Update base for Update on "update llama runner to decode single token"

Right now, we don't print the generated response in the eager runner until all tokens are generated. This is not good experience as we need to wait until all tokens are generated to see the response.

This PR updates it to decode each new token immediately after it is generated.

Differential Revision: [D65578306](https://our.internmc.facebook.com/intern/diff/D65578306/)

[ghstack-poisoned]

Author: helunwencser

.github/workflows/ghstack_land.yml

@@ -5,6 +5,7 @@ on:
     branches:
       - 'gh/cccclai/[0-9]+/base'
       - 'gh/dbort/[0-9]+/base'
+      - 'gh/dvorjackz/[0-9]+/base'
       - 'gh/guangy10/[0-9]+/base'
       - 'gh/helunwencser/[0-9]+/base'
       - 'gh/jorgep31415/[0-9]+/base'

backends/arm/_passes/arm_pass_manager.py

@@ -43,6 +43,7 @@
 from executorch.backends.arm._passes.unsqueeze_scalar_placeholders_pass import (
     UnsqueezeScalarPlaceholdersPass,
 )
+from executorch.backends.xnnpack._passes.remove_getitem_op import RemoveGetItemPass
 from executorch.exir import ExportedProgram
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from executorch.exir.pass_manager import PassManager
@@ -58,6 +59,7 @@ def transform_to_backend_pipeline(
     ):
         """Apply passes before transforming program to backend"""
         self.add_pass(CastInt64ToInt32Pass(exported_program))
+        self.add_pass(RemoveGetItemPass())
         self.add_pass(UnsqueezeScalarPlaceholdersPass(exported_program))
         self.add_pass(SizeAdjustConv2DPass())
         self.add_pass(RemoveClonePass())

backends/arm/arm_backend.py

@@ -13,7 +13,7 @@
 
 import logging
 import os
-from typing import final, List, Optional
+from typing import cast, final, List, Optional
 
 import serializer.tosa_serializer as ts
 from executorch.backends.arm.arm_vela import vela_compile
@@ -31,6 +31,7 @@
 from executorch.exir.backend.backend_details import BackendDetails, PreprocessResult
 from executorch.exir.backend.compile_spec_schema import CompileSpec
 from torch.export.exported_program import ExportedProgram
+from torch.fx import Node
 
 # TOSA backend debug functionality
 logger = logging.getLogger(__name__)
@@ -225,6 +226,7 @@ def preprocess(  # noqa: C901
         node_visitors = get_node_visitors(edge_program)
 
         for node in graph_module.graph.nodes:
+            node = cast(Node, node)
             if node.op == "call_function":
                 process_call_function(node, tosa_graph, node_visitors)
             elif node.op == "placeholder":
@@ -236,9 +238,6 @@ def preprocess(  # noqa: C901
                 # any checking of compatibility.
                 dbg_fail(node, tosa_graph, artifact_path)
 
-        # TODO: It would be awesome if this dump could somehow be done on top level and not here.
-        # Problem is that the desc.json has to be created on the tosa_graph object, which we can't
-        # access from top level.
         if artifact_path:
             tag = _get_first_delegation_tag(graph_module)
             dbg_tosa_dump(
@@ -259,6 +258,4 @@ def preprocess(  # noqa: C901
         else:
             raise RuntimeError(f"Unknown format {output_format}")
 
-        # Continueing from above. Can I put tosa_graph into this function?
-        # debug_handle_map = ...
         return PreprocessResult(processed_bytes=binary)

backends/arm/arm_partitioner.py

@@ -55,6 +55,7 @@ def is_node_supported(self, submodules, node: torch.fx.Node) -> bool:
             exir_ops.edge.aten._native_batch_norm_legit_no_training.default,
             exir_ops.edge.aten.native_layer_norm.default,
             exir_ops.edge.aten.avg_pool2d.default,
+            exir_ops.edge.aten.max_pool2d_with_indices.default,
             exir_ops.edge.aten.sigmoid.default,
             exir_ops.edge.aten.mm.default,
             exir_ops.edge.aten.repeat.default,

backends/arm/operators/__init__.py

@@ -20,6 +20,7 @@
     op_get_item,
     op_hardtanh,
     op_log,
+    op_max_pool2d,
     op_mm,
     op_mul,
     op_permute,

