.. DO NOT EDIT. THIS FILE WAS AUTOMATICALLY GENERATED BY .. TVM'S MONKEY-PATCHED VERSION OF SPHINX-GALLERY. TO MAKE .. CHANGES, EDIT THE SOURCE PYTHON FILE: .. "how_to/tune_with_autoscheduler/tune_sparse_x86.py" .. only:: html .. note:: :class: sphx-glr-download-link-note This tutorial can be used interactively with Google Colab! You can also click :ref:`here ` to run the Jupyter notebook locally. .. image:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/utilities/colab_button.svg :align: center :target: https://colab.research.google.com/github/apache/tvm-site/blob/asf-site/docs/_downloads/293f8d0753933b706a0b588f909fe38a/tune_sparse_x86.ipynb :width: 300px .. rst-class:: sphx-glr-example-title .. _sphx_glr_how_to_tune_with_autoscheduler_tune_sparse_x86.py: Auto-scheduling Sparse Matrix Multiplication on CPU with Custom Sketch Rule =========================================================================== **Author**: `Chengfan Jia `_ This is a tutorial on how to use the auto-scheduler to tune a sparse matrix multiplication for CPUs. Auto-scheduler is designed to explore the schedule with best performance for a given computation declaration automatically. While sometimes, we may have a demand to try some special ops which may not been well-supported by auto-scheduler's default sketch rules and result in poor performance. Fortunately, auto-scheduler currently allows user to provide a CustomSketch to cover these cases. We use sparse matrix multiplication as an example in this tutorial to demonstrate how to implement and plug a custom sketch rule to the auto-scheduler's search policy. Note that this tutorial will not run on Windows or recent versions of macOS. To get it to run, you will need to wrap the body of this tutorial in a :code:`if __name__ == "__main__":` block. .. GENERATED FROM PYTHON SOURCE LINES 37-49 .. code-block:: default import os import numpy as np import tvm import tvm.testing from tvm import te, auto_scheduler, runtime, topi from tvm.auto_scheduler import _ffi_api from tvm.topi.utils import get_const_tuple from tvm.topi.sparse.utils import random_bsr_matrix .. GENERATED FROM PYTHON SOURCE LINES 50-55 Define the computation ^^^^^^^^^^^^^^^^^^^^^^ To begin with, let us define the computation of a sparse matmul with several relu and bias add. The function should return the list of input/output tensors. From these tensors, the auto-scheduler can get the whole computational graph. .. GENERATED FROM PYTHON SOURCE LINES 55-72 .. code-block:: default @auto_scheduler.register_workload def sparse_dense(M, N, K, w_data_shape, w_indices_shape, w_indptr_shape, dtype): X = te.placeholder(shape=(M, K), dtype=dtype) W_data = te.placeholder(shape=w_data_shape, dtype=dtype) W_indices = te.placeholder(shape=w_indices_shape, dtype="int32") W_indptr = te.placeholder(shape=w_indptr_shape, dtype="int32") B = te.placeholder(shape=(M, N), dtype=dtype) out = topi.nn.sparse_dense(topi.nn.relu(X), W_data, W_indices, W_indptr) out = te.compute((M, N), lambda i, j: out[i, j] + B[i, j], name="BiasAdd") out = topi.nn.relu(out) return [X, W_data, W_indices, W_indptr, B, out] .. GENERATED FROM PYTHON SOURCE LINES 73-82 Special step for sparse workload ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ During schedule tuning, auto-scheduler will use random inputs to measure the performance of a generated schedule. While we cannot directly use a random array as the input of a sparse op, for the "indices" and "indptr" array are meaningful for the computation. To solve this problem, we register these as special buffers, and load them when