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Qualcomm AI Engine Direct - [Multimodal] granite-speech-3.3-2b #18740

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162 changes: 87 additions & 75 deletions backends/qualcomm/_passes/canonicalize_conv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,86 +115,98 @@ def call(self, graph_module: torch.fx.GraphModule):
)

with graph_module.graph.inserting_after(qdq_node_after_unsqueeze):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
filter_node.meta["val"] = (
filter_node.meta["val"].unsqueeze(2).contiguous()
)
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)

bias_node = node.args[2] if num_args > 2 else None
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = [1] + node.args[5] if num_args > 5 else [1, 1]
groups = node.args[6] if num_args > 6 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
groups,
# conv2d must be inserted before conv1d in the graph to preserve correct
# topological ordering. This is required due to conv-bn fusion: when conv1d
# has no bias, the fused bias (from batchnorm) is introduced as a new node,
# and its corresponding dq (dequantize) node must appear before conv2d in
# the execution order.
with graph_module.graph.inserting_before(node):
filter_arg = node.args[1]
filter_node = (
filter_arg
if filter_arg.op == "placeholder"
else node.args[1].args[0]
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
filter_node.meta["val"] = filter_node.meta["val"].unsqueeze(
2
)
groups = node.args[6] if num_args > 6 else 1
dilation = [1] + node.args[7] if num_args > 7 else [1, 1]
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv1d_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
qdq_node_after_conv2d = append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(qdq_node_after_conv2d):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
filter_tensor = get_parameter(
filter_node, self.edge_program
).unsqueeze(2)
set_parameter(
(
qdq_node_after_conv2d,
[2],
torch.nn.Parameter(filter_tensor)
if filter_tensor.dtype == torch.float
else filter_tensor
),
filter_node,
self.edge_program,
)

num_args = len(node.args)

bias_node = node.args[2] if num_args > 2 else None
stride = [1] + node.args[3] if num_args > 3 else [1, 1]
padding = [0] + node.args[4] if num_args > 4 else [0, 0]
if node.target == torch.ops.aten.conv1d.default:
dilation = (
[1] + node.args[5] if num_args > 5 else [1, 1]
)
groups = node.args[6] if num_args > 6 else 1
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
dilation,
groups,
)
else:
output_padding = (
[0] + node.args[5] if num_args > 5 else [0, 0]
)
groups = node.args[6] if num_args > 6 else 1
dilation = (
[1] + node.args[7] if num_args > 7 else [1, 1]
)
conv_args = (
qdq_node_after_unsqueeze,
node.args[1],
bias_node,
stride,
padding,
output_padding,
groups,
dilation,
)
conv2d_node = graph.create_node(
"call_function",
self.conv1d_op_map[node.target],
conv_args,
)
conv2d_node.meta = copy_meta(
node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)}
)
squeeze_node.meta = copy_meta(node.meta)
qdq_node_after_conv2d = append_qdq(
graph_module=graph_module,
node=conv2d_node,
qdq_node=list(node.users)[0],
)

with graph_module.graph.inserting_after(
qdq_node_after_conv2d
):
squeeze_op = torch.ops.aten.squeeze_copy.dims
squeeze_node = graph.create_node(
"call_function",
squeeze_op,
(
qdq_node_after_conv2d,
[2],
),
)
squeeze_node.meta = copy_meta(node.meta)

for user in node.users.copy():
user.replace_input_with(node, squeeze_node)
Expand Down
18 changes: 18 additions & 0 deletions backends/qualcomm/tests/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -490,6 +490,24 @@ def example_inputs(self):
}


class Conv1dBn(torch.nn.Module):
def __init__(self, bias=True):
super().__init__()
self.conv = torch.nn.Conv1d(
in_channels=2048,
out_channels=2048,
kernel_size=15,
groups=2048,
bias=bias,
)
self.batch_norm = torch.nn.BatchNorm1d(2048)

def forward(self, x):
x = self.conv(x)
x = self.batch_norm(x)
return x


class Conv1dSequential(torch.nn.Module):
def __init__(self, bias=True):
super().__init__()
Expand Down
124 changes: 123 additions & 1 deletion backends/qualcomm/tests/test_qnn_delegate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -377,6 +377,13 @@ def test_qnn_backend_conv1d(self):
with self.subTest(i=i):
self.lower_module_and_test_output(module, sample_input)

def test_qnn_conv1d_batch_norm(self):
modules = [Conv1dBn(), Conv1dBn(bias=False)] # noqa: F405
sample_input = (torch.randn([1, 2048, 858]),)
for i, module in enumerate(modules):
with self.subTest(i=i):
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_conv2d(self):
modules = [Conv2dSequential(), Conv2dSequential(bias=False)] # noqa: F405
sample_input = (torch.randn([1, 1, 3, 3]),)
Expand Down Expand Up @@ -2637,6 +2644,14 @@ def test_qnn_backend_conv1d(self):
module = self.get_qdq_module(module, sample_input)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_conv1d_batch_norm(self):
modules = [Conv1dBn(), Conv1dBn(bias=False)] # noqa: F405
sample_input = (torch.randn([1, 2048, 858]),)
for i, module in enumerate(modules):
with self.subTest(i=i):
module = self.get_qdq_module(module, sample_input)
self.lower_module_and_test_output(module, sample_input)

def test_qnn_backend_conv2d(self):
modules = [Conv2dSequential(), Conv2dSequential(bias=False)] # noqa: F405
sample_input = (torch.randn([1, 1, 3, 3]),)
Expand Down Expand Up @@ -6870,13 +6885,30 @@ class MLLMSpecs:
tok_embedding_pte_size: float
decoder_pte_size: float

