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Over the years, we’ve had the privilege of collaborating closely with the Meta team to contribute features to PyTorch, including a significant number of features added in PyTorch 2.0—see our 2022 PyTorch contributions blog post.
In this post, we’ll go into deeper technical detail on some of our contributions to a specific portion of the PyTorch codebase, the torch.func module. As with all of our work on PyTorch, everything described here was a collaboration between Quansight and Meta.
torch.func (previously known as functorch) is a PyTorch module designed to offer JAX-like transforms. Within this module, various higher-order functions, such as grad, vmap, and vjp are made accessible. These transforms help users to easily compute gradients for the parameters of their model or write batch-size agnostic code. The beauty of these transformations lies in their ability to compose with one another. Thanks to this composability, the process of calculating per-sample gradients becomes a straightforward nested function call: vmap(grad(model)).
Note: We offer PyTorch support as part of our consulting services offerings, so if you’re working with PyTorch and could use some assistance, please take a look at our PyTorch Services page.
Now let’s dive in.
vmapvmap is a transformation that accepts a function that operates on non-batched tensors and returns a new function that operates on batched tensors. When processing a batched input, an additional dimension, denoted by in_dims, is introduced to indicate which dimension to apply the function over. Conceptually, it emulates a for loop that iterates through all the data points and stacks the results. Importantly, it performs this operation efficiently by pushing the for loop into internal PyTorch machinery, allowing the batches to run in parallel.
Consider the following example:
import torch
# Written to handle only single sample.
def my_simple_model(feature_vec, weight):
return torch.dot(feature_vec, weight).relu()
batch_size = 4
batched_inputs = torch.randn(batch_size, 3)
weight = torch.randn(3)
# For Loop version
expected = []
for input in batched_inputs:
expected.append(my_simple_model(input, weight))
expected = torch.stack(expected)
# Vmap
# `in_dims` specifies the dimension that should be mapped over.
# In this case, we map only over 0-dim of `batched_inputs`.
actual = torch.vmap(my_simple_model, in_dims=(0, None))(batched_inputs, weight)
# Verify that the results match.
torch.testing.assert_close(expected, actual)
To support vmap for PyTorch operators, we need to specify the batching rule—i.e., how to map the given function over a batched input. A batching rule is essentially a function which takes one or multiple batched inputs and computes the batched operation. In the above example, to support vmap for my_simple_model, we need to know the batching rule for torch.dot and torch.relu to be able to vectorize our model. PyTorch has more than 2000 operators and we need to have coverage for all of them to support vmap. That being said, there is a for-loop fallback in case an operator is not supported, so as not to crash the code.
From the point of view of adding batching rules, PyTorch operators can be roughly categorized as primitive or composite. Primitive operators are the ones for which we specify the batching and gradient rules. Composite operators are implemented using these primitive operators and other, simpler composite operators. If we implement batching rules for every primitive operator, we automatically get the batching rules for composite operators.
There are now two ways to add batching support for an operator:
torch.dot.torch.vdot.As mentioned earlier, we obtain batching rules effortlessly for composite operators but this holds true only under certain constraints. These constraints include refraining from accessing the tensor’s data pointer and avoiding the use of out= variants of the operators. For the full list of constraints, see this documentation. When all these hold for an operator, we say that it is ‘composite compliant.’
Unfortunately, operators that claim to be composite may occasionally deviate from these constraints. While such deviations may not pose issues when utilizing plain eager PyTorch, they can lead to complications when using torch.func transformations.
We now have tests to ensure that operators tagged as composite are indeed composite compliant. We test this by creating a new subclass CompositeCompliantTensor that utilizes the __torch_dispatch__ mechanism. This mechanism is invoked for all operators in the testing, enabling us to detect any non-compliant behavior exhibited by an operator.
