add kv and attention

Signed-off-by: Kyle Sayers <[email protected]>

Author: kylesayrs

src/compressed_tensors/modeling/attention.py

@@ -0,0 +1,186 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. 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.
+
+import inspect
+from typing import Callable, Optional
+from weakref import ref
+
+from compressed_tensors.modeling.kvcache import initialize_hooked_kv_cache
+from compressed_tensors.quantization import (
+    QuantizationArgs,
+    QuantizationScheme,
+    QuantizationStrategy,
+    forward_quantize,
+)
+from compressed_tensors.quantization.lifecycle.initialize import (
+    _initialize_scale_zero_point,
+)
+from compressed_tensors.utils import getattr_chain
+from compressed_tensors.utils.internal import InternalModule
+from torch import Tensor
+from torch.nn import Module
+from torch.utils.hooks import RemovableHandle
+from transformers import AttentionInterface, PretrainedConfig, PreTrainedModel
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
+
+
+__all__ = [
+    "QuantizedAttentionImpl",
+    "initialize_hooked_attention",
+    "register_query_hook",
+]
+
+
+IMPL_ATTR = "impl"
+HOOKED_ATTENTION_NAME = "ct_hooked_attention"
+
+
+class QuantizedAttentionImpl(InternalModule):
+    """
+    QuantizedAttentionImpl module which wraps the functionality of the original
+    attention implementation. Unlike the original attention function, this
+    implementation is a `torch.nn.Module` which can be hooked to trigger
+    transforms and calibration hooks.
+
+    This module works by being registered as a submodule to attention modules via
+    `initialize_hooked_attention`, registering a new attention implementation function
+    which calls this module, then setting the model attention implementation to the new
+    function. After triggering hooks and quantization, this module calls the original
+    attention implementation function.
+
+    :param attn_module: parent attention module
+    """
+
+    def __init__(self, config: PretrainedConfig, attn_module: Module):
+        super().__init__()
+        self.config = config
+        self.attn_module = ref(attn_module)  # avoid circular references
+        self._qparams_initialized = False
+
+    def forward(
+        self,
+        module: Module,
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+        *args,
+        **kwargs,
+    ):
+        # quantization
+        quant_args_attr = "quantization_scheme.input_activations"
+        quant_args = getattr_chain(module, quant_args_attr, None)
+        quant_enabled = getattr(module, "quantization_enabled", True)
+        if quant_args is not None and quant_enabled and self._qparams_initialized:
+            query = forward_quantize(module, query, "q", quant_args)
+
+        # original attention
+        return ALL_ATTENTION_FUNCTIONS[_original_impl](
+            module,
+            query,
+            key,
+            value,
+            *args,
+            **kwargs,
+        )
+
+    def initialize_qparams_once(self, model: PreTrainedModel, module: Module):
+        """
+        Initialize attention quantization parameters if they have not already been
+        initialized. KV cache quantization parameters are initialized by the
+        `QuantizedKVCache`
+
+        :param model: parent model of attention module
+        :param module: attention module to initialize with
+        """
+        # TODO: move to initialize.py
+        assert module is self.attn_module()
+        scheme: Optional[QuantizationScheme] = getattr(
+            module, "quantization_scheme", None
+        )
+        quant_args: Optional[QuantizationArgs] = getattr(
+            scheme, "input_activations", None
+        )
+
+        if (
+            not self._qparams_initialized
+            and quant_args is not None
+            and not scheme.kv_cache_only
+        ):
+            assert quant_args.strategy == QuantizationStrategy.TENSOR
+            _initialize_scale_zero_point(module, "q", quant_args)
+            self._qparams_initialized = True
+
+
+# ----- initialize ----- #
+
+
+def _ct_hooked_attention(module: Module, *args, **kwargs):
+    if hasattr(module, IMPL_ATTR):
+        return module.impl(module, *args, **kwargs)
+    else:
+        return ALL_ATTENTION_FUNCTIONS[_original_impl](module, *args, **kwargs)
+
+
+def initialize_hooked_attention(
+    model: PreTrainedModel, module: Module, quantize: bool = True
+):
+    """
+    Initialize `QuantizedAttentionImpl` and `QuantizedKVCache` instances
+    attached to attention
+
+    :param model: parent model of attention module
+    :param module: attention module to initialize with
+    :param quantize: initialize attention quantization parameters
+    """
+    if not hasattr(module, IMPL_ATTR):
+        module.register_module(IMPL_ATTR, QuantizedAttentionImpl(model.config, module))
+        if model.config._attn_implementation != HOOKED_ATTENTION_NAME:
+            # assumes only one model at a time
+            global _original_impl
+            _original_impl = model.config._attn_implementation
+
+            AttentionInterface.register(HOOKED_ATTENTION_NAME, _ct_hooked_attention)
+            model.config._attn_implementation = HOOKED_ATTENTION_NAME
+
+    impl: QuantizedAttentionImpl = getattr(module, IMPL_ATTR)
+    if quantize:
+        impl.initialize_qparams_once(model, module)
+
+    initialize_hooked_kv_cache(model, module, quantize=quantize)
+
+
+# ----- hooks ----- #
+
+
+def register_query_hook(
+    module: Module, hook: Callable[[Module, Tensor], Optional[Tensor]]
+) -> RemovableHandle:
+    """
+    Register a hook which takes post-rope query states as an argument and
+    returns the modified query states or `None`
+
+    :param module: attention module to add hook to
+    :param hook: query hook function
+    """
+    impl = getattr(module, IMPL_ATTR)
+
+    def _hook(impl: QuantizedAttentionImpl, args, kwargs):
+        bound = inspect.signature(impl.forward).bind(*args, **kwargs)
+        value = hook(module, bound.arguments["query"])
+        if value is not None:
+            bound.arguments["query"] = value
+
+        return bound.args, bound.kwargs
+
+    return impl.register_forward_pre_hook(_hook, with_kwargs=True)

