[LLM]support QWenVL second part

llm/predictor.py

@@ -1386,11 +1386,16 @@ def create_predictor(
                 )
                 model.eval()
             elif "qwen" in config.architectures[0].lower():
-                from paddlenlp.experimental.transformers import (
-                    QWenForCausalLMInferenceModel,
-                )
-
-                model = QWenForCausalLMInferenceModel.from_pretrained(
+                if model_args.model_type == "qwen-img2txt":
+                    # we use qwen for img2txt.
+                    from paddlenlp.experimental.transformers import (
+                        QWenForQWenVLInferenceModel as QWenInferenceModel,
+                    )
+                else:
+                    from paddlenlp.experimental.transformers import (
+                        QWenForCausalLMInferenceModel as QWenInferenceModel,
+                    )
+                model = QWenInferenceModel.from_pretrained(
                     predictor_args.model_name_or_path,
                     config=config,
                     dtype=predictor_args.dtype,

paddlenlp/experimental/transformers/qwen/modeling.py

@@ -39,7 +39,7 @@
 )
 from paddlenlp.transformers.qwen.modeling import QWenLMHead, QWenPretrainingCriterion
 
-__all__ = ["QWenForCausalLMInferenceModel"]
+__all__ = ["QWenForCausalLMInferenceModel", "QWenForQWenVLInferenceModel"]
 
 
 class FusedQWenRMSNorm(nn.Layer):
@@ -244,6 +244,19 @@ def remove_padding(self, input_ids, seq_lens_this_time):
         )
         return ids_remove_padding, padding_offset, cum_offsets
 
+    # This function is a little different from prepare_input_ids_for_generation in paddlenlp/transformers/generation/utils.py,
+    # it is used to generate fake input_ids according to inputs_embeds length.
+    @staticmethod
+    def prepare_input_ids_for_generation(bos_token_id, encoder_output=None):
+        batch_size = 1
+        seq_len = 1
+        if bos_token_id is None:
+            raise ValueError("`bos_token_id` should be defined when no " "`input_ids` are provided.")
+        if encoder_output is not None:
+            batch_size = encoder_output.shape[0]
+            seq_len = encoder_output.shape[1]
+        return paddle.full([batch_size, seq_len], bos_token_id, dtype="int64")
+
     def forward(
         self,
         input_ids=None,
@@ -270,17 +283,21 @@ def forward(
         elif input_ids is None and inputs_embeds is None:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
+        # generate a fake input_ids according to inputs_embeds
+        # this is usually occurred in img2txt multimodal model when first enter into this forward function.
+        if input_ids is None and inputs_embeds is not None:
+            input_ids = self.prepare_input_ids_for_generation(self.config.bos_token_id, inputs_embeds)
+        if inputs_embeds is not None:
+            batch, seq_len, hidden_dim = inputs_embeds.shape
+            inputs_embeds = inputs_embeds.reshape([batch * seq_len, hidden_dim])
+
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         use_cache = use_cache if use_cache is not None else self.config.use_cache
         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 
-        if inputs_embeds is not None:
-            batch, seq_len, hidden_dim = inputs_embeds.shape
-            inputs_embeds = inputs_embeds.reshape([batch * seq_len, hidden_dim])
-
         if past_key_values is None:
             past_key_values = tuple([None] * self.config.num_hidden_layers)
 
