etops
=====
The `etops` package provides a Python interface for einsum tree operations. It enables users to define, configure, optimize, and execute complex tensor contractions and elementwise operations. The package is built on top of the einsum_ir C++ backend and exposes advanced features such as dimension fusion, dimension splitting, and backend-specific optimizations.
Main Features
-------------
- Abstractions for tensor operations and configuration
- Support for multiple data types (float32, float64)
- Primitive operations: zero, copy, relu, gemm, brgemm, etc.
- Dimension execution strategies: primitive, sequential, shared, space-filling curve (SFC)
- Dimension and stride configuration for advanced memory layouts
- Interface for built-in contraction optimizer
- Pythonic API with dataclass-based configuration
Installation
------------
Install the package using pip:
.. code-block:: bash
pip install etops
Quick Example
-------------
Below is a minimal example showing how to configure and execute tensor operations:
.. code-block:: python
import etops
# -----------------------------------------
# First example:
# Column-major GEMM operation
# Compares the result with NumPy's einsum
# -----------------------------------------
# Define a column-major GEMM configuration
top_config = etops.TensorOperationConfig(
data_type = etops.float32,
prim_first = etops.prim.zero,
prim_main = etops.prim.gemm,
prim_last = etops.prim.none,
dim_types = (etops.dim.m, etops.dim.n, etops.dim.k ),
exec_types = (etops.exec.prim, etops.exec.prim, etops.exec.prim),
dim_sizes = (64, 32, 128 ),
strides_in0= (1, 0, 64 ),
strides_in1= (0, 128, 1 ),
strides_out= (1, 64, 0 )
)
# Create the TensorOperation instance
top = etops.TensorOperation(top_config)
# Create input and output arrays
import numpy as np
A = np.random.randn(128,64).astype(np.float32)
B = np.random.randn(32,128).astype(np.float32)
C = np.random.randn(32, 64).astype(np.float32)
# Execute the operation
top.execute(A, B, C)
C_np = np.einsum("km,nk->nm", A, B)
# Compute absolute and relative errors
error_abs = np.max( np.abs(C - C_np) )
error_rel = np.max( np.abs(C - C_np) / (np.abs(C_np) + 1e-8) )
print("Column-major GEMM operation:")
print(f" Max absolute error: {error_abs:.6e}")
print(f" Max relative error: {error_rel:.6e}")
# -----------------------------------------
# Second example:
# Batched GEMM operation
# Compares the result with torch's einsum
# -----------------------------------------
# Define a batched GEMM configuration
batched_config = etops.TensorOperationConfig(
data_type = etops.float32,
prim_first = etops.prim.zero,
prim_main = etops.prim.gemm,
prim_last = etops.prim.none,
dim_types = (etops.dim.c, etops.dim.m, etops.dim.n, etops.dim.k ),
exec_types = (etops.exec.shared, etops.exec.prim, etops.exec.prim, etops.exec.prim),
dim_sizes = (48, 64, 32, 128 ),
strides_in0= (128*64, 1, 0, 64 ),
strides_in1= (32*128, 0, 128, 1 ),
strides_out= (32*64, 1, 64, 0 )
)
# Create the batched TensorOperation instance
top = etops.TensorOperation(batched_config)
import torch
# Create input and output arrays
A = torch.randn(48, 128, 64, dtype=torch.float32)
B = torch.randn(48, 32, 128, dtype=torch.float32)
C = torch.randn(48, 32, 64, dtype=torch.float32)
# Execute the operation
top.execute(A, B, C)
C_torch = torch.einsum("bkm,bnk->bnm", A, B)
# Compute absolute and relative errors
error_abs = torch.max(torch.abs(C - C_torch))
error_rel = torch.max(torch.abs(C - C_torch) / (torch.abs(C_torch) + 1e-8))
print("Batched GEMM operation:")