Our testing approach involves running tests on the actual operator, as well as their backward formula and forward AD formula. Testing both the backward and forward formulas is crucial because we may encounter scenarios involving vmap(vjp(fn)) or vmap(jvp(fn)).
chunk_size in vmap and jacrevThe computation of the Jacobian can be memory intensive, and users have raised concerns about this high memory usage, (e.g., this one). In response to these concerns, we have introduced a feature that allows for the calculation of jacrev and vmap in smaller, user-defined chunks, determined by the chunk_size argument. This adjustment serves to reduce the peak memory usage during computation. With this argument, users can specify the number of rows of the Jacobian to be computed at once, instead of computing the entire Jacobian at the same time. This enhancement was incorporated into both jacrev and vmap.
linearize TransformThe jvp transform is designed to calculate both f(x) and the Jacobian-vector product. Consequently, even when one intends to compute the Jacobian-vector product for fixed inputs, the jvp transform still redundantly evaluates f(x). For these scenarios, we have the linearize transform. This transform only computes the Jacobian-vector product, and avoids evaluating f(x) whenever possible. This proves valuable when multiple jvp computations are needed for constant inputs.
Note that, in order to implement this efficiently, linearize stores some intermediate computations, which can result in higher memory requirements compared to directly applying jvp. The linearize transform was implemented in this PR.
torch.func Transforms Within torch.compilePyTorch 2.0 introduced a JIT compiler under torch.compile, similar to jax.jit. This opened up the possibility of compiling the existing transforms to enhance their performance. To understand how these transforms can be compiled, it is essential to discuss the workings of the three layers within the compilation stack, namely dynamo, aot_autograd, and inductor.
The dynamo and aot_autograd layers primarily focus on capturing the computation graph and converting the captured operations into more basic operations. This captured graph is then passed to inductor, the compiler. inductor then applies various optimization passes before generating specialized code.
To gain insight into the different stages of this stack, let us compile a simple program in debug mode using these tools.
# Run this file with `TORCH_COMPILE_DEBUG=1`
import torch
def fn(x):
return torch.sin(x) + torch.square(x)
torch.compile(fn)(torch.randn(4, 4))
dynamo: The primary responsibility of dynamo is to trace the Python program and convert it into the FX graph format. The FX graph generated by dynamo represents PyTorch operations using the public API, such as torch.sin. Below, you can observe the graph captured by dynamo for the above program.
class GraphModule(torch.nn.Module):
def forward(self, L_x_ : torch.Tensor):
l_x_ = L_x_
# File: test/test_scratch.py:334, code: return torch.sin(x) + torch.square(x)
sin = torch.sin(l_x_)
square = torch.square(l_x_); l_x_ = None
add = sin + square; sin = square = None
return (add,)
aot_autograd: aot_autograd retraces all PyTorch operations to produce a lower-level FX graph using aten functions (from the private API). Additionally, aot_autograd decomposes composite operations into primitive operations. For instance, a composite operation like torch.square is traced down to aten.pow(x, 2).
Moreover, aot_autograd also manages the creation of the backward graph when requested. This is useful for transforms like grad, vjp, etc. Below, you can see the graph generated by aot_autograd for the above program.
def forward(self, arg0_1: f32[4, 4]):
# File: test/test_scratch.py:334, code: return torch.sin(x) + torch.square(x)
sin: f32[4, 4] = torch.ops.aten.sin.default(arg0_1)
pow_1: f32[4, 4] = torch.ops.aten.pow.Tensor_Scalar(arg0_1, 2); arg0_1 = None
add: f32[4, 4] = torch.ops.aten.add.Tensor(sin, pow_1); sin = pow_1 = None
return (add,)
inductor: As discussed above, it is inductor‘s job to apply optimizations and generate specialized code. In this case, it has fused sin and square to run within the same for-loop. This allows the generated program to do more compute per read/write, effectively improving the memory bandwidth utilization.
