Qualcomm AI Engine Direct - Fix the regression of whisper model

Summary:
- Resolve the Whisper model  accuracy issue caused by upgrading the Transformers.
- Modify decompose_sdpa.py to support kwargs "scale"
- fixed internal CI

Author: shewu-quic

backends/qualcomm/tests/models.py

@@ -1530,12 +1530,13 @@ def forward(self, x):
 
 
 class ScaledDotProductAttention(torch.nn.Module):
-    def __init__(self):
+    def __init__(self, scale=None):
         super().__init__()
+        self.scale = scale
 
     def forward(self, query_layer, key_layer, value_layer, attn_mask):
         attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_layer, key_layer, value_layer, attn_mask
+            query_layer, key_layer, value_layer, attn_mask, scale=self.scale
         )
         return attn_output
 

backends/qualcomm/tests/test_qnn_delegate.py

@@ -1008,7 +1008,11 @@ def test_qnn_backend_rsqrt(self):
         self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_sdpa(self):
-        module = ScaledDotProductAttention()  # noqa: F405
+        modules = [
+            ScaledDotProductAttention(),  # noqa: F405
+            ScaledDotProductAttention(scale=0.5),  # noqa: F405
+            ScaledDotProductAttention(scale=1.0),  # noqa: F405
+        ]
         mask = torch.tril(torch.randn(1, 1, 100, 100))
         mask[mask == 0] = float("-inf")
         sample_input = (
@@ -1017,7 +1021,9 @@ def test_qnn_backend_sdpa(self):
             torch.randn(1, 4, 100, 64),
             mask,
         )
-        self.lower_module_and_test_output(module, sample_input)
+        for i, module in enumerate(modules):
+            with self.subTest(i=i):
+                self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_sigmoid(self):
         module = Sigmoid()  # noqa: F405
@@ -2414,7 +2420,11 @@ def test_qnn_backend_rsqrt(self):
         self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_sdpa(self):
-        module = ScaledDotProductAttention()  # noqa: F405
+        modules = [
+            ScaledDotProductAttention(),  # noqa: F405
+            ScaledDotProductAttention(scale=0.5),  # noqa: F405
+            ScaledDotProductAttention(scale=1.0),  # noqa: F405
+        ]
         mask = torch.tril(torch.randn(1, 1, 100, 100))
         mask[mask == 0] = torch.finfo(torch.float32).min
         sample_input = (
@@ -2423,8 +2433,12 @@ def test_qnn_backend_sdpa(self):
             torch.randn(1, 4, 100, 64),
             mask,
         )
-        module = self.get_qdq_module(module, sample_input)
-        self.lower_module_and_test_output(module, sample_input)
+        for i, module in enumerate(modules):
+            with self.subTest(i=i):
+                module = self.get_qdq_module(
+                    module, sample_input, quant_dtype=QuantDtype.use_16a8w
+                )
+                self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_select_copy(self):
         module = SelectCopy()  # noqa: F405
@@ -4949,13 +4963,14 @@ def test_gMLP(self):
                 self.assertGreaterEqual(msg["top_1"], 60)
                 self.assertGreaterEqual(msg["top_5"], 85)
 
-    def test_mobilevit_v1(self):
+    @unittest.skip("Only outputs good accuracy in QNN 2.29")
+    def test_mobilevit_v2(self):
         if not self.required_envs([self.image_dataset]):
             self.skipTest("missing required envs")
 
         cmds = [
             "python",
-            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit_v1.py"
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit_v2.py",
             "--dataset",
             self.image_dataset,
             "--artifact",
@@ -4973,6 +4988,8 @@ def test_mobilevit_v1(self):
         ]
         if self.host:
             cmds.extend(["--host", self.host])
+        if self.shared_buffer:
+            cmds.extend(["--shared_buffer"])
 
         p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
         with Listener((self.ip, self.port)) as listener:
@@ -4982,17 +4999,16 @@ def test_mobilevit_v1(self):
             if "Error" in msg:
                 self.fail(msg["Error"])
             else:
-                self.assertGreaterEqual(msg["top_1"], 70)
+                self.assertGreaterEqual(msg["top_1"], 50)
                 self.assertGreaterEqual(msg["top_5"], 85)
 
-    @unittest.skip("Only outputs good accuracy in QNN 2.29")
-    def test_mobilevit_v2(self):
+    def test_mobilevit1(self):
         if not self.required_envs([self.image_dataset]):
             self.skipTest("missing required envs")
 
         cmds = [
             "python",
-            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit_v2.py",
+            f"{self.executorch_root}/examples/qualcomm/oss_scripts/mobilevit1.py",
             "--dataset",
             self.image_dataset,
             "--artifact",
@@ -5010,8 +5026,6 @@ def test_mobilevit_v2(self):
         ]
         if self.host:
             cmds.extend(["--host", self.host])
-        if self.shared_buffer:
-            cmds.extend(["--shared_buffer"])
 
         p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
         with Listener((self.ip, self.port)) as listener:
@@ -5021,7 +5035,7 @@ def test_mobilevit_v2(self):
             if "Error" in msg:
                 self.fail(msg["Error"])
             else:
-                self.assertGreaterEqual(msg["top_1"], 50)
+                self.assertGreaterEqual(msg["top_1"], 70)
                 self.assertGreaterEqual(msg["top_5"], 85)
 
     def test_pvt(self):
@@ -5031,7 +5045,11 @@ def test_pvt(self):
         cmds = [
             "python",
             f"{self.executorch_root}/examples/qualcomm/oss_scripts/pvt.py",
+            "--dataset",
             self.image_dataset,
+            "--artifact",
+            self.artifact_dir,
+            "--build_folder",
             self.build_folder,
             "--device",
             self.device,

backends/transforms/decompose_sdpa.py

@@ -6,6 +6,8 @@
 
 # pyre-strict
 
+import math
+
 import torch
 from executorch.exir.pass_base import ExportPass, PassResult
 from torch._decomp import get_decompositions
@@ -30,6 +32,7 @@ def call(
         for node in graph.nodes:
             if node.target == torch.ops.aten.scaled_dot_product_attention.default:
                 input_tensors = (arg.meta["val"] for arg in node.args)
+                scale = node.kwargs.get("scale", None)
 