print(f" Max absolute error: {error_abs:.6e}")
print(f" Max relative error: {error_rel:.6e}")
# -----------------------------------------------
# Third example:
# Batch-reduce GEMM operation with optimization
# Compares the result with torch's einsum
# -----------------------------------------------
# Define a batch-reduce GEMM configuration
batched_config = etops.TensorOperationConfig(
data_type = etops.float32,
prim_first = etops.prim.zero,
prim_main = etops.prim.gemm,
prim_last = etops.prim.none,
dim_types = (etops.dim.k, etops.dim.m, etops.dim.n, etops.dim.k ),
exec_types = (etops.exec.seq, etops.exec.seq, etops.exec.seq, etops.exec.seq),
dim_sizes = (48, 64, 32, 128 ),
strides_in0= (128*64, 1, 0, 64 ),
strides_in1= (32*128, 0, 128, 1 ),
strides_out= (0, 1, 64, 0 )
)
# Optimize the configuration
optimized_config = etops.optimize( batched_config,
target_m=16,
target_n=12,
target_k=64,
num_threads=1,
br_gemm_support=True,
packed_gemm_support=True )
# Create the optimized TensorOperation instance
top = etops.TensorOperation(optimized_config)
import torch
# Create input and output arrays
A = torch.randn(48, 128, 64, dtype=torch.float32)
B = torch.randn(48, 32, 128, dtype=torch.float32)
C = torch.randn( 32, 64, dtype=torch.float32)
# Execute the operation
top.execute(A, B, C)
C_torch = torch.einsum("bkm,bnk->nm", A, B)
# Compute absolute and relative errors
error_abs = torch.max(torch.abs(C - C_torch))
error_rel = torch.max(torch.abs(C - C_torch) / (torch.abs(C_torch) + 1e-8))
print("Batch-reduce GEMM operation:")
print(f" Max absolute error: {error_abs:.6e}")
print(f" Max relative error: {error_rel:.6e}")
See the source code and inline documentation for more advanced usage.
Raw data
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"description": "etops\n=====\n\nThe `etops` package provides a Python interface for einsum tree operations. It enables users to define, configure, optimize, and execute complex tensor contractions and elementwise operations. The package is built on top of the einsum_ir C++ backend and exposes advanced features such as dimension fusion, dimension splitting, and backend-specific optimizations.\n\nMain Features\n-------------\n- Abstractions for tensor operations and configuration\n- Support for multiple data types (float32, float64)\n- Primitive operations: zero, copy, relu, gemm, brgemm, etc.\n- Dimension execution strategies: primitive, sequential, shared, space-filling curve (SFC)\n- Dimension and stride configuration for advanced memory layouts\n- Interface for built-in contraction optimizer\n- Pythonic API with dataclass-based configuration\n\nInstallation\n------------\nInstall the package using pip:\n\n.. code-block:: bash\n\n pip install etops\n\nQuick Example\n-------------\nBelow is a minimal example showing how to configure and execute tensor operations:\n\n.. code-block:: python\n\n import etops\n\n # -----------------------------------------\n # First example:\n # Column-major GEMM operation\n # Compares the result with NumPy's einsum\n # -----------------------------------------\n # Define a column-major GEMM configuration\n top_config = etops.TensorOperationConfig(\n data_type = etops.float32,\n prim_first = etops.prim.zero,\n prim_main = etops.prim.gemm,\n prim_last = etops.prim.none,\n dim_types = (etops.dim.m, etops.dim.n, etops.dim.k ),\n exec_types = (etops.exec.prim, etops.exec.prim, etops.exec.prim),\n dim_sizes = (64, 32, 128 ),\n strides_in0= (1, 0, 64 ),\n strides_in1= (0, 128, 1 ),\n strides_out= (1, 64, 0 )\n )\n\n # Create