[GitHub] [tvm] comaniac commented on a change in pull request #7313: [AutoSchedule] Sparse dense tuning support with custom sketch rule

[GitBox <gi...@apache.org>]

comaniac commented on a change in pull request #7313:
URL: https://github.com/apache/tvm/pull/7313#discussion_r586185313



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File path: tutorials/auto_scheduler/tune_sparse_x86.py
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+Auto-scheduling Sparse Matrix Multiplication for CPU by Custom Sketch Rule
+==========================================================================
+**Author**: `Chengfan Jia <https://github.com/jcf94/>`_
+
+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 search policy. Auto-scheduler currently allows
+user to provide a CustomSketch to cover these cases.
+
+We use sparse matrix multiplication as an example in this tutorial.
+
+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.
+"""
+
+import os
+import itertools
+
+import numpy as np
+import tvm
+from tvm import te, auto_scheduler, topi
+from tvm.auto_scheduler import _ffi_api
+from tvm.topi.utils import get_const_tuple
+
+import scipy.sparse as sp
+
+######################################################################
+# 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.
+
+# We use this function to generate a random bsr matrix
+def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype):
+    import itertools
+
+    Y = np.zeros((M, N), dtype=dtype)
+    assert M % BS_R == 0
+    assert N % BS_C == 0
+    nnz = int(density * M * N)
+    num_blocks = int(nnz / (BS_R * BS_C)) + 1
+    candidate_blocks = np.asarray(list(itertools.product(range(0, M, BS_R), range(0, N, BS_C))))
+    assert candidate_blocks.shape[0] == M // BS_R * N // BS_C
+    chosen_blocks = candidate_blocks[
+        np.random.choice(candidate_blocks.shape[0], size=num_blocks, replace=False)
+    ]
+    for i in range(len(chosen_blocks)):
+        r, c = chosen_blocks[i]
+        Y[r : r + BS_R, c : c + BS_C] = np.random.randn(BS_R, BS_C)
+    s = sp.bsr_matrix(Y, blocksize=(BS_R, BS_C))
+    assert s.data.shape == (num_blocks, BS_R, BS_C)
+    assert s.indices.shape == (num_blocks,)
+    assert s.indptr.shape == (M // BS_R + 1,)
+    return s
+
+@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]
+
+######################################################################
+# 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 :any:`auto_scheduler.measure` code for more details.
+
+# Define the basic shapes of this sparse computation
+M = K = 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
+
+# Register the sparse data to special buffer
+prefix = "sparse_dense_bsr_%d_%d_%d_%d_%d_%.2f_" % (M, N, K, BS_R, BS_C, density)
+auto_scheduler.measure.register_special_buffer(prefix + "W_data", W_sp_np.data)
+auto_scheduler.measure.register_special_buffer(prefix + "W_indices", W_sp_np.indices)
+auto_scheduler.measure.register_special_buffer(prefix + "W_indptr", W_sp_np.indptr)
+
+######################################################################
+# 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
+
+target = tvm.target.Target("llvm")
+
+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
+)
+
+# Inspect the computational graph
+print("Computational DAG:")
+print(task.compute_dag)
+
+######################################################################
+# 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 use this sketch rule. For example, we can match the op
+#     by their name or tag.
+#   - apply function: describe how to generate the initial sketch. Auto-scheduler provides a set of
+#     loop state APIs.
+
+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
+
+######################################################################
+# Next, we set parameters for the auto-scheduler.
+#
+# * :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 `matmul.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`.
+
+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")
+    ]
+)
+
+######################################################################
+# 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.
+
+# Run auto-tuning (search)
+task.tune(tune_option, search_policy)

Review comment:
       Well...the failed case in #7548 also runs two trials. We should avoid potential CI flaky as possible.




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