process program measuring. See the `tvm.auto_scheduler.measure.py` for more details. .. GENERATED FROM PYTHON SOURCE LINES 82-102 .. code-block:: default # Define the basic shapes of this sparse computation M = 128 K = 256 N = 512 BS_R = 16 BS_C = 1 density = 0.6 # Generate the test data with numpy X_np = np.random.randn(M, K).astype("float32") X_np = np.maximum(np.zeros((M, K), dtype="float32"), X_np) # Relu W_sp_np = random_bsr_matrix(N, K, BS_R, BS_C, density=density, dtype="float32") W_np = W_sp_np.todense() Y_np = X_np @ W_np.T # Process the matrix multiplication B_np = np.random.randn(M, N).astype("float32") Y_np = Y_np + B_np # Bias add Y_np = np.maximum(np.zeros((M, N), dtype="float32"), Y_np) # Relu .. GENERATED FROM PYTHON SOURCE LINES 103-110 Create the search task ^^^^^^^^^^^^^^^^^^^^^^ We then create a search task with M=N=K=512 and dtype="float32" If your machine supports avx instructions, you can - replace "llvm" below with "llvm -mcpu=core-avx2" to enable AVX2 - replace "llvm" below with "llvm -mcpu=skylake-avx512" to enable AVX-512 .. GENERATED FROM PYTHON SOURCE LINES 110-138 .. code-block:: default target = tvm.target.Target("llvm") # Register the sparse data to task inputs prefix = "sparse_dense_bsr_%d_%d_%d_%d_%d_%d_" % ( N, K, BS_R, BS_C, W_sp_np.indices.shape[0], W_sp_np.indptr.shape[0], ) task = tvm.auto_scheduler.SearchTask( func=sparse_dense, args=(M, N, K, W_sp_np.data.shape, W_sp_np.indices.shape, W_sp_np.indptr.shape, "float32"), target=target, task_inputs={ prefix + "W_data": runtime.ndarray.array(W_sp_np.data), prefix + "W_indices": runtime.ndarray.array(W_sp_np.indices), prefix + "W_indptr": runtime.ndarray.array(W_sp_np.indptr), }, task_inputs_save_to_file=True, ) # Inspect the computational graph print("Computational DAG:") print(task.compute_dag) .. rst-class:: sphx-glr-script-out .. code-block:: none Computational DAG: placeholder = PLACEHOLDER [33] placeholder = PLACEHOLDER [4916, 16, 1] placeholder = PLACEHOLDER [4916] placeholder = PLACEHOLDER [128, 256] compute(i0, i1) = max(placeholder[i0, i1], 0f) compute(i, nb_j, j) += (placeholder[(placeholder[nb_j] + elem_idx), j, c]*compute[i, (placeholder[(placeholder[nb_j] + elem_idx)] + c)]) compute(m, n) = compute[m, floordiv(n, 16), floormod(n, 16)] placeholder = PLACEHOLDER [128, 512] BiasAdd(i, j) = (compute[i, j] + placeholder[i, j]) compute(i0, i1) = max(BiasAdd[i0, i1], 0f) .. GENERATED FROM PYTHON SOURCE LINES 139-149 Write the custom sketch for sparse dense op ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Before tuning, we will need to define the CustomSketchRule for the sparse dense op. CustomSketchRule consists of two parts: the condition function and the apply function. - condition function: describe when to apply this sketch rule. For example, we can only apply the rule to the sparse ops by matching their name and tag. - apply function: describe how to generate the initial sketch. You can implement it using auto-scheduler provided loop state APIs. .. GENERATED FROM PYTHON SOURCE LINES 149-204 .. code-block:: default def meet_condition_func(search_policy, state, stage_id): state = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag) if state.stages[stage_id].op.tag in [ "sparse_dense_sp_rhs_bsrmm", "sparse_dense_sp_rhs_bsrmm_block", ]: return auto_scheduler.PreloadCustomSketchRule.APPLY_AND_SKIP_REST else: return auto_scheduler.PreloadCustomSketchRule.PASS def apply_func(search_policy, state, stage_id): ret = [] s0 = auto_scheduler.loop_state.State(state, search_policy.search_task.compute_dag) if s0.stages[stage_id].op.tag == "sparse_dense_sp_rhs_bsrmm_block": return [s0.state_object, stage_id - 1] sparse_dense = s0.stages[stage_id].op sparse_dense_block = s0.stages[stage_id - 1].op assert sparse_dense.tag == "sparse_dense_sp_rhs_bsrmm" assert sparse_dense_block.tag == "sparse_dense_sp_rhs_bsrmm_block" # Set the default consumer of compute block consumer = sparse_dense # If sparse dense has a single elementwise consumer # We can compute inline the sparse_dense output stage consumers = _ffi_api.SearchPolicyUtilsGetConsumers( search_policy.search_task, s0.state_object, stage_id ) if len(consumers) == 1: consumer_id = int(consumers.items()[0][0]) if _ffi_api.SearchPolicyUtilsIsElementwiseMatch( search_policy.search_task, s0.state_object, stage_id, consumer_id ): consumer = s0.stages[consumer_id].op s0.compute_inline(sparse_dense) i, nb_j, j, row_offset, c = s0[sparse_dense_block].iters m, n = s0[consumer].iters i0, i1, i2 = s0.split(sparse_dense_block, i, [None, None]) m0, m1 = s0.follow_split(consumer, m, len(s0.transform_steps) - 1, 1) j0, j1 = s0.split(sparse_dense_block, nb_j, [None]) n0, n1 = s0.follow_split(consumer, n, len(s0.transform_steps) - 1, 1) s0.reorder(sparse_dense_block, [i0, j0, i1, j1, row_offset, i2, j, c]) s0.reorder(consumer, [m0, n0, m1, n1]) s0.compute_at(sparse_dense_block, consumer, n0) ret.append([s0.state_object, stage_id - 2]) return ret .. GENERATED FROM PYTHON SOURCE LINES 205-217 Next, we set parameters for the auto-scheduler with the custom sketch plugged in. * :code:`num_measure_trials` is the number of measurement trials we can use during the search. We only make 10 trials in this tutorial for a fast demonstration. In practice, 1000 is a good value for the search to converge. You can do more trials according to your time budget. * In addition, we use :code:`RecordToFile` to dump measurement records into a file `sparse_dense.json`. The measurement records can be used to query the history best, resume the search, and do more analyses later. * see :any:`auto_scheduler.TuningOptions` for more parameters * Here, we need to create a :code:`auto_scheduler.SketchPolicy` object, and add the custom sketch rule as a `init_search_callbacks`. .. GENERATED FROM PYTHON SOURCE LINES 217-234 .. code-block:: default log_file = "sparse_dense.json" tune_option = auto_scheduler.TuningOptions( num_measure_trials=10, measure_callbacks=[auto_scheduler.RecordToFile(log_file)], verbose=2, ) search_policy = auto_scheduler.SketchPolicy( task, program_cost_model=auto_scheduler.XGBModel(), init_search_callbacks=[ auto_scheduler.PreloadCustomSketchRule(meet_condition_func, apply_func, "SparseDense") ], ) .. GENERATED FROM PYTHON SOURCE LINES 235-241 Run the search ^^^^^^^^^^^^^^ Now we get all inputs ready. We can kick off the search and let the auto-scheduler do its magic. After some measurement trials, we can load the best schedule from the log file and apply it. .. GENERATED FROM PYTHON SOURCE LINES 241-292 .. code-block:: default def tune_and_evaluate(tune_option, search_policy): # Run auto-tuning (search) task.tune(tune_option, search_policy) # Apply the best schedule sch, args = task.apply_best(log_file) # We can lower the schedule to see the IR after auto-scheduling. # The auto-scheduler correctly performs optimizations including multi-level tiling, # layout transformation, parallelization, vectorization, unrolling, and operator fusion. print("Lowered TIR:") print(tvm.lower(sch, args, simple_mode=True)) # Check correctness and evaluate performance # We build the binary and check its correctness and performance. func = tvm.build(sch, args, target) dev = tvm.cpu() X_tvm = tvm.nd.array(X_np, device=dev) W_data_tvm = tvm.nd.array(W_sp_np.data, device=dev) W_indices_tvm = tvm.nd.array(W_sp_np.indices, device=dev) W_indptr_tvm = tvm.nd.array(W_sp_np.indptr, device=dev) B_tvm = tvm.nd.array(B_np, device=dev) Y_tvm = tvm.nd.empty(Y_np.shape, device=dev) func(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm) # Check results tvm.testing.assert_allclose(Y_np, Y_tvm.numpy(), atol=1e-4, rtol=1e-4) # Evaluate execution time. evaluator = func.time_evaluator(func.entry_name, dev, min_repeat_ms=500) print( "Execution time of this operator: %.3f ms" % ( np.median( evaluator(X_tvm, W_data_tvm, W_indices_tvm, W_indptr_tvm, B_tvm, Y_tvm).results ) * 1000 ) ) # Notice: We do not run the tuning in our webpage server since it takes too long. # Uncomment the following line to run it by yourself. # tune_and_evaluate(tune_option, search_policy) .. GENERATED FROM PYTHON SOURCE LINES 293-331 .. note:: Tuning result example .. code-block:: c ---------------------------------------------------------------------- Lowered TIR: primfn(placeholder_5: handle, placeholder_6: handle, placeholder_7: handle, placeholder_8: handle, placeholder_9: handle, compute_1: handle) -> () attr = {"global_symbol": "main", "tir.noalias": True} buffers = {placeholder_2: Buffer(placeholder_10: Pointer(float32), float32, [9831, 16, 1], []), placeholder_4: Buffer(placeholder_11: Pointer(int32), int32, [33], []), placeholder_3: Buffer(placeholder_12: Pointer(float32), float32, [512, 512], []), compute: Buffer(compute_2: Pointer(float32), float32, [512, 512], []), placeholder_1: Buffer(placeholder_13: Pointer(float32), float32, [512, 512], []), placeholder: Buffer(placeholder_14: Pointer(int32), int32, [9831], [])} buffer_map = {placeholder_7: placeholder, placeholder_9: placeholder_1, placeholder_6: placeholder_2, compute_1: compute, placeholder_5: placeholder_3, placeholder_8: placeholder_4} { for (i0.outer.i1.outer.fused: int32, 0, 1024) "parallel" { attr [compute_3: Pointer(float32)] "storage_scope" = "global"; allocate(compute_3, float32, [256]) { for (nb_j.inner: int32, 0, 2) { for (i.inner.init: int32, 0, 8) { for (j.init: int32, 0, 16) { compute_3[(((i.inner.init*32) + (nb_j.inner*16)) + j.init)] = 0f32 } } for (elem_idx: int32, 0, ((int32*)placeholder_11[(((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner) + 1)] - (int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)])) { for (i.inner: int32, 0, 8) { for (j: int32, 0, 16) { compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] = ((float32*)compute_3[(((i.inner*32) + (nb_j.inner*16)) + j)] + ((float32*)placeholder_10[((((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)]*16) + (elem_idx*16)) + j)]*max((float32*)placeholder_12[(((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i.inner*512)) + (int32*)placeholder_14[((int32*)placeholder_11[((floormod(i0.outer.i1.outer.fused, 16)*2) + nb_j.inner)] + elem_idx)])], 0f32))) } } } } for (i0.inner: int32, 0, 8) { compute_2[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)] = max(((float32x32*)compute_3[ramp((i0.inner*32), 1, 32)] + (float32x32*)placeholder_13[ramp((((floordiv(i0.outer.i1.outer.fused, 16)*4096) + (i0.inner*512)) + (floormod(i0.outer.i1.outer.fused, 16)*32)), 1, 32)]), broadcast(0f32, 32)) } } } } .. _sphx_glr_download_how_to_tune_with_autoscheduler_tune_sparse_x86.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: tune_sparse_x86.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: tune_sparse_x86.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_