read_result(os.path.join(self.output_dir, "predict.json"))

tests/llm/test_prefix_tuning.py

@@ -55,7 +55,6 @@ class PrefixTuningTest(LLMTest, unittest.TestCase):
 
         prefix_tuning_config["dataset_name_or_path"] = self.data_dir
         prefix_tuning_config["output_dir"] = self.output_dir
-
         with argv_context_guard(prefix_tuning_config):
             from finetune_generation import main