extern "C" void kernel(const float* in_ptr0, float* out_ptr0) {
for (long i0 = 0L; i0 < 16L; i0 += 8L) {
auto tmp0 = at::vec::Vectorized<float>::loadu(in_ptr0 + i0);
auto tmp1 = tmp0.sin();
auto tmp2 = tmp0 * tmp0;
auto tmp3 = tmp1 + tmp2;
tmp3.store(out_ptr0 + i0);
}
}
dynamo about torch.func transformsNow that we have a basic understanding of how torch.compile works, let us delve into how we extended the support for torch.func transforms. Given that aot_autograd is already capable of tracing through the transforms, our task is to teach dynamo to validate whether the user-defined function intended for transformation is free of side effects affecting the global state or of graph-breaks. In cases where the function meets these criteria, we can put the torch.func transform into the FX graph and delegate the remaining processing to the lower layers of the stack.
However, if the function cannot be successfully traced due to its failure to meet the above constraints, we fall back to the eager implementation, and this particular portion of the code remains uncompiled.
Let us have a look at what dynamo and aot_autograd generate when we compile a program with grad.
# Run this file with `TORCH_COMPILE_DEBUG=1`
import torch
def user_fn(x):
return torch.sin(x)
def wrapper_fn(x):
return torch.func.grad(user_fn)(x)
torch.compile(wrapper_fn)(torch.randn(()))
The output from dynamo is presented below. The initial GraphModule pertains to the wrapper_fn, clearly indicating a call to grad on the traced representation of the user’s function intended for transformation. Subsequently, the second GraphModule corresponds to the function provided by the user. In this instance, our function didn’t have side effects or graph-breaks. Thus, we were able to successfully trace through this program in one graph.
class GraphModule(torch.nn.Module):
def forward(self, L_x_ : torch.Tensor):
l_x_ = L_x_
# File: torch/_functorch/apis.py:363, code:
# return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs)
grad_body_0 = self.grad_body_0
grad_proxy = torch.func.grad(grad_body_0, 0, False); grad_body_0 = None
call = grad_proxy.__call__(l_x_); grad_proxy = l_x_ = None
contiguous = call.contiguous(); call = None
return (contiguous,)
class GraphModule(torch.nn.Module):
def forward(self, l_x_):
# No stacktrace found for following nodes
_set_grad_enabled = torch._C._set_grad_enabled(True)
# File: test/test_scratch.py:382, code: return torch.sin(x)
sin = torch.sin(l_x_); l_x_ = None
# No stacktrace found for following nodes
_set_grad_enabled_1 = torch._C._set_grad_enabled(True)
return sin
The graph shown above is handed over to aot_autograd for the subsequent phase of the compilation process. aot_autograd performs a trace through the transformation, resulting in the generation of the transformed graph. This explains why we observe a call to cos instead of sin: aot_autograd has traced through the forward and backward graph, as we have applied the grad transform, then optimized away the forward computation as grad discards that value.
def forward(self, arg0_1: f32[]):
# File: torch/_functorch/apis.py:363,
# code: return eager_transforms.grad_impl(func, argnums, has_aux, args, kwargs)
full: f32[] = torch.ops.aten.full.default([], 1, dtype = torch.float32,
layout = torch.strided,
device = device(type='cpu'),
pin_memory = False)
cos: f32[] = torch.ops.aten.cos.default(arg0_1); arg0_1 = None
mul: f32[] = torch.ops.aten.mul.Tensor(full, cos); full = cos = None
return (mul,)
The inclusion of torch.func support within torch.compile is currently under active development. At present, our support extends to the compilation of grad and vmap. However, it is important to note that there are certain limitations that restrict the range of cases we can compile.
Looking ahead, our roadmap aims to extend the support for all transforms with minimal limitations, providing a more comprehensive compilation support for torch.func transforms.
This project was yet another instance of the tight collaboration between Quansight and Meta within PyTorch. In particular, we would like to thank Richard Zou and Horace He, the torch.func creators, for all the design discussions and guidance throughout these years.
As we noted above, in addition to working directly on PyTorch, Quansight also offers support services to assist you with your use of PyTorch. Check out our PyTorch Support page or reach out to us for more information.