@dataclass(frozen=True)
class ALMSpecs(MLLMSpecs):
audio_path: str
golden_audio_feature: str

@dataclass(frozen=True)
class VLMSpecs(MLLMSpecs):
image_path: str
golden_image_feature: str

# TODO: refactor to support different backends
def setUp(self):
self.alm_specs = {
"granite_speech_3_3-2b": TestExampleMultimodalityScript.ALMSpecs(
max_seq_len=512,
sm8650_token_rate=5,
sm8750_token_rate=8,
encoder_pte_size=900_000_000, # 900MB
tok_embedding_pte_size=240_000_000, # 240MB
decoder_pte_size=3_000_000_000, # 3GB
audio_path="https://huggingface.co/ibm-granite/granite-speech-3.3-2b/resolve/main/10226_10111_000000.wav?download=true", # Audio content: after his nap,...
golden_audio_feature="after his nap,",
),
}
self.vlm_specs = {
"smolvlm_500m_instruct": TestExampleMultimodalityScript.VLMSpecs(
max_seq_len=128,
Expand All @@ -6900,6 +6932,96 @@ def setUp(self):
),
}

def test_static_asr(self):
if not self.required_envs([self.model_name]):
self.skipTest("missing required envs")

if self.enable_x86_64:
# Running on host is extremely slow for large models, so we skip this check to avoid timeouts.
# Please verify the output on the actual device instead.
self.skipTest(
"Skipping the check for the static ASR model on x86 due to long execution time."
)

alm_specs: TestExampleMultimodalityScript.ALMSpecs = self.alm_specs[
self.model_name
]
prompt = "can you transcribe the speech into a written format?"
audio_path = alm_specs.audio_path
cmds = [
"python",
f"{self.executorch_root}/examples/qualcomm/oss_scripts/llama/llama.py",
"--artifact",
self.artifact_dir,
"--build_folder",
self.build_folder,
"--model",
self.model,
"--ip",
self.ip,
"--port",
str(self.port),
"--prompt",
prompt,
"--audio_path",
audio_path,
"--temperature",
"0",
"--decoder_model",
f"{self.model_name}",
"--model_mode",
"kv",
"--max_seq_len",
f"{alm_specs.max_seq_len}",
]
if self.compile_only:
cmds.extend(["--compile_only"])
elif self.device:
cmds.extend(["--device", self.device])
if self.host:
cmds.extend(["--host", self.host])
if self.pre_gen_pte:
cmds.extend(["--pre_gen_pte", self.pre_gen_pte])

p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL)
with Listener((self.ip, self.port)) as listener:
conn = listener.accept()
p.communicate()
msg = json.loads(conn.recv())
if "Error" in msg:
self.fail(msg["Error"])
else:
if not self.compile_only:
model_out = msg["result"][0]
self.assertTrue(
alm_specs.golden_audio_feature in model_out.lower(),
f"Expected Output contains feature: '{alm_specs.golden_audio_feature}' Actual Output: '{model_out}'",
)
print(f"Audio Path: {audio_path}")
print(f"Query: {prompt}")
print(f"Answer: {model_out}")

encoder_pte_size = msg["audio_encoder_pte_size"]
tok_embedding_pte_size = msg["tok_embedding_pte_size"]
decoder_pte_size = msg["pte_size"]
self.assertLessEqual(encoder_pte_size, alm_specs.encoder_pte_size)
self.assertLessEqual(
tok_embedding_pte_size, alm_specs.tok_embedding_pte_size
)
self.assertLessEqual(decoder_pte_size, alm_specs.decoder_pte_size)
print(f"Encoder PTE Size: {encoder_pte_size} bytes")
print(f"Token Embedding PTE Size: {tok_embedding_pte_size} bytes")
print(f"Text Decoder PTE Size: {decoder_pte_size} bytes")

attr_name = f"{self.model.lower()}_token_rate"
if not self.compile_only and hasattr(alm_specs, attr_name):
device_inference_speed = msg["inference_speed"]
expected_inference_speed = getattr(alm_specs, attr_name)
print(f"Prompt Evaluation: {device_inference_speed} tokens/second")
self.assertGreaterEqual(
device_inference_speed, expected_inference_speed
)

def test_static_vlm(self):
if not self.required_envs([self.model_name]):
self.skipTest("missing required envs")
Expand Down Expand Up @@ -6964,7 +7086,7 @@ def test_static_vlm(self):
print(f"Query: {prompt}")
print(f"Answer: {model_out}")
if not self.enable_x86_64:
encoder_pte_size = msg["encoder_pte_size"]
encoder_pte_size = msg["vision_encoder_pte_size"]
tok_embedding_pte_size = msg["tok_embedding_pte_size"]
decoder_pte_size = msg["pte_size"]
self.assertLessEqual(encoder_pte_size, vlm_specs.encoder_pte_size)
Expand Down
24 changes: 24 additions & 0 deletions examples/models/granite_speech/BUCK
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Original file line number Diff line number Diff line change
@@ -0,0 +1,24 @@
load("@fbcode_macros//build_defs:build_file_migration.bzl", "fbcode_target", "non_fbcode_target")
load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime")

oncall("executorch")

fbcode_target(_kind = runtime.python_library,
name = "granite_speech",
srcs = [
"__init__.py",
"convert_weights.py",
],
_is_external_target = True,
base_module = "executorch.examples.models.granite_speech",
resources = {
"config/2b_config.json": "config/2b_config.json",
},
deps = [
"//caffe2:torch",
"//executorch/examples/models/llama:llama2_model",
"fbcode//pytorch/torchtune:lib",
"fbsource//third-party/pypi/safetensors:safetensors",
],
visibility = ["PUBLIC"],
)
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