the TensorOperation instance\n top = etops.TensorOperation(top_config)\n\n # Create input and output arrays\n import numpy as np\n A = np.random.randn(128,64).astype(np.float32)\n B = np.random.randn(32,128).astype(np.float32)\n C = np.random.randn(32, 64).astype(np.float32)\n\n # Execute the operation\n top.execute(A, B, C)\n\n C_np = np.einsum(\"km,nk->nm\", A, B)\n\n # Compute absolute and relative errors\n error_abs = np.max( np.abs(C - C_np) )\n error_rel = np.max( np.abs(C - C_np) / (np.abs(C_np) + 1e-8) )\n print(\"Column-major GEMM operation:\")\n print(f\" Max absolute error: {error_abs:.6e}\")\n print(f\" Max relative error: {error_rel:.6e}\")\n\n # -----------------------------------------\n # Second example:\n # Batched GEMM operation\n # Compares the result with torch's einsum\n # -----------------------------------------\n # Define a batched GEMM configuration\n batched_config = etops.TensorOperationConfig(\n data_type = etops.float32,\n prim_first = etops.prim.zero,\n prim_main = etops.prim.gemm,\n prim_last = etops.prim.none,\n dim_types = (etops.dim.c, etops.dim.m, etops.dim.n, etops.dim.k ),\n exec_types = (etops.exec.shared, etops.exec.prim, etops.exec.prim, etops.exec.prim),\n dim_sizes = (48, 64, 32, 128 ),\n strides_in0= (128*64, 1, 0, 64 ),\n strides_in1= (32*128, 0, 128, 1 ),\n strides_out= (32*64, 1, 64, 0 )\n )\n # Create the batched TensorOperation instance\n top = etops.TensorOperation(batched_config)\n\n import torch\n # Create input and output arrays\n A = torch.randn(48, 128, 64, dtype=torch.float32)\n B = torch.randn(48, 32, 128, dtype=torch.float32)\n C = torch.randn(48, 32, 64, dtype=torch.float32)\n\n # Execute the operation\n top.execute(A, B, C)\n\n C_torch = torch.einsum(\"bkm,bnk->bnm\", A, B)\n\n # Compute absolute and relative errors\n error_abs = torch.max(torch.abs(C - C_torch))\n error_rel = torch.max(torch.abs(C - C_torch) / (torch.abs(C_torch) + 1e-8))\n\n print(\"Batched GEMM operation:\")\n print(f\" Max absolute error: {error_abs:.6e}\")\n print(f\" Max relative error: {error_rel:.6e}\")\n\n # -----------------------------------------------\n # Third example:\n # Batch-reduce GEMM operation with optimization\n # Compares the result with torch's einsum\n # -----------------------------------------------\n # Define a batch-reduce GEMM configuration\n batched_config = etops.TensorOperationConfig(\n data_type = etops.float32,\n prim_first = etops.prim.zero,\n prim_main = etops.prim.gemm,\n prim_last = etops.prim.none,\n dim_types = (etops.dim.k, etops.dim.m, etops.dim.n, etops.dim.k ),\n exec_types = (etops.exec.seq, etops.exec.seq, etops.exec.seq, etops.exec.seq),\n dim_sizes = (48, 64, 32, 128 ),\n strides_in0= (128*64, 1, 0, 64 ),\n strides_in1= (32*128, 0, 128, 1 ),\n strides_out= (0, 1, 64, 0 )\n )\n\n # Optimize the configuration\n optimized_config = etops.optimize( batched_config,\n target_m=16,\n target_n=12,\n target_k=64,\n num_threads=1,\n br_gemm_support=True,\n packed_gemm_support=True )\n\n # Create the optimized TensorOperation instance\n top = etops.TensorOperation(optimized_config)\n\n import torch\n # Create input and output arrays\n A = torch.randn(48, 128, 64, dtype=torch.float32)\n B = torch.randn(48, 32, 128, dtype=torch.float32)\n C = torch.randn( 32, 64, dtype=torch.float32)\n\n # Execute the operation\n top.execute(A, B, C)\n\n C_torch = torch.einsum(\"bkm,bnk->nm\", A, B)\n\n # Compute absolute and relative errors\n error_abs = torch.max(torch.abs(C - 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