Skip to content

Update quantized matmul tests to DQ/Q format supported by fx_importer #3815

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Oct 24, 2024
Merged

Update quantized matmul tests to DQ/Q format supported by fx_importer #3815

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 0 additions & 9 deletions projects/pt1/e2e_testing/xfail_sets.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -394,15 +394,6 @@
"AtenIntBoolOpModule_basic",
"AtenIntMM_basic",
"AtenItemFpOpModule_basic",
"AtenMatmulQMixedSigni8Transpose_basic",
"AtenMatmulQMixedSigni8_basic",
"AtenMatmulQint8MV_basic",
"AtenMatmulQint8_basic",
"AtenMatmulQint8VM_basic",
"AtenMatmulQint8VV_basic",
"AtenMmQMixedSigni8_basic",
"AtenMmQint8_basic",
"AtenMmQuint8_basic",
"QuantizedReluInt32_basic",
"QuantizedReluInt8_basic",
"QuantizedReluUint8_basic",
Expand Down
130 changes: 75 additions & 55 deletions projects/pt1/python/torch_mlir_e2e_test/test_suite/matmul.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -337,6 +337,8 @@ def AtenMmIntTypes_basic(module, tu: TestUtils):


# ==============================================================================
# For DQ-Q fake quantization ops
import torch.ao.quantization.fx._decomposed


class AtenMmQint8(torch.nn.Module):
Expand All @@ -352,12 +354,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.0215, -25)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0176, 18)
qy = torch.dequantize(qy)
qz = torch.mm(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.0215, -25, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0176, 18, -128, 127, torch.int8
)
z = torch.mm(x, y)
return z


@register_test_case(module_factory=lambda: AtenMmQint8())
Expand All @@ -384,12 +388,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.199, 65)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0215, 160)
qy = torch.dequantize(qy)
qz = torch.mm(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.199, 65, 0, 255, torch.uint8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0215, 160, 0, 255, torch.uint8
)
z = torch.mm(x, y)
return z


@register_test_case(module_factory=lambda: AtenMmQuint8())
Expand All @@ -416,12 +422,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.03, -66)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.025, 160)
qy = torch.dequantize(qy)
qz = torch.mm(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.03, -66, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.025, 160, 0, 255, torch.uint8
)
z = torch.mm(x, y)
return z


@register_test_case(module_factory=lambda: AtenMmQMixedSigni8())
Expand Down Expand Up @@ -475,12 +483,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.0215, -25)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0176, 18)
qy = torch.dequantize(qy)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.0215, -25, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0176, 18, -128, 127, torch.int8
)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQint8VM())
Expand All @@ -505,12 +515,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.0215, -25)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0176, 18)
qy = torch.dequantize(qy)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.0215, -25, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0176, 18, -128, 127, torch.int8
)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQint8VV())
Expand All @@ -535,12 +547,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.0215, -25)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0176, 18)
qy = torch.dequantize(qy)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.0215, -25, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0176, 18, -128, 127, torch.int8
)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQint8MV())
Expand All @@ -565,12 +579,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.0215, -25)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.0176, 18)
qy = torch.dequantize(qy)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.0215, -25, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.0176, 18, -128, 127, torch.int8
)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQint8())
Expand All @@ -597,12 +613,14 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.03, -66)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.025, 160)
qy = torch.dequantize(qy)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.03, -66, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.025, 160, 0, 255, torch.uint8
)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQMixedSigni8())
Expand All @@ -629,13 +647,15 @@ def __init__(self):
]
)
def forward(self, x, y):
qx = torch._make_per_tensor_quantized_tensor(x, 0.03, -66)
qx = torch.dequantize(qx)
qy = torch._make_per_tensor_quantized_tensor(y, 0.025, 160)
qy = torch.dequantize(qy)
qy = torch.transpose(qy, 1, 2)
qz = torch.matmul(qx, qy)
return qz
x = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
x, 0.03, -66, -128, 127, torch.int8
)
y = torch.torch.ops.quantized_decomposed.dequantize_per_tensor.default(
y, 0.025, 160, 0, 255, torch.uint8
)
y = torch.transpose(y, 1, 2)
z = torch.matmul(x, y)
return z


@register_test_case(module_factory=lambda: AtenMatmulQMixedSigni8Transpose())
Expand Down
Loading