@@ -502,3 +519,122 @@ def set_state_dict(self, state_dict):
             lm_head_weight = paddle.to_tensor(state_dict["lm_head.weight"], dtype=self.lm_head.weight.dtype)
             self.lm_head.weight.set_value(lm_head_weight)
         self.qwen.set_state_dict({k: state_dict[k] for k in state_dict.keys()})
+
+
+class QWenForQWenVLInferenceModel(QWenForCausalLMInferenceModel):
+    """
+    This class is 99% like QWenForCausalLMInferenceModel.
+    Used only for QWenVL's second part.
+    """
+
+    # This function corresponds to QWenVL's second part, only used for QWenVL.
+    @paddle.no_grad()
+    def generate_text_with_image_features(
+        self,
+        input_ids: paddle.Tensor,
+        image_features: paddle.Tensor,
+        img_pos: paddle.Tensor,
+        attention_mask: paddle.Tensor,
+        position_ids=None,
+        penalty_score=None,
+        frequency_score=None,
+        presence_score=None,
+        min_length=None,
+        max_length=None,
+        temperature=None,
+        top_p=None,
+        eos_token_id=None,
+        seq_len_encoder=None,
+        seq_len_decoder=None,
+        step_idx=None,
+        stop_flags=None,
+        tgt_ids=None,
+        tgt_pos=None,
+        tgt_generation_mask=None,
+        pre_ids=None,
+        stop_nums=None,
+        cache_kvs=[],
+        inputs_embeds=None,
+        **generate_kwargs
+    ) -> paddle.Tensor:
+        inputs_embeds = self.qwen.wte(input_ids)
+        inputs_embeds_dtype = inputs_embeds.dtype
+        if inputs_embeds_dtype != paddle.float32:
+            inputs_embeds = paddle.cast(inputs_embeds, paddle.float32)
+            image_features = paddle.cast(image_features, paddle.float32)
+
+        for idx, (i, image_start_idx, image_end_idx) in enumerate(img_pos):
+            index = paddle.arange(image_start_idx + 1, image_end_idx).unsqueeze(-1)
+            inputs_embeds[i] = paddle.scatter(inputs_embeds[i], index, image_features[idx])
+
+        if inputs_embeds_dtype != paddle.float32:
+            inputs_embeds = paddle.cast(inputs_embeds, inputs_embeds_dtype)
+
+        outputs = self.generate(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            penalty_score=penalty_score,
+            frequency_score=frequency_score,
+            presence_score=presence_score,
+            min_length=min_length,
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            eos_token_id=eos_token_id,
+            seq_len_encoder=seq_len_encoder,
+            seq_len_decoder=seq_len_decoder,
+            step_idx=step_idx,
+            stop_flags=stop_flags,
+            tgt_ids=tgt_ids,
+            tgt_pos=tgt_pos,
+            tgt_generation_mask=tgt_generation_mask,
+            pre_ids=pre_ids,
+            stop_nums=stop_nums,
+            cache_kvs=cache_kvs,
+        )
+        return outputs
+
+    # rewrite to_static function in generation_utils.py
+    def to_static(self, output_path: str, config: dict):
+        dtype = config.get("dtype", paddle.get_default_dtype())
+        cache_kvs_shapes = self.get_cache_kvs_shape(self.config, max_length=config.get("max_length", None))
+        input_spec = [
+            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids"),  # input_ids
+            paddle.static.InputSpec(
+                shape=[None, None, None], dtype="float32", name="image_features"
+            ),  # image_features
+            paddle.static.InputSpec(shape=[None, 3], dtype="int64", name="img_pos"),  # img_pos
+            paddle.static.InputSpec(shape=[None, None], dtype=dtype, name="attention_mask"),  # attention_mask
+            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="position_ids"),  # position_ids
+            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="penalty_score"),  # penalty_score
+            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="frequency_score"),  # frequency_score
+            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="presence_score"),  # presence_score
+            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="min_length"),  # min_decode_length
+            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="max_length"),  # max_decode_length
+            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="temperature"),  # temperature
+            paddle.static.InputSpec(shape=[None, 1], dtype="float32", name="top_p"),  # top_p
+            paddle.static.InputSpec(shape=[None], dtype="int64", name="eos_token_id"),  # eos_token_id
+            paddle.static.InputSpec(shape=[None, 1], dtype="int32", name="seq_len_encoder"),  # seq_len_encoder
+            paddle.static.InputSpec(shape=[None, 1], dtype="int32", name="seq_len_decoder"),  # seq_len_decoder
+            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="step_idx"),  # step_idx
+            paddle.static.InputSpec(shape=[None, 1], dtype="bool", name="stop_flags"),  # stop_flags
+            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="tgt_ids"),  # tgt_ids
+            paddle.static.InputSpec(shape=[None, 1], dtype="int64", name="tgt_pos"),  # tgt_pos
+            paddle.static.InputSpec(
+                shape=[None, 1, 1, None], dtype=dtype, name="tgt_generation_mask"
+            ),  # tgt_generation_mask
+            paddle.static.InputSpec(shape=[None, None], dtype="int64", name="pre_ids"),  # pre_ids
+            paddle.static.InputSpec(shape=[1], dtype="int64", name="stop_nums"),  # stop_nums
+            [
+                paddle.static.InputSpec(
+                    shape=shape,
+                    dtype=dtype,
+                    name="cache_kvs_{}".format(i),
+                )
+                for i, shape in enumerate(cache_kvs_shapes)
+            ],  # cache_kvs
+        ]
+
+        model = paddle.jit.to_static(self.generate_text_with_image_features, input_spec=input_spec)
+        paddle.jit.save(model, output_path, skip_prune_program=True)

paddlenlp/transformers/qwen/configuration.py

@@ -47,6 +47,9 @@ def __init__(
         tensor_parallel_output=True,
         no_bias=True,
         tie_word_embeddings=False,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -72,4 +75,10 @@ def __init__(
         self.use_fused_rope = use_fused_rope
         self.no_bias = no_bias
 
-        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

tests/llm/test_predictor.py

@@ -20,7 +20,9 @@
 import pytest
 from parameterized import parameterized_class
 
+from paddlenlp.experimental.transformers import QWenForQWenVLInferenceModel
 from paddlenlp.transformers import (  # ChatGLMForCausalLM,
+    AutoConfig,
     AutoTokenizer,
     BloomForCausalLM,
     ChatGLMForCausalLM,
@@ -325,3 +327,108 @@ def test_predictor(self):
 
