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Linear #1901

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2 changes: 2 additions & 0 deletions backends/xnnpack/operators/op_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,7 @@ def define_node(
xnn_graph,
vals_to_ids,
quant_params=weight_quant_params,
fp32_static_weights=True,
)
filter_id = vals_to_ids[weight_node]

Expand All @@ -73,6 +74,7 @@ def define_node(
xnn_graph,
vals_to_ids,
quant_params=bias_quant_params,
fp32_static_weights=True,
)
bias_id = vals_to_ids[bias_node]
else:
Expand Down
16 changes: 9 additions & 7 deletions backends/xnnpack/runtime/XNNCompiler.cpp
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Expand Up @@ -256,23 +256,25 @@ Error defineTensor(
/*flags=*/0, // this is netiher external input or output
/*id_out=*/&id);

// this is the FP32 external value that is dynamically quantized
uint32_t fp32_id;
// this is the FP16 or FP32 external value that is being dynamically
// quantized
uint32_t float_id;
enum xnn_datatype fp_datatype = getDataType(tensor_value->datatype());
status = xnn_define_tensor_value(
/*subgraph=*/subgraph_ptr,
/*datatype=*/xnn_datatype_fp32, // always fp32
/*datatype=*/fp_datatype,
/*num_dims=*/tensor_value->num_dims(),
/*dims=*/dims_data.data(),
/*data=*/buffer_ptr,
/*external_id=*/tensor_value->external_id(),
/*flags=*/tensor_value->flags(),
/*id_out=*/&fp32_id);
executor->addDynamicQinput(fp32_id);
/*id_out=*/&float_id);
executor->addDynamicQinput(float_id);

// Define dynamic conversion from fp32 to qdint8
// Define dynamic conversion from float to qdint8
status = xnn_define_convert(
/*subgraph=*/subgraph_ptr,
/*input_id=*/fp32_id,
/*input_id=*/float_id,
/*output_id=*/id,
/*flags=*/0);
break;
Expand Down
30 changes: 25 additions & 5 deletions backends/xnnpack/test/ops/linear.py
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Expand Up @@ -23,6 +23,17 @@


class TestLinear(unittest.TestCase):
def test_fp16_linear(self):
for use_bias in (True, False):
self._test_linear(
lambda in_size, out_size: torch.nn.Linear(
in_size, out_size, bias=use_bias # noqa
),
uses_bias=use_bias,
dtype=torch.float16,
atol=5e-2,
)

def test_fp32_linear(self):
for use_bias in (True, False):
self._test_linear(
Expand Down Expand Up @@ -284,7 +295,14 @@ def forward(self, x):
quant=True,
)

def _test_linear(self, make_module, uses_bias, quant=False):
def _test_linear(
self,
make_module,
uses_bias,
quant=False,
dtype: torch.dtype = torch.float,
atol=1e-03,
):
aten_op, edge_op = (
(
"aten.addmm.default",
Expand All @@ -309,9 +327,10 @@ def _test_linear(self, make_module, uses_bias, quant=False):
in_size = int(in_sizes[i])
input_size = int(input_sizes[i])
output_size = int(output_sizes[i])
print(f"Testing {in_size} {input_size} {output_size}")

module = make_module(input_size, output_size).eval()
inputs = (torch.randn(in_size, input_size),)
module = make_module(input_size, output_size).eval().to(dtype)
inputs = (torch.randn(in_size, input_size).to(dtype),)

tester = Tester(module, inputs)

Expand All @@ -336,7 +355,8 @@ def _test_linear(self, make_module, uses_bias, quant=False):
tester.to_executorch()
tester.serialize()
tester.run_method()
tester.compare_outputs(qtol=quant)
tester.compare_outputs(qtol=quant, atol=atol)
print("success")

def _test_dqlinear(
self,
Expand Down Expand Up @@ -370,7 +390,7 @@ def _test_dqlinear(
tester.export()
tester.check_count({aten_op: linear_count})
tester.check(["torch.ops.quantized_decomposed"])

tester.dump_artifact()
tester.to_edge()
tester.check_count({edge_op: linear_count})

Expand Down