Qualcomm AI Engine Direct - Model sharding for LLM

backends/qualcomm/partition/qnn_partitioner.py

@@ -44,16 +44,7 @@ def __init__(
     ):
         self.node_visitors = node_visitor.get_node_visitors(edge_program)
 
-        self.skip_node_op_builder_set = set()
-        if skip_node_op_set is not None:
-            self.skip_node_op_builder_set = set(
-                [
-                    self.node_visitors[val]
-                    for val in skip_node_op_set
-                    if val in self.node_visitors
-                ]
-            )
-
+        self.skip_node_op_set = skip_node_op_set
         self.skip_node_id_set = skip_node_id_set
         self.nodes_to_wrappers = defaultdict(dict)
         self.qnn_manager = PyQnnManager.QnnManager(
@@ -75,14 +66,9 @@ def is_node_supported(self, _, node: torch.fx.Node) -> bool:
         if node.target in allow_list_operator:
             return True
 
-        if self.skip_node_id_set is not None and node.name in self.skip_node_id_set:
-            print(f"[QNN Partitioner Op Support]: {node.target.__name__} | Skipped")
-            return False
-
         if (
-            self.skip_node_op_builder_set is not None
-            and self.node_visitors[node.target.__name__]
-            in self.skip_node_op_builder_set
+            node.name in self.skip_node_id_set
+            or node.target.__name__ in self.skip_node_op_set
         ):
             print(f"[QNN Partitioner Op Support]: {node.target.__name__} | Skipped")
             return False
@@ -124,8 +110,8 @@ def __init__(
             QnnBackend.__name__, self.compiler_specs_snapshot
         )
         self.partition_tags: Dict[str, DelegationSpec] = {}
-        self.skip_node_id_set = skip_node_id_set
-        self.skip_node_op_set = skip_node_op_set
+        self.skip_node_id_set = set() if skip_node_id_set is None else skip_node_id_set
+        self.skip_node_op_set = set() if skip_node_op_set is None else skip_node_op_set
 
     def generate_partitions(
         self, edge_program: torch.export.ExportedProgram

backends/qualcomm/quantizer/quantizer.py

@@ -116,7 +116,7 @@ def _update_per_channel_weight_quant_ops(self, ops: Set[OpOverload], enable: boo
         if enable:
             self.use_per_channel_weight_quant_ops.update(ops)
         else:
-            self.use_per_channel_weight_quant_ops.difference(ops)
+            self.use_per_channel_weight_quant_ops.difference_update(ops)
 
     def add_16bit_quant_ops(self, ops: Set[OpOverload]) -> None:
         for op in ops:

examples/models/llama2/export_llama_lib.py

@@ -193,6 +193,12 @@ def build_args_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Whether or not to export a model using kv cache",
     )
+    parser.add_argument(
+        "--num_sharding",
+        type=int,
+        default=0,
+        help="Specify the number of splits by inserting the fallback custom op. The graph will be split evenly by layers.",
+    )
     parser.add_argument(
         "--use_sdpa_with_kv_cache",
         default=False,
@@ -455,6 +461,9 @@ def _validate_args(args):
             " Please use --disable_dynamic_shape."
         )
 
+    if args.num_sharding > 0 and not args.qnn:
+        raise ValueError("Model shard is only supported with qnn backend now.")
+
 
 def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
     _validate_args(args)
@@ -501,11 +510,11 @@ def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
         modelname = f"coreml_{modelname}"
 
     if args.qnn:
+        from executorch.extension.llm.custom_ops import model_sharding
+
         partitioners.append(
             get_qnn_partitioner(
-                quant_dtype,
-                args.use_kv_cache,
-                args.pt2e_quantize,
+                args.use_kv_cache, args.pt2e_quantize, args.num_sharding
             )
         )
         # pyre-ignore: Undefined import [21]: Could not find a module corresponding to import `executorch.backends.qualcomm.utils.utils`
@@ -514,14 +523,27 @@ def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
         # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`, Optional type has no attribute `exported_program`
         _transform(builder_exported_to_edge.edge_manager.exported_program())
 
+        if args.num_sharding > 0:
+            model_sharding.split_graph(
+                builder_exported_to_edge.edge_manager.exported_program(),
+                builder_exported_to_edge.metadata["get_n_layers"],
+                shares=args.num_sharding,
+            )
+
     if args.generate_etrecord:
         if not builder_exported_to_edge.edge_manager:
             raise ValueError("Unable to generate etrecord due to missing edge manager.")
 
         logging.info("Generating etrecord")
         # Copy the edge manager which will be serialized into etrecord. This is memory-wise expensive.
         edge_manager_copy = copy.deepcopy(builder_exported_to_edge.edge_manager)
-        builder = builder_exported_to_edge.to_backend(partitioners).to_executorch()
+        builder = builder_exported_to_edge.to_backend(partitioners)
+        if args.num_sharding > 0 and args.qnn:
+            from executorch.backends.qualcomm.utils.utils import canonicalize_program
+
+            canonicalize_program(builder.edge_manager.exported_program())
+
+        builder = builder.to_executorch()
 
