[Model] Add Granite model (#7436)

Co-authored-by: Nick Hill <nickhill@us.ibm.com>

tests/models/test_granite.py

+"""Compare the outputs of HF and vLLM for Granite models using greedy sampling.
+
+Run `pytest tests/models/test_granite.py`.
+"""
+import importlib.metadata
+
+import pytest
+
+from .utils import check_logprobs_close
+
+TRANSFORMERS_VERSION = tuple(
+    map(int,
+        importlib.metadata.version("transformers").split(".")))
+
+MODELS = [
+    "ibm/PowerLM-3b",
+]
+
+
+# GraniteForCausalLM will be in transformers >= 4.45
+@pytest.mark.skipif(TRANSFORMERS_VERSION < (4, 45),
+                    reason="granite model test requires transformers >= 4.45")
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    # TODO(sang): Sliding window should be tested separately.
+    with hf_runner(model, dtype=dtype) as hf_model:
+        hf_outputs = hf_model.generate_greedy_logprobs_limit(
+            example_prompts, max_tokens, num_logprobs)
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy_logprobs(
+            example_prompts, max_tokens, num_logprobs)
+    check_logprobs_close(
+        outputs_0_lst=hf_outputs,
+        outputs_1_lst=vllm_outputs,
+        name_0="hf",
+        name_1="vllm",
+    )

vllm/model_executor/models/__init__.py

@@ -65,6 +65,7 @@ _GENERATION_MODELS = {
     "EAGLEModel": ("eagle", "EAGLE"),
     "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
     "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
+    "GraniteForCausalLM": ("granite", "GraniteForCausalLM")
 }
 
 _EMBEDDING_MODELS = {

vllm/transformers_utils/configs/granite.py

+# coding=utf-8
+# Copyright 2024 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+"""Granite model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import rope_config_validation
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class GraniteConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of
+    a [`GraniteModel`]. It is used to instantiate an Granite
+    model according to the specified arguments, defining the model architecture.
+    Instantiating a configuration with the defaults will yield a similar
+    configuration to that of the Granite-3B.
+
+    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 32000):
+            Vocabulary size of the Granite model. Defines the number of
+            different tokens that can be represented by the `inputs_ids`
+            passed when calling [`GraniteModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            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`.
+        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 2048):
+            The maximum sequence length that this model might ever be used with.
+        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-06):
+            The epsilon used by the rms normalization layers.
+        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`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        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*):
+            Dictionary containing the scaling configuration for the RoPE
+            embeddings. Currently supports two scaling strategies: linear and
+            dynamic. Their scaling factor must be a float greater than 1. The
+            expected format is
+            `{"type": strategy name, "factor": scaling factor}`.
+            When using this flag, don't update `max_position_embeddings` to
+            the expected new maximum. See the following thread for more
+            information on how these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/.
+            This is an experimental feature, subject to breaking API changes
+            in future versions.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output
+            projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers
+            in the MLP layers.
+        embedding_multiplier (`float`, *optional*, defaults to 1.0):
+            embedding multiplier
+        logits_scaling (`float`, *optional*, defaults to 1.0):
+            divisor for output logits
+        residual_multiplier (`float`, *optional*, defaults to 1.0):
+            residual multiplier
+        attention_multiplier (`float`, *optional*, defaults to 1.0):
+            attention multiplier
+
+    ```python
+    >>> from transformers import GraniteModel, GraniteConfig
+
+    >>> # Initializing a Granite granite-3b style configuration
+    >>> configuration = GraniteConfig()
+
+    >>> # Initializing a model from the granite-7b style configuration
+    >>> model = GraniteModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "granite"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        embedding_multiplier=1.0,
+        logits_scaling=1.0,
+        residual_multiplier=1.0,
+        attention_multiplier=1.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        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.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+
+        self.embedding_multiplier = embedding_multiplier
+        self.logits_scaling = logits_scaling
+        self.residual_multiplier = residual_multiplier
+        self.attention_multiplier = attention_multiplier
+
+        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,
+        )
+
+        rope_config_validation(self)