[ExecuTorch] Add quantized kv cache to llama

examples/models/llama2/TARGETS

@@ -73,6 +73,7 @@ runtime.python_library(
         "source_transformation/apply_spin_quant_r1_r2.py",
         "source_transformation/prune_output.py",
         "source_transformation/quantize.py",
+        "source_transformation/quantized_kv_cache.py",
         "source_transformation/rms_norm.py",
         "source_transformation/rope.py",
         "source_transformation/sdpa.py",

examples/models/llama2/export_llama_lib.py

@@ -53,7 +53,11 @@
     get_quant_embedding_transform,
     get_quant_weight_transform,
 )
+from .source_transformation.quantized_kv_cache import (
+    replace_kv_cache_with_quantized_kv_cache,
+)
 from .source_transformation.rms_norm import replace_rms_norm_with_native_rms_norm
+
 from .source_transformation.rope import materialze_broadcast_of_rope_freq_cis
 from .source_transformation.sdpa import (
     replace_causal_mask,
@@ -206,6 +210,12 @@ def build_args_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Whether or not to export a model using kv cache",
     )
+    parser.add_argument(
+        "--quantize_kv_cache",
+        default=False,
+        action="store_true",
+        help="Whether or not to export a model using quantized kv cache",
+    )
     parser.add_argument(
         "--num_sharding",
         type=int,
@@ -428,7 +438,6 @@ def _prepare_for_llama_export(modelname: str, args) -> LLMEdgeManager:
 
     Returns a LLMEdgeManager prior to calling export_to_edge with quantizers
     """
-
     # load model from checkpoint and params.json
     checkpoint_path = canonical_path(args.checkpoint) if args.checkpoint else None
     checkpoint_dir = (
@@ -446,6 +455,41 @@ def _prepare_for_llama_export(modelname: str, args) -> LLMEdgeManager:
     else:
         dtype_override = None
 
+    # source transforms
+    transforms = []
+    if args.quantization_mode:
+        modelname = f"{modelname}_q"
+        transforms.append(
+            get_quant_weight_transform(args, dtype_override, verbose_export())
+        )
+
+    if args.embedding_quantize:
+        modelname = f"{modelname}_e"
+        transforms.append(get_quant_embedding_transform(args))
+
+    if args.expand_rope_table:
+        transforms.append(materialze_broadcast_of_rope_freq_cis)
+
+    if args.use_sdpa_with_kv_cache:
+        transforms.append(replace_sdpa_with_custom_op)
+
+    if args.quantize_kv_cache:
+        assert (
+            args.use_kv_cache and not args.use_sdpa_with_kv_cache
+        ), "quantize_kv_cache requires use_kv_cache=True and use_sdpa_with_kv_cache=False"
+        transforms.append(replace_kv_cache_with_quantized_kv_cache)
+
+    if args.use_kv_cache:
+        if args.qnn:
+            transforms.append(replace_kv_cache_with_simple_kv_cache)
+            transforms.append(replace_sdpa_with_flex_sdpa)
+            transforms.append(replace_causal_mask)
+
+        elif args.coreml or args.mps:
+            # Currently qnn/coreml/mps doesn't support sdpa op, use the simpler decomposition
+            # to get free perf gain.
+            transforms.append(replace_sdpa_with_simple_sdpa)
+            transforms.append(replace_causal_mask)
     return (
         _load_llama_model(
             modelname=modelname,

examples/models/llama2/source_transformation/TARGETS

@@ -0,0 +1,28 @@
+load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")
+
+oncall("executorch")
+
+runtime.python_library(
+    name = "quantized_kv_cache",
+    srcs = [
+        "quantized_kv_cache.py",
+    ],
+    _is_external_target = True,
+    base_module = "executorch.examples.models.llama2.source_transformation",
+    visibility = ["//executorch/..."],
+    deps = [
+        "//caffe2:torch",
+    ],
+)
+
+runtime.python_test(
+    name = "quantized_kv_cache_test",
+    srcs = [
+        "test_quantized_kv_cache.py",
+    ],
+    deps = [
+        ":quantized_kv_cache",
+        "//caffe2:torch",
+        "//executorch/examples/models/llama2:llama_transformer",
+    ],
+)