         # Generate ETRecord
         if edge_manager_copy:
@@ -532,7 +554,13 @@ def _export_llama(modelname, args) -> LLMEdgeManager:  # noqa: C901
             )
             logging.info("Generated etrecord.bin")
     else:
-        builder = builder_exported_to_edge.to_backend(partitioners).to_executorch()
+        builder = builder_exported_to_edge.to_backend(partitioners)
+        if args.num_sharding > 0 and args.qnn:
+            from executorch.backends.qualcomm.utils.utils import canonicalize_program
+
+            canonicalize_program(builder.edge_manager.exported_program())
+
+        builder = builder.to_executorch()
 
     if args.profile_memory:
         generate_memory_trace(builder.export_program, "memory_profile.json")
@@ -575,6 +603,7 @@ def _load_llama_model_metadata(
         "get_max_seq_len": model_args.max_seq_len,
         "get_n_bos": 1,
         "get_n_eos": 2 if is_fairseq2 else 1,
+        "get_n_layers": model_args.n_layers,
         "get_vocab_size": model_args.vocab_size,
         "use_kv_cache": use_kv_cache,
         "use_sdpa_with_kv_cache": use_sdpa_with_kv_cache,

extension/llm/custom_ops/model_sharding.py

@@ -0,0 +1,104 @@
+# Copyright (c) Qualcomm Innovation Center, Inc.
+# 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 re
+from typing import List
+
+import torch
+
+from executorch.backends.qualcomm.utils.constants import QCOM_QUANT_ATTRS
+from executorch.exir.dialects._ops import ops as exir_ops
+from executorch.exir.pass_base import ExportPass, PassResult
+from torch.export.exported_program import ExportedProgram
+from torch.library import impl, Library
+
+
+fallback_op_lib = Library("llama", "DEF")
+# registering an operator.
+fallback_op_lib.define("fallback(Tensor input) -> Tensor")
+
+
+@impl(fallback_op_lib, "fallback")
+def fallback_impl(a: torch.Tensor) -> torch.Tensor:
+    return a
+
+
+# registering the out variant.
+fallback_op_lib.define("fallback.out(Tensor input, *, Tensor(a!) output) -> Tensor(a!)")
+
+
+@impl(fallback_op_lib, "fallback.out")
+def fallback_out_impl(a: torch.Tensor, *, out: torch.Tensor) -> torch.Tensor:
+    out.copy_(a)
+    return out
+
+
+class SplitGraph(ExportPass):
+    """
+    Class to split the model to multiple partitions.
+    Because there is limited memory on the device, it could
+    not load all llama model in one pte.
+    """
+
+    def __init__(self, shard_layers: List[int]):
+        super().__init__()
+        self.shard_layers = shard_layers
+
+    def _insert_fallback_op(
+        self, graph_module: torch.fx.GraphModule
+    ) -> torch.fx.GraphModule:
+        """
+        Insert fallback op before layer that needs to be shard.
+        Example:
+            There is 12 layers llama model and num_sharding is 3.
+            The first partition will contain layers [0, 4) and embedding.
+            The second partition will contain layers [4, 8).
+            The third partition will contain layers [8, 12) and output.
+        """
+        pattern = r"layers.(\d+)"
+        prev_node = None
+        prev_layer = None
+        for node in graph_module.graph.nodes:
+            if node.op != "call_function" or "nn_module_stack" not in node.meta:
+                continue
+
+            module_values_list = list(node.meta["nn_module_stack"].values())
+            full_qualified_name = module_values_list[-1][0]
+            # Search which layer this node belongs to
+            match = re.search(pattern, full_qualified_name)
+            if match is None:
+                continue
+
+            cur_layer = int(match.group(1))
+            # Check the current node which is the last node of the layer
+            if cur_layer in self.shard_layers and prev_layer == cur_layer - 1:
+                with graph_module.graph.inserting_after(prev_node):
+                    users = list(prev_node.users.keys())
+                    inserted_node = graph_module.graph.create_node(
+                        "call_function",
+                        exir_ops.edge.llama.fallback.default,
+                        (prev_node,),
+                    )
+                    inserted_node.meta["val"] = prev_node.meta["val"]
+                    if prev_node.meta.get(QCOM_QUANT_ATTRS, None):
+                        inserted_node.meta[QCOM_QUANT_ATTRS] = prev_node.meta[
+                            QCOM_QUANT_ATTRS
+                        ]
+                    for user in users:
+                        user.replace_input_with(prev_node, inserted_node)
+
+            prev_layer = cur_layer
+            prev_node = node
+
+    def call(self, graph_module: torch.fx.GraphModule):
+        self._insert_fallback_op(graph_module)
+        graph_module.recompile()
+        return PassResult(graph_module, True)
+
+
+def split_graph(edge_program: ExportedProgram, num_layers: int, shares: int):
+    graph_module = edge_program.graph_module
+    shard_layers = list(range(0, num_layers, int(num_layers / shares)))
+    return SplitGraph(shard_layers)(graph_module)

