| """ |
| Scan and Recurrent Kernel |
| ========================= |
| **Author**: `Tianqi Chen <https://tqchen.github.io>`_ |
| |
| This is an introduction material on how to do recurrent computing in TVM. |
| Recurrent computing is a typical pattern in neural networks. |
| """ |
| from __future__ import absolute_import, print_function |
| |
| import tvm |
| import numpy as np |
| |
| ###################################################################### |
| # TVM supports a scan operator to describe symbolic loop. |
| # The following scan op computes cumsum over columns of X. |
| # |
| # The scan is carried over the highest dimension of the tensor. |
| # :code:`s_state` is a placeholder that describes the transition state of the scan. |
| # :code:`s_init` describes how we can initialize the first k timesteps. |
| # Here since s_init's first dimension is 1, it describes how we initialize |
| # The state at first timestep. |
| # |
| # :code:`s_update` describes how to update the value at timestep t. The update |
| # value can refer back to the values of previous timestep via state placeholder. |
| # Note that while it is invalid to refer to :code:`s_state` at current or later timestep. |
| # |
| # The scan takes in state placeholder, initial value and update description. |
| # It is also recommended(although not necessary) to list the inputs to the scan cell. |
| # The result of the scan is a tensor, giving the result of :code:`s_state` after the |
| # update over the time domain. |
| # |
| m = tvm.var("m") |
| n = tvm.var("n") |
| X = tvm.placeholder((m, n), name="X") |
| s_state = tvm.placeholder((m, n)) |
| s_init = tvm.compute((1, n), lambda _, i: X[0, i]) |
| s_update = tvm.compute((m, n), lambda t, i: s_state[t-1, i] + X[t, i]) |
| s_scan = tvm.scan(s_init, s_update, s_state, inputs=[X]) |
| |
| ###################################################################### |
| # Schedule the Scan Cell |
| # ---------------------- |
| # We can schedule the body of the scan by scheduling the update and |
| # init part seperately. Note that it is invalid to schedule the |
| # first iteration dimension of the update part. |
| # To split on the time iteration, user can schedule on scan_op.scan_axis instead. |
| # |
| s = tvm.create_schedule(s_scan.op) |
| num_thread = 256 |
| block_x = tvm.thread_axis("blockIdx.x") |
| thread_x = tvm.thread_axis("threadIdx.x") |
| xo, xi = s[s_init].split(s_init.op.axis[1], factor=num_thread) |
| s[s_init].bind(xo, block_x) |
| s[s_init].bind(xi, thread_x) |
| xo, xi = s[s_update].split(s_update.op.axis[1], factor=num_thread) |
| s[s_update].bind(xo, block_x) |
| s[s_update].bind(xi, thread_x) |
| print(tvm.lower(s, [X, s_scan], simple_mode=True)) |
| |
| ###################################################################### |
| # Build and Verify |
| # ---------------- |
| # We can build the scan kernel like other tvm kernels, here we use |
| # numpy to verify the correctness of the result. |
| # |
| fscan = tvm.build(s, [X, s_scan], "cuda", name="myscan") |
| ctx = tvm.gpu(0) |
| n = 1024 |
| m = 10 |
| a_np = np.random.uniform(size=(m, n)).astype(s_scan.dtype) |
| a = tvm.nd.array(a_np, ctx) |
| b = tvm.nd.array(np.zeros((m, n), dtype=s_scan.dtype), ctx) |
| fscan(a, b) |
| tvm.testing.assert_allclose(b.asnumpy(), np.cumsum(a_np, axis=0)) |
| |
| ###################################################################### |
| # Multi-Stage Scan Cell |
| # --------------------- |
| # In the above example we described the scan cell using one Tensor |
| # computation stage in s_update. It is possible to use multiple |
| # Tensor stages in the scan cell. |
| # |
| # The following lines demonstrate a scan with two stage operations |
| # in the scan cell. |
| # |
| m = tvm.var("m") |
| n = tvm.var("n") |
| X = tvm.placeholder((m, n), name="X") |
| s_state = tvm.placeholder((m, n)) |
| s_init = tvm.compute((1, n), lambda _, i: X[0, i]) |
| s_update_s1 = tvm.compute((m, n), lambda t, i: s_state[t-1, i] * 2, name="s1") |
| s_update_s2 = tvm.compute((m, n), lambda t, i: s_update_s1[t, i] + X[t, i], name="s2") |
| s_scan = tvm.scan(s_init, s_update_s2, s_state, inputs=[X]) |
| |
| ###################################################################### |
| # These intermediate tensors can also be scheduled normally. |
| # To ensure correctness, TVM creates a group constraint to forbid |
| # the body of scan to be compute_at locations outside the scan loop. |
| # |
| s = tvm.create_schedule(s_scan.op) |
| xo, xi = s[s_update_s2].split(s_update_s2.op.axis[1], factor=32) |
| s[s_update_s1].compute_at(s[s_update_s2], xo) |
| print(tvm.lower(s, [X, s_scan], simple_mode=True)) |
| |
| ###################################################################### |
| # Multiple States |
| # --------------- |
| # For complicated applications like RNN, we might need more than one |
| # recurrent state. Scan support multiple recurrent states. |
| # The following example demonstrates how we can build recurrence with two states. |
| # |
| m = tvm.var("m") |
| n = tvm.var("n") |
| l = tvm.var("l") |
| X = tvm.placeholder((m, n), name="X") |
| s_state1 = tvm.placeholder((m, n)) |
| s_state2 = tvm.placeholder((m, l)) |
| s_init1 = tvm.compute((1, n), lambda _, i: X[0, i]) |
| s_init2 = tvm.compute((1, l), lambda _, i: 0.0) |
| s_update1 = tvm.compute((m, n), lambda t, i: s_state1[t-1, i] + X[t, i]) |
| s_update2 = tvm.compute((m, l), lambda t, i: s_state2[t-1, i] + s_state1[t-1, 0]) |
| s_scan1, s_scan2 = tvm.scan([s_init1, s_init2], |
| [s_update1, s_update2], |
| [s_state1, s_state2], inputs=[X]) |
| s = tvm.create_schedule(s_scan1.op) |
| print(tvm.lower(s, [X, s_scan1, s_scan2], simple_mode=True)) |
| |
| ###################################################################### |
| # Summary |
| # ------- |
| # This tutorial provides a walk through of scan primitive. |
| # |
| # - Describe scan with init and update. |
| # - Schedule the scan cells as normal schedule. |
| # - For complicated workload, use multiple states and steps in scan cell. |