src/compressed_tensors/modeling/kvcache.py

@@ -0,0 +1,192 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. 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.
+
+import inspect
+from typing import Callable, Optional, Tuple
+from weakref import ref
+
+from compressed_tensors.quantization import QuantizationStrategy, forward_quantize
+from compressed_tensors.quantization.lifecycle.initialize import (
+    _initialize_scale_zero_point,
+)
+from compressed_tensors.utils import getattr_chain
+from compressed_tensors.utils.internal import InternalModule
+from torch import Tensor
+from torch.nn import Module
+from torch.utils.hooks import RemovableHandle
+from transformers import Cache, PretrainedConfig, PreTrainedModel
+
+
+__all__ = [
+    "QuantizedKVCache",
+    "initialize_hooked_kv_cache",
+    "register_key_hook",
+    "register_value_hook",
+]
+
+
+KV_CACHE_ATTR = "kv_cache"
+
+
+class QuantizedKVCache(InternalModule):
+    """
+    QuantizedKVCache module which wraps the functionality of any existing kvcache args.
+    Unlike transform Cache instances, this cache is a `torch.nn.Module` which can be
+    hooked to trigger transforms and calibration hooks.
+
+    This module works by being registered as a submodule to attention modules via
+    `initialize_hooked_kv_cache`, then adding a hook which replaces `past_key_values`
+    kwargs with this module. This module adopts the functionality of the replaced cache,
+    preserving caching functionality such as sliding window attention, ect.
+
+    :param attn_module: parent attention module
+    """
+
+    def __init__(self, config: PretrainedConfig, attn_module: Module):
+        super().__init__()
+        self.config = config
+        self.attn_module = ref(attn_module)  # avoid circular reference
+        self.past_key_values: Optional[Cache] = None
+        self._qparams_initialized = False
+
+    def update(self, *args, **kwargs) -> Tuple[Tensor, Tensor]:
+        return self(*args, **kwargs)
+
+    def forward(
+        self,
+        key_states: Tensor,
+        value_states: Tensor,
+        *args,
+        **kwargs,
+    ) -> Tuple[Tensor, Tensor]:
+        # quantization
+        module = self.attn_module()
+        quant_args_attr = "quantization_scheme.input_activations"
+        quant_args = getattr_chain(module, quant_args_attr, None)
+        quant_enabled = getattr(module, "quantization_enabled", True)
+        if quant_args is not None and quant_enabled and self._qparams_initialized:
+            key_states = forward_quantize(module, key_states, "k", quant_args)
+            value_states = forward_quantize(module, value_states, "v", quant_args)
+
+        # original cache
+        if self.past_key_values is not None:
+            ret = self.past_key_values.update(key_states, value_states, *args, **kwargs)
+        else:
+            ret = (key_states, value_states)
+
+        self.past_key_values = None
+        return ret
+
+    def initialize_qparams_once(self, model: PreTrainedModel, module: Module):
+        """
+        Initialize kv cache quantization parameters if they have not already been
+        initialized
+
+        :param model: parent model of attention module
+        :param module: attention module to initialize with
+        """
+        # TODO: move to initialize.py
