3 ms·

ROCm (AMD) Now Upstreamed into TensorFlow

20 points by jamesblonde 7y ago

hammeiam 7y ago
So does this mean that I no longer need an nvidia gpu for doing ML at home?
- 0-_-0 7y ago
  "We are motivated by the results of MLIR and XLA, and we are working towards enabling and optimizing these technologies for AMD GPUs." So the answer is "not yet".
dragontamer 7y ago
Okay, so I like ROCm but... I just don't see how AMD can strategically win here. NVidia has Tensor-cores. NVidia GPUs literally have an assembly statement that performs a 4x4 FP16 matrix multiplication, while AMD hardware at best can perform FP16 dot-products. (which... is still greatly accelerated and pushing 20+TFlops, but no where near as fast as the 100TFlops that some NVidia GPUs are putting out). What's cool about AMD GPUs is that they have better general-purpose compute numbers than NVidia. An AMD Vega64 is over 10TFlops with 480GBps HBM2 RAM and is priced under $400 now. Vega64 is a few years old, but its still a behemoth in compute power. Radeon VII is still around 10-TFLOPs (specified as 13.84 TFlops), but has 1000GBps HBM2 bandwidth and 16GB of RAM. Radeon VII is an absolute monster in parallel compute and only costs $700. Since many problems are Memory-bandwidth bound (163840 hardware SIMD threads of execution uses up a LOT of memory bandwidth), the +memory bandwidth on Radeon VII is quite practical (YMMV of course: it always depends on your specific workload). Crushing a Terrabyte of memory bandwidth per second is a huge amount of memory bandwidth, no matter how you look at it. Memory-hard problems will almost certainly want to execute on the Radeon VII. ROCm is a decent programming environment to unlock this "general purpose" compute power... but Tensorflow is simply going to be in the bag for NVidia due to those 4x4 FP16 Matrix multiplication instructions. Hmm... its still nice I guess for AMD to catchup. But I feel like AMD should maybe focus on other compute problems where they can better demonstrate their advantages? For example, OpenCV probably would run better on AMD hardware than equivalently priced NVidia hardware. I understand that there's a lot of hype to Deep Learning, but the compute world is bigger than just that. There's a lot of problems out there, and AMD GPUs seem better (!!) for a lot of these other problems. But there just isn't very much discussion or marketing that points this fact out. If AMD worked on optimizing OpenCV, or maybe some other libraries, for the ROCm platform, surely they'd beat NVidia in price/performance. AMD's hardware is pretty kick-ass, the main issue is the amount of programming effort you need to put forth since AMD's software stack is just very far behind NVidia's CUDA environment. ----------- Maybe AMD could show how memory bandwidth can become a unique asset in some workloads? The specs are downright amazing, the benchmarks prove its possible. Its just a matter of playing with the hardware and figuring out what problems that 4096 SIMD-ALUs + 1TBps memory bandwidth can crush.
- 0-_-0 7y ago
  > 100TFlops that some NVidia GPUs are putting out... Theoretically. In practice you need to make sure you're not bandwidth bottlenecked, which is heavily dependent on workload. E.g. large fully connected layers are bandwidth bound, so the FLOPS difference doesn't matter. Besides, nothing prevents AMD from implementing its own matrix multiply OP in the next generation of GPUs.
  - dragontamer 7y ago
    > large fully connected layers are bandwidth bound Hmm, well that might be useful for the Radeon VII and its 1TBps bandwidth. The #1 network of interest to me is the Leela-Zero network, which is basically a "deep" neural net with 50 layers of tiny 3x3. Something like Leela-Zero would definitely benefit from those Tensor Cores, and it'd be small enough to fit inside of L1 cache (or maybe even shared memory. I haven't looked into it too deeply though). > Besides, nothing prevents AMD from implementing its own matrix multiply OP in the next generation of GPUs. While that's true, its not really something this generation of hardware buyers can think about. I think your point about large and fully connected layers may really benefit from the Radeon VII however. Someone should probably benchmark that kind of network between Radeon VII vs RTX 2080 (both $700-class GPUs).
- jamesblonde 7y ago
  I think you underestimate the challenge in programming TensorFlow to use Tensor-cores, currently. It is a hell of a lot of fp16 and fp32 casts, and, frankly, most Python programmers / Data Scientists do not have the skills to do it. There is some work in getting TensorFlow to automatically do it for you, but i haven't seen the evidence in benchmarks, yet. From this thread, it looks like (for Resnet-50), the Radeon VII is closing in on the nvidia 2080Ti - with more memory (16 vs 11 GB), can be used in a data-center EULA, and 2/3 of the price. https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/173 https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/... In any case, Google are not going the tensorcores route with TPUs. Fast general purpose matmuls. Same as AMD. I don't think tensorcores is a clear winner, yet.

