Gemm#
Gemm - 13#
Version
name: Gemm (GitHub)
domain: main
since_version: 13
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 13.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A’ = transpose(A) if transA else A
B’ = transpose(B) if transB else B
Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX. This operator has optional inputs/outputs. See ONNX for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B.
beta: Scalar multiplier for input tensor C.
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
Between 2 and 3 inputs.
A (heterogeneous) - T: Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (heterogeneous) - T: Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (optional, heterogeneous) - T: Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
Outputs
Y (heterogeneous) - T: Output tensor of shape (M, N).
Type Constraints
T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ): Constrain input and output types to float/int tensors.
Examples
_default_zero_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_zero_bias")
_default_no_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b"], outputs=["y"])
a = np.random.ranf([2, 10]).astype(np.float32)
b = np.random.ranf([10, 3]).astype(np.float32)
y = gemm_reference_implementation(a, b)
expect(node, inputs=[a, b], outputs=[y], name="test_gemm_default_no_bias")
_default_scalar_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 3]).astype(np.float32)
b = np.random.ranf([3, 4]).astype(np.float32)
c = np.array(3.14).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_scalar_bias"
)
_default_single_elem_vector_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 7]).astype(np.float32)
b = np.random.ranf([7, 3]).astype(np.float32)
c = np.random.ranf([1]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node,
inputs=[a, b, c],
outputs=[y],
name="test_gemm_default_single_elem_vector_bias",
)
_default_vector_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_vector_bias"
)
_default_matrix_bias
import numpy as np
import onnx
node = onnx.helper.make_node("Gemm", inputs=["a", "b", "c"], outputs=["y"])
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.random.ranf([3, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c)
expect(
node, inputs=[a, b, c], outputs=[y], name="test_gemm_default_matrix_bias"
)
_transposeA
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transA=1
)
a = np.random.ranf([6, 3]).astype(np.float32)
b = np.random.ranf([6, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transA=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeA")
_transposeB
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], transB=1
)
a = np.random.ranf([3, 6]).astype(np.float32)
b = np.random.ranf([4, 6]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, transB=1)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_transposeB")
_alpha
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], alpha=0.5
)
a = np.random.ranf([3, 5]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.zeros([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, alpha=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_alpha")
_beta
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gemm", inputs=["a", "b", "c"], outputs=["y"], beta=0.5
)
a = np.random.ranf([2, 7]).astype(np.float32)
b = np.random.ranf([7, 4]).astype(np.float32)
c = np.random.ranf([1, 4]).astype(np.float32)
y = gemm_reference_implementation(a, b, c, beta=0.5)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_beta")
_all_attributes
import numpy as np
import onnx
node = onnx.helper.make_node(
"Gemm",
inputs=["a", "b", "c"],
outputs=["y"],
alpha=0.25,
beta=0.35,
transA=1,
transB=1,
)
a = np.random.ranf([4, 3]).astype(np.float32)
b = np.random.ranf([5, 4]).astype(np.float32)
c = np.random.ranf([1, 5]).astype(np.float32)
y = gemm_reference_implementation(
a, b, c, transA=1, transB=1, alpha=0.25, beta=0.35
)
expect(node, inputs=[a, b, c], outputs=[y], name="test_gemm_all_attributes")
Gemm - 11#
Version
name: Gemm (GitHub)
domain: main
since_version: 11
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 11.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A’ = transpose(A) if transA else A
B’ = transpose(B) if transB else B
Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX. This operator has optional inputs/outputs. See ONNX for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument’s name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B.
beta: Scalar multiplier for input tensor C.
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
Between 2 and 3 inputs.
A (heterogeneous) - T: Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (heterogeneous) - T: Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (optional, heterogeneous) - T: Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N).
Outputs
Y (heterogeneous) - T: Output tensor of shape (M, N).
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ): Constrain input and output types to float/int tensors.
Gemm - 9#
Version
name: Gemm (GitHub)
domain: main
since_version: 9
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 9.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A’ = transpose(A) if transA else A
B’ = transpose(B) if transB else B
Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B.
beta: Scalar multiplier for input tensor C.
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
A (heterogeneous) - T: Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (heterogeneous) - T: Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (heterogeneous) - T: Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
Outputs
Y (heterogeneous) - T: Output tensor of shape (M, N).
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64) ): Constrain input and output types to float/int tensors.
Gemm - 7#
Version
name: Gemm (GitHub)
domain: main
since_version: 7
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 7.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3
A’ = transpose(A) if transA else A
B’ = transpose(B) if transB else B
Compute Y = alpha * A’ * B’ + beta * C, where input tensor A has shape (M, K) or (K, M), input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N), and output tensor Y has shape (M, N). A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB. This operator supports unidirectional broadcasting (tensor C should be unidirectional broadcastable to tensor A * B); for more details please check Broadcasting in ONNX.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B.
beta: Scalar multiplier for input tensor C.
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
A (heterogeneous) - T: Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero.
B (heterogeneous) - T: Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero.
C (heterogeneous) - T: Input tensor C. The shape of C should be unidirectional broadcastable to (M, N).
Outputs
Y (heterogeneous) - T: Output tensor of shape (M, N).
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Gemm - 6#
Version
name: Gemm (GitHub)
domain: main
since_version: 6
function: False
support_level: SupportType.COMMON
shape inference: True
This version of the operator has been available since version 6.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
beta: Scalar multiplier for input tensor C, the default value is 1.0.
broadcast: Whether C should be broadcasted
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
A (heterogeneous) - T: Input tensor A
B (heterogeneous) - T: Input tensor B
C (heterogeneous) - T: Input tensor C
Outputs
Y (heterogeneous) - T: Output tensor.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.
Gemm - 1#
Version
name: Gemm (GitHub)
domain: main
since_version: 1
function: False
support_level: SupportType.COMMON
shape inference: False
This version of the operator has been available since version 1.
Summary
General Matrix multiplication: https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms#Level_3 Compute Y = alpha * A * B + beta * C, where input tensor A has dimension (M X K), input tensor B has dimension (K X N), input tensor C and output tensor Y have dimension (M X N). If attribute broadcast is non-zero, input tensor C will be broadcasted to match the dimension requirement. A will be transposed before doing the computation if attribute transA is non-zero, same for B and transB.
Attributes
alpha: Scalar multiplier for the product of input tensors A * B, the default value is 1.0.
beta: Scalar multiplier for input tensor C, the default value is 1.0.
broadcast: Whether C should be broadcasted
transA: Whether A should be transposed
transB: Whether B should be transposed
Inputs
A (heterogeneous) - T: Input tensor A
B (heterogeneous) - T: Input tensor B
C (heterogeneous) - T: Input tensor C, can be inplace.
Outputs
Y (heterogeneous) - T: Output tensor.
Type Constraints
T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input and output types to float tensors.