examples/models/llama2/source_transformation/quantized_kv_cache.py

@@ -0,0 +1,204 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+from enum import Enum
+
+import torch
+import torch.nn as nn
+from executorch.examples.models.llama2.llama_transformer import KVCache
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+
+
+"""
+ Heavily "inspired" by AO's implementation of the same in torchao/_models/llama/model.py
+"""
+
+
+# Doesnt have to abide by affine quantizaiton laws
+# However, if we do implement quantized sdpa, then this might be handy
+class QuantizedCacheType(Enum):
+    AffineSymmetric = 0
+    AffineAsymmetric = 1
+    AffineSymmetricGroupWise = 1
+    AffineAsymmetricGroupWise = 2
+
+
+class QuantizedKVCache(nn.Module):
+    def __init__(
+        self,
+        max_batch_size,
+        max_seq_length,
+        n_heads,
+        head_dim,
+        cache_type: QuantizedCacheType = QuantizedCacheType.AffineSymmetric,
+        tranposed=False,
+        enable_dynamic_shape=False,
+    ):
+        super().__init__()
+        if cache_type not in (
+            QuantizedCacheType.AffineSymmetric,
+            QuantizedCacheType.AffineAsymmetric,
+        ):
+
+            raise ValueError(
+                f"Only affine symmetric and asymmetric cache types are supported: got {cache_type}"
+            )
+        # For now supporting int8 only
+        self.quantized_cache_dtype = torch.int8
+        self.cache_fp_type = torch.float32
+        self.is_transposed = tranposed
+        self.enable_dynamic_shape = enable_dynamic_shape
+        if self.is_transposed:
+            cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+            scale_shape = (max_batch_size, n_heads, max_seq_length, 1)
+        else:
+            cache_shape = (max_batch_size, max_seq_length, n_heads, head_dim)
+            scale_shape = (max_batch_size, max_seq_length, n_heads, 1)
+        self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=torch.int8))
+        self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=torch.int8))
+        self.register_buffer(
+            "k_cache_scales", torch.ones(scale_shape, dtype=torch.double)
+        )
+        self.register_buffer(
+            "v_cache_scales", torch.ones(scale_shape, dtype=torch.double)
+        )
+        if cache_type == QuantizedCacheType.AffineAsymmetric:
+            self.register_buffer(
+                "k_cache_zero_points", torch.ones(scale_shape, dtype=torch.int64)
+            )
+            self.register_buffer(
+                "v_cache_zero_points", torch.ones(scale_shape, dtype=torch.int64)
+            )
+
+    def update(self, input_pos, k_val, v_val):
+        # quantize current k_val and store it in the cache
+        k_scales, k_zero_points = (
+            torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default(
+                k_val, torch.int8  # no other value is supported by this op anyway
+            )
+        )
+        quantized_k_val = torch.ops.quantized_decomposed.quantize_per_token(
+            k_val,
+            k_scales,
+            k_zero_points,
+            torch.iinfo(torch.int8).min,
+            torch.iinfo(torch.int8).max,
+            torch.int8,
+        )
+
+        v_scales, v_zero_points = (
+            torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric(
+                v_val, torch.int8
+            )
+        )
+        quantized_v_val = torch.ops.quantized_decomposed.quantize_per_token(
+            v_val,
+            v_scales,
+            v_zero_points,
+            torch.iinfo(torch.int8).min,
+            torch.iinfo(torch.int8).max,
+            torch.int8,
+        )
+
+        if self.enable_dynamic_shape:
+            start_pos = input_pos[0].item()
+            torch._check_is_size(start_pos)
+            if self.is_transposed:
+                dim_to_slice = 2