+        assert module is self.attn_module()
+        scheme = getattr(module, "quantization_scheme", None)
+        quant_args = getattr(scheme, "input_activations", None)
+
+        if not self._qparams_initialized and quant_args is not None:
+            assert quant_args.strategy == QuantizationStrategy.TENSOR
+            _initialize_scale_zero_point(module, "k", quant_args)
+            _initialize_scale_zero_point(module, "v", quant_args)
+            self._qparams_initialized = True
+
+
+# ----- initialize ----- #
+
+
+def _kv_cache_attention_hook(module: Module, args, kwargs):
+    kv_cache: QuantizedKVCache = getattr(module, KV_CACHE_ATTR)
+    _past_kv_name = (
+        "past_key_values"  # transformers#39956
+        if "past_key_values" in inspect.signature(module.forward).parameters
+        else "past_key_value"
+    )
+    kv_cache.past_key_values = kwargs.get(_past_kv_name, None)
+    kwargs[_past_kv_name] = kv_cache
+
+    return args, kwargs
+
+
+def initialize_hooked_kv_cache(
+    model: PreTrainedModel, module: Module, quantize: bool = False
+):
+    """
+    Initialize a `QuantizedKVCache` instance attached to attention
+
+    :param model: parent model of attention module
+    :param module: attention module to initialize with
+    :param quantize: initialize kv cache quantization parameters
+    """
+    if not hasattr(module, KV_CACHE_ATTR):
+        module.register_module(KV_CACHE_ATTR, QuantizedKVCache(model.config, module))
+        module.register_forward_pre_hook(_kv_cache_attention_hook, with_kwargs=True)
+
+    kv_cache: QuantizedKVCache = getattr(module, KV_CACHE_ATTR)
+    if quantize:
+        kv_cache.initialize_qparams_once(model, module)
+
+
+# ----- hooks ----- #
+
+
+def register_key_hook(
+    module: Module, hook: Callable[[Module, Tensor], Optional[Tensor]]
+) -> RemovableHandle:
+    """
+    Register a hook which takes post-rope key states as an argument and
+    returns the modified key states or `None`
+
+    :param module: attention module to add hook to
+    :param hook: key hook function
+    """
+    kv_cache: QuantizedKVCache = getattr(module, KV_CACHE_ATTR)
+
+    def _hook(cache: QuantizedKVCache, args, kwargs):
+        bound = inspect.signature(cache.forward).bind(*args, **kwargs)
+        value = hook(module, bound.arguments["key_states"])
+        if value is not None:
+            bound.arguments["key_states"] = value
+
+        return bound.args, bound.kwargs
+
+    return kv_cache.register_forward_pre_hook(_hook, with_kwargs=True)
+
+
+def register_value_hook(
+    module: Module, hook: Callable[[Module, Tensor], Optional[Tensor]]
+) -> RemovableHandle:
+    """
+    Register a hook which takes value states as an argument and
+    returns the modified value states or `None`
+
+    :param module: attention module to add hook to
+    :param hook: value hook function
+    """
+    kv_cache: QuantizedKVCache = getattr(module, KV_CACHE_ATTR)
+
+    def _hook(cache: QuantizedKVCache, args, kwargs):
+        bound = inspect.signature(cache.forward).bind(*args, **kwargs)
+        value = hook(module, bound.arguments["value_states"])
+        if value is not None:
+            bound.arguments["value_states"] = value
+
+        return bound.args, bound.kwargs
+
+    return kv_cache.register_forward_pre_hook(_hook, with_kwargs=True)