Llama 3.1 conversion

Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com>

src/transformers/modeling_rope_utils.py

@@ -129,6 +129,7 @@ def _compute_dynamic_ntk_parameters(
         Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
         post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
     """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
     if config is not None and len(rope_kwargs) > 0:
         raise ValueError(
             "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
@@ -249,6 +250,7 @@ def _compute_longrope_parameters(
         Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
         post-processing scaling factor applied to the computed cos/sin.
     """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
     # No need to keep BC with longrope, unreleased when this new pattern was created.
     if len(rope_kwargs) > 0:
         raise ValueError(
@@ -293,6 +295,50 @@ def _compute_longrope_parameters(
     return inv_freq, attention_factor
 
 
+def _compute_llama3_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+    new_freqs = []
+    for freq in inv_freq:
+        wavelen = 2 * math.pi / freq
+        if wavelen < high_freq_wavelen:
+            new_freqs.append(freq)
+        elif wavelen > low_freq_wavelen:
+            new_freqs.append(freq / factor)
+        else:
+            assert low_freq_wavelen != high_freq_wavelen
+            smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+            new_freqs.append((1 - smooth) * freq / factor + smooth * freq)
+    inv_freq = torch.tensor(new_freqs, dtype=inv_freq.dtype, device=inv_freq.device)
+    return inv_freq, attention_factor
+
+
 # This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
 # from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
 # parameterizations, as long as the callable has the same signature.
@@ -302,6 +348,7 @@ def _compute_longrope_parameters(
     "dynamic": _compute_dynamic_ntk_parameters,
     "yarn": _compute_yarn_parameters,
     "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
 }
 
 
@@ -339,6 +386,20 @@ def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
         raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling["rope_type"]
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
 def _validate_yarn_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling["rope_type"]
@@ -374,7 +435,8 @@ def _validate_longrope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
     rope_type = rope_scaling["rope_type"]
     required_keys = {"rope_type", "short_factor", "long_factor"}
-    optional_keys = {"attention_factor", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
 
@@ -417,13 +479,50 @@ def _validate_longrope_parameters(config: PretrainedConfig):
             )
 
 
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling["rope_type"]
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        raise ValueError(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        raise ValueError(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor < low_freq_factor:
+        raise ValueError(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        raise ValueError(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        raise ValueError(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
 # Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
 ROPE_VALIDATION_FUNCTIONS = {
     "default": _validate_default_rope_parameters,
     "linear": _validate_linear_scaling_rope_parameters,
-    "dynamic": _validate_linear_scaling_rope_parameters,  # `dynamic` has the same validation pattern as `linear`
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
     "yarn": _validate_yarn_parameters,
     "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
 }
 
 

src/transformers/models/llama/configuration_llama.py

@@ -73,25 +73,28 @@ class LlamaConfig(PretrainedConfig):
             End of stream token id.
         pretraining_tp (`int`, *optional*, defaults to 1):
             Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
-            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
-            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
-            issue](https://github.com/pytorch/pytorch/issues/76232).
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
         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. IMPORTANT: RoPE scaling expects
-            `max_position_embeddings` to remain unchanged -- some methods, like 'longrope', require the original value
-            to determine which scaling to apply.
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
             Expected contents:
                 `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope'],
-                    with 'default' being the original RoPE implementation.
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
                 `factor` (`float`, *optional*):
                     Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                     most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    `max_position_embeddings`.
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
                 `attention_factor` (`float`, *optional*):
                     Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                     computation. If unspecified, it defaults to value recommended by the implementation, using the
@@ -104,12 +107,16 @@ class LlamaConfig(PretrainedConfig):
                     ramp function. If unspecified, it defaults to 1.
                 `short_factor` (`List[float]`, *optional*):
                     Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `max_position_embeddings` * `factor`). Must be a list of numbers with the same length as the hidden
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                     size divided by the number of attention heads divided by 2
                 `long_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `max_position_embeddings` * `factor`). Must be a list of numbers with the same length as the hidden
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                     size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
         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):