         self.assertGreaterEqual(full_match / len(result_0), 0.25)
         self.assertGreaterEqual(count / len(result_0), 0.4)
+
+
+class QWenVLTest(LLMTest, unittest.TestCase):
+    config_path: str = "./tests/fixtures/llm/predictor.yaml"
+    model_name_or_path: str = "__internal_testing__/tiny-fused-qwen"
+    model_class = QWenForCausalLM
+
+    def setUp(self) -> None:
+        super().setUp()
+        paddle.set_default_dtype("float32")
+        self.model_class.from_pretrained(self.model_name_or_path, dtype="float16").save_pretrained(self.output_dir)
+        AutoTokenizer.from_pretrained(self.model_name_or_path).save_pretrained(self.output_dir)
+
+    def test_forward(self):
+        self.disable_static()
+        config = AutoConfig.from_pretrained(self.output_dir)
+        config.quant_type = None
+        config.weight_only_quant_bits = None
+
+        paddle.set_default_dtype("float16")
+        model = QWenForQWenVLInferenceModel.from_pretrained(self.output_dir, config=config, dtype="float16")
+
+        batch = 1
+        seq = 31
+        max_len = 50
+        dtype = "float16"
+        input_ids = paddle.randint(0, 100, [batch, seq], dtype="int64")
+        image_features = paddle.randn([batch, 16, config.hidden_size], dtype="float16")
+        tgt_generation_mask = paddle.full([batch, 1, 1, max_len], 1, dtype=dtype)
+        img_pos = paddle.to_tensor([[0, 4, 21]], dtype="int64")
+        attention_mask = paddle.full([batch, 1, max_len, max_len], 0, dtype=dtype)
+        attention_mask[:, 0, :seq, :seq] = paddle.tril(paddle.ones(shape=(seq, seq), dtype=dtype))
+        position_ids = paddle.full([batch, seq], 0, dtype="int64")
+        for i in range(batch):
+            position_ids[i, :] = paddle.to_tensor([i for i in range(seq)], dtype="int64")
+
+        inputs = [
+            input_ids,  # input_ids
+            image_features,  # image_features
+            img_pos,  # img_pos
+            attention_mask,  # attention_mask
+            position_ids,  # position_ids
+            paddle.full([batch, 1], 1.0, dtype="float32"),  # penalty_score
+            paddle.full([batch, 1], 0.0, dtype="float32"),  # frequency_score,
+            paddle.full([batch, 1], 0.0, dtype="float32"),  # presence_score,
+            paddle.full([batch, 1], 1, dtype="int64"),  # min_length,
+            paddle.full([batch, 1], max_len - seq, dtype="int64"),  # max_length,
+            paddle.full([batch, 1], 1.0, dtype="float32"),  # temperature,
+            paddle.full([batch, 1], 0.0, dtype="float32"),  # top_p,
+            paddle.full([1], 151643, dtype="int64"),  # eos_token_id,
+            paddle.full([batch, 1], seq, dtype="int32"),  # seq_len_encoder,
+            paddle.full([batch, 1], seq, dtype="int32"),  # seq_len_decoder,
+            paddle.full([batch, 1], 0, dtype="int64"),  # step_idx,
+            paddle.full([batch, 1], False, dtype="bool"),  # stop_flags,
+            paddle.full([batch, 1], -123, dtype="int64"),  # tgt_ids can be be initialized arbitrarily
+            paddle.full([batch, 1], seq - 1, dtype="int64"),  # tgt_pos,
+            tgt_generation_mask,  # tgt_generation_mask,
+            paddle.full([batch, max_len], -100, dtype="int64"),  # pre_ids, can be initialized arbitrarily
+            paddle.full([1], batch, dtype="int64"),  # stop_nums, be batch
+        ]
+        for i in range(config.num_hidden_layers):
+            tmp = paddle.rand(shape=[2, batch, 1, max_len, 64], dtype=dtype)
+            inputs.append(tmp)
+
+        model.eval()
+        model.generate_text_with_image_features(
+            input_ids=inputs[0],
+            image_features=inputs[1],
+            img_pos=inputs[2],
+            attention_mask=inputs[3],
+            position_ids=inputs[4],
+            penalty_score=inputs[5],
+            frequency_score=inputs[6],
+            presence_score=inputs[7],
+            min_length=inputs[8],
+            max_length=inputs[9],
+            temperature=inputs[10],
+            top_p=inputs[11],
+            eos_token_id=inputs[12],
+            seq_len_encoder=inputs[13],
+            seq_len_decoder=inputs[14],
+            step_idx=inputs[15],
+            stop_flags=inputs[16],
+            tgt_ids=inputs[17],
+            tgt_pos=inputs[18],
+            tgt_generation_mask=inputs[19],
+            pre_ids=inputs[20],
+            stop_nums=inputs[21],
+            cache_kvs=inputs[22:],
+        )
+
+    def test_export(self):
+        self.disable_static()
+        config = load_test_config(self.config_path, "inference-to-static")
+        config["model_name_or_path"] = self.model_name_or_path
+        config["output_path"] = self.output_dir
+        config["dtype"] = "float16"
+        config["inference_model"] = True
+        config["model_prefix"] = "qwen"
+        config["model_type"] = "qwen-img2txt"
+
+        with argv_context_guard(config):
+            from export_model import main
+
+            main()