20 points by jamesblonde 7y ago

hammeiam 7y ago

So does this mean that I no longer need an nvidia gpu for doing ML at home?

0-_-0 7y ago
"We are motivated by the results of MLIR and XLA, and we are working towards enabling and optimizing these technologies for AMD GPUs." So the answer is "not yet".

dragontamer 7y ago

Okay, so I like ROCm but... I just don't see how AMD can strategically win here. NVidia has Tensor-cores. NVidia GPUs literally have an assembly statement that performs a 4x4 FP16 matrix multiplication, while AMD hardware at best can perform FP16 dot-products. (which... is still greatly accelerated and pushing 20+TFlops, but no where near as fast as the 100TFlops that some NVidia GPUs are putting out). What's cool about AMD GPUs is that they have better general-purpose compute numbers than NVidia. An AMD Vega64 is over 10TFlops with 480GBps HBM2 RAM and is priced under $400 now. Vega64 is a few years old, but its still a behemoth in compute power. Radeon VII is still around 10-TFLOPs (specified as 13.84 TFlops), but has 1000GBps HBM2 bandwidth and 16GB of RAM. Radeon VII is an absolute monster in parallel compute and only costs $700. Since many problems are Memory-bandwidth bound (163840 hardware SIMD threads of execution uses up a LOT of memory bandwidth), the +memory bandwidth on Radeon VII is quite practical (YMMV of course: it always depends on your specific workload). Crushing a Terrabyte of memory bandwidth per second is a huge amount of memory bandwidth, no matter how you look at it. Memory-hard problems will almost certainly want to execute on the Radeon VII. ROCm is a decent programming environment to unlock this "general purpose" compute power... but Tensorflow is simply going to be in the bag for NVidia due to those 4x4 FP16 Matrix multiplication instructions. Hmm... its still nice I guess for AMD to catchup. But I feel like AMD should maybe focus on other compute problems where they can better demonstrate their advantages? For example, OpenCV probably would run better on AMD hardware than equivalently priced NVidia hardware. I understand that there's a lot of hype to Deep Learning, but the compute world is bigger than just that. There's a lot of problems out there, and AMD GPUs seem better (!!) for a lot of these other problems. But there just isn't very much discussion or marketing that points this fact out. If AMD worked on optimizing OpenCV, or maybe some other libraries, for the ROCm platform, surely they'd beat NVidia in price/performance. AMD's hardware is pretty kick-ass, the main issue is the amount of programming effort you need to put forth since AMD's software stack is just very far behind NVidia's CUDA environment. ----------- Maybe AMD could show how memory bandwidth can become a unique asset in some workloads? The specs are downright amazing, the benchmarks prove its possible. Its just a matter of playing with the hardware and figuring out what problems that 4096 SIMD-ALUs + 1TBps memory bandwidth can crush.

0-_-0 7y ago
> 100TFlops that some NVidia GPUs are putting out... Theoretically. In practice you need to make sure you're not bandwidth bottlenecked, which is heavily dependent on workload. E.g. large fully connected layers are bandwidth bound, so the FLOPS difference doesn't matter. Besides, nothing prevents AMD from implementing its own matrix multiply OP in the next generation of GPUs.
- dragontamer 7y ago
  > large fully connected layers are bandwidth bound Hmm, well that might be useful for the Radeon VII and its 1TBps bandwidth. The #1 network of interest to me is the Leela-Zero network, which is basically a "deep" neural net with 50 layers of tiny 3x3. Something like Leela-Zero would definitely benefit from those Tensor Cores, and it'd be small enough to fit inside of L1 cache (or maybe even shared memory. I haven't looked into it too deeply though). > Besides, nothing prevents AMD from implementing its own matrix multiply OP in the next generation of GPUs. While that's true, its not really something this generation of hardware buyers can think about. I think your point about large and fully connected layers may really benefit from the Radeon VII however. Someone should probably benchmark that kind of network between Radeon VII vs RTX 2080 (both $700-class GPUs).
jamesblonde 7y ago
I think you underestimate the challenge in programming TensorFlow to use Tensor-cores, currently. It is a hell of a lot of fp16 and fp32 casts, and, frankly, most Python programmers / Data Scientists do not have the skills to do it. There is some work in getting TensorFlow to automatically do it for you, but i haven't seen the evidence in benchmarks, yet. From this thread, it looks like (for Resnet-50), the Radeon VII is closing in on the nvidia 2080Ti - with more memory (16 vs 11 GB), can be used in a data-center EULA, and 2/3 of the price. https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/issues/173 https://github.com/ROCmSoftwarePlatform/tensorflow-upstream/... In any case, Google are not going the tensorcores route with TPUs. Fast general purpose matmuls. Same as AMD. I don't think tensorcores is a clear winner, yet.