+            else:
+                dim_to_slice = 1
+            torch._check(start_pos < self.k_cache.size(dim_to_slice))
+            seq_length = k_val.size(dim_to_slice)
+            narrowed_k = self.k_cache.narrow(dim_to_slice, start_pos, seq_length)
+            narrowed_k_scales = self.k_cache_scales.narrow(
+                dim_to_slice, start_pos, seq_length
+            )
+            narrowed_k_zp = self.k_cache_zero_points.narrow(
+                dim_to_slice, start_pos, seq_length
+            )
+            narrowed_k.copy_(quantized_k_val)
+            narrowed_k_scales.copy_(k_scales)
+            narrowed_k_zp.copy_(k_zero_points)
+            # pyre-ignore: Incompatible parameter type [6]
+            narrowed_v = self.v_cache.narrow(dim_to_slice, start_pos, seq_length)
+            narrowed_v_scales = self.v_cache_scales.narrow(
+                dim_to_slice, start_pos, seq_length
+            )
+            narrowed_v_zp = self.v_cache_zero_points.narrow(
+                dim_to_slice, start_pos, seq_length
+            )
+            narrowed_v.copy_(quantized_v_val)
+            narrowed_v_scales.copy_(v_scales)
+            narrowed_v_zp.copy_(v_zero_points)
+        else:
+            if self.is_transposed:
+                self.k_cache[:, :, input_pos] = quantized_k_val
+                self.k_cache_scales[:, :, input_pos] = k_scales
+                self.k_cache_zero_points[:, :, input_pos] = k_zero_points
+                self.v_cache[:, :, input_pos] = quantized_v_val
+                self.v_cache_scales[:, :, input_pos] = v_scales
+                self.v_cache_zero_points[:, :, input_pos] = v_zero_points
+            else:
+                self.k_cache[:, input_pos] = quantized_k_val
+                self.k_cache_scales[:, input_pos] = k_scales
+                self.k_cache_zero_points[:, input_pos] = k_zero_points
+                self.v_cache[:, input_pos] = quantized_v_val
+                self.v_cache_scales[:, input_pos] = v_scales
+                self.v_cache_zero_points[:, input_pos] = v_zero_points
+
+        k_out = torch.ops.quantized_decomposed.dequantize_per_token(
+            self.k_cache,
+            self.k_cache_scales,
+            self.k_cache_zero_points,
+            torch.iinfo(torch.int8).min,
+            torch.iinfo(torch.int8).max,
+            self.quantized_cache_dtype,
+            self.cache_fp_type,
+        )
+        v_out = torch.ops.quantized_decomposed.dequantize_per_token(
+            self.v_cache,
+            self.v_cache_scales,
+            self.v_cache_zero_points,
+            torch.iinfo(torch.int8).min,
+            torch.iinfo(torch.int8).max,
+            self.quantized_cache_dtype,
+            self.cache_fp_type,
+        )
+        return k_out, v_out
+
+    @classmethod
+    def from_float(cls, kv_cache, cache_type: QuantizedCacheType):
+        cache_shape = kv_cache.k_cache.shape
+        if kv_cache.is_tranposed:
+            max_batch_size, n_heads, max_seq_length, head_dim = cache_shape
+        else:
+            max_batch_size, max_seq_length, n_heads, head_dim = cache_shape
+        return cls(
+            max_batch_size,
+            max_seq_length,
+            n_heads,
+            head_dim,
+            cache_type,
+            kv_cache.is_tranposed,
+            kv_cache.enable_dynamic_shape,
+        )
+
+
+def replace_kv_cache_with_quantized_kv_cache(module):
+    logging.warning(
+        "Replacing KVCache with QuantizedKVCache. This modifies the model in place."
+    )
+    for name, child in module.named_children():
+        if isinstance(child, KVCache):
+            setattr(
+                module,
+                name,
+                QuantizedKVCache.from_float(child, QuantizedCacheType.AffineAsymmetric),
+            )
+        else:
+            replace_kv_cache_with_quantized_kv_cache(child)
+    return module