extension/llm/custom_ops/op_fallback.cpp

@@ -0,0 +1,48 @@
+/*
+ * Copyright (c) Qualcomm Innovation Center, Inc.
+ * 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.
+ */
+#include <executorch/extension/kernel_util/make_boxed_from_unboxed_functor.h>
+#include <executorch/extension/llm/custom_ops/op_fallback.h>
+#include <cstring>
+
+namespace torch {
+namespace executor {
+
+namespace native {
+
+// Copy from op_clone.cpp
+Tensor& fallback_out(RuntimeContext& ctx, const Tensor& in, Tensor& out) {
+  (void)ctx;
+
+  ET_KERNEL_CHECK(
+      ctx,
+      resize_tensor(out, in.sizes()) == torch::executor::Error::Ok,
+      InvalidArgument,
+      out);
+
+  // The input and out shall share same dtype and size
+  ET_KERNEL_CHECK(
+      ctx, tensors_have_same_shape_and_dtype(in, out), InvalidArgument, out);
+
+  if (in.nbytes() > 0) {
+    // Note that this check is important. It's valid for a tensor with numel 0
+    // to have a null data pointer, but in some environments it's invalid to
+    // pass a null pointer to memcpy() even when the size is zero.
+    memcpy(out.mutable_data_ptr(), in.const_data_ptr(), in.nbytes());
+  }
+
+  return out;
+}
+
+} // namespace native
+} // namespace executor
+} // namespace torch
+
+EXECUTORCH_LIBRARY(
+    llama,
+    "fallback.out",
+    torch::executor::native::fallback_out);

extension/llm/custom_ops/op_fallback.h

@@ -0,0 +1,20 @@
+/*
+ * Copyright (c) Qualcomm Innovation Center, Inc.
+ * 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.
+ */
+
+#pragma once
+
+#include <executorch/runtime/kernel/kernel_includes.h>
+
+namespace torch {
+namespace executor {
+
+namespace native {
+Tensor& fallback_out(RuntimeContext& ctx, const Tensor& in, Tensor& out);
+} // namespace native
+} // namespace executor
+} // namespace torch

extension/llm/custom_ops/targets.bzl

@@ -8,8 +8,8 @@ def define_common_targets():
     """
     runtime.cxx_library(
         name = "custom_ops",
-        srcs = ["op_sdpa.cpp"],
-        exported_headers = ["op_sdpa.h"],
+        srcs = ["op_sdpa.cpp", "op_fallback.cpp"],
+        exported_headers = ["op_sdpa.h", "op_fallback.h"],
         exported_deps = [
             "//executorch/runtime/kernel:kernel_includes",
             "//executorch/kernels/portable/cpu:scalar_utils",

extension/llm/export/partitioner_lib.py

@@ -105,7 +105,9 @@ def get_coreml_partitioner(
 
 
 def get_qnn_partitioner(
-    quant_dtype, use_kv_cache: bool = False, pt2e_quantize: Optional[str] = None
+    use_kv_cache: bool = False,
+    pt2e_quantize: Optional[str] = None,
+    num_sharding: int = 0,
 ):
     assert (
         use_kv_cache is True
@@ -132,15 +134,18 @@ def get_qnn_partitioner(
         )
 
     use_fp16 = True
-    skip_node_op_set = {}
+    skip_node_op_set = {"llama.fallback.default"}
     if pt2e_quantize is not None:
         use_fp16 = False
 
     return QnnPartitioner(  # pyre-fixme[16]
         generate_qnn_executorch_compiler_spec(  # pyre-fixme[16]
             soc_model=QcomChipset.SM8650,  # default to SM8650  # pyre-fixme[16]
             # pyre-fixme[16]
-            backend_options=generate_htp_compiler_spec(use_fp16=use_fp16),
+            backend_options=generate_htp_compiler_spec(
+                use_fp16=use_fp16,
+                use_multi_contexts=num_sharding > 0,
+            ),
             debug=False,
             saver=False,
         ),

extension/llm/export/quantizer_lib.py

@@ -177,6 +177,12 @@ def get_qnn_quantizer(
         quant_dtype = QuantDtype.use_8a8w  # pyre-fixme[16]
     elif quant_config == "16a16w":
         quant_dtype = QuantDtype.use_16a16w  # pyre-fixme[16]
+        # Due to the error with 16a16w in Qnn Htp, we need to disable per channel linear quantization when use 16a16w
+        # TODO: enable it after the issue is fixed
+        logging.warn(
+            "Disable per channel quantization for linear due to the error with QNN HTP 16a16w."
+        )
+        qnn_quantizer.set_per_channel_linear_quant(enable=False)
         qnn_quantizer.add_16bit_quant_ops(qnn_quantizer.SUPPORTED_OPS)
         qnn_quantizer.set_bit16_op_quant_config(
             # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`.