backends/arm/operators/op_max_pool2d.py

@@ -0,0 +1,77 @@
+# Copyright 2024 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+from typing import cast, List
+
+import serializer.tosa_serializer as ts
+import torch
+from executorch.backends.arm.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.arm.tosa_mapping import TosaArg
+from executorch.backends.arm.tosa_utils import get_quant_node_args
+
+from serializer.tosa_serializer import TosaOp
+
+
+@register_node_visitor
+class MaxPool2dVisitor(NodeVisitor):
+    target = "aten.max_pool2d.default"
+
+    def __init__(self, *args):
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        tosa_graph: ts.TosaSerializer,
+        inputs: List[TosaArg],
+        output: TosaArg,
+        is_quant_node: bool,
+    ) -> None:
+
+        input_tensor = inputs[0]
+        kernel_size = inputs[1].special
+        stride = inputs[2].special
+
+        try:
+            padding = [*inputs[3].special, *inputs[3].special]
+        except IndexError:
+            padding = [0, 0, 0, 0]
+
+        accumulator_type = input_tensor.dtype
+
+        if is_quant_node:
+            # Accumulator type always is int8 when input tensor is an integer type.
+            accumulator_type = ts.DType.INT8
+
+        # Initilize zero point to zero.
+        input_zp = 0
+        output_zp = 0
+
+        if is_quant_node:
+            input_zp = get_quant_node_args(
+                cast(torch.fx.Node, node.all_input_nodes[0])
+            ).zp
+            output_zp = get_quant_node_args(list(node.users)[0]).zp
+
+        attr = ts.TosaSerializerAttribute()
+        attr.PoolAttribute(
+            kernel=kernel_size,
+            stride=stride,
+            pad=padding,
+            input_zp=input_zp,
+            output_zp=output_zp,
+            accum_dtype=accumulator_type,
+        )
+
+        tosa_graph.addOperator(
+            TosaOp.Op().MAX_POOL2D,
+            [input_tensor.name],
+            [output.name],
+            attr,
+        )

backends/arm/quantizer/arm_quantizer_utils.py

@@ -147,6 +147,7 @@ def is_share_obs_or_fq_op(op: Callable) -> bool:
         # TODO: remove?
         torch.ops.aten.adaptive_avg_pool2d.default,
         torch.ops.aten.avg_pool2d.default,
+        torch.ops.aten.max_pool2d.default,
         torch.ops.aten.full.default,
         torch.ops.aten.flatten.using_ints,
         torch.ops.aten.dropout.default,

backends/arm/test/common.py

@@ -91,6 +91,17 @@ def pytest_sessionfinish(session, exitstatus):
 
 # ==== End of Pytest hooks =====
 
+# ==== Custom Pytest decorators =====
+
+
+def expectedFailureOnFVP(test_item):
+    if is_option_enabled("corstone300"):
+        test_item.__unittest_expecting_failure__ = True
+    return test_item
+
+
+# ==== End of Custom Pytest decorators =====
+
 
 def load_libquantized_ops_aot_lib():
     so_ext = {
@@ -181,19 +192,15 @@ def get_tosa_compile_spec_unbuilt(
     the compile spec before calling .build() to finalize it.
     """
     if not custom_path:
-        intermediate_path = maybe_get_tosa_collate_path() or tempfile.mkdtemp(
-            prefix="arm_tosa_"
-        )
-    else:
-        intermediate_path = custom_path
+        custom_path = maybe_get_tosa_collate_path()
 
-    if not os.path.exists(intermediate_path):
-        os.makedirs(intermediate_path, exist_ok=True)
+    if custom_path is not None and not os.path.exists(custom_path):
+        os.makedirs(custom_path, exist_ok=True)
     compile_spec_builder = (
         ArmCompileSpecBuilder()
         .tosa_compile_spec()
         .set_permute_memory_format(permute_memory_to_nhwc)
-        .dump_intermediate_artifacts_to(intermediate_path)
+        .dump_intermediate_artifacts_to(custom_path)
     )
 
     return compile_spec_builder

backends/arm/test/misc/test_debug_feats.py

@@ -107,7 +107,10 @@ def test_numerical_diff_prints(self):
             ArmTester(
                 model,
                 example_inputs=model.get_inputs(),
-                compile_spec=common.get_tosa_compile_spec(permute_memory_to_nhwc=False),
+                compile_spec=common.get_tosa_compile_spec(
+                    permute_memory_to_nhwc=True,
+                    custom_path=tempfile.mkdtemp("diff_print_test"),
+                ),
             )
             .export()
             .to_edge()

backends/arm/test/ops/test_cat.py

@@ -121,7 +121,7 @@ def test_cat_tosa_MI(self, operands: tuple[torch.Tensor, ...], dim: int):
     def test_cat_4d_tosa_MI(self):
         square = torch.ones((2, 2, 2, 2))
         for dim in range(-3, 3):
-            test_data = ((square, square), dim)
+            test_data = ((square, square.clone()), dim)
             self._test_cat_tosa_MI_pipeline(self.Cat(), test_data)
 
     @parameterized.expand(Cat.test_parameters)