                 # refer to pytorch/test/test_decomp.py
                 decomposed_module = make_fx(
@@ -81,6 +84,16 @@ def call(
                                 )
                             continue
 
+                        if scale is not None and decomposed_node.target in [
+                            torch.ops.aten.mul.Scalar
+                        ]:
+                            new_args = list(decomposed_node.args)
+                            # Based on the implementation of _scaled_dot_product_attention_math,
+                            # the scale is applied to q and k before matmul.
+                            # refer to pytorch/aten/src/ATen/native/transformers/attention.cpp#L873
+                            new_args[1] = math.sqrt(scale)
+                            decomposed_node.args = tuple(new_args)
+
                         subgraph_node = graph.node_copy(
                             decomposed_node,
                             arg_transform=lambda x: decomposed_node_to_subgraph_node[  # noqa: B023

examples/qualcomm/oss_scripts/whisper/whisper.py

@@ -36,8 +36,8 @@
 
 from executorch.devtools.backend_debug import print_delegation_info
 from executorch.examples.qualcomm.oss_scripts.whisper.whisper_model import (
-    Seq2SeqLMDecoderExportableModuleWithStaticCache,
-    Seq2SeqLMEncoderExportableModule,
+    QnnSeq2SeqLMDecoderExportableModuleWithStaticCache,
+    QnnSeq2SeqLMEncoderExportableModule,
 )
 
 from executorch.examples.qualcomm.utils import (
@@ -169,14 +169,14 @@ def __init__(
         )
 
         self.whisper_encoder = (
-            Seq2SeqLMEncoderExportableModule(whisper_model.get_encoder())
+            QnnSeq2SeqLMEncoderExportableModule(whisper_model.get_encoder())
             .to("cpu")
             .eval()
         )
         self.encoder_passes_job = get_capture_program_passes()
 
         self.whisper_decoder = (
-            Seq2SeqLMDecoderExportableModuleWithStaticCache(
+            QnnSeq2SeqLMDecoderExportableModuleWithStaticCache(
                 whisper_model=whisper_model,
                 max_cache_length=self.max_seq_length,
                 batch_size=batch_size,
@@ -190,20 +190,21 @@ def __init__(
         self.exported_whisper_encoder = None
         self.exported_whisper_decoder = None
         self.has_quant_io = False
+        self.kv_shape = {
+            (self.max_seq_length, self.head_dim),
+        }
 
     def _tag_ios(self, node, fixed_point_type):
         if not self.has_quant_io:
             return
 
         quant_io_type = None
-        if node.op == "placeholder" and "static_cache_" in node.name:
+        if node.op == "placeholder" and node.meta["val"].size()[-2:] in self.kv_shape:
             quant_io_type = fixed_point_type
 
         if is_graph_output(node):
             # shape of k caches and v caches
-            if node.meta["val"].size()[-2:] in {
-                (self.max_seq_length, self.head_dim),
-            }:
+            if node.meta["val"].size()[-2:] in self.kv_shape:
                 quant_io_type = fixed_point_type
 
         return quant_io_type

examples/qualcomm/oss_scripts/whisper/whisper_model.py

@@ -6,10 +6,11 @@
 
 
 import torch
-from transformers import StaticCache, WhisperForConditionalGeneration
+from transformers.cache_utils import DynamicCache, EncoderDecoderCache, StaticCache
+from transformers.models.whisper.modeling_whisper import WhisperForConditionalGeneration
 
 
-class Seq2SeqLMEncoderExportableModule(torch.nn.Module):
+class QnnSeq2SeqLMEncoderExportableModule(torch.nn.Module):
     """
     A wrapper module designed to make a Seq2Seq LM encoder exportable with `torch.export`.
     This module ensures that the exported encoder model is compatible with ExecuTorch.
@@ -29,7 +30,7 @@ def get_metadata(self):
         return {}
 
 
-class Seq2SeqLMDecoderExportableModuleWithStaticCache(torch.nn.Module):
+class QnnSeq2SeqLMDecoderExportableModuleWithStaticCache(torch.nn.Module):
     """
     A wrapper module designed to make a Seq2Seq LM decoder exportable with `torch.export`,
     specifically for use with static caching. This module ensures the exported decoder
@@ -57,11 +58,7 @@ def __init__(self, whisper_model, max_cache_length, batch_size):
             device="cpu",
             dtype=torch.float32,
         )
-
-        # Register cache buffers to make them exportable
-        for i in range(len(self.static_cache.key_cache)):
-            self.register_buffer(f"key_cache_{i}", self.static_cache.key_cache[i])
-            self.register_buffer(f"value_cache_{i}", self.static_cache.value_cache[i])
+        self.cache = EncoderDecoderCache(self.static_cache, DynamicCache())
 
     def forward(
         self, decoder_input_ids, attention_mask, encoder_hidden_states, cache_position
@@ -71,7 +68,7 @@ def forward(
             input_ids=decoder_input_ids,
             attention_mask=attention_mask,
             encoder_hidden_states=encoder_hidden_states,
-            past_key_values=self.static_cache,
+            past_key_values=self.cache,
             use_cache=True,
             cache_position=cache_position,
         )