making streamllm work with phi multimodal

examples/run_streaming_phi.py

@@ -0,0 +1,134 @@
+# Description: phi_streaming_llm_v1.py This script demonstrates how to enable streaming LLM for a multimodal model.
+import warnings
+warnings.filterwarnings("ignore")
+
+import torch
+import argparse
+import os
+from streaming_llm.utils import load, download_url, load_jsonl
+from enum import Enum
+from transformers import AutoTokenizer
+
+from streaming_llm.enable_streaming_llm import enable_streaming_llm
+
+class InputMode(Enum):
+    LANGUAGE = 0
+    VISION = 1
+    SPEECH = 2
+    VISION_SPEECH = 3
+
+@torch.no_grad()
+def greedy_generate(model, tokenizer, input_ids, past_key_values, max_gen_len):
+    input_mode = torch.tensor([InputMode.LANGUAGE.value], dtype=torch.long, device=model.device)
+    outputs = model(
+        input_ids=input_ids,
+        past_key_values=past_key_values,
+        use_cache=True,
+        input_mode=input_mode,
+        num_logits_to_keep=1  # Only compute last token's logits
+    )
+    past_key_values = outputs.past_key_values
+    pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
+    generated_ids = [pred_token_idx.item()]
+    pos = 0
+    for _ in range(max_gen_len - 1):
+        outputs = model(
+            input_ids=pred_token_idx,
+            past_key_values=past_key_values,
+            use_cache=True,
+            input_mode=input_mode,
+            num_logits_to_keep=1 
+        )
+        past_key_values = outputs.past_key_values
+        pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
+        generated_ids.append(pred_token_idx.item())
+        generated_text = (
+            tokenizer.decode(
+                generated_ids,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=True,
+                spaces_between_special_tokens=False,
+            )
+            .strip()
+            .split(" ")
+        )
+
+        now = len(generated_text) - 1
+        if now > pos:
+            print(" ".join(generated_text[pos:now]), end=" ", flush=True)
+            pos = now
+
+        if pred_token_idx == tokenizer.eos_token_id:
+            break
+    print(" ".join(generated_text[pos:]), flush=True)
+    return past_key_values
+
+
+@torch.no_grad()
+def streaming_inference(model, tokenizer, prompts, kv_cache=None, max_gen_len=1000):
+    past_key_values = None
+    for idx, prompt in enumerate(prompts):
+        prompt = "USER: " + prompt + "\n\nASSISTANT: "
+        print("\n" + prompt, end="")
+        input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+        input_ids = input_ids.to(model.device)
+        seq_len = input_ids.shape[1]
+        if kv_cache is not None:
+            space_needed = seq_len + max_gen_len
+            past_key_values = kv_cache.evict_for_space(past_key_values, space_needed)
+
+        past_key_values = greedy_generate(
+            model, tokenizer, input_ids, past_key_values, max_gen_len=max_gen_len
+        )
+
+def main(args):
+    model_name_or_path = args.model_name_or_path
+    model, tokenizer = load(model_name_or_path)
+    test_filepath = os.path.join(args.data_root, "mt_bench.jsonl")
+    print(f"Loading data from {test_filepath} ...")
+
+    if not os.path.exists(test_filepath):
+        download_url(
+            "https://raw.githubusercontent.com/lm-sys/FastChat/main/fastchat/llm_judge/data/mt_bench/question.jsonl",
+            args.data_root,
+        )
+        os.rename(os.path.join(args.data_root, "question.jsonl"), test_filepath)
+
+    list_data = load_jsonl(test_filepath)
+    prompts = []
+    for sample in list_data:
+        prompts += sample["turns"]
+
+    if args.enable_streaming:
+
+        print("---------------------------") 
+        print("Enabling streaming LLM ...")
+        print("---------------------------") 
+        kv_cache = enable_streaming_llm(
+            model, start_size=args.start_size, recent_size=args.recent_size
+        )
+    else:
+        kv_cache = None
+
+    streaming_inference(
+        model,
+        tokenizer,
+        prompts,
+        kv_cache,
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        default="microsoft/Phi-4-multimodal-instruct"
+    )
+    parser.add_argument("--data_root", type=str, default="data/")
+    parser.add_argument("--enable_streaming",default=True, action="store_true")
+    parser.add_argument("--start_size", type=int, default=200)
+    parser.add_argument("--recent_size", type=int, default=800)
+    args = parser.parse_args()
+
+    main(args)

phi4mm/configuration_phi4mm.py

@@ -0,0 +1,235 @@
+# coding=utf-8
+# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+""" Phi-4-MM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Phi4MMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi4MMModel`]. It is used to instantiate a Phi-4-MM
+    model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 200064):
+            Vocabulary size of the Phi-4-MM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi4MMModel`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        partial_rotary_factor (`float`, *optional*, defaults to 0.5):
+            Percentage of the query and keys which will have rotary embedding.
+        bos_token_id (`int`, *optional*, defaults to 199999):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 199999):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 199999):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi4MMModel, Phi4MMConfig
+
+    >>> # Initializing a Phi-4-MM style configuration
+    >>> configuration = Phi4MMConfig.from_pretrained("TBA")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi4MMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi4mm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=200064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        partial_rotary_factor=1,
+        bos_token_id=199999,
+        eos_token_id=199999,
+        pad_token_id=199999,
+        sliding_window=None,
+        embd_layer: str = "default",
+        img_processor=None,
+        audio_processor=None,
+        vision_lora=None,
+        speech_lora=None,
+        **kwargs,
+    ):
+        self.embd_layer = embd_layer
+        self.img_processor = img_processor
+        self.audio_processor = audio_processor
+        self.vision_lora = vision_lora
+        self.speech_lora = speech_lora
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self._rope_scaling_adjustment()
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_adjustment(self):
+        """
+        Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
+        """
+        if self.rope_scaling is None:
+            return
+
+        rope_scaling_type = self.rope_scaling.get("type", None)
+
+        # For backward compatibility if previous version used "su" or "yarn"
+        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
+            self.rope_scaling["type"] = "longrope"
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        rotary_ndims = int(self.hidden_size // self.num_attention_heads * self.partial_rotary_factor)
+        if not len(rope_scaling_short_factor) == rotary_ndims // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == rotary_ndims // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {rotary_ndims // 2}, got {len(rope_scaling